Record

Linkage

Techniques—

1985

Proceedings of the Workshop on

Exact Matching Methodologies

Arlington, Virginia

May 9-10, 1985

Co-Sponsored with the

Washington Statistical Society

and the

Federal Committee on Statistical Methodology

Compiled and Edited by

Beth Kilss and Wendy Alvey

Department of the Treasury

Internal Revenue Service

Statistics of Income Division

December 1985
Pf?EFACE

The Workshop on Exact Matching Methodologies

was held on May 9-10, 1985, at the Rosslyn

Westpark Hotel in Arlington, Virginia. The

conference grew out of the efforts of the

Matching Group, Administrative Records

Subconnnittee, of the Federal Committee on

Statistical Methodology. It was co-sponsored

with the Washington Statistical Society. This

volume contains the papers from that event.

The current volume; Record Linkage Techniques

-- 1985, is more than just a proceedings of the

_nference. It is intended to serve as a

handbook on modern matching theory, as well as

to report on the current state of the art. For

this reason, not only were the papers from the

Workshop included here, but extensive background

material and bibliographic citations have also

been added.

Contents. -- The format for this vo1ume

essentially follows that of the Workshop agenda,

with several sections added to help round out

the actual presentations. The collection begins

with an Introduction, which summarizes the

objectives of the Matching Group in conducting a

Workshop of this sort. It also proposes some

recommendations for the statistical community to

consider w“th regard to the future of exact

matching. (This latter portion is based on

comnents made by the participants during and

after the Workshop.)

The rest of the volume is set up as follows:

o Section I provides selected background

material, which lays the historical

groundwork for current methodological

thought. Some of these papers were

distributed at the conference, but they

were not presented as part of the

agenda.

o Section 11 begins the program for the

Workshop. This contains three papers

presented at the Opening Session, to

introduce the theory and provide an

overview of matching applications. A

fourth (contributed) PaPer, describing

the present state of general methodological issues, is also included.

o Section 111 focuses on current theory and

practice. It is comprised of three

invited papers and their resu7ting

discussions, as well as two related

contributed papers.

o Sections IV and V follow with papers

which describe recent application case

studies. Once again, two relevant papers

have been added to the six invited papers

and their discussions presented at the

conference.

December 1985

0

The

o

0

Finally, Section VI deals with computer

software for exact matching. It provides

the papers presented during the last

portion of the Workshop.

volume also contains two appendices:

Appendix A consists of selected

bibliographies of exact matching

methodologies and applications. Five

separate collections of references are

provided, each with a slightly different

orientation.

Appendix B concludes the volume m“th

information specific t, the workshop,

itself -- the agenda, the list of

attendees, and the list of sponsors.

Copy Preparation. -- The contents of the papers

Included here are the responsibility of the

authors. With the exception of previously

published background papers, which were simply

reproduced as is, all of the papers in this

VOILMW underwent only a limited peer review

process. Each paper was read by at least one

person familiar with the subject matter. It

should be noted, however, that reviewers were

instructed to focus on editorial concerns and

gross factual problems. Since this did not

constitute a formal referee process, authors

were also encouraged to obtain their own

technical review. Corrections and changes were

either made by the authors themselves or cleared

through them by the editors. Final layout of

the papers was done by the editorial staff, with

minor changes of a cosmetic nature considered

the prerogative of the editors.

Acknowledgments. -- First of all, I would like

to thank the Steering Committee -- Maria

Gonzalez, Thomas B. Jabine, Matthew Jaro, Nancy

Kirkendall, and Carol Utter -- and the Workshop

Coordinators -- Beth Kilss and Wendy Alvey --

for their outstanding job in organizing such a

successful conference.

Next, I join the editors of this volume in

thanking the Workshop participants for their

insightful remarks and helpful suggestions

regarding both bibliographic references and

recommendations for future research. Along

those lines, a special thanks goes to Thomas B.

Jabine and Nancy Kirkendall for their wrk in

drafting the Matching Group Recommendations,

which appear following the Introduction.

Appreciation is also due to all of the

reviewers, for providing their thoughtful

comments in such a timely manner. Finally, many

thanks go out to the typists -- Michelle Cobb,

Denise Herbert, Dawn Nester, Denise Reeder, and

Susan Rhodes -- and especially, the editorial

staff -- Clementine Brittain, Nancy Outton, and

Elettye Jamerson -- for their extensive

contributions to the copy preparation process.

Fritz Scheuren

Director

Statistics of Income Division

i
TABLE OF CONTENTS

Page

Preface .........0.. 00 ..0....0 . ...0..0000 ● . . ...0.... . 00 .0.0..00 .00 . . . . . i

Introduction ..0..● ..*O● ● ● . . . . ● ,. . ● . ● . . , ● . ● ● ● ● ● . ● . . . ● ● . . ● ● . ● . , ● ● . . ● . . . . 1

Reconmnendations,MATCHINGGROUP, Administrative Records

Subcommittee, Federal Committee on Statistical Methodology ● ,.....00.● . 3

Selected Background Papers (1959 - 1983)

Automatic Linkage of Vital Records. H.B. NEWCOMBE and

J.M. KENNEDY, Atomic Energy of Canada Limited;

S.J. AXFORD, Dominion Bureau of Statistics, and

A.P. JAMES, Atomic Energy of Canada Limited ...● .● ,,....● ● .● ● .● ● .● ..● .●

Record Linking: The Design of Efficient Systems for

Linking Records into Individual and Family Histories.

HOWARD B. NEWCOMBE, Atomic Energy of Canada Limited ● ● ● ● ● ● * ● . . . . ● . .0 ● . ●

AModel for Optimum Linkage of Records. BENJAMIN J.

TEPPING, Bureau of the Census ......● ● ..● .....0..,● .● ● .0● .0 . . ● ● . ● . . ● ● . .

A Theory for Record Linkage. IVAN P. FELLEGI and

ALANB. SUNTER, Dominion Bureau of Statistics .........................

Fiddling Around with Nonmatches and Mismatches,

FRITZ SCHEUREN and H. LOCK OH, Social Security

Administration ...0..● .● ..● .......● .● ● ● .● ● ● ..● ...● ● *● ● .● ....● ......● ..●

1

An Application of a Theory for Record Linkage.

RICHARDW. COULTER, Department of Agriculture .........................

A Generalized Iterative Record Linkage Computer System

for Use in Medical Follow-Up Studies. G. R. HOWE, University

of Toronto, and J. LINDSAY, Statistics Canada .........................

Reliability of Computerized Versus Manual Death

Searches in a Study of the Health of Eldorado Uranium

Workers. H.B. NEWCOMBE, Consultant, Eldorado Nuclear

Limited and Statistics Canada; M.E. SMITH, Statistics

Canada; G.R. HOWE, University of Toronto; J. MINGAY,

A. STRUGNELL, and J.D. ABBATT, Eldorado Nuclear Limited ..● ...● ● .....● .

7

13

39

51

79

89

97

111

iii
Page

Overview of Amlications and Introduction to Theorv

Tutorial on the Fellegi-SunterModel for Record Linkage.

IVAN P. FELLEGI, Statistics Canada .........● ..● .● ..● .......● .● .● ● ...,.

Why Are Epidemiologists Interested in Matching Algorithm?

GILBERTW. BEEBE, National Cancer Institute ...● O● ..● .**..● ● ...● . . . ● . . .

Exact Matching of Microdata Sets in Social Research: Benefits

and Problems. ROBERT BORUCH, Northwestern University, and

ERNST STROMSDORFER, Washington State University .*..● ● ..............● ..

Methodologic Issues in Linkage of Multiple Data Bases.

FRITZ SCHEUREN, Internal Revenue Service and Consultant,

Panel on Statistics for an Aging Population ......● *.....● ..● ........● ..

Current Theory and Practice

Preprocessing of Lists and String Comparison.

WILLIAM E. WINKLER, Energy Information Administration

Weights in Computer Matching: Applications and an

● ..0..... . . . ..*.O

Inf6nnation Theoretic Point-of View. NANCY J. KIRKENDALL,

Energy Information Administration ● ..● ..● ...● .............● ......● ..● ..

Advances in Record Linkage Methodology: A Method

for Determining the Best Blocking Strategy.

R. PATRICK KELLEY, Bureau of the Census .,......● ● . . . ● ● ● . ● ● . . . . . . ● . . ● . .

Discussion. ELI S. MARKS, Consultant ....● .● ......● ..● ● ....0...6● .● ...

Discussion. BENJAMIN J. TEPPING, Westat, Inc. ● 0● .● .● .● .....● ● ● ● ● ● ..● .

Rejoinder. WILLIAM E. WINKLER, Energy Information

Administration ● .● ..0● .● ....● ..● .● . ● . . . . ● . . ● ● ● ● . ● , . . . .* ● . ● ● ● ● . . ● . . . ● . ● .

Rejoinder. R. PATRICK KELLEY, Bureau of the Census ● .● ....● .....● .....

Properties of the Social Security Number Relevant

to Its Use in Record Linkages. THOMAS B. JABINE,

Consultant, Committee on National Statistics ...● ...● .....● ● ....,......

Exact Matching Lists of Businesses: Blocking,

Subfield Identification,and Information Theory.

WILLIAM E. WINKLER, Energy Information Administration . ...0...00 . ..0.0.

127

139

145

155

181

189

199

205

207

209

211

213

227

iv
Page

Application Case Studies I

The National Death Index Experience: 1981-1985.

JOHN E. PATTERSON and ROBERT BILGRAD,

National Center for Health Statistics ..........● . . . ...00. .**, . . . . . . . . . 245

An Implementationof a Two-Population Fellegi-Sunter

Probability Linkage Model. MAX G. ARELLANO,

University of California, San Francisco ● ,.....0 . . . , . . . . . .0. ● . . . ● . . .*. . 255

Deriving Labor Turnover Rates From Atiinistrative

Records. MALCOLMS. COHEN, University of Michigan .................... 259

Discussion. NORMANJ. JOHNSON, Bureau of the Census .................. 267

On Matching with Personal Names. J.T. KAGAWA,

Cancer Research Centerof Hawaii, andM.P. MI,

University of Hawaii, Honolulu .● .,......● ● ......● .● ● ........● . ..0 . ● . . . 269

Surname Blocking for Record Linkage. F. QUIAOIT,

Cancer Reseamh Center of Hawaii, and M.P. MI,

University of Hawaii, Honolulu .,....**.....,..● .....● ..● .....**....*.. 275

Application Case Studies 11

1979 Sole Proprietorship Employment and P~roll :

Processing Methodology. NICK GREENIA, Internal Revenue Service ....... 285

The Development of the Master Establishment List.

DAVID HIRSCHBERG, Small Business Administration ....................... 291

Enhancing Data from the Survey of Income and Program

Participationwith Data from Economic Censuses and

Surveys--A Brief Discussion of Matching Methodology.

DOUGLAS K. SATER, Bureau of the Census ● ● O● ● *● ● 0.. ● ● ● ● . . ● . .0.0 ● ● , ● .0 . ● ● 297

Discussion. JOSEPH STEINBERG, Survey Design, Inc. . . . . . . . . ● . . . . . . . . . . . 303

Rejoinder. NICK GREENIA, Internal Revenue Service .................... 305

Rejoinder. DAVID HIRSCHBERG, Small Business Administration ........... 307
Page

Computer Software

Project LINK-LINK: An Interactive Database of

Administrative Record Linkage Studies. JANE L. CRANE,

National Center for Education Statistics, and

DOUGLAS G. KLEWENO, U.S. Department of Agriculture ...● ......● . . . . . . . ● .

Current Record Linkage Research. MATTHEW JARO,

U.S. Bureau of the Census . . . ...0.. . ..0..... ● . ...*... . . . . . . . . . . . . . . . ..O

Record-keeping and Data Preparation Practices

to Facilitate Record Linkage. MARTHA SMITH,

Statistics Canada ● . . . . . . . . . . . . . . . . . . . . ...0.. . ...0..0. . . . . . . . . . ● ...0.0.

Generalized Iterative Record Linkage System.

TED HILL and FRANCIS PRING-MILL, Statistics Canada ..0.00....● ...0.....

Appendix A: Selected Bibliographies of Exact

Matching Methodologies and lcatlons

1.

2.

3.

4.

5.

Updated Bibliography of Work on Exact Matching. Compiled

through 1985 by WENDY ALVEY, Internal Revenue Service .**.*,......*

Selected Bibliography on the Matching of Person

Records from Different Sources. Compiled through

1974 by FRITZ SCHEUREN and WENDY ALVEY, Social

Security Administration .● .● .● .......● ● ● ● .● .....● ....● ● ● ● ,.0..● ● ● ..

Record Linkage: Its Methodology and Application

in Medical Data Processing. Compiled through 1969 by

G. WAGNER, Information und Statistik am Deutschen

Krebsforschungszentrum,and H.B. NEWCOMBE, Atomic

Energy of Canada Limited ...● ● ● ● ...........● ..● .● ● .● ● . . ● . ● ● . . ● ● . . . .

The Development of the Small Business Data Base

of the U.S. Small Business Administration: A

Working Bibliography. Compi1ed through 1985 by

BRUCE PHILLIPS, Small Business Administration .....................

Bibliography of Methodological Techniques Related

to Exact Matching. Compiled through 1984 by WRAY SMITH,

Harris-Smith Resea~h, Inc. . . . . . . . . . . ● . . . . ● . . . . . . . . . . . . . . . ● . ● ● . . . .

311

317

321

327

337

347

357

375

381

vi
d2a

W%

.-

.●..........●........................●.●............●.

$2zzk00f

cmG...........●..........●...........●●.●.........●●...● %

u) u

(nG0+u$acI

r--

mmm.................●●....●●..●●●...●●..●..●....●........

-1

-F

u) +0+$Ew0r

,. .-
IIITRODUCTIOW

In June l!W, the Administrative I?ecords

Subcommittee recommended to the Federal Committee on Statistical Methodology that a subcommittee be set up to explore integration of

surveys and afiministrative records. The result

was the creation of a Matching Group, whose

initial (ambitious) goals were to examine policy

strategies in conducting data linkages; look at

such methodological issues as measurability of

matching and analysis of statistical techniques

in view of matching errors; and study previous

linkage studies for suggestion of possible

alternative approaches to matching problems.

Both population-based linkages and establishment

matches were of interest.

The Matching Group began by reviewing the

available literature on exact matching. It soon

became apparent that some gaps in knowledge

existed that perhaps could be addressed by a

workshop conducted by experts currently working

in that field. Thus was born the Workshop on

Exact Matching lMethodologies,

The Workshop was designed to balance the

disparate interests of the many different people

involved in exact matching: statisticians,

research analysts, and computer progranmners.

SLlbject matter interests ranged from the

epidemiologists’ concerns about person-matches

to occupation and mortality data, to economists’

desires to create estimates based on establishment linkages. As such, the Workshop was viewed

as a means of summarizing the work done on

matching over the past ten to fifteen years,

filling in some of the holes we had discovered,

and drawing this more current information

together in one place -- this volume -- for use

as a ready resource aid by the statistical

community and its users. The conference was

also seen as a means of building a netmrk of

people interested in matching, with a view

towards establishing a more coordinated approach

to future policy and research efforts.

The Workshop

The Workshop drew 140 registrants from both

the U.S. and Canada, representing 47 different

agencies, universities, and businesses -- a very

sizable segment of the major contributors to the

field of exact matching today. Not surprisingly, well over half of those who attended

represented Federal agencies; most notably, the

13ureauof the Census, Internal Revenue Service,

Social Security Administration, Energy Information Administration, and the Bureau of Labor

Statistics. Furthermore, about half of those

who came expressed primary interest in application issues. The remainder were about equally

divided between statistical theory and computational developments.

Workshop Results

In addition to the interactions which took

place at the Workshop, there were also several

important tangible products which resulted from

that effort. First, based on discussions at the

Workshop and subsequent correspondence, some

recommendations for next steps in exact matching

were developed. These were summarized by the

Matching Group and appear following this Introduction. Next, selected papers representing the

historical development of modern matching

methodological thought were assembled. These

have been represented here in Section I. Then,

in Sections II through VI, presentations from

the Workshop and a few additional related papers

are provided. Their inclusion is intended to

document the current state of exact matching

methodology, application and computer software

development.

Finally, extensive efforts e re made to

develop a comprehensive bibliography of exact

matching literature. What resulted was a

collection of five separate reference lists,

each slightly different in orientation. These

are provided in Appendix A. Also, along similar

lines, the Matchinq Group developed a special

software package containing a menu-prompt

~~~ib~~ of information on recent exact matching

This effort was dubbed Project LIHKLIWK aid is described in Section VI of this

volume.

one of the most important outgrowths of the

Workshop, however, was that it provided a long

overdue forum for persons working in the area of

exact data linkage. It not only sparked new

interest in matching, but provided an atmosphere

where participants could interact on a more

personal basis -- a very important factor which,

in the past, has been lacking, resulting in unnecessary duplication of effort in some

cases. If nothing else, the Workshop has served

its purpose if it acts as a catalyst for

initiating more concerted efforts with regard to

matching policy, methodological development and

application. It was with this aim in mind that

the Matching Group assembled the Recommendations

which follow.

1
RECO~ENDATIONS

rive reconsoendations are provided here from

the Matching Group, Administrative Records Subcommittee, Federal Committee on Statistical

Methodology. The first recommendation calls for

the establishment of a continuing interagency

working group on record linkage systems and

techniques: such a working group would be

expected to play a significant role in implementing recommendations 2 through 5. The second

recommendation calls for careful monitoring of

external developments that might affect the

prospects for undertaking record linkages for

statistical purposes. Recommendations 3, 4 and

5 identify specific aspects of record linkage

systerns and techniques that deserve special

emphasis in future research, development and

evaluation activities. The five recommendations

are:

1. Documentation should be improved and

Information on record linkage systems and

lechnltauesshould be shared.

t 1s recommended that the Matching Group

of the Administrative Records Subcommittee

be reconstituted as a Technical Working

Group on Record Linkage Systems and

Techniques, continuing to function under

the auspices of the Federal Committee on

Statistical Methodology. The main goal of

the Working Group would be to promote the

effective use of record linkage techniques

for statistical purposes by encouraging the

documentation of individual record linkage

systems and techniques and the sharing of

relevant technical information. A primary

activity would be sponsorship and

organization of workshops and meetings of

professional societies to discuss relevant

new developments and research, and to

disseminate information on existing systems

and techniques. In addition, the

reconstituted working group would

contribute, in appropriate ways, to the

implementation of recommendations 2 through

5 below.

2. Changes in the external environment for

record llnkages should be monitored.

tatlstlcal users of record linkage

techniques should track external developments that may influence their ability to

perform record linkages. Such developments

include changes in laws, regulations and

policies affecting access to records and

changes in the content of data files used

in record linkages. Examples of the latter

would include increased use of four-digit

ZIP code add-ens (“ZIP + 4“) o~d steps

taken to promote the use unique

addresses in rural areas. In so far as

possible, statistical users of record

linkage techniques, working through the

reconstituted Working Group (see recommendation 1), should attempt to influence the

course of these developments in ways that

will facilitate statistical applications.

For example, the Working Group might try to

promote the development of standards for

reporting names and addresses of both businesses and individuals.

3. Comparative evaluation studies of record

Tinkage systems should be undertaken.

3everal aqencles of the Unlted States and

Canadian - governments have invested

substantial resources in the development of

automated record linkage systems for use in

a variety of statistical programs. For

many new applications, use of an existing

system is likely to be more cost-effective

than development of a new one. To aid

potential users of record linkage systems,

it is recommended that resources be sought

for comparative evaluations of existing

systems and some of their components, such

as name and address standardizers and

blocking rules. The evaluation design

should recognize that record linkage

systems vary in their objectives,

especially with respect to the kinds of

units for which records are to be matched:

persons or businesses. A much-needed first

step is the development of a detailed

evaluation plan that specifies the measures

of quality and cost to be used in the

evaluation and the nature of the files to

be matched. Such evaluations may reauire

data sets for which true match status is

known. One possibility would be to create

such data sets by simulation.

4. Research and development aimed at the

improvement of record linkage systems and

techniques should glve priority to selected

%$%%ing that resources for the

devejopmen~ of improved record linkage

systems are limited, it is recommended that

priority be given to the following aspects:

(1) systems for linking business records,

(2) name and address standardizers, (3)

string comparators, (4) the choice of

blocking strategies, (5) the development of

“learning” systems, and (6) the role of

manual intervention.

5. Errors associated with record linkages and

helr effects on analyses should be

measured.

-It is recommended that more research be

carried out on the error characteristics of

record linkage systems and on the effects

of errors on analyses performed with the

linked data sets. To enhance the value of

such research, consensus is desirable on

standard measures of record linkage errors

and on methods of measuring them. Promising error measurement methods include

3

—
p

g q

contacts with samples of linked pairs to

determine their true match status.

By design, the principal focus of the Workshop

discussions and followup comments by

participants was on methodological aspects of

record linkages for statistical purposes. Legal

and ethical considerations in such linkages were

not part of the main agenda.

Nevertheless, the Matching Group of the

Administrative Records Subcormnittee recognizes

that legal and ethical considerations must be

weighed carefully by any organization that links

perceptions of the appropriateness of various

kinds of record linkages are also of critical

importance. More research in these areas WUIC!

also be desirable, addressing, in particular:

(1) public understanding of and attitudes toward

linkages performed for statistical and other

purposes; (2) survey respondents’ comprehension

of informed consent statements currently being

used, especially when such statements cover

linkages of survey data and administrative

records; and (3) the effects on survey response

of varying the amount and kinds of information

included in informed consent statements to

respondents.

MATCHING GROUP:

Members of the

ADMINISTRATIVE RECOf?DSSUBCOMMITTEE

(as of May

Mary Bentz

Internal Revenue Service

David Cordray

General Accounting Office

Jane Crane

National Center for Education Statistics

Maria Elena Gonzalez

Office of Management and Budget

Nick .Greenia

Internal Revenue Service

David Hirschberg

Small Business Administration

Howard Hogan

Bureau of the Census

Thomas B. Jabine

Consultant, Committee on National Statistics

Matt Jaro

Bureau of the Census

Patrick Kelley

Bureau of the Census

1985)

Beth Kilss

Internal Revenue Service

Nancy Kirkendall

Energy Information Administration

Douglas Kleweno

Department of Agriculture

Thomas Reilly

Bureau of the Census

@ouglas Sater

Bureau of the Census

Fritz Scheuren (Chair)

Internal Revenue Service

Wray Sinith

MathematicalPolicy Research

Wendel Thompson

Department of Energy

Carol Utter

Bureau of Labor Statistics

William Minkler

Energy Information Administration

4
.

Section 1:

Selected Background

Papers (1959 - 1983)

A

-
Automatic Linkage of Vital Records*

‘.

Computers can be used to extract “follow-up”

statistics of families from files of routine records.

H. B. Newcombe, J. M. Kennedy, S, J. Axford, A. P. James

The term record linkage has been

used to indicate the bringing together

of two or more separately re?orded

pieces of information concerning a particular individual or family (I). Defined

in this broad manner, it includes almost

any use of a file of records to determine what has subsequently happened

to people about whom one has some

prior information,

The various facts concerning an individual which in any modern society

are recorded routinely would, if brought

together, form an extensively documented history of his 1ife. In theory at

least, an understanding might be derived from such collective histories concerning many of the factors which operate to influence the welfare of human

populations, factors about which we are

at present almost entirely in ignorance.

Of course, much of the recorded information is in a relatively inaccessible

form; but, even when circumstances

have been most favorable, as in the

registrations of births, deaths, and marriages, and in the census, there has been

little recognition of the special value of

the records as a source of statistics

when they are brought together so as to

relate the successive events in the lives

of particular individuals and families.

The chief reason for this lies in the high

cost of searching manually for large

numbers of single documents among

vast accumulations of files. It is obvious

that the searching could be mechanized,

but as yet there has been no clear demonstration that machines can carry out

the record linkages rapidly enough,

cheaply enough, and with sufficient accuracy to make this practicable.

The need for various follow-up studies

such as might be carried out with the

aid of record linkage have been discussed in detail elsewhere (I, 2), and

there are numerous examples o{ important surveys which could be greatly

extended in scope if existing record files

were more readily linkable (3). Our

special interest in the techniques of record linkage relates to their possible use

(i) for keeping track of large groups of

individuals who have been exposed to

low levels of radiation, in order to determine the causes of their eventual

deaths (see 4, chap. 8, para. 48; 5),

and (ii) for assessing the relative importance of repeated natural mutations

on the one hand, and of fertility differentials on the other, in maintaining

the frequency of genetic defects in

human populations (see 4, chap. 6,

para. 36c).

Our own studies (6) were started as

part of a plan to look for possible differentials of family fertility in relation

to the presence or absence of hereditary

disease (through the use of vital records

and a register of hm-sdicapped children).

The first step has been the development

of a method for linking birth records

to marriage records automatically with

a Datatron 205 computer. For this purpose use has been made of the records

of births which occurred in the Canadian province of British Columbia

during the year 1955 (34, 138 births)

and of the marriages which took place

in the same province over the 10-year

period 1946-55 (114,47 1 marriages).

Fortunately, these records were already

in punch-card form as a part of Canada’s National Index, and from them

could be extracted most of the necessary information on names and other

identifying particulars. An intensive

study of the various sources of error in

the automatic-linkage procedure has

now been carried out on approximately

one-fifth of these files.

Technical Problems

One of the chief difficulties arises

from the unreliability of the identifying

information contained in successive records which have to do with the same

individual or married pair. The spellings of the surnames may be altered,

the first Christian name on one record

may become the second on another, and

the birthplaces and ages may not be

correctly stated. Much of the design

effort must be directed toward ensuring

that records can be linked in spite of

such discrepancies, which in our files

occurred with frequencies of about 10

percent of all record linkages involving

live births and 25 percent of all linkages involving stillbirths.

A second problem relates to anlbiguous linkage, in which it is uncertain

whether or not a birth has arisen out of

a particular marriage, or where there

are two or more marriages any one of

which might be that of the parents.

These problems tend to occur when the

husband’s surname and the wife’s maiden name are both common in the region

studied, but they can also be associated

with rarer family names, as in the marriage of two brothers to two sisters, and

in certain racial minority groups. The

difficulty increases with the size of the

population under study.

At first sight these considerations

might seem to preclude any extensive

use of automatic record linkage as a

source of statistics, since it is not at all

obvious that the rules of judgment as

exercised by a human being can be

adapted to machine use. Also, partially

mechanized record-linkage operations

have proved laborious in the past (7).

Nevertheless, satisfactory procedures

were eventually developed. These began

with a series of small-scale attempts to

link records visually, and thus to gain

insight into the causes of any failures.

The first of these studies was carried

out at the Bureau of Statistics by one

of us (S.J. A.) and made use of one of

the standard phonetic name-coding systems to reduce the undesirable consequences of spelling discrepancies in linking records of sibling stillbirths. The

gradual evolution of the method since

thattime has served to make it evident

that further refinements can undoubted-

*Reprinted with permission from Science, Copyright 1959, by the American Association

for the Advancement of Science, Vol. 130, No. 3381, October 16, 1959, pp. 954-959.

7
SINGLE SURNAMES

w

p- ;n sight.

? I

Of primary interest was the development of a procedure which would be

4 16 & 2k toi4

FREQUENCIES PER 108

.

ly redeveloped and that nolimit to the

possible reliability of the linkages is yet

PAIRS OF SURNAMES

1

-

FREQuENCIES PER 109

Fig. 1. (Top) Frequency distribution of brides’ maiden names, in Smsndex coded fore,

from records of 114,471 marriages in British Columbia for 1946-55. (Bottom) Fre.

quency distribution of family-name pairs for married couples, in Smsndex coded form,

from the same records. Two East Indian names, of which one is customarily passed from

mother to daughter and the other from father to son, were omitted. ‘flew occurred

togethr.r in the same combination in approximately 100 marriages.

fully automatic and free from piecemeal

operations which might later limit the

usefulness of the approach. This aim

was achieved, chiefly because the use of

a computer made it possible to compare

each birth record in turn with all of

the marriage records in appropriate sec-

---- tions of the marriage file. Since groups

of marriages were sometimes scanned a

number of times, it is apparent that this

operation could not have been carried

out with conventional card-handling

equipment. Thus, without the computer,

a visual search through printed lists

would have been required to achieve

some of the linkages.

To reduce the number of marriage

records with which the computer must

compare a birth record, it was decided

to make use of both the husband’s surname and the wife’s maiden name, these

being present on both the marriage and

the birth cards. The surnames were fir@

reduced to phonetic codes, consisting in

each case of the first letter of the name

followed by three numeric digita and

known as the Russell Soundex Code

(8), the computer being used for the

coding operation. The codes served two

purposes: They were designed to remain

unchanged with many of the common

spelling variations and in the present

application were thus expected to bring

together linkable records which would

have been widely separated if arranged

in a strictly alphabetic sequence. The

coding also simplified the subsequent

use of the Datatron computer, which is

essentially a mathematical instrument

and works more readily with numbers

than it does with letters.

The extent to which two surnames

are more efficient than one for identifying a family group has probably not

been generally recognized. Thus, of the

various brides’ maiden names encountered in the marriage file, more than

half recurred (in their coded forms)

with frequencies in the range from 64

up to 1024 per 105. In contrast to this,

nearly 80 percent of the pairs of family

names (in their coded forms) were

unique; that is, they occurred only once

in our file in that particular combination, and extremely few had frequencies

exceeding 4 per 105 (see Fig. 1). This

8
.

meant that we could mechanically compare each birth for the entire year with

all of the marriages, using the same pair

of surname codes, and that only rarely

would the number of code matchings

exceed one or two per bkth.

To enable the computer to decide

whether or not a birth and a marriage

relate to the same married pair, use

must be made of other identifying particular. We relied chiefly on six items:

the full alphabetic family names of the

husband and wife (limited to nine letters each), their provinces or countries

of birth (each coded as a two-digit

number), and their first initials. In addition, the ages of the married pair were

available on our cards for all of the

birth records and for about half of the

marriage records (that is, for marriages

in the period 1951-56); the second initials were present in the case of the

birth file; and the name of the city or

place of the event (restricted to six letters) WASavailable throughout both files.

As mentioned earlier, no one piece of

information was entirely reliable. Usually it was obvious on inspection that

the two events did, or did not, relate

to the same married pair, but occasionally the decision was difficult. For this

reason the computer had to calculate

a probability that the couples were the

same, or were different. The operation

was performed automatically when the

files were first matched.

The principle on which such a probability was based is fairly simple. If,

for. example, the province or country

of birth of both the husband and wife

WIrtiow AOES

,

‘.

-

two—

—

+

4G# —

-so -20 -m o 10 20 so 40

LOG2 OF THE PROSA61LITIES

WITH AGES

eoo -

● xs -h

II

f-l

I -1

1

T-,

-s -40 -so -20 -10 0 {0 ?b sb’4b’

LOGZ OF THS PRGGABILITIES

Fig. 2. (Top) Frequency distribution of the probabilities (in binits) obtained on comparing birth and marriage records having identical Soundex code pairs (calculated without using ages), based on records contained in the first fifth of the birth and marriage

files (husband’s surname beginning with A, B, or C). For this comparison only legitimate live births and marriages recorded in 1951-55 (a period for which ages are available) were considered. There were 2174 cases of genuine linkage and 1232 cases of

accidental Soundex agreement. (Bottom) Same as above, except that the ages were used

in calculating the probabilities.

agree on the two records, these facts

may influence somewhat our belief that

these records relate to the same married

pair. Of course, the weight which one

attaches to the information will be small

if both have been born in the home

province of British Columbia, but it will

be large if they happen to have been

born in, let us say, Switzerland and

New Zealand, respectively. To give this

a mathematical form it is necessary to

know the frequencies for the various

birthplaces of brides and grooms, and

these can be determined quite readily

either from published statistics or from

the files themselves.

Similar reasoning can be applied to

any item of identifying information,

and to both agreements and disagreements. In order that the probabilities

may be added together they must be

converted to logarithms, and it is conventional practice in information theory

to use logarithms to the base 2 of the

probabilities expressed in’ the form of

the “odds,” for or against. l%e units

are known as “binits.” Thus, if the odds

were 16 to 1 in favor of a genuine linkage, this would be represented as plus 4

binits, and odds of 16 to 1 against wouId

be minus 4 binits. It is convenient to

remember that a value of 10 binits is

equivalent to odds of approximately

1000 to 1.

For present purposes, the probability

or odds associated with a given agreement or disagreement may be obtained

in binit units from the expression:

% Pr, - ‘O& PF (1)

where pL and p~ are the frequencies

with which the agreement or disagreement occurs, respectively, in the linked

paira of records and in pairs which have

been brought together by accident. The

expression will have a positive value in

the case of agreement and a negative

value in the case of disagreement.

As applied to agreements of initials

and birthplaces, the expression can usually be simplified without any great loss

of accuracy, since the particular letter

or place should agree in the linked records almost as often as it appears in the

individual records, and the chance of a

fortuitous agreement will in most cases

be approximately the square of this

frequency. By substitution, expression

1 thus becomes:

log, pR – loft, (pR)* = – 1% PR (2)

where pR is the frequency of the particular initial or birthplace in the individual records.

9
The approach also lends itself to com

parisons of the ages as stated on the two

records, the lapse of time between the

two events, and whether a discrepancy,

if present, is slight or large, being taken

into account. Even such an unlikely

item as the place of the event can be

used; if the marriage and the birth occurred in different places the fact carries little weight, but if they occurred

in the same place (provided it was not

the largest city in the province) the fact

is important.

The items from which the probabilities were calculated in our study were

the two alphabetic surnames, the two

birthplaces, the two firat initials, the two

ages (where these were given on the

cards ), and the place of the event. For

possible future use the computer also

compared the birth order with the apparent duration of the marriage at the

time of the birth, and wherever a first

initial failed to agree, the computer

looked for agreement between the first

initial on the marriage record and the

corresponding second initial on the birth

record.

This sort of treatment can be adapted

to linking almost any types of records

where the information in common is

sufficient for the purpose, Although

tables of probabilities (in binits) containing over 300 items were used in the

present study, they did not exhaust the

capacity of the computer’s memory unit.

The limiting factor is the discriminating

power inherent in the information supplied, and it is apparent that additional

items of information can be of use even

where they are of limited reliability.

The extent to which ages, for example, enable the computer to separate

the genuine linkages from the fortuitous

Sounr!ex agreements can be seen from

the data of Fig. 2. In this case, the number of record comparisons falling in

the region from minus 10 to plus 10

binits, where the degree of certainty is

less than 1000 to 1, is reduced by a

factor of 3 when use is made of the

additional information.

Reliability of the Linkages

Studies of the accuracy of the present

computer-handling procedures indicate

that about 98.3 percent of the potential

linkages are detected in the existing record files, and that contamination with

spurious linkages is 0.7 percent [see

(9) ]. This degree of accuracy is considered adequate for the statistical studies

Table 1. Surnamespelling discrepattcies.

__._-l Dkcrepancies

Number of Total spelling affecting the

Name linkages discrepancies phonetic codes

in sample

No. IPercentage No.

I

Pcrcerrt8ga

Husband’s surname

———

3622 41 1.1 15 0.4

Wife’s maiden name 3501 115 3.3 42 1.2

Combined 4.4 t.6

● Based on visualIinkaaes of birrbswkthmarriages. To detect sp$f!ing discrepanci~ in a random umrtment of

.._

the fmrrifyttamcsof onepartner,w wasmade nf the parts of the files m w’htc.hthe fantdy name Of the Spu= -n

wrh 4, B, or C. Thus, the Iwo sunpfes of roeords each represented approxlmatcly 19 percentof the total tiles.

Table 2. Dkcrepancies in birtlsplwes and fmatinitials*.

Number of

Category

Discrepancies

linkages

in sample No. I

Percentage

Birthplace of husband 2174 22 I.0

Birthplace of wife 2174 21 I.0

Firstinitialof husband 2174 60 2.8

First initial of wife 2174 53 3.8

Total S.6

Total, including surnames 11.4

Linkages having discrepancies in one or more of the six items 1o.3

● Discrepancies in com,puler Iinktges of records contained in the first fifth of the birth and marriage film (bus.

bands’ surnames begmnmg with ,A, 8, or C): Only lmk~ser of legitimate hve births with marria~ti in the ~ri~d

1951-S6(fm which aECSwere m’mlable)were uswt. For :bc ‘Iotas, including S“mamcs,,- useW- madeof tie data

from Table 1.

which have been planned, since the loss

of such a small amount of data cannot

in itself constitute a source of bias.

Further, both the losses and the contaminations can be detected in the majority of cases by means of a subsequent

check on the continuity of birth orders

within families.

Variations in the spelling of the family names occur in about 4 to 5 percent

of all linkages, but the losses ‘from this

source are reduced by the use of the

phonetic codings to approximately a

third of that value (see Table 1). The

detection of such losses was accomplished by the simple expedient of resorting the files in a sequence which

ignored the suspect code but trusted

other identifying items, the files then

being listed and examined visually. This

operation could have been performed

by the computer, and since the six main

identifying items all agree in about 90

percent of the linked pairs of records

(see Table 2), two additional arrangements of the files, each of which ignored

one of the two Soundex codes, would

be sufficient to reduce losses of this kind

from the present 1.6 percent to about

0.16 percent. For the projected statistical studies such a procedure would

hardly be worth while, the computer

time being the limiting factor. It might

become of value for other purposes,

however, as computer speeds increase,

especially as it is customary for central

registry offices to keep two separate

listings of marriages for searching purposes, arranged under grooms’ surnames

and brides’ maiden names, respectively.

Failure of the calculated probabilities

to make a correct distinction contributed a few additional losses and a few

spurious linkages. These were detected

by comparing the full Christian names

as given on the original registration

forms wherever the calculated probability fell within the range from minus 10

to plus 10 binits. Where age was used

in calculating the probabilities there

were only one loss and four spurious

linkages from this source in a sample

of over 2000 linkages (see Table 3).

Although this degree of accuracy is adequate for almost any purpose, to make

a further reduction in the number of

spurious linkages would not be difficult.

Table 3. Losses and spurious linkages due to

Iack of sufficient identifying information, which

occurred in the linkage reported in Table 2

(9).

Spurious

No. of Losses tinkaaes

Item linkages

in No. sample ce%ge ‘0’ rcentage ‘peAge data

used 2174 1 0.0s 4 0.23

Age data

not used 2174 S 0.2 26 1.2

10
.

●

The contamination with spurious linkages will tend, however, to vary in direct

proportion to the size of the marriage

tile with which the births are compared.

Thus, in any future studies of larger

populations it might be desirable to

make use of additional identifying information. Christian names (perhaps restricted to four letters each), the city of

birth of the husband and of the wife,

respectively (likewise restricted to a few

letters), and the province and year of

marriage (not shown at present on the

birth registration form) would all be

suitable data for this purpose. The last

of these three groups of items, however,

would be of special value in effectively

reducing the size of the marriage file

with which any one birth would have

to be compared, and in this manner reducingthe false linkages. Occasional inaccuracies in the additional information

would not greatly alter its usefulness in

view of the nature of the handling procedures.

It is doubtful whether the present accuracyof the procedure can be matched

by that of conventional survey and interview techniques, and its potential accuracy is certainly much greater than

that of conventional techniques.

Speed of Record Lisskage

By far the largest part of the effort in

this undertaking has gone into the preparation of the card files. This has included, in the case of the marriage cards,

a mechanical reproduction of the information contained in the existing National Index marriage cards for brides and

for grooms, respectively, on a single card

of our own format. Likewise, a part of

the contents of our birth cards was obtained by reproduction from existing

National Index birth cards, but in this

case the maiden name of the mother and

a number of other items were then

added from cards which had been especially key-punched for the purpose. The

family names on all cards in both files

were Soundex coded by means of the

computer, and the files were sorted into

a Soundex sequence by pairs of codes,

and listed. For the purpose of the initial

record-linkage study the part of the

marriage file for married pairs in which

the groom’s surname began with A, 1?,

or C (approximately one-fifth of the

total file) was transferred to magnetic

tape.

This done, the computer made the

necessary birth-to-marriage comparisons

when presented with the birth cards,

matchings with respect to the pairs of

name codes being achieved .at a rate of

approximately one comparison every 3

seconds. About half of these code agreements represented genuine linkages ( /0).

(Subsequently the whole of the birth

and marriage files were put on magnetic

tape and linked automatically by the

computer. )

The initial steps would be largely

eliminated were the format of the cards

which are prepared routinely designed

with a view to their possible use for

record-linkage purposes. Also, an improvement in the rate at which the computer makes the comparisons can be

gained in later operations by limiting

the longer computations to the relatively small number of comparisons where

simpler tests are inadequate. Some other

short cuts might well be effected in the

program if it were used sufficiently to

justify the time involved. Such improvements can be thought of as reducing the

cost of record linkage, in which computer rentals may be a major item, and

of incrwsing the ease with which statistics can be derived from the linkage

process.

The use of a computer especially designed to handle alphabetic information

would further reduce the time required

for the linkages by virtue of this special

designalone,and therearelargercomputersin which the basic logical steps

are more rapid by an order of magnitude. Thus, the present rate of something like one linkage every 6 seconds

might be increased perhaps 20- or 30-

fold—that is, to 200 or 300 linkages per

minute, with existing equipment.

It is difficult to guess to what extent

these speeds will be exceeded in the next

10 years or so. However, circuits have

been described in the literature in which

the basic logical steps take much less

time than those in any equipment at

present on the market (11). Research

with the more novel kinds of electrical

switching devices, some of which are

not only fast but extremely compact,

may extend the present limit by at least

another order of magnitude (12).

Well before such equipment becomes

available, however, it should be possible

to develop the data-processing methods

by which record linkages are achieved

to the point at which the extraction of

a wide variety of family and follow-up

statistics becomes practicable from any

records which are in an accessible form.

References ad Notes

L H. L. Dunn, Am. J. Public Health 3S {Dec.

1946): J. T. Marshall, Popufafhm Studies 1,

204 (1947).

2. H. L: Dunn ● nd M. Gilbert, Public Health

RePfs. (U. S.) 71, 1002 (1956); H. B. Nawcomha, in Eflect of Radfation on Human

Heredlry (World Health Organization, Geneva, 19S7): —, A. P. James, S. J. Ax.

ford, “Family Linkage of Vital and Health

Records,” Atomic EnerKy Can. Rept. No. 47o

(Chalk River, 1957); H. B. Newcombe. S. J.

Axford, A. P. James, “A Plan for the Study of

Fertility of Relatives of Children Suffering

from Hereditary and Other Defects,” A fomic

Energy Can. Repf. No. S5/ (Chalk River,

1957); H. B. Newcombe, A. P. James, S. J.

Axford, “Genetic hazards and vital s!atisitcs, ” Proc. lnfrrn. Conm. Genet. 10th Conxr,.

Montreal ( 1958), vol. 2: p. 205.

-.

3. S. C. Reed and J. D, Palm, Sctcnce 113, 294

(1951); S, C. Reed, E. W. Reed, J. D. Palm,

Eugenics Quurf. L 44 ( 1954): T. E. Reed,

Japan. J. Hutwm Genet. 2, SUPPL,4S ( 1957);

— and E. L. Kelly. Amt. Human Genrt.

22, part 2, 165 (1958); A. B, Hill, R. Doll,

T. M. Galloway, J. P. W. Hushes, 3fril. J.

Prercnf. & Social Med. 12, 1 ( 195S ).

4. Reoort of the United Nations Sclcnt{fic Com5%

6,

7.

s.

. .—— mitt- on the Effects of Atomic Rodintlon,

Suppl. No. 17 (A/3838) ( United Nations,

New York, 1958).

H. B. Newcomhe, Science 126, 549 (1957).

We are indebted to John H. Doughty for

his encoum$ement and constructive criticism

in the course of this work, 10 Robert J. Momgomery for makins available facilities for the

preparation of the marriage file, and to George

.SeIby for his help in this initial operation. We

would also like to thank Elizabeth Kinsey for

collaborating in the preparation of the record

tiles and in the analysis of the results, and

Ardcn Okasaki for her work in pro!$rammins

the computer. Permission to use the vital records in this study was obtained thmush the

Dominion aureau of Statistics, from the

Health Branch, Department of Health and

Welfare. Province of British Columbia. The

permission was conditional upon strict observ.

imce of the oath of secrecy respecting the

nonstatistical information contained in tbe

records.

S. Shapiro and J. Schachter, Estadktka 10,

6.SS (1952).

The rules of Soundex coding are as follows. (i) The first letter of a surname is uncoded and serves 8s the prefix letter. (ii) W

and H are isnored completely. (iii) A, E, 1,

0, U, and Y are not coded but serve as aep

ar’dtOm(See v below). (iv) Other letters are

coded as follows, until three disi!s have been

used up (the remaining letters are ignored):

B, F, P, V, coded 1: D, T, coded 3; L, coded

4; M, N, coded 5; R, coded 6; all other consonants (C, G, J, K, Q, S, X, Z). coded 2.

(v) Exceptions are letters which follow letters

havin$ the same code, or prefix letters which

would, if coded, have the same code. These

ore ignored in all cases unless a separator

(see iii above) nreced.es them.

9. since ages wer~ available on only atjqut half

of [he marriase cards, the average lossaa from

this cause were 0.12 percent of all linkages,

and the average spurious linkages were 0.7

percent. When these are added to the losses

resulting from the Smtndex ducrepancies, as

shown in Table 1, the total loss is 1.72 percent,

10. It is known that approximately 19 per cent of

the surnames in the marriage file begin with

A, B, or C, as determined from studies of

the frequencies of brides’ Sotmdex codes.

Thus, of the 114,471 marriage records and

34,138 birth records, approximately 21,750

and 6500 records, respectively, were used in

the initial linkage study. In all, 6375 comparisons (3484 with positive bittit values and 2S91

with negative) between birth raeords and marriase records having identical pairs of !Jcwndex

codes were made by the computer. Of these,

418 (20 positive and 39S negative) related to

illegitimate births, 2549 ( 12S5 positive and 1264

negative ) related to legitimate births and to

1946-50 marriages, and 34oS (2179 positive and

1229 negative as determined by memts of

ases) related to legitimate births and to

1951-55 marriages. Since age records were

available in the case of the 1951-55 marriasea,

11
this latter group of 3408 compariaona was

used for a detailed study of the reliabilky of

the machine linkage proms.% (Revised tables

of bktit values were also derived from them

comparisons.) Two of the 3408 comparison

cards were removed bccauaa in each c- ona

of the ages was missing. Of the remaining

3d06 cards, 2174 reprcscntad genuine linkage

(2173 positive cards plus one negative card)

and 1232 represented accidental Soundex avec.

menta (4 p08itive plus 1228 negative cccda), puter linkages) were uacd to aaccsa the loaam

aa judged by comparisons of the full Christian due to SPC1lICISdiscrepancic$, aea footnote to

names in alf caaca where the binit valuec fall Table 1.

within the range from minus 10 to plus 10. 11. R.M. Walker, D. 5?. Roscnheim, P.A. f#wia,

It will ba noted that of the 6500 birth of A. G. Anderson, IBM J. Research and De.

1955 which were studied, 3484 (54 parccnt) velop. 1, 257 (1957).

were from marriagcc cmctcacted in British 12. R. F. Rutz, ibid, 1, 212 (1957); D. A. Buck,

Columbic during the If)-year parind 1946-S5. Proc. I,R.E, (Inst. Radto En.yrs.) 44, 482

For a dcacription of the manner in which (1956); J. W. Crowc, IBM J. Research and

visual record linkages (as distinct from com- D,vdOP. 1, 295 (1957).

Editors’ Note: In 1959 Dr. Newcombe and Dr. James were affiliated with the

biology branch of Atomic Energy of Canada, Ltd., Chalk River, Ontario. Dr.

Kennedy was affiliated with the theoretical physics branch of Atomic Energy

of Canada. Dr. Axford was affiliated with the health and welfare division

of the Dominion Bureau of Statistics, Ottawa.

12

—
Record Linking: The Design of Efficient Systems for

Linking Records into Individual and

Family Histories*

HOWARD B. NEWCOMBE

Biology Branch,

Chalk River Nuc.kar Laboratories,

Chalk Riuer, Ontario.

INTRODUCI’ION

THE APPLICATIONSof computer technology to genetic problems discussed so

far in this Supplement make use, primarily, of the ability of the machines to

carry out involved mathematical procedures. In contrast, the application which

I shall describe uses the computer as a kind of filing clerk. The task given it

is that of building family histories of births, marriages, procreations, deaths,

and ill health from the individual registrations of these events, and of doing

so on a substantial scale.

Although the computer is at no point asked to carry out any mathematical

operation more complicated than simple addition and subtraction, it must

nevertheless perform a function that is much more unconventional for machines. It is required to simulate the judgment of a human clerk who attempts

to file correctly the incoming correspondence from people who are careless

about the way they spell their family names, who may sometimes use their

middle names as if these were their first, and who may be writing from places

that are not their usual addresses.

Provided that a computer can be instructed to carry out an operation of

this kind with a degree of accuracy similar to that of a human filing clerk,

the special talent which it maybe expected to apply to the task is its speed.

Current experience with this sort of computer application is particularly

encouraging, in terms of accuracy, speed, and cost, and the capabilities of

the machines will undoubtedly increase as time goes on. Thus, it is not unrealistic to think of integrating, in due course, some major fraction of the routine personal documenhtion dealing with reproduction and health into the

form of individual and family histories.

~NCEPTS

A number of concepts will be discussed that are inherently simple, but

the implications of these concepts will not necessarily be self evident.

The idea of linking records, for example, is particularly simple—the phrase

record linking just means bringing together information from two independent

sources about the same person —but with successive linkings the information

may take on the characteristics of a collection of personal or family histories.

*Reprinted with permission from American Journal of Human

Genetics, University of Chicago Press, Vol. 19, No. 3,

Part I (May) , 1967.

13

.

—
Even such familiar file upkeep operations as the insertion of address changes

into a mailing list are elementary forms of record linking. However, the

process as applied to human genetics will involve successive linkings of

routinely collected records of procreative and health events to derive, eventually, rnultigeneration pedigrees for whole populations.

The two principal steps in any linking operation, namely, those of searching

out the potentially linkable pairs of records for detailed comparison and of

deciding whether or not a given pair is correctly matched, are commonplace

in almost any operation by which a file is kept up-to-date. However, both of

these steps, if they are to be carried out efficiently by machines, involve the

use of stratagems of kinds that are employed almost unconsciously by a human

filing clerk. For the searching step the aim must be to reduce the number of

failures to bring potentially linkable records together for comparison, such as

may occur as a result of discrepancies in the file sequencing information, but

this must be done without resorting to excessive amounts of additional searching. For the matching step, the problem is that of enabling the machine to

apply in numerical form the rules of judgment by which a human clerk would

decide whether or not a pair of records relates to the same person when some

of the identifying information agrees and some disagrees.

Similarly, the idea of arraying pedigree information in linear fashion to

facilitate storage, updating, and retrieval by machines using magnetic tapes

as the storage medium is simple and by no means new. Nevertheless, the

forms which such linear arrays may take bear little resemblance to the conventional pedigree charts with which geneticists are most familiar. The great

flexibility of the linear pedigrees and the ease with which family relationships

of unlimited complexity may be represented in such a fashion are, for this

reason, not generally appreciated. In comparison, however, the usual twodimensional representations are exceedingly cumbersome (Fig. 1).

Finally, it has not been uncommon in the past to derive partial histories

of individuals and families from the routine oital and health records, on

a small scale, by manual means. However, the idea that some substantial

fraction of these enormous files might be so organized and that we are at

the point now where this would be technically feasible and not too expensive

is one that has been slow in gaining acceptance. Nevertheless, the inherent

possibilities are beginning to be recognized. A colleague of mine is reported

to have remarked recently that we are still using old data on hemophilia, that

there are many hemophiliacs in Canada, almost all of whom will wind up in a

computer sooner or later, and “what a shame if it is only opposite a dollar

sign.”

The concepts may not be new, but such implications are,

METHODSOFRECORDLINKING

The two essential steps in the linking of records by computer, that is, the

search ing step and the matching step, have precise counterparts in many

manual filing operations. Although the accuracies of such operations and the

times required are generally regarded as important, it is unusual to judge the

efficiencies in numerical terms or to set down the conditions under which

14
FANNINGFORWARD

(!%%o

FANNINGBACKWARD

w

d

c)3%5

w

FIG. 1. Conventional pedigree charts, Note the difficulty of representing in a single

chart the ancestors, descendants, cousins, and in-laws,

an optimum balance may be achieved between the level of accuracy and its

cost as indicated by time required to achieve that level. Where such an undertaking is to be carried out on a very large scale by a computer, however, some

thought may profitably be given to the efficiency of the operation in these

terms.

L Optimizing the Searching Step

In the case of the searching step, errors in the form of failures to bring

potentially Iinkable pairs of records together for comparison could be reduced

to zero simply by comparing each incoming record with all of the records

already present in the master file. Where the files are large, however, such a

procedure would generally be regarded as excessively costly in terms of the

enormous numbers of wasted comparisons of pairs of records that are

unlinkable.

For this reason, it is usual to arrange the file in some orderly sequence,

using identifying information that is common to both the incoming records

and those already present in the master file. Detailed comparisons then only 

need to be carried out within the small portions of the master file for which

the sequencing information is the same as that on the incoming recosxls

(Fig. 2). For many purposes, it is common practice to ,MI the alphitbctic

surnames and first given names for sequencing a file of personal records.

The price that must be paid for the saving of time is an increase in the failures

to bring potentially linkable pairs of records together for comparison, owing

to discrepancies in the sequencing information on pairs that in fact relate

to the same person. However, different kinds of information that might

be used for the sequencing differ widely, both in their reliability and in the

extents to which they subdivide a file.

Although alphabetic surnames are commonly employed, they are not particu15

.

—

—
A)

B)

c)

NO SUBDIVISION

SUBDIVISION TO

H

2

SUBDIVISION TO

(100,000 RECORDS)

-NUMBER OF COMPARISONSFOR EACH

INCOMINGRECORD: 100,000

(OR 50,000 OEPENDINGONTHE RULES)

- CHANCEOF FAILURETO BRINGPOTENTIALLY

LINKABLE PAIRS TOGETHER = O

~ (e.g. BY SEX)

- NUM6ER OF COMPARISONSREQUIRED

IS HALVED

- CHANCEOF FAIWRE OEPENOSON THE

FALLIBILITY OR LIKELIHOODOF DISCREPANCY

OF THE ONE ITEM OF SEQUENCING

INFORMATION

)’100,000

- NUMBEROF COMPARISONSIS REOUCED

FROM 100,000 TO ONE PER NEW RECORO

- CHANCEOF FAILURETO COMPAREIS

INCREASEOBY THE FALLIBILITY OF EACH

SEQUENCINGITEM (THE CORRECT

MATCHINGRECOROCOULO BE IN ANY

ONE OF 99,999 OTHER PLACES)

FIG. 2. Optimizinga single sequence search. Subdivision must be based on items

of identifying informationwith the highest eilkiency ratios and must be adjusted to an

acceptablelow level of lossesor of wastedcomparisons.

lady efficient for sequencing, because of the high frequency with which they

are misspelled or altered. Considerable improvement can be achieved by setting aside temporarily the more fallible or labile parts of the information

which the surnames contain, while retaining as much as possible of the

inherent discriminating power. There are a number of systems for doing this,

the most common of which is known as the Russell Soundex code. This is

essentially a phonetic coding, based on the assignment of code digits which

are the same for any of a phonetically similar group of consonants. (Details

of a number of such surname coding systems are given in the Appendix. )

In practice, we have found that the Soundex code remains unchanged

with about two-thirds of the spelling variations observed in linked pairs of vital

records, and that it sets aside only a small part of the total discriminating

power of the full alphabetic surname. The system is designed primarily for

Caucasian surnames, but works well for files containing names of many

different origins (such as those appearing on the records of the U. S. Immigration and Naturalization Service ). This particular code is less satisfactory,

however, where the files contain names of predominantly Oriental origin,

because much of the discriminating power of these resides in the vowel

sounds which the code ignores.

16
\

Any kind of identifying information that is available on all of the records

may, of course, be used for sequencing the files, and it should not be assumed

that surnames necessarily possess special merit for this purpose. The qualities

required are reliability and discriminating power, both of which may be

measured numerically. Usually, where the discriminating power of any one

kind of information alone is insufficient to divide the file finely enough, two

or more kinds of information may be used together to achieve a required

degree of subdivision. However, each additional kind of information carries

its own likelihood of discrepancy and thus contributes to the over-all tendency

for the sequencing infmrnation to be reported differently on successive records

relating to the same person, with a resulting increase in the frequency with

which potentially Iinkable records will fail to be brought together for comparison. It is important, therefore, to choose the most appropriate kinds of information from among those that are available.

Fortunately, there are numerical tests which will indicate the relative merits

of the different items of identifying information for the purpose of sequencing

the files. Three values will be discussed, the coefficient of specificity, the

discriminating power, which is simply another way of describing the specificity,

and a so-called merit ratio, which may be used to indicate the amount

of discriminating power per unit likelihood of discrepancy. This latter value

can be used in selecting the most appropriate information to be employed in

sequencing a file.

The fineness with which a file will be divided by a particular kind of identifying information may be represented by a single number, the coefficient of

specificity,

C8= ~pE2 (1)

where P= is the fraction of the file falling in the xth block (see Fig. 3). CS

may be thought of as the fraction of the file falling within a block of strictly

representative size. Since most identifying information divides a file unevenly

into a mixture of small and large blocks, it is convenient to be able to indicate

the effective degree of division of the file in this simple manner.

Unlike the coefficient of specificity, which gets smaller as a file becomes

more finely divided, the discriminating power increases with the extent of the

subdivision. Furthermore, it is usually regarded as an “addable” quantity. Thus,

the discriminating power may be taken as the logarithm of the inverse of the

coefficient of specificity, and in practice we have found it convenient to use

logarithms to the base two ( see Table 1 ):

D. = log, ( I/cg) (2)

Finally, the merit of any particular kind of identifying information for sequencing the files may be taken as the ratio of the discriminating power to

the likelihood of discrepancy or inconsistency of such information in linkable

pairs of records:

M~ = D# (3)

In calculating this so-called merit ratio, we normally use the percentage likelihood of inconsistency as the numerical value of 1.

17

.

.
671

471/

/2

H

1/3

1/3

1/3

/

/4

1/4

/2

H

lx~

/xl

c.= ( 1/1)2 =1/1 =1

C*= (1/2)2 + (1/2)2 = 2/4 = 1/2

c.= (1/~)2 + (1/~)2 + (1/~)2 =g/* =1/rj

C.= (1/2)2 + (1/4)2 + (1/4)2 ‘6/16 = 1/2,7

c.= (1/=1)2 + (V=, )2 + —~p=2 . . . . ..—

(where P. is the proportionin the Xth block)

FIG. 3. Examplesof coefficientsof specificity.

TABLE 1. RELATIONSHIPOF COEFFICIENTOF SPECIFIUTTAND

DIXRIMINATINC POWER

COetllci&..~$e&icity

Equivalent number of

Discriminating power blocks{ffile

,--= 10& ( l/c, ) equally divided

1 0 20=1

1/2 1 21=2

1/4 2

22=4

1/8 3 2S=8

1/16 4 24 = 16

1/1024 10 210= 1024

1/106 20 220= 106

The most efficient sequencing of a file will be based on the items of

identifying information that have the highest merit ratios, using enough different items to achieve a combined discriminating power that will subdivide

the file to the required degree of fineness. In this manner, the minimum total

likelihood of discrepancy or inconsistency will have been introduced into the

sequencing items for any required degree of subdivision.

By means of such numerical values, the usefulness of surname information

in its Soundex coded form can be shown to be considerably greater than

18
.

.

T.mm 2. Ikwrsw Mmwrs OF ALPHABETICVERSUSSournmx CODED

SURNAMESFORSEQUENCINGFIhwiv:len:k:mber

Discriminating Percentage likelihood

Surname power - of equal size of disc= pancy* Merit ratio

information Dp I/c, 1 Mt = D$l

Alphabetic +9 512 ~.z 4.1 -

Soundex +8 258 0.8 10.0

Residual +1 2 1.4 0.7

●Averagefor husbands’and wives’birth surnames.

that of the full alphabetic surnames for the purpose of sequencing the files,

the merit ratio being about two or three times as large (Table 2). The residual

information that is omitted from the Soundex codes is of very low quality

indeed, having a merit ratio that is less than one-tenth that of the Soundex

codes.

The approach permits the searching step of a linkage operation to be

optimized, in terms of the numbers of ( 1 ) wasted comparisons to which an

incoming record must be subjected in order to be brought together with a

potentially linkable counterpart from the master file, and (2) failures to

bring such records together. A tolerable level may be set for either the wasted

comparisons or the failures, and the other value may then be minimized.

Adjustment is achieved by adding or deleting an item from the sequencing

information, thus increasing or decreasing the fineness of subdivision and the

errors simultaneously until the required balance is struck. At no time should

the sequencing information include an item with a lower merit ratio where

one with a higher ratio is available. The cost of the searching step is thus

balanced against its precision with a view to getting the best possible bargain.

In practice, we have found that by sequencing a master file of 114,000 marriage records in order of the ptiirs of surname codes for the grooms and

brides, d-senumber of wasted comparisons was kept at a very low level, i.e.,

0.6 per incoming birth record where the births had arisen from marriages

represented in the master file and 1.6 for all other incoming birth records.

The number of failures to bring potentially linkable records together for

comparison due to spelling discrepancies that altered one or other of the

Soundex codes amounted to 1.6%of the potentially possible linkages.

The discussion so far has assumed that all of the linkings will be carried

out using files arranged in a single sequence. However, the cost of sorting

by computer is rapidly diminishing. Where more than one sequence is permitted, an even better bargain may be struck in terms of the precision that

can be achieved for any given number of wasted comparisons. Linkings

may then be carried out using very fine subdivisions of the file sequences,

based on information of quite limited reliability, with the assurance that

potentially linkable pairs of records which are not brought together on the

first search will be compared in one of the alternative sequences based on

other identifying information.

One quite large manual test of such a procedure has been carried out in

19

.——
TABLE 3. IDENTIFYING INFORMATION ON VXTAL RECORDS

Birth Birth- Birth date

Event and individual name place* (or age)

Marriage

Groom + + (+)

Bride + + (+)

Father of groom + +

Mother of grwm + +

Father of bride + +

Mother of bride + +

Birth

Child + +

Father + + (;)

Mother + + (+)

Death

Deceased + + +

Spouse +

Father + +

Mother + +

*i.e., city or place, and province or country.

which initials and provinces of birth were substituted in the secondary sequences for one or other of the two surname codes. This test showed that a

reduction in errors by more than tenfold could be achieved at the price of a

two- to three-fold increase in wasted comparisons.

Where the avoidance of “lost” linkages is of special importance, the use of

multiple alternative sequences represents an ultimate in refinement.

2. Optimizing the Matching Step

When pairs of records are brought together for comparison, decisions must

be made as to whether these are to be regarded as linked, not linked, or

possibly linked, depending upon the various agreements and disagreements

of items of identifying information. It is also desirable that such decisions

be based on numerical estimates of the degrees of assurance that the records

do or do not relate to the same persons. The computer is asked, in effect,

to simulate the processes of human judgment and to make the best use it can

of the items of identifying information that are individually unreliable but

collectively of considerable discriminating power.

The extent of the personal information that is usually entered in the vital

registration makes the potential accuracy of the linkings of these records high

indeed. Newborn children, grooms and brides, and deceased persons are

commonly identified by their full birth names, their birth dates or ages, and

their birthplaces. Together with this personal identification, there is a substantial amount of family information. The full names of the parents, including

the maiden surname of the mother, are usually given, as well as their birthplaces. In addition, the ages of married couples are entered in the records

of their marriages and the records of the births of their children (Table 3 ).

20
llus, there is an abundance of overlapping information that may be used

to link ( 1 ) deaths to births, (2) births to the parental marriages and to the

births of older siblings, and (3) marriage records of brides and grooms to

their birth records, to the marriage records of their parents, and to the birth

and marriage records of their siblings (Table 4). Even where some of the

items fail to agree, the combined discriminating power of such information is

almost always large.

A human filing clerk attempting to carry out such a grouping operation

would intuitively attach greater positive weight to some of the agreements

than to others and greater negative weight to some of the disagreements

than to others. In each instance, the question that is asked, almost unconsciously, is, “Would such an agreement be likely to have occurred by chance

if the pair of records did not relate to the same person?” or “Would such a

disagreement be likely to have occurred by chance if the pair of records did

in fact relate to the same person?” The answer in each case will depend upon

prior knowledge gained from experience. An initial known to be rare, such as

‘Z,” will be regarded as less likely to agree by chance on a pair of records

than would a commonly occurring initial such as “J.” Similarly, a highly reliable and stable item of identification, such as sex, when it fails to agree,

will argue more strongly that the people referred to are not the same than

would, for example, disagreement of province of birth, which is known from

our own experience to be discordant in about one per cent of genuinely linked

pairs of records.

The mathematical basis of such intuitive assessments is really quite simple.

In general, agreements of initials, birth dates, and such will be more common

in genuinely linked pairs of records than in pairs brought together for comparison and rejected as unlikable. The greater the ratio of these two frequencies, the greater will be the weight attached to the particular kind of

agreement.

If we wkh to obtain numerical weights that can be adclml to other such

weights, the above rat io mav simply be converted to a logarithm. In practice,

,

the logarithm to the base two has proved particularly convenient. These socalled hinit weights are simply

W, = log2 (.4/B) (4)

where A and B arc the frequencies of the particular agreement, defined as

specifically as one wishes, among linked pairs of records and among pairs that

are rejected as unlikable. The binit weights for agreements will have positive

values because A in such circumstances is always greater than B ( Fig. 4), and

these weights mi~y he regarded as strictly analogous to the discriminating

powers discussed earlier except that they relate to particular values of the

various items of identifying information.

There is no need to alter this formula when deriving the weights for disagreements, A and B may be regarded simply as the frequencies of the particular disagreement, defined in any way, among linked and milinked pairs of

records. Usually the weights will then be negativ~ in sign, because disagree21
0s9s1

aoa x xo~ Vf fm 60 60 —— pal~

0s”9”s1

qvaa SS61

WJ!fl 0S61 3oa x xo~ Vf fm 60 60 0s Sz paid

lf$DL?a 0$ t@l?q

plp!A!pul uO!qmuJOJu!

I

Iwlua.wd uogwmo~u~ ptmqsnq) X (aJ!*

I

luaA3
.

.

.

..

,

*

()

H

LINKED

PAIRS

()

H

UNLINKA6LE

PAIRS

FREQ = A FREQ = B

(“BINIT WEIGHTS”= log2A/B)

Examples

Frequency in Frequency in Binit

l(indBofagreements linkedpairs unlikablepairs R:;; weight

ordisagreements A B 10IT, AIB 1 1 1

Agreements

Male sex 1/2 1/4 2 +1

Initial “J” 1/16 1/256 16 +4

Initial “Z’” l/looo l/l,ooo,ooo 1000 +10

Disagreements

City of residence 1/3 2/3 1/2 –1

Initial (any) 1/40 32/40 1/32 –5

Sex 1/8000 1/2 1/4000 –12

FIG, 4. Calculating “binit weights.”

ments are, in most instances, less common among the linked than among the

unlinked pairs; i.e., A will be less than B, and the logarithm of A/B will be

negative.

Exceptions will occur in which an apparent disagreement is in reality a

partial agreement. For example, a discrepancy of one year of age, after

allowance is made for the interval of time between the two registered events,

will frequently be a reflection of an underlying genuine agreement. Fortunately, however, it is not necessary to prejudge the issue, If the apparent

discrepancy is predominantly a reflection of a partial agreement, the calculated weight will automatically turn out to be positive.

In practice, the formula is used to derive from the actual files a set of

look-up tables of weights for agreements and disagreements of various items

of information, broken down by the natures of these agreements and disagreements to whatever extent is necessary to make nearly full use of the

thsctiminating po~rs, Swh tables are stored in the memory of the computer.

For each detailed comparison of a pair of records, the positive and negative

weights appropriate for the different agreements and disagreements are

added together, and the total weight is used to indicate the degree of

assurance that the pair do, or do not, relate to the same person. The procedure

assumes as a tolerable approximation that the weight for the individual agree-

--

-merits or disagreements are uncorrelated with each other; corrections are

possible where this is not strictly true, but in our own experience these have

been too small to be worth applying,

23
The derivation and use of the binit weighting factors have been described

in greater detail elsewhere ( Newcombe et al,, 1959; Newcombe and Kennedy,

1962 ). For present purposes, it is sufficient to indicate that there is great

flexibility in the manner in which the weights can be employed and that they

permit the introduction of numerous refinements so as to make nearly full

use of the discriminating power inherent in the identifying information. For

anyone planning an actual application, I would recommend that a number of

small linking studies be carried out by hand to provide an opportunity to

experiment with the system and become familiar with its characteristics.

The total binit weight represents the extent to which assurance of a genuine

linkage is increased, or decreased, as a result of the comparisons made. Such

weights are, in fact, logarithms to the base two of the factors by which the

odds in favor of a linkage are increased over and above what they would have

been in the absence of the comparisons.

In our own operation, the linkages are earned out within the very small

“double surname pockets” of the master file, which contain on the average

between one and two records apiece. Furthermore, an incoming record is

quite likely to find a linkable counterpart there. Thus, even in the absence of

the detailed comparisons, the probability of a match with a record drawn at

random from the correct pocket of the master file will not be so very much less

than 50% (i.e., odds of 1:1). In this situation, the total binit weight will closely

approximate the Iogz of the odds in favor of a linkage. Weights of +10 and of

+20, for example, may in this situation be regarded as indicating favorable

odds of approximately 1,000 to 1 and 1,000,000 to 1, respectively.

Using the double-surname sequenced files in this manner, no weights are

attached to agreements of the items of sequencing information, i.e., to agreements of the surname codes. The reason is that the discriminating powers of

these have already been taken into account automatically, since it is this

information which determines the sizes of the pockets in the master file,

If binit weights were attached to agreements and disagreements of the

sequencing information, incoming records would then have to be thought of

as linking within a population of records consisting of the whole of the master

file. Suppose, for example, that this contained 108 records and was known to

include one which matched each of the incoming records. Under these conditions, the chance of an incoming record linking with a randomly chosen

record from the master file would be 1/108 ( = 2 – 20). However, if the detailed

comparisons yielded a weight of +24, this would raise the odds from 2 –20 up

to 24, i.e., to 16:1 in favor of a genuine linkage.

Thus, to derive from the total binit weights the odds in favor of a linkage,

allowance must be made for the size of the population of records within

which the linkage is carried out by subtracting logz of this population size,

Similarly, allowance must also be made for the limited probability that there

is, in fact, a matching record within that particular population. The logz of

this probability will be negative in sign and when added to the total binit

weight will further reduce its value.

In practice, thresholds must be set which specify the ranges of binit weights

24
‘%

.

?

information I Word*

Soundex pair 1

List word 2

Event (date, etc. ) 3-o

Husband (name, etc. ) 7-9

Wife 10-12

Offspring 13-14

Record linkage cross reference 1s-17

Sibship cross reference 18-19

Statistics 20-24

Other cross reference 25

‘One word equals ten octal digits or five alphanumeric characters.

which are to be regarded as representing linkage, no linkage, and possible

linkage. Initially, these thresholds may be set to what seem intuitively to be

reasonable values, but empirical tests are needed to ensure that false linkages,

failures to link, and tentative linkages are balanced in a reasonable fashion.

In an actual operation, the total weights for linked pairs should be recorded

permanently as evidence of the degree of assurance on which the linking was

based. Similarly, for pairs of records that are judged to be neither positively

Iinkable nor positively nonsinkable but which represent the most likely linkage

available, it is prudent to retain permanently information about each such

doubtful link and the weight associated with it. As more information accumulates about the family groupings, such as the sequences of birth orders in

the families and the intervals between the births, this further knowledge

may assist with the resolution of some of these doubtful linkings, provided

that the information about them is retained on the files.

3. Factors Affecting the Speed of the Record Linking Operation

A number of practical considerations will influence the speed of a record

linking operation.

The individual magnetic tape records should not be unnecessarily large, as

this will increase the times required for input and output and for sorting the

records. It will also limit the number of records that can be manipulated

within the available core memory at any one time, The record format chosen

for our own linking operation, using the vital registrations, consists of 25

words of 30 or 32 bits each (depending upon the magnetic tape units used).

Each word may contain ten octal digits or five alphanumeric characters, This

size of record was found to be sufficient for the storage of the individual and

family identifying information, the statistics, and the cross-referencing information pertaining to a vital registration (Table 5).

Speeds are also affected by the amount of unused space on the magnetic

tapes between records or between “blocks” of records. On the tapes used

with the Control Data G20 computer, on which most of the recent work was

done, records are stored in addressable blocks of 800 words each, i.e., con-

.

“.

25
TABLE 6. EXAMPLE OF LIST PROCESSING

New

Limk.s

record Position Record Forward I

Back a

G (1) Go o 0

B (1) G o 2

(2) B* 1 0

D (1) G o 3

(2) B* 3 0

(3) D 1 2

F (1) G o 4

(2) B“ 3 0

(3) D 4 2

(4) F 1 3

A (1) G 0 4

(2) B 3 5

(3) D 4 2

(4) F 1 3

(5) A* 2 i)

‘Indicates “flag” for head of list.

taining 32 records per block. If records are read singly onto tape rather than

in blocks, a substantial fraction of the tape is used up in the inter-record gaps.

A special time-saving feature in our own linking operation has been the

use of a so-called “list processing” method. Records entering a husband-wife

double surname pocket in the master file are arranged, physically, simply in

order of their entry or acquisition, regardless of the appropriate logical sequence in the family groups. The logical position of each record is indicated

by the inclusion on it of the “entry number” (i.e., acquisition number) of the

record that logically preceeds it and that of the record that logically succeeds

it. These numbers are known respectively as the backward and forward links,

When a new record enters the double surname pocket, known as a “superfamily,” it is placed physically at the end; backward and forward links are

then entered in the incoming record, and the existing links on the records

that immediately precede and succeed it in the logical sequences are updated

(Table 6). The saving of time occurs because with this procedure there is

no need to alter the physical positions of the records already in a pocket to

make room for a new record each time one is to be interfile. The list processing method used has been described in detail by Kennedy et al. (1964).

Another factor that affects the speed of a linking operation has been mentioned earlier, namely, the size of the units into which the file is broken by

the sequencing information. In our own experience, the use of two phonetically

coded surnames relating to the husband-wife pair has divided a master file of

114,000 marriage records into units containing on the average ahol~t 1.6 records

each. For approximately 60% of the file the pairs of surname codes are unique,

i.e., they occur only once in that combination throughout the whole file.

Under the various conditions described above as pertaining to our own

26
.

.

g g

file of parental marriages and earlier births at a rate of 2,300 per minute.

Thus for the British Columbia population of 1.6 million people, with which

this study is concerned, a year’s crop of 35,000 birth records can be merged

and linked with the master family file of ten years of marriages in somewhat

less than 30 minutes of machine time, once the magnetic tape records have

been prepared in the proper format and appropriately sequenced, At a machine rental of two dollars per minute this is equivalent to a cost of 0.1 cents

per record, i.e., it is minute in comparison with the cost of producing the

punchcards in the first place, as is done routinely for administrative and

statistical purposes,

The ways in which these various time-saving devices have been employed

are described in greater detail by Kennedy et al. (1965).

STORAGE AND RETRIEVAL

In the sections that follow, we will consider the manner in which records

relating to sibship groups may be stored together, certain extensions of the

procedures to permit the inclusion of pedigree information covering an indefinite number of generations, and methods of retrieving information from

the sibship grouping and multigeneration pedigrees. The records pertaining

to the sibships, of course, fall within the main file sequence based on the

surname pairs in their phonetically coded forms (Table 7 ).

1. Storage of Sibship Groupings of Records

There is a natural sequence in which the vital and health records pertaining

to a sibship group may be linked and stored, Starting with the parental marriage registration, which may be regarded as a ‘lead-of-family” record, birth

records are linked to the marriage record in chronological order, and records

of the various events of ill health, including death, are linked to the birth

records of the children to whom they relate, those for a particular child falling

likewise in chronological order after his or her birth record (TabIe 8).

The experience which we have had with this kind of file organization relates

to records of marriages, Iivebirths, stillbirths, and deaths, together with those

from a special register of handicapping conditions of children and adults. In

addition, detailed plans have been worked out for the possible future inclusion of substantial numbers of records from a universal scheme of hospital

insurance. Off-line linkings with the birth registration records are needed in

the case of the handicap and hospital records in order to pick up the mother’s

maiden name which is lacking on the original form. Only after this has been

done can the handicap and hospital records be merged and linked with the

master family file, which is arranged in order of the two parental surname

codes.

Incompleteness of a sibship grouping of records poses no special problem.

In the absence of the parental marriage record, for example, the birth record

of the oldest child represented in the file may serve as the head-of-family

record, and records of the births of younger siblings will be linked to it. A

27
TABLE 7. EXAMPLROF DOUBLESOUNDEXFILE SEQUENCE*

Adams X Adair” A 352 A 360

Adams X Baron A 352 B 650

Adams X Caird A 352 C 630

Adams X Danys A 352 D 52o

t

Baker X Allen B 260 A 450

Baker X Barks B 260 B 620

Baker X Caron B 260 C 650

Baker X Duffy B 260 D200

J

Baird X Aubry B 630 A 160

Baird X Baker B 630 B 260

(and so on )

“i.e., by husband’s surname code followed by the wife’s maiden surname code.

TABLE8. EXAMPLEOF A SIBSHIPGROUPOF 13ECORDS

Reccml

1--- ‘:’;~;’] J_ Child

Parental marriage Doe X COX - —

Birth 1 Doe X Cox Alan

Birth 2 Doe X Cox Carl

Ill health Doe X COX Carl

Death Doe X Cox Carl

Birth 3 Doe X Cox Edna

death record may serve likewise as a head-of-family record where it relates to

the oldest child represented in the family group and the birth record for

this child is missing. Thus, all of the available records of vital and health

events may be merged and linked into sibship arrays, regardless of the degx’ee

of completeness or incompleteness of these groupings, and the master file may

be updated periodically by the introduction into it of successive crops of current records.

The times required to merge and link the death and handicap records to

the master file are somewhat greater than those for the corresponding operation as applied to birth records. There are two reasons for this. First, an ill

health or’ ~eath record must scan all of the birth records present in the appropriate double surname pocket of the master file, and these will tend to be

more numerous than the head-of-family records which the incoming births

must scan. Second, where an incoming ill health or death record fails to find

a matching birth record, it must scan the double surname pocket a second

time in an attempt to find a head-of-family record with which to link.

In our own operation, handicap and death records were merged and linked

with the master file at a rate of approximately 1,100 per minute, i.e., at about

one-half of the speed for the merging and linking of birth records.

2. Storage of Multigeneration Pedigrees

The modifications of the above procedures needed to permit the linking and

28
.

,

storage of the vital and health records in the form of multigeneration pedigrees are surprisingly simple. For most registration areas, the marriage records

contain sufficient information to serve as bridges between the generations

and between the in-law sibships.

Information from a marriage record may be treated in two ways. We have

discussed already how it can be arranged into the form of a head-of-family

record representing the marriage of a parental couple. Similarly, information

from the registration form may also be fitted into the format of a record such

as is used to describe an event in the life of an individual. The part of this

latter kind of record entry that is assigned to family information would then

contain the names and other identifying particulars of the parents of the newly

married person, and the part of the record assigned to personal identification

would contain his or her own name, age, and birthplace. This kind of entry of

the marriage information is almost precisely analogous to a death record,

since both relate to events in the lives of members of a sibship group. In the

master file, the three entries pertaining to a particular event of marriage (i.e.,

the groom’s entry, the bride’s entry, and the head-of-family entry) will each

become part of a different sibship group of records.

The only special requirement for the three marriage entry records is that

each of them, before being placed in these various locations on the master

tape, be cros~-referenced to [hc oti~er iwo. This is dorm by inserting :n the

cross-reference field of each record entry the double surname codes for the

other two. These codes, together with the marriage registration number which

is common to all three entries, provide both a means of access within the

master file from one of the double surname pockets to the other two and a

positive identification of the alternative entries when the pockets in which

they occur have been located. 1 he cross-referencing is illustrated in Tables 9

and 10.

The simplicity of the procedure resides in the use of essentially the same

format for the marriage entries of grooms or brides as for their death records.

In our own operation, the same programs that are used to build the sibship

groupings of records will also be employed to insert into these groupings the

grooms’ and brides’ marriage entries, just as they would the records of any

other kinds of events in the lives of the same individuals.

The idea of thus putting family groups of records into a single linear array

and of using cross references to indicate the relationships between the groupings that are filed as units is basic to any system by which computers may be

employed to store and retrieve large quantities of pedigree information of unlimited complexity, The special features of the system described are merely

matters of convenience. The choice of the sibship group as the unit of storage

and of the surname pair as the sequencing information may have fairly wide

application, but the details of the use of idcntifyil]g Ixwticulars have been

dictated largely by the nature of the vital rccorcls.

It would, of course, l]e feasible to store the same pwligree information more

compactly if the family relationships were worked out in advance so that

every individual could be assigned an identifying rmmher containing as few

29
TABLE 9. EXAMPLE OF A MARRIAGERECXSTRATIO~ANDOF THE MARRIAGE

ENTRY RECORDSDERIVEDFROM IT

Marriage registration

Groom Dunn, Alex

Bride Rowe, Anna

Groom’s father Dunn, Carl

Groom’s mother Bell, Edna

Bride’s father Rowe, Paul

Bride’s mother Hill, Jean

Marriage entry records

Parental couple Offspring

1. Head of family entry Dunn X Rowe —

( Alex) (Anna)

2. Groom’s entry Dunn X Bell Alex

(Carl) (Edna)

3. Bride’s entry Rowe X Hill Anna

(Paul) ( lean)

TABLE 10. EXAMPLE OF.CROSS-REFERENCING A SIBSHIP TO THE

RELATED SIBSHIPS

Parental

Record couple Offspring Crossreferences

I 1 1

Parental marriage Dunn X Bell

{

Birth 1 Dunn X Bell Alex

Groom’s entry Dunn X Bell Alex

{

Birth 2 Dunn X Bell Stan

Groom’s entry Dunn X Bell Stan

{

Dunn X Nash—father’s sibship

Bell X Mann—mother’s sibship

Dunn X Rowe-new family

Rowe X Hill—bride’s sibship

Dunn X Knox-new family

Knox X Fynn—bride’s sibship

digits as possible, but the disadvantages of this approach where large populations are involved should perhaps be mentioned. A main objective of the

present handling procedures has been to avoid entirely all manual manipulations so that full use can be made of the speeds of electronic computers. If

this feature is to be preserved, the present kind of linking operation would

have to be carried out anyway. A more important problem would be what

to do with the borderline linkings when condensing the pedigree information

into its more compact form, since both the extents of the uncertainties and the

means for their later resolution would tend to be lost in the process, It might

also be difficult to keep open the possibility, as the present system does, of

merging at some future time the pedigrees drawn from a limited region, such

as a province or a state, with those for a wider region such as the country as

a whole.

3. Retrieval of Pedigree Information

The need for writing detailed programs does not end with the establishment

30
of a master family file containing the required pedigree information. For almost any kind of genetic study, the extraction of the required tabular information from a printed listing of the master file would be almost unthinkably

laborious and expensive.

In general, it is necessary first to prepare programs that will summarize

in a single record whatever information is required about a particular family.

A further program is then written to extract information in tabular form from

the resulting file of these summary records. Two examples of such procedures

will be described, relating to sihship groups and to multigeneration pedigrees,

respectively.

Where the family units under study arc restricted to the sibships, summaries of the events of birth, ill health, and death in the lives of the various

members of a sibship will usually be derived in two steps. First, individual

histories will be condensed so that there is just a single summary record for

each child replacing the separate records for the various events. The resulting

magnetic tape file of individual or personal summaries can be used repeatedly

to prepare the much more compact family summary records, which may be of

a variety of kinds depending upon the natures of the studies for which they are

to be used (Table 11).

To facilitate subsequent tabulations, the family summary records will have

a different fixed field for each of the sil]lings. There must also be provision for

large families, \vhich will sometimes overrun a family summary record of

modest size. This is lmst takm care of by arranging for trailing records to act

as extensions where needed.

In one study which we have done using this procedure, the coded causes of

stillbirths, handicaps, and deaths were entered into the fields of the family

summary record assigned to the particular siblings who ~~me affected, and for

the unaffected siblings just the fact of birth, the birth order, and the sex of

the child were entered.

In this particular study. usc was rntidc of the familv summaries to derive

information about the magnitudes of the risks to the’ later-born siblings of

children ~~liohad been stillborn, handicapped. or hacl died, as the result of

diseases of various kinds. The tabulations contained, typically, the number

of index cases of a disease, the numbers of earlier and later siblings of the

index cases, and the number of later-born siblings suffering from the same

condition (Table 12). For detaih of the computer programs hy which the

different steps in the extraction were carried out, the reader is referred to

Smith et al. ( 1965).

A more elaborate procedure is requirecl where muitigcneration pedigrees

are to be summarized, because as an initial step the sibship groupings of

records relating to a particular family must be brought together from different

parts of the master file. Pmfore st:mting this step. ccrt:lin si?lships ~vhose relatives one wishes to ascertain will have hem extracted from the master file.

These may bc called “index sil)ships.” and they ~~’illin most instances have

been chosen because they include indi~’iduals ~vho an affected by some clisease of special interest.

31
Euent records for a sibship (one per euent)

Event Birth Dis~e

code order FnmilY Child code

J (birth) 1 Fox X DOW Alan —

J (birth) 2 Fox X DOW John —

J (birth) 3 Fox X DOW Vera —

Q (handicap) Fox X DOW Vera 123

J (birth) 4 Fox X DOW Leon —

R (death) Fox X DOW Leon 456

Individual summary records (one per child)

(J) 1 Fox X DOW Alan —

(J) 2 Fox X DOW John —

(Q) 3 Fox X DOW Vera 1!23

(R) 4 Fox X DOW Lam 456

Family summury record ( one per sibship)

(Fox X DOW) 1 (J)---> Z(J)---> 3 (Q) lx> 4(R)4S6.

TABLE 12. EXAMPLE OF A TABULATIONFROM FAMILY SUMMARYRRCOrms

Dis-e codeS25(mental deficiency)

Normal

H:::ca-aed

Sti/Kl&rnHandicapped Dead

(J) (Q) (R) (s)

Index cases o 0 506 9 58

Earlter sibs 208 2 6 16 0

Later sibs, same cause o 0 11 0 1

Other later sibs 286 2 11 14 0

The records of the index sibships may contain cross-referencing information

(in the form of double-surname codings and marriage registration numbers)

indicating links with as many as six diflerent kinds of related sibships, i.e.,

1. From the parental marriage (head-of-family) records to

(a) the fathers’ sibships and

(b) the mothers’ sibships.

2. From the marriage records of the “affected individuals who got married

(i.e., from the grooms’ and brides’ entries) to

(c) their offspring’s sibships and

(d) their spouses’ sibships,

3. From the marriage records of the brothers and sisters who got married to

(e) the sibships of the nephews and nieces of the affeckcd individuals and

(f) the sibships of the spouses of the brothers and sisters who got

married.

These six different kinds of cross references may be used in a single scan

to draw from the master family file all of the groups of records pertaining to

sibships that are removed by one degree of relationships from those in which

the affected individuals occurred, including the in-law groups (Fig. 5).

32

.
*

a’IN LAWS1 FATHERIS

SIE!SHIPS SmSHIP

**

JNLAWSr

<

OWN > WIFE 1S

S133SHIPS SIBSHIP SIHSHIP

1-

D -

NEPI-IEWS

+NIECES1 CHILDREN’S

SIBSHIPS SIBSHIP

‘i.e., those of the paternal uncles and aunts by marriage.

**i.e., those of brothers’ wives and sisters’ husbands.

Fm. 5. Scanning themaster file correlated sibships.

Similarly, in a second scan of the master tape, use may be made of the

further cross-referencing information contained in the sibship groups of these

six diflerent kinds to extract the sibships that are removed by two degrees of

relationship from those in which the affected individuals occurred. Again, the

in-law sibships may be extracted in the same way as those of the blood relatives. And so, with each successive scan, an expanding circle of more distant

relatives may be identii3ed and retrieved from the master file.

Each such scan will be exceedingly rapid even where large numbers of sibships groups are extracted. Thus, it is feasible to carry out the retrieval of

muhigeneration pedigrees on a truly massive scale.

From this point on, the making of summaries would follow much the same

pattern as described earlier, except that the family summary record might be

more complex than the sibship summary record.

The chief limiting factor in work of this kind is not the speed of the computer but the time required to develop the appropriate programs.

THE LIKELIHOODOF FUTURE “TOTAL UTILIZATION”

OF PEDIGREEINFORMATION

Geneticists will at first tend to think of the possible uses of record linking as

applied simply to the familiar kinds of ad hoc studies of limited size and duration. The question arises whether it is realistic to go beyond this and to consider using for scientfic purposes all of the pedigree information gathered

33
routinely for whole populations through the vital registration systems, of doing

so on a continuing basis, and of adding an increasing amount of medical

documentation M time goes on,

Clearly, the cost would appear large if it were paid wholly from budgets for

scientific research. But this would not necessarily be the case, because the

information that is unlocked by linking and integrating the files into individual

and family histories has many statistical and administrative uses, as well as

other scientific uses beyond those of the geneticist.

Those geneticists who attempt to apply the methods of record linking will

be in a particularly good position to see a variety of possible uses for the linked

files and to develop procedures that will serve more than one purpose. Their

own long-term interest may be furthered most where they exploit the fact

that there arc other potential users.

Of course, with time the various files of routine records will, to an increasing

extent, be linked and integrated anyway for administrative purposes, whether

or not scientists take an interest in the matter. But the only way to ensure that

scientific by-products will come out of this trend is for the scientists themselves to participate actively while the administrative procedures are being

established.

APPENDIX

Surname Coding

Surnames may be converted into coded forms for either of two reasons:

to set aside temporiwi]y some unreliable component of the information that

may vary on successive records relating to the same person, or for the sake

of compactness. A number of systems have been designed to achieve one or

other of these purposes, or both simultaneously. Some of the more useful of

these codes will be described.

THE RUSSELL SOUNDEX CODE

This code is particularly efficient at setting aside unreliable components of

the alphabetic surname information without losing more than a very small

part of the total discriminating power. It is the method of choice for almost all

populations, except where the names are predominantly of Oriental origin.

Rule.Y:

1, The first letter of the surname is used in its uncoded form and serves ils

the prefix letter,

2. w :\ndH are ignored entirely.

3. A, E, 1, 0, U, Y are not coded but serve as separators ( scc item 5 lw]owr).

4. Othm letters me coded as follows until three digits :Irc llsc>d up ( the

remaining letters are ignored ):

B, P, F, V coded 1

D, T coded 3

L coded 4

M, N coded 5

34

.

.-—
5.

R coded 6

Another consonants coded 2

(C, G, J, K, Q, S,X, Z)

Exceptions are letters which follow prefix letters which would, if coded,

have-the same code. These are ign&ed in all cases unless a separator

(see item3 above) precedes them.

Examples:

Anderson

Bergmans, Brigham

Birk, Berque, Birck

Fisher, Fischer

Lavoie

Llwellyn

NAMECOMPRESS1ON

= A 536

= B 625

= B 620

= F260

=L1OO

= L 450

As indicated by its name, this form of coding is designed mainly to condense

surnames, given names, and place names. However, the code does remain unchanged with some of the common spelling variations, although it is less

efficient in this respect than the Soundex code.

Rules:

1. Delete the second of any pair of identical consonants.

2. Delete A, E. J, O, U, Y, except when the first letter of the name.

Examples:

BENNETT = BNT

FISHER = FSHR

ILL-SPELLEDNAMEROUTINE

Where the insertion, deletion, or substitution of a single letter of a surname

alters the coded form, recognition that a pair of names are the same necessarily

depends upon residual similarities in the sequences of the letters in the two,

despite any interruptions in these sequences. The “ill-spelled name routine”

is not, strictly speaking, a system of coding but rather a system of comparison

which employs the coded forms of the names as derived by “name compression.” The system was designed for use with airline bookings (Davidson, 1962 ).

RtJes:

1.

2.

3.

Use “name compression” procedure, up to a total of four letters.

Search for and count the numbers of letters or blanks, up to a total of four

in all, that agree without altering the sequence.

Where the agreements equal 3 or 4 in a pair of names, compare other

identifying information.

35
Examples:

Score

BOWMANN = B M N -

=JJJI

BAUMAN

McGONE = MCGN

. .l&l J

McKONE

ANGREIFF = A N G R

= ~~11~

SINGER .

MCGINESS = M C G N

MAGINNES = ~ G’N’S

LU

: :7];

ROO --

ALPHANUMERIC~NvEIISION

4

3

3

3

3

This is a highly specific numeric coding for all surnames. It is not designed

to set aside the less stable parts of the information but rather to retain virtually

all of the original specificity of the alphabetic form. The numeric form of the

surname is compact, is more readily sorted on an electromechanical card

sorter than the alphabetic form, and is nonrevealing to anyone who lacks the

relevant look-up table. Furthermore, when sorted in numerical sequence the

names fall in alphabetic order or a close approximation to it.

The coding is done by computer using a look-up table containing over 8,000

different enties. (See International Business Machines, 1960. )

Examples:

ABBIT = 0008

ADLER = 0105

BORNE = 1058

BRYAN = 1070

CLARK = 1646

Cox = 1721

&

ZZINA = 9776

HOCBE~SURNAMECODE

This is a simple two-digit code for surnames based on a division of the

names in a large telephone directo~ into 100 approximately equal parts. Although compact, it loses much of the discriminating power inherent in the

full name and is therefore chiefly of historical interest. (Originally this was

just a part of a much longer numeric code derived from the surname, first

given name, sex, and bti date. See Hogben et aZ., 1948.)

36
Examples:

00= AA – AK

01 = AL

02=AM –AR

03 = AS – AZ

04 = BAA – BAJ

05 =BAK –BAQ

00 =BAR

(and so on)

REFERENCES

DAVIDSON, L. 1962. Retrieval of misspelled names in an airlines passenger record system.

Cornmun. Assoc. Computing Machinery 5: 169-171.

HOCBEN,L., JOIiNSTONE,M. M., AND CROSS,K. W. 1948. Identification of medical documents. Brit. Med. J. 1: 625-635.

International Business Machines. 1960. Cenerd Information Manual. A Unique Computable

Name Code for Alphabetic Account Numbering.

KENNBDY,J.M.,NEWCONIBE,H. B.,OKAZAKI,E. A.,AND SMITH,M. E. 1964.List Processing

Methods for Organizing Files of Linked Records. Chalk River, Ontario: Atomic

Energy of Canada, Ltd. Document AECL-2078.

KENNEDY, J.M., NEWCOMBE, H. B.,OKAZAKI, E. A., ANDSMITH,M. E. 1965.Computer

Methods for Family Linkage of Vital and Health Recor&. Chalk River, Ontario:

Atomic Energy of Canada, Ltd. Document AECL-2222.

NEWCOMBE, H. B.,AND KENNEDY, J.M. 1962.Record linkage: Making maximum use of

the discriminating power of identifying information. Commun. Assoc. Computing

Machinery 5:563-566.

IQEWCOMBE, H. B,, KENNEDY, J. M., AKFORD,S. J., AND JAMES, A. P. 1959. Automatic

linkage of vital and health records. Science 130:954-959.

SMITH, M. E., SCHWARTZ,R. R., ANDNEWCOMBE, H. B. 1965. Computer Metho& far

Extracting Sibship Data from Family Groupings of Records. Chalk River, Ontario:

Atomic Energy of Canada, Ltd. Document AECL-2530.

37
A MODEL FOR OPTIMUM LINKAGE OF RECORDS*

BENJAMIN J.TEPPING

Bureau of the CenwLs

A model is presented for the frequently recurring problem of linking

records from two lists. The criterion for an optimum decision rule is

taken to be the minimization of the expected total costs associated with

the various actions that may be taken for each pair of records that may

be compared. A procedure is described for estimating parameters of the

model and for successivelyimproving the decision rule. Illustrative results for an application to a filemaintenance problem are given.

1. INTRODUCTION

T

HE problem of record linkage arises in many contexts. A typical example

is that of file maintenance. In this example there is a file, which we shall

call the master file, whose constitution is to be changed from time to time, by

adding or deleting records or by altering specific records. hTotice of these required changes is given by means of ahother file of records, which we shall call

the transaction file. Presumably, each transaction record specifies the addition

of a new master file record, or the deletion of an existing master file record, or

the alteration of an existing master file record. It may not be known whether

there exists a master file record that corresponds to a given transaction record

so that the determination of whether a master file record is to be changed or a

new master file record added must wait until it is found whether a corresponding master file record exists. Thus, the fundamental problem is to determine,

for each transaction record, which master file record corresponds to it or that

no master file record corresponds to it.

If each master file record and each transaction record carried a unique and

error-free identification code, the problem would reduce to one of finding an

optimum search sequence that would minimize the total number of comparisons. In most cases encountered in practice, the identification of the record is

neither unique nor error-free. Thus it becomes necessary to make a decision

as to whether or not a given transaction record ought to be treated as though

it corresponded to a given master file record. The evidence presented by the

identification codes of the two records in question may possibly be quite clear

that the records correspond or that they do not correspond. On the other hand,

the evidence may not clearly point to one or the other of these two decisions.

Thus it may be reasonable to treat the records temporarily as if they corresponded or to treat them temporarily as if they did not correspond, but to seek

further information. Or it may be reasonable in a particular case to take no

overt action until further information has been obtained. The amount of effort

that it is reasonable to expend in resolving a particular problem is also a variable. Thus it is clear that in making the decision on the correspondence between

a transaction record and a master file record, there are available at least two

and perhaps more possible decisions. If one considers now the costs of the

various actions that might be taken and the utilities associated with their pos-

*Reprinted with permission from the Journal of the American

Statistical Association, American Statistical Association,

December 1968, Vol. 63, pp. 1321-1332.

39
sible outcomes, it appears to be desirable to choose decision rules that will in

some sense minimize the costs of the operation.

There are many other contexts in which record linkage takes place. One example is that in which two files are to be consolidated. Information about

some individuals may be contained in one or another of the two files, while for

other individuals some information may be in one file and some in the other.

Another example is that of multi-frame sample surveys in which it may be

necessary to determine which of the sampling units in one frame are also included in the other frame. A third example is that of geographic coding in

wtilch the master file consists of a street address guide and the transaction

records are particular addresses; the problem here is to assign to each address

a geographic code as given by the street address guide. The reader can doubtless supply many other examples.

The literature on this subject is replete with descriptions of actual matching

operations ([2], [3], [4], [7], [8], [10], [11], [12], [13], [17], [18]). Several

also deal with principles for the design of matching operations ([4], [7], [8],

[9], [11], [12]). Some formulate mathematical models to serve as a basis for

the design of a matching process that will be optimum in some sense. Thus, in

analogy to the Neyman-Pearson theory of testing statistical hypotheses, Sunter

and Fellegi [14] 1 IIX the probabilities of erroneous matches and erroneous

non-matches and minimize the probability of cases for which no decision is

made. Nathan ([5 ], [6]) proposes a model that involves minimization of a cost

function, but restricts detailed discussion to cases in which the information

used for matching appears in precisely the same form whenever the item exists

in either list. Du Bois’ [1] approach is to attempt to maximize the set of correct matches while minimizing the set of erroneous matches.

This paper proposes a mathematical model of the record linkage problem and

a decision rule which minimizes the cost. The implementation of this model in

practice depends upon the estimation of the parameters of the model. These

parameters are costs and certain probabilities. The parameters may be difiicult

to determine. Also, it will be seen, the mathematical model (as usual) is not

an exact representation of the real world. Nevertheless, the model provides

useful guides for the construction of efficient linkage rules, as will be illustrated

in the sequel.

2. A MATHEMATICAL MODEL

There are given two lists: a list A (the master file, say) which consists of a

set of labels { a ] and a list B (the transaction file, say) consisting of a set of

labels {p}. (See Section 6 for a simple example.) Each label a is to be compared

with each label @ and an action taken on the basis of that comparison. The

action taken must be one of a list of possible actions exemplified by, but not

confined to, the following:

1. Treat the labels a and p as if they designated the same individual of some

population. We shall say that the pair (a, P) is a “link”.

1The notation and terminology used here follow, generally, those of the SunterFellegi paper.

40
2.

3.

4.

5.

Temporarily treat the labels a and @as a link but obtain additional information before classifying the pair as a link or a non-link.

Take no action immediately but obtain additional information before

classifying the pair as a link or non-link.

Temporarily treat the labels a and 9 as if they were associated with

different individuals of the population, but obtain additional information

before classifying the pair as link or non-link.

Treat the labels a and ~ as if they were associated with different individuals of the population (non-link) ~

Other actions may be added to the list, including for example the use of a randomizing device to determine the treatment of the pair (a, @. Each pair

(a, /3) will be called a “comparison pair.” It is assumed that each pair (a, /3)

is either a “match” (the labels a and @are associated with the same individual

of the population) or a “nonrnatchfl (the labels a and @are associated with

different individuals of the population). Thus the set of all comparison pairs is

the sum of mutually exclusive sets M (the “matchn pairs) and U (the “nonmatch” pairs).

It should be noted that the labels a and /3 are, in general, vector-valued.

Thus a label may contain, for example, a name, address, age, and other characteristics of a person.

Theoretically, any comparison of the label a with the label/3 consists of construtting a vector-valued function -yof the comparison pair (a, /3). (See Section

6 for a simple example of a comparison function.) The comparison function -y

serves to classify all pairs into classes: (al, M and (m, i%) are members of the

same class if and only if 7 (m, L3)= ~(az, I%).The comparison pairs in each given

class are to be subjected to exactly one of s possible “actionsn al, U-Z,. . . , a,.

(Examples of five possible actions were given above.) A “linkage rule” consists

of the assignment of an action to each class.

Let a label a be selected at random from list A and a label @from list B,

and let a non-negative loss g(ai; a, /3) be associated with taking action ai on

a pair (a, 6). Let

P[fif \ y] = Prob[(a, P)dl I ~(cu,P)]

denote the conditional probability that the pair (a, t?) is a match, given the

value of ~.

We assume here that G, the expected value of g(ai; a, /3), is a function only

of a~ and P [M 17 ]. (This assumption is discussed below, in Section 4.) Thus

G = G{g(ai; a, B) [ ai, z’[MI y]] = G(ai, p[~l ~]).

Given a linkage rule, the total expected loss of the rule is

~ P(T) )( G(a,, P[M ] ~])

where a; is the action specified for 7 by the linkage rule, ancl the summation

extends over all -y.To minimize the total loss, we need only minimize each term

of the sum, each term being non-negative.

A special case of the above is that in which there is a loss Gil associated

41
with taking act ion a~ on a pair (a, d) when in fact that pair is a match, and a

loss Giz when in fact the pair is a nonrnatch. In this case G, the expected value

of the loss, can easily be seen to be a linear function of the conditional probability that the comparison pair is a match, given ~, for each action ai.

If the functions G are linear in P(M I-y),the interval (O, 1) for the probability y

of a match is divided into at most s “action intervals* each of which corresponds to one of the possible s actions. The action interval for a given action

is the interval in which the cost function G for that action is less than the cost

function for any other action.

Ifigure 1 illustrates a case in which (i(a~, P [M \-y]) is a linear f{mction of

6 :,

,

0 P,

PIMla’]

R 1

FIG. 1,

P [Ml Y ] for each ai. In this illustration, the optimum linkage rule specifies:

If the functions G are not linear in P [M I~], an “action set” of points of the

interval (O, 1) that correspond to one of the possible actions will not be an

interval in general. The treatment of the nonlinear case, however, proceeds

along the same lines.

The conditional probability that a comparison pair is a match, given that

the comparison function ~ has a stated value depends upon the prior definition

of the comparison function v or, equivalently, upon the definition of the corresponding clarification of comparison pairs.

As noted above, any comparison function y defines a classification of the

pairs (a, /3). Let # be any other comparison function, which therefore defines

another classification. It is possible to pass from the classification 7 to the

classification # by a sequence of steps, each of which consists either of splitting

a class into two classes or of combining two classes into a single class. Therefore,

if we begin with a tentative comparison function y, we may seek ways of splitting some classes or combining some classes in such a way as to reduce the contribution of the cla,wes involved to the loss function.

Consider the case of splitting a class 7 into two classes -y, and ~Z. Without

42

—
loss of generality, we may assume that

But then, clearly,

If P(MI -rJ and P(M \72) are in the same action set as P(M \-y), there is no

gain in making the split. But if either P(M I~J or P(MI WJ falls into a different action set, the loss is necessarily (and sometimes materially) reduced.

To determine for which classes splits should be considered, one may fkst

calculate the expected loss contribution for each class. It is evident that if the

expected loss for a class is a small proportion of the total, little can be gained

by splitting that class. Therefore, attention should be given first to classes

whose expected loss contribution is a substantial proportion of the total. The

illustration given subsequently shows that large reductions in the total expected cost can be attained by this technique.

With regard to the combining of classes, it is clear that this cannot result in

reducing the expected cost. But if the classes to be combined are in the same

action set, no increase in the cost will be sustained while the combination may

reduce somewhat the operational costs of implementing the linkage rule. The

combining of classes is useful also as an initial step, for the purpose of reducing

the number of classes for which estimates need to be made, as detailed in

Section 3, below.

3. ESTIMATIONPROBLEMS

The application of the mathematical model involves estimating the cost

function for each action as a function of the probability of a match, and estimating the probability that a comparison pair is a match.

The estimation of the cost function is often extremely difficult. Usually the

cost consists of two classes of components, one class consisting of the cost of

actual operations that may be involved and the other of the less tangible losses

associated with the occurrence of errors of matching. The former can often be

estimated very well, but estimates of the latter may depend upon judgment in

large part. Despite the possible dependence on judgment, in the framework of

the mathematical model even rough guesses at the cost function are extremely

useful.

It may be noted that the first class of components of the cost function usually contains some components that are functions of the linkage rule (specifically, of the classification imposed). This is not reflected in the model, which

only defines an optimum linkage rule for a fixed classification or comparison

function.

It should be noted in connection with the estimation of the probabilities

that it is necessary only to determine in which of the action sets a given probability falls. Ordinarily the probabilities will be estimated by selecting a sample

in each comparison class. The sampling designs used should be chosen with

the whole problem in mind, so that unnecessary sampliig costs are avoided

when, for example, the probability being estimated is near the center of an

43
action interval or when an error in the estimate of the probability will have

little effect on the total cost. The latter may occur if the frequency of the given

comparison class is small or if the alternative actions in the neighborhood of

a given probability lead to costs which are only slightly different.

The successive steps in the application of the mathematical model may be

described as follows:

1. The possible actions that may be taken on a comparison pair are listed.

2. For each action, the mathematical expectation of the cost as a function

of the probability of a match is estimated.

3. An initial comparison function, i.e., an initial classification of comparison

pairs into comparison classes, is determined on the basis of judgment or

past experience (see, for example, [2], [3],. [4], [7], [8], [9], [10], [11 ],

[12], [15], [17], [18]), or on the basis of mathematical conclusions following from specified assumptions about the interaction of the components

of the labels a and /?. The more nearly the initial classification resembles

the optimum classification, the less is the amount of subsequent work

required to attain the classification that will finally be used.

4. Samples are selected from each comparison class and the probability of a

match estimated for each comparison class. This determines the optimum

action pattern for the given classification.

5. The contributions of the several comparison classes to the total cost is

now analyzed, and the classes that provide large contributions to that

total cost are identified.

6. On the basis of that analysis, the classification is revised by splitting and

recombining classes.

7. Steps 4 to 6 are repeated until step 6 indicates that no substantial additional reduction of cost can be made.

4. SOME COMMENTS ON THE MODEL

As is usually the case with a mathematical model, the model does not, in

every respect, faithfully represent the real world that it is intended to describe.

The model assumes that every possible comparison pair will actually be examined. With large files, this would involve an inordinate number of comparisons. In practice, comparisons would be confined to specified subsets of the

master file, and corresponding subsets of the transaction file. From the point

of view of the mathematical model, the comparisons not actually made are

being treated as non-links.

A limitation of the model is that it permits a given element of the transaction

file to be treated as a link with more than one element of the master file. In

man y situations, this treatment may be intolerable. The difficulty can be

handled by subjecting all such multiple-link cases to a subsequent stage in

z Thus Sunter and Fellegi [14] suggest that the components of the comparison vector

may be grouped into sub-vectors which are statistically independent on each of the sets

M and U. They then show how the value of a parameter equivalent to P[M I~] may be

estimated on the basis of a knowledge of the frequency d~tribution of y. This would serve

to define an initial comparison function, even if the assumption of independence is not a

satisfactory one.

44

—.— .-
which the transaction record is linked with at most one of the master file records associated with it in the first stage. If the cost or frequency of such awes

is small, the mathematical model described in this paper remains a useful one

for guiding the design of the linkage rule.

Similarly, there exist situations in which the linkage of a master file record

with more than one transaction record is not tolerated.

There are some situations in which the cost is not only a function of the

probability of a match but also of some other characteristic of the comparison

pair. Thus, there may be two types of master file records, with the cost of an

erroneous link being different for the two types. In such a situation, the comparison pairs may be classified in such a way that the characteristic is constant

within each class and then the problem of optimum linkage may be treated as

a separate problem in each of these classes.

The model is applicable also to cases in which the master file is not fixed but

changes from one time period to another. Each transaction record is to be compared with the master file as it exists at the time period when the transaction

record enters the system. We may consider the sequence of master files as constituting list A and a corresponding sequence of transaction files as constituting

list B. The identity of the particular file becomes a component of the comparison vector ~, and we may define (a, 6) to be a member of U if a and /3are not

from corresponding files. In this manner, this situation is covered by the model.

Some comments on the characteristics of useful comparison function are in

order. Typically, the cost function

G(P) = min G(a~, P[M 1~])

U

is a concave function of P, with G(0) = G(1) = O. Thus, the ideal comparison

function is one for which P [Ml ~] is either Oor 1 for every value of 7 that may

be observed. This ideal is usually not attained. However, one can usually find

an initial comparison function such that the distribution of P [M \ T] over the

set of all comparison pairs is U-shaped, with low frequency where the cost

function is high and high frequency where the cost function is low. Carrying

through the steps given in Section 3 will often result in revtilng the comparison

function -y so that the distribution of P [Ml ~] is shifted nearer the endpoints

,of the interval (O, 1).

Finally, it should be noted that the successive steps listed in Section 3 do not

necessarily converge to the optimum decision rule. The procedure does provide

an effective means of reducing the cost, as illustrated in Section 5.

6. AN ILLUSTRATION

The model described above was developed in connection with a file maintenance application, the rmwter flea being the Iiits of subscribers of two large

magazine publishers ([15], [16]). In connection with the development of a

system employing a large-scale electronic computer for the maintenance of the

files of subscribers, it waa necessary to develop explicit rub for matching the

transaction file with the master file of subscribers. Initially, matching rules

were developed on an intuitive basis, but the subsequent development of the

45
mathematical model indicated ways in which the matching rules could be

substantially improved. The illustration presented here is confined to transactions which are subscription orders. (Other types of transactions included

changes of address, complaints of non-delivery, subscription cancellations, and

so forth. Separate linkage rules should be established for each type.)

TABLE 1. TENTATIVE UNIT COSTS

!

True Status

Action

Match Non-match

1 $0.00 $6.01

2 .41 1.13

3 .77 .77

4 .82 .41

5 2.59 ,00

Table 1 shows tentative unit costs developed by the staff of one of the

publishers on the basis of consideration of the character of the actions and the

consequences of these actions. The actions listed are roughly the same as those

given above as examples in the description of the model. Computation from

these unit costs would indicate that the optimum action intervnls are as follows:

Action Probability of a Match

1 P> .92

2 .64< P<.92

3 —

4 .19< P<.64

5 P<.19

Figure 2 shows the cost function for each of the possible actions. NTotethat

action 3 is never used, since its cost function lies everywhere above some other

cost function.

A systematic sample of approximately 10,000 subscription orders during a

period of four months was selected. The portion of the master file used for this

study consisted of those records for which the post office and the first four

letters of the surname were the same as some record in the sample of transactions. Thus, comparison pairs to be examined were confined to those in which

the post office and the first four letters in the surname were the same in the

two members of the pair. (TKIs is consonant with the comment made above

in Section 4 that, in practice, comparisons are usually confined to specified

subsets of the master file and the transaction file. This procedure adds, to the

cost of any of the alternative linkage rules considered, the contribution from

linking errors made for pairs (a, /3)that are not actually examined.) TO reduce

the size of the master file for the purpose of this study, a subsample of one in

ten of the master file records not matching a transaction record was selected

from those sets that contained 100 or more records, a set here being defined as

46

——
6

s

4

3

2

1

n

o .1 .2 ,3 .4 .5 .6 .7 .8 .9 1.0

FIQ. 2, Cost function for each of five actions, and the optimum action intervals.

a group of master file records having the same post office and first four letters

of surname. The number of master file records in the final sample was about

83,000 and the number of comparison pairs about 192,000.

The comparison pairs in the sample were then classified into comparison

classes that corresponded to the initial intuitive rule already being employed

in the system. The probability of a match in each comparison class was estimated as the proportion of the comparison pairs in that class that were judged

to correspond to each other. The determination as to whether a given comparison pair was or was not a match cannot be regarded as definitive since that

determination was based upon judgment. However, there were at least two

independent judgments for each case, and discrepancies between the judgments were resolved by further review and judgments. It was planned, but

never carried out, that results should be refined by selecting a subsample of

comparison pairs from the classes defined and then making more intensive investigations of each of the subsample pairs in an effort to determine definitively whether or not the pair was a match. However, it is suggestive to consider some of the consequences if the match status assigned is assumed to be

correct. For example, it is interesting to consider the difference in the cost of

the initial intuitive rule and the optimum rule based upon the assumed cost

system.

Table 2 lists the 52 classes of comparison pairs with the size of each class

and the estimated probability of a match in each class. For the initial intuitive

rule and for the optimum rule, the table shows the action to be taken for e:~ch

class, the expected cost for this sample, and the percentage of the total cost.

Thus, it is estimated that the expected cost using the initial rule would have

been $1,800 for this sample while the cost using the optimum rule was reduced

.

47
.

TABLE 2. COSTS FOR THE SAMPLE, FOR TWO MATCHING RULES,

ASSUMING THE TENTATIVE UNIT COSTS

Estijmted ExpectedCosts

CorfiparisonTotal l?s~imated InitialRule I Optimm Rule

class percent I 1’<pof

I I

~oof pairs Act + mstch total Act $ total

1

2

i’

2

:

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

2

z

29

z

32

33

9

3

37

38

::

kl

4P

43

44

45

:;

48

Jig

50

51

52

Totfils 192,125

9.5

47.1

87.5

96.8

$r/.h.

100.0

lCO.O

98.4

50.0

lCO.O

92.3

$.0

94.1

53.8

70.0

86.0

l&k

‘33.2

o

8.1

3(0.o

1;.5

~:;

{::

7*7

6.7

::;

29.2

0

0.2

0

1.1

0

0.005

.&

o

0

0

0

3!:;

1.2

100.0

20.2

0.2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

:

2

2

:

2

2

f

:

4

4

4

4

4

4

4

5

;

5

5

5

5

5

5’

5

5

:

5

5

5

5

5

42.07

54.09

4C8.68

6.01

6.01

0.00

0.00

6.01

48.(x3

:::

~.::

36:06

18.05

84.14

lao, xl

23.68

29.38

172.57

21.73

19.21

76.21

48.66

8.61

18.04

41*OO

13*53

2.46

22.96

13.12

45.51

15.99

18.31

0.00

2.59

0.00

77.70

1%’

15.54

12,95

0.00

0;00

o.co

0.00

5.18

7. T(

15.54

2.59

88.06

_J&QL

$1;799.65

2.3

3.0

22.7

.3

.3

.0

.0

.3

2.7

;0

●3

1.7

.3

2.0

;:;

10.0

1.3

1.6

9.6

1.2

1.1

4.2

2.7

1::

2.3

.8

.1

1.3

2:;

.9

1.0

.0

.1

4:;

.0

.9

.9

.7

.0

.0

.0

::

.4

.9

.1

2

99.8$

42.07

lj.jj

272.03

6.Oi

6.01

0.00

0.00

6.01

g.84

:::

30.05

6.01

8.2o

6.26

48,.?l

3;.%

.

3%

0.00

3;::

31.47

5.25

0.00

7.77

6.01

1::$

4.10

23.90

7.77

12.71

0.00

2.59

7;:;

1;:s

15.54

12.95

0.00

0.00

O.rlcl

0.00

5.18

4.51

15.54

O.ml

4.4

l.h

23.7

.,<

.6

.0

.0

.6

1.6

.0

.6

3.2

.6

.9

~:;

3.6

.6

;:

.0

(:

3.3

.6

::

.6

1:;

.4

2.5

.8

1.3

‘:;

8:;

1::

1..6

1.4

.0

.0

.0

.0

●5

1:2.

.0

48
to about $950, or about one-half. The estimated

percentage reduction in cost is approximately

suggestive to note that 4 of these comparison

standard error of the estimated

2 percentage points. It is also

classes account for more than

hai~of the expected cost of the optimum rule but involve fewer than 2 per cent

of all comparison pairs. There is a distinct possibilityy that an intensive investigation of these 4 comparison classes could markedly reduce the cost of the

optimum rule by subdividing these comparison classes.

6. A SIMPLEEXAMPLEOF A COMPARISONFUNCTION

To clarify the notion of a comparison function, the following simple example

is given. The example is given for illustration only and bears no direct relationship to the numerical illustration given above, in which the comparison classes

are defined in a more complex way.

I.et each label a or j3 consist of the following components, a “blank” being

an admissible entry for a component:

1. Surname

2. Given name

3. House number

4. Street name

,s. l’ost office zip code

Then y(a, /3) may be defined as a vector (M, W, 7s, -Y4,75) where

~1= O if the surname is blank in either a or d.

1 if the surname is the same in a and @,and is a member of a specified

list of common surnames.

2 if the surname is the same in a and /3, and is not a member of the

specified list of common surnames.

3 if the surname is different in a and /3, and at least one of them is a

member of the specified list of common surnames.

4 if the surname is different in a and D, and neither is a member of the

specified list of common surnames.

-YZ= O if the given name k blank in either a or @.

1 if the given name is the same in a and p.

2 if the given name is different in a and /3.

~a= O if the house number is blank in either a or @.

1 if the house number is the same in a and b.

2 if the house numbers are different in a and B, but one is a permutation of the other.

3 if the house numbers are different in a and /3, and one is not a permutation of the other.

74= O if the street name is blank in either a or /3.

1 if the street names are the same in a and /?.

2 if the street names are different in a and 6.

yi = 1 if the zip codes are the same in a and @.

2 if the zip codes are different in a and B.

(It is assumed that the zip code is always present or can be supplied.) Thus the

function ~ may have up to 360 distinct values in thk example.

49
p

function may be reduced by a process of combination. That is, we may define

another comparison function # in terms of sets of values -r. Let the 360 possible

values of 7 be classified into sets L% Then y’(a, /3)= ~[~)if and only if -Y(CY, j3)dli.

I thank the referees for their helpful comments.

REFERENCES

[1] Du Bois, N. S. D’Audrea. “On the problem of matching documents with rniwing and

inaccurately recorded items (Preliminary report). ” Annals of Mathematical Statidics,

35 (1964), p. 1404 (Abstract).

[2] Fasteau, Herman H. and Mintrm, George. Auf.omated Geographic Coding S@e,n.

1963 Economic Census: Research Report No. 1, U. S. Bureau of the Census (unpublished). (1965).

[3] Kennedy, J. M. Linkuge of Birth and Marriage ~iecords Using a Digital Computer.

Document N-o. A.E. C.L.-1258, Atomic Energy of Canada Limited, Chalk River,

Ontario. (1961).

[4] Kennedy, J. M. “The use of a digital computer for record linkage. ” The Use of Vital

and Health Statistics for Gsnetic and Radiation Studies, United Nations, New York,

(1962), pp. 155-60.

[5] Nathan, Gad. On optimal Matching Processes. Doctoral Dissertation, Case Institute of

Technology, Cleveland, Ohio (1964).

[6] Nathan, Gad. ‘Outcome probabilities for a record matching process with complete

invariant information.” .fournal oj the American Statistical .4ssociatiors, 62 (1967),

pp. 454-69.

[7] Newcombe, H. B. “The study of mutation and selection in human populations.” The

Gerzetia Review, 57 (1965), pp. 109-25.

[8]Newcombe, H. B. and Kennedy, J. M. “Record linkage: Making maximum use of the

discriminating power of identifying information. ” Communications of the Association

jor Computmg Machinery, 5 (1962), pp.563-66.

[9]Newcombe,H. B., Kennedy,J. M., Axford,S.J., andJames, A. P. “Automatic linkage

of vital records.” Science,130 (1959), pp. 954-9.

[10] Newcombe, H. B. and Rhynas, P. O. W. “Chdd spacing followingstillbirth and infant death.” Eugenics Quarterly, 9 (1962), pp. 25-35.

[11] Nitzberg, David M. and Sardy, Hyman. “The methodology of computer linkage of

health and vital records. ” Proceedings, Social Statistics Section, American Statistical

A8sociatiw. (1965), pp. 100-6.

[12] Perkins, Walter M. and Jones, Charles D. “Matching for Census Coverage Checks. n

Proceedings, Social Statistics Section, American Statistical Association. (1965), pp.

122-39.

[13] Phillips, William and Bahn, Anita K. ‘Experience with matching of names.” Proceedings, Social Stuti8tice Section, .4 rrsericanStatistical Association. (1963), pp.26-9.

[14]Sunter, A. B. and Fellegi, I. P. An Optimal Theory oj Record Linkage. Unpublished

paper presented at the 36 Session of the International Statistical Institute, Sydney,

Australia (1967).

[15] Tepping, Benjamin J. Progress Report on the 1969 Matching Study. National Analysts,

Inc., Phdadelphia, Pa. (1960).

[16] Tepping, Benjamin J. and Chu, John l’. A Report on Matching Rules. National

Analysts, Inc., Philadelphia, Pa. (1958).

[17] U.S. Bureau of the Census. Evaluation and Rewzrch Program oj the U.S. Censuses of

Population and Housing, 1960: Record Check Studies oj Population Cowruge. Series

‘lJR.60, No. 2. U. S. Goverliment Pril,ting (Mice, Washington, D. C. (1964).

[18] U.S. Bureau of the Census. Evaluui,ion and Research f+o~rau~ o.f the U.S. (’ensuscs of

Population and Housingt 1960: Accuracy of Data on Population Characteristics as

Measured b~ CPS-Census Match. Series KR fit), No. 5. U. S. Goverlunel~t Pril]ti ng

office, Wasbiugton, 1). C. (1964)

50
A THEORY FOR RECORD LINKAGE*

.

.

.

IVAX P. FELLEGIANDALANB. SUNTEB

Dominion Bureau of Statistics

A mathematical model is developed to provide a theoretical framework for a computer-oriented solution to the problem of recognizing

those records in two files which represent identical persons, objects or

events (said to be matched).

A comparison is to be made between the recorded characteristics and

values in two records (one from each file) and a decision made as to

whether or not the members of the comparison-pair represent the same

person or event, or whether there is insufficient evidence to justify either

of these decisions at stipulated levels of error. These three decisions are

referred to as link (A J, a non-lin}c (AJ), and a possible link (iii). The

first two decisions are called positive dispositions.

The two types of error are defined as the error of the decision A]

when the members of the comparison pair are in fact unmatched, and

the error of the decisionA? when the members of the comparison pair

are, in fact matched. The probabilities of these errors are defined as

M = ;r IL(-r)P(A, I -r)

and

~ = ~rm(Y)fW IY)

rmpectively where u(y), m(y) are the probabilities of realizing y (a

comparison vector whose components are the coded agreements and

disagreements on each characteristic) for unmatched and matched

record pairs respectively. The summation is over the whole comparison

space 1’ of possible realizations.

A linkage rule assigns probabilities P(AI IY), and P(A2 I -r), and

P(A$ Iy) to each possible realization of y c r. .4n optimal linkage rule

L (p, A, r) is defined for each value of (p, A) as the rule that minimizes

P(AJ at those error levels. In other words, for fixed levels of error, the

rule minimizes the probability of failiug to make positive dispositions.

A theorem describing the construction and properties of the optimal

linkage rule and two corollaries to the theorem which make it a practical

working tool are given.

1. INTRODUCTION

T

HE necessity for comparing the records contained in a file LA with those

in a file LB in an effort to determine which pairs of records relate to the

same population unit is one which arises in many contexts, most of which can

be categorized as either (a) the construction or maintenance of a master file

for a population, or (b) merging two files in order to extend the amount of

information available for population units represented in both files.

The expansion of interest in the problem in the last few years is explained by

three main factors:

1) the creation, often as a by-product of administrative programmed, of

large files which require maintenance over long periods of time and which

oft en cent ain important statistical information whose value could be increased by linkage of individual records in different files;

.

*Reprinted with permission from the Journal of the American

Stat istical Association, American Statistical Association,

December 1969, Vol. 64, No. 328, pp. 1183-1210.

51
2) increased awareness in many countries of the potential of record linkage

for medical and genetic research;

3) advances in electronic data processing equipment and techniques which

make it appear technically and economically feasible to carry out the

huge amount of operational work in comparing records between even

medium-sized files.

A number of computer-oriented record linliage operations have already been

reported in the literature ([4], [s], [6], [~], [S], [11], [12], [13]) as well as

at least two attempts to develop a theory for record linkage ([1], [3]). The

present paper is, the authors hope, an improved version of their own earlier

papers on the subject ([2], [9], [10]). The theory, developed along the lines

of classical hypothesis testing, leads to a linkage rule which is quite similar to

the intuitively appealing approach of ~ewcombe ([4 ], [5], [6]).

The approach of the present paper is to create a mathematical model within

the framework of which a theory is developed to provide guidance for the

handling of the linkage problem. Some simplifying assumptions are introduced

and some practical problems are examined.

2. THEORY

There are two populations A and B whose elements will be denoted by a

and b respectively. We assume that some elements are common to A and B.

Consequently the set of ordered pairs

A X B = {(a, b); acil, b~B)

isthe union of two disjoint sets

and

u= {(a, b); a#b, aeA, b&] (2)

which we call the matched and unmatched sets respectively.

Each unit in the population has a number of characteristics associated w-ith

it (e.g. name, age, sex, marital status, address at different points in time,

place and date of birth, etc.). We assume now that there are two record generating processes, one for each of the two populations. The result of a record

generating process is a record for each member of the population containing

some selected characteristics (e.g. age at a certain, date, address at a certain

date, etc.). The record generating process also introduces some errors and some

incompleteness into the resulting records (e.g. errors of reporting or failure to

report, errors of coding, transcribing, keypunching, etc.). As a result two unmatched members of A and B may give rise to identical records (either due to

errors or due to the fact thnt an insufficient number of characteristics are included in the record) and, conversely, two matched “(identical) members of

A and B may give rise to different records. We denote the records corresponding to members of A and B by a(a) and P(b) respectively.

We also assume that simple random samples, denoted by A, and B, respectively, are selected from each of A and B. We do not, however, exclude the

52
possibility that A, =.4 and B,= B. The two given files, L..l and L3, are considered to be the result o“fthe application of the record generating process to

A, and B, respectively. For simplicity of notation we will drop the subscript s.

The first step in attempting to link the records of the two files (i.e. identifying

the records which correspond to matched members of A and B) is the comparison of records. The result of comparing two records, is a set of codes encoding

such statements M “name is the same, “ “name is the same and it is Brown, ”

“name disagrees, “ “name missing on one record, ” “agreement on city part of

address, but not on street, n etc. Formally we define the comparison vector as a

vector function of the records a(a), @(b):

7[4), N)] = {71[4), W)], “ “ “ , 7K[44, P(b)]} (3)

It is seen that y is a function on A XB. We shall write y(a, b) or y(a, p) or

simply y as it serves our purpose. The set of all possible realizations of y is called

the comparison space and denoted by 17.

In the course of the linkage operation we observe y(a, b) and want to decide

either that (u, b) is a matched pair (a, b)●.?1 (call this decision, denoted by Al,

a positive link) or that (a, b) is an unmatched pair (a, b) ~ u (call this decision,

denoted by A8, a positive non-link). There will be however some cases in which

we shall find ourselves unable to make either of these decisions at specified

levels of error (as defined below) so that we allow a third decision, denoted AZ,

a posst..k link.

A linkage rule L can now be defined as a mapping from I’, the comparison

space, onto a set of random decision functions D = {d(y) } where

NY) = {Wh[ Y), WL I Y), WL I Y)]; @ (4)

and

~ P(A, I Y)= 1.

i-1

(5)

In other words, corresponding to each observed value of y, the linkage rule

assigns the probabilities for taking each of the three possible actions. For some

or even all of the possible values of y the decision function may be a degenerate

random variable, i.e. it may assign one of the actions with probability y equal to 1.

We have to consider the levels of error associated with a linkage rule. We

assume, for the time being, that a pair of’ records [a(a), /i?(b)] is selected for

comparison according to some probability process from L.l )( LB (this is equivalent to selecting a pair of elements (a, b) at random from A )( B, due to the

construction of LA anti LB). The resulting comparison vector y [a(a), p(b) Jis

a random variable. We denote the conditional probability of y, given that

(a, b) GJ4 by m(y). Thus

m(y) = P{y[a(a), 19(b)]] (a, b)~ilf}

= ~ P{y[a(a), p(b)]] .P[(a, b) \ M].

(6)

(a,b)ehf

Similarly we denote the conditional probability of y, given that (a, b) ~ V by

u(y) . Thus

53
u(y) = ~{y[a(a), p(b)] I (a, b)c~]

= ,o~u P{y[cr(a), p(b)]] .P[(a, b) I IV].

(7)

There are two types of error associated with a linkage rule. The first occurs

when an unmatched comparison is linked and has the probability

P(A*I u) = ~u(y)”l’(fi, [ y).

-fE r

(8)

The second occurs when a matched comparison is non-linked and has the

probability

P(A, \ M) = ~n2(y).F’(A, Iy). (9)

-(t r

A linkage rule on the space I’ will be said to be a linkage rule at the levels

p, ~ (O<P<l and O<X< 1) and denoted by L@, ~, I’) if

P(A, / u) =/4 (lo)

and

P(A, I M) = x. (11)

Among the class of linkage rules on I’ which satisfy (10) and (11) the linkage

rule L(P, k, r) will be said to be the optimal linkage rule if the relation

P(.4, [ L) s P(A, I L’) (12)

holds for every L’(v, k, I’) in the class.

In explanation of,our definition we note that the optimal linkage rule maximizes the probabilities of positive dispositions of comparisons (i.e. decisions

A.1 and AJ subject to the fixed levels of error in (10) and.(11) or, put differently, it minimizes the probability of failing to make a positive disposition.

This seems a reasonable approach since in applications the decision Az will require expensive manual linkage operations; alternatively, if the probability of

AZis not small, the linkage process is of doubtful utility.

It is not difficult to see that for certain combinations of I.Land ~ the class of

linkage rules satisfying (10) and (11) is empty. We admit only those combinations of u and Afor which it is possible to satisfy equations (10) and (11) simultaneously with some set D of decision functions as defined by (4) and (5). For

a more detailed discussion of admissibility see Appendix 1. At this point it is

sufficient to note that a pair of values (P, ~) will be inadmissible only if one or

both of the members are too large, and that in this case we would always be

happy to reduce the error levels.

2.1. A fundamental theorem

We fikstdefine a linkage rule LO on l“. We start by defining a unique ordering

of the (finite) set of possible realizations of y.

If any value of y is ‘such that both m(y) and u(y) are equal to zero, then the

(unconditional) probability of realizing that value of y is equal to zero. and

54
hence it need not be included in I

. \Ye no!v assign an order arbitrarily to ally

for which m(y) >0 but u~yj = O.

~ext we order u1l remaining y in such a

quence of

m(y)/u(y)

is monotone decreasing. When the value of

than one y we order these y arbitrarily.

way that the corresponding se

n?(y)/u(y) is the same for more

We index the ordered set {y] by the subscript i; (i= 1, 2, . c ., lVr); and

write Ui= U(Yi);?n,i=?lt(Yi).

Let (p, A) bean admissible pair of error levels and choose n and n’ such that

n— 1

~Ui<~S~Ui (13)

i-l i=.1

where Nr is the number of points in r.

We assume for the present that when (13) and (14) are satisfied we have

1<n< ~’– 1< Nr. This will ensure that the levels (~, k) are admissible. Let

.L& A, I’) denote the linkage rule defined as follows: having observed a comparison vector, yi, take action Al (positive link) if i ~ n – 1, action A2 when

n <is n’ -1, and action AS (positive non-link) when i 2 n’ + 1. When i= n or

i= n) then a random decision is required to achieve the error levels p and h

exactly. Formally,

1

1,0, o) i< n-1 (a)

(PM, 1 – P., o) ~=n (b)

d(-yJ = ‘(0,1,0)

1

n<isn’-l (c) (15)

(O, 1- Pi, Pi) i = n’ (d)

1(0,o,1) i>nt+l (e)

where P@ and Pi are defined as the solutions to the eqtiations

.-1

i-1

{-n’+1

7’lZ~OIL??~l: Let LO(P, A, I’) be the linkage rule

(16)

(17)

defined by (15). Then L is

a best linkage rule on r at the levels (P, k). The proof is given in Appendix 1.

The reader will have observed that the whole theory could have been

formulated, although somewhat awkwardly, in terms of the classical theory of

hypothesis testing. We can test first the null hypothesis that (a, b) ~ U against

]Aslightlyextendedversion ofthetheormiegiveninAppendiI1.

55
the simple alternative that (a, b) 6-M, the action .41 being the rejection of the

null hypothesis and p the level of significance. Similarly the action & is the

rejection at the significance level k of the null hypothesis that (a, b) G .11 in

favour of the simple alternative that (a, b) ~ U. The linkage rule L is equivalent

to the likelihood ratio test and the theorem above asserts this to be the uniformly most powerful test for either hypothesis.

We state, without proof, two corollaries to the theorem. These corollaries,

although mathematically trivial, are important in practice.

Corollary 1: If

~=~u,, ~=~mi, n<n’,

i= 1 i-n

the LO(U,X, I’), the best linkage rule at the levels (P, A) becomes

[

(1,0,0) if I<ign

~(7i) = (0, 1,0) if n < i < n’

(O, O, 1) if n’ S i S ZVr.

If we define

m(y.)

TP=—

U(yn)

then the linkage rule (18) can be written equivalently as

{

(1, O,O) if T,< mfty)/u(y)

d(Y) = (0,1, O) if TA < m(y)/u(y) < T.

(O, O, 1) if rn(y)/u(y) 5 TA.

Corollary 2: Let T. and T~ be any two positive numbers such that

Then there exists an

T. and TA such that

(~, X) are given by

where

T.> Ti.

admissible pair of error levels

the linkage rule (19) is best at

(18)

(19)

(Y, A) corresponding to

these levels. The levels

(20)

(21)

(22)

(23)

s We me grateful tothe‘mfemefor pointing out that (19) and (1S) are exactly equivalent only if

%/% <%+1/%+,and m~’.~/un’.l <mm:’u,,.

56
In many applications we may be willing to tolerate error levels sufficiently high

to preclude the action .42. In this case we choose n and n’ or, alternatively,

T~ and Ti so that the middle set of y in (18) or (19) is empty. In other words

every (a, b) is allocated either to -lf or to U. The theory for the allocation of

observations to one of two mutually exclusive populations may thus be regarded as a special cose of the theory given in this paper.

● s. APPLICATIONS

3.1. Some Practical Problems

In attempting to implement the theory developed in the previous section

several practical problems need to be solved. They are outlined briefly below

and taken up in more detail in subsequent sections.

a)

b)

c)

d)

e)

The large number of possible values of m(y) and u(y). Clearly the number

of distinct realizations of y may be so large as to make the computation

and storage of the corresponding values of m(y) and u(y) impractical.

The amount of computation and storage can he substantially reduced on

the basis of some simplifying assumptions.

llethods to calculate the quantities m(~) and u(~). Two methods are

proposed.

Blocking the files. Implicit in the development of the theory is the assumption that if two files are linked then all possible comparisons of all

the records of both files will be attempted, It is clear that even for medium

sized files the number of comparisons under this assumption would be

very large, (e.g. 10Krecords in each file would imply 10tOcomparisons).

In practice the files have to be “blocked” in some fashion and comparisons

made only within corresponding blocks. The impact of such blocking on

the error levels will be examined.

Calculations of threshold values. It should be clear from Corollary 2 that

we do not have to order explicitly the values of y in order to apply the

main theorem since for any particular y the appropriate decision (Al,

Az or AJ can be made by comparing m(y) /u(y) with the threshold values

T. and TA. We shall outline a method of establishing these threshold

values corresponding to the required error levels p and L

Choice of the comparison space. The main theorem provides an optimal

linkage rule for a given comparison space. Some guidance will be provided on the choice of the comparison space.

3.2. Some simplifying assumptions

In practice the set of distinct (vector) values of y may be so large that the

estimation of the corresponding probabilities m (Y) and u(y) becomes comIetely impracticable. In order to make use of the theorem it will be necessary

to make some simplifying assumptions about the distribution of y.

We assume that the components of y can be re-ordered and grouped in such

a way that

y=(y’, yz, . . ..p)

and that the (vector) components are mutually statistically independent with

57
respect to each of the conditional distributions. Thus

nz(7) = ml(yl) vnz(yz) . . . m~(y~j

u(y) = Ul(yl) .u2(y~) o . . Uk(;=)

where m(y) and u(y) are defined by (4) and (5) respective y and

??li(yi) = ~(yi I (a, b)~.ii!)

Ui(yi) = ~(y’1 (a, b)c V).

(24)

(25)

For simplicity of notation we shall write m(yi) and U(yi) instead of the

technically more precise ~i(yi) and ui(yi). As an example, in a comparison of

records relating to persons yl might include all comparison components that

relate to surnames, yz all comparison components that relate to addresses.

The components yl and yz are themselves vectors; the subcomponents of y~

for example might represent the coded results of comparing the different components of the address (tit y name, street name, house number, etc.). If two

records are matched (i.e. when in fact they represent the same person or event),

then a disagreement configuration could occur due to errors. Our assumption

says that errors in names, for example, are independent of errors in addresses.

If two records are unmatched (i.e. when in fact they represent different persons

or events) then our assumption says that an accidental agreement on name, for

example, is independent of an accidental agreement on address. In other words

what we do assume is that yl, yz, . . . , yK are conditionally independently distributed. We emphasize that we do not assume. anything about the unconditional distribution of y.

It is clear that any monotone increasing function of m(y)/u(y) couId serve

equally well as a test statistic for the purpose of our linkage rule. In particular

it will be advantageous to use the logarithm of this ratio and define

w~(y~) = Iogm(yk) – logu(p). (26)

We can then write

W(7) =w1~w2~...wKwK (27)

and use w(y) as our test statistic with the understanding that if u(y) = O or

m(~) = O then w(y) = + @ (or w(y) = - ~ ) in the sense that w(y) is greater (or

smaller) than any given finite number.

Suppose that y~ can take on nk different con.$gurations, y;, Y1, . “ “ , y~~. We

define

w; = logm(y;) - log U(y;). (28)

It is a convenience for the intuitive interpretation of the linkage process that the

weights so defined are positiive for those configurations for which m(Y!) > u(y~),

negative for those configurations for which m (y;) < u(y$), and that this property is preserved by the weights associated with the total configuration y.

The number of total configurations (i.e. the number of points y~l”) is obviously nl. n?. . . . n~. However, because of the additive property of the

58
weights defined for components it will be sufficient to determine nl + % + . “ “

+nK weights. We can then always determine the weight associated with any

y by employing this additivity.

3.3. The Calculation of Weighk

An assumption made at the outset of this paper was that the files LA and LD

represent samples A, and B, of the populations A and B. This assumption is

often necessary in some applications when one wishes to use a set of values of

m(~) and u(~), computed for some large populations A and B while the actually observed files LA and LB correspond to some subpopulations A, and B,.

For example, in comparing a set of incoming records against a master file in

order to update the file one may want to consider the master file and the incoming set of records as corresponding to samples .4, and B, of some conceptual

populations A and B. One might compute the weights for the full comparison

space I’ corresponding to A and B and apply these weights repeatedly on different update runs; otherwise one would have to recompute the weights on each

occasion.

Of course it seldom occurs in practice that the subpopulations represented

by the files LA and LB are actually drawn at random from any real populations

.4 and B. However it is clear that all the theory presented in this paper will

still hold if the assumption is relaxed to the assumption that the condition of

entry of the subpopulation into the files is uncorrelated with the distribution

in the populations of the characteristics used for comparisons. This second

assumption obviously holds if the first does, although the converse is not

necessarily true.

In this paper we propose two methods for calculating weights. In the first

of these we assume that prior information is available on the distribution in

the populations A and B of the characteristics used in comparison as well as

on the probabilities of different types of error introduced into the files by the

record generating processes. The second method utilizes the information in the

files LA and LB themselves to estimate the probabilities m(~) and u(~). The

validlty of these estimates is strongly predicated on the independence assumption of the previous section. Specifically it requires that the formal expression

for that independence should hold almost exactly in the subpopulation LA X LB,

which, in turn, requires that the files LA and LB should be large and should

satisfy at least the weaker of the assumptions of the previous paragraph.

Another procedure, proposed by Tepping ([11], [13]), is to draw a sample

from LA XLB, identify somehow (with negligible error) the matched and unmatched comparisons in this sample, and thus estimate m(y) and u(y) directly.

The procedure seems to have some difficulties associated with it. If and when

the identification of matched and unmatched records can in fact be carried out

with reasonable accuracy and with reasonable economy (even if only at least

occasionally) then it might provide a useful check or corroboration of the reasonableness of assumptions underlying the calculation of weights.

Finally, the weights w(y) or alternatively the probabilities m(y) and u(y),

derived on one occasion for the linkage LAX LB can continue to be used on a

59
subsequent occasion for the linkage, say .Lx(X LBt, provided A, and B. can be

regarded as samples from the same populations as A. and B, and provided the

record generating processes are unaltered.

3.3.1. Method I

Suppose that one component of the records associated with each of the two

populations A and B is the surname. The comparison of surnames on two

records will result in a component of the comparison vector. This component

may be a simple comparison component such as ‘name agreesn or “name disagrees or ‘name missing on one or both recordsn (in this case Ykis a scalar);

or it may be a more complicated vector component such as for example %ecords agree on Soundex code, the Soundex code is B650; the first 5 characters of

the name agree; the second 5 characters of the name agree; the surname is

BROWNING.n

In either of the two files the surname maybe reported in error. Assume that

we could list all error-free realizations of all surnames in the two populations

and also the number of individuals in the respective populations corresponding

to each of these surnames. Let the respective frequencies in A and B be

f4, f+, “ “ “ ,fAm; ~ f., = NA

and

Let the corresponding frequencies in AnB be

jl, f2, “ “ “ ,fm; ~ f, = .N.B.

3

The following additional notation is needed:

t?A or f?B the respective probabilities of a name being misreported in LA

or LB (we assume that the probability of misreporting is independent of the particular name);

e.40or eBO the respective probabilities of a name not being reported in

LA or LB (we assume that the probability of name not being

reported is independent of the particular name);

e~ the probab~lty the name of a person is differently (though correctly) reported in the two files (this might arise, for example, if

LA and LB were generated at different times and the person

changed his name).

Finally we assume that eA and eB are sufficiently small that the probability

of an agreement on two identical, though erroneous, entries is negligible and

that the probabilities of misreporting, not reporting and change are independent of one another.

We shall first give a few rules for the calculation of m and u corresponding

60
to the follon-ing configurations of 7: name agrees and it is the jth listed name,

name disagrees; name missing on either record.

m (name agrees and is the jth listed name)

= -+1 –fj’J(l —.E?B)(l—@)(l – CAO)(l — @I?O)

i~AB

a&(l–eA -eB-eT–eAO-eBO)

m (name disagrees)

= [1-(1 – eA)(l – eB)(l – eT)](l – eAO)(l - eBO)

‘f?A+eB+t?T

m. (name missing on either file)

. 1 – (1 - .!?A))(l – f?BO) = e.40+ eBO

u (name agrees and is the jth listed name)

fAj fBj

~A x(1 – e,4)(l – e.)(1 - eAO)(l – @80) .—

~ .fAj .fBj

‘(l–t?A -C? B-@-eAO-eBO)

NA NB

u (name disagrees)

[

.fAi fBj

. 1 – (1 - e~)(l – e~)(l - 1

eT)~—— (1-eAO)(l - @BO)

j .~.4 NB

“[

fA, fBi

. l–(l-f?A-eB– 1

e~)~~ (1-eAO – @BO)

(29)

(30)

(31)

(32)

(33)

u (name missing on either file)

= 1 – (1 - eAO)(l – C?BO) = eAO + eBO. (34)

The proportions j.4j/NAr jBi/NB, .fi/N may be taken, in manY application% to

be the same. This would be the case, for example, if two large files can be

assumed to be drawn from the same population. These frequencies may be

estimated from the files themselves.

A second remark relates to the interpretation of weights. It will be recalled

that according to (28) the contribution to the overall weight of the name component is equal to log (m/u) and that comparisons with a weight higher than a

specified number will be considered linked, while those whose weight is below a

specified number will be considered unlinked. It is clear from (29-34) that an

agreement on name will produce a positive weight and in fact the rarer the

name, the larger the weight; a disagreement on name will produce a negative

weight which decreases with the errors eA, eB, e~; if the name is missing on either

record, the weight will be zero. These results seem intuitively appealing.

61
We should emphasize that it is not necessary to list all possible names for the

validity of formulae (29) to (34). We might only list the more common names

separately, grouping all the remaining names. In the case of groupings the

appropriate formulae in (29) to (34) have to be summed over the corresponding

values of the subscript j. The problem of how to group configurations is taken

up in a later section.

Finally we should mention that formulae (29) to (34) relate to reasonably

simple realizations of y, such as a list of names, or list of ages, or lists of other

possible identifiers. In more complex cases one maybe able to make use of these

results, with appropriate modifications, in conjunction with the elementary

rules of probability calculus. Alternatively one may have recourse to the

method given below.

3.3.2. Method II

The formulae presented in Appendix 2 can be used, under certain circumstances, to estimate the quantities m(~), u(~) and N, the number of matched

records, simply by substituting into these formulae certain frequencies which

can be directly (and automatically) counted by comparing the two files.

Mathematically, the only condition for the validity of these formulae is that y

should have at least three components which are independent with respect to

the probability measures nt and u in the sense of (24) and (25). It should be

kept in mind, however, that for agreement configurations m(~) is typically

very close to one, u(#) is very close to zero, and conversely for disagreement

configurations. Therefore the estimates of u(~) and zn(#) can be subject to

substantial sampling variability unless the two files represent censuses or large

random samples of the populations A and B.

The detailed formulae and their proofs are included in the Appendix. At this

point only an indication of the methods will be given. For simplicity we present

the method in terms of three components. If, in fact, there are more than three

components they can be grouped until there are only three left. Clearly this

can be done without violating (24) and (25).

For each component vector of y designate the set of configurations to be considered as “agreements’} and denote this set (of vectors) for the hth component

by &. The designation of specific configurations as “agreements” may be

arbitrary but subject to some numerical considerations to be outlined in the

Appendix.

The following notation refers to the frequencies of various configurations of

y. Since they are not conditional frequencies, they can be obtained as direct

counts by comparing the files LA and LB:

~h: the proportion of “agreement” in all components except the hth; any

configuration in the kth component;

Uk: the proportion of “agreement” in the hth component; any configuration

in the others;

J4: the proportion of “agreement” in all components.

Denote also the respective conditional probabilities of ‘(agreements” by

62
() (35)

yts~

Uh = ~ u(y). (36)

yesh

It follows from the assumptions (24) and (2.5)that the expected values of f~h,

~h, and .11with respect to the sampling procedure (if any) and the record generating process through which the files LA and LB arose from the populations

.4 and B can be expressed simply in terms of mh and uh as fo~ows.

.VAN~E(.T1h) = II(N) ; Tllj + [NANB - E(N)] : 246; h = 1,2,3 (37)

~=1 a.-1

j#h j~h

.~AfvB.??(Uh) = J??@)??th + [fvA~B – ~(~)]Uh (38)

where NA and NB are the known number of records in the files LA and LB and

N is the unknown number of matched records.

Dropping the expected values we obtain seven equations for the estimation

of the seven unknown quantities IV, m~, uk(h = 1, 2, 3). The solution of these

equations is given in Appendix 2.

Having solved for mh, uh and N the quantities m(+) and u(~) are easily computed by substituting some additional directly observable frequencies into

some other equations, also presented in Appendix 2. The frequency counts required for all the calculations can be obtained at the price of three sorts of the

two files.

It is our duty to warn the reader again that although these equations provide

st artistically consistent estimates, the sampling variability y of the estimates may

be considerable if the number of records involved (NAND) is not su5ciently

large. One might get an impression of the sampling variabilities through the

method of random replication, i.e., by splitting both of the files at random

into at least two parts and by performing the estimation separately for each.

Alternatively, one can at least get an impression of the sampling variabilities

of .lfk, ~h and 1! by assuming that they are estimated from a random sample

of size NANB.

Another word of caution maybe in order. The estimates are computed on the

basis of the independence assumptions of (24) and (25). In the case of departures from independence the estimates, as estimates of the probabilities

m(~) and u(~), may be seriously affected and the resulting weights m(~)/

u(-#) would lose their probabilistic interpretations. What is important, of

course, is their effect on the resulting linkage operation. We believe that if

sufficient identifying information is available in the two files to carry out the

linkage operation in the first place, then the operation is quite robust against

depart ures from independence. One can get an impression of the extent of the

departures from independence by carrying out the calculations of Appendix 2

on the basis of alternative designations of the “agreement” configurations.

63
f ppp

In practice of course we do not select comparisons at random from LAX LB.

But then in practice we are not concerned with the probability of the event

(All U) or the event (Atl M) for any particular comparison but rather with the

proportion of occurrences of these two events in the long run. Clearly if our

linkage procedure is to examine every comparison (a, @ ~L~ X LB then we could

formally treat any particular comparison as if it had been drawn at random

from LA XLB. The only change in our theory in this case would be the replacement of probabilities with proportions. In particular the probabilities of error

p and A would then have to be interpreted as proportions of errors. N’ith this

understanding we can continue to use the notation and concepts of probability

calculus in this paper even though often we shall think of probabilities as

proportions.

We have now made explicit a second point which needs to be examined. We

would seldom be prepared to examine every (a, O)~ L4 X LB since it is clear

that even for medium sized files (say 105 record each) the number of comparisons (1010)would outstrip the economic capacity of even the largest and fastest

computers.

Thus the number of comparisons we will examine explicitly will be restricted

to a subspace, say I’*, of l’. This might be achieved for example by partitioning

or “blockingw the two files into Soundex-coded Surname “blocks” and making

explicit comparisons only between records in corresponding blocks. The subspace I’* is then the set of y for which the Soundex Surname component has

the agreement status. All other y are implicit positive non-links (the comparisons in I’- I’* will not even be actually compared hence they may not be either

positive or possible links). We consider the effect that this procedure has on the

error levels established for the all-comparison procedure.

Let r. and I’x be established (as in Corollary 2) for the all-comparison procedure so as to satisfy

where

If we now regard all ye (I’ – I’*) as implicit positive non-links we must

adjust our error levels to

lJ*=P - X U(Y) (40)

rp~ r*

(41)

where f’x and I’* denote complements taken with respect to 1’ (i.e. r- r~ and

r– r*, respectively).

64
The first of these expressions indicates that the Ievel of ~ is reduced by the

sum of the u-probabilities of those comparisons which would have been links

under the all-comparison procedure but are implicit non-links under the blocking procedure. The second expression indicates that the actual level of A is increased by the sum of the n-probabilities of the comparisons that would be

links or possible links under the all-comparison procedure but are implicit

non-links under the blocking procedure.

The probabilities of a failure to make a positive disposition under the blocking procedure are given by

the second term on the right in each case being the reduction due to the blocking procedure.

These expressions will be found to be useful when we consider the best way

of blocking a file.

3.5. Choice of Error Levels and Choice of Subspace

In choosing the error levels (P, k) we may want to be guided by the consideration of losses incurred by the different actions.

Let Gjf(A J and Gu(Ai) be non-negative loss functions which give the 10SS

associated with the disposition A i; (i= 1, 2, 3); for each type of comparison.

Normally, we would set

Gaz(A,) = Gcr(AJ = O

and we do so here. Reverting to the all-comparison procedure we set (P, k) so

as to minimize the expected loss given by the expression

P(M) .E[G~(A,)] + P(U) .E[Gu(AJ]

= P(M) [I’(A2 I M) “GadAJ + A“GM(AJ] (44)

+ P(U) [W”GU(AI)+ HAA I U)”GU(AJ]

Note that P(A* IJf ) and P(A21 U) are functions of H and A We give later a

practical procedure for determining the values of (P, ~) which minintize (44).

Suppose that (P, h) have been set so as to minimize (44). We now consider

the effects of blocking the files and introduce an additional component in the

loss function which expresses the costs of comparisons, GIS*(LA XLB), under a

blocking procedure equivalent to making implicit comparisons in a subspace

I’*. We seek that subspace I’* which minimizes the total expected loss,

c{ P(M) .E[G~(Ai)] + P(U) .E[Gu(AJ]}

+ G I-(LA X LB)

= c{P(M) [P*(A * I JJ)G.w(A J + A*GM(AJ ] (45)

+ ~(u) [~*Gu(Al) + F’*(A2 I L9Gu(AJ]]

-i- G’r*(LA X LB)

65
pg pgy () 

(43) respectively and c denotes the number of comparisons in L. x LB. ~ow if

the processing cost of comparisons under any blocking I’* is simply proportional to the number of comparisons, c*, i.e.

G r*(LA X LB) = ac*

then we can minimize

P(M) [P”(A, I M) G~(A,)x*G~(AJ]

+ P(U) [p*GcL4J + F’”(A2 I OW4Z)] + : ~ (46)

The last term is the product of the cost, a, per comparison and the reduction

ratio in the number of comparisons to be made explicitly.

NTOexplicit solution of (46) seems possible under such general conditions.

However, (46) can be used to compare two different choices of l“*. Once a

choice of I’* has been made, the “theoreticaln error levels ~, 1 can be chosen,

using (40) and (41), so that the actual error levels p*, A* meet the error specification. The threshold values TP, T~ are then calculated from the “theoretical error levels.

3.6. Choice of comparison space

Let X’and 17’be two comparison spaces, with conditional distributions m(w),

u(w) and m’(w), u’(w) and threshold values TM, TX and T;, T~ respectively

(the threshold values being in both cases so determined that they lead to the

same error levels ~, A).

Now in a manner precisely analogous to our linkage criterion we might say

that a comparison space I’ is better than a comparison space I“ at the error

levels (P, A) if

P(T, < W(Y) < TJ < P(T( < W’(y’) < T;) (47)

where it is assumed that the comparisons are made under the optimal linkage

rule in each case. The linkage criterion developed for a given I’ is independent

of (P, k) and ~(ikf). Clearly we cannot hope for this to be the case in general

with a criterion for the choice of a comparison space.

Expanding the expression (47) we have as our criterion at the level (~, k)

P(M) . ~ m(w)+ P(u). ~ u(w)

?’~<11<T* Th<w<?’P

< P(M). ~ m(w’) + P(U). ~ u(w’) ’48)

TA<W< Tp TX<W’<TP

In most practical cases of course P(M) is very small and the two sides of (48)

are dominated by the second term. However if a ‘blockingn procedure has

reduced the number of unmatched comparisons greatly it would be more appropriate to use P*(M) and P*(U) appropriate to the subspace I’* (i.e. to the

set of comparisons that will be made explicitly), than to use F’(M) and P(u)

provided the same ‘blocking” procedure is to be used for each choice of comparison space. P(M) and P(U), or alternatively P*(M) and P*(U), have to be

66
.

,

guessed at for the application of (-48). The difference betm-een the right hand

side and the left hand side of (4S) is equal to the reduction of P(AJ due to the

choice of the comparison space.

In practice the difference between two comparison spaces will often be the

number of configurations of component vectors which are listed out in addition

to the simple “agreement’’-” disagreement” configurations (e.g. “agreement

on name Jones, n “agreement on name Smith, ” etc.). The formula (48) can be

used to compare the loss or gain in dropping some special cordigurations or

listing out explicitly some more.

:3.7. Calculation of threshold values

Having specified all the relevant configurations y; and determined their

associated weights ~; k = 1, 2, . . . , K; j= 1, 2, “ “ “ , m it remains to set the

threshold values T, and Tk corresponding to given P and k and to estimate the

number or proportion of failures to make positive dispositions of comparisons.

As shown before, the number of weights to be determined is equal to

nl+n2 . “ . +~~. The total number of different configurations is, however,

nlm”.. WC.Since the number of total configurations will, in most practical

situations, be too large for their complete listing and ordering to be feasible

we have resorted to sampling the configurations in order to estimate TNand T~.

Since we are primarily interested in the two ends of an ordered list of total

configurations we sample with relatively high probabilities for configurations

which have very high or very low weights w (y).

The problem is made considerably easier by the independence of the component vectors yk. Thus if we sample independently the component configurations -y~, 7;,, . “ o

K. . . .

J TjK. ~th Probablhtles zjl~ z~2t - -

. , ZfK respectively we will

have sampled the total configuration Yj = (-Y},, ~~,, “ . “ , ~~) with probability

Zj 2

=2;1, Z,* . . . z;. Hence we do not need to list all configurations of y for

sampling purposes, only all configurations of ~~ for each k.

We speed up the sampling process and increase the efficiency of the sample

by ordering the configurations listed for each component by decreasing values

d, and sampling according to the following scheme:

1) Assign selection probabilities z!, z:, . . “ , z.: roughly proportional to Izv~\”

~) Choose a configurationfrom each component. If the configuration T; is

chosen from the kth component (with probability z;) choose also the

configuration #fi .,*1.

3) Combine the first members of the pairs chosen from each component to

give one total configuration and the second members to give another.

4) Repeat the whole procedure S/2 times to give a with-replacement sample

of B total cor@urations.

The sample is then ordered by decreasing values of

w=wl+wz+””. +wK. (49)

Let Y~(h=l,2, “ “ ., S) be the hth member of the ordered listing of the sample.

(Note: If a configuration with the same value of w occurs twice in the sample,

it is listed twice.) Then ~(w(y) < zv(yh)\y ●31) is estimated by

.

67
where

and

s

7r(y.h) = ;. z’(yJ

2 (%J = Z:lZ:*“ “ “ 2: + Z:+i+lz:,++l . . . 2: K-h +1 KK

(30)

(51)

(52)

while

~’(~(y) < ~(~h) [ Y = ~) is estimated by

P,= ~ ~(yf#)/7r(y,~).

(53)

h’-l

The threshold values !l’(~~~)and Z’(PV), are simply the weights ~(yhf) and

W(yh.).

We have written a computer program which, working from a list of configurations for each vector component and associated selection probabilities, selects

a sample of total configurations, orders the sample according to (49), calculates

the estimates (50) and (53) and finally prints out the whole list giving for each

tOtal COd@ratiOn itS associated hh, ph, T(AJ, and T(#h).

We can use the same program to examine alternative blocking procedures

(see Section 3.4). Thus in the ordered listing of sampled configurations we can

identify those which would be implicit positive non-links under a blocking procedure which restricts explicit comparisons to a subspace r*. Thus corresponding to any values of TMand Tk (or ~ and k) we can obtain the second terms in

each of the expressions (40), (41), (42), and (43). Alternative] y if the implicit

positive non-links are passed over in the summations (40) and (41) we can read

off the values of the left-hand sides of those expressions. If we arrange this for

alternative blocking procedures we are able to use the output of the program to

make a choice of blocking procedures according to (46).

4. ACKNOWLEDGMENTS

The authors would like to express their gratitude to the Dominion Bureau

of Statistics for providing opportunities for this research and in particular to

Dr. S. A. Goldberg for his continued support.

The authors would also like to express their appreciation to H. B. Newcombe

for his pioneering work in the field of record linkage and for his generous encouragement of an approach which, in many respects, differs from his own. The

contributions of J. N. Gauthier, the systems analyst in charge of programming

for our pilot project, have been essential to whatever success we have enjoyed

so far and will continue to be essential in what remains for us to do.

REFERENCES

[1] Du Bois, N. S. D., ‘A solution to the problem of linking multivariate documents,

Journal oj the American Statistical Association, 64 (1969) 163-174.

68
[~1

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

Fellegi, I. P. and Sunter, A. B., “An optimal theory of record linkage, ” Proceedings of

the International Symposium on Automation of Population Register Systems, Volume 1,

Jerusalem, Israel, 1967.

Nathan, G., “Outcome probabilities for a record matching process with complete

invariant information, n Journal of the American Stotisticul Association, 62 (1967)

454-69.

Newcombe, H. B. and Kennedy, J. Nf., “Record linkage: Making maximum use of

the discriminating power of identifying information, ” Communications of the A. C’.M.

5 (1962) 563.

Newcombe, H. B., Kennedy, J. M., Axford, S. L., and James, A. P., “.kutomatic

linkage of vital records, ” Science 130, (1959) 954.

Newcombe, H. B. and Rhynas, P. O. W., “Family linkage of population records,”

Proc. U.N. / W. H. O. Seminar on Use of Vital and Health Statistics for Genetic and

Radiation Studies; United Nations Sales No: 61, XVII 8, New York, 1962.

Nitzberg, David M. and Sardy, Hyman, “The methodology of computer linkage of

health and vital records,” Proc. Sot. Statist. Section, American Statistical Association,

Philadelphia, 1965.

Phillips, Jr., William and Bahn, Anita K., “Experience with computer matching

of names, ” Proc. Sot. StatisL Section, American Statistical Association, Phdadelphia,

1965.

Sunter, A. B., “A statistical approach to record linkage; record linkage in medicine, ”

Proceedings of the International Symposium, Oxford, July 1967; E. & S. Livingstone

Ltd., London, 1968.

Sunter, A. B., and Fellegi, I. P., ‘An optimal theory of record linkage, ” 36th Session

of the International Statistical Institute, Sydney, Australia, 1967.

Tepping, B. J., “Study of matching techniques for subscriptions fulfillment,” National

Analysts Inc., Philadelphia, August, 1955.

Tepping, B. J., “A model for optimum linkage of records, ” Journal of the American

Statistical Association, 63 (1968) 1321-1332.

Tepping, B. J., and Chu, J. T., “A report on matching rules applied to readers digest

data, ” National Analysts Inc., Philadelphia, August, 1958.

APPENDIX I

A FUNDAMENTAL THEOREMFORRECORDLINKAGE

We stated that (P, A) is an admissible pair of error Ievels provided ~ and x

are not both too large. We will make this statement more precise.

Let

L-n = kui;

i..1

U,=o

NP

.11., = ~ mi;

i-n *

.Uxr+l = o

and define j(p), as shown in Figure

~=l,~...r.-r ? (1)

(2)

,L~=l, ~,. ..vrvr (3)

(4)

1, on the interval (O, 1) as the monotone

decreasing ‘polygon line passing through the points (u”, l~ffn+l) for ??= 0,

1, ..., N. It is possible of course to state the definition more precisely, but

unnecessary for our purposes.

The area contained by the axes and including the line k =!(P) defines the

region of admissible pairs (~, X). In other words (P, A) is an admissible pair if

69
K?r+1

‘\&--- k=f~)

!/

!--

u

1

t

mnl

/

/

L.

L- U

0

U. 1

u,,

FIG.1

.,“

(5)

(6)

(7)

(8)
al-d

– Gn(.)-l

P.=p (9)

It follows from the way in which the configurations were ordered and the restrictions cm p and L that the denominators of the expressions cm the right of

(S) and (9) are positive.

It is easy to see from Figure 1 that

O<PxSl and O< P.S1. (lo)

It is also clear from Figure 1 that (P, A) are admissible if and only if

(a) n’(~) 2 n(p) + 1

(e.g. (Pa, X=) in Figure 1)

or (11)

(b) n’(A) = n(p) and Pi+ P. <1

(e.g. (M, b) in Figure 1).

Thus (a) and (b) simply divide the admissible region into two areas, one

bounded by the axes and the broken lines in Figure 1, and the other bounded

by the broken lines and the polygon line k =j(&).

Finally, from Figure 1 and the definitions of n(p) and n’(X) we see that

~ =~(u) if and only if

(a) n’(~) = n(p) + 1 and P, = PA (@

(i.e. the vertices of x = ~(y)).

or

(b) n’(~) = n(~) and PX + P@ = 1 (13)

(i.e. points on x = ~(p) other than vertices).

Let (v, X) be an admissible pair of error levels on I’. We define a linkage rule

LO(U,X, r) as follows:

1) If n’(~) >n(~)+l then

f{l, o, ) ifi<n(~)–1

I(PM, 1 – F’p, O) if i = n(~)

d~(y,) = ~(0, 1, O)

1’

ifn(~)+l<isn’(k)–l

\(o, 1 – F’,, PA) if i = n’(~)

[(0, o, 1) if i 2 n’(~) + 1

2) If n’(~) =n(p) and Pi+Pp< 1

f(l, o, o) ifign(u)–1

Jo! y,) = ~(P,, 1 – f’, – P,, PJ if i = n(p) = n’(h)

,(0, o, 1) if i 2 n’(~) + 1.

71
(It is easy to see that (P, X)is admissible if and onlv if one of the two conditions

above holds.)

We have now defined a linkage rule for an arbitrary pair of admissible levels

(K, k). It follows immediately from the definition of L& k, I’) that l’(zi,) =0

if and only if i =~(P)

Theorem: If (~, k) is an admissible pair of error levels on I’ then Lo(A, X, I’)

is the best linkage rule on r at the levels P and L If (P, A) is not admissible on

r then there are levels (PO, Ao) with

(with at least one of the inequalities in (14) being a definite inequality) such

that L$ (w, h, r) is better than Lo(P, h r) and for ~hich

PLO(A,) = o. (15)

This theorem explains the terminology “inadmissible.” This simply means

that we should not consider linkage rules at inadmissible error levels, since in

this case L: always provides a linkage rule at lower error levels for which we

still have ~(.4J = O (i.e. only the positive dispositions Al and .4s occur).

Proof :

Let L’[p, X, I’) be any linkage rule with admissible levels (~, k). Then

L’(p, X, I’) can be characterized by the set of decision functions

~’(YJ = (% P:z, E3) }

~P~j=l i=l,2,. .jN~ (16)

3-1

where

P{j = P(Aj I Yi), j=l,2,3; i=l,2, . . ..Nr.

Clearly

i=1

(17)

(18)

(19)

i-l

Consider the linkage rule Lo(P, ~, l“).It is Charaeternzed b equations anahws

to (16) to (19) but P;j replaced by Pij as defined above. We shall prove that

P(A, I Lo) S P(~2 I L’) (20)

According to the construction of Lo the u{ -which happen to be zero have the

smallest subscripts, the mj which happen to be zero have the largest subscripts.

More rigorously, there are subscripts r and s such that

ui=O ifisr–1, ui>O ifizr (21)

mi=O ifi Zs+ l,- 7ni>0 ifigs (22)

We have seen previously that

u.(p) > 0

72
and

hence

hence

Pi~ = “1 fori=l,2, ,.. ,r–l (23)

Pi% = 1 fori=s+ l,s+2, . . ..Nr (24)

that is, whenever ui is zero then F’il= 1 and whenever mi = O then Pia = 1.

By definition of p, it follows that

Putting n = n(p) and observing that Pil = 1 if is n —1 we can express (25) as

follou-s :

or

With the possible exception of the last term on the left it is clear that every

term in (26) is non-negative. We assume, without loss of generality, that the

term in question h non-negative for, if it were negative, we would simply

transfer it to the other side of the equality and all of the steps to follow would

hold. It follows that if not every term in (26) is equal to zero then both sides

are positive. Assume for the moment that this is the case.

It follows from the ordering of r that

It is now seen that

[j~lm’p;ll[Eui(l -p’l)+un(pp-p:o’)l

[

n-1

~ ~ mi(l – PL) + 772.(P,

i-.1 ‘p:’)l[jslu’p;ll’28)

since by (27) every term in the expansion of the left hand side is of the form

mJ’UiP~l(1 – P{J or 71ZjUnP~(Pp – P., J (isn <j)

73
and corresponding to each there is a similar term on the right hand side but

with m,tii replaced by miuj and mj% replaced by mnufi Dividing (2S) by (26)

we get

~ mj~~~ s ~’mj(l - ~~i) + mm(~. - ~~,1)

j-a+ 1 j-l

or

(29)

If every term in (26) was zero (29) would still hold since in that case we would

have

i.e. whenever ui#O and we would have

becauae of (23) and because P&s 1 for every i. Hence (29) would hold in this

case as well.

By definition

From (29) and (30) we get

i-1 i-1

or

f m,(l - P{*) $ ~ mi(l - Pi,).

i-1 i= 1

Because

2Vr

~ mi = 1, we get

i-1

i-1 i-1

or

PLO(A, I *M) s PLt(.42 I M).

It can be shown similarly that

(30)

(31)

(32)

(33)

74
But (32) and (33) together state that

P(.42 I Lo) s P(A2 I ~’) (34)

which completes the proof of the first pafi of the theorem. Nrote that we have

actually proved more than (34) since we have proved that LO is optimal separately under both the conditions M and the condition U. This also explains

why the prior probabilities P(M) and P(U) do not enter either the statement

or the proof of the theorem; our result ia independent of these prior probabilities. The underlying reason, of course, lies in the fact that the error levels are

concerned with conditional probabilities of misallocation. The situation would

change if one tried to minimize the unconditional probability of misallocation

or if one tried to minimize some general loss function.

As for the proof of the second PM%)let (K’, A’)bean inadmissible pair of error

levels (O< M<1, 0 <A < 1). Since f(p) is a strictly monotone decreasing continuous function in the range determined by

O<M<l

o< f(#)<l

it will intersect at a unique point the straight line drawn through (O, O) and

(P’, ~’). This is illustrated in Figure L Denote this Point by (AO, ~0). Then

O<po<p’<1

o<ho<A’<1

and

Ao = f(xo). , (35)

The linkage rule LO(~o, 10, I’) is, in light of (36), (12), and (13) such that

P(A, I LO) = O.

Hence L&LO, Xo, r) is a better linkage rule than any other linkage rule at the

level (P’, k’).

This completes the full proof of our theorem.

The form of the theorem given in the text is an immediate corollary of the

theorem above and the expression (11).

APPENDIX 11

METHOD IIFOE THE CALCULATION OF WEIGHTS

Denoting

NANB = c

the equations resulting from (37) to (39) by dropping expected values can be

written as

75

(1)
k=l,2,3

We introduce the transformation

mz ‘mk — uk

*

~k = uk – Uk.

Substituting ‘mk and Uk from (4) and (5) into (2) we obtain

N * C-iv *

— ‘mk + ‘Uk = o k=l,2,3,

c c

Substituting (4) and (5) into (1) and then substituting in the

tions U1 from (6) we obtain

(~)

(3)

(4)

(5)

(6)

resulting equafi ??t~=~[il~k- fi ~’j] k = 1,2,3. (7)

i-l. j#k ;-1,;&

Denoting

& = Jr, - fi Vj k=l,2,3 (8)

j_l,j#k

we obtain by multiplying the three equations under (7) and by taking square

roots

iw=(%w!iRl (9)

Dividing (9) by (7) and putting

x = #(c – IV)/m (lo)

B,= ~;___~Rj/Rk k= 1,2,3 (11)

we get

*

mk = BkX k=l,2,3 (p)

and, from (4) to (6),

‘mk =Uki-&x k=l,2,3 (13)

?,@= vk - Bk/X k=l,2,3. (14)

we can now substitute into (3) rnk aIId Uk from (13) and (14) respectively and

IV as expressed from (10). We obtain

x, ~ ~ ,x(t-j+ BjX) + “(Uj - Bj,/X) = -]l. (15)

X’+1

76
After expanding (15), some cancellations and substitution of Bk from (11) we

get the following quadratic equation in iX:

3

$/rI [

3

Rj (xY’ – 1) + fi L-i + ,2 RJ~’i 1

-.VX=O. (16)

i=1 i-1

The positive root of this equation is

X = {M’– ~ RjL-j - fi Uj

The estimates of mk, U, and N are now easily obtained from (10), (13) and (14).

Having solved these equations we can proceed to estimate the specific values

of m(y) and U(y) which are required. We introduce some additional notation

which, as before, refers to observable frequencies:

l~fk( y:) = the proportion of “agreement” in all components except the kt h;

the specific configuration y: in the kth component

U,(y?) = the proportion of “agreement” in the first, y; in the second and any

configuration in the third component

ul(y~) = the proportion of “agreement” in the first, y: in the third and

any configuration in the third component

Uz(yl) = the proportion of y: in the first, “agreement” in the second and

any configuration in the third component.

The required values of nt( y:) and U( y:) are estimated as

my:) - %uz(y:)

?n(y;) = (x’ + 1)

mz(ma — Ua)

.M,(y:) - IQul( y:)

?n(y:) = (x’ + 1)

7Ttl(71Z3- ‘lLa)

.,-? 7n@’z.2— Iq)

7nzu2(Y:) - Jfl(yi)

U(y:) =

V,Z(??Z8— ‘UJ

m8Ul(y~) – M:(y;)

U(y:) =

‘U1(?7Z8- ~J

m2ul(Y:) - Mz(y;)

u(y:) =

Ul(mz — ?42)

. . .

X2+1

X2

(18)

(19)

(~o)

(21)

X2+1

X2

(22)

X2+1

F

(23)

The formulae (18) to (23) are easily verified by expressing the expected values

of the quantities fl~k (y:), VI(y?), etc. in terms of m~, ‘Ukj m(y~) and u(y~),

77
dropping the expected values and solving the resulting equations (there will be

two equations for each pair m(y~) and u(yf)).

The necessary and sufficient conditions for the mechanical validity of the

formulae in this section are that

??tk # Uk k=l,2,3

and

R,, >0 k=l,2,3

clearly for sensible definitions of “agreement” ???&>w should hold fork= 1, 2, 3.

In this case Rk >0 will hold as well. The latter statement can easily be verified

by substituting (1) and (2) into (8).

78
FIDDLING AROUND WITH NONMATCHES AND MISMATCHES

Fritz Scheuren and H. Lock Oh~ Social Security Administration

The necessity of linking records from two or more

sources arises in many contexts. One good example

would be merging files in order to extend the

amount or improve the quality of information

available for population units represented in both

files. In developing procedures for linking

records from two or more sources, tradeoffs exist

between two types of mistakes: (1) the bringing

together of records which are for different

entities (mismatches), and (2) the failure to link

records which are for the same entity (erroneous

nonmatches) . Whether or riot one is able to

utilize one’s resources in an “optimal” way, it is

almost certainly going to be true that in most

situations of practical interest some mismatching

and erroneous nonmatching will be unavoidable.

How to deal with these problems depends, of

cocrse, to a great extent on the purposes for

which the data linkage is being carried out.

Because these reasons can be so diverse, no

general strategy for handling mismatches and

nonmatches will be offered here. Instead, we will

examine the impact of these difficulties on the

analyais of a specific study. The study chosen is

a large-scale matching effort, now nearing

completion, which had as ita starting point the

March 1973 Current Population Survey (CPS).

THE 1973 CENSUS - SOCIAL SECURITY

EXACT MATCH STUDY

The primary identifying information ~n the 1973

Census-Social Security study was the social

security number (SSN) , The problems which arise

when using the SSN to link Current Population

Survey interview schedules to Social Security

records differ in degree, but not in kind, from

the problems faced by other “matchmakers,”

In the 1973 study, as in prior CPS-SSA linkages,

the major difficulty encountered was incompleteness in the identifying information [1]. Manual

searches had to be carried out at SSA for over

22,000 individuals for whom no SSN had been reported by the survey respondent [2]. Another

major problem was reporting errors in the social

security nunber or other identifiers (name and

date of birth, etc.). SSN’S were manually

searched for at SSA in cases where severe

discrepancies between the CPS and SSA information

were found after matching the two sources using

the account number initially provided [3].

Because of scheduling and other operational

constraints, an upper limit of 4,000 manual

searches had to be set for this part of the

project. Therefore, it was possible to look for

account numbers only in the most “likely” instances of CPS misreporting of the SSN. The cases

sent through this search procedure were those for

which both name and date of birth were in

substantial disagreement. For social security

beneficiaries, computerized (machine) searches at

SSA were also conducted for both missing and

misreported SSN’S. This was made possible through

an administrative cross-reference system which

79

links together persons who receive benefits on the

same claim number. About 1.000 potentially usable

SSN’S were obtained in this-way.-

Operational Restrictions on the Matching--- One of

.—

the concerns the 1973 work has in common with

earlier Census-SSA linkage efforts is the great

care that is being taken to ensure the

confidentiality of the shared information. The

laws and regulations under which the agencies

operate impose very definite restrictions on such

exchsnges, and special procedures have been

followed throughout, so as to adhere to these provisions--in particular, to ensure that the shared

information is used only for statistical purposes

and not for administrative ones.~/ Another major

restriction on the study was, of course, that it

had to be conducted using data systems which were

developed and are used principally for other purposes. The CPS, for instance, lacks a number of

pieces of information that would, if available,

have materially increased the chances of finding

the surveyed individual in SSA’S files. Finally,

the manual searching for over 26,000 account numbers at Social Security imposed a sizable addition

to the normal administrative workload in certain

parts of the agency. Therefore, in order to

obtain a reasonable priority for the project,

numerous operational compromises were made which

precluded the employment of “optimal” matching

techniques [e.g., 4, 5, 6, 7, 8]. One of the most

serious of these was the decision basically not to

“re-search” for the missing and misreported SSN’S

of individuals for whom no potentially usable number was found after just one search.

——Basic Match Results.--There were 101,287

interviewed persons age 14 or older who were

included in the 1973 Census-Social Security Exact

Match Study. Of the total, about 2 percent had

not yet been issued an SSN at the time of the

interview and, hence, were not eligible for

matching, In another 8 percent of the cases, no

potentially usable social security numbers could

be found even though one was believed to exist.

For the remaining 90,815 sampled individuals, an

SSN was available, and CPS and SSA data could be

linked. Of these account numbers, 77,465 were

supplied by CPS respondents initially. There were

also 3,347 cases where the SSN provided originally

was replaced with an account number obtained from

the manual and machine searches of SSA’S files

which were described above. In a few of these

cases--about 200--the SSN’S used as replacements

were taken from a supplementary Census source.

Finally, there were 10,003 sampled individuals for

whom no account number had been provided

initially, but one was obtained subsequently by a

search of SSA’S files.

ALTERNATIVE COMPUTERIZED MATCH RULES

In general, aside from certain obvious errors

(which have already been eliminated), it is not
possible to determine whether the SSN we have for

a particular individual is his own or haa been erroneously ascribed to him. One can, however,

esttmate the likelihood that a potentially usable

account number is incorrect. To do this, five

confirmatory variables common to both data sets

were used: surname (first aix characters), age

attained in 1972 (in years), race, sex, and month

of birth, The pat tern of agreements and

disagreements that might be expected between the

CPS and SSA reporting on these variablea depends,

of course, on whether the records brought together

are “mismatches” or “truematches.” (See figure 1

below for definitions.)

Figure I -- Match Definitions

TRUEMATCH -- A match between a Social Security

Administration (SSA) record and a Current Population Survey (CPS) interview schedule where the

two sets of documents were for the s eme

individual.

F

MISMATCH -- The erroneous matching of data from

the two sources when the information brought

together was not for the same individual. I

TRUE NONMATCHES -- Individuals in the Current

Population Survey who have not yet been issued a

social security number (SSN) and therefore do not

have a Social Security Administrative record.

I

ERRONEOUS NONMATCH -- A case where either no SSN

could be found even though it had been issued

(making it impossible to match the sources together) ~ the two sources were brought together

ut because of the rule used to decide what would

e called a “match” ~ were treated erroneously

s nonmatches.

Mismatches. --If mismatches arise on a purely

chance basis, then the probability of agreement on

any one variable would depend just on the marginal

distribution of that variable in the two data aeta

being linked. This is the assumption we have made

here. The conditional probability given a

mismatch of a particular combination of agreements

(disagreements) on the confirmatory information,

denoted by {pm} , was thus estimated as the

product of the observed mergfnsl proportions of

agreement and disagreement for each variable

separately.

TWO separate mismatch models were fit: one for

SSN’S obtained in manual searching and one for all

other SSN’S. This waa neceaaary becauae of the

nature of SSA’S manual searching procedures where,

for a number to be returned from the search, there

usually must be at least sough agreement on

surname and age. (Hence, these two variablea

could not be used for evaluating mismatches among

persons with SSN’ S obtained from manual

searching.)

Truemetchea--- Differences between the CPS and SSA

variables can arise quite frequently even when the

data is for the same person. The information in

the two systems is collected at very different

times; perhaps as long as 30 or more years

separate the two observationa. Furthermore, the

respondent on the two occasions may very well be

different. For the most part, the Social Security

variables were obtained from the individual

himself, while in the CPS, over half the

information was obtained by proxy.

The extent of agreement for “truematches” has also

been modelled by assuming independence among the

confirmatory variables. However, the conditional

probabilities of agreement, given a truematch,

denoted by {P~}, cannot be estimated separately

from the overall mismatch rate, “ a,” that exists

among the 90,815 individuals with potentially

usable SSN’S. To obtain estimates an Information

Theoretic approach was taken; the {pm} and a were

obtained by (iteratively) fitting the observed

proportions {T} for each of the combinations of

agreement or disagreement on the confirmatory

variables that were found in the sample. The

estimating equation was of the form

where the {~~’n’l} were calculated as described

above, with aand the {pm} being chosed such that

.

= E IT In: (2) I(lr;lr) “ “

.

was a minimum. The {~} are given by the

expression

and were used in obtaining table 1.

These models were judged to be adequate except for

cases where there was perfect or near perfect

agreement on the confirmatory variables. For such

Individualsp research from other SSA studies

indicated that the estimated number of mismatches

was probably too small, and some upward

adjustments were made to the fitted results.~/

—Alternate ——Match Rules.--The match rules considered

in the remainder of this paper all use the extent

of agreement on age, race, sex, month of birth,

end surname to determine whether CPS and SSA

records linked by common SSN’S should be treated

as “matches” or “nonmatchea.r’ Four ad hoc rules

were examined:

1. “Perfect” Agreement Rule,--For this rule

all five confirmatory ?ariables had to

agree within tolerance. For surname, which

80
Table 1. -- Estimated Number of Mismatches and Erroneous Nonmatches by Match

Rule for March 1973 CPS Interviewed Persons 14 Years of Age and Older

Perfect Surname CPS-SER Potentially

I tern Agreement Agreement Agreement Usable

Rule F!ule Rule Rule

Total ....... 90,815 90,815 90,815 90,815

Matched, Total 76,294 85,293 86,910 90,815

Truematches............ 76,276 84,784 86,537 88,962

Mismatches............. 18 509 373 1,853

Mismatches as a Percent

of Total Matches . . . . . . . . 0.02 0.60 0.43 2.04

Nonmatches, Total 14,521 5,522 3,905

True Nonmatches........ 1,835 1,344 1,480

Erroneous Nonmatches... 12,686 4,178 2,425

Note: Based on an unweighed CPS sample of all individuals with potentially

usable SSN’s,incluclinga small number of Armed Forces members excluded from

the weighted figures in the remaining tables.

depends on a character-by-character agreement of the first six letters of the last

name, a tolerance of two letters was

allowed. Similarly, a difference of four

years was permitted in defining agreement

on age. For sex, race, and month of birth,

no tolerance was allowed.

2. Surname Agreement Rule.--This rule requires

at least four of t-irst six letters of

the surname to be the same. (The other

confirming variables were not considered.)

The surname rule is based on a modified

version of the administrative procedures

now in use at IRS and SSA to verify the

correctness of the social security number

supplied.

3. CPS-SER Agreement Rule. --This rule

basically requires that four out of the

five confirmatory variables agree (within

the tolerances mentioned in the first rule

above) . In selected cases (361

altogether), agreement on just three variables was enough to consider the individual

a match. It was this rule, discussed in

report no. 4 of SSA’S Series on Studies

from Interagency Data Linkages, which has

= employed for the first public-use

match file prepared from the project and

described in reporta nos. 5 and 6 of that

Series.

4, Potentially Usable Rule.--Thia is the leaat

stringent on — rules in that no

restrictions are placed on what is to be

called a “match.”

IMPACT OF ALTERNATE MATCH RULES ON EARNINGS

In assessing the four match rules being

considered, it is not enough simply to look at

them in terms of their respective mismatch and

erroneous nonmatch rates. What we need to do is

to take account of the bias and variance

implications of the matching error on some of the

chief variables to be provided by the linkage.

Among the most important of these data items are

the 1972 earnings information reported to the

Census Bureau and to Social Security, In this

81
section, therefore. we will compare these earninga

data under each match rule. First, we will

examine the extent to which one’s overall “level”

estimators of the CPS or SSA aarninga distribution

are affected by the different match rules. The

level estimates are of interest principally

because a standard exists for these against which

a comparison can be made. What is crucial to our

evaluation, however, is the sensitivity of the

relationahipa between CPS and SSA earninge amounts

.to the match rule choeen. Here, of course, no

outside standard exists, since it was to examine

these relationship that the study was mounted.

Lavel ComParisons.--Tables 2 and 3 below compare

~ercentage distributions of CPS and SSA earninga for each procedure with preliminary overall

survey or administrative control figures. No

correction hae been made for erroneous nonmatchea

or mismatches, but the sample has been raweighted

to make a rough adjuetnent for differences which

arise because of survey undercoverage [9].

Sizable diacrepanciee among the varioua estimates

can be observed in the tables. For example, from

table 2, it can be seen that the difficulty of obta~n~rig an SSS may have been relatively greater

for individuals who were not identified in the CPS

as having worked in 1972. Large differences

(etatietically significant at a = 0.01) exist, in

fact, between each of the match results and the

control for the “no earnings” category of the CPS

classifier. On the other hand, both tablea 2 and

3 ahow that persons with CPS or SSA earnings of

$9,000 or more are always proportionately overrepresented in the sample. For the SSA claaaifier

the observed differences for the $9,000 or more

clase are all significant at the a = 0.01 level.

Relationship Comparisons. --The relationships between CPS and SSA reported earnings can be investigated in a number of ways. One of the standard

methods is to cross-classify the two amounts by

the same dollar size-classes and count the

fractio~ of cases which fall into the same

interval or into a higher or lower interval [11],

Table 4 provides a summary of such crosstabulations for each match rule where the dollar

size-claaees used are the same as those shown in

tables 2 and 3.

Table 2. -- Unadjusted CPS Earnings Percentage Distributions

Under Alternate Match Rules, as Compared to the

Overall Survey Estimate: Civilians 140r Olclerwith SSN’S

Size of Overal1

Match Rule

CPS Survey

Earnings Estimate Perfect Surname CPS-SER Potentiall

Agreement Agreement Rule Usable

Rule Rule Rule

TOTAL.......... 100.0

None ............... 35.0

$1 to $9990r Loss.. 10.9

$1,000to $1,999....

$2,000 to $2,999.... :::

$3,000to $3,999... 4.4

$4,000to $4,999.● . 4.4

$5,000 to $5,999... 4.5

$6,000 to $6,999...

$7,000 to $7,999... ::;

$8,000 to $8,999...

$9,000 or More . . . . . 1::;

100.0

32.8

10.5

5.9

4.5

4.5

4.5

4.7

100.0

33.6

10.6

5.9

4.5

4.6

4.5

4.7

4.3

4.3

3.5

19.5

100.0

34.0

10.7

6.0

4.5

4.6

4.5

4.7

4.2

4.2

3.5

19.2

100.0

34.2

10.6

6.0

4.5

LI. (j

4.5

4.7

4.2

4.2

3.5

19.0

Note: Based on weighted

text. Detail may

sample counts for civilians, adjusted as explained in the

not add to totals because of rounding.

82
Table 3. -- Unadjusted SSA Earnings Percentage Distributions

Under Alternate Match Rules, as Compared to the

Administrative Controls: Civilians 140r Older with SSN’S

Size of Match Rule

SSA Administrative

Earnings Perfect Surname Potentially

Control Agreement Agreement CPS-SER

Rule Usable

Rule Rule Rule

TOTAL.......

None.............

$1 to $999.......

$1,000 to $1,999.

$2,000 to $2,999.

$3,000 to $3,999.

$4,000to $4,999.

$5,000 to $5,999.

$6,000 to $6,999.

$7,000to $7,999.

$8,000to $8,999.

$9,000 or More...

40.9

10.2

6.5

4.7

4.4

4.3

4.1

3.7

3.3

3.1

14.8

100.0

39.2

::;

4.6

4.4

4.5

4.2

3.9

3.6

3.0

16.5

100.0

40.0

9.8

6.3

4.7

4.4

4.4

4.1

3.9

;:;

15.8

100.0

40.6

9.9

6.2

4.7

4.4

4.4

4.1

3.8

3.5

2.9

15.5

100.0

41.0

9.8

6.2

4.6

::;

4.0

3.8

3.5

2.9

15.3

Note: Based on weighted sample counts for civilians, adjusted as explained in the

next. Detail may not add to totals because of rounding.

As can be seen from table ~, marked differences

exist among the procedures in the proportion of

individuals whose CPS and SSA eaminga class

agree. The percentages vary from a high of 68

percent for the perfect agreement rule to a low of

66 percent for the potentially usable one, with

the surname and CPS-SER rules having class

agreements of around 67 percent. The standard

errors for the four estimators of the extent of

earnings class agreement average about 0.25

percentage points. The range of the agreement

figures (at 2.0 percentage points) is thus eight

times the standard error.

Since our focus is on the matching process itself,

we will leave to others [12, 13] a detailed study

of the relationship between the earnings

distributions shown in table 4. Inetead, we will

proceed (in the next section) to examine the bias

end variance impact of adjustments designed to

lessen the effect of errors in the matching.

UTILITY OF POST-HOC ADJUSTMENT PROCEDURES

In this section a combination of procedures is examined which is designed to adjust for mismatching

and erroneous nonmatches. Successive adjustments

will be made to the data: first, by reweighing to

account for the nonmatches; then, by “raking” the

results to the overall survey and administrative

controls shown in tables 2 and 3; and, finally, by

“subtracting out” estimates of the effect of the

mismatching. The utility of each step taken will

be evaluated in terms of its bias and variance

impact.

Reweightin& ~ Nonmatches.--No matter which of

the four match rules is used, important

differences exist between those who are treated as

“matches” and those believed to have SSN’S but for

whom no usable account number could be determined.

This is evident not only from tables 2 and 3, but

also from previous papers which have discussed the

reporting of social security numbers in the March

1973 Current Population Survey [i.e., 1, 2, 3],

For example, large differences exist between the

two groups by earnings, age, race, sex, and

respondent status.~1

One way to “correct” for these differentials (the

method adoptad in this paper) is to consider the

caaes where SSN’S were obtained through manual

searching as a sample from the entire group of

83
Table 4. 

Percentage Distribution of Earnings Class Agreement Between CPS and SSA

Reported Amounts Under Alternate Match Rules Before Adjustment:

Civilians 140r Older with SSN’S

Extent of Perfect Surname CPS-SER Potentially

Earnings Class Agreement Agreement Agreement Usable

Agreement Rule Rule Rule Rule

Total............. 100.00 100.00 100.00 100.00

SSA Earnings in Higher

Interval than CAPS....... 10.84 11.35 11.05 11.70

CPS and SSA Earnings Class

Agree.................... 68.08 67.13 67.42 66.05

CPS Earnings in Higher

Interval than SEA........ 21.08 21.52 21.53 22.25

Note: Based on weighted sample counts for civilians, adjusted as explained in the

text. Detail may not add to totals because of rounding.

individuals ‘who “should” have usable numbers but

do not. The exact procedure followed was to subtract from the estimated total with SSN’S, the

weighted number of adults who had an acceptable

SSN but who had not obtained it from the manual

search. The weighted manual search cases were

then ratioed up to this difference and added to

the estimates obtained from the rest of the

sample. These steps were carried out for each of

the eight CPS rotation groups separately in order

to be able to come up with an approximation to the

variance.~/ The overall adjustment factors

applied are shown below for each match rule along

with the (weighted) fraction of sample cases with

SSN’S but for which no usable SSN could be found.

“Perfect agreement rule.. 26.9 3.4

Surname agreement rule.. 13.2 2.2

CPS-SER rule . . . . . . . . . . . . 10.9 2.0

Potentially usable rule. 5.9 1.5

The reweighing procedure just described, while

crude in many respects, does have a certain logic

to it since the great bulk of the cases for whom

no SSN is available were searched for manually in

84

SSA’S files. It might also be noted in passing

that such an approach is quite analogous to the

classical method for utilizing follow-up samples

of those persons who, in the survey’s initial

wave, were nonreapondents [14].

TO help evaluate the impact of the reweighing

scheme, table 5 is provided below. As can be

seen, for all match rules, the reweighing reduces

the amount of CPS-SSA earnings-class agreement.

In fact, the average declined by about 0.8

percent, from 67.17 percent to 66.40 percent.

From internal evidence in the CPS, there aeeme to

be a definite tendency for persons who provide

usable SSN’a to be better respondents than those

who do not.. Thus, this reduction in earningsclass agreement (with accompanying increases

elsewhere) probably reduces the overall nonmatch

bias which exists for all of the estimators.

There is, of course, no way of knowing whether the

magnitude of the changes is appropriate, but it is

encouraging to note that the net effect of the reweighing is to bring the estimates for the four

rules closer together. (The range of the percentages for earnings-class agreement dropped from 2.0

percent to l.l percent.

For the probable reduction in the nonmatch bias, a

price has been paid in increasing the standard

error of nearly all the estimators shown in the

table. These increases range from small to

moderate for the potentially usable,surname,and

CPS-SER rules. However, for the perfect agreement
Table 5. -- Percentage llistribution of Earnings Class Agreement Between CPS and SSA

Reported Amounts Under Alternate Match Rules After Reweighing:

Civilians 140r Older with SSN’S

Extent of Perfect Surname CPS-SER Potentially

Earnings Class Agreement Agreement Agreement Usable

Agreement Rule Rule Rule Rule

Total . . . . . . . . . . . . . . . 100.00 100.00 100.00 100.00

SSA Earnings in Higher

Interval than CAPS......... 11.99 12.01 11.50 12.01

CPS and SSA Earnings Class

Agree...................... 66.74 66.34 66.81 65.70

CPS Earnings in Higher

Interval than SEA. . . . . . . . . . 21.26 21.65 21.60 22.29

Note: Based on weighted sample counts for civilians, adjusted as explained in the

text. Detail may not add to totals because of rounding.

rule, the increase is sizable; if such a rule were

seriously being contemplated, some other method of

adjustment would, in all likelihood, be desirable.

Rakin&Ad_Justment ~Nonmatches.--The reweighing

scheme just described tends to bring the matched

CPS and SSA earnings distributions closer to the

control totals shown in tables 2 and 3. However,

the remaining discrepancies are still large.

Unlike biases in the CPS-SSA interrelationships,

which can only be adjusted indirectly and

incompletely, it is possible to alter the sample

earnings marginals so they conform simultaneously

to both sets of controls more or less exactly.

There are a number of well-known procedures for

doing this. The approach employed here is due to

Deming and Stephan [15], and we have referred to

it, following the practice at the Census Bureau,

as “raking.” (Perhaps it ia better known

elsewhere as “the method of iterative proportions”

[16].)

Table 6 provides a summary of the impact of the

raking on the extent of agreement between CPS and

SSA earnings. Aa will be seen, our estimators of

the amount of agreement have declined still more

as a result of this additional adjustment (from an

average of 66.4 percent after reweighing to 66.2

percent after raking). The range in the extent of

agreement has also narrowed further, from 1.1

percent to 0.9 percent, respectively, with the

largeat proportion on the main diagonal being 66.4

percent (CPS-SER) and the smallest, 65.5 percent

(potentially usable rule). Again, we believe that

this change representa a further reduction in the

nonmatch biaa. Not unexpectedly, the raking has

also produced reductions in the standard errors,

although not uniformly so. (For 8 of the L2

estimators in the table, there was some reduction.

In the four instances where increases occurred,

they were slight.)

Mismatch Adjustment. --If two linked records have

been brought together just by chance, then it is

highly unlikely for them to agree on earnings

class. Thus, a “natural” consequence of the

mismatching which exists under each rule is that

the estimatea of the extent of agreement, as shown

in table 6, understate the true underlying amount

of agreement. Some further adjustment, therefore,

ia necessary. There are a number of ways of

taking account of the mismatches, depending on the

assumptions one is willing to make about their

affect on the relationship between the CPS and SSA

classifiers. The model chosen here is a fairly

simple one which may not be too unrealistic.

Basically, it assumea that the mismatch rates do

not depend on earnings levels and that, when a

mismatch occurs, the matched CPS and SSA amounts

are independently distributed. Put another way,

the mismatches can be thought of as having the

same row ‘pi.} and column {P.j} marginal

proportions for CPS and SSA earnings, respectively, as the truematches; but such that the

85
Table 6. -- Percentage Distribution of Earnings Class Agreement Between CPS and SSA

Reported Amounts Under Alternate Match Rules After Reweighing and Raking:

Civilians 140r Older with SSN’S

Extent of Perfect Surname CPS-SER Potentially

Earnings Class Agreement Agreement Agreement Usable

Agreement Rule Rule Rule Rule

Total............... 100.00 100.00 100.00 100.00

SSA Earnings in Higher

Interval than CAPS......... 11.78 11.82 11.47 11.98

CPS and SSA Earnings Class

Agree...................... 66.01 65.89 66.36 65.45

CPS Earnings in Higher

Interval than SEA.......... 22.21 22.30 22.17 22.57

Note: Based on weighted sample counts for

text. Detail may not add to totals

proportion of mismatches for any particular

combination ij of CPS and SSA earnings classes,

denoted{p~} , is given by

p[

(4) ij = ‘i. ‘.j “

The expected value of the observed relationship

between the two classifiers is assumed to consist

of two components. First, there Ist;n estimate of

the truematch proportion in the(ij) cell of the

earnings cross-tabulation, denoted p: , times

the fraction of the total sample that were

truematches, denoted by (1 - a). The second term

consists of the mismatch proportion p? times

the fraction of the total samplelJthat were

mismatches (i.e., “a “). Thus, we have that the

observed ceil— proportions{n ij} can be expressed

as

(5)

‘Xij =(l-a)P: +aP:

From (4) this becomes

(6) ETij = (1 - a) Pfl + a Pi. P.j

Since estimates of the mismatch rate W, the CPS

civilians, adjusted as explained in the

because of rounding.

marginal {pi.}, and SSA marginal {p.j} were all

readily available (tables 1 to 3), it was a simple

matter to ~btaiq estimate? of the{p~} by. substituting ~ , Pi. , and p.j in (6). JThe {p~~}~

obtained were then used to produce the result~

table 7. ~/

For the perfect agreement rule, the mismatching

had only a small effect, but, for the other rules,

changes in the percent with CPS and SSA eamlngs

in the same interval were substantial. For the

potentially usable rule, where the amount o

mismatching was estimated to be greatest, that

proportion increased by 1 percent, from 65.45

percent to 66.45 percent. Increases for the CPS

SER and surname rules were smaller but still

sizable (0.3 and 0.4 percentage points, respectively). The range of the four estimates of the

extent of agreement narrowed again as a result o

this final adjustment (from 0.91 percent after

raking to 0.59 percent). The “cost” of the

mismatch adjustment was a very slight increase in

the variance over that of the raked estimator.

Summary of Impact of Adjustments.--Overall, whe

we look ~ the co~ined affect of all three

adjustments, we see that the range of earnings

class agreement under the four rules has been

reduced to less than one-third of what it was t

begin with (i.e., from 2.0 percent to O

percent). This narrowing of the range o

agreement suggests that the techniques employe
Table 7. -- Percentage Distribution of Earnings Class Agreement Between CPS and SSA

Reported Amounts Under Alternate Match Rules After All Adjustments,

Including the Adjustment for Mismatching: Civilians 140r Older with SShJ’s

I I I I

Extent of Perfect Surname CPS-SER Potentially

Earnings Class Agreement Agreement Agreement Usable

Agreement Rule Rule Rule Pule

I 1 1 I

Total . . . . . . . . . . . . . . . 100.00 100.00 100.00 100.00

SSA Earnings in Higher

Interval than CAPS. . . . . . . . . 11.77 11.63 11.34 11.46

CPS and SSA Earnings Class

Agree . . . . . . . . . . . . . . . . . . . . . . 66.03 66.25 66.62 66.45

CPS Earnings in Higher

Interval than SEA. . . . . . . . . . 22.20 22.12 22.05 22.10

Note: Based on weighted sample counts for civilians, adjusted as explained in the

text. Detail may not add to totals because of rounding.

nay have been “moderately” successful in reducing

the various biases which affect each rule (and may

even have some merit in general). Iiowever,since

the range in earnings-class agreement after

adjustment is still. about twice the standard

deviation, it seems likely that residual

uncorrected biases remain an important part of the

total mean square error.

Except for the perfect agreement rule, the price

that was paid for this bias reduction appears to

be “small.” The median increase in the standard

errors was about 10 percent of the original

standard errors. (However, since the sample sizes

involved are so large, this amounted to only 0.025

percentage points.)

In the light of our computations, it might be of

interest to comment on which match rule is “beat.”

Because the final results are so close, this question has lost some of its force but is still worth

pursuing. By and large, the results suggest that

in this case, and for the statistics considered,

the best choice of the four match rules examined

is the potentially usable rule. &/ It tends to

have the amalleat standard error after all adjustments; its initial and final estimates change

the least; and, its initial and final estimates

are the closest of any rule to the overall average

for all rules after adjustment. Partly as a consequence of this finding, all subsequent pUbliCuse data tapes to be “prepared from the 1973

Census-Social Security Study will be made

available with all the potentially usable

“matches” included. II Also, since information on

the extent of agreement on the confirmatory

variablea i.s available on these data tapes,

another consequence of this decision is that users

will have the option of choosing the match rule

best suited for their purposea.

Conclusion,--Matched statistical samples have much

in common with other surveys and, as we have seen,

adjustment techniques normally encountered in

standard practice (~.~., raking), can be applied

successfully to linked data acts aa well. The

problems of choosing a suitable match rule and of

dealing with mismatches are, however, Unique to

record linkage studies. Usually, in the

literature on data linkage, match rules (and

mismatching) have been dealt with in the context

of the research design and hoh, to choose “optimal”

strategies for allocating resources. With few

exceptions [17], there has been insufficient

attention given to the analyais aspects of

imperfectly matched samples. In the 1973 CensuaSocial Security Study, the administrative (and, to

some extent, confidentiality) constraints imposed

on the design and execution of the data linkage

make these analysis issues particularly pointed.

87
Our approach to them baa, of course, been quite

applied. Obviously, theoretical examinations are

xarranted as an adjunct to the empirical work on

matching commented on here, We invite

participation in this endeavor.

[1]

[2]

FOOTNOTES

*The authors would like to thank Wendy Alvey and

Gina Savinelli for their assistance, especially

for helping to prepare the basic tabulations.

Thanks also must be extended to Ben Bridges and

Dean Leimer for their careful reading of an

earlier draft.

For details on the confidentiality precautions

taken, see the invited paper session on the

Reconciliation ~ Survey and Administrative

Sources through Data Linkage~hown elsewhere

in these Proceed%.

A paper is in preparation which provides more

detaila on the procedures employed in

estimating the number of mismatches with particular attention to other estimation methods.

In the public-use file (with the CPS-SER match

rule), the reweighing adjustment being made

attempts to take account of most of these

factora. See report nos. 5 and 6 in Studies

from Interagency Data Linkages for details.

The raking and mismatch adjustments were also

carried out separately by CPS rotation group

to make it possible to approximate their

variance impact as well.

The mismatch rates used were not those ahown

in table 1 but were calculated (by rotation

group) in terms of the weighted data after

having taken account of the adjustments for

nonmatches.

Readers should carefully note the qualifications on this “endorsement” of the

potentially usable rule. While for the

example chosen here the nonmatch and mismatch

errors of this rule tended to cancel each

other out, this would not alwaya be the case.

In fact, the potentially usable rule, if not

adjusted for mismatches, in many situations

might even be the worst rule one could choose.

For reasons of confidentiality, social

security information for CPS respondents who

refused to provide their SSN’S to the Census

Bureau are not includable on the public-use

files from this project, even though it was

poaaible to find on account number for them.

With the CPS-SER rule, 619 such cases were

eliminated. With the potentially usable rule,

641 cases would have to be treated as

nonmatches for this reason.

88

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

REFERENCES

Vogel, L., and Coble, T., “Current Population Survey Reporting of Social Security

Nurnbera,’” 1974 Amer. Stat. Asan. Proc. Sot.

Stat. Sec.,~5~. Z13T — —

as,?., and Tyler, E., “Searching for

Missing Social Security Numbers,” 1974 Amer.

Stat. Aasn. Proc. Sot. Stat. Sec.,~5~.

m150. — — — —

Cobleigh, C., a~d Alvey, W., “Validating Reported Social Security Numbers,” 1974 Amer.

——Stat. Asan. Proc. ~. at. ~.~75, pp..

137-144.

Tepping, B. J., “A Model for Optimum Linkage . .

of Records,” ~. —.— Amer. Stat. —As&., vol. 63,

1968, pp. 1321-1332.

Felleghi, I. P., and Sunter, A. B.,”A Theory

for Record Linkage,” ~. —.Amer. Stat. —Assn.-,

vol. 64, 1969, pp. 1183-1210.

DuBoia, Jr., N: S. D., “A Solution to the

Problem of Linking Multivariate Documents,”

~. ——Amer. Stat. ~, vol. 64, 1969, pp. 163-

174.

Wells, B., Optil’cum Matching Rules,

University of North Carolina, 1974..

Nathan, G., “Outcome Probabilities for a Record Matching Proceaa with Complete

Invariant Information,” J. Amer. Stat.

*. , vol. 62, 1967, Pp. 454-46~

Vaughan, D. R., and Ireland, C. T., “Adjusting for Coverage Errors in the March 1973

Current Population Survey,” ——1975 Amer. Stat.

.

Aaan. Proc. Sot. Stat. Sec.

Moatel~ C ~oc~ion and Estimation

in Contingency Tables,” ~. ——Amer. Stat.

~. , vol. 63, 1968, pp. 1-28.

Scheuren, F. .J., and Oh, H. L., “A Data

Analyais Approach to Square Tablea,” Comm.

in Stat., July 1975.

Alvey, W., and Cobleigh, C., “Exploration of

Differences Between Linked Social Security

and Current Population Survey Earninga Data

for 1972,” 1975 Amer. Stat. ~. Proc. ~.

Stat. Sec. ‘— —

~at~M. P., “Evaluation of Current Population Survey Simulations of Payroll Tax

Changea,” 1975 Amer. ——Stat. Assn. —.

Proc. Sot.

——Stat. Sec.

Hansen, M., and Hurwitz, W., “The Problems

of Non-Response in Sample Surveys,” ~. ~.

—.

Stat. Asan., vol. 41, 1946, pp. 517-528.

Deming, N. E., and Stephan, F. F., “On a

Leaat Squares Adjustment of a Sampled Frequency Table When the Expected Marginal

Tables are Rnown,” Annala Math. Stat., vol.

11, pp.427-444, 194~— —

Feinberg, S. E., “An Iterative Procedure for

Estimation in Contingency Tablea,” Annala

—.

Yith. Stat., vol. 41, 1970, pp. 907-10~

Iieter, J., t4aynea, E. S., and Ramanathan,

R “The Effect of Mismatching on the

M&urement of Response Errors,” ~, Amer.

Stat. Aasn. , VO1. 60, 1975, pp. 1005-1027. ——
AN APPLICATION OF A

Richard W. Coulter,

I. INTRODUCTION

As part of the effort by the Statistical Reporting Service to build a master list sampling

frame of farms in each State, a record linkage

system is being developed for use in detecting

duplication in a list. To build this master,

lists from several sources are combined and

duplication, both between and within the lists,

is removed. In selecting a linkage technique,

an important consideration was the paucity of

identifying data on most records. The table

below illustrates the information available for

one fairly typical State.

As the table indicates, only given name, surname, and place name are guaranteed to be

present. Address information for the rural

population is scarce and most often is only a

rural route number. The presence of identifier

numbers is rare. It is estimated that in making

comparisons, nearly 60 percent of the comparison

pairs will have no information in addition to

given name, surname, place name, and possibly

route number. In an attempt to best use this

limited information in linkage, a probability

model is used which incorporates some of the

concepts developed by Ivan Fellegi and Alan

Sunter [11. A number of modifications and

extensions have been made to portions of the

original theory. (See [31.) Some of these Will

be examined in the following. Prior to this

some background information on the model is

necessary.

Let LA be the set of records, a(a), pertaining

to the population

consideration.

Define M = {(ai,

U={(ai,

A, with elements aicA, under

aj);ai ‘aj. i<j)

aj); ai#a., i<j}

J

THEORY FOR RECOF!PLINKAGE

Department of Agriculture

as the matched and unmatched sets, respectively. Denote by Y = (yk)the coded result of

the comparison of the variables in the compari-

[ 1

son pair cx(ai ), ci(aj) where the result of t~

comparison on the k

th component is denoted by Y .

The comparison space can be defined as the set

of all realizations of Y generated as a result of the comparison of records associated

with members of M or U. Two Drohabilities are

k

estimated for eachY .

1.

[

m(Yk) = P{Yk a(ai), ~

2.

L

U(Yk) = P{Yk a(ai), a’

A component weight for each

1

aj) ; (ai, aj) EM)

—

J

aj) ; (ai, aj) Cul

Yk is defined by:

[ 1

W(yk) = loglo m(yk) / u(Yk) .

The component weights for those variables

compared are then summed to yield a total

weight, w (Y), for each comparison pair.

Two threshold values are calculated to which

the total weight is compared. If the total

weight is less than the lower threshold, then

the pair is classified as a nonlink. If the

total weight is larger than the upper threshold,

then the pair is classified as a link. Pairs

with total weight between the two thresholds are

classified as possible links.

As an illustration of this general technique,

the specific calculations for surname - surname

code will be examined. In addition, the manner

in which several other variables are used will

be briefly described. Since the same general

technique is used for these, the specific

Table A.--Availability of Identifying Data

Variable % Presence in File

Prefix 3 (82% of these are ‘MR’)

Given Name 100 (24% of these are an initial only)

Middle Name 52 (90% of these are an initial only)

Surname 100

Rural Route 76 (43% of these are ‘RT 1’)

Box Number 43

House Number ~

Street Name

Place Name 10:

Social Security Number o

Employer Identification Number 2

Telephone 4

89
computations (some of which are rather lengthy)

will not be given at this time.

11. USE OF SURNAME - SURNAME CODE

AS A MATCHING VARIABLE

Surname and surname code are used as a joint

variable in the linkage model. (See [71.) When

surnames agree, the appropriate weight is assigned and surname code is not considered. However, when surnames disagree, then surname codes

are compared. Depending upon this outcome, the

appropriate weight is assigned. Under the

present blocking scheme, surname codes must

agree and, thus, the weight assigned when surnames disagree will always be the weight for

agreement on the particular surname code. The

manner in which weights are calculated for this

variable is described below.

A. Notation

Let, X ={xj, j = 1,2,....n} represent the set

of all possible realizations of

surnames in the file;

Y ={Yk, k = 1,2,...,}representnt the set

of al1 possible realizations of

surname codes on the file;

Y’ ={Yd, d = 1.2,...,n”} represent the subset of Y that consists of surname

codes associated with more than one

surname;

fx, f ,..., fx denote the frequencies of

1 ‘2 n

the surname realizations;

n

Zfx ‘N +J

ff f

Y1’ Y2’”””’ Yn

the surname

e= P (surname

denote the frequencies of

realizations;

= N’

in error in the file of

records associated with the matched

set);

‘T = P (error-free forms of the surnames

in a pair associated with the matched

set are different);

91 = p-(a surname in error in a pair associated with the matched set receives

the same code as the correct surname);

92 = p (a valifi change in surname occurs

in matched records and both receive

the same surname code);

m(yh) = P(Yh Ithe pair represents records

from M), h = 1,2,3; and

u(Yh)

where, Y

1

‘2

‘3

❑ P[Yh \the pair represents records

from u), h = 1,2,3;

denotes agreement on surname,

denotes agreement on surname code

and disagreement on surname, and

denotes disagreement on both surname and surname code.

B. Assumptions

1. The distribution of matching surnames

(surname codes) in the matched set is the

same as the distribution in the file.

2. The distribution of surnames (surname codes)

in the unmatched set is the same as the distribution in the file.

3. The !J, and 92 ‘probabilities are

independent of surname code.

c. Calculations (for surname x.

code Yd) J

[

m’{, (xj) = (fx /N) (1 -e)2

j

& 1

(Xj)- = (fx /N)2

-.

and surname

(1 -eT)

J

r

m[Y2 (yd~ = (fYd/N’) :91 e(l - e)(l -eT)

+ 9fe2(l -eT)+g2(l-e)2.

eT + Zglg2e(l- e)eT

2

1

+ 91 92 e2 eT

u ~2 (yd)l = u(agree on sn code) o u(disagree on sn lagree on sn code)

[

= u(agree on sn code) “ 1 - u

(agree on sn\agr~eonsn code)]

[ 1

=(1/N2) f 2-;d fx2 ,

Yd j=l j

where n; = the number of surnames with surname code Yd

M(Y3)= 2(1 - 91)e(l - e)(l- ‘T) + (1

- g12) e2(l - eT) + (1 - 92)(1 - e)2

eT + Z(i - g~g2)e (1 - e)eT

+(1- 91292) ‘2eT

1

U(Y3) = 1 - ; (f /NJ2

k=l yk

90
weight = W(Yh) = 10910 ~(yh)/u(Yh~ , h = 1,2,3

Under the present blocking scheme, surname

code is used as the first blocking factor and,

thuS, Y3 does not occur; i.e., m(y3) and U(Y3)

are both zero. To fit the supplied probabilities to the actual situation, the probabilities

~n~ ~oth m and u should be redistributed over Y,

2“

For h = 1,2 the revised probability functions

would be:

m(Yh)’ =

.

u(Yh)’ =

.

m(Yh

I

Y3 does not occur)

m(yh)/ [1 - ‘!y~)]

I.I(Y Y3does not occur)

u&/ [1 - U(Y3)] .

Since most of the probability for the unmatched set will be concentrated in y3 , the net

effect of this redistribution would be a significant reduction in the derived weights for exact

matches on surname and surname code. For this

reason, we have chosen to ignore this effect of

blocking for weight calculation purposes. For

example, in a test file of 150,000 records, a

surname which occurs 1,000 times receives a

weight for agreement of 2.16. The revised

weight using the redistributed probabilities

hould be -.51.

The wight for Y1 depends primarily on the

frequency of the particular surname, with the

more rare surnames receiving the larger weights.

The weight for Y2 depends on the frequency of

the surname code, on the size of the error rates

e and eT and on the number of distinct surnames

within that codes. Infrequent surname codes,

large error rates and few different surnames all

tend to make the weight for this condition large.

III. OTHER VARIABLES

Modifications have been made to other variables in an attempt to improve the linkage

results. These will be outlined below.

A. Gfven Name - First Name

AS Dart”of the Drocessfna Drier to linkaae.

each given name on the file “is’assigned a fotiai

or first name. (See [8].) A dictionary of the

most common given name is utilfzed for this

purpose. For given names not in the dictionary,

the given name will also serve as the first

name. Common examples of gfven - first names

are: Bill=William, Dick=Richard, Jack=John.

First name is used in the model in a manner

similar to surname code. If given names agree,

then first names are not compared. However, if

given names disagree, then first names may

either agree or disagree. Weight calculation

routines have been develoDed for the three

possible conditions using the same general

technique as discussed for surname - surname

code. An additional factor which has to be

considered for this variable is that one name

may be an initial, while the other may be a

complete name. In this case, the initial is

compared against the first letter of both the

given and first names of the complete name. The

probability of this occurring is estimated using

frequencies of initials on the file and weights

for the various outcomes are also calculated.

B. Place Name

A place name dictionary for each State is

utilized to standardize all spellings and

abbreviations of place names and to assign a

latitude - longitude location to each. (See

[111.) The standardization eliminates disagreement due to different spellings of place names.

The location of each is, then, used to compute

the distance between two places, in a comparison

when the place names are different. This distance is classified into one of seven intervals,

and a different weight is calculated for each

interval. The intervals are:

Oto 1 miles

;: 1 to 10miles

10 to 25miles

:: 25 to 50miles

50 to 100 miles

:: 100 to 200 miles

i’. over 200 miles.

The m and u probabilities and subsequent

weights for the agreement condition on place

names are calculated in the same manner as is

done for surname. The weight computation for

place name disagreement is outlined below.

1. The m values are based on counts for

each interval of matched pairs with

place name disagreement taken from a

sample. These are then fitted, using

least squares estimates to a monotonically decreasing function of

bd

the form y = ae . The fitted values

form the distribution for m.

2. The u values are estimated from the

file. Every pair of distinct place

names is compared, their distance

apart calculated, and the product of

their relative frequencies summed in

the appropriate interval. This

yields the probability of getting

place name disagreement in a particular interval by chance; i.e.,

u(disagreement in Ith interval) =

2’E(fx/N) (f /N), where fx, f are

Y Y

frequencies of

distance apart

and N = total

file.

In practice, the further

place names whose

is in interval I;

number of records on

away two place names

91
are, the larger their disagreement weight

becomes.

c. Box Number and House Number

Disagreement weights for these variables are

based on the amount of disagreement present.

This is measured by comparing these on a

character-by-character basis. (See [131.) Box

and house number are up to five characters long

and, thus, there are 15 different combinations

of number of agreements - number of disagreements when the variable is present in both

records and not identical. Different m and u

probabilities and weights are calculated for

each of these conditions. The key to the

calculations is to estimate the appropriate

probabilities for one character, given that data

are present, and, then, to make the assumption

that the probability of misreported data is

independent of the particular character and is

equal for each of them. In general, the more

disagreement present, the larger the disagreement weight will be.

D. Social Security lJumberand Other

dentlflers

Weights for identifier numbers, such as SSN,

are also partitioned. Only one agreement weight

is calculated for these. SSN, for example, is

broken into four partitions which are assumed to

be independent. (See [161.) The m and u values

are calculated for one partition and independence assumptions allow these to be extrapolated

to the entire number. For SSN, sixteen different wsights are calculated for conditions

ranging from complete agreement to complete

disagreement.

See the following papers for additional

information on identifier comparisons: [91 for

derivation of the middle name comparison; [10]

for a derivation of the negative weight to be

used when one record has “Jr.” and the other has

no suffix; and [121 for a discussion of the

additional negative Wight when more than one

address variable disagrees.

IV. ERROR RATES AND THRESHOLDS

Implicit in the use of the model is the assumption that the two error rates -- probability

of a recording error and probability of a valid

change for records associated with the matched

set -- are known or can be estimated for each

Variable prior to processing the file through

the linkage system. In the absence of prior

knowledge, the current system is designed to

Process a sample of blocks through links e in

order to estimate these errors. (See [4? and

[171.) Initial estimates are provided and the

linkage decisions for the sample are manuallY

reviewed and questionable decisions a~ resolved. Once this is completed, counts of error

conditions are kept by variable for those pairs

which are links. These are then used to

estimate the necessary error rates.

TO aid in this process, counts an maintained

within the software for those pairs originally

classified as definite links. As decisions are

changed, based upon the review, these cOUntS a~e

updated. The importance of these estimates 1s

demonstrated by the graph in Figure 1, ~~v~

gives the frequency distribution of

comparison wights for three sets of error

rates, where the rates were varied for four of

the variables. As the graph indicates, the

major effect of an increase in error rates

(decrease in quality) is to shift the frequency

curve to the right, particularly at the lower

end of the scale, resulting in an increase in

the number of pairs classified as possible links

(weight between 5.0 and 7.5). That is, the

model is unable to classify as many pairs as

definite nonlinks. Pairs with smal1 total

weights are most affected, since it is in these

pairs that there is the most disagreement in

components, and the error rates affect most the

weights assigned to the disagreement condition.

The final parameters to be supplied are the

threshold values. It is these tw values which

ultimately determine the classification of each

pair. Fellegi and Sunter suggest a technique of

estimating these by sampling from the tails of

the m and u probability distributions for the

comparison pairs. In practice, a technique of

initially estimating these -- based on a combination of Wights for selected components--

and revising, as necessary -- as a result of the

review of the sample used to estimate error

rates -- has proven to be more satisfactory.

The initial estimate of the lower threshold is

made by summing the agreement weights for the

most common given name, surname, and place

name. This has proven to be an excellent “first

guess.” Another tool which can be us~{u;ot~;

setting thresholds is the distribution

weights. This distribution for one sample of

2,200 records is given in Figure 2. The

thresholds could expect to be most efficiently

set at points on either side of the lowest point

on the U-shape portion of the curve (about a

total weight of six in the example). The percentage of pairs classified as links after the

manual resolution is also indicated for each

interval in this example. Specifying the

allowable rates of misclassification would,

then, also determine where the thresholds will

be set.

v. REMARKS

Research and analysis of results is continuing

in order ta further improve the procedure. FOr

example, the possibility of using a coding procedure for given name is now being investigated.

Also$ questions concerning the stability of the

error rates across States and, more generallY

the amount of preprocessing of a sample that is

necessary are being investigated. The amount of

manual review that is necessary after the automated procedure is also a concern. The limited

amount of identifying data that is present on

the lists necessitates using each item to the

fullest extent possible, but it ZIISO implies

that a manual review of, at least, some decisions will always be necessary.

92
Figure 1. --Total Weights by Frequency

(Approximately 39,000

Frequency

30,000

20,000

10,000

5,000

2,000

1,000

500

100

50

25

,1

a

.e ,

II

i

I

\.

1 \

lo

e

i L=5.O

for Three Sets

comparisons)

I

i

I

I

i

I

;

I

I

I

I

1

●

i

U=7.5

of Error Rates

V?L \● 3

.

\-

1

$

I I

1 )

-10-8 -6 -4 -2 0 2 4 6 8 10 12 14 Total Weights

Key for Figure 1

Recording Error Change Error

Variable . . . . ---- —. .*. I

----

Given Name .001 .01 .1 .001 .01 .1

Middle Name .001 .01 .1 .001 .C1 .1

Surname .001 .01 .1 .001 .01 .1

Place Name 000 .001 .01 .1

93
Figure 2. --South Carolina Sample - Weight Distribution

2000

1500

1000

800

600

400

300

200

150

120

110

100

2 3 4

:*

6

—

5

i4

I

5454

6

77

—

19

0(

.0

—

7 8 9 10 11 12 13 14

Total Weights

*Numbers in each bar indicate the percentage Of resolved pairS itl that

interval that were links.

The computed thresholds used prior to any resolution were 4.5 and 8.3.

94
NOTES AND PREFERENCES

[1] Fellegi, Ivan P. and Sunter,

(1969) “A Theory for Pecorcl

Alan R.

Linkaue,”

Journal of the American Statistical

Association, vol. 64 no. 378 PP.

(AlSO re’orinted in’ this

volume.)

Editors’ Note:

This report is part of a series of Working

Papers documenting the development of a record

linkage system by the Statistical Reporting

Service (!X~~A~f the U.S. Department of AgriCU1ture The collection represents

various stages in the research and modification

of matching theory to construct a master list

sampling frame of fawn operators by State. The

work was begun under the direction of Max

Arellano and later refined by Richard Coulter

and others.

Thanks to the help of Nancy Kirkendall, we

have added annotated references to this paper to

tie it in with related reports prepared as part

of the same series. With the exception of [6],

none of the papers have been Deviously published, and they are only available in draft

form from:

Henry Power

Statistical Reporting Service

U.S. Department of Agriculture

S. Agriculture Bldg., Room 5864

Washington, DC 20250.

It is the hope of the editors that interest

generated by this Workshop will lead to the

eventual publication of this valuable set of

papers.

[?1

[31

[4]

[5]

Arellano, Max G. (1976) “Application of

the Fellegi-Sunter l?ecordLinkage Model to

Agricultural List Files,” SRS, USDA.

Arellano, Max G. (1976) “The Development

of a Linkage Rule for Unduplicating Agricultural List Files,” SRS, USDA. This

paper describes the differences between

the USDA assumptions and the FellegiSunter assumptions as applied to probabilistic matching. Major differences are

in the definition of the error rates and

the assumptions concerning errors in the

files used to derive agreement weights.

(6 pages)

llfi?lano,Max G. (1976) “The Estimation of

,“ SRS, USDA.

Coulter, Richard W. and Mergerson, James

w. (1977) “An Application of a Record

Linkage Theory in Constructing a List

Sampling Frame,” SRS, USDA. From the

Coulter paper reprinted here, one might

think that the SRS record linkage system

is strictly an application of the proba-

[61

[71

[81

[91

[10]

[11]

95

bilistic matching procedures. In [5],

Coulter “and Mergerson describe the SRS

system in more detail than is found in any

of the other papers. This latter paper

describes preprocessing and variable

identification procedures; it, then,

discusses the method used to classify

records as being partnership, corporate or

individual records. The partnership and

corporate record linkages are handled

manually. Only the individual records are

processed through the probabilistic

linkage. The overall system adjusts for

some of the matches missed because of

blocking on surname by identifying for

manual review all of the record pairs

which agree exactly on address. This

paper gives a nice overview of the entire

systern. (29 pages)

Lynch, Billy T. and Arends, William L.

(1977) “Selection of a Surname Coding

Procedure for the SRS Record Linkage

System,” SRS, USDA. This is the only

paper in the series which was published by

SRS. In it, Lynch and Arends describe the

analysis of surname coding systems performed by USDA. These efforts led to the

selection of a revised NYSIIS (New York

State Identification and Intelligence

System) coding system as the most

appropriate system for SRS purposes. (31

pages)

Arellano, Max G. and Coulter, Richard W.

(1976) “Weight Calculation for the Surname

Comparison,” SRS, USDA. This paper provides the mathematical derivation for the

w?ights used for the comparison of

surname, including surname code. It

details the assumptions and the error

terms needed in the implementation. (6

pages)

Arellano, Max G. and Coulter, Richard W.

(1976) “Weight Calculation for the Given

Name Comparison,” SRS, USDA. This paper

provides the mathematical derivation for

the weights used for the comparison of

given names. It recognizes nicknames and

initials. As in [71, it details the

assumptions. (9 pages)

Arellano, Max G. and Coulter, Richard W.

(1976) “Weight Calculation for the Middle

Name Comparison,” SRS, USDA. This paper

provides the mathematical derivation for

the weights used for the comparison of

middle names. It also accounts for agreement on middle initial. As in [7], it

details assumptions. (5 pages)

Coulter, Richard W. (1976) “A Weight for

‘Junior’ vs. Missing,” SPS, USOA. This

paper derives the disagreement weight for

the case when one record includes “Jr.”

and the other record does not. (4 pages)

Arellano, Max G. (1976) “Weight Calculation for the Place Name Comparison,” SRS,
[121

USDA. This paper provides the mathematical detail for the comparison of place

names. Disagreement weights for the place

name comparison are based on how far apart

the two different places are (as calculated by using the latitude and longitude

for each place). This paper also details

assumptions. (5 pages)

Coulter, Richard W. (1976) “Processing of

Comparison Pairs in Which Place Names Disagree,” SRS, USDA, This paper compares

addresses and their components -- street.

name, street number, etc. Since these

variables are probably not independent,

the paper derives an additional negative

weight for use when there is a disagreement on more than one address variable.

(4 pages)

[131 Arellano, Max G. (1976) “Calculation of

Weights for Partitioned Variable Comparisons,” SRS, USDA. This paper describes

the calculation of agreement weights when

variables are to be compared by splitting

them into different partitions and comparing the pieces -- for example, if two 3-

digit numt)ers were compared by examining

one digit at a time. (This is how house

number and box number are compared.) (10

pages)

[14] “Partitioned Variable Comparison/Algorithm

for Identifying Configurations,” SRS,

USDA. This paper translates three outcome

comparison configurations on n variables

to integers in the interval frcm O to

2**(n+l)-2 for purposes of indexing. (1

page)

[15] Nelson, D.O. (1976) “On the Solution of a

[16]

Polynomial Arising During the Computation

of Weights for Record Linkage Purposes,”

SRS, USDA. The procedure described in

[131 for determing weights for partitioned

variables needs a root of a polynomial.

This paper shows that a root in the

appropriate range exists and that it can

be evaluated numerically. (2 pages)

Arellano, Max G. (1976) “Optimum

Utilization of the Social Security Number

for Matching Purposes,” SRS, USDA. This

paper presents the derivation of weights

to be used in the comparison of social

security numbers. The social security

number is partitioned into four pieces (of

length 2,2,2, and 3) for purposes of comparison. For more on this technique, see

also [131. (10 pages)

[17] Arellano, Max G. and Arends, William L.

(1976) “The Estimation of Component Error

Probabilities for Record Linkage Purposes,”

SRS, USDA. This paper describes the estimation of error rates used in calculating

most of the agreement and disagreement

weights for individual variable comparisons. There are three types of errors

recognized in the USDA system: errors

resulting from the erroneous reporting or

recording of a value, errors which are a

result of a valid change in the value of a

variable, and missing values. (14 pages)

[18] Coulter, Richard W. (1975) “Sampling Size

in Estimating Component Error Probabilities,” SRS, USDA. This paper describes

the determination of the sample size

required to estimate the error rates

described in [17]. It also refers to

[41. (12 pages)

96
A Generalized IterativeRecord Linkage Computer System

for Use in Medical Follow-up Studies*

.,

G. R. HOWE

NCICEpidemiology Unit, FacultyofMedicine, McMmrichBnildin~, Universityof Toronto,

Toronto, Ontario M5S lA5, Canuda

AND

J. LINDSAY

Vital Statistics and Disease Registries Section, Health Division, Statistics Canada, Ottawa,

Ontario KIA OT6, Canada

The development of a generalized iterative record linkage system for use in follow-up of

cohorts in epidemiologic studies isdescribed.The availability of this system makes such

large-scale studies feasible and economical. The methodology for linking records is

described as weU as the dWerent modules of the computer system developed to apply the

methodology. Two applications of record linkage usingthe generalizeds ystem are discussed

together with some considerations regmdi~ strategies forconducting linkages efficiently.

The primary focus of epidemiologic studies of chronic disease is the

determination of factors which may be associated with increased risk of such

diseases. Two classic approaches to identifying such factors are the casecontrol and cohort studies (I).

In a cohort or follow-up study one starts with a group of individuals some or

all of whom may have been exposed to the factor under study, and ascertains

their subsequent morbidity or mortality experience. In order to accumulate

sufficient person-years of experience to provide a sufficiently powerful

statistical test of any association between exposure and disease, it may be

necessary to follow large groups of individuals for many years, and this is

particularly true if the excess risk in question is a small one. However, even in

the latter case it is possible that if exposure to the factor is widespread, the

population attributable risk can be substantial and consequently the factor can

be a significant health hazard. Conventional methods for following cohorts

include personal contact, telephone, and mail inquiries (1) and when the cohort

is large such methods can be prohibitive y difficult, expensive, and time

consuming.

*Reprinted with permission from Computers and Biomedical

Research 14, Copyright Q 1981 by Academic presss lnC o s

pp. 327-340.

97
An alternative method for following cohorts is the use of computerized

record linkage in which records of individual members of a cohort are

compared with records from files of morbidit y and mortality data (24). When a

unique identification number (such, for example, as the Canadian Social

Insurance Number or the U.S. Social Security Number) is present on both the

exposure records and the morbidity or mortalit y records, such linkages simply

involve sorting both files using the unique identifier as key and then directly

matching records from the two files. However, such unique identifiers rarely

exist, especially on data which have been assembled retrospectively. In this

case, it is necessary to use identifying characteristics such as surname, given

name, date of birth, etc. in order to link records from the two files, and this

involves two practical problems. In the first place, such identifying items are

not unique to a particular individual and even combinations of identifying items

may not be unique; and in addition, identifying items may be misrecorded or

missing on certain records. It is therefore necessary to devise algorithms for

comparing the two records in order to produce some quantitative measure

which is a fimction of the probability that those two records do indeed refer to

the same individual. Secondly, given such algorithms, it is necessary to devise

a computer system in order to efficiently carry out the data processing

involved.

Considerable attention has been paid to the first of these two problems and

the methods most widely used are those which have been developed by

Newcombe and his associates (5) and Fellegi and Sunter (6). However, the

implementation of these methods in terms of computer programs has generally

been done on an ad hoc basis for each specific application. This paper describes

some extensions of the Newcombe methodology, in particular to cope with the

problem of partial agreement of identifying items, and also a generalized

computer system which has been developed in order to carry out linkages

between any two files of interest. The system may also be used to internally

link records from a single file, where one individual may have more than one

record, but again no unique identifier exists. The application of the system to

two studies in cancer epidemiology is also described.

METHODOLOGY

A. Basic Principles

Conceptually carrying out a record linkage between two files A and B

involves the following steps:

Step 1. Every record on file A is compared with every record on file B. The

result of each comparison is a series of outcomes, one outcome resulting from

each identifying item being used for linkage such as surname, first given name,

year of birth, etc. An outcome may be defined as specifically as desired; for

example, the two records agree on the first five characters of the surname and

the value is SMITH, or the first given name agrees on first character

irrespective of value, but remaining characters disagree.

98
Step 2. A statistic called the total weight (W*) is calculated for the

comparison of any two particular records. The weight is an estimate of the odds

that the two records under consideration do in fact refer to the same individual,

i.e., that they are linked (L) as opposed to referring to di!Terent individuals,

i.e., they are not linked (~).

Thus the weight is an estimate ofi

P(L/lOtOaO. . .) [1]

P(E/102030. . .) ‘

where P(L/102030. . .) is the probability that the two records are linked

conditional that the outcome from comparing the first identifying item is 10,

etc. If one assumes that the values of the identifying items on the records are

statistical y independent then it follows that:

P(L) w*=1w+2w +3w. ..+log2p~, [2]

where ~w is Iotq of the estimate of the odds of obtaining outcome ~O conditional

upon the two records being linked. It is convenient as is customary in

information theory to use log2 in Eq. [2] in order to make the equation additive.

In practice the final term in Eq. [2] is usual] y impossible to evaluate since it

requires a priori knowledge of the number of links among the set of all

comparisons and this is usually unknown. Thus a modified total weight may be

defined as:

W=1W+2W+3W . . . . [3]

If W can be estimated from Eq. [3] for all possible comparisons between the

records on the two files and these comparisons are then ordered by the value of

W, they represent potential links in decreasing order of believability, and, in

particular, the difference W 1 – W2 for any two particular comparisons is an

estimate of Iogz of the odds ratio, Thus, if two comparisons result in W’s which

difFer by 1.0 the odds in favor of the first comparison being a true link are twice

the odds for the second comparison being a true link. Details of weight

calculations including the case of partial agreements are given below.

Step 3. Having ordered the comparisons by W, upper and lower threshold

values are chosen. These are used to divide the set of all comparisons into

three; namely, the ‘*definite links” —those with a weight above the upper

threshold, the’ ‘nonlinks” —those below the lower threshold, and the’ ‘possible

links” —those between the thresholds. The possible links may be manually

inspected and if possible resolved. If further identif ying information is available

which is not in machine-readable form, this may be used to supplement the data

for the possible links in order to resolve them. If no such data are available,

manual resolution is probably undesirable and one possible approach is to

choose a single threshold value (2). Fellegi and Sunter (6) have developed a

likelihood ratio test based upon the total weight statistic which leads to

optimum values of the upper and lower thresholds. Alternatively, and

99
fiequentl y more conveniently, their values may be empirically assigned from

inspection of the set of potential links.

B. Blocking

In order to compute W it is therefore on] y necessary to estimate ~w, Zw, ~w,

etc. for each identifying item, for each possible outcome from comparing the

possible values of that item. There is, however, a further practical

consideration. When dealing with files of any appreciable size the total number

of possible comparisons between records becomes extremely large and

resulting computer costs are inordinate. It is therefore necessary to block the

files using a combination of identifying items or derivatives of identifying items

to define the blocks. Comparisons are then only carried out between records in

corresponding blocks on the two files. The block identifier used in the

applications described in the last section of this paper, for example, was the

combination of sex and the NYSIIS code of surname (7). The NYSHS code is

an alphabetic code designed so that surnames of similar sound have the same

code and frequently encountered errors of misreporting do not result in change

in the NYSIIS code. Thus this blocking system will generally bring together

records belonging to a single individual even when errors of recording have

occurred. The effect of blocking on the calculation of weights is taken into

account in the general formulation given below.

C. Derivation of Formulas for Weights

The w’s of Eq. [3] may now be computed from simple probability theory. The

general formulation proposed leads to slight modifications of the original

formulas of Newcombe and Fellegi and Sunter as discussed subsequently.

It is convenient for illustrative purposes to consider a specific identifying

item; the most useful one in the present context is surname since this involves a

consideration of the blocking factor, namely, the NYSIIS code. Although the

number and types of outcome in comparing the surnames horn two records is

arbitrary, we have found it most convenient to consider five possible types of

outcome defined as follows. The subscript used to identify the particular

identifying item is omitted from these formulas. (For outcomes 1 to 4 surname

is assumed to be present on both records.)

(1) 0,.,: Surname agrees on first seven characters with value i.

(2) Og.j: Surname agrees on first four characters with valuej, but disagrees

within next three charactem.

(3) 0,.~: Surname agrees on NYSIIS code with value k, but disagrees within

the first four characters.

(4) 0,: Surname disagrees on NYSIIS code.

(5) 0,: Surname is missing on one or both records.

100
The weight corresponding to Os is obviously zero unless the linked and

unlinked set of records have different frequencies for the reporting or

nonreporting of identif ying items. If an estimate can be made of any differential

reporting for the two sets, w~maybe computed correctl y from its definition. No

further consideration need be given to missing data, as all probabilities and

frequencies are assumed to be conditional upon a value for the identifying item

in question being present.

In order to compute WI to w, it is necessary to specify the frequency with

which surname is misreported. These frequencies, referred to as transmission

rates, are defined as follows:

r~: The probability that the surname on a particular record has the same first

seven characters as the “true” value.

lZ: The probabilityy that the surname has at least the first same four characters

as its “true” value.

r~: The probability that the surname has the same NYSIIS code as its “true”

value.

By this definition there is a single set of transmission coefficients, tlto ?3,for

each identifying item. It should be noted that the transmission coefficients

correspond to the various possible outcomes listed above in the sense that if

both records in a particular comparison are transmitted from the “true” value

to the recorded value so that the first seven characters remain the same the

outcome will be 01 and the probability of such a transmission is tl for each

record. It should also be noted that various components can contribute to the

transmission coefficients, such as a genuine change in the “true” value of

surname between the creation of the two records, errors of recording, etc. If

such components can be identified and numerical values estimated, these

values can be used to compute the transmission coefficients. The approach we

have used is to compute the transmission coefficients in an iterative fashion

from the records themselves as described subsequently.

In order to calculate the weights corresponding to each possible outcome the

basic definition is used. For example, the probability of exact agreement on the

first seven specific characters of a certain surname when the two records

origimte from the same individual is given by

t12fi ,

where ff is the relative frequency of occurrence of the particular sevencharacter value among the individuals who give rise to the linked set. In order

to estimate such frequencies it is usually necessary to use the frequencies as

observed on the records in the files themselves. This involves a decision as to

whether the frequencies on the linked set are most similar to the frequencies on

file A or file B, and this obviously depends on the particular data sets under

consideration and involves essentially an empirical decision. Given the

particular file to be used for estimating the frequencies there are two possible

models. In the first, it is assumed that errors in recording are such that the

original “true” value is transmitted to some value that does not already exist

101
within the linked set. This leads to the observed frequent y value within the file

being set equal to t,Zji,which is the formulation proposed by Fellegi and

Sunter. Alternatively it may be assumed that when a recording error is made it

results in some value which already exists within the linked set. If this process

happens randomly the observed frequency within the file will be equal to~f. We

have used the second model since we feel it to be more realistic and since it

leads to a formulation in which transmission and frequency components of the

weights are separable and the weight for any particular outcome can be

factorized into these two components.

The probability for any outcome with the unlinked set of comparisons is most

simply determined from consideration of frequencies as the y occur on the files.

Thus the probability of agreement by chance on the first seven characters of

surname in the unlinked set is given by:

Ah Bfi,

where ~~i and B~~refer to the relative frequencies on files A and B, respectively.

(The contribution to all possible comparisons from the linked set is negligibly

small and is therefore ignored in this formulation.) Using this approach the

weights for 1-4 above can be shown to be:

w~=~= log2 flz + log2 —,

;

[4]

Bi

w~=j = log2(t22 – t~q + log~

[

Agj 1

[5]

‘4g,Bl?j - ~ ‘JiJ, ‘

if.j

[

h

w~=~ = log2(t32 —t~z) + log~ Ak 1

7 [6]

,.lhk Bhk – ~ Agj Bg,

jck

Wq= Iogz(o) , m

where ~~{ is as before; Ag j is the relative frequency of first four characters

of surname equal toj, and Ah k is the relative frequent y of NYSIIS code equal to

k (for file A). Equation [7] is applicable only to the item used as a pocket

identifier.

These formulas apply when the frequency distributions in the linked set are

taken as being the same as those on file A.

In all the above expressions it will be seen that the transmission and

frequency components of the weight are separable and their log2s are additive.

It should be noted that the value for WAmeans that no two records from

different blocks can link. In order to estimate the various values oft, we have

used an iterative procedure as follows. The linkage is carried out using

estimates for t, usually based on previous experience. Given an estimate of the

upper threshold value, a sample of links may be drawn from the linked set and

estimates made of the transmission coefficients from the number of times that

102

.—
full or partial agreements on surname occur within the linked set. These new

values may then be used as the basis for another linkage and the process

repeated iterative y until reasonably stable values for the transmission

coefficients are obtained. Altemativel y, as previously mentioned, the

transmission coefficients may be estimated empirical y.

SYSTEM DESIGN

The particular series of programs, which were written in order to apply the

above methodological principles to specific data sets, relies heavily upon use of

a data base system (Relational Access Processor for Integrated Data Bases

(RAPID)) which is available within the facility where the programs were

developed (Statistics Canada). The programs as such, therefore, are of no

direct use in any other environment, but the principles of the system involved

are readily generalizable to any other computer environment, and may be

programmed within the particular limitations of the hardware/software

available.

The system has been deliberate y designed to be modular in nature. In

particular, the most time-consuming element, name] y, the comparison of all

records within each block, was developed as a single module. Only one pass of

the complete data is necessary, which will eliminate any comparisons which

result in any obvious nonlinks and will produce a file of potential links with

their corresponding outcomes. These potential links may then be subjected to a

number of d~erent weighting runs in order to refine the linkage results at a

much lower cost than would be incurred by rerunning comparisons between the

entire data files. This modular approach also facilitates the iterative process of

calculating transmission weights. The modules involved in the system are

shown in block diagram form in Fig. 1 and their specific functions are now

described.

A. Preprocessing

This step involves editing and correcting of the original data files, including

such fimctions as creating a unique sequence number for each record and the

NYSIIS code of surname, left @ifying fields such as given name, removing

blanks within names, recoding variables, etc. Following the editing step the

files are sorted by whichever identifying item is to be used as the pocket

identifier, e.g., NYSIIS code.

B. Calculation of Frequency Component of Weights

Frequency counts are earned out on the preprocessed files for all levels of

agreement and partial agreement for all identifying items. From these

frequency distributions are computed the frequency components of the weights

as given in Eqs. [4] to [7]. In practice it will often be found that for many items

the frequency distribution is similar from one file to another and consequent y a

103
Module

Recalcul*ta

Transmission

Grouping

FIG. 1. Generalized iterative record linkage system.

single set of frequent y weights will suffice. For other items, such as birth year,

the distribution will vary considerably from file to file and may need

recomputing each time.

C. Comparison Module

The function of the compare module as stated is to create a file of potential

links and their corresponding outcomes and to eliminate all obvious nonlinks.

In this module all records within a given pocket are compared with each other,

each comparison giving rise to a series of outcomes such as, e.g., “seven

character agreement on surname, and the value is Smith.” Identifying items on

the two records are compared in an order which is specified at execution time.

This ordering is decided by two factors, the discriminating power of the

identifying item and the CPU time necessary to make the comparison. An

option is provided to carry a crude “running total of disagreement weights. ”

104
Each item is assigned an appropriate preliminary disagreement weight, and

where a disagreement occurs, the running total is decremented by the

disagreement weight for the item concerned. When the running total achieves a

value below a preselected cutoff value, the comparison between the two

records in question is abandoned and the module then proceeds to the next

comparison. This procedure ensures that records which are in obvious

disagreement are not considered as potential links. For any comparison which

does not yield a value for the running weight below the critical, a “link record”

is created consisting of the two record numbers and an outcome code and,

where appropriate, a value for each identifying item, in question. At the

completion of this phase the link record file thus contains all potential links and

fimther processing is concerned with this particular file.

D. Weighting Module

The function of this module is to add both frequency and transmission

components of the weights to the link record file. Components maybe added in

separate passes as they are completely independent of each other as in the

formulation of the previous section. The particular method used to add the

weights will of course depend on the hardware configuration available. In

general, the procedure will involve table lookups using the outcome code and

value where appropriate as an index. Since the link records are ordered in the

same sequence as the pocket identifier, the weights for the pocket identifier

(e.g., NYSIIS of surname) maybe added conveniently from a sequential file.

For items with relatively limited numbers of values such as birth year the tables

may be convenient y stored in core; for alphabetic data other than the pocket

identifier, such as given name, random access disk files probably provide the

most convenient means. As there are relatively few transmission coefficients

these generally can be stored in core, and a weighting pass to change @st the

transmission coefficients can be carried out rapidly. Subsequent to applying the

weights to the link record file, a sample of this can be printed out for manual

inspection and this can be used to assign tested threshold values. Given these

threshold values new estimates of transmission weights can be made using the

set of links which are above the upper threshold. These new values can be

applied to the links and the process repeated until some measure of consistency

is achieved.

E. Grouping Module

The fimction of this module is to bring together all records which have linked

with each other. The speciilc algorithm to be used is of course dependent upon

the nature of the records concerned, and whether the linkage is two file or

internal. For an internal linkage generally there is no limitation upon the

number of records that can constitute a “group” corresponding to a single

individual. Often in the case of two-file linkage onl y a one-to-one relationship is

possible as for example in linking records for specific individuals to a file of

105
death records. However, in the latter case, since some links will occur by

chance, it is necessary to identify records which appear in more than one link.

For grouping records from an internal linkage we utilized the following

method which involves starting with a single record, identifying all links to that

record, then identifying all links to those links, and so on. We defined definite

groups of records as those in which each member is linked to at least one other

member of the group with a weight which is above the upper threshold (a

definite link). Possible groups are then defined as being composed of a series of

definite groups in which there is at least one possible link between members of

the definite groups concerned. Any possible groups which are formed can then

be printed out for visual inspection and a decision made as to whether the

definite groups which constitute them should be amalgamated into a single

group or whether the original definite groups should be maintained as

individuals. The reservations concerning the utility of manual resolution when

no further identifying data are available, expressed in the methodology y section,

should be taken into account when deciding whether to adopt such a grouping

procedure.

In order to group links from a two-file linkage where only a one-to-one link is

permissible, the links are sorted by weight, then proceeding from the link with

the largest value downward, each link is checked to see whether either record

concerned has appeared in a previous link. If either has, the link maybe printed

out as a conflict and the situation resolved by visual inspection. Alternatively,

the link with the highest weight may be accepted.

Since processing up to this point has involved record numbers rather than the

actual records themselves at this stage a number is assigned to each group or

pair of records that has been linked. These group numbers may then be

assigned sequential y using the record number of one of the original records,

and sorting the records on this group number brings together those records

which have been linked so they may thus then be processed firther as desired.

It should be noted that although the identifying items on any particular record

which has entered into a possible link are essentially contained on the link

record file, and are there available for inspection if needed, it is also desirable

to provide a mechanism for accessing the origiml complete data records. In the

system we have developed this is done by maintaining a parallel file containing

those data records which have formed at least one link so that they may be

accessed via the data base used.

APPLICATIONS

The system described has been primarily developed for use in monitoring the

morbidity and mortality experience of various groups of individuals with

various exposures, by linking such exposure records to national morbidity and

mortality files. Two such specific applications are now described in more detail.

106
Linkage of TB Patient File to Mortality File

Between 1930‘and 1952 extensive use was made of collapse therapy in the

treatment of tuberculosis. This involved considerable X-ray exposure from

fluoroscope machines which were extensively used for examination of the

chest cavity. A major study of cancer mortality in relation to this radiation

exposure is being conducted (3), by collecting data on individual patients from

all existing hospital and sanatorium records in Canada.

The TB patient file was first internally linked using the generalized iterative

linkage system described here to bring together treatment data from d~erent

institutions to form one complete treatment history per patient. The TB patient

file containing 118,000 records was then linked to the national mortality file

covering the years 1950 to 1977 containing 5,000,000 records. (1950 is the first

year for which sufficiently well-identified mortality records are available in a

format suitable for computerized record linkage.)

The identifying items used were the following: NYSIIS code and surname;

first and second given names; day, month, and year of birth; place of birth; sex;

NYSIIS of mother’s maiden name; mother’s first initial; mother’s birthplace;

father’s first initial; and father’s birthplace. Year of last contact on the TB

records was compared with year of death on the mortality records in order to

eliminate unnecessary comparisons. Use was made of the facility to

incorporate partial agreements as follows: Surnames were considered to be in

fill agreement if they agreed on seven characters; the first level of partial

agreement was on the first four characters and the second level of partial

agreement, on NYSIIS only. Full agreement for given names was on the first

four characters, and partial agreement, on initial only. Birth year was treated as

being in full agreement if it was within plus or minus 1 year. The first level of

partial agreement was within 5 years, and the second level, within 10.

The records were blocked by NYSIIS code of surname and sex. Alternate

surname spellings and maiden names were also available. These were included

as comparison items by creating duplicate records for alternate surnames at the

preprocessing stage. Following the linkage, duplicate records were combined.

The total file of TB patients was linked to 1 year of mortality records at a time.

This provided the advantage of allowing the runs to be checked closely rather

than risking costly errors over the entire linkage.

Initially, the number of potential links formed between the TB and mortality

iiles was 787,800 for males and 554,800 for females, using a very conservative

cutoff weight to ensure that no potential links were missed. The preliminary

weights used were average values or approximations of the final weights. Afler

the final weights were calculated and threshold values set, there were 82,828

possible and definite links generated by the male files and 67,490 by the fernale

files. This was considered to be an application where only a one-t-one link was

acceptable, i.e., one TB record could validly link with one death record.

Following the application of the one-toone rule, there remained 20,293 male

links and 12,697 female links which were considered to be definite for the

purpose of the subsequent statistical analysis.

107
The cost of this record linkage was just over $5000 (Canadian). This cost

includes the comparison of the records, assignment of preliminary weights used

to determine whether each link was a potential link, insertion of the final

weights, setting of the thresholds and resulting classification of each link as

definite, possible or rejected, the listing of a sample of links from each run, and

resolution of duplicate links within each run. In addition, duplicate links

involving records over different years of death were resolved. Over two-thirds

of the cost was accounted for by the comparison of the records. As previously

mentioned, this demonstrates the advantage of a modular system, where all

other steps may be carried out iterative] y at relatively minimal cost. The next

most expensive step was the weighting which accounted for approximately

14%. The steps listed above took 179 min of CPU time for the males and 175

min for the fernales. It should be noted that testing was carried out first on a

very small sample of the file consisting of a few blocks of records from the two

files. At this point, the mortality records were selected from a single year of

death. When preliminary testing was completed, an entire year of death records

was linked with the TB records and further refinements made. For example, it

was found that test runs where no cutoff weight was used were about 15!?4more

expensive than those where a cutoff weight was used that was sufficiently low

for no potential links to be missed. The cost of this linkage using the

generalizeds ystem was substantially lower than the cost of linkages carried out

previously using ad hoc programs.

Linkage of Occupational Cohort to Cancer Incidence

Between 1965 and 1971, data were collected by Statistics Canada for a 10%

sample of the Canadian labor force (approximate y 700,000 individuals). The

data included identifying information together with the industry and occupation

in which the individual was engaged in each particular year. In order to follow

the mortality and cancer morbidity experience of this cohort with respect to

their industrial and occupational exposure, these records were linked to the

national mortality data base and the cancer incidence files. For the linkage to

the cancer incidence files, Ontario occupational records were excluded, since

identifiable cancer incidence records were not available for that province,

leaving 476,174 occupational records.

The 287,786 male and 188,388 female occupational records were linked to

171,628 male and 215,651 female cancer incidence records covering the years

1969 to 1976. (Cancer incidence data were first collected nationally in 1969.)

The identifying items available on both files were NYSIIS of surname; surname

and alternate surname; first and second given names; day, month, and year of

birth; and sex. As in the previous example, the records were blocked by

NYSIIS of surname and sex. In this case only two separate runs were made

since the files were split by sex, but not according to the year of diagnosis of

cancer. The same levels of full and partial agreement were used as for the TBmortaiit y linkage.

108

—.
The number of potential links generated was 96,100 from the male files and

82,482 from the female files. After the insertion of final weights and the setting

of threshold values, and resolution of links of multiple occupation records to

single cancer records, the number of possible and definite male links was 5315

and there were 2885 female links. In this case, multiple cancer incidence links

to occupation records were considered acceptable since the cancer incidence

file contains one record for each primary site of cancer. The number of

occupation records involved in these links or the number of individuals linking

to cancer records was 4953 men and 2747 women. The cost of this linkage was

approximately $600 and the CPU time used was about 30 min for the males and

23 min for the females, including the same steps for which cost was calculated

for the TB-mortality linkage. The proportion of time spent on the comparison

of records and weighting was comparable to the TB-mortalit y linkage.

Strategy for Using Linkage System

There are three main factors which affected the cost of these linkage runs

using the system described. The order in which comparisons are carried out is

extremely important, as has been mentioned. Obviously it would be very costly

to compare alphabetic fields first, knowing that at some point later in the

comparison the records could be rejected as potential links. Efficiency can be

maximized by first comparing numeric fields on the basis of which pairs of

records can be immediate y rejected. It may be decided, for example, that the

quality of the two files concerned is sufficiently high that disagreement on birth

year of more than 10 years means that the link would not possibly be believed.

The second factor affecting cost is the extent to which records have missing

identifying items of information. If one or both files contain many records with

very little information present, these records will generate large numbers of

potential links because there is little or no basis on which to reject these links,

i.e., there will not be a sufficient number of disagreements to bring the

disagreement weight below the cutoff weight. As a result, comparison of

records takes longer since more records go through the comparison of all items

and weighting will also be more expensive due to the volume of potential links.

The third consideration is the setting of the cutoff weight. The apparent

efficiency of a linkage maybe increased by using a less strongly negative cutoff

weight. However, depending on the purpose of the application, this may have

subsequent adverse effects. If ordy the definite links are of interest, no

problems may arise, but if the purpose of conducting the linkage is statistical

anal ysis, it is then important to be able to identify the records or individuals

whose status is unknown. This is the case with respect to the applications

described here.

CONCLUSION

The system which was developed provides a very powerfid tool for medical

research in general, and the concepts can be implemented fairly readily on any

109
medium-sized computer. Since the processing is sequential in general it can

also be adapted to any small installation which has the facility for processing

large volumes of sequential data.

ACKNOWLEDGMENTS

The authors wish to acknowledge the contributions of systems analysts Ted Hill and Steve

Hobbs, and methodologists Simon Cheung, Mike Eagen, and Dave Binder.

REFERENCES

/. MACMAHON, B., AND PUCH, T. F. “Epidemiology Principles and Methods.” Little, Brown,

Boston, 1970.

2. HOWE, G. R., LINDSAY, J., COPPOCK,E., AND MILLER, A. B. Isoniazid exposure in relation to

cancer incidence and mortality in a cohort of tuberculosis patients. Int. J. Epidemiol. 8, 4, 305

( 1979).

.?. HOWE, G. R. Breastcancermortalityinrelation to fluoroscopic X-ray exposure. Presented at

the 4tb International Symposium of the Detection and Prevention of Cancer, London, July 1980.

4. HOWE, G. R., LINDSAY, J., AND MILLER, A. B. A national system for monitoring occupationally related cancer morbidity and mortality. Prev. Med., in press.

5. SMITH, M. E., AND NEWCOMBE, H. B. Methods for computer linkage of hospital adrnissionseparation records into cumulative health histories. Methods of Information in Medicine 14, 118

(197s).

6.FELLEG!, I. P., AND SUNTER, A. B. A theory for record linkage. J. Amer. Stat. Assoc. 64, 1183

(1%9).

7. LYNCH, B. T., AND ARENDS, W. L. “Selection of a Surname Codiig Procedure for the SRS

Record Linkage System.” U.S. Department of Agriculture, Washington, D. C., 1977.

110
RELIABILITY OF COMPUTERIZED VERSUS MANUAL

DEATH SEARCHES IN A STUDY OF THE HEALTH OF

ELDORADO URANIUM WORKERS **

H. B. NEWCOMBE*, M. E. S~THt, G. R. HOWE~, J. MINGAY$,

A. STRUGNELLfj and J. D. ABBATT~II

* P.O. Box 135,Deep River, Ontario, KOJ 1PO, Canada; t Vital Statistics and Disease Registries,

Statistics Canada; $National CancerInstitute of CanadaEpidemiology Unit, University ofToronto;

\Eldorado Nuclear Limited, Ottawa

Abstract-An epidemiological follow-up study of 16,000 uranium mine and refinery employees has

made use of computerized techniques for searching a national death file. The accuracy of this

computerized matching has been compared with that of corresponding manual searches based on

one-eighth of the worker file. The nationa[ death file-Canadian Mortality Data Base—at

Statistics Canada includes coded causes of death for all deaths back to 1950. The machine search

was carried out using a generalized record linkage system based upon a probabilistic approach. The

machine was more successful than the manual searchers and was also less likely to yield false

linkages with death records not related to the study population. In both approaches accuracy was

strongly dependent on the amount of personal identifying information available on the records

being linked.

Uranium Radium Cancer Risks Follow-up Epidemiology

Industrial cancer Death searches Computer searches Automated follow-up

INTRODUCTION

Eldorado Nuclear Limited (E.N.L.) is conducting a retrospective epidemiological study

of the health of its former employees. Eldorado operations involve the mining, milling and

refining of uranium and these activities have been carried on continually from the early

1930s. Initially radium was extracted for medical and other purposes, and more recently

uranium metal and nuclear fuel materials have become the main products.

The objectives of this study are:

(a) to identify former employees who may have a potential compensation claim, and to

inform them or their survivors of these potential compensation claim rights, and

(b) to obtain dose-response data for evaluation of the risks to workers, especially with

respect to atmospheres containing radon and radon-daughters.

The main study design and details regarding the assembly of the nominal roll have been

described elsewhere [1]. The purpose of the present study, which serves both the shortterm and the long-term aims of the broader investigation and of other similar studies, was

to investigate the reliability of searches of all relevant death registration material using the

study nominal roll and the Canadian Mortality Data Base (C. M. D.B. ) operated by Statistics Canada. In an attempt to assess the reliability of machine record linkage for which the

C. M.D.B. was designed [2, 3], the results of rapid computer searching and file linkage

have been compared with manual searching and file linkage.

It has rarely if ever been possible to judge, much less quantify, how many false positive

(incorrect) and false negative (missed) linkages result from conventional manual searches

for death registrations where the dead or alive status of the members of the nominal roll

is unknown. The present study is designed to provide quantitative information on both

manual and machine file searching. The comparison has demonstrated the extent of the

influence of an abundance or scarcity of personal identifiers on the efficiency of both types

**Reprinted with permission from Computers in Biology and

Medicine, Vol. 13, No. 3, Copyright 01983 by Pergamon

Press Ltd. , pp. 157-169.

111

——
Table 1. Manual matches of worker records with death records, by degree of assurance

Degree of awu ramx I

C:itegory

I

Number of worker records

A definite link 137

B+ very gc~,)dpossihlc 35

}

~1{)

B good possi blc 47

B– unlikely possible 23

c poor possible 17

D not enough identification 10

other no link 1602

From a sample of 1871 male worker records in which the surnames begin with the letters A or B

of file matching. It has also demonstrated the greater efficiency of machine than manual

matching.

The Eldorado study, although retrospective in nature, is being carried out with the

intention of merging it into a prospective health monitoring instrument. It is the hope of

many that similar prospective undertakings will come to be regarded in the future as

desirable and feasible. Only thus can full use be made of available records to assess the

adequacy of current standards of protection against delayed harm from the working experience.

MATERIALS AND METHODS

The Eldorado nominal roll used for the present study of linkage accuracy consists of a

total of 16,658 names. These relate to past workers at the Port Radium mine (4526),

Beaverlodge mine (9336), the Port Hope refinery (2514) and Research and Development

(282), and involve employment as far back as 1932.

The Canadian Mortality Data Base file contains over five million death registrations with

coded cause of death for the years 1950 to 1977.

For the computer linkage study, only E.N.L. records with a sex code equal to male or

unknown (15 ,937) were used to initiate searches of the male half of the C.M. D .B. Searches

for deaths relating to female workers (721) were not attempted because of the small

numbers and the practical problems associated with changes of name at marriage. Such

searches should be possible in the future, however, using the maiden surnames which occur

on the death registrations of ever-married women, in the form of fathers’ surnames.

For the manual linkage part of the operation, a sample of the E.N .L, file was used to

initiate the searches representing all surnames of males beginning with the letters A and

B (1871). A and B were chosen because they are known to provide a good sample of

common and uncommon names (Andersons and Browns), and there is no evidence that

they introduce a bias. The manual search used the C.M. D .B. microfiche listings.

The degree of assurance that a correct match has been achieved is assessed quantitatively by the computer. The decision is based upon prior information about the discriminating

powers of various possible agreements and disagreements of the personal identifying

information. The manual searchers assessed the degree of assurance subjectively and

ranked the matches (links) they achieved on a scale that was qualitative (Table 1).

The principles are the same in both cases. Greater weight is attached to agreements of

rare names, rare birthplaces, etc., than to agreements of their commoner counterparts.

Similarly disagreements that occur only rarely, in a pair of records, argue more strongly

against a correct match than will disagreements that are common. These fairly obvious

inferences are taken into account by both the computer and the searcher. The chief

difference is that the computer works from look-up tables that tell it by how much a given

agreement, or disagreement, will shift the odds in favour of, or against, a correct match. The

man relies on judgement with regard to the same matter, based on similar information and

reasoning.

112
Table 2. Coincident identifiers in potentially matching worker records and death records (estimated)

Percentage available in

Identifiers for searching and linkage Worker records

I

Death records I

Both simultaneously

alone alone (est.)

Surname plus at least one given name 100 100 100

plus a middle initial or name 50 47 23

Birth date in full 79 95 75

province or country 55 98 54

Parental initials, on? or more 23 87 20

birth province/country, one or both 8 87 7

The system used for searching the death records was developed by Statistics Canada and

the Epidemiology Unit of the National Cancer Institute of Canada for use in medical

studies at Statistics Canada [4] and is described as a Generalized Iterative Record Linkage

System (GIRLS). It is an extension of the probabilistic approach to record linkage

developed at Chalk River [5-8]. Record linkage has been described in detail in numerous

other publications (see references [9- 13] and for a complete bibliography [14]), The

mathematical derivation of ‘weighting factors’, from the frequencies of the various identifier comparison outcomes (agreements, disagreements, etc. ), in linked vs unlinked pairs

of records, has been described in detail elsewhere [4–7]. The weighting factors serve to

represent in numeric form the discriminating powers of different identifier comparisons

and their outcomes.

The assurances calculated by the computer are conveniently expressed on a logarithmic

scale using the base 2 as in information theory. On such a scale, zero represents odds of

1:1 that the linkage is a correct one, each added unit doubling the odds and each subtracted

unit halving them. For example, + 1 and +2 represent odds of 2:1 and 4:1 respectively, in

favour of a correct match; whereas – 1 and –2 represent odds of 1:2 and 1:4 and so argue

against a correct match. With an abundance of personal identifying information common

to a pair of records, the evidence for or against a correct match tends to become more

decisive, and stronger positive or negative ‘weights’, as they are called, are likely to be

associated with the comparisons. Thus, for genuinely Iinkable pairs of records, total

weights of +10 to +20 may be common, representing favorable odds of 1000:1 to

1,000,000:1. For unlikable pairs, the weights and the odds will tend to be similar in

magnitude but opposite in direction.

The degrees of assurance of a correct match, in both approaches, may be expected to vary

widely. In large part this is due to differences in the amount of personal identifying

information common to a potentially linkable pair (Table 2). For example, without the full

birth date, the name information alone will usually not carry enough discriminating power

to enable the correct death record to be selected from among a million or so others. And

in part it is due to differences in the rarity or commonness of the names, birthplaces and

such. Assurance is similarly affected whether the search is carried out by computer or by

man.

A major purpose in performing the analysis of the data yielded by the combined efforts

of the computer and the human searchers is to determine to what degree the accuracy of

the death searches depends upon the amount of personal identifying information which

can be applied to the problem of distinguishing good matches from bad.

RESULTS AND DISCUSSION

Assurances associated with the computer and manual searches

As a result of the computer search, approximately 2000 of 15,937 Eldorado worker

records were linked to matching death registrations with varying degrees of assurance

(Table 3). As a result of the manual search, somewhat over 200 of the 1871 records from

113
Table 3. Computer matches of workerrecordswithdeathrecords,by degree of assurance

Range of odds Number of worker

Weight range Category (inferred from records

weights)

+4 and over positive link (11:1 and over) 1490

+lto+3 probable link (1.4:ltoll:l) 362

}

2023

zero possible (1:1.4 tol.4:1) 171

–lto–3 probable non-link (l:lltol:l.4) 794

–4 to –8 positive non-link (1:256 to 1:11) 2339

other no link — 10,781

From a total of 15,937 records where sex is male or unknown.

the sample (relating to surnames beginning with A or B) were similarly linked (Table 1).

In each case, the precise number of ‘acceptable’ links depends upon where one sets the

‘threshold’ for acceptability. If one places it where the implied odds in favour of a correct

match are 50:50 or better, either as calculated by the computer or as judged subjectively

by the manual searchers, the precise number of ‘acceptable’ links would be 2023 and 219

respectively.

Because the setting of the threshold for acceptance is necessarily arbitrary in both cases,

one must consider how best to estimate the numbers of accepted links that are in fact

wrong, and the numbers of rejected matches that were correctly paired.

Estimating the false positive and false negative computer matches

There are two ways in which the accuracy of the computer linkages may be judged

without reference to parallel manual searches, The first approach is based on the simple

fact that where a worker’s record links ‘acceptably’ to two different death records, only one

of these links can be correct; the frequency of such instances tells us something about the

potential for producing false positive outcomes. The second approach takes at face value

the calculated odds, in favour of or against a correct match, and derives both an estimated

number of false matches that lie above the threshold for acceptance, as well as another

estimated number of potential correct matches that fall below the threshold for rejection.

Table 4. ‘Runners up’ as indicators of the potential for false positive linkages (computer searching)

I

Range of odds I I

Number of worker Number of matches I‘Runners up’ (% of

Weight range (inferred from records (’best’ not the ‘best’ ‘best’)

weights) match for each ) (’runners up’)

+10 and over (724:1 and up) 1057

}

10

}

1

+4 to +9 (11:1 to724:l) 433 2023 64 325 15

+1 to +3 (1.4:1 to 11:1) 362 150 41 }

16%

zero (l:l,4tol.4:1) 171 101 59

–lto–3 (l:lltol:l.4) 794 680 86

–4 to –8 (l:256to 1:11) 2339 5053 216

Note: (1) Weighting factors are rounded for simplicity, the precise dividing lines in the above table being +9.5,

+3.5, +0,5, –0.5, and -3.5.

(2) In the’+ 10 and over’ group, a substantial fraction carry weights in the region of +20 and even +30,

representing odds of a million-to-one and a billion-to-one in favour of a correct linkage.

(3) Where such high weights occur among the ‘runners up’, which cannot be true links, they nevertheless

correctly refer to similarities of identifying information which are exceedingly unlikely to have

occurred by chance alone. Sometimes, such a pair of records will relate to two members of a family,

one of whom was named after the other. Also, twins, who share the same birth date, are apt to turn

up in such pairs of records, and so do members of small ethnic groups who share the same rare birth

places and rare surnames. Manual searchers and the computer, both correctly tend to pay special

attention to such non-random pairings of records, which signify correlations other than those dueto

theidentityoftheindividual.

114
Table 5. Calculated ‘weights’ as indicators of probable false positives and false negatives (computer searching)

Range of odds Number of worker Probable correct Probable false

Weight range (inferred from ~ecords (’best’ matches (est. ) matches (est.)

weights) matches)

+10 and over (724: 1 and up) 1057

}

1057

+4 to +9

)

—

(11:1 to724:l) 433 2023 424 9

+lto+3 (1.4:1 to 11:1) 362 279 1845 83

}

177

zero (1:1.4 tol.4:1) 171 85 85

(1:11 to 1:1.4)

& } 3133 51 t 204 \ 2929 153

(1:256 to 1:11) 2%

-l to-3

-4 to –8

Note: Whichever weight one chooses as representing a threshold for acceptance, those ‘false matches’ which

fall above the threshold will become ‘false positives’. and those ‘correct matches’ which fall below the

threshold will become ‘false negatives’.

For the first approach, one may compare the numbers of ‘best’ matches with the numbers of ‘runners up’, broken down by the calculated ‘weight’ or odds in favour of a correct

match (Table 4). The number of runners up increases with progressively lower weights.

With the threshold for acceptance set just below zero, the ‘runners up’ (representing death

records to which workers’ records might have linked ‘acceptably’ if they hadn’t found a

better match) number sixteen per hundred ‘best’ matches. These are potential rather than

actual false positives, but they indicate what might happen to the record of a worker who

hadn’t yet died and for whom there was therefore no correct matching death registration.

This problem arises chiefly where the personal identifying information is limited.

For the second approach, the calculated weights (and their associated odds) were used

to derive the probable numbers of links and non-links. For example, a weight of zero

represents odds of 1:1 in favour of a correct linkage. Therefore half of the matches which

have been assigned this weight, probably do relate to the same person and the other half

do not. Taking the weighting factors at face value, the likely proportions of correct and fake

matches associated with each value of the total weights were calculated (Table 5). From

this sort of calculation it was inferred that, for a threshold set just below zero weight, and

with 2203 ‘accepted’ links, 178 of these or just under 9 ~0 are likely to be false positives. In

addition there are a probable 205 potential correct links that were not accepted, representTabte 6. Numbers of matches achieved by manual vs computer searching, by degree of assurance (based on worker

records having surnames beginning with A orB)

Computer Degree of manual assurance No

weight , .-

1 I I

man. Total

range A B+ B B- C D match

+10 and up

i-4 to +9

+lto+3

zero

121

13

2

16

8

4

1

7

9

8

3

1

1

3

1

2

1

2

14 161

21 53

23 42

11 16

–lto–3

-4to -8

no comp.

match

3

9

6

3

10

5

2

5

7

1 4

1

1

79 92

266 300

1188 1207

9

Total 137 35 45 24 19 9 1602 1871

Note: (1)

(2)

Where the thresholds for acceptance are set at zero and above for the computer. and at B and above

for the manual searches, the following would be the result:

accepted by both = 192

accepted by computer only= 80

accepted by manual only = 25

rejected by both = 1574.

The table includes cases in which the death record selected by the computer differs from that selected

by the manual searcher (see next table).

115
pgp

(Parentheses indicate which were judged correct on subsequent review.)

Computer Degree of manualassurance

weight

I 1

Total

range A B+ B B– c D

+10 and up - l(M) I(c) I(C) l(c) 4

+4 to +9 1(?) l(c) 1(?) l(c) = 4

+lto+3 l(c), l(x) 1(?) 2(?) 5

zero

–lto–3 - 1

-4to–8 -

l(?)~l(x) 3(?)~2(X) 2(?~!~\x) 2;?) 12

Total 2 6 8 8 2 26

Note: These numbers are included in the previous table.

M = manual choice correct

C = computer choice correct

X = both manual + computer choices incorrect

? = uncertain

ing a false negative rate of about 10’?to.If the threshold were raised to get rid of the false

positives the false negatives would increase, and lowering the threshold would have the

opposite effect. With the threshold in the vicinit y of zero the number of false positives and

false negatives are expected to be about equal. The only way to simultaneously reduce the

frequencies of false positives and false negatives is to obtain a greater amount of personal

identifying information for each record.

The human searcher is faced with the same problem, except that in this case it is not

quantified. For both the man and the computer there may be additional false negatives that

arise because some of the worker records are grossly deficient in identifying information;

e.g, an absent birth date may result in insufficient discriminating power to distinguish

between multiple possibilities for linkage.

Comparisons of computer vs manual linkages

Further insights into the respective levels of accuracy may be gained from comparisons

of the performance of the computer vs that of a human searcher. Specifically, where the

two approaches fail to agree, (a) they may yield different deaths, (b) the human may

appear to succeed and the computer not at all, and (c) the reverse may be the case.

It might be supposed that the ultimate test of the accuracy of the computer searching

would be for a man to carry out the same searches as the machine to see where the

computer had gone wrong. This assumes, without evidence, that the man is more accurate

than the computer. Instead, however, the problem is actually quite symmetrical, because

lack of specificity in the identifying information adversely affects the accuracy of both the

computer and the human searcher, and it remains to be shown which is the more accurate

in the present setting.

Direct comparisons serve to indicate where the two approaches have yielded the same

Table 8. Proportions of worker records linked with death records by the computer. when birth year is absent vs

present

Birth year* I

Linkages (weights zero

I

Worker records I

‘%linked

(presentlabsent) and over)

Absent 18 3323 0.5

Present 2004 12614 15.9

Total 2022 15937 12.7

*Note: Virtually all of the worker records that lack year of birth, also lack the rest of the birth date.

116

—
Table 12 Calculation of ‘weighting factors’ for place of death vs pkaee of work

Number in Expected for Ratio (inferred Weighting factor

place of death linked pairs average odds in favour of

Canadians

(log, of the

linkage) ratio)

Port Radium and Beaverlodge workers (145 pairs)

Que.-Atlantic 8

Ont. 30

Man.–Sask. 19

Alta.–B.C. 51

Y.T.-N.W.T. 8

Edmonton 27

Port Hope workers (59 pairs)

Que.-Atlantic

Ont. 44

Man.-Sask. 3

Alta.–B.C. 12

Y.T.-N. W.T.

Port Hope 20

53

52

12

27

0,4

3.5

22

21

5

11

0,05

1:6.6

1:1.7

1.5:1

1.9:1

20:1

8:1

1:43

2.1:1

1:1.7

1.1:1

400:1

–2.7

–0.8

+().6

+0.9

+4.4

+3.0

–5.4..

+1,1

–0.8

+0. 1

+8,7

Note: (1) Where no death occurred, the ratio is based on an assumed 0.5 deaths; the resulting ‘weighting

factor’ will then tend to be conservative.

(2) The expected numbers ‘for average Canadians” are based simply on the populations of the regions,

unlikable pairs argue against linkage. ) The conversion of this ratio into a logarithm to the

base 2 is just a convenience to make the weights addable. The first of the two frequencies

is obtained by direct observation of the linked pairs of records, and the second is normally

calculated from the frequency of the particular value of an identifier in the files themselves.

Examples are given of the use of such data as derived from the present study after its

completion. These have to do with (a) simple disagreement weights (Table 10), (b) weights

for a spectrum of outcome values ranging from complete agreement through various

degrees of partial agreement-disagreement to complete disagreement (Table 11), and (c)

weights for the occurrence in matched pairs of records, of identifier combinations which

are correlated but cannot be regarded as either agreeing or disagreeing (Table 12). The

latter two tables represent relatively fine groupings of the full range of possible outcome

values. Such breakdowns are designed to avoid unnecessary pooling of outcomes with high

and with low discriminating power, which would degrade the usefulness of the identifiers

(rather as the usefulness of panned gold dust is degraded by re-mixing it with the sand).

The setting of the ‘zero point’ on the weight scale has proved more complicated than

originally expected. This is the point at which the total weight for a matched pair of records

indicates 50:50 odds in favour of, or against, a correct linkage. The total weight as initially

envisaged did not take into account either the increased likelihood of chance similarities

where the file being searched is particularly large, or the degree to which age and sex may \

influence the likelihood that an individual will be represented in that tile where it is a death

file. The hope was that the zero point could be adequately pinpointed by manual examination of borderline linkages. However, the present extensive work of this sort leaves one

less confident about use of the manual approach alone, for this purpose. Substantial biases

are now suspected, from a human tendency to reject out-of-hand those troublesome pairs

which lack sufficient identifiers on which to base a judgement but might non-the-less be

correctly matched. For a total of the calculated weights to represent ‘absolute odds’, as

distinct from just ‘relative odds’, components are required which will take into account (a)

the size of the death file over a given period, (b) the likelihood of an individual dying in

that period, and (c) the likelihood of his being alive at the start of the period so as to be

‘available’ to die within the period. This approach is now being developed as a result of

the need indicated by the present manual studies. And ways of estimating, and perhaps

correcting for, any biases in the total weights arising out of this approach are being

considered.

117
outcomes, and where they have differed. But judgments concerning which is the correct

outcome when the approaches disagree are necessarily subjective, except where an actual

oversight/error of some kind can be detected, or where additional identifying information

can be obtained and used. The comparisons between the outcomes of the computer vs the

manual searches that will be considered relate to the sample of 1871 Eldorado worker

records in which the surnames began with A or B.

The degree of assurance of a correct linkage with a death record, or of a non-linkage,

was variable both for the computer and for the manual searches. To a large extent, where

the computer was ‘very sure’ that a correct decision had been made, so was the manual

searcher, but the correlation is a fairly loose one when all degrees of assurance are considered (Table 6).

The conclusions one may draw from this comparison are best described in terms of a

possible arbitrary threshold for ‘acceptance’ as a linkage, or ‘rejection’ as a non-linkage.

Suppose, for example, that this threshold isset so that computer weights of zero and above,

and manual assurances of B and above, are taken to indicate acceptable linkages. Then

for 94?Z0of worker records the outcomes from the two types of search both indicate either

an appropriate linkage (192 cases or 10.3% of the records) or a non-linkage (1574 cases

or 84.l~o of the records).

For about 6% of the worker records the computer and the manual searcher were in

disagreement as to whether an appropriate matching death record had been found (Table

6). If the results of the human searching are believed the computer approach resulted in

80 false positives and 25 false negatives (i.e. 4.3% and 1.3$Z0,respectively, of the 1871

worker records, or, when based on the 219 manual linkages, 37 ~0 and 11 x of the potentially linkable records). If the results of the computer searching are believed, the manual

approach is similarly inaccurate and results in 25 false positives and 80 false negatives (out

of 1871 worker records, or, when based on the 272 computer linkages, 970 and 29910of the

potentially linkable pairs). This comparison serves chiefly to suggest that both approaches

may involve considerable inaccuracy where the personal identification lacks discriminating power. And, of course, such comparisons cannot indicate how many relevant death

records were missed by both kinds of searching.

There is evidence, however, that the computer searching results in fewer false negatives

than does the manual searching. Thus, in Table 6 there are only seven cases of ‘acceptable’

manual matches of which the computer was apparently unaware, as against 69 cases of

‘acceptable’ computer matches of which the manual searchers were seemingly unaware.

Evidence that the computer is likewise less prone to the production of false positive

linkages, may be obtained from those instances in which both approaches appeared to be

successful but each identified a different death record as the appropriate one, For all 26

examples of disagreement of this kind, the source documents (E.N. L. work records and

death certificates) were re-examined for additional information with which to resolve

alternative choice ‘matches’ (Table 7). The resulting ‘final’ judgments are not infallible,

but they do show that the computer is more reliable than the manual searchers where the

two find different death records. The computer ‘accepted’ thirteen matches for the 26 ENL

records, later judged to consist of six ‘right’, two ‘wrong’, and five ‘doubtful’. The manual

searchers ‘accepted’ just eight matches, later judged to consist of one ‘right’, five ‘wrong”,

and two ‘doubtful’.

From the above evidence, the computer searches appear to result in substantially fewer

false positive and false negative outcomes than do the manual searches. Appropriate

empirical tests and procedural adjustments will further improve the quality of machine

linkage. Some of the proposed procedural changes will be described in what follows.

DISCRIMINATING POWER AS A LIMITING FACTOR

Since record linkage in the absence of unique identifier numbers depends upon multiple

identifiers, it follows that discrimination decreases rapidly as personal identifying inform118
Table 9. EtTectsof differences in the availabilityy of identifying particulars on the estimated proportions of false

positives and false negatives (matched pairs with computer weights of zero and above being ‘accepted’ as

‘linked)

Available identifiers I

Number of

I

Calculated false positives I Calculated false negatives

matched pairs No. iv o of accepted No. I % of acce~ted

Year of birth, but not month and day

Accepted 291 47.8 16.4

Rejected 805 — — 54.2 18.6

Full birth date

Accepted 1684 122.9 7.3

Rejected 2092 136.6 8.1

Birth date and place, plus two given names

Accepted 166 4.8 2.9

Rejected 89 5.2 3.1

Note: (1)

(2)

(3)

Columns headed ‘No.’ contain estimated numbers. They will therefore not be integers. For the

method of estimation, see Section on ‘Estimating the false positive and false negative computer

matches’.

For the purpose of this table an identifier is said to be ‘available’ as a basis for linkage when it is

present cm both a worker record and the death record to which it is matched, regardless of whether

it agrees or disagrees.

Where not specifically mentioned, an identifier may be either available or unavailable,

ation diminishes in abundance. In other words, the number of false negatives increases

disproportionately as identifying information decreases.

Some indication of the quantitative importance of different amounts of identifying

information may be gained from a few comparisons. For example, where information on

birth year was present on the ENL records, some 16% were successful in finding a matching

death record. But when it was absent, the success rate was only 0.5% (Table 8).

A better comparison involves three different levels of discriminating power in records

that have the birth year (Table 9). ‘Full identifying information’ results in an estimated 3’%0

of false positives and 3% of false negatives. Records reduced to birth date without place,

etc., double both error rates to 7 and 8% each. Records with year of birth only again

double the error rates to 16 and 1996. The comparisons are not precise, because different

data sets are involved. But, in the absence of more elaborate and expensive tests, it would

be unwise to disregard the practical guidance from such internally consistent evidence, of the

need for multiple identifiers.

A redundancy of identifiers maybe needed for a rather different reason. Strictly speakTable 10. Frequency of discrepancies in personal identifying information, and the ‘weighting factors’ derived

from these frequencies (based on 269 matched pairs of worker and death records, with weights of zero and up)

Frequency in Weight for

Kindofidentifier Discrepant Totallinkedpairs linkedpairs discrepancy

(log,freq.)

Surname spelling ]~ 269 1122 –4.5

First initial 27 269 1/10 –3.3

Fkst given name 74 268 1/3.6 –1.8

Second initial 19 119 1/6 –2.6

Second given name 18 65 1/3.6 –1.8

Birth province or country 7 114 1/16 –4.0

Parental initials 18 73 1/4 –2.0

Parental birth provincel 11 25 1~2.3 –1.2

count ry

Note: For simplicity. the frequency of the discrepancy in unlinked pairs istaken to be virtually unity. Thus. log~

of the frequency in linked pairs approximates closely. Iogz of the ratio of the frequencies in linked/

unlinked pairs.

119
Table 11. Calculation of ‘weighting factors’ for birthdate discrepancies

Number in Expected in Ratio (inferred Weighting factor

Degree of discrepancy linked pairs unlinked pairs odds in favour of (log2 of the

linkage) ratio)

Year of birth (268 pairs)

o 170 2 85:1 +6,4

1 45 4 11:1 +3.5

2-3 38 8 5:1 +2,3

4-9 8 24 1:3 –1.6

10+ 7 230 1:33 –5.0

Month of birth (243 pairs)

o 219 20 11:1 +3.5

1 10 37 1:3.7 –1.9

2-3 8 64 1:8 -3.0

4-9 5 112

} }

–4.3

10-11 1 10 1:20

Day ofbirth (241 pairs)

o 189 8 24:1 +4.6

1 11 16 1:1.5 -0.6

2-3 10 29 1:2.9 –1.6

49 17 76 1:4.5 –2.2

10+ 14 112 1:8 -3.0

Note: The numbers expected in unlinked pairs are calculated as follows:

For exact agreements the expectation is taken to be nln2 times the number of matched pairs, where

n is the number of different values of the identifier.

For discrepancies of degree d, the expectation is taken to be 2(n–d)/n2 times the number of matched

pairs.

These equations represent approximations based on the assumption that the different values are equal

in frequency. Where they are not equal, a more detailed calculation is required and this has been carried

out in the case of year of birth.

ing, total weights reflect only the likelihood or unlikelihood that the observed similarity

of identifying information on pairs of records has arisen other than by chance. But the

ruling out of chance does not necessarily establish that the same person is involved:

Family members may be named after each other, and twins maybe confused because of a

common birthplace, birth date, and perhaps because of similar given names.

There are fashions in given names with small communities, and surnames repeat in

localized ethnic groups and communities.

In short, similar or identical identifiers occasionally refer to attributes associated with

particular groups of people, but not uniquely with any individual person.

The above kinds of problems can be minimized by abundant information, and to some

extent by manual resolution using additional identifiers.

IMPROVING THE WEIGHTING PROCEDURES

The present manuaUmachine matching study has revealed needs for improvements in

the weighting procedures used by the machine, and has provided some of the data required

for the purpose. Such improvements would have to do in particular with (a) putting to use

more of the potential discriminating power that could otherwise remain latent in the

available identifiers, and (b) finding a better way of setting the ‘zero-point’ on the weighting

scale.

The data used for calculating the weighting factors consists of the frequencies of various

identifier comparison outcomes (agreements, disagreements, etc. ) in pairs of records

judged to be correctly linked, together with the corresponding frequencies for unsinkable

pairs. Quite simply, the ratio between these two frequencies indicates the degree of

assurance associated with a particular comparison outcome. (Outcomes that are more

fashionable in linked pairs argue for linkage, and those that are more fashionable in

120
.

Table 13. Discrepancies of given names, by kind of discrepancies (based on 92 discrepancies of the first and

second names combined, among 333 given names compared in record pairs with weights of zero and above)

Kind of discrepancy I Examples

All discrepancies (92 cases)

Position only, same spelling

Different initial and name

(John - William John) 24

(John - Fred) 16

Different spelling, same initial (Louie - Louis) 52

Spelling discrepancies (52 cases)

Vowel change only (Ralph - Rolph) 15

Shortened only (Fred - Frederick) 11

Nicknames,notjustshortened (John - Jack) 5

Phonetic similarities (Ouide - Ovide) 4

Anglicizations (Kenneth - Kazimie) 3

Double consonants (Riser - Risser) 2

Other (Bjom - Bjorvi) 12

Note: Of 46 disagreements of first or second initials. 11were associated with simple reversals of the sequence

on one of a matched pair of records as compared with the other (inversions). and 22 were due to one of

the initials being transposed from first to second place (frame shifts).

Various other possible improvements in the weighting system, which will not be

described here, are under development as a result of the present manual comparisons.

Some of these have to do with (a) the handling of given name similarities where precise

agreement is lacking (see examples in Table 13), (b) comparisons involving inverted

sequences (e.g. of initials, and of birth month and day), and (c) practical means for making

better use of the discriminating powers of very rare surnames, without recourse to excessively long look-up tables of weights.

IMPLICATIONS FOR ALL RETROSPECTIVE AND PROSPECTIVE

STUDIES

Safety standards

(1) It is in everyone’s interests to know where problems of safety are greatest and where

they are least.

(2) Neither workers, management nor society in general benefit where undue emphasis is

directed to non-problems, while real problems are neglected because they remain undetected.

(3) The limited public funds available earmarked for administration and enforcement of

safety standards ought to be used so that attention to low-risk situations never results in

the neglect of higher risks.

Fears about possible loss of privacy have tended recently to further reduce the specificity

of personal identification on personnel records, notably on application forms for employment. At the same time, the public has increasingly demanded investigations of the

delayed risks in various work situations, and has emphasized the right of the worker to

know the risks.

To detect and measure delayed personal harm of almost any sort, and resulting from

almost any kind of ‘exposure’, individual people require to be identified in a reasonably

unambiguous fashion. This istrue whether one follows exposed individuals forward to look

for harm, or sick individuals backward in time to look for exposures. With both

approaches, the most serious stumbling block is often a lack of sufficient specificity and

redundancy in the personal identifiers (names, birth dates and such) by which people are

known and represented on their various records, including their work records.

SUMMARY

Computerized searching of a national death file has been tested and compared for

accuracy with the corresponding manual searches. The test formed a part of an

121

.
epidemiological follow-up study of some 16,000 former Eldorado employees, in which

employment records are being used to initiate the searches for related death registrations

contained in the Canadian Mortality Data Base at Statistics Canada. This facility includes

the coded cause for all deaths back to 1950. The computer searching was guided by a

generalized record linkage program, based on a probabilistic approach; the program was

developed by Statistics Canada and the Epidemiology Unit of the National Cancer Institute of Canada. The corresponding manual searches used microfiche printouts from the

Mortality Data Base tapes.

The results from the test showed the machine to be more accurate than the manual

searchers. Not only was it more successful in extracting the relevant deaths, but it was also

much less likely to yield false linkages with death records not relating to members of the

study population. For both approaches, however, accuracy was strongly dependent on the

amount of personal identifying information available on the records being linked.

Ackrrowledgments-The authors wish to thank Mr. J. Silins, Mrs. C. Poliquin and Mrs. M. Warner for their

invaluable assistance. The advice and criticism of many other colleagues, particularly Mr. S. E. Frost and Mr.

R. G. Dakers, is gratefully acknowledged.

REFERENCES

1. J. D, Abbatt, The Eldorado Epidemiology Project; Health Follow-up of Eldorado Uranium Workers.

Eldorado Nuclear Limited, Ottawa, Ont. (1980),(Available on request by writing to Eldorado Nuclear Limited,

255 Albert Street, Suite 400, Ottaw% Ont. K 1P 6A9.)

2. M, E. Smith and H, B. Newcombe, Automated follow-up facilities in Canada for monitoring delayed health

effects, Am. J, pub. Hlrh 70, 1261–1268 (1980).

3. G. W. Beebe, Record linkagesystems-CanadavstheUnitedStates,Am. J. pub. Hlth 70,1246-1247 (1980).

4. G. R. Howe and J. Lindsay, A generalized iterative record linkage computer system for use in medical follow-up

studies, Compur. biorned.Res. 14, 327–340 (1981).

5. H. B. Newcombe, J. M. Kennedy, S.J. Axford and A. P. James, Automatic linkage of vital record$ Science 130,

954-959 (1959).

6. H. B. Newcomrke and J. M. Kennedy, Record linkage: making maximum use of the discriminating power of

identifying information, Comnrurrs Ass. Compur. Mach. 5, 363–566 (1962).

7. H. B, Newcombe, Record linking: the design of efficient systems for linking records into individual and

family histories, Am. J. hum. Genef. 19,335-359 (1967),

8, M. E. Smith and H. B. Newcombe, Methods for computer linkage of hospital admission–separation records

into cumulative health histories, ?deth, lnf. filed. 14, 118–125 (1975).

9. E, D. Acheson, Record Linkage in Medicine, E. and S. Livingstone, Edinburgh. (1968).

10. J. A. Baldwin, Linked medical information systems, Proc. R. Sot. 184,403-420 (1973).

11. P. Beauchamp, H, Charbonneau and B. Desjardins, La reconstitution automatique des famines; un fait acquis,

clansla mesure des phenomenes demographiques, Homage a Louis Henry, Popul 1977, numero sp6cial (mars

1977).

12. M. E. Smith, Record linkage of hospital admission–separation records, Chalk River Nuclear Laboratories,

Chalk River, Ont. Publication No. AECLA4507(Sept. 1973).

13. M. E. Smith and H, B, Newcombe, Accuracies of computer versus manual linkages of routine health record$

Meth. Inf. Med. 18, 89-97 (1979).

14. G. Wagner and H. B. Newcombe,Record linkage: Its methodology and application in data processing (a

bibliography), Merh, ln~ Med. 9, 121-138 (1970).

About the Author—HowARD B. NEWCOMBE,B. SC. (Acadia University 1935), Ph. D., D.SC.,

F. R.S.C. Born 1914. Dr. Newcombe was a Research Scholar at the John Innes Horticultural

Institute in 1939and after wartime service as a Lieutenant, R. N.V. R., 1941–46, from 1947–79 was

Head of the Biology Branch and later Population Research Branch, Atomic Energy of Canada

Limited, Chalk River, Ontario. He was Visiting Professor of Genetics to the University of Indiana

in 1963, Member of the International Commission on Radiological Protection and is the author of

numerous scientific papers (mutations in microorganisms; effects of ionizing radiations; methods

of study of human population genetics).

Dr. Newcombe is a Past President of the American Society of Human Genetics and the Genetics

Society of Canada. At the present time he is Consultant to Eldorado Nuclear Limited and Statistics

Canada.

122
About the AUthOr-MARTHA SMITHreceived her B. SC.from the University of Manitoba and her

M. SC.in Computing and Information Science from Queen’s Universit y in 1973. She was employed

for several years in the Biology and Health Physics Division at Atomic Energy of Canada Limited,

working with Dr. H. B. Newcombe on the British Columbia Record Linkage Study. Thk work

involved developing new computer record linkage techniques for studying the effects of radiation

on human populations. In 1978 she joined Statistics Canada and is currently Head of the Occupational and Environmental Health Research Unit. She is involved in planning and setting up some

of the national tiles and facilities required to do long-term medical follow-up studies.

About the Author—GEoFFREYR. HOWE, B. SC. (universh y college, London 1%5), Ph.D. 1969.

Born 1942. Dr. Howe was initially a Research Chemist with I,C.I. in England. He has subsequently

been Research Fellow at Brock University and is now Senior Biostatistician to the N.C.I.C.

Epidemiology Unit, University of Toronto. In addition, Dr. Howe is Professor in the Department

of Preventive Medicine and Biostatistics at the University of Toronto and a Faculty Member of the

School of Graduate Studies, University of Toronto. He is the author of numerous scientific papers,

mostly on epidemiology and computerized record linkage.

He is a Fellow of the Chemical Society of London, Consultant to Eldorado Nuclear Limited and

Atomic Energy of Canada Limited, Member of the American Statistical Association, Biometric

Society and the Scoiety for Epidemiologic Research.

About the Author—J.4NE MINGAYreceived her Bachelor of Journalkm degree in 1977 from Carleton University, having received practical experience in journalism. She subsequently worked on

contract on a number of data collection and editing projects including medically oriented studies.

After one of these projects organizing an historical research project for Eldorado Nuclear Limited,

she became an Occupational Health Researcher on the E. N.L. Epidemiology Project, She is now

on the staff of Maciean Hunter.

About thehthor-ARLENE STRUGNELLgraduated from business college in Montreal, Quebec

in 1965 and worked as Secretary in the Department of Meteorology, McGill University until 1971.

After working for a number of years in Toronto and Belleville, Ontario, she subsequently moved

toOttawaandispresentlyResearchAssistantwiththeEpidemiologyProjectatEldoradoNuclear

Limited.

About the Author—JoHN D. ABBAIT, B.SC.. MB., Ch.B. (University of Edinburgh 1945),

D. M.R., C.C, B.O.M. Born 1923. After wartime service with R. A. F.V. R. and hospital appointments in Edinburgh was Member of the U.K. M.R. C. External Scientific Staff at Hammersmith

Hospital and Consultant Radiotherapist. After subsequent service as a Canadian Federal Civil

Servant, he retired as Director General of Laboratory Centre for Disease Control, D, N.H.&W.

and is now Medical Adviser to Eldorado Nuclear Limited, Ottawa.

Author of numerous scientific papers on the early applications of nuclear medicine and the

therapeutic effects of radiation in man and animals, followed by epidemiological studies on human

radiation effects.

123
Section 11:

Overview of Applications

and Introduction

to Theory
TUTORIAL ON THE FELLEGI-SUNTER MODEL FOR RECORD LINKAGE

Ivan P. Fellegi, Statistics Canada

EDITORS’ NOTE the exhibits and requested copies.

The exhibits are presented here, withThe following exhibits, numbered 1

to 22, were used at the Workshop on

Exact Matching Methodologies (in the

form of transparencies) as the basis

for a presentation of the essential

features and some of the consequences

of the Fellegi-Sunter model and theory

for record linkage. Many Workshop

participants commented favorably on

out additional commentary, for the

benefit of those who would like to

have a convenient summary of the main

points. The following chart shows the

relationship between groups of exhibits

and s~ecific sections

“A Theory for Record

can be found on pages

volume.

of the article;

Linkage,” which

51-78 of this

Figure 1.--Exhibits for Fellegi-Sunter Article

Exhibit Numbers ToPiC Section of Article Pages

1 to 6, 7a Basic model and theory 2 52-57

7b, 8 to 10 Method of constructing 2.1 54-57

an optimum linkage

rule; consequences

11 to 14 Assumptions used in 3.2 57-59

estimating weights

15to17 Calculation of weights, 3.3.1 60-62

Method I

18 Calculation of weights, 3.3.2 62-63

Method II

19, 20 Blocking 3.4 64-65

21 Choice of comparison 3.6 66-67

space

22 Calculation of threshold 3.7 67-68

values

127
Two sets

Vector of

of units: A = {a}, B = {b}

characteristics ~(a), /3(b)associated w

LA = {~(a);

LA x LB

M

u

ad)

M-N

{[4,

{[@05

a

a

—

#

~(b); b EA

b,

bY

b&B}

E A,

th unit

x LB unmanageable.

\

Code

Y[@)l

of

/3(b)]

Exhibit 2

comparing /3(b):Y(a,

n

Y(% w=(Y’, y% ● ● ● J Yk)(%

Exar lples: Yi = O if

1 if

2 if

sex is same

sex is different

b)

b)

sex is missing on either record 

128
Exhibit 3

Yj = O if name is same and is Brown

1 if name is same and is Smith

2 if name is same and is Jones

3 if name is same and not Brown, Smith, Jones

4 if name is different

5 if name is missing on either record

r = {y(a, b)}: comparison space.

Exhibit 4

Linkage rule: decision regarding match status of

(a, b) based on Y(a, b)

d(y) = Al: link (inference is “match”)

d(y) = A2: possible link (“don’t know”)

d(y) = A3: non-link (inference is “unmatched”)

129
Exhibit 5

y(a, b) = y. k a subset of LA x LB

M(y) M

u(y) u

Y

I M(y)l

In(y) = P{Y(a, b)l(a, b)& M}= \,M ,’

Iu(y)ll

u(y) = P{y(a, b)l(a, b)c U}= ‘ “

I

Exhibit 6

A linkage rule partitiorls LA x LB:

*

Al

For any y E Al all record pairs

p = P(A1 I U) = ~ U(y)

ycA1

M

u

in U(y)are linked in error.

proportion of linked

record pairs in U

A= P(A31M)= ~ m(y) proportion of unlinked

yLA3 record pairs in M

130
Exhibit 7

a) Definition: Consider all linkage rules R on r with

error levels PO,Ao. Then RI is optimal

if P(A2 I Rl) s P(A2 I R) for all R.

b) Heuristic: arrange LA x LB so that m(y) monotone

decreases and u(y)increases Choose Al, A3

to correspond to desired p, 1. Then this

linkage rule is optimal.

w \

Exhibit 8

Optimal rule: order ~ by decreasing values of

m(Y)/u(Y).

Al if TP S m(Y)/u(Y)

A 2 if T~ < m(~)/u(Y) < TK

A 3 if m(y)/u(y) S T~

Tl{ chosen so that p = po, Ti so that A = ~.

Likelihood ratio tests: Al at level p, A3 at level A.

Uniformly most powerful.

Tepping’s test (JASA, 1968) functionally equivalent.

/

131
Exhibit 9

HIGH -’ l~~(Y)/u(Y)-“ LOW

Exhibit 10

1. Trade-off between decreasing po, A. ~ AZ

2. AZ can be eliminated if Tp = TA

3. Typically ~. < < A. should hold. If N is the

number of matched record pairs, (NANB – N)

the number of unmatched record pairs, then

condition for number of linked record pairs

[o be N is

N(1 – l.) + (NANB – N)pO = N.

True if PO=

N

ao

NANB – N

4. Randomized decision may be needed to achieve

/( = l-~o,k = A. exactly.

4

132
Exhibit I I

Estimating m/u

If y=(yl, y2, . . ..yq

yk has nk values

then y has nl. nz . . . nK values.

Simplifying assumption:

m(y) = m(yl ). m(y2) . . . m(yK)

u(y) = U(yq. U(yq . . . U(yq

Components of y are conditionally independent w.r. to m and u.

\,

I

1

‘~—.. --- .— .—

.)

Exhibit 12

Matched records: Without errors, all Y

k

should

shou “agreement”. Hence independence+ errors In

different ldent. variables of a and b are independent.

Unmatched records: accidental agreement on one

variable (e.g. name) 1s independent of accidental

agreement on another (e.g. address).

Estlmands: mom, . ..m(y K)-- nl+n2+.. o + nK

(also for u).

133
Exhibit 13

Need care in defining y :

agreement on female given name

agreement on male given name

disagreement on given name

[ Qivenname mlssln90nelther record

yz.

(

agreement on sex

disagreement on sex

sex missing on either record

1 2

Accidental agreement on Y +agreement on Y .

Independence might hold if first two codes of Y

1

combined.

\ /“

/

Exhibit94

Prefer to use log (m/u) - monotone incr. function of

(m/u).

log (m/u) - w’ + U2 + ,.. + w

k where

Wk= log [mu]

We have

wk%o if m(Yk) > u(Yk)

(lntultlvely appealing).

Similar to Newcombe-KennedY (Communications of ACM,

1962).

I

134
Exhibit 15

METHOD 1 FOR WEIGHT CALCULATION (ILLUSTRATION)

Weights for “name” component.

Let proportions of different names .ln A, B and AnB be

pA(l), p@), P(1) (x p-l). For simpllclty:

pA(l) = pA(i) = p(i)

eA. eB: prob. of misreporting name in A, B

respectively

P observable, e separately to be estimated.

Exhibit16

w (agreement on .jthname)= log (l/pj)

– Positive

– I.e. large positive weight for agreement on rare

charoctcrlstlc

w(agreement) e log (l/p) where p = ~ PJ2

– Large for uniformly well dlscrlmlnating variable

– p decreases fast lf common.outcomes are separated.

135
/ —Y

Exhibit 17

e~+eB

w (disagreement) = log —

l-p

- Typically negative

- The smaller the error, the lar9er the np9atjVp

weight

l.c. disagreement on well reported variable

-large negative weight

- E.g.: sex. Don’t restrict linkage variables

to high discrimination.

w (name missing on either file) = O

neutral contribution.

/

Exhibit 18. SECOND METHOD (ILLUSTRATION)

flsc(mleonly three components; each coded to two

‘-tales: “agreement”, “disagreement.”.

[’nr]ditional probabilities of “agreement” are mh? uh.

‘nNouh = N ‘h + ‘NANB- ‘) “h h = 1,2,3

wtlrre Uh: proportion of reco~d pairs with “agreement”

in h-lh component.

“h’ ‘A’ ‘B observable; N, mhj Uh unknown.

Atlnve 3 equations can be supplemented by other 4;

fill involve observable quantitie~ + 7 unknown

variables.

‘;olvable; generalizable; heavy dependence on

i[lfll’f]r’fldence.

\

136
Exhibit 19

Blocking

Objective: reduce number of comparisons.

Implicit assumption: comparisons not made are non-linked (A3).

Exhibit 20. IDEALBLOCKING VARIABLE

1. If a variable 1s such that disagreement results

in very large negative weight -- corresponding

eA, ea very small. Does not increase ~.

2. High dlscrlmatlon results in maximum file

blocklng (comparisons restricted to records

which agree on the blocklng variable).

Frequent compromise: coded name where code 1s

designed to reduce Impact of misspellings.

Additional use of any well reported variable,

even of low discrlmlnatlon (e.g. sex), is net bonus.

137
Exhibit =1. CHOICE m COMI)ARISON SPACE

1. How many separate valljes to recognize for

agreement?

Trade-off between complexity and reduction

in Zpjz

2. How many of the variables common to both files

should we use?

Generally: the more the better.

3. w is positive for agrrement, negative for

disagreement almost certainly.

4. If (2A + e~ <+ < l-p, then each additional

variable increases total weight for matched

records, decrcascs tol_al weight for unmatched

records -- both with probability >~z.

Exhibit 22. EsTIMATING nimstiolDS

1.

k

Select at random one value of each Y . Higher

probabllltles for high Iw!;

2. Cnmblne into Y; compute corresponding

W(:lght(w);

J. Repeat n times;

4. Arrange Y by decreasing w;

5“ sctTp , Ta as in r, but counting each Y

with inverse of probability of selection.

138
WHY ARE EPIDEMIOLOGISTS INTERESTED IN MATCHING ALGORITHMS?

Gilbert W. Beebe, National Cancer Institute

INTRODUCTION

Both public and scientific concerns about

hazards to health determine the agenda of epidemiology. The more we learn about health hazards

the more there is to be learned, it seems, and the

more the public comes to recognize health hazards

the more it demands risk identification, risk

estimates, and control measures. In recent

decades new chemicals have been entering the environment at a very rapid pace. Under the Toxic

Substances Control Act [I], passed in 1976, the

Environmental Protection Agency (EPA) has been

receiving over 1,000 pre-manufacture notices annually. There is now a list of about 30 chemicals

and industrial processes recognized by the

International Agency for Research on Cancer (IARC)

as carcinogens for man, and another 61 thought to

be probable carcinogens [z]. Another 103 are

known to be carcinogenic for experimental animals,

but IARC has reviewed only somewhat more than 600

chemicals and industrial processes on which there

is adequate published information. I think we

must assume that the carcinogens for man are far

from identified and that the pace of industrial

change exceeds our capacity for refined etiologic

studies. We need inexpensive surveillance systems

that will tell us where to look for significant

hazards to health, and we need alert medical practitioners and industrial physicians to spot the

unusual and unexpected [3].

The public is increasingly concerned with risks

of a size that would have passed unnoticed in

earlier years, risks associated with ionizing

radiation, foods, drugs, toxic wastes, non-ionizing radiation, and the quality of our air and

water. The MMR vaccine against measles, mumps,

and rubella may cause brain damage in only one in

a million vaccinees, but this risk is now sufficient to discourage manufacture of the vaccine

because of the burden of litigation [4]. To identify small risks requires large samples, which in

some instances may not be possible.

Ours has been aptly called an information

society. Our capacity for recording, storing,

transmitting, and manipulating information has

been growing by leaps and bounds under the impetus

of the computer revolution. I commend to you the

recent (26 April 1985) computer issue of Science.

The epidemiologist contributes to our understanding by bringing together for examination facts

about individuals derived from different contexts.

Increasingly these facts, or leads to them, are to

be found in computer files. And since his unit of

study is generally the individual, the epidemiologist wants to link files, which means matching,

and to transfer data from files other than his

own. And when he matches files he wants to be

sure he is identifying the same person in each

file.

In the U.S. we are experiencing a budgetary

crunch. Funds for research are being reduced and

staffs are being cut. The use of administrative

records in research through record linkage, which

means computer matching, is often the most economical way of obtaining information. For reasons

of economy alone we should be looking more to

record linkage as an adjunct to the more expensive

procedures that we may have been following.

THE SPECTRUM OF EPIDEMIOLOGIC INTERESTS

The following illustrations are drawn from the

field of chronic disease epidemiology with which I

am more familiar, but record-matching routines are

also of interest to epidemiologists working in the

infectious diseases.

Etiology. -- (1) The cause of multiple sclerosis

‘an enigma but epidemiologists are developing a great deal of information on differentials

in risk; and (2) we may be getting closer to an

understanding of the role of viruses in human

cancer. There are animal cancers of known viral

etiology and several human cancers are now being

linked to viruses.

Risk Estimation. -- (1) There is a widespread desire to know the carcinogenic risk of exposure to

low doses of ionizing radiation; and (z) we are

interested in the hazards of certain prescription

drugs such as oral contraceptives.

Value of Early Diagnosis. -- A prime example is

breast cancer. At issue is the value of a screening regimen that includes mammography.

Prevention of Disease. -- (1) Epidemiologists are

involved in intervention trials to prevent coronary heart disease, as illustrated by the Multiple

Risk Factor Intervention Trial (MRFIT) program of

the National Heart, Lung and Blood Institute; and

(2) numerous intervention trials are also being

conducted against cancer; for example, the

National Cancer Institute (NCI) has trials in

righ-risk areas of China where micronutrients,

principally vitamins, beta-carotene, and minerals,

are being prescribed on a controlled basis.

Treatment. -- Breast cancer is a recent example.

?lt iSSUe are the extent of the surgery and the

value of adjuvant drugs and radiation.

Natural History. -- Acquired Inwnune Deficiency

me, or AI0S, is a current example.

RECORD LINKAGE

Whether epidemiologists are working retrospectively or prospectively, in case-control or

cohort mode, or are testing hypotheses or generating new ones, they are typically trying to link

together, within the lives of individuals, events

that are displaced in time and independently recorded. This underlies our dependence on record

linkage; i.e., on matching and data-transfer.

Matching requires rules of agreement, an

algorithm, whether it be done manually or electronically.

Epidemiologists create their files from their

own observations and from such records as are

139
available to them. Often they must reach out to

administrative record files of large organizations

such as medical care providers, insurers, state

government agencies, and even the Federal

agencies, for some of the facts they need to

complete the history of the individual subject.

It may even be necessary, for example, to go to

the Internal Revenue Service (IRS) to obtain

addresses needed to locate subjects for examination or interview.

Agencies with large files tailor their matching

algorithms to the identifying information they

characteristically deal with and understand. One

cannot, for example, go to IRS for an address or

to the Social Security Administration (SSA) for a

mortality check, without a social security account

number. The Health Care Finance Administration

(HCFA), on the other hand, can search its files

for addresses on the basis of a name and date of

birth, after first passing the incoming file

through a nominal index file that provides the

SSNS essential for the address search of its Medicare file. The Veterans Administration (VA) has a

very flexible approach to matching with algorithms

that will work on almost any variable or combination of variables the requestor may provide.

Epidemiologists often do not have any number other

than the date of birth, and lack of a SSN will

often keep Federal agency files beyond their

reach.

Matching algorithms must depend on the identifiers available but they also reflect the

scientific imagination and experience of those

responsible for the progranwning. Newcombe has

stressed the importance of experience in the

manual matching of representative records as preparation for designing programs for matching by

computer. He also emphasizes the value of redundancy in identifying variables when matching is

involved. It was his 1959 paper, more than any

other single contribution, I believe, that paved

the way for technically adequate machine matching

in the absence of a central ID number like the SSN

[5]. With a number like the SSN it is possible to

insist on an exact match. Even though the SSN is

not precisely a unique number and lacks a check

digit, it is nevertheless a very good number in

most situations requiring linkage. If you transpose digits of your SSN in your tax return you

will soon receive a query from the IRS. Names may

be abbreviated to 4-6 letters of the surname if

main reliance is placed on the SSN, but in other

contexts the surname may be coded phonetically in

New York State Identification and Intelligence

System (NYSIIS) or Soundex fashion.

The investigator wants the benefit of a matching algorithm that minimizes both false positive

and false negative matches but he may have no idea

of the false negative rate in the absence of

formal tests such as are being made on the

National Oeath Index of the National Center for

Health Statistics (NCHS) [6]. If the false positives are frequent, and in some applications NCHS

algorithms have returned two false positives for

each true positive match, the consumer may be hard

put to evaluate the output without a weighting

scheme such as Newcombe has devised.

Record linkage is now often being required on

such large files that matching must be performed

electronically or not at all. One cannot think of

the IRS file of individual taxpayers being

searched for addresses in any fashion except electronically. 1 am told the file contains 155

million records and takes three weeks to run. And

if you want to locate a large roster of subjects

under age 65 and 20-40 years after some occupational exposure, alternative sources of addresses

would probably be expensive and inefficient.

THE BACKGROUND OF MY OWN INTEREST

From the medical experience of World War 11

came the suggestion, by Dr. Michael E. DeBakey,

the heart surgeon, that a medical research program

be established to follow up the injuries and

diseases of the war [7]. We both served as staff

for a corrunitteeof the National Research Council

(NRC) that looked into his idea and I wound up in

charge of the statistical work of the group known

today as the Medical Follow-up Agency of the NRC.

Knowing that work with records would be a large

part of the effort, one of the first persons I

hired was Nona-Murray Lucke. She had been working

with Dr. Halbert Dunn, then director of the Vital

Statistics Division of the Bureau of the Census

and originator of the term “record linkage,” on

his scheme for matching birth and death records at

the state level [8]. Although there were Army

punchcard indices to the entire medical experience

of the war, the cards contained Army serial numbers but not names. A manual look-up was required

to obtain the corresponding names that we could

then match to the nominal VA Master Index in order

to find VA claim numbers and to locate the offices

having custody of the hard-copy VA files. All the

linkage was manual, but usually there was enough

detail beyond name and Army serial number to rule

out misidentification. Identification was a problem in only about 2-4 per cent of the cases and

records were unavailable in less than one percent.

Starting in 1972 we benefitted from automation of

the VA Master Index, now the Beneficiary Identification and Records Locator Subsystem (BIRLS) file,

as well as from the automated record

systems for hospital discharges and for compensation and pension status. Tape-to-tape matching

has long been the rule. But the detailed medical

records, not only those of World War II but also

those generated today as well, are available only

in hard copy.

One of the matching efforts I personally

directed was a test of the completeness of VA

information on the mortality of war veterans,

matching known deaths obtained from NCHS against

the military files in St. Louis to determine veteran status, and then submitting the resulting file

intermingled with living veterans to the VA for a

blind search [9]. We learned that the VA had

about 95 percent of the mortality information on

WW II veterans.

At the Atomic Bomb Casualty Commission (ABCC)

in Japan, where I directed the epidemiologic and

statistical work for some years, we followed two

main samples of 55,000 and 110,000 for mortality,

using the Japanese family registration system

devised in 1871 [10]. Each Japanese citizen has a

place of family residence (his honseki), and the

city office for that place keeps a running family

record, the koseki, that shows vital events for

all the family members, no matter where in Japan

140
these events take place or where the individuals

live. The koseki tells where any death certificate is retained and for the cause of death one

must go there. To enter the system both the name

and the honseki must be known. There is very

little slippage in this system, but it is manually

operated. At ABCC mortality was checked every

three years on a rotational scheme that levelled

out the workload.

An interesting matching problem arose in the

late 1950’s when I first went to Japan. The U.S.-

Japan Joint Commission had created a file of about

14,000 records of its medical investigations in

1945 that were stored at the Armed Forces Institute of Pathology (AFIP) in Washington. To recapture the 1945 observations for the ABCC files we

obtained blow-ups of microfilm copies retained at

AFIP. For the Hiroshima portion of the sample,

names were written in the Romanized fashion, not

in the Japanese idiographs, or kanji. Location at

the time of the bomb was given in terms of a

numbered radial zone and the direction from the

hypocenter, not in terms of a postal address, and

age was usually given in the Japanese style which

is equivalent to the western style plus one year.

That is, in Japan, children are one year old at

birth. Under Seymour Jablon’s supervision this

file was later matched to the ABCC records so that

the 1945 data could be added to the ABCC files

that represented largely individuals alive in

1950. About 42 percent could be matched, largely

because of the considerable ancillary detail on

both record sources. The false negatives could

not be assessed but tests showed that the false

positives probably numbered no more than 5 percent. The matching rate in Nagasaki, for which

the records did contain the name in kanji and the

postal address, was higher, 60 percent.

At the National Institutes of Health I have

also been very much concerned with record linkage,

trying to make it easier to link some of the large

files of Federal agencies in the furtherance of

medical research [11]. We need to restore access

to the IRS address file for a broader class of

investigators than just National Institute for

Occupational Safety and Health (NIOSH) investigators who are concerned with occupational health,

and Federal investigators studying the occupational hazards of military service, these being

the privileged classes under current law. We also

need to restore the kind of freedom we had before

the Tax Reform Act of 1976, when SSA was willing

to define industrial employment cohorts and determine their mortality. With Dr. Scheuren’s help I

have been trying to learn how to strengthen the

Continuous Work History Sample of SSA so that it

might provide some national mortality data by both

industry and occupation. In addition, I’m engaged

in a research project that has involved extensive

matching to the files of the VA, IRS, and HCFA.

POSSIBLE LIMITATIONS OF COMPUTER-LINKED DATA

If the only observations available to the epidemiologist derive from the linkage of

administrative files, his study may be useful for

screening a large experience or for developing

working hypotheses, but it will probably not illuminate the meaningful aspects of exposure or

define end-points precisely. If we link files as

part Of a larger process, e.g., to obtain

addresses so that we can examine or interview

subjects, or to learn that deaths have occurred

and where we can find the death certificates, such

limitations do not apply. Even as an index to

hard-copy records, however, a largecomputer file

may prove disappointing: recently I found that a

VA diagnostic-index I must depend on contains so

much coding error for the cancer I am investigating that I will have to review the underlying

hard-copy records for validity of diagnosis.

LANDMARK STUOIES BASED ON MATCHING RECORDS

Any list of landmark studies is bound to be

very selective and the following is further

limited by my own reading and knowledge of the

field:

Framingham Heart Study [12];

Follow-up Studies of War Injuries and Diseases,

and Registry of Veteran Twin Pairs, NRC

Follow-up Agency [7];

Mancuso’s Studies of Occupational Risks Based

on Industrial Employment Rosters of

the SSA [131:

Studies of-A-66mb Survivors in Japan [10];

Court-Brown and Doll’s Study of Ankylosing

Spondylitis Patients Treated by X Ray [14];

Dorn’s Study of the Health Effects of Smoking,

WW I Veterans [15];

Oxford Record Linkage Project [16];

Selikoff’s Study of Asbestos Workers [17];

The Mayo Clinic Studies of Olmstead County,

Minnesota [18];

The Canadian Studies of Newcombe, Statistics

Canada, and the National Cancer Institute of

Canada [19]; and

The British Office of Population Surveys and

Statistics Longitudinal Study [20].

SOME OF THE LARGER COMPUTER FILES OF

INTEREST TO THE EPIDEMIOLOGIST

It would be fruitless to enumerate all the

files used by epidemiologists but generated independently of their own efforts. They cover a wide

range of classes: employment, medical care, vital

records, finance, life insurance, disability, city

directories, licensing, etc. But some examples

follow in Table 1.

Table 1. Some Large Files Used by Epidemiologists

Name of File Millions

of Records

IRs. File of Indlvldual TaxDavers 155

SSA; Master Beneficiary Recor6 35-40

(MBR File)

HCFA, Medicare Beneficiaries 30

VA, BIRLS 35

National Archives Records Agency,

“Registry” File of Military Records

in National Personnel Records Center,

St. Louis 30

NCHS, National Death Index 10

SSA, File of Oeceased 30

California Automated Mortality Linkage

System (CAMLIS) 3.6

Army WW II Hospital Diagnosis Index 12

141
SOME CURRENT EPIDEMIOLOGIC STUDIES TAPPING

LARGE COMPUTER FILES

Apart from current studies that are already

represented on our program today, some that I am

particularly familiar with include:

The Johns Hopkins Study of Nuclear Shipyard

Workers. -- he lnvestlqators are sam~llnq the

700,000 nuclear shipyard ~orker population, ‘stratifying on radiation dose, and seeking to relate

cause of death to radiation dose, demographic

characteristics, occupation, and other specific

risk factors. External linkage has been established with the VA BIRLS file, the SSA MBR file,

state death files, the NDI file of NCHS, and OPM

files. In addition there is considerable internal

file linkage to unduplicate the eight yards and to

update study files with radiation dose, job classification, and the like. About 90,000 deaths

have been ascertained.

Study of X-Ray Technologists. -- The NCI Radiation

Epidemiology Branch has initiated a study, together with NI~SH investigators and epidemiologists of

the University of Minnesota, of about 160,000 xray technologists in the U.S. whose exposure has

long been monitored by radiation badges. Investigative interest centers not only on the

carcinogenic effect of low doses of radiation, but

also on the highly fractionated character of their

exposure. Linkage will involve the SSA MBR file,

the NDI file of the NCHS, the HCFA Medicare file,

the IRS address file, and possibly other files.

Hepatitis B Virus and Primary Liver Cancer. -- In

the NCI Clinical Epidemiology Branch I am doing a

study with 6 VA hospitals and the Medical Followup Agency of the National Research Council to

learn whether the contaminated yellow fever vaccine that led to 50,000 cases of acute hepatitis

in the Army in 1942 has also produced excess liver

cancer among the vaccinees. Record linkage has

involved the Army World War 11 diagnostic index,

the National Archives “Registry” file in St.

Louis, the VA BIRLS file, the IRS address file,

and the HCFA Piedicarefile. About 60,000 men are

under study.

Study of Atomic Veterans. -- The NRC Medical

Follow-up Agency is completing a study of 50,000

“atomic veterans” exposed in weapons tests in the

Pacific and at the Nevada Test Site. Rosters of

exposed individuals assembled by the Department of

Defense were linked with the VA BIRLS file, the VA

Master Index (a microfilm file), the NDI file of

NCHS, and various military service files. This is

another low-dose study, stimulated by the earlier

finding of some excess leukemia among men exposed

to the Smoky shot.

Study of Cancer from Fallout from the Weapons

m. -- Epidemiologists at the Unlverslty of

M under a contract with the NCI, are studying

leukemia and thyroid cancer among Utah residents

downwind from the Nevada Test Site, trying to

establish whether fallout from the atmospheric

tests of the 1950’s caused excess cancer. Linkage

involves two files of the Church of Jesus Christ

of Latter-Day Saints (Mormons), one of about two

million membersregistered in church censuses, the

other of 400,000 deceased members. Matching also

extends to the state mortality files and to the

population-based cancer registry in the state of

Utah.

Health Effects of A ent Oran e and Service in

Vietnam. -- The~~e-Control have

~ay a complex investigation of the effect of

the exp&ure of’servicemen-to Agent Orange in the

Vietnam War. A sample of about 30,000 men is

under study and record linkage procedures involve

the IRS address file, the SSA MBR file, the VA

BIRLS file, and the NCHS NDI file.

OUTLOOK FOR THE FUTURE

I think we can expect the computer to play an

ever larger role in future epidemiologic studies

through record linkage. There will be no let-up

in the demand of society to know its risks and to

learn how to control them, and no let-up in the

forward march of computer science. We can expect

to find more and more data in computer files, with

less dependence on them as mere indexes to hardcopy records. And matching algorithms will

provide the key to the record linkage. But there

are obstacles and there will be missed opportunities. Files that might have been useful for

epidemiologic research may not be so because

insufficient identifying information will have

been collected. For the epidemiologist a critical

item is often the social security number but SSA

policy seems to be against its widespread use as

concern for privacy and confidentiality has led to

restraints on access to data that have been placed

without regard for the special needs for epidemiologic information on health risks. These restraints are made doubly difficult to deal with by

the fractionation of Federal statistical programs

and responsibilities, each agency collecting its

own statistics in support of its own narrow mission and having laws to limit access to its data.

We might wish for a Statistics USA akin to Statistics Canada, but I doubt that day will ever come.

The concern for privacy stems in part from a

public fear of “data banks” on the ground that

they could too easily be misused. But record

linkage need not imply the necessity for huge data

bdnks. It requires only that conmmnication be

permitted between files on an ad hoc basis under

restrictions that reflect the Dublic interest in

both privacy and adequacy of information.

[11

[2]

[3]

[4]

[5]

REFERENCES

PL 94-469, Oct. 11, 1976.

Tomatis, L., “Exposure Associated with

Cancer in Humans,” J. Cancer Res. Clin.

Oncol. 108:6-10, 1984.

Miller, R.W., “The Alert Practitioner As a

Cancer Ethologist,” Cancer Bull. 29:183-185,

1977.

—

Medical News, “AMA Offers Recommendations

for Vaccine Injury Compensation,” J. An.

Med. Assn. 252:2937-2946, 1984.

Newcombe, H~, Kennedy, J.M., Axford, S.J.

and James, A.P., “Automatic Linkage of Vital

Records,” Science 130”954-959, —. 1959.

142
[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

Wentworth, D.N., Neaton, J.D. and Rasmussen,

W.L., “An Evaluation of the Social Security

Administration Master Beneficiary Record

File and the National Death Index in the

Ascertainment of Vital Status,” ~. J.

Public Health 73:1270-1274, 1983.

DeBakey, M.E.—and Beebe, G,W., “Medical

Follow-up Studies on Veterans,” J. Am. Med.

Assn. 182:1103-1109, 1962.

Dunn, ~., “Record Linkage,” Pun.J. Public

Health 36:1412-1416, 1946.

Beebe, ~W. and Simon, A.H., “Ascertainment

of Mortality in the U.S. Veteran Population,” Am. J. Epidemiol. 89:636-643, 1969.

Beebe, G.W., “Reflections_?inthe Work of the

Atomic Bomb Casualty Commission in Japan,”

Epidemiol. Rev. 1:184-210, 1979.

Beebe, G.W., “~ecord Linkage and Needed

Improvements in Existing Data Resources,”

Banbury Report 9, Cold Spring Harbor, New

York, Cold Spring Harbor Laboratory, 1981,

pp. 661-673.

Dawber, T.R., Kannel, W.B. and Lyell, L.p.,

“An Approach to Longitudinal Studies in a

Corununity: The Framingham Study,” Ann. N.’f.

Acad. Sci, 107:!539-556,1963.

Mancuso, T.~and Coulter, E.J., “Methods of

Studying the Relation of Employment and

Long-term Illness--Cohort Analysis,” Pm. J.

Public Health 49:1525-1536, 1959.

[14] Court-Brown, KM. and Doll, R., “Mortality

from Cancer and Other Causes After Radiotherapy for Ankylosing Spondylitis,” Brit.

Med. J. 2: 1327-1332, 1965.

[15] Dorn, HT., “The Mortality of Smokers and

Nonsmokers,” Proc. Sot. Statist. sec. ~.

Statist. Assoc., 1958, pp. 34-71.

[16] Acheson, E.D., “Medical Record Linkage,”

London, Oxford Univ. Press, 1967.

[17] Selikoff, I.J., “Cancer Risk of Asbestos

Exposure,” In Origins of Human Cancer (Hiatt,

H.H., Watson, J.D. and Winsten, J.A., eds.),

Cold Spring Harbor, New York, Cold Spring

Harbor Laboratory, 1977, pp.1765-1784.

[18] Kurland, L.T. and Molgaard, C,A., I!The

Patient Record in E9idemioloqy,” Sci. Am.

245:54-63, 1981.

-.

[19] me, G.R. and Lindsay. J.. “A Generalized

Iterative Record Linka~e Computer System for

Use in Medical Follow-Up Studies,” Comput.

Biomed. Res. 14:327-340, 1981.

[20] Office of Pop~ation Censuses and Surveys,

“Cohort Studies: New Developments,” Studies

in Medical and Population Subjects No. Q,

London, Her Majesty’s Stationery Office,

1973.

143

●
EXACT MATCHING OF MICRO DATA SETS IN SOCIAL RESEARCH: BENEFITS AND PROBLEMS

Robert Boruch, Northwestern University

Ernst Stromsdorfcr, Washington State University

1. INTRODUCTION

The first objective here is to review some

applied social research projects that have

benefited from exact matching. The examp]es are

merely illustrative but stem from a variety of

disciplines.

The secund objective is to discuss the

negative aspects of matching. In particular, our

argument is that, by espousing the opportunity to

match too ardently, we may constrain or misdirect

our ability to respond to other research issues

and problems. An issue of special interest here

is obtaining unbiased estimates of the effects of

manpower projects.

?’heidea of matching records in the interest

of science has a long pedigree. For instance,

R.A. Fisher lectured at a Zurich public health

congress in 1929, arguing the usefulness of

public records supplemented by (and presumably

linked with) family data, in human genetics

research (Box, 1978, p. 237). Earlier, Alexander

Graham Bell exploited genealogical records,

administrative records on rsarrlages, census

results and others, apparently linking some

sources, to sustain bis familial studies of

deafness (Bruce, 1973; Bell, 1906).

2. HOW AND WHY HAS WATCHING BEEN HELPFUL

The fundamental reasons that matching has

been useful do not differ appreciably from those

implied by the above examples. Nor do the

reasons differ much across the social and

behavioral sciences. The following illustrations

are taken from Boruch and Cecil (1979); unless

otherwise noted, specific references are given

there.

2.1 klatchingto Understand Phenomena and Avoid

Egregious Error

ln psychology, for example, graphs of the

sort used in Figure 1A were commonly used during

the 1940’s and 50’s to describe the gradual

Increase in IQ with age, an IQ plateau and

gradual decrease in IQ with age. The data are

based on cross-sectional surveys.

The ability to match, as in 1inking

individuals’ records obtained at one point in

time to those collected at another to generate

longitudinal files, yielded an entirely different

picture of behavior. This, given in Figure lB,

tells us that earlier declines in IQ are an

artifact of cross-sectional studies and that

cohort differences are important and account for

the misleading interpretations of the earlier

data.

Lest you think the example confined to a

quantitatively naive discipline, consider an

Figure 1, Confounding of Age and

Differences in Cross-sectional Research.

I K & # a a

Graph A 10 20 30 40 50

Chronological Age

Graph B 10 20 30 40 50

Cbronologicol Age

Cohort

1950

1940

1930

1920

I91O

From: Boruch, R.F., and Cecil, J.S. Assuring

the Confidentiality of Social Research Data.

Philadelphia: University of-Pennsylvania Press,

1979.

economic example. Table 1, based on simple

cross-sectional surveys, suggests that a graph

ainilar to Type A is appropriate for earnings

data as well as IQ data. Such earnings data were

commonly used during the 60’s to describe

increases, plateau, and gradual decline in

income. Table 2 gives cohort earnings obtained

in longitudinal surveys, matching on individuals.

It shows a different picture, one that is less

dramatic and more similar to the Type B figure.

Studies that try to separate genetic and

environmental influences in schizophrenia are

bound to be more controversial. But they are

important and worth pursuing... So, for example,

i,*

$,

145
Table I.--Estimates of Mean Annual Income in

Dollars for Men Aged 25-64

(Data ia baaed on independent samples taken in

1947, 1948, and 1949.)

I

t

Age

Year 25-34 I 35-44 I

45-54 I 55-64

used Swedish data that were better than data

available to either Samuelson or Jencks:

matching individual records from miiitary

screening; birth registries, tax registries on

earnings of the respondent, census records rJn

occupational mobility. These analyses favor

Samuelson’s theory.

I947 2,704 3,344 3,329 2,795

1948 2,898 3,508 3,37s 2,s+’(.

1949 2,842 3,281 3,331 1,777”

From: Boruch, R.F., and Cecil, J.S. Assurin&

the Confidentiality of Social Research Data

Philadelphia: University of Pennsylvania Press,

1979.

Table 2.--Estimates of Mean Annual Income in

Dollars Over Ten-Year Intervala for Six Cohorts

A gcs

Year 25-34 I 35-!4 I

45-54

1. 1947 2,704 (1947) $,30U(1957) 8,342 ([967)

2. 1948 2,898(1948) 5,433(1958) 8,967 (1968)

3. 1949 2,842 (1949) 5,926 (1959) 9,873 (1969)

t

Ages

Year 3544

1

45-54

I

55-64

4, 1947 3,344([947) 5,227(1957) 7,C04( 1967)

5. 1948 3,508 (1948) 5,345 (1958) 7,828 (]968)

6. 1949 3,281 (1949) 5.587(1959) 6,405 (1969)

Nnte: Each cohort waa surveyed every ten yeara.

The firat cohort, for example, contains individual who =re 25-34 yeara of age in 1947 and had

an average income of $2704; in 1967, when they

-re 45-54 years of age, their mean income was

d8342.

From: Boruch, R.F., and Cecil, J.S. Assuring

the Confidentiality of Social Research Data.

Philadelphia: University of Pennsylvania Press,

1979.

Danish-U.S. collaboration supported by the

National Institute of Mental Health (NIMH) has

involved intensive record matching to determine

how children born of schizophrenic parents fare

when they are adopted and reared by nonschizophrenic, foster parents. Matching among

records of hospitals, surveys, and psychiatric

systems was required to execute the research.

The work appears to confirm a genetic component

in that incidence of schizophrenia among such

children is higher than ita incidence among

adopted children born of nonschizophrenic

parents, including children adopted by

schizophrenic parents.

That use of matched records can improve

scientific analysis seems clear from studies of

tbe economjc impact of education. Paul

Samuelson, for exasnple, has argued that returns

on higher education are substantial. Christopher

Jencks has analyzed various survey data sets to

argue that the returns are marginal. Fagerlind

Neither the schizophrenic study nor the

Samuelson-Jencks-Fagerlind work is unambiguous,

of course. There has been considerable debate

about the models exploited in each. The mssi

pojnt is that improvements in data, notably

through linkage of records from a varietY of

sources, can enhance the analyst’s ability to

explore ideas and test hypotheses. The “sources”

may be additional survey panels in a longitudinal

design. Or they may be administrative records

that are at least as gond as survey data.

2.2 Matching to Avoid Aggregation Error and

Ecological Fallacy

We often compute correlations between X and

Y based on aggregate data, being cautious, of

course, in generalizing to the individual level.

The opportunity to match individual records often

gives us the opportunity to entirely avoid the

problems and caution engendered by aggregaticrn.

One of the oldest illustrations is still the

most dramatic. At a particular point in time,

the correlation between literacy rate and color

(black vs. white) computed on the basis of nine

census regions in the United States was .95.

When the data are aggregated by State instead of

region, the correlation becomes .77. Finally,

access to individual records led to a

correlation of .20.

2.3 Matching Records in Randomized Tests of

Social and Education Programs

In Middlestart education programs at Oberlin

College, for instance, a series of experiments

was undertaken to understand whether precollege

programs worked for promising but poor

adolescents. The evaluators relied on

randomization to assure statistically unbiased

estimates of long--run program effect. They

relied on records matched among surveys, high

school records, and standardized precollege

records to avoid the problem of low validity in

student reports of grades, and to enhance the

statistical power of the tests.

Randomized field experiments, designed to

understand how one can increase compliance with

food stamp registration rules, have been mounted

by the Office of Analysis and Evaluation of the

Us. Department of Agriculture’s Food and

Nutrition Service (1984). These tests depend on

matches of records among participant reports and

records of State Employment Security agencies and

the Food Stamp Agency. Results show remarkable

decreases in food stamp costs and employment

benefits for certain innovative approaches to

compliance assurance.

Police research is relevant, too, of course.

In the Minneapolis Domestic Violence Experiments,

146
the object was to understand how to handle

domestic violence effectively, for example,

immediate arrest versus referral to social

services, within limits. Undertaken by the

Police Foundation, the experiment involved

matching among police patrolman records, precinct

arrest records, and the experimenters’ records.

Arrest, incidentally, seems to work in the sense

of reducing subsequent incidence of domestic

violence (Sherman and Berk, 1984).

Motor vehicle research is pertinent to

matching, too. Work done some years ago by the

Insurance Institute for Highway Safety, for

example, involved linking an experimenter’s

observations on vehicle registration, the

drivers’ seat belt use, and advertisements on the

topic, to motor vehicle records that contained

data on the drivers’ residence area. The

residence area match with the other information

■ade it possible to determine how effective

alternative TV commercials, directed to different

areas, were in encouraging seat belt use.

Program Implementation and Validity of f@DOrtinX

The New Jersey Negative Income Tax

Experiments attended to the potential problem of

overpaying welfare recipients. This set a

standard for validity studies in later

experiments. Overpayment of benefits in such

experiments was critical insofar as (a) other

sources of assistance were available to

participants in the experiment, and (b) they

might receive such assistance illegitimately

through error (welfare rules ~ complicated) or

deceit (crime is still a bastion.of the free

enterprise system). All participants reported

their income based on recall. Matching these

reports with administrative records helped to

assure reasonable implementation of the program

and to assess quality of reporting.

For example, welfare audits were created to

reduce or prevent the problems: these depended

heavily on the experimenters’ ability to match

research records with records of welfare

boards. Internal Revenue Service (IRS) W-2

forms were required of families and permitted

comparisons between lRS-reported income and

income reported

(Underreports of inco~e to

the experiment.

the experiment

relative to IRS appear to have been less than 15

per cent). The Social Security Administration

(SSA) cooperated by taking the experimental

data, matching to its own records on

individuals, and providing aggregate earnings

data (not individual records) t? permit

estimates=f underreporting of earnings in the

experiment (Kersbaw and Fair, 1979). (The SSA

comparison suggests that about 80% of families

underreport to researchers by 1S% or less even

when they have incentives to misreport. )

In the Seattle and Denver Income Maintenance

Experiments (SIMS/DIME), research records were

matched to public agency recordq on food stamp

purchase, rent subsidy, and wages. The

experiment produced some small surprises through

evidence that public records on rent support and

food stamps were less accurate than respondents’

reports <in the.experiments, evidence that was

later strengt.henedby independent investig~tion.

Underreporting of wages appeared in the expected

direction based on matches with IRS records

(Halsey, 1980).

In the New Jersey Negative Income Tax Experiments, Mercer County Welfare Board records

were used in a pilot test to determine composition, work history, and residential mobility of

families that attrited from tb experiment and

could not ke intervie~d without great difficulty. More generally, the attrited families

in five cities were traced through post office

change-of-addrese cards, motor vehicle registration agencies, helfare boards, prisons, and

communtty groups. Apparently, face-to-face

intervie /s with foruer neighkors e re most :

productive (Kershaw and Fair, 1979).

The use of administrative records to trace

attriters and assess misreporting in all the

income maintenance experiments iS an important

but underexamined topic. The experiments

themselves were wel] run, relative to any

pragmatic standard. They cover a sufficient

number of sites to tantalize any scholar with an

interest in regional differences in record

accuracy, misreporting models and so on. Sample

sizes for validity studies were small, however.

This may account partly for the disinterest of

scholars. Still, it is a bit distressing to some

that otherwise thoughtful commentators such as

Hausman and Wise (1985) fail to recognize the

policy ~mport of misreporting and the

methodological contributions of randomized tests

of economic programs to this area.

2.4 Matching and Testing New Ways

Information

Innovative ways to elicit informat.

o Elicit

on, such

as randomized response, need to be tested despite

their. cleverness. We are unaware of any

individual match studies in this arena. But

studies that compare marginals or point estimates

for individuals on whom both responses and

archival records are available are done.

so, for example, Bradburn, Locander and

Sudman found that a randomized response method

worked at times to reduce response distortion on

sensitive topics such as drunk driving arrests,

The basis for comparison was administrative

records on the same individuals, e.g., arrest

records. Individual records were not matched;

comparison are based on margina~counta or

averages. But matching in this and relatbd

research is possible in principle, An,dit may be

useful insofar as it helps us to understand how

-response distortion varies with sensitivity of

the traits that are being examined and

characteristics of individual respondents.

A fascinating example of a near match study

on reporting energy use to the Census Bureau was

given by Tippett (1984) in recent. 1984

Proceedings of the ASA. Her experiment involved

encouraging utility companies to send a randomly

147
year’s utility bills. A randomly assigned

comparison group was not sent the statement.. The

statements were sent prior to the 1980 census to

understand whether providing such records could

enhance quality of respondents’ reports of

utility costs to Census. Both groups overstated

costs ; the “primed” group overstated costs

appreciably less than the control group, Again,

matching could be helpful in understanding how

degree of reporting error varies with the true

state of the individual.

2.5 Matching Records to Understand Validity of

Response and inferential Errurs

We know that error in measurement of a

response variable degrades statistical power.

More important, it can lead to invidious biases

in covariance analyses based on fallibly measured

covariates. That is, the analyses can make

programs iook useless when their effects are in

fact slightly positive, and can make programs

look harmful when indeed they are merely useless

(Riecken et al., 1974). The recent work by

Andersen, Kasper, Frankel and their colleagues

(1979) on total survey error clarifies the effect

of imperfections in observational studies

generally.

The point is that understanding validity of

the measures is important in applied social

research, especially policy research, as well as

in basic work. Matching studies undertaken in

education and supported by the National Institute

of Education and the National Center for

Education Statistics, for instance, show that

females are appreciably more accurate than males

in responding to questions about their own grades

and coursework, and more accurate in reporting on

income and education levels of parents. There

are race differences as well as gender

differences in respondents’ ability and

willingness to furnish information. Failure to

recognize these differential validities can lead

to errors in understanding which programs work

and for whom. Matching helps us to avoid those

errors merely by showing which subgroup

differences .tnreporting quality may account for

differences id performance.

imperfect measures of employment and

occupation can produce similar biases in

explanatory models of income gain and other

response variables. Matching studies of the sort

undertaken by Mathiowetz and Duncan (1984) in

which private employer records are linked to

survey records of the Panel Study on Income

Dynamics are not common. But they have potential

for revising ideas about error structure. Errors

in retrospective reporting on employment and

occupation seem to depend less on time or recency

than on salience of events in a particular month

(e.g., a raise) and task difficulty (e.g., a

single unemployment spell vs. multiple spells).

Gender and race differences in reporting error

are reduced when these variables are taken into

account.

148

LEAST NOT SO CLEAR

Having the optjon to capitalize on existing

records and to match so as to obtain a better

file is important because the idea and the

relevant technology have been so useful. For

jnstance, the 1984 Proceedings of the ASAL

section on Survey Research Methods contains

over 30 articles that concern exact matching

methods or analysis or depend heavily on matching

for conclusions (validation studies, ca@urerecapt~, others). Unlike the 1984 Proceedings,

~~ 1978 Proceedings of the same section

contained no sessions on using administrative

records in conjunction with surveys or on quality

control of statistical systems (partly through

linkage).

The Interagency Linkage Study participants

--Internal Revenue Service, Census, and Social

Security Administration--deserve special credit

for advances in this arena. Other agencies have

worked at least as vigorously and as often,

however, e.g. , the National Center for Education

Statistics and the National Center for Health

Statistics. And a good many research projects

undertaken with support of the U.S. Department of

Labor’s Employment and Training Administration,

the National Institute of Justice, the National

Center for Health Services Research (and the

Department of Health and Human Services more

generally) have made use of matching where it has

been useful and legs’lly possible to match.

Matching is a seductive option, however.

That is, we may capitalize on matching existing

records to obtain estimators that are efficient

and cheaply produced, but W-. They are wrong

at times partly on account of the administrative

system in which matching must take place. They

are wrong partly because the matched data

(observational data more generally) are

inappropriate despite their accessibility and

ostensible relevance.

Consider a recent case, one in which the

role of matching is important.

3.1 The Case at Hand

Estimating the effect of manpower employment

and training programs in this country is a

significant policy issue. Since 1985 or so,

most estimates have been based on observational

data, i.e., sample surveys. Two kinds of

observational data are most reIevant here--the

Continuous Longitudinal Manpower Survey (CLMS)

and the Current Population Survey (CPS). Both

are based on large, well-designed samples. Both

have been augmented by matching respondent

records with social security (SSA) earnings

records.

The CLMS-SSA match works as follows. The

Bureau of the Census, under agreement with the

Department of Labor, designs the CLMS probability

sample and collects the data. The record on each

individual includes identifying information and

social security number. A list of respondent SSA

numbers is given tn the SSA which then searches

——
SSA files for records on the relevant

individuals. The SSA records include the social

security number, earnings, birth year, six

letters of surname, and other bits of

information. These SSA records are then given to

Census for matching to the CLMS survey records

under an interagency agreement that assures

confidentiality of both sets of files. Census

matches the records, deletes identifying

information and geographic area related

characteristics. ‘l’hegeographic data are deleted

to prevent deductive disclosure.

Recently, the U.S. Department of Labor

contracted for two kinds of analyses bearing on

the impact of manpower programs and based on

these files. In the first kind, different, well

regarded contractors were asked to use such data

to estimate tbe effects of training programs

(Westat, 1984; Dickinson, et al., 1984; Bassi, et

al., 1984). In the second kind of study,

estimates based on observational survey data,

similarly constructed, were compared to estimates

yielded by randomized field experiments. In

particular, the models used on CLMS and CPS data

were used to construct quasi-experimental

comparison groups. The performance of these

comparison groups was compared to randomized

control groups generated in the National

Supported Work Demonstration (Fraker & Maynard,

1985).

The results of three independent analysts

generating models and using them to estimate

program effects based on CLMS and CPS data

yielded the following results:

(a) Effects of training on earnings are

positive and significant, especially for females

and all post Comprehensive Employment and

Training Act follow-up years (Westat, 1984, p.

61).

(b) Effects on earnings for men are not

generally significant; effects on women’s

earnings are significant (Bassi, et al., 1984, P.

xv).

(c) Effects on earnings for men tend to be

significant and negative, but effects on women

are positive and significant but smal1

(Dickinsen, et al., 1984, p. xiii).

We have oversimplified here, of course.

“Significance” is emphasized too much and the

statements are misleadingly blunt. But the

conclusions are as they appear in the final

reports.

Comparing estimates of control group

performance similarly constructed to estimates of

control group behavior based on randomized

experiments had the following results: depending

on the particular model and matching strategy

used, estimated effects on earnings range from

* 2000% of “true” earnings to plus 50% of

“true” earnings, “true” being estimated from the

randomized trial.

These results should be a bit disconcerting.

They are indeed puzzling and potentially

embarra~ng. The Labor Department deserves

praise for scholarship in disclosing the puzzle

149

and for its political fortitude in willingness to

tolerate potential embarrassment.

More to the point, what are the reasons for

the discrepancies? Sampling variations may

account for some of the differences. But it is 

not likely to account for all. In the next

section, the reasons engendered by another line

of argument are discussed, in the interest of

understanding the strength and weakness of the

argument.

3.2 Line of Argument

The critic can propose that part of the

reason for discrepant results lies in relying----

(a) solely on observational data, matched

or otherwise, and

(b) on models whose validity is untestable with the data at hand.

Critics who are more blunt may further suggest

that the CPS, SSA, and CLMS are used because they

are available and seemingly appropriate and not

because they are sufficient.

Finally, the administrative system in which

matching occurs demands that one give up some

opportunities that should not be given up if the

object is to produce good estimatea of program

effects.

To illuminate the contentions, consider SSA

earnings matches with observational data from

surveys. Problems similar to onea discussed here

occur in other contexts. The material that

follows is based on thoughtful reports by Basai,

et al. (1984), Dickinson, et al. (1984), and

Westat (1984), that is, the producers of the

estimates of manpower program effects.

State Identifiers and Areas as Missing Data

Welfare iaws differ appreciably among

states. These laws determine who gets welfare

and how much they get. It makes sense to

incorporate such data into any analysis of the

way a federal employment program is used by the

poor and what the impact of the program is.

Local labor market information is also crucial to

thoughtful analyses of why people do or do not

get joba as a consequence of programs.

Yet such information is absent from public

use microdata files that are released after

matching records. The result is that the

economist must be content with data that are

bound to generate estimatea of program effect

that are likely to be biased. That is, important

major variables are left out of the left hand

side of explanatory equations becauae they are

deleted from public use files or remain

unmeasurable variablea. The incompleteness of

the model is responsible for biased estimates of

effect.

Why are they left out of such files?

Because their inclusion will permit deductive

disclosure. That is, it becomes possible to

deduce the identity of anonymous respondents if
information about geographic area is supplied.

The Census, for example, cannot countenance the

possibility of deductive disclosure of

information that it has collected, and invokes

Title 13 to justify its position. Census

perspective on this matter is important not only

for this case: The Bureau “uerforms a mainr

por

bas

P.

and

the

ion of its survey work on a reimbursable

s for other Federal agencies” (Cox, et al.,

1, 1985). It is important as a survey agency

as a model of virtue in this respect.

Exclusion of relevant data seems to us to be

most serious consequence of our use of

Census-SSA in data collection

such a matching system,

credible estimates without

variables.

Earnings not Covered by SSA

and matching. From

we cannot produce

the appropriate

Many public sector jobs are not covered by

SSA reporting. Insofar as the employment and

training program leads to jobs that are public

sector and not covered, two problems occur. When

earnings are a dependent variable, estimates of

impact will be understated when the comparison

groups jobs are more likely to be SSA covered.

When earnings are used as a covariate, e.g.,

“prior base year,” estimates of program impact

will be biased because the covariate is fallible.

One way to assess the problem is by looking

at interview-based earnings reports and SSA

earnings, of course. Dickinson, et al. (1984)

did so. They found substantial error in CLMS

interview reports, e.g., 33% of CLMS respondents

who said they did not work in 1977 had positive

SSA earnings reported. The rate for CPS is about

lo%. We still have a dilemma: SSA is clearly

better than self-reports of earnings, although

they are imperfect.

SSA earnings data are also truncated at both

ends. For example, the maximum earnings subject

to SSA tax is the maximum recorded earnings

level. Dickinson, et al. (1984) examined

interview earnings @ SSA cap earnings to find

no appreciable difference between analyses using

each. i.e., estimates of program effect are about

the same (p. 98).

Updatedness: A Possibly Tractable Problem

As of 1983-84, the period of 00L analyses of

interest here, 1979 SSA records merged with CPS

and CLMS data are incomplete. mat is, not all

1979 SSA earnings for members of these samples

were available. A “zero” entry for the ❑issing

data means we cannot tell how ~ missing data

there ia. Bias cannot be estimated. Still, this”

problem seems tractable.

Program Participation not Measured: A Possibly

Tractable Problem

The CPS does not now measure p!3rtiCipf3tiOn

in employment programs. Consequently, a public

use file will not permit construction of a

comparison group that is “uncontaminated.” Among

youth in the CPS comparison group, for example,

.

150

it has been estimated that between 1975-78 30%

entered CETA. So the contamination issue seems

important. It, too, seems tractable but not

without substantial effort.

Alignment Problems

According to Dickinson, et al. (1984), in

Westat’s analysis of the FY76 cohort, SSA

earnings in calendar year 1975 were used to match

individuals, despite the fact that calendar year

1975 earnings included up to six months of postenrollment earnings for some CLMS

35). Dickinson, et al., used

cohorts rather than fiscal year

disadvantage is in potentially

preprogram drop in earnings.

4. RESTATEMENT OF THE PROBLEMS

SOLUTIONS

4.1 Core Problems

members, (p.

calendar year

cohorts. The

missing the

AND POSSIBLE

There are two kinds of problems implicit in

the case just presented. The first concerns

reliance solely on surveys coupled to

administrative records to understand relative

effects of programs. Problems engendered by

relying on such data affects not only efforts to

estimate impact of manpower training programs, of

course. They also appear in health services

research, psychiatric and mental health services

evaluations, assessments of court procedures, tax

compliance, and police procedures (Riecken, et

al., 1974). We attribute the problems partly to

the seductiveness of matching and partly to the

more dangerous problem of untestable ❑odels.

The second kind of problem stems from our

inability to use ~ the data in ways that permit

confidence that the analysis is statistically

unbiased. Denial of access to micro-records on

account of deductive disclosure affects research

by Bureau of Labor Statistic (Plewes, 1985) as

well as the DOL Employment and Training

Administration, by the National Institute of

Justice (e.g., in victimization studies), and

others. The issue is also likely to affect newer

statistical programs, e.g., tbe Survey of Income

and Program Participation (David, 1984). We

attribute this problest to the administrative

environment in which matching technology must be

exploited.

4.2 Resolving the First Kind of Problem and

Exacerbating the Second

A scientifically reasonable solution to the

first kind of problem is to actively experiment.

That is, we need to run randomized trials of

projects, project components, or project

variations. The research policy option that

seems worth exploring is routinely adjoining

randomized experiments to the longitudinal

studies and/or record files that are matched.

See for instance, the Hollister, et al. (1985)

report on evaluating the effectiveness of youth

employment programs.
Exercising the option of randomized

experiments can exacerbate the second problem,

i.e., of deductive disclosure. That is,

experiments generally involve a smaller number of

individuals than national probability samples and

more detailed information on each individual.

This makes deductive disclosure easier. It also

makes it difficult to adopt sampling rates as a

partial index of likelihood of deductive

disclosure (Cox, et al., 1985). if an agency

with restrictive rules is involved in data

collection then no public use tapes with

sufficient detail will be released and no

sensible competing analyses will be done.

Apart from the information demands of

randomized experiments, the demand for microdata

is increasing. Cox, et al. (1985) recognize that

this increase has strong implications for Census

policy on disclosure and they provide a

thoughtful analysis.

4.3 Reso

The

problems

procedura

following

ving the Second Problem

possible resolutions to the disclosure

are of at least three kinds;

statutory, and empirical. ‘l’he

options illustrate each.

Avoiding Restrictive Agencies

One may stay away from agencies that have

data worth matching but that also have

restrictive disclosure policies. Indeed, it is

not hard to argue that private agencies are as

capable of producing good data with equal privacy

protection for the respondent and fewer

constraints on the research than a government

agency. The case is especially arguable for

controversial topics of research such as AIDS,

but it is also relevant here (Boruch, 1984).

Still, doing without. micro-records from

agencies such as the Census Bureau, Social

Security Administration, or others, and doing

without their capacity to serve as a broker for

linking records from independent sources, is not

an attractive prospect. We may gratuitously

abandon Omortunities to do soc.ie]ly useful and

reliable research by foregoing collaboration with

such agencies. So it is sensible to consider

other options in addition to this one.

Proactive Change in Law and Policy

Alteration of law and more feasibly the

interpretation of law is possible and seems

desirable. The battles for statistical enclaves

suggest, however, that this war will not be won

easily, if at all. Still, sensible work has been

done and some progress in clarifying issues has

been made (Alexander, 1983). Assaults on

Census’s stewardship of Title 13 seem not to have

been productive, for example (Plewes, 1985).

Still, working toward legitimate reinterpretation

of law seems an effort worth making, especially

if more empirical research can be brought to bear

on the issue of perceived risks of disclosure to

populations. This brings us to the next option.

EmDirical Research

Research on the role that privacy and

consent have in record matching contexts seems

sensible. How much the assurance of

confidentiality means to respondents and how it

influences the cooperation rate has received some

attention from empiricists. For example,

randomized field tests have been run under the

auspices of the NAS Committee on National

Statistics to understand whether people attend to

assurances about privacy (Panel on Privacy and

Confidential ity, as Factors in Survey Response,

1979) . We agree with Thomas Plewes (1985) of the

Bureau of Labor Statistics (BLS) in urging that

more related work needs to be done.

In particular, obtaining respondent consent

to disclose and link records for research

purposes is an avenue for resnlving deductive

disclosure/confidentiality problems at Census,

SSA , and elsewhere. We are aware of no good

field experiments to determine effective

strategies to elicit consent or their

consequences. The BLS has been successful,

according to Plewes, in eliciting consent for

disclosure of its data to the Department of

Agriculture, for instance, so that better

sampling frames for forms could be developed.

But this evidence is anecdotal and few hard data

from controlled trials are available.

Both Cox, et al. (1985) at Census and Plewes

(1985) at BLS recognize that public perceptions

of government agencies are important in this

context. That is, public confidence in

government affects cooperation in surveys and

resultant public data.

This chain of reasoning is plausible. But

our agreement is a matter of intuition, not hard

evidence. Moreover, the politicians’ view of the

idea and its implications for a bureaucracy and

votes seem important. Neither the Census Bureau

nor BLS (nor other agencies) can work on this

tangle of issues with impunity, at least not

always. Academic researchers have some

responsibility to do so if they expect to have

access to good data. We know of very few who are

involved in such work, e.g. , Flaherty, Hanis, and

Mitchell (1979) in Canada, Mochinann and Mul~er

(1979) and Damman and Simitis (1977) in Germany.

Research: Analytic

The Department of Labor’s support of

competing analyses, and of comparisons of the

results of randomized tests to the results of

nonrandomized assessments, is admirable.

Research in the same spirit on matching and

disclosure is warranted.

The thoughtful vbserver ought to admire the

work by Nancy Spruill and Joe Gastwirth (1982) on

microaggregation and masked data and work by

George Duncan and Diane Lambert (1985) on

disclosure limited dissemination. Their analysis

helps to actualize a balance between privacy

needs and the need to assure quality of released

data. The thoughtful observer will also

recognize, however, that not much work has been

151
done on the costs, traps, flaws, and benefits of

using the suggestions uf these analysts. We

ought to know more about these issues. And so we

ought to invest.some resources routine~ in the —-—.

design of side studies to illuminate the limits

on the utility of their work.

The importance of this matter stems partly

from the fact that the effects of social programs

in tax compliance, police, training, and

employment effects are usual~ small. Expecting

small effects, we should then be better able to

anticipate the effects of micro-aggregation,

random perturbation (contamination), random

rounding, collapsing, and other strategies used

to transform data so as to make it suitable for

public use. All such tactics are used by the

Census and other agencies to protect individual

(and at times institutional) privacy (Cox, et

al., 1985). But very little bas been published

about their implications for the validity of

inferences based on analyses

data.

Administrative Procedures

Suppose that we create

of such public use

a matching system

under which public use tapes that are first

expurgated or “adjusted” to reduce deductive

disclosure problems are used for crude analyses.

These analyses are eventually verified using the

unexpurgated records by the agency that maintains

the more detailed micro-records. The procedure

achieves a balance between privacy concerns and

scientific demands for quality in analysis.

But it demands substantial resources, i.e.,

a sequential system of crude analyses, based on

pub]ic use tapes, followed closely by

confirmatory analyses, based on within-agency

analysis of micro–records. Still, the option

seems worth considering especially because the

procedure seems generalizable, e.g., to ❑atching

economic variables in the Survey of Income and

Program Participation (David, 1984).

For example, 1976 Annual Housing Survey data

on energy use were matched on geographic area to

local utility company data. Census created the

file. To protect against deductive disclosure,

the Census adjusted the accuracy of energy use

data “prior to release to guard against the

possibility that the utility companies could

uniquely identify individuals on the released

file from their reported cost data” (Cox et al,,

1985, p. 22). The adjustment involved random

perturbation (that can be accommodated up to a

point in analyses, given the perturbation

parameters) and rounding. We are unaware of any

formal benefit-cost analysis of this case. We

believe that some sort of evaluation of such

cases should be undertaken g~~ published.

5. REPRISE AND CONCLUSION

There is no doubt that matching can be and

has been useful in a variety of social research

projects. Moreover, the analytic work on the

topic by Felligi and Sunter (1969) and others is

152

remarkable for its thoughtfulness. The

technology fur matching, considered apart from

the matching system (organization and data), has

stimulated fascinating research by academic

and bureaucratic scholars. But solutions to the

problem of getting the benefit of matching

without reducing interpretability of data are not

yet clear.

The ingeniousness of a matching algorithm is

one thing. The system in which the algorithm is

applied is quite another. It is clear that the

administrative envirrrnmentof the matching system

can lead to invidious problems in analysis at the

policy level. The problems lie not so much in

matching technology as in other elements of the

matching system: the data and rules under which

it was collected, the institutional vehicle for

matching and the rules governing it, and the

procedures one uses to understand the errors we

make based on analyses of matched data. The

problems are severe enough to warrant the serious

concern of applied statisticians and social

scientists. Unless attention is dedicated to the

matter we will do far less than we should for

science, society, and the profession.

ACKNOWLEDGMENTS

The background research for this paper was

supported by the National Science Foundation

during 1984-85 to the Social Science Research

Council (Measurement and Methodology Division,

NSF) and 1985-86 to Northwestern University (RII84181’79 Ethics and Values in Science and

Technology, NSF). We are indebted to Robert

Pearson of the Social Science Research Council

and Richard Beatty of Westat for background

information. Readers should be grateful, as I

am, to Theodore Clemence of the Census Bureau for

graciously providing references on Alexander

Bell.

REFERENCES

Alexander, L. Proposed legislation to improve

statistical and research access to federal

records. In R.F. Borucb and J.S. Cecil

(Iids.), Solutions to ethical and legal

problems in social research. New York:

Academic Press, 1983, 273-292.

Andersen R., Kasper, J., Frankel, M.R. and

Associates. Total survey error. San

Francisco: Jossey Bass, 1979.

Bassi, L.J., Simms, M.C., Burbridge, L.C., and

Betsey, C.L. Measuring the effect of CETA

on youth and the economically disadvantaged.

Washington, D.C.: The Urban Institute,

April 1984.

Bell, A.G. The deaf. In: U.S. Department of

Commerce and Labor, Bureau of the Census.

Special Reports: The blind and the deaf,

~ Washington, D.C.: U.S. Government

Printing Office, 1906.
Boruch, R.F,, and Cecil, .l.S. Assuring the

confidentiality of social research data.

Philadelphia: University of Pennsylvania

Press, 1979.

Boruch, R.F. Should private agencies maintain

federal research data? ~, 1984, 13(6),8-

9.

Box, J.F. R. A. Fisher: The life of a

scientist. New York: Wiley, 1978.

Bruce, R.V. Alexander Graham Bell and the

conquest of solitude. Boston: Little,

Brown, and Company, 1973.

Cox, L.G., Johnson, B., McDonald, S.K., Nelson,

D ., and Vazquez, V. Confidentiality issues

at the Census Bureau. Presented at the

first Annual Census Bureau Research

Conference. Reston, Virginia, March 20-23,

1985.

Damman, u ., and Simitis, s.

Bundesdatenschutzgesetz. Baden-Baden:

=mos Verla=el=aft, 1977.

David, M. Discussion. Proceedings of the

American Statistical Association: Social

Statistics Section. Washington, D.C.: ASA ,

1984, pp. 534–536.

Dickinson, K.P., Johnson, T.R., and West, R.W.

An analysis of the impact of CETA programs

on components of earnings and other

outcomes. Menlo Park, CA: SRI

International, November 1984.

Duncan, G.T., and Lambert, D. Disclosure limited

data dissemination. Journal of the American

Statistical Association. 1985, in press.

Fellegi, I.P., and Sunter, A.B. A theory for

record linkage. Journal of the American

Statistical Association, 1969, ~, 1183-

1210.

Flaherty, D.G., Hanis, E.H,, and Mitchell, s.p.

Privacy and access to government data for

research. London: Mansell, 1979.

Fraker, T., and Maynard, R. Tbe use of

comparison group designs in evaluations of

employment related programs. Princeton,

N.J.: MathematicalPolicy Research, 1985.

Halsey, H.I. J)ata validation. Chapter 2 of P.K.

Robins, R.G. Spiegelman, S. Weiner, and J.r3.

Bell [Eas.) A guaranteed annual income:

evidence from a social experiment. New

York: Academic, 1980, pp. 33-.55.

Hausman, J.A. and Wise, D.A. (Eds.) Social

experimentation. University of cPress, 1985.

iiollister, R. and others (Eds.) Report of the

Committee on Youth Employment Proirraq.

Washington, D.C.: National Academy of

Sciences, 1985,

Kershaw, D. and Fair, J. The New .Jerseyincome

maintenance experiment: Operations surveys,

153

and adrninistration, Volume I, New York:

Academic Press. 1979.

Locander, W., Sudman, S. and Bradburn, N.M. An

investigation of interview method, threat,

and response distortion. Journal of the

American Statistical Association, 1976, U,

269-275.

Mathiowetz, N.A., and Duncan, G.J. Temporal

patterns of response errors in retrospective

reports of unemployment and occupation.

Proceedings of the American Statistical

Association: Section on Survey Research

Methods. Washington, D.C.: 1984, 652-657.

Mochmann, E., and Muller, P.J. (Eds.), Data

protection and social science resea~

Frankfurt/New York: Campus Verlag. 1979.

Newcombe, H.B., Smith, M.E., Howe, G.R., Mingay,

J.Strugnell, A. and Abbatt, J.D.

Reliability of computerized versus manual

death searches in a study of the health of

Eldorado uranium workers. Computers in

Biology and Medicine, 1983, 13(3), 157-169.

Panel on Privacy and Confidentiality as Factors

in Survey Response. Committee on National

Statistics. Privacy and confidentiality as

factors in survey resDonse. Washington,

D.C.: National Academy of Sciences, 1979.

Plewes, T.J. Confidentiality principles and

practice. Paper presented at the first

Annual Census Bureau Research Conference.

Reston, Virginia, March 20-23, 1985

(available from the author, Bureau of Labor

Statistics).

Riecken, H.W., and others. Social

experimentation: A method for glanning and

evaluating social programs. New York:

Academic, 1974.

Sherman, L.W., and Berk, R.A. The specific

deterrent effects of arrest for domestic

assault. American Sociological Review~ 1984,

49, 261-272,

Spruill, N.L., and Gastwirth, J. On the

estimation of the correlation coefficient

with grouped data. Journal of the American

statistical Association, 1982, ~, 614-620.

Tippett, J.A. An experimental project to imDrove

Us.

reporting of selected costs- in the “1980

census. Proceedings of the American

Statistical Association: Survey Research

Methods Section. Washington, D.C.: ASA ,

1984, 323-328.

Department of Agriculture, Food and

Nutrition, Office of Analysis and Evaluation.

Food stamp work registration and job search

demonstration. (Contract No. 533198085).

Alexandria, VA: DOA, 1984.

Westat, Inc. Continuous Longitudinal Manpower

Survey: Net impact results. Rockville, MD:

Westat, April 1984.
METHOI)OLOGICISSUES IN LINKAGF OF

,-. MULTIPLE DATA RASES

Fritz Scheuren *

‘.

Data linkage offers several obvious benefits

in studying the dynamics of aging. Retrospective and prospective approaches are possible.

Many ad hoc epidemiological studies could serve

as ex~mp~ here (e.g., Beebe, 19851. Perhaps

of even more importance are broad-based statistical samples composed of linked administrative

records, either used alone or in conjunction

with survey data (e.g., Kilss and Scheuren,

1980: Scheuren, 1983).

In general, linked administrative records,

when structured longitudinally (e.g., Ruckler

and Smith, 1980), can be very effective in

tracing changes with age in income and family

relationships--including the onset of some forms

of morbidity (e.g., Klein and Kasprzyk, 1983);

and, m“th the advent of the National Death

Index, mortality as well (e.g., Patterson and

8ilgrad, 1985).

Survey data can be used, among other things,

to explore the underlying causal mechanisms for

these administratively recorded outcomes. The

design challenge, of course, is how to build a

data collection process which exploits the

comparative advantages of both administrative

and survey information.

The present paper examines settings where

linkages of U.S. federal government records for

individuals are feasible and of interest in the

study of the dynamics of aging. Both administrative and survey records will be considered. Our

focus will be on the barriers to and benefits

from data linkages, with examples drawn from

studies conducted using records from the SociaT

Security Administration (SSA), the Health Care

Financing Administration (HCFA), the h!ational

Center for Health Statistics (NCHS), the Bureau

of the Census and, of course, the Internal

Revenue Service (IRS).

Organizationally, the paper has been divided

into three main sections. Structural auestions

(e.g., legal and procedural) in the development

of a data linkage system are taken UP first

(Section 1). Technical issues in the matching

process itself are discussed next (Section 2).

The paper concludes (in Section 3) with Some

recoimnendationson areas for future study. An

extensive set of references is also provided,

along with some additional bibliographical

citations (See Appendix A).

1. STRUCTURAL DESIGN CONSIDERATIONS

During the last several decades numerous data

systems have been built by linkage techniques in

an attempt, among other objectives, to study

various aspects of the aged population. some of

these, like the Continuous Work History Sample,

remain enormously valuable (e.g., Kestenbaum,

1985) but are no longer fully exploited because

of access problems and severe resource

constraints (e.g., Cartwright, 1978). Others,

notably the Retirement History Survey (Irelan

and Finegar, 1978), have not been continued.

Many studies had an ad hoc character to begin

with. While successful,—they have not been

repeated (e.g., The 1973 Exact Match Study,

Kilss and Scheuren, 1978; the Survey of Low

Income Aged and Disabled, Barron, 1978). Still

other studies originally envisioned as standalone survey systems have not exploited

available data linkage opportunities to extend

their useful life beyond the point at which

interviewing has stopped (e.g., the Na~~~~~l

Longitudinal Survey, Parries, et al.,

What can we learn from these ~xpe~ences an~

others that are similar--

● First, agency support for the activity has

to be very strong and continuing. Social

Security, which supported most of the

projects listed above, has moved away from

such general research efforts and shifted

towards examining improvements in program

operations (Storey, 1985). A sustained

long-run conmnitmentto basic research simply

may not be possible in what is inherently a

policy-oriented environment (President’s Reorganization Project for the Federal Staistlcal System, 1981).

Second, strong user support is essential.

The products must have high, perceived

public value, be delivered in a timely

manner and with sufficient regularity to

sustain continued interest. Start-up

problems with the Retirement History Survey

caused it some major difficulties from which

it may never have been able to fully recover

(Maddox, Fillenbaum, and George, 1978). The

Continuous Work History Sample has,

especially in recent years, been unable to

sustain user interest outside of Social

Security because of access issues raised by

the 1976 Tax Reform Act. Also, the emphasis

on employee-employer relationships, long a

main feature of the Continuous Work History

Sample, may not have been seen to be as

important as the resource commitment

required to maintain it.

Third, start-up costs may be high for data

linkage systems, especially if based in part

on survey data. Linkage systems tend to be

easily maintained at 1Ow cost unless

*Prepared for the Panel on Statistics for an Aging Population and presented September 13, 1!985.

Reprinted with permission from the National Academy of Sciences, Committee on National Statistics

(to appear in their forthcoming report).

155
yg 

certain data problems, due to insufficient

attention in obtaining good matching information, can cause continuing expense and

difficulty at the analysis stage. Obviously

also, as turned out to be the case with the

Continuous Work History Sample, data quality

limitations in the administrative records

may necessitate considerable additional

expense.

@ Fouhth, data 7inkage systems emp70y methods

that may not be seen as entirely ethical

(e.g., Gastwirth, 1986) or that have confidentiality constraints that make the systems

hard to maintain as with the Retirement

History Survey or hard to use as with the

Continuous Work History Sample (e.g.,

Alexander, 1983). These controversial

e7ements in data linkage techniques, it may

be speculated, could be one of the reasons

linkages to the National Longitudinal Survey

(NLS) have never been attempter!(despite the

collection of social security numbers in the

NLS).

It is only with the last of these points that

we touch on risks that data linkage systems

encounter, which are not also encountered to

some degree in more conventional data-capture

approaches. The force of these concerns will be

discussed below.

Confidentiality and Disclosure Concerns

Data linkage operations bring us face-to-face

with a “dense thicket” of laws, regulations and

various ad hoc practices justified on heuristic

grounds.— ~re are statutory considerations

which apply either to the particular statistical

agencies involved or to the federal government,

as a whole. These include the Privacy Act; the

Freedom of Information Act; special legislative

protections afforded to statistical data, fOr

example, at the Census Bureau and the National

Center for Health Statistics; and, of course,

legislative protections afforded to administrative data, notably the 1976 Tax Reform Act.

The paper by Wilson and Smith (1983) gives a

good summary of the legal protections afforded

tax data. For a more general treatment of legal

issues and one which advocates change, see Clark

and Coffey (1983); also see Alexander and Jabine

(1978).

The regulations and practices of each federal

statistical agency differ too, not only because

of the different .le islative

which they operate, fut also ~’~~;llsotn%;

varying approaches that they have taken in the

accomplishment of their missions. Indeed,

interagency data sharing arrangements almost

defy description; they vary, among other

reasons, depending on which agencies are sharing

whose data and for wha~rpose. One excellent,

7iTEiETtincomplete, ~nory of current practice

is found in the work of Crane and Kleweno (1985).

Despite the complexity of this topic, several

general trends emerge that are wvrth noting:

● First, the American People are at best

ambivalent about letting their government

gy,

specifically between different agencies and

for purposes not obviously central to the

missions of both agencies. For example, in

a recent sur~ questions were asked about

the sharing of tax records with the Census

Bureau, something which is a longstanding

practice specifically permitted by law.

Three-fourths of those surveyed did not

support this use of administrative reco~

even though an attempt was made to put the

matter in a very favorable light, arguing

for it on efficiency grounds. (Gonzalez and

Scheuren, 1!?85; see also Appendix B for

exact question wrding).

o Second, bureaucratic practices which do not

respect this general unease about linkage

may need to be reexamined (e.g., Gastwirth,

7986). It is the duty, after all, of

government statisticians to upho~d both the

letter and the spirit of the law. The whole

tenor of the post-Watergate, Privacy Act and

Tax Reforfn Act era has been to limit

administrative initiatives (both big and

little “a”) and only to permit the expansion

of access after the enactment of positive

law. The failed initiative regarding

Statistical Enclaves illustrates this point

quite nicely. The Enclave proposal (Clark

and Coffey, 1983) sought what many regarded

as a degree of reasonable discretion on data

linkage and data access; however, the

authority requested was too broad for the

current political climate. The arguments

put forward in the proposed legislation’s

defense, for example, that it would increase

efficiency and bring order to a patchwork of

disparate practices, simply did not carry

the day. In sumnary, we do not seem to be

even close b a general solution on access

to data for statistical purposes.

● Third, absent new legislation, many

statistical agencies have begun to reexamine

their traditional access arrangements and

tighten still further their practices (e.g.,

Cox et al., 1985). For example, the use of

specl~ ~ensus agents to facilitate linkages

or to improve their subsequent analysis has

been drastically curtailed resulting in a

clear short-run loss in the utility to

outsiders of linkage methods at the Census

Bureau. On the other hand, new linkage

practices have emerged from such reviews

which may be superior to what otherwise

might have been done. The linkage between

the Current Population Survey and the

National Death Index is an excellent example

(Rogot, et al.,1983). Neither the Census

Bureau no~ me National Center for Health

Statistics felt it could give up access of

its data to the other agency; however, a

compromise was worked out where joint access

was maintained during the linkage operation

and this has proved satisfactory. In fact,

similar arrangements have been made successfully between the Center and the Internal

Revenue Service as part of a study of

occupational mortality (Smith and Scheuren,

1985b).

156

—
o Fourth. the extent to which public use files

can be-made available from linked data sets

has been greatly curtailed because cf new

concerns about what is called the “reidentification” proble~ (Jabine and Scheuren,

lgpK). Simply put, this means that if

enough linked data are provided in an

otherwise unidentifiable (PUbliC-LJ5e) form,

then each contributing agency could reidentify at least some of the linked units,

almost M matter what efforts at disguise

are attempted (Smith and Scheuren, 1985b).

The only major exception occurs when the

data made public from the contributing

agencies are extremely limited (Oh and

Scheuren, 1984; F’sass, 1985): M then,

usually, the incentives for cooperation on

the part of the contributing agencies are

limited as well. In practice, of course,

there is almost no incentive for the

contributing agencies to reidentify; thus,

legally binding contractual obligations

might be entered into that could stipulate

that there was no such interest. Contractl)al

guarantees, however, may mt satisfy all

parties to the linkage, because of the

pL!blic perception issues mentioned earlier.

It is conceivable, moreover, that no degree

of legal or contractual reassurance would be

adeauate at the present time to permit the

release of certain public use linked data

sets--for example, those involving Census

surveys linked to Internal Revenue Service

information. }!istorically it was only the

impossibility of reidentification which made

the release of matched CPS-IRS-SSA public

use files possible (Kilss and Scheuren,

1978).

It goes almost without saying that confidentiality and disclosure concerns pose the

greatest barriers to the development of data

linkage systems for studying aging. We will,

however. defer to Section 3 a discussion of what

might b; done to deal with such issues and

to explore the technical side of matching.

2. MATCHING C!ESIGNCONSIDERATIONS

This section is intended to provide a

discussion of matchina design auestions

go on

brief

that

must be looked at in developing data linkage

systems. We begin with some historical

background and then focus specifically on

“person” matches, where the social security

number is a possible linking variable. Linkage

systems based in part on survey information are

emphasized. Analysis problems also are covered,

particularly ways of estimating and adjusting

for errors arising from erroneous links or

nonlinks.

}!istoricalObservations

The main theoretical underpinnings for

computer-oriented matching methods were firmly

established by the late nineteen sixties with

the papers of Tepping (1968) and especially

Fellegi and Sunter (1969). Sound practice dates

back even earlier, at least to the nineteen

fifties and the work of Newcombe and his collaborators (e.g., Newcombe, et al., 1959).

The Fellegi-Sunter approach is basically a

direct extension of the classical theory of

hypothesis testing to the problem of record

linkage. A mathematical model is developed for

recognizing records in two files which represent

identical units (said to be matched). As part

of the process there is a comparison between all

possible pairs of records (one from each file)

and a decision made as to whether or not the

members of the comparison-pair represent the

same unit, or tiether there is insufficient

evidence to justify either of these decisions.

These three decisions can be referred to as a

“link,“ “non- link” or “potential link.”

In point of fact, Fellegi and Sunter contributed the underlying theory to the methods

already being used by Newcombe and showed how to

develop and optimally employ probability weights

to the results of the comparisons made. They

also dealt with the implications of restricting

the comparison pairs to be looked at, that is of

“blocking” the files, something that generally

has to be done when linking files that are at

all large.

Despite the early seminal work of Newcombe,

Fellegi and others, ad hoc heuristic methods

abound. There are mafi r~sons for this state

of affairs:

●

o

●

First, until recently (and maybe even now)

there have been only a handful of people

whose main professional interest is data

linkage. This means, among other things,

that most of the applied wrk done in this

field has been carried out by individuals

who may be solving matching problems for the

first time. Because the basic principles of

matching are deceptively simple, ad hoc

solutions have been encouraged that c~ld~

far from optimal.

Second, statisticians typically get involved

very late in the matching step, often after

the files to be matched have already been

created. Even when this is not the case,

little emphasis may be placed on the data

structures needed for linkage because of

other higher priorities. Design opportunities have. therefore. been qenerally

limited to what steps b-take

which were produced largely w

~“

Third, until the late nineteen seventies

good, portable, general-purpose matching

software had not been widely available

(e.g., How g;;lyLindsay, 1981), despite some

important attempts (e.g., Jaro,

1972). Even in the presence of generalpurpose software, the uniqueness of each

matching environment may lead practitioners

to write complex customized programs,

thereby absorbing resources that might have

been better spent elsewhere.

Fourth, especially for matches to administrative records, barriers tKJ the introduction of improved methods have existed

157
●

because cruder methods were thought to be

more than adeauate for administrative

purposes.

Fifth, the analysis of linked data sets,

with due consideration to matching errors,

is still in its infancy (Smith and Scheuren,

1985a). @Ui31ib3tiVe statements about such

limitations typically have been all that

practitioners have attempted.

More will be said below concerning these

issues in the context of computerized person

matching.

Person Matching

Typically in a computerized matching process

there are a number of distinct decision points:

● First, design decisions have to be made

about the linking variables that are to be

used, including the extent to which

resources are expended to make their

reporting both accurate and complete. (This

step may be the most important but it is

likely also to be the one over which

statisticians have the least control,

especially when matching to administrative

records.)

● Second, decisions have to be made about what

preprocessing will be conducted prior to

linkage. Some of the things done might

include correcting common spelling errors,

calculating SOUNDEX or NYSIIS Codes, etc.

(Winkler, 19851. Decisions about how to

sort and block the files also fall here

(Kelley, 1985).

● Third, decisions about the match rule itself

come next. If a probabilistic approach is

taken, as advocated by Fellegi and Sunter

(1969), then we have to estimate a set of

weights that represent the extent to which

agreement on any particular variable provides evidence that the records correspond

to the same person (and conversely, the

extent to which disagreements are evidence

to the contrary).

● Fourth, invariably there are cases where

status is indeterminate regardless of the

approach taken and a decision has to be made

about excluding them from the analysis,

going back for more information, etc.

TO give some realism and specificity to our

discussion, let us consider potential linkage

settings in which we could bring together two

files based on common identifying information:

name, social security number, sex, date of

birth, and address. As appropriate we will

contrast the linkage as taking place either

entirely in an administrative context or between

survey and administrative data.

Linking Variables--The social security number

(SW ) is the most important linking variable

that we in the United States have for person

matching purposes. SSNS were first issued so

that the earnings of persons in emp~oyment

covered by the social security program could be

reported for eventual use in determining

benefits. SSF!Swere also used as identifiers in

state-operated unemployment insurance programs

but no other major uses developed until 1961

when the Internal Revenue Service decided to use

the SSM as the taxpayer identification number

for individuals. Other uses by federal and

state governments followed rapidly and now the

social security number is a nearly universal

identifier. The Privacy Act of 1974 placed

restrictions on the use of SSNS but exempted

those formally established prior to 1975. So

far these restrictions have had only a minor

impact on the widespread use of the social

security number by governments and private

organizations (Jabine, 1985).

The social security number is nearly a unique

identifier all by itself and extremely well

reported, even in survey settings, as well as on

records SUC h as death certificates (e.~.,

Cobleigh and Alvey, 1974; Alvey and Aziz,

1979). In survey contexts, error rates may run

to 2 or 3 percent; but this depends greatly on

the extent to which respondents are reauired to

make use of records in order to provide the

requested information. Typically, driver’s

licenses, pay stubs, and the like are excellent

sources (in addition to the use of the social

security card itself).

Both administrative and survey reporting of

social security numbers are subject to possible

mistakes in processing, but these can be guarded

against by using part of the individual’s

surname as a confirmatory variable. For

example, IRS and SSA use this method as one way

of spotting keying errors.

A difficulty with current administrative

approaches is that name changes (especially for

females) may lead to considerable extra effort

in confirming (usually through correspondence)

that the social security number was indeed

correct to begin with. (It is a requirement of

the social security system that notification is

to be made when name changes occur, but many

people fail to do this until the omission is

called to their attention.)

One disadvantage of the social security number

is the absence of an internal check digit

allowing one to spot errors by a simple

examination of the number itself. At the time

the social security system started in the

mid-thirties, the widespread use of the SS}J as

an identifier was not envisioned. Indeed, there

is not a one-to-one correspondence between

individuals and the social security numbers they

use. In some instances more than one person

uses the same social security number. Historically, the most important cases of this type

arose because SSN’S were used by advertisers in

promotional schemes. Perhaps the best known

such instance is the number 078-05-1120

(Scheuren and Herriot, 1975). It first appeared

on a sample social security number card

contained in wallets sold nationwide in 1938.

!,lanypeople who purchased the wallets assumed

the number to be their own. The number was

subsequently reported thousands of times by

different individuals; 1943 was the hiuh year,

with 6,000 or more wage earners reporting the

number as their own.

158
.

While there have been over 20 different

“pocketbook” numbers, like 078-05-1120, they are

probably no longer the main cause of multiple

use of the same number. Confusion can arise

(and go largely undetected) when one member of a

family uses the number of another. Also, there

are incentives for certain individuals, like

illegal aliens, to simply “adopt” the social

security number of another person as their own.

The extent to which these problems exist is

unknown, but they are believed, at least by some

authorities, to be less prevalent than the

opposite problem--issuances of multiple numbers

to the same person (HEW Secretary’s Advisory

Committee, 1973).

lJntil1972, applicants for SSNS were not asked

if they had already been issued numbers, nor was

proof of identity sought. This led to perhaps

as many as 6 million or more individuals having

two or more social security numbers (Scheuren

and I-!erriot,1975). A substantial fraction of

the multiple issuances have been cross-

-referencedso that multiple reports for the same

individual can be brought together if desired.

Based on work done as part of the 1973 Exact

Match Study, it appears that, despite the

freauency of the problem, multiple issuances can

largely be ignored unless one is looking at

longitudinal information stretching back to the

early days of the social security program. (In

other words, people tend consistently to use

only one of the numbers they have been issued.)

While the social security number is nearly

ideal as a linking variable it is not always

available. For example, in the Current

Population Survey for adults the number is

missing between 20 and 30 percent. of the time

(Scheuren, 1983). Evidence exists, however,

from work done in connection with the Survey of

Income and Program Participation, suggesting

that with a modest effort the SSN missed rate

can be lowered significantly, to less than 10%

in Census surveys (Kasprzyk, 1983). Pecent

experience with death certificates shows a

missed rate of about 6% for adults (Patterson

and Bilgrad, 1985).

What, then, do we do when the SSN is missing

or proves unusable? We are obviously forced

either to seek more information or to try to

make a match using the other linking variables.

Now, as a rule, none of these other linking

variables is uniaue alone and all of them, of

course, are subject in varying degrees to

reporting problems of their own. Some examples

of the problems typically encountered are--

● Surname--As already mentioned, name changes

~marriage or divorce are, perhaps, the

main difficulty. For some ethnic groups,

there can be many last names and the order

of their use may vary.

● Given Name--The chief problem here is the

widespread use of nicknames. Some are

readily identifiable (“Fritz” for

“Frederick”) but others are not (like

“Stony” for “Paul”).

● Middle Initiai--People may have many middle

names (includfng their maiden name) and the

middle name they employ may vary from

occasion to occasion. Often, too, this

variable may be missing (Patterson and

Bilgrad, 1985!.

o Sex--This is generally well reported and,

~ept for processing errors, can he relied

upon. The main difficulty with this

variable is that it is not always available

in administrative records. (IRS does not

have this variable except through the

recoding of first names which simply cannot

be done with complete accuracy.)

o Date of Birth--Pay and month are generally

well reported even by proxy respondents.

Year can be used with a tolerance to good

effect as a match~ v%rlable. Again, as

with “sex,” this item is not available on

all the administrative files we are

considering.

o Address--This is an excellent variable for

~ing otherwise questionable links.

I?isagreements are hard to interpret,

however, because of address changes; address

variations (e.g., 21st and Pennsylvania

Avenue for 2122 Pennsylvania Avenue): and,

of course, differences between mailing

addresses (usually all that is available in

administrative files) and physical addresses

(generally all that is obtained in a household survey). Recent research on this

variable has been done by Childers and Hogan

(1984).

Still other linkage variables could t?avebeen

discussed, for example, race and telephone

nunber. Race is a variable that is similar to

sex except not nearly as well reported (unless

it is recoded as black, nonblack (e.g., U.S.

Bureau of the Census, 1973). Telephone numbers

have problems similar to addresses and, while

potentially of enormous value eventually, are

not now widely available in administrative files.

Preprocessing Steps--In general, any method of

standardization of Identifier labels, such as

names and addresses, will improve the chances of

linking two records that should be linked during

the actual matching process; however, it will

also, to an unknown degree, result in some

distortion and loss of information in the

identifying data and may even increase the

likelihood of designating some pairs of records

as a positive link when, in fact, the pair is

not a match.

Typically, for person matches to SSA or IRS

information, two preprocessing steps have been

undertaken: (1) to validate reported social

security numbers; and (2), if missing or

unusable, to search for SS1!s using surname and

other secondary Tinking variables. Both of

these steps have had to be conducted largely

within the existing administrative arrangements. The cost of mounting a wholly separate

effort has been judged to be prohibitive. (The

data sets involved are simply enormous: Social

Security has roughly 300 million SSNS now

issued. In recent years. IRS has been processing

about 100 million individual income tax returns

annually, containing well over 150 million

taxpayer social security account numbers.)

159
The “Validation Step” itself consists of two

parts: first, a simple match on SSN alone is

attempted; and, if an SSN is found, then

secondary information from Social Security or

Internal Revenue records is made available on

the output computer file. Further processing

then takes place so that the confirmatory

matching information (names, etc.) can be

examined and coded as t~ the extent of agreement.

It is possible that this part of the current

administrative procedure can be readily modified

to accord with modern matching ideas. What is

needed is to institute probability-based weights

for the agreements (disagreements) found. At

present administrators and statisticians alike

simply employ a series of ad hoc rules to

separate what will be consid~ed~ link from

cases that have questionable SSNS (e.g.,

Scheuren and Oh, 1975; Jabine, 1985).

The “Search Step” is an elaborate and fairly

sophisticated computerized procedure (which

differs in detail at SSA and IRS). The files

used are in sort; and, for the most part, the

only possible links that can be looked at are

cases that agree on surname. Since other

blocking variables are used as well, the current

administrative methods tend to be very sensitive

to small reporting errors. This is believed to

be true despite the fact that the computer

linkage procedures go to great lengths to

protect against more common reporting errors

(such as those mentioned above). At Social

Security they do this by systematically varying

the linking information on the record for which

an SSN is being searched. An extensive set of

manual procedures also exists for cases where

computer methods prove unsuccessful.

Unlike the “Validation Step,” it may not be

possible to bring the “Search Step” into full

accord with modern practice. First of all, we

would need to reexamine the decisions about what

blocking variables to use (Kelley, 1985).

Ideally we want variables that are without error

themselves, or nearly so, in both sources

(Fellegi, 1985) and that divide the files into

blocks or “packets” of reasonably small size,

within which we can look at all possible linkage

combinations (e.g., Smith, 1982). Research is

now underway in both agencies to find ways of

improving the blocking variables, but it is

unlikely that the current deterministic methods

will ever be replaced by probability-based ones

and for good reason. Linkage techniques for

administrative purposes must be employed with

high frequency in a great variety of situations

and hence be extremely efficient in the use of

computer time since the basic files involved are

so large.

A compromise that naturally arises within the

wrld of large computer files is to employ some

form of multiple, albeit still deterministic,

scheme. This is the approach taken with the

National Death Index. The NDI currently employs

over a dozen different combinations of matching

variables. Some give a primary role tn the

social security number, some to the surname;

still others place primary emphasis on the given

name or on date of birth (Patterson and Bilgrad,

1985). Adopting the NDI approach at SSA or IRS,

if feasible, might be one way to make a real

advance.

Match Rules--Usually the computerized matching

phase in a data.linkage system consists of three

steps: (1) comparisons between the linkage

variables on the files being matched; (2)

generation of codes which indicate the extent to

which agreements exist or disagreements are

present; and (3) decisions regarding the status

of each comparison pair. This structure is the

same, whether probability-based methods are

being implemented (e.g., HOW and Lindsay, 1981)

or heuristic approaches are taken (e.g.,

Scheuren and Oh, 19751.

● Cornarisen Step--In a sense, we have already

~step earlier. It depends

heavily on what linkage variables are

present; the reformatting, etc., done of

those variables to facilitate comparisons;

and the degree to which blocking is required

because of resource or other considerations. What is desired here conceptually is

to compare every record on each file with

every record on the other. Blocking, of

course, limits (sometimes severely) the

extent to which such comparisons can be

carried out. Any recoding of the linkage

variables (say SOUNOEX for surname) may

possibly, as we have noted, reduce the

utility of this step. Generally, if

resources permit, all the linking variables

should he used in the computer comparisons.

When this is not possible, they can still be

employed later in manually settling cases

where the outcome might otherwise be indeterminate. However, it almost goes

without saying that manual intervention

needs to be carefully limited and closely

controlled. Manual matching is extremely

costly and, while individual manual

decisions can sometimes be better than with

computer matching, usually humsns lack

consistency of judgnent and can be

distracted by extraneous information, such

that they act more decisively than the facts

would warrant.

Step--As a result of the comparison

“e a series of codes can be generated

indicating the degree of agreement which has

been achieved. These agreement outcomes may

be defined quite specifically, e.g., “Agrees

on Surname and the value is GILFORC.” They

might be defined more generally: agree,

disagree or unknown (the last arising

because of missing information, perhaps),

It becomes very difficult to talk about

the coding step without looking ahead to the

decision step and the specific approach that

will be taken there. Nonetheless, some

general observations can be made.

Obviously, when we have, fn fact, brought

together records for the same person, we

would like the agreement coding structure

not to obscure this point. For example, to

protect against trivial spelling errors, we

might use the same agreement code even

though there are transposition or singlecharacter differences in the name. (The

preprocessing of the files should have taken

care of some of this but it may, again, be a

consideration in the agreement coding

itself.)

160
.

.

.

In most applications of the

Fellegi-Sunter approach the assumption iS

made that agreement (or disagreement) on one

linking variable is independent from that on

any other, conditional only on whether or

not the records brought together are, in

fact, for the same person. To aid in making

this assumption plausible, special care

needs to be taken in structuring agreement

codes for such variables as sex and first

name, which are inherently related (Fellegi,

19~5).

Decision Step--An assessment can now be made

as to the extent to which an aqreement on

any particular linking variable,-or set of

variables, constitutes evidence that the

records brought together represent the same

person. Conversely, an assessment can be

made as to the extent to which disagreements

are due to processing or reporting errors or

at-e evidence that the records do not

represent information for the same person.

Typically, the records are divided into

those (1) where a positive link is deemed

to have been “definitely” established, (2)

where a “possible” link may exist but the

evidence is inconclusive, and (3) where it

can “definitely” be said that no link exists.

In probability-based methods a statistical weight function is calculated to order

the comparison pairs. The weights are

developed by examining the probability

ratio--

Prob (result of comparison, given natch)

Prob (result of comparison, given nonmatch~

The numerator represents the probability that

comparison of two records for the same person

would produce the observed result. The

denominator represents the probability that

comparison of records for two different persons,

selected at random, would produce the observed

result. In general, the larger the ratio, the

greater our confidence that the two records

match, i.e., are for the same person.

Let lJsconsider a particular example in which

we are matching on both sex and race; where sex

is always represented as either male or female

and where race has been recoded black or

nonblack. Further suppose the proportion of

males and females is each 50% and that blacks

constitute 10% of the population and nonblacks

g~ . Also suppose that the chances of a

reporting error on race are 1/100 and for sex

1/1000. Finally, we will assume that sex and

race are independently distributed in the

population and that reporting errors are

independent as well.

Nith these stipulations and assumptions, we

have the following table of possible probability

or “odds” ratios, say for blacks. Usually,

given the independence assumption, the

probability ratio is broken up into a series of

ratios, one for each agreement or disagreement, and logs are taken (to the base 2). One

is now working with simple sums, such that the -

larger (more positive) the total, the more

likely that the pair is a match; conversely, the

MOre negative the sum, the the

likelihood that the tw records areg~o~te{or the

same person.

Probability Ease ?

Outcome Ratio ILog of

,Ratio

I?ace and sex agree:

Race is black........... 197.?020 7.62’7?

Race is nonblack........ 2.442C 7.?Pel

Race agrees, sex does not:

Race is black........... 0.lg~o -?.3364

Race is nonblack........ C.0C24 -~.7027

Sex agrees, race does not. 0,171C -?.1714

P/eitheragree............. 0.(!001 -13.2877

See Computational Note at end of paper.

In our particular example it is only when both

sex and race agree that the sum of the logs is

positive. If the race is b7ack, the Tog is

between +7 and +?, moderately strong evidence in

favor of a match. If the race is nonb~ack,

however, the log is only slightly more than +1.

As one would expect, the strongest evidence in

favor of a nonmatch occurs when both race and

sex disagree; for this outcome the log of the

probability is about -J3. (Parenthetically, it

might be noted that this example illustrates

nicely the fact that outcomes that are frequent

in the population do not add very much to one’s

ability to decide if the pair should be treated

as a link; but if there are disagreements on

such variables and reporting is reasonably

accurate, then the variable may have a great

deal of power in identifying comparison pairs

that represent nonlinks.)

Now it can be shown in qeneral, as by Fellegi

and Sunter (1969) or by Kirkendall (1985), that

we can divide the weight distribution into three

parts, as seen in figure A. The points “a” and

“b” optimally divide the distribution of weights

so that we can simultaneously minimize the error

of accepting as a positive link cases that we

should not have matched, plus minimize the error

of rejecting as nonlinks cases that we should

have kept. Assumptions, like independence, must

be made, as a rule, and formidable computational

problems exist. Nonetheless, the approach is

entirely workable, especially since the development of the Generalized Iterative Record

Figure A.--Hypothetical Distribution of

Linkage Weights

— 7

matched

unmatched

++ Linkage Weights +

(adapted from Fellegi, 1985; comparison pairs

above the line are matched , those below

nonmatched)

161
Linkage System (GIRLS), which provides a stateof-the-art solution to the major computational

problems (Howe and Lindsay, 1981). Other

notable approaches in advanced linkage software

include the vmrk of Jaro and his collaborators

(Jam, 1985).

Indeterminate Outcomes--Virtually all computerlzed record llnkage schemes may leave at least

some cases where the status is indeterminate.

Three kinds of indeterminacy might be

distinguished:

● Nonlinks--Cases that were “definitely:

iletermned by the method to have no suitable

match, given the approach taken, but which

might have been matched if another technique

had been used (e.g., if we had employed a

different set of blocking variables). The

difficulty here is that, while all the

potential links that get looked at may have

proved inadequate, not all possible links

are examined and we cannot tell the

difference necessarily between a case that

should have been a link and one that should

not. The only way this issue can be skirted

directly is in the implausible situation

when the probability of a match between

blocks is zero. (An indirect “solution” to

this problem can be developed using contingency table ideas as will be discussed

below.)

● Multiple Links--These can occur in the

tellegi-Sunter formulation; that is, there

may be more than one comparison pair for a

unit whose match weight or score exceeded

the threshold for acceptance. In some

cases, these many-to-one links might be

appropriate but, usually, a further step has

to be taken to select “the best” one. This

problem also can occur with some frequency

in administrative contexts and with the

National Death Index. Manual resolution is

usually the approach taken, especially if

further information is going to be sought or

is available to help make the selection.

Jaro (1985) offers a computerized transportation algorithm to solve multiple linkage

problems. His approach is most effective

when all the linking information has already

been computerized and when there are

contention problems in the linkages, that

is, “n” records on one file are matching “m”

records on another. Smith and Scheuren

(1985a) suggest ways of carrying through the

statistical analysis using all the links.

● Potential Links--This type may be the

largest form of indeterminacy. These are

the cases that fall in the middle area in

figure A. The usual advice, resources

permitting, is to collect more information

to resolve the match status. If statistical

estimates are to be made, and the resources

needed to seek further information are not

available, the potential links may be

treated as nonlinks and a survey-type nonresponse adjustment may be made (Scheuren,

1980)+ It is possible, also, b consider

keeping some of the potential links and then

162

conducting the analysis, with an adjustment

being made for mismatching (Scheuren and Oh,

1975).

Often, the difficulty with indeterminate cases

can be traced back to a design flaw in the data

linkage system. For example, not enough linking

information may have been obtained on one or

both files to assure uniqueness. Maybe the

degree of redundancy in the identifiers was

insufficient to compensate completely for the

reporting errors. In an administrative context,

the linkage process may be so constrained for

operational reasons that, even if there are

sufficiefltlinkage items, they cannot be brought

fully to bear.

Analysis Issues

Statements about the nature of the matching

errors are typically provided in data linkage

studies; generally, however, there is no real

attempt to quantify the implications of matching

errors for the specific inferences being drawn.

Data linkage systems, like other survey-based or

sample-based techniques, need to be “measurable”

and to be structured to be as robust as possible

in the face of departures from underlying

assumptions. What can be done to achieve this

is a separate and sizable subject (Smith and

Scheuren, 1985a). For our present purposes it

may be enough to sketch some of the issues and

indicate general lines of attack.

o Linkage Documentation--Documentation should

routinely be provided which tabulates the

results of the-match effort along dimensions

that turned out to be important in the

analysis. A distribution of the weights

would be one example, perhaps shown for

major subgroups. If a public-use file is

being created, then the match weight might

be placed in the file along m“th summary

agreement codes, so that secondary analysts

can “second-guess” some of the decisions

made. Providing potential links, at least

near the cut-off point, is another example

of good practice. Most of the above, by the

way, were part of the documentation and

computer files made available from the 1973

Exact Match Study (Aziz, et al 1978). ——”~

o Adjusting for Nonlinks--It is generally

Worthwhile conslrler reweiqhtina the

linked record pairs actually ~btai~ed to

adjust for failures to completely link all

the proper records to each other (Scheuren,

1980). Conventional nonresponse procedures

can be followed (Oh and Scheuren, 1983).

Imputation strategies are also possible, but

may be less desirable because they tend to

disturb the estimated relationships across

the tm files being brought together (Oh and

Scheuren, 1980; Rodgers, 1984). An important problem in this adjustment process,

however conducted, is in being able to

estimate whether a link should have

occurred. Sometimes, by the nature of the

problem, w know all the records should have

been linked. In other cases (Rogot et al

1983), one of the key

——”$

things we are

interested in is, in fact, the linkage
,

.

.

0

rate. Elser&r&, (Scheure~iv:983; Smith and

Scheuren, , w advocated a

capture-recapture approach to this

estimation problem. Such an approach, in

the presence of blocking, will actuallY

allow us to improve the links obtained, as

well as make it possible to measure the

extent to tiich our best efforts still lead

to erroneous nonlinks. Capture-recapture

ideas are w1l described in the literature

(e.g., Bishop et al 1975; Marks et ~.,

1974).

— —.” ~

Were we WT1l only indica~e the

application.

If w emDlov more than one set of blocks

and keep t~ac~ for each blocking procedure

whether w would have found (and linked) the

case in every other blocking scheme, then

for any subpopulation of linked records we

can construct the usual 2n table, where we

look at the link/nonlink status for each

blocking (with “n” being the number of

separate blocking schemes). To estimate the

number of records not caught by any scheme,

three or imre sets of blocks are recommended; otherwise, the assumptions made may

be unrealistically strong. (The National

Death Index, or NDI, already emPloYs manY

more than this, as w have noted earlier.)

For best results the blocks need to be as

independent functionally and statistically

as is possible, given the linkage information. (Improvements in the current NDI

would be recorirnendedhere, but these seem to

be coming in any case.) Application of

these ideas in an IRS or SSA context seems

worthy of study (Scheuren, 1983), although

the expense of developing such an approach,

say at SSA, may never be incurred unless

there were a compelling administrative need.

Adjusting for Mismatches--In most linkage

s~ ave operated in what

they considered to be a conservative manner

with regard to the links they would accept.

Sometimes this may have meant heavy additional expense in obtaining more information

or the risk of seriously biasing results by

~f;[~ng out a large number of the potential

. In any event, further research is

needed on how to apply more complex analytic

techniques that take explicit account of the

mismatch rate, possibly by use of errors-invariable approaches where the mismatch rate

is estimated, e.g., as in Scheuren and Oh

(1975), so that a correction factor can be

derived. We must also attempt to find ways

of estimating the mismatch rate that make

weaker assumptions than those made in most

Fellegi-Sunter applications. (Some further

ideas on this are found in Smith and

Scheuren, 1985a).

In sumnary, the main issues in the analysis of

linked data sets are that, at a minimum, we need

to examine the sensitivity of the results to the

assumptions made in the linkage process. Where

possible, we need b quantify uncertainties in

the results; specifically, indetenninacies in

the linkages should translate into wider confidence intervals in the estimates. To achieve

these goals we need to bring in techniques from

other areas of statistics and apply them creatively to linked data sets. Examples here

include information theory, error-in-variable

approaches and contingency table (capturerecapture) ideas.

3. SOME CONCLUSIONS AND AREAS FOR FUTURE STUDY

In this paper we have dealt with the topic of

data linkage in abroad conceptual framework,

using examples from recent practice. It is

appropriate now to draw out the implications of

the point of view expressed for studies of aging

and to use that surmnary as a basis for recommending further research.

Overall Perspective

We have argued elsewhere that the potential

for the statistical use of data linkage systems

is truly enormous (e.g., Kilss and Scheuren,

1980; Jabine and Scheuren, 1985). The

suggestion has even been made that data linkages

among administrative records (with some supplementation) might eventually replace conventional

censuses in the United States (Alvev and

Scheuren, 1982). Such ideas are not new,

certainly not tn Europeans, where many developed

nations have been rapidly moving in this

direction (e.g., Pedfern, 1!?83). Indeed some

countries, like Penmark (Jensen, 1983), may have

“already arrived.”

In the United States there has been some

reluctance and resistance to accepting th~

inevitability of such a future. Grave concerns

have been expressed (Butz, 1985) about movinq

too fast or in the wrong way. Pfter all, while

Denmark has succeeded in its efforts, other

countries (notably West Germany) have

encountered major problems which did grave

damage to their statistical programs.

In view of what has happened elsewhere and,

especially, given the current state of public

opinion, we would caution that any planned use

of data linkage systems be grounded firmly in

existing practice and not be based on new

legislation designed to expand on what it is

currently possible to do. On the other hand, it

is important to conceptually integrate what is

now possible with what might be possible ten or

twenty years from now. Some further observations are--

First, if a data linkage approach is going

to be taken, it should be a necessary means,

not just a sufficient one, for achieving

some reouired specific purpose. It is

simply not enough to argue the need for data

linkage on efficiency grounds.

Second, the linkage should be seen as

important by all the cooperating agencies

and part of their mission. It is simply not

enough that the law can be interpreted to

permit such linkages. Positive law, and

indeed social custom, must exist which

encourages the research. at least in broad

outline-(Cox and Boruch,-1985).

163
●

●

●

Third, strong continuing user support is

essential if a long-term basic research

effort is W he successful. Program

agencies cannot. be relied on for really

lonu-run undertakings without this support.

Opportunity costs are simply too high. If

the linkage system is to be placed in a

statistical aaency, user involvement is,

again, essential (from the outset., if

possible). Without strong user involvement,

statistical agencies will tend to emphasize

continuity of measurement over relevance

(while proqram agencies tepd to the reverse).

Fourth, cost considerations suggest. that

most data linkage systems be based on, or

augment, an existing survey or administrateve system. Further, maintenance coSts

should he low so that in the long run most

of the resources can be focussed on

exploiting the analytic potential of the

system.

Fifth, access to the results of the linkaae

system must be basically open not only to

the primary user(sl, but to secondary users

as well. Ways to solve the “reidentification” problm must be built into the

undertaking from the beginn;ng and firmly

rooted in the best statistical practice.

Still other considerations come to mind, such

as adeauate physical security during the linkage

operation and minimizing the risks by removing

identifiers from workino files as soon as

possible (Kilss and Scheuren, 1978: Steinberg

and Pritzker, 1967; Cox and 130ruch, 1985: and

Flahertv, 1978).

Many ad hoc efforts have succeeded without

strictly—adb~ing to one or more of the above;

nonetheless, if one is working towards a future

which encompasses still more data linkages, it

is essential that the strategy taken be

absolutely sound and above reasonable reproach.

Potential Data Systems Deserving Further Study

Within the framework Just Cllven, there seems

to be a clear need to ‘inten~ively examine the

potential of particular data linkage systems to

answer certain auestions. We will illustrate

this point by looking at one of the most

preSSing areas in the United States where better

data are needed -- this is on our rapidly

grawing aged population. Even if we confine

ourselves to this single area, many subsidiary

issues must be addressed. For example, where

are the greatest gaps: in data on health,

general demographic information, financial data,

or the extent to which federal programs provide

support? In what follows, there has been no

attempt to answer this ouestion. To do so, we

would go well beyond the scope of the present

paper. Instead, there is a discussion of four

data linkage environments that, depending on the

answer to the Question, may warrant further

study. Special emphasis has been placed on the

limitations of working in each of these settings

and of the role that a strang outside user might

play in overcoming those limitations.

Sacial Security and Health Care Financing

Administrations -- The Social Security (SSA) and

Fea Ith Care tlnancing Administrations (HCFA) are

unlikely to take the lead in building and

maintaining general purpose statistical data

inkage systems, in part because of a reduced

emphasis on basic and applied research.

Nevertheless, the program-oriented statistical

activities of these agencies will continue to

give them an important role in data linkage

efforts which are consistent with agency

missions. The potential at SSA and HCFA for

praviding improved sources of statistics on the

aging population depends on the extent to which

they are able to: (1) maintain major in-house

data linkage efforts, like the Continuous Work

History Sample (e.g., Buckler and Smith, 1980)

and the Medicare Statistical System (U.S. Health

Care Financing Administration, 1983); (2)

continue to sponsor or co-sponsor periodic or ad

hoc surveys; and (3) cooperate in Iinka@

~dies sponsored elsewhere (for example, in the

Survey of Income and Program Participation or in

the Health Interview Survey) if they are in

support of the agencies’ missions.

However, these efforts would need to be

caupled with strong outside user support. At

SSA and HCFA, there may be a particularly

pressing need for outside users to aid in the

resumption of some form of public release of

subsets, at least, of the administrative samples

now being employed almost solely for in-house

purposes.

Internal Revenue Service -- It seems pointless

ta speculate upon the degree to which

interagency data linkages can or should take

place involving Internal Revenue Service (IRS)

data. Formidable statutory barriers narrowly

limit access to tax records and, even when the

legal requirements can be met, many other

agenCieS, notably the Census Bureau, feel they

would be unable to engage in a cooperative study

because of concerns about public perception.

American social customs, particularly concerns

about “Big Brother,” stand as nearly

insurmountable obstacles in the short run.

It is possible, though, to use IRS records

essentially all by themselves as a basis for

studying the aged population. This may seem

surprising because the statistical program of

the Internal Revenue Service is not looked at

typically as a source of such information.

Certainly the Statistics of Income publication

series has focused very llttle on the aged, and

then mainly through the use of the age exemption

to identify taxpayers 65 years or older (e.g.,

Holik and Kozielec, 1984). Broader-based

research has been possible through occasional

linkages between the IRS’s Individual Income Tax

Model File and Social Security information. In

a few cases, these linkages have resulted in

public-use files (DelBene, 1979). What has not

been done is to look at the aging population

longitudinally, although this is fairly

164
straightforward, at 1east back to 1972.

Furthermore, with the recent addition of

complete SSA year-of-birth information to IRS

files, it will be possible to routinely study

age cohorts by means other than the age

exemption. It is also noteworthy of mention

that linkages between IRS files and the recently

instituted National Death Index have just been

successfully instituted (Bentz, 1985).

Tax returns probably represent the single best

source of financial information and could,

therefore, prove of value in studying the aging

process. There are, however, three main

limitations to their use:

● First, the income data, while of exceedingly

high auality (relative to surveys), are

incomplete since certain nontaxable incomes

have been omitte~lf:;~g., taX-eXeMpt “l::;

interest and payments). -

recently, social security benefits were

unavailable but they are now potentially

taxable (beginning With 1984).

● Second, the population coverage of income

tax returns is incomplete. In fact, only

about half the population ages 65 years or

older show up as taxpayers on income taX

returns. Again, recent changes have a

bearing here since information documents,

notably Forms 1099 from Social Security, are

filed with the Internal Revenue Service for

all social security beneficiaries. This

change permits an expanded population

concept that could be essentially complete

for the aged population.

● Third, the tax return is exceedingly awkward

as a unit of analysis for some purposes

since it does not always conform to

conventional family and household concepts

(Irwin and Herriot, 1982). It is possible

though, using information documents like

Forms W-2 (for wages), Forms W-2P (for

private pensions), and Forms 1099 (for

social security payments, dividend,

interest, etc.), to develop approximate

financial profiles of virtually all

individuals aged 65 or older. (Major gaps

would exist, of course, for supplemental

security income recipients and recipients of

veterans disability benefits.) There does

not appear to be much hope in inferring

changes in lifestyles directly from the

current IRS information, althOUgh the

proposed addition of dependent social

security numbers could lead to real progress

(Alvey and Scheuren, 1982).

Depending on its extent, the cost of

maintaining an IRS data linkage system to study

aging could be ouite modest. Public-use files

are possible; but, as with the Social Security

and Health Care Financing Administrations,

strong outside support would be needed.

National Center for Health Statistics --

Recent changes (s”Irken and Greenberg, 1983) at

the National Center for Health Statistics

suggest that the Center may be assuming a

leading role in sponsoring data linkage

systems. Naturally and appropriately, the focus

of these systems will be suite narrow, looking

almost solely at health concerns. The National

Health Interview Survey (HIS), involving about

40,000 households annually, appears to be the

Center’s main survey vehicle for the approach

it is planning to take. Continued periodic

matching to Medicare records seems planned (Cox

and Folsom, 1984) and, of course, the National

Death Index can be expected to be fully

exploited (Patterson and Bilgrad, 1985). Stil1

other linkage efforts are underway (e.g.,

Johnston, et al., 1984) which, taken together, ——

suggest that the Center is pursuing a coherent,

fully integrated approach, both ameng its

surveys and towards needed vital record systems.

When the social security number ouestion was

added to the HIS a few years ago, it was largely

for matching to the National Death Index. Great

care initially was given to securing informed

consent from respondents before obtaining the

information. This approac~ved tedious and

expensive. Now the social security number

auestian is simply asked without much

explanation; and, only if reouested, are reasons

given for why the information needs to be

obtained (see Appendix C). Response rates are

quite high, about 90%, and it appears that the

HIS may constitute a major vehicle for a

successful data linkage appraach ta studying

aging. Concerns exist abaut the reidentification problem, but exactly how the Center

will deal with this factor is unclear.

Bureau of the Census -- Historically, the

Census Bureau has played a major role in federal

data linkage systems involving surveys,

sometimes as the sole spansor (e.g., Childers

and Hagan, 1984), but often as a partner in

conducting a particular study (e.g., as with

Social Security, Bixby, 1970). Much of this

work has focussed on the Current Population

Survey (Kilss and Scheuren, 1978). Of more

promise in future studies of aging has been the

development of the Survey of Income and Program

Participation (SIPP), which has as one of its

design elements the nation that data linkages

wauld be attempted, at least to Social Security

information (Kasprzyk, 1983). SIPP, which may

settle down to a sample size of about 30,000

househo:;~ annually, is certainly of sufficient

size scope to laok at many general

demographic, financial and program related

questions concerning aging. The SSN reporting

rate is on the arder of 90%; hence, the needed

resources to “perfect” the linkage (and the

analysis problems resulting from faulty ar

incomplete linkage) should be entirely

manageable. Oversampling is possible for

particular subgroups (e.g., those aged 65 or

alder); however, unfortunately, SIPP, like the

HIS, is confined to the noninstitutional

population and for studies of the very old it

may not be suitable alone.

TW difficulties exist with SIPP that further

research may resalve. First is the extent to

which informed consent is being obtained when

the social security number is being secured

(SIPP’S approach is similar to that in the HIS--

see Appendix D). Related to this concern, of

course, is the extent to which such consent is

165
felt to be needed. The second issue, and one

that seems exceedingly troublesome to the Census

Bureau, is the “reidentification” problem.

(Briefly stated, the reidentification problem is

particularly acute where linkage is concerned,

because the cooperating agencies might have

enough data on the linked file to reidentify

virtually all of the individuals linked.)

The Census Bureau appears to be searching for

a solution that involves either simply rmt

releasing public-use files of linked data or

releasing public-use files where only very

limited linked data have been provided and some

kind of masking technioue has been employed to

prevent reidentification. Given these restrictions, it must be said, there seem to be real

difficulties in concluding that there are

sufficient benefits to outside users of a

SIPP-based data linkage system. Some further

conrnents on this dilemma and ways a general

research program could address it are given

below.

General Issues Deserving Further Study

Further research is needed on a wide range of

data linkage issues, both structural and

technical. Four, in particular, stand out from

the rest and deserve special attention: ethical

and legal concerns, public perception ouestions,

finding solutions to the reidentification

problem, and finally, analysis issues in the

presence of matching errors.

Ethical concerns such as those raised by

Gastwirth (1986) seem to need a more specific

answer than they have been given so far (e.g.,

as by Dalenius, 1983). What might be done is to

obtain some data directly bearing on how

respondents actually think about data linkage.

We could approach this in a way similar to the

earlier study by the CotIsnitteeon National

Statistics concerning confidentiality guarantees

(Committee on National Statistics, 1979).

Within the context of current survey efforts in

HIS and SIPP it might be extremely valuable to

know how often respondents ask for clarification

before providing social security numbers and to

code the cases accordingly so we can look at

differential refusal rates, for example. Again,

exactly what is said (by respondents and

interviewers) typically when respondents do

ask? Legal and procedural issues abound here,

too. For example, how long, even assuming

informed consent, can the consent be treated as

binding? Social Security practices with outside

researchers (when they obtain consent to gain

access to individual records) is to treat the

consent as binding potentially only once; thus,

reouests for information on the same subjects

may reauire a renewal of the consent. Signed

consent agreements are also reauired of outside

researchers. Such a requirement has never been

imposed, say, in Census Bureau surveys,

should it be? If it were, what wauld be

costs of such a practice in interview t’

reduced response, and cooperation generally?

Public perception concerns deserve to

examined in depth. To what extent are

already violating the public’s sense of

social customs within which statisticians

supposed to wark? The public opinion pol

but

the

me,

be

t:

are

ing

results reported in Ganzalez and Scheuren (1985)

need to be follawed up. It does not seem

defensible simply to speculate about whether

this or that approach to data linkage would be

acceptable to the public. While we can never

use opinion palling to answer all the many

specific issues that exist here, much can be

done. Of particular interest may be the extent

to which the public knows or assumes such

linkages take place now and for what purposes;

the perceived legitimacy of actual and perceived

purpases; whether statutory ar contractual

prohibitions against effarts at reidentification

would be seen to be adeouate; and so on.

We do not believe that an entirely

satisfactory technical solution to the

reidentification prablem is possible; but a

great deal more can be done to allow for at

least limited release af linked information.

The work of Paass (1985) and Smith and Scheuren

(1985a) is suggestive here. The line of attack

that appears mast promising is what might be

termed a three-step pracess. First, “slice” the

data up into small enough bits sa that each of

the “bits” can be adequately masked. (The data,

for example, might be divided up into disjoint

subsets and for each subset of observations,

say, only 2 to 4 different items of administrative data would be provided.) Second, if

the slices are chasen appropriately, then one

can “splice” back together the camplete data set

using statistical matching; but in a setting

where the conventional--and usually false

conditional--independence assumption (e.g.,

Radgers, 1984) does not nave ta be made.

Finally, the masking step can add “noise” to the

data set in such a way that certain analytic

results are either invariant under the noise

transformation or correction factors can be

ca~:;:;ted and readily applied.

are some serious losses in this

approach. Far example, the effective sample

SiZf? of the linked data items may have shrunk

considerably. In any case more research on this

problem is definitely warranted, (maybe even if

contractual and legal solutions turn out ta be

eventually possible). Either way, public access

to the linked data sets must be seen as a key

objective when such studies are undertaken and,

to the extent possible, release practices shauld

be as open as with any other data set (Cammittee

on National Statistics, 1985).

Finally, a number af analysis issues have been

mentianed which deserve further research,

especially in measuring matching errors and

adjusting the matched results accordingly. In

particular, we need to find a way to escape the

historical dilemma that the dissemination and

growth af saund theory and practice have been

retarded by the perceived uniqueness of many

linkage problems (and the customized salutions

this perception has led to). The profaund

nature of the comman sense principles upan which

good practice is based are nat widely enough

appreciated. Insufficient attention has been

paid ta the analysis issues in data linkage

systems, perhaps because so much creative energy

and financial resources typically go into the

linkage steps (Smith and Scheuren, l?&5a). It

may be too optimistic to suppose that things are

now changing, but there is some evidence to this

166
.

L

.

,

.

,

.

effect in the success of the 19C5 Washington

Statistical Society Horkshop on Exact Matching

Hethodo?ogies (Kilss and }Ivey, l!?~~). In any

case, it is time to stop treating natching as a

necessary but dirty business, isolated from

other parts of statistical theory and practice.

ACKNOWLEDGMENTS P,NDP,FTERWORDS

The ideas in this paper owe much to my

associations with other professionals in the

field of-matching. Particular thanks are due to

Dan Kasprzyk, for his useful renarks, and,

especially, Tom Jabine, whose insightful

cements were much appreciated, even though I

was unable to incorporate them all in the

present version. Tom also acted as a discussant

when this paper was originally given and, anong

other things, corrected a computational error in

the calculation of the probability ratios shcwn

in the example. All the remaining errors are,

of course, my responsibility.

COMPUTATIONAL NOTE

The Probability Ratios shown in the table

above were calculated as follows:

Race and Sex Agree (Race is Black)

g.g&l\/;+ + ;.+)= ,,7.8020

Race and Sex Agree (Race is Nonblack)

~“~~[$”$]~~”$+;”+)= 2.4420

Race Agrees, Sex Does !!ot(Race is Black)

%“A/(+”d)(;”; +i”+)’ 0.”80

Race Agrees, Sex Does Not (Race is Nonblack)

&&/(_&](~.;+~;]= 0.0024

Sex Agrees, Race Does Not

1 . 999 9. 1 1. 9 1.1

100 1000 /( mmhm, K )

~z+;”+= O.111o

!!eitherAgree

A“& /( )( )

+“++’; +“; ++”; =O”OOO1

167
REFERENCES

Alexander, L. and Jabine, T.

1978 Access to Social Security Microdata

Files for Research and Statistical

Purposes: An Overview. Social Securitv

Bulietin, U.S. Social ‘Se-y Adrunl - ‘~

stratlon.

Alexander, L.

1983 There Ought to be a Law..., proceedings, Section on Survey Rese37Th

Flethods, American Statistical Association.

Alvey, W. and Aziz, F.

1979 Mortality Reportinq in SSA Linked

Data: Preliminary- Results, Social

Security Bulletin, U.S. Social Security

Xdmlnlstratlon.

Alvey, W. and Scheuren, F.

1982 Background for an Administrative Record

Census, Proceedings, Social Statistics

Section, American Statlsticaf

~tion.

Aziz, F., et al.

1978 STiid~s from Interagency Data Linkages

Report No. 8) us Social Security

Administration. ‘ “ “

Barron,

1978

E.

The Survey of Low-Income Aged and

Disabled: Survey Design and Data

System, Policy Analysis with Social

Security Research Files, U.S. SociaT

Security Admlnlstratlon. .

Beebe, G.

1985 Why Are Epidemiologists Interested in

Matching Algorithins? Record Linkage

Techniques--l985, U.S. Internal Revenue

Service.

Bentz, M.

1985 The Intergenerational Wealth Study:

Prospects for Data Analysis and

Methodological Research, presented at

the Canadian Conference in Tax

Modelling, September 1985.

Bishop, Y., et al

1975

— —.

Discrete Multivariate Analysis: Theory

and Practice,~ ress:

Bixby, L

1970 Income of PeoDle Aaed 65 or Older:

Overview from ‘the 1~68 Survey of the

Aged, Social Security Bulletin, U.S.

Social 3ecurity Admlnlstration.

Buckler W. and Smith, C.

1980 The Continuous Work History Sample

(CWHS): Description and Contents,

Economic and Demographic Statistics,

. . Soc7al Security Admlnlstratlon.

Butz, W.

1985 The Future of Administrative Records in

the Census Bureau’s DemoaraDhic

Activities, Journal of Business ‘and

Economic Statlstlcs, American

tatlst~cal Assoclatlon.

Cartwright, D.

1978 Major Limitations of CWHS Files and

Prospects for Improvement, Policy

Analysis with Social Security Research

Files, us Social Security Admlnl-

=ion. “ .

Childers. O. and Hoqan. H.

1984

Clark C.

1983

“Matching ‘IR5 Records to Census

Records: Some Problems and Results,

Proceedings, Section on Survey Research

Rethods, American

~tion.

tatisticaf

and Coffey, J.

HOW Many People Can Keep a Secret?

Statistical Data Exchange Within a

Decentralized System,

Section on Survey Researc_

Xmerlcan Statlstlcal Assoclatlon.

Cobleigh, C. and Alvey, W.

1975 Validating the Social Securitv Number.

Studies from Interagency Data” Linkage;

Report No. 4), Us. Social Security

Administration.

Committee on National Statistics

1985 Sharing Research Data, National

>cademy of Sciences.

Committee on National Statistics

1979 Privacy and Confidentiality as Factors

In Survey Response, NatTonal Academy of

_Sclences.

Cox. B. and Folsom. R.

1984-

Cox, L.,

1985

Evaluation of Alternate Designs for a

Future NMCUES, Proceedings, Section on

Survey Researcl Methods, American

Statistical Assoc7atlon.

et al.

~n~dentialitv Issues at the Census

Bureau, Proceeclingsof the First Annual

Census Bureau Research Conference, U.S.

Bureau of the Census.

Cox, L. and Roruch, R.

1985 Emerging Policy Issues in Record

Linkage and Privacy, presented at the

45tk Session of the International

Statistical Institute.

Crane, J. and Kleweno, l?.

1Q85 Project LINK-LINK: An Interactive

Database of Administrative Record

Linkaae Studies. Record Linkaae

Techn~aues--l985, U.S. Internal Revenue

Service.

168
Irwin. R. and Herriot. R. Dalenius. T.

1983 ‘

DelBene,

1979

Felleqi,

1985

Fellegi,

1969

Informed Consent or R.S.V.P.,

Incomplete Data in Sample Surveys

Volume 11, Academic Press.

L.

1972 Augmented Individual Income Tax

Model Exact Patch File, Studies from

Interagency Data Linkages (Report No.

1, [J. . Social Security Administration.

1.

Tutorial on the Fellegi-Sunter Model

for Pecord Linkage, Pecord Linkage

Techniques--l985, U.S. Internal Revenue

3ervlce.

I. and Sunter, A.

A Theory of PecorclLinkage, Journal of

the American Statistical Association,

vol. 6a, PP. 1183-1710.

Flaherty, 0.

1978 The 8ellaqio Conference on Privacy,

Confidentiality and the Use of

Government I!icrodata, Pew Directions in

Program Evaluation, vol. 4, PP. 19-30.

Castwirth, J.

1~p~ Discussion Comnents to paper by George

Ouncan and Diane Lambert, A Model for

Statistical Disclosure Control Based on

Predictive Distributions and

Uncertainty Functions, Journal of the

American Statistical Association,

?lmerlcanStatlstlcal Assoclatlon.

(%nzalez, M. and Scheuren, F.

1985 Future Work by the Conference of

European Statisticians on Population

and Housing Censuses, presented before

the Thirty-Third Plenary Session of the

U.N. Conference of European

Statisticians.

Holik, D. and Kozielec, J.

1984 Taxpayers Age 65 or Older, 1977-81,

Statistics of Income Bulletin, U.S.

Department ot he Treasury, Internal

Revenue Service.

HEW Secretary’s Advisory Committee

1973 Records, Computers and the Rights of

Cltlzens, us Department of Health,

~n anriWelfare.

Howe, G.

1981

and Lindsay, J.

A Generalized Iterative Record Linkaoe

Computer System for Use in Medic~l

Follow-up Studies, Computer and

Biomedical Research, vol. 14, pp.

340. .

Irelan, L. and Finegar, W.

1978 Surveys Relatinq to Retirement and Survivor~hip, Polity Analysis with Social

Security Research Files, U.S. SoclaT’

Security Administration.

1982

Jabine,

1985

Jabine,

1985

Jaro, M.

Jaro, M.

1972

An Initial ‘Look at Preparing Local

Estimates of Household Size from Income

Tax Returns, Proceedings, Section on

Survey Research F?ethods, Pmerlcan

tatlstlcal Assoclatlon.

T.

Properties of the Social Security Number Relevant to Its Use in Record

Linkages, Record Linkage Techniaues--

~, U.S. Internal Revenue Service.

T. and Scheuren, F.

Goals for Statistical Uses of Administrative Records: The Next Ten Years,

Journal of Business and Economic

tatlstlcs, American Statistical

lssoclation.

Current Record Linkage Research, Record

Linkage Techniques--l9&?5,U.S. InRevenue Service.

UNIMATCH--A Computer System for Generalized Record Linkage ‘Under Conditions

of Uncertainty, AFIPS-Conference

Proceedings.

Jensen, P.

1983 Towards a Register-Based Statistical

System--Some Panish Experience,

Statistical Journal of the United

Nations, vol. 1s PP. 341-355.

Johnston, D. et al.

1984 1980— ~A Hospital and National

Natality/Fetal Mortality Survey Linkage

Methodology, “Proceedings, Section on

Survey Research Methods, American

_StatisticalAssoclatlon.

KasDrzYk. D.

1983-

Kelley,

1985

‘Social Security Number Reporting, the

Use of Administrative Records and the

Multiple Frame Design in the Income

Survey Development Program, Technical,

Conceptual and Administrative Lessons

o~ e ncome urve envelopment

Program, Social SCIence Researcfi

~: New York.

R.

Advances in Record Linkage MethodOloov: A Method for Detennininu the

Bes~810cking Strategy,

Technicwes--l985, U.S. In %%%2% erna

3ervlce.

Kestenbaum, B.

1985 The Measurement of Early Retirement,

Journal of the American Statistical

Association, vol. 80, PP. 38-45.

169
Kilss, B. and Alvey. M.

1985 (Ed.) R;cord Linkage Techniques --

1985, Us. Department of the Treasury,

~rnal f?evenueService.

Kilss, B. and Scheuren, F.

~$18~ CfialS and Plans for a Linked Administrative Statistical Sample, Proceedings, Section on Survey ~h

hlethods, American Statistical Asso-

~.

Kilss, B. and Scheuren, F.

1978 The 1973 CPS-II?S-SSAExact Match Study,

Social Security Bulletin, U.S. Social

3ecurlty Administration.

Klein, B. and Kasprzyk, D.

1983 Designing an Integrated Disability Data

System from Social Security Administrative Records, Proceedings, Section

on Survey Research Methods, American

tatlstlcal Association.

Kirkendall, N.

1985 Weiqhts in ComDuter Matching: Applications and an Information Theoretic

Point of View, Record Linkage

Techniques--l9&35,1!.S. Internal Revenue

Service.

Maddox, G.: Fillenbaum, G. and George, L.

1978 Extending the Uses of the LRHS’ Rata

Set, Policy Analysis with Social

Security F?esearch }lles, U.S. Socla~

Security Admlnlstratlon.

Marks, E., et al.

1974 Po@~ion Growth Estimation: A

~andbook of V7tal St atlstlcs Fleasurement, The Population Council: New

m.

Newcombe, H., et al.

1959 Automfii?_ Linkage of Vital Records,

Science, vol. 130, pp. 954-95!9.

Oh, H. L. and Scheuren, F.

1984 Statistical Disclosure Avoidance,

presented before a May 1984 meeting of

the Washington Statistical Society.

Oh, Ii.L. and Scheuren, F.

1983 Weighting Adjustments for Unit

Nonresponse, Incomplete Data in Sample

Surveys (Volume 2) P 1 Incomplete

-ational Acad’emya~fS%ences.

Oh, H.L. and Scheuren, F.

1980 Differential Bias Impacts of Alternative Census Bureau Hot Deck Procedures for Imputing Missing CPS Income

Data, Proceedings, Section on Survey

Research Methods, American Statistical_

Issoclatlon.

Paass, G.

1985 Disclosure Risk and Disclosure

Avoidance for Microdata, presented at

the May 1985, meetings of the

International Association for Social

Service Information and Technology

(IASSISTI.

Parries,H., et al.

1979 Froii_t~ Middle to Later Years: Longfludlnal St udies of the Preretlremenf

and Postretlrement txperlences of Men,

Uhlo State University.

Patterson, J. and Bilgrad, R.

1985 The National Death Index Experience:

1981-1985, Record Linkage Techniques --

1985, U.S. _Oepartment of the Treasury,

~rnal Revenue Service.

President’s Reoganization Project for the

Federal Statistical System

1981

Redfern,

1983

Rodgers,

1984

Improving the Federal Statistical

System: Issues and Options, Statistical ReDorter.

P.

A Study of the Future of the Census of

Population: Alternatlve Approaches,

conmnssloned by the stat- 1stlcal off1“ce

of the Europea~ Communities.

w.

An Evaluation of Statistical Matchinq.

Journal of Business and Economic

3tatistics, American Statistical

Association, vol. 2, pp. 91-102.

Rogot, E., et al.

1983 Th~~e of Probabilistic Methods in

Matchina Census SamDles to the National

Death index, Proceedings, Section on

Survey Research Mettlods , American

tatlstical Assoclation.

Scheuren, F.

1983 Design and Estimation for Large Federal

Surveys Using Administrative Records,

Proceedings, Section on Survey Research

American Statistical AssociaScheuren, F.

1980 Methods of Estimation for the 1973

Exact Match Study, Studies from Interagency Data Linkages (Report No. ~

. . Soclal Security Administration.

Scheuren, F. and Herriot, R.

1975 The Role of the Social Security Number

in Matchinq Administrative and Survey

Records, S{udies from Interagency Data

w A!i!$?%-a!;n!)’ ‘.s. ‘OclaT

Scheuren, F. and Oh, H. L.

1975 Fiddling Around with Nonmatches and

Mismatches, Proceedings, Social Statistics Section, Anerlcan stat”1stncal

k.satiation.

Sirken, M. and Greenberg, M.

1983 Redesign and Integration of a Population-Based Health Survey Program,

presented at 44th Sessian of the

International Statistical Institute.

170
$

Smith M.

1982 Oevelopnent of a National Record

Linkage Program in Canada

Section on Survey Rese~rc_~

lmerican s tatlstlcal Association,

Smith, W. and Scheuren, F.

1985a Multiple Linkage and Measures of

Inexactness: Methodology Issues,

presented at the Workshop on Exact

Matching Methodologies, Arlington,

Virginia, May 9-10, 1985.

$

Smith, W. and Scheuren, F.

1985b Some New Methods in Statistical

Disclosure Avoidance, presented at the

1985 Annual Meetings of the American

Statistical Association, in a session

sponsored by the Section on Survey

Research Methods.

Steinberg, J. and Pritzker, L.

1967 Some Experiences with and Reflections

on Data Linkage in the United States,

Bulletin of the International

tatistlcal Institute, vol. 42, PP.

Storey, J.

1985 Recent Changes in the Availability of

iederal Data on he Aged, reporf

prepared for the G?rontological Society

of America.

Tepping, B.

1968 A Model for Optimum Linkaqe of Records,

Journal of the America; Statistical

Xssoclatlon, vol. 63, PP. 1321-1332.

U.S. Bureau of the Census

1973 The Medicare Record Check: An Evaluation of the Coverage of Persons

Years of Age and Over in the 19/0

_Census,PHC(E)-.

U.S. Health Care Financinq Administration

1983 Medicare Statistical Files Manual.

Wilson, O. and Smith, W.

1983 Access to Tax Records for Statistical

Purposes, Proceedings, Section on

Survey Methods, American Statistical

Xssoclatlon.

Winkler, W.

lg~~ Preprocessing of Lists and Strin$ Comparison, Record Linkage Technlques--

T9&5, U.S. Internal Revenue Service.

*

“

.

.

.

,

*

,

.

,

171
Appendix A

SUPPLEMENTAL BIBLIOGRAPHIC SOURCES

In this paper we have cited some of the

literature on exact and statistical matching

when the discussion warranted. Further

bibliographic material can be found in the

followino publications:

Record Linkage Tecbnioues--l985 (1!?85), U.S.

Internal Revenue Service. (Edited by Beth

Kilss and Wendy Alvey.) Many of the citations

in the present paper come from this volume,

which contains the proceedings of the Workshop

on Exact Matching Methor!oloqies, held Vay

9-10, 1985, in Arlinqton, Virainia.

Statistical Working Paper Series (1977-1985),

}ederal Committee on tatlstlcaT Methodology.

(Produced under the general editorial guidance

of Maria Elena Gonzalez.) See especially, Na.

5, on “Exact and Statistical Matching,” and

No. 6, on the “Statistical Uses of Administrative Records.” Some of the publications

in the Series wre prepared by the U.S.

Department of Commerce; more recently the

publications have been issued by the (!.S

Office of Management and Budget.

Statistics of Income and Related

Administrative Record Research (1981 lq8a) -.

. . Internal Revenue Service. (Edited “b;

Beth Kilss and Wendy Alvey.) This annuaj

publication series contains numerous papers on

record linkage topics and is a successor to

the Social Security publications: Statistical

Uses of Administrative Pecords With Lmphasls

on Mortality and Dlsabll~ty Research (T~

and Economic and Demograp~at.i sties

(1980), which also may be useful.

Statistical lJses of Administrative Records:

Recent Research and Present Prospects (m

. . Internal Revenue Service. (Edited b;

Thomas Jahine, Beth Kilss and Wendy Alvey.)

This handbook of recent wrk includes mafiy

papers on data linkage, most of which are also

found in the series listed above.

Studies From Interagency Data Linkages

977--,.S0} . . Social Security Admlnlstration. (Produced uncler the general

editorial supervision of Fritz Scheuren.) Of

special interest may be the bibliography by

Scheuren, F. and Alvey, W. (1975), “Selected

Bibliography on the Matching of Person Records

from Different Sources,” which will be found

in Report No. a in the Series, pages 127-136.

● Policy Analysis with Social Security Research

Hles (lQ78~

=istratYon. ‘

us s 1 s

(Edi~e~ by ‘~~~dy Alv~yur~~

●

Fritz Scheuren.1 Most of the research files

described are based on data linkage

methodologies.

Accessing Individual Records from Personal

Data Using Lton-Unlaue dentlflers, NatlonaT

Eureau of St andards, NBS Special Publication

500-?.

Additional citations to the recent literature on

disclosure which may be of value are aiven

below. Some of these are of interest.as general

background; others focus specifically on disclosure barriers to data linkage.

Crank, S. (1985)

Evaluation of Privacy and Disclosure Policy in

the Social Security Administration, Social

Security Bulletin, U.S. Social Security

dmlnlst.rat~on.

12alenius,T. (1985)

Privacv and Confidentiality in Censuses and

Survey~, Proceedirigs”, Section on Survey

Research Methods, American StatlsticaT

Assoclatlon.

Hansen, M. (1971)

The Role and Feasibility of a Hational Data

Bank, Based on Matched Records and

Alternatives, Federal Statistics, Report of

the President’s Commission (vol. TI).

Spruill. N. (1984)

Protecting Confidentiality of Business

Ftlcrodata by Masklna, The Public Research

Tnstltute: Alexandria, VA.

Spruill, N. (1983)

The Confidentiality and Analytic Usefulness of

Masked Business Microdata, Proceedings,

Section on Survey Research Methods, American

tatlstlcal Assoclatlon.

Younq, P. (1984)

Legal and Administrative Impediments to the

~onduct of Epldemlologic Research, Task Force

on Environmental Cancer and I{eart and Lung

Disease: Washington, DC.

172
Appendix B

TAXPAYER OPINIONOUESTION

ON SHARING IRS DATA

Yankelovich, Skelly and White, Inc. (1984)

1984 General Purmse TaXDaVer Oninion Survev

60a. As you may know, the IRS has been reouired by law to keep all of their

records confidential. However, some people feel the IRS should share

this information with other government departments in order to save money

and reduce bureaucratic waste since those departments also need this

information to do their wrk. Others feel that the taxpayer’s right to

privacy is more important. For which, if any, of these departments or

purposes do you think it wuld be all right for the IRS to provide

information?

a. The Census Bureau.................................................. 24%

b. Major criminal investigations {such as drugs and organized crime).. 43%

c. Investigationsof illegal aliens................................... 34%

d. Welfare fraud investigations.0.● .................● ................. 48%

e. Draft

f. Other

g. State

h. Child

i. Fraud

Boards or Selective Service .0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17%

U.S. Federal departments.....● .● .*.● *....*..● ..● ...0.....*... 12%

governments● ................................................. 13%

support investigations.....● .......● ......................... 38%

and embezzlement investigations.....● ....................● ● .. 43%

j. Other..● ● ..● ...● ● .● ● ● . ● . . . . . . . . . . . . ● ● . . . . . . . . ● ● ● . . . . . . . . . . ● . . . . ● . . . 1%

k. None [should keep records private)..........,...................... 31%

1. Don’t know/no answer............*............0.....0..... . . . . . . . . . . 4%

Author’s Note:

Tom Jabfne, Dan Kasprzyk and others have commented on the many

problems this ouestion may have had when it was asked. In my

opinion the responses are far from definitive, but they do make the

main point I wished to make--that we need more and better research

cn this issue.

173
Appendix C

RECORD MATCHING INFORMATION FOR HIS

(Ouestion 16)

R.*6 to r.. ~om! - In o?d.r to d.t. rmhw hot. h.. tthpvucflc. a wui c.ndtvlo.. .r. mtat.d t. how long puw1. Ilv., w would llh.

la*8!.?10.t. tl. tlcal mcorda maint. irmdby th. N.tlon. i C.mlm f- Ua. hh lilstl. tic.. R

,,, I h... VOW*&ta of ldrth ● . (b<~~<[afufromrf.rn3 on HIS. I Hou$etml~ I

ComOO~,fiOnP*g81. k Uut—i}

j F5Enl ‘-” ----- ----- ----- ----- ----- ----- ---- ----- -- ----- ----- ------ ----- ---

b. lntit K@Wor~wwaycmtbom? M DDK -@I

!

Wrr?olnrfu /uflrlameo/ftwsr#toorl-Mti Ifu 4wmpNrcb0andu state

#4n’@OcW’aol w- Wtbomm Ih4Ufvltisfawn. :

t

I oiD P.DMOma mu Cubs 1

1 a2D Viinkktxk a $.4*X,CQ

t

I UD Guam

t

MoAJOttbw-s

1 *O Gnbde

----- -- ------- ------- ------- -. ---d -------- ------- ------- ------- ----

c. Tow~yt9Jvdk.a, Wtui b * Ml —. ~ ;bat -Ei

I

r

tFnst ~

)

I

k

,Mlddo nltml 4a

:

------------- ------------- -------A----- -------------------------- -

Vmt? b flub: nk tar lm?lAb. 1 E

d. What -s ~ f-’. lAST-?

t

I

vantv ~. DO NOT - ““Same.’” Fmhw’? LNS- --_------------------------------&________ --------------

w B~t -~*~fiti%&b~~=UJ~ —DDK

Ii.@

:

9rtAc*9umWhy 8#LtmPubJkt4&ti ~

vka Aa ~wlllknoticeon~

oqOthu knf)ts

~.

d

Qew9muud

Mld~.-—we

qDlll-J* s ;

,

t

un-m-um

Cculblsauit’v h.!fmu

R@sdifzms&’y.W?ublk Hdth 8avhAmlL8t$tlo~ 1

Udtd BlmwlCodo,OUtkmauk. :

1

kttu~obmrd~ ! c1 M*mla+v E

9. whB1by!?Ur a0ck18.UdtfnUmbU? J o Roco?ds

Instructions ka. If auestions arise in 16c. we want.the name

1. Read the introductory statement above item

16 to explain the purpose of obtaining the

information.

*2, When asking 16a, insert the birthdate from

the HIS-1, Household Composition Page. If

the hirthdate recorded in the HIS-1 is in

error, make no changes to the HIS-1 entry,

but enter the correct birthdate in the

answer space in 16a and note “Date

verified.” If YOU determine that the

person is actually under 55 years of age,

footnote the situation and continue the

interview. Do not make any changes to the

HIS-1(D16-?) or to the supplement. Mark

Check Item S2 in Section S based on the

original HIS-1 age.

.?. Enter the ful1 state name on the Iine in

16h: do not use abbreviations. If the

samDle person was not born in one of the 50

states or the District of Columbia, mark

the appropriate box in 16b, leaving the

state line blank.

174

*4b.

the sample person is legs’llykrIownby. If

the person has more than one middle name,

enter the initial of the first one given.

Some women use their maiden name as a

middle name: accept the response as given.

Be sure to verify the sDellinq and record

the last name first in this item.

It is acceptable to record an initial as

the first name in 16c if this is how the

nerson is legalIv known. Even if such a

person uses their full middle name, only

the middle initial is necessary. For

example, G. Watson Levi would be recorded

as Levi, G., W. in 16c. Do not record name

suffixes such as “Sr.,” “Jr.,” “II I,” etc.

5a. When verifying 16d for males, ask “Was your

father’s last name ?“ Always ask

the auestion for females, regardless of

their marital status. Be sure to verify

the spelling.
5b. Enter the last name of the sample person’s

father in the answer space, whether it is

the same as the person’s name or not,

Always verify the spelling, even if the

names sound alike. If it is volunteered

that the person was legally adopted, record

the name of the adoptive father.

NOTE: Take special care to make the entries in

16b-d legible. Printing is preferred.

6. Read the introduction to 16e to all

respondents. If you are asked for the legal

authority for collecting social security

numbers, cite the title and section of the

United States Code, as printed below the

introduction. If you are given more than

one number, record the first 9-diqit number

the respondent mentions, not the first one

issued. If the number has more than 9

digits, record the first 9-digits. Do not

record alphabetic prefixes or suffixes.

7. After recording the social security number,

mark the appropriate box indicating whether

the number was obtained from memory or

records.

* Revised February 1984

.

SENSITIVE QUESTIONS

,

,

There are no auestions

sensitive on either the core

considered to be

series of items or

the supplement. However, certain information

may be considered sensitive and the following

explanation of the need for the data is provided

regarding social security number and the subject

of incontinence.

● Social Security Number and National Death

Index Match

So that in the future the National Center for

Health Statistics (NCHS) may investigate the

relationship between the results of the

“Supplement on Aging” data and causes of death,

the supplement collects

information (items

the appropriate

lla-lle of questionnaire

Section 3, Occupation/Retirement), particularly

the social security number, that will enable

monitoring the National Death Index records for

sample persons.

The cost-effectiveness of this supplement is

enhanced by the availability of the National

Death Index (NDI). Data on the future mortality

of the survey population will be available with

minimum expenditures by means of a computer

search of the NDI. Information on age at death,

cause of death, residence at time of death and

place of death can be easily ascertained from a

copy of the death certificate obtained from the

appropriate vital records office. This

additional information can be integrated with

data from the original survey to greatly enrich

the scope of the analysis. Extensive

information on the health status of the elderly

is being collected on the original survey.

Information obtained from death certificates

will allow investigators to relate these health

status measures to longevity and cause of

death. It will also be possible to determine

whether selected behavioral and socioeconomic

factors collected at the time of the oriainal

survey, such

relationship

mortality.

as living arrangements, affect the

between health characteristics and

Several years after the data collection and

preparation is completed, a list of all survey

respondents will be submitted to the NDI and a

search made to determine which respondents had

died during the interim period. Additional

searches of the NDI will be carried out on a

periodic basis. In order to optimize the

successfulness and reduce the cost associated

with these searches, the following information

must be collected as part of the original

survey: social security number, full (legal)

name, Date of birth, State of birth, race, sex,

and marital status. Ascertainment of social

security number is most essential. A search of

the NDI which uses social security number should

produce only one match if the subject is

deceased. The other information is then used to

verify the match. The result of such a match

identifies a death certificate which can be

obtained from the State with reasonable

certainty that it is in fact for the subject.

If a social security number is not available,

multiple matches within the age range

established will occur, especially for common

names. This would necessitate obtaining death

certificates from several States and attempting

to determine whether any of them is for the

subject. These false positives would add both

acquisition costs and staff costs to the death

search process, as well as introducing error.

Interviewers will verify the person’s name

and birth date (which may have been provided by

the household respondent on the core

questionnaire), and obtain the last name of the

person’s father. The social security number

will also be requested and if the person is

unable to recall the number, he or she will be

asked to check their card. This information is

not thought to be sensitive; however,

respondents will be reminded of the voluntary

and confidential nature of the survey, the

purpose of the data collection, the legislative

authority under which the information is being

collected, and the absence of any penalty for

refusal. Nonresponse to any of these items will

175
not affect most of the analyses planned for the

supplement; however, provision of social

security numbers allows for future epidemiologic

research for this population without the

necessity of conducting a separate longitudinal

or followback survey.

● Incontinence

NCHS‘S and NIA’s interests in general

physical problems of older people, which relate

directly to their quality of life, include

auestions on urination and bowel control

(Pretest Questionnaire Section V, Items 6a-6e,

7a-7e). One issue is the relationship of

incontinence to the aging process. In this

case, incontinence can be viewed as a health

problem, independent of other illnesses. In

order to examine this issue, it will be

necessary to collect data from all persons in

the 55-and-over age group (so that their effects

can be examined) and from people both with and

without other illnesses.

In addition, a substantial part of the

interest in the problem of incontinence results

from the relationship between incontinence and

institutionalization. It is the view of some

experts consulted that incontinence is one of

the main reasons for the decision to

institutionalize an older person.

Considerable effort went into wording these

questions both to minimize sensitivity and to

assure comparability with similar items proposed

for the 1984 National Nursing Home Survey.

Attachment VIII presents planned analysis of

comparable data for both the institutionalized

and noninstitutionalized populations from the

two surveys.

176
Appendix D

RECORD MATCHING INFORMATION

(Question 33)

CARD B - Continued

FOR SIPP

COMIVK)NQUESTIONS AND SUGGESTED ANSWERS

I thought that the Bureau of the Census operated only every 10 years, when

they counted people. What is the Bureau of the Census doing now?

In addition to the decennial census, which is conducted every 10 years, the

Bureau collects many different kinds of statistics. Other censuses required

by law are conducted on a regular basis including the Census of Agriculture,

the Censuses of Business and Manufactures, and the Census of State and Local

Governments. In addition, we collect data on a monthly basis to provide

current information on such topics as labor force participation, retail and

wholesale trade, various manufacturing activities, trade statistics, as well

as yearly surveys of business, manufacturing, governments, family income, and

education.

Why does the Census Bureau want to know my Social Security Number?

We need to know your Social Security Number so we can add information from

administrative records to the survey data. This will help us avoid asking

questions for which information is already available and help to ensure the

completeness of the survey results. The information we obtain from the Social

Security Administration and other government agencies will be protected from

unauthorized use just as the survey responses are protected.

177
------- 1

-------- Ii

-------, 1-

.

178
.

Section Ill:

Current Theory

and Practice

,
PREPROCESSING OF LISTS AND STRING COMPARISON

.

.

William E. winkler, Energy

1. INTRODUCTION

By combining data on enfities from different

sources, researchers are often able to perform

anslysas that would not be possible if they were

to use data from individual sources separately.

When a unique common identifier (such aa a

verified Social Security Number) is available on

individual sources of data, matching files

merely involves using the unique identifier as

the sort key and then directly matching records

from the two files.

When a unique common identifier is not available, it is necessary to use other identifying

information. Characteristic identifying information might consist of surname, street addreas,

or ZIP code in matching files that contain name

and address information. Uae of such information involvea several practical problems.

First, if the precise locations of identifiers (such as first name and surname) are not

conatstent from record to record, computer

matching using the identifiers cannot be performed. Second, some identifiers may be miscodad or missing on some records. Third, such

identifiers, or even combinations of them, are

not unique for individuals .or buainessea.

This paper presents examples of some of the

solutions for problems arising in preparing name

and address information for uae in matching

files.

Most of the work described has taken place at

the U.S. Bureau of the Census, the Statistical

Reporting Service in the U.S. Department Of

Agriculture, the Energy Information Admtnfstration, and Statistics Canada. The problems,

examples, and resultant methodologies should be

representative of problems that arise in

general.

2. BACKGROUND

2.1. Why Preproceaslng is Needed

Match/merge strategies generally perform

better (i.e., have lowar rates of erroneous

matches and nonmatches) when address lists have

been preprocessed to produce more consistent

formats and spellings and to dalineate records

representing different types of entities (such

aa records associated with individuals sole

proprietorships, partnerships, and buslnessea).

2.2. Definitions

As the terminology of matching is not always

consistent from reference to reference, we

present deftnitiona.

A match is a pair of records that represent

the asme unit and a nonmatch is a pair of

records that do not. Blockin~ is a procedure

for subdividing files into a set of mutually

exclusive subsets under the assumption that no

matches occur acrosa blocks. Each mutually

exclusive subset consists of records agreeing on

the blocking characteristics.

181

.

Information Administration

A positive link ia a pair of records that is

designated as a match. A positive nonlink la a

pair of records that is designated as a

nonmatch. A possible link ia a pair of records

that ia not designated as a positive link or

nonlink. Additional steps, such as manual

review or collection of additional information,

are needed to designate it as a positive link or

nonlink.

A Type I Error is the designation of a pair

of records as a positive nonlink when it is a

match. Type I Errors have been referred to as

erroneous or falae nonmatches (U.S. Department

of Commerce, 1980) . A Type II Error is the

designation of a pair of records as a positive

link when it is a nonmatch. Type 11 Errora have

been referred to as erroneous or false matches.

2.3. Nature of the Problem

The specific types of matchlmerge procedures

adopted depend on the identifiability and consistency of corresponding information in the

address lists to be merged. For instance. if an

addresa list were in free format, then merging

would have to be done manually becauae computer

software could not use corresponding information

such aa NAME or ZIP for blocking pairs of

records.

Even if fields such as NAPE, ADDRESS, CITY,

STATE, and ZIP are identified (possibly using

manual techniques), it may not be possible to

block records accurately if words in correspond ing fields do not contain consistent

spellings. For instance, the STATE field and

worda such as ‘COMPANY,’ ‘CORPORATION,’ ‘P O

BOX,’ and ‘STREET’ should be spelled or abbreviated in a consistent manner.

If subfielda such as FIRST NAME, MIDDLE

INITIAL(S), SURNAME, STREET NUWBER, STREET NAME,

PO BOX NUMBER, ROUTE NUMBER, and SUITE NUXBER

are identified and placed in fixed locations,

then they can be used for delineating true and

false matches. If FIRST NAME and SURNAME

subfields are in inconsistent order within the

NAME fields of two lists, than it will not be

possible to block records accurately using the

NAME field.

2.4. Match/Merge Stages

As the need for sDecific trees of preprocessing is closaly conne~ted to d~kferent match/

merge strategies, these strategies and their

relationship to specific data needs will be

summarized.

Matching records within or across lists

consists of two stagea. In the blocking atage,

pairs of records are blocked Into sets of pairs

using a few common characteristics with substantial discri.minatlng power. Some such

characteristics are the SOUNDEX abbreviation of

SURNAME (see e.g. Bourne and Ford (1961)) or ZIP

code. Records for which such common characteristics do not agree are assumed to represent

different entities.
In the discrimination stage, blocked pairs

are categorized as positive links, poaitlve

nonlinka, or potential links using all available

discriminating characteriatica within blocked

pairs of records.

At both stages preprocessing can play an

important role. For instant@, if records of

individuals are blocked using the SOUNDEX abbreviation of the surname, the location of surname

needs to be identified and the spelling of

surnames needs to be moderately accurate. If

records of establishments or buaineases are

blocked using ZIP code, then ZIP codes need to

be accurate.

If the first name, firat four character of

the street address, and state abbreviation are

used for designating links and nonlinks within a

aet of blocked pairs, then those fields and

aubfields need to be located and accurate.

2.5. Topics Addressed in Paper

The remainder of this paper presenta examplea

of the kinda of name and addresa lists that are

encountered and the types of praproceasing that

are performed. ‘he third section presents

examples illustrating problems with namas and

addresses in lists that are normally available

for updating. The fourth section presents a

summary of the various typea of preproceaaing

software and procedures to i’dentify different

typea of entities, clean up fields and subfields, and identify subfields of the NAME and

STREET ADDRESS fields.

The fifth section describes methods for

comparing strings that are used to overcome some

speiling- variat-ions and

The final section poses

ther research.

3. EXAMPLES OF PROBLEMS

LISTS

to create sort keys.

some problems for furIN NAME AND ADDRESS

In addition to the problem of locating

sources of lists for uae in updating, there are

problems associated with lists that can make

them difficult to use. Problems can include

transferal of hardcopy lists to computer files,

identification of fields and aubfields, and

different name andlor address representation of

similar entities or similar representation of

different entities.

This section provides examples of the problems that affect ~ list’s suitability for uae as

an update source.

3.1. Keypunch Error in Consistently Formatted

Subffelds

Addresses in a source list might contain a

significant number of typographical arrors --

which do not seriously affect manual processing

-- while the computerized mailing list does not.

The following two pairs of namea and addreaaes

representing two entities, from source lists and

mailing lists being updated, respectively,

illustrate the problem.

(a) J K Smoth 114 E Main Stret

J K Smith 114 Main St

(b) Southside Feul 898 Northwat Hghwy

Seth Side Fuel 8895 Northwest Hwy

3.2. Unidentified Fields

Address records in which the five fields

NAME, STREET, CITY, STATE, and ZIP occur in free

format generally cannot be placed in consistent

formats using straightforward computer code.

They must be reformatted manually. Free format

records often exist as address labels in which

the five fields occur In no fixed format.

The following examplea illustrate the problem

of free formats:

(a) A A Fuel Oil

CIO Marvel Distribution Co

PO Box 519

Larsmie, Wyoming 66519

(b) Smith Distributing

5632 Westheimer

Suite 43

Houston TX 77514

(c) ABC Oil, PO Box 54

Grand Rapids

Michigan 49506

In example (a) the name occurs on the second

line whereas in examples (b) and (c) it occurs

on the first. The STREETfPO BOX field appears

on the third, second, and first lines o

examples (a), (b), and (c), respectively. Th

CITY field appears in the second to last line i

example (c) but on the last line in examples (a

and (b).

3.3. Inconsistently Formatted Subfields

If formatting conventions within subfields o

the name and address field vary substantially,

merging procedures may not perform as well as I

the situation in which corresponding subfields

can be readily identified using computer software. For instance, one or more lists migh

contain records with names and addresses in th

following forma:

(a)

(b)

(c)

In

J K Smith Co 113 Main

Smith J K Co 113 EMain St

Smith Jonathon K Co PO BOX 16

A A Fuel Co PO Box 105

AA Fuel Distribution Inc Drawer 105

R Smith Fuel Co 1171 Northwest

Highway

Robert Smith Highway 65 Wea

Smith Co Route 1

the first two lifies of example (a), bot

SURNAME and STREET NAME are not ot&ious u&tchas

using a straightforward computer comparison an

the billing addresa in the third entry makes 

difficult to determine if the three entries

represent the same company.

In example (b), the COMPANY NAME aubfields

cannot be easily identified and the ADDRE

fielda may be difficult to compare. In th

example (c), SURNAMES may not be identified an

the equating of street addreasea of the firat

two entries requites specific geographic information. Without additional information, it 

difficult to detarmine whether the third entry

represents the asme company as that given by th

firat two entries.

3.4. Name and Address Representation

3.4.1. Same Entity, Different Name and Addreaa

Entities in some potential updata sources a

represented in substantially different form

182
.

.

than the entities are represented in the mafn

mailing list. When this happens, it is difficult to determine those records representing

entities that are out-of-scope or duplicate to

records in the main mailing list.

For instance, a list of individuals licansed

by a state to sell petrolaum products might be

considered aa an update source for a list of

businesses selling petroleum products in the

atate. The reason that the list of ownara might

be considered ia that aending a form to elthar

the owner of a small fuel oil dealership or the

appropriate corporate billing addresa (which

might exist in the main mailing list) could

yield correct sales information.

Combining such a list of owners with a list

of businesses can yield difficulties. Without a

suitable additional data source, it may be

impossible to identify records representing the

same entity that take the following form:

J K Smith 116 Main St

Anytown 66591

~ A Fuel PO Box 68

Othertown 66442

3.4.2. Same or Different Entity, Similar Name,

Different Address

If the purpose of a mailing list is to provide

one address record for each corporate entity,

then additional difficulties can arise.

Businesses often maintain substantially different mailing addresses, sometimes even

requiring survey forma to be sent to locations

in different statas. For instance, addresaes

could take the following form:

ABCFuel Co 116 Main St

Anytown CA 96591

ABC Fuel Oil PO Box 534

Othertown NY 10091

J K Smith ABC Co PO Box 68

Sometown KS 66442

The firat two records could represent the

aeme corporate entity, independent but

affiliated companiea, or unaffiliated companiea.

The third addreaa could represent a aubaidiary

of one of the companies represented by the firat

two records, a aubaldiary of an unidentified

company, or an affiliated but independent distributor of products for some ABC Co.

3.4.3. Different Entity, Identical Address

andlor Phone

With soma lists, different entltiea mav. be

represented aa follows:

(a) Pargaa of Illinois PO BOX 661

NY 10015 202/664-2139

Pargas of Ohio PO BOX 661

NY 10015 202/664-2139

(b) ABC Diatrib.ting 1345 t?eatheimer

TX 71053 703/789-5439

Lone Star Oil 1345 Westheimer

TX 71053 703/789-5439

Example (a) illustrate a situation in which

a parent company reports separately for two

subsidiaries. Example (b) could reprasent a

situation in which an accountant reports for two

different companies. The addreaa and phone

number could be the accountants.

Example (b) could also represent different

companies which are both located in the same

office building or two different companies, one

of which haa gone out of business. If companies

are matched using TELEPHONE, manual followup may

be required to determine whether one has gone

out of business or is an affiliate of the other.

4. PREPROCESSING METHODS

Methods of preprocessing, using manual procedures or software, have been developed to (1)

delineate corresponding classes of records such

as those associated with corporations, partnerships, or individuals within a list of

businesses; (2) identify corresponding subfielda

such aa HOUSE NUMBER, STREET NAME, and PO BOX;

(3) make consistent the spelling of words such

as ‘STREET,’ ‘CORPORATION,’ and ‘ROUTE;’ and (4)

clean up ZIP codes.

4.1. Identification of Individual, Partnerships, and Corporations

As racorda associated with individualsfaole

proprietorahipa, partnerships, and corporations

within a list of buafnesaes have different

characteristics, they are sometimes distinguished and processed separately. The U.S.

Department of Agriculture/Statistical Reporting

Service (USDA/SRS, 1979) and the U.S. Department

of Commerce (1981) have developed software

andlor procedures for identifying individuals,

partnerahfpa, and corporations in lists of

farina.

It appears that partnershipa are identified

aa thoaa records having ‘6’ In the NAME field.

Corporation are those records having worda such

as ‘CORP,’ ‘CO,’ ‘INC,’ ‘FARM,’ and ‘DAIRY’ in

the NAME field. Individual are those records

not classified aa partnerships or corporation.

Records aaaociated with partnerships are more

difficult to procasa (may require more manual

followup) because partnerahipa can be

erroneously matched more times than records

aaaociated with individuals and because partnership records can take the followlng inconsistent forms:

Smith John A & Mary B

Smith John 6 Jones Lee

Smith John A, Smith Mary B, & Lee Jones

Smith Mary B & Jones Lee

Smith Mary B 6 Smith John A

Tha first entry containa only one SURNAME

entry while others contain one SURNAME for each

partner. The third entry represents a partnership of three individuals while the others

represent only two. Due to ordering differences

in entries two through four, it is difficult to

datermine if Jones or Lea is the individual’a

aurnama.

4.2. Formatting and CLeanup of the Name Field

Subfields

Cleanup of the name field consists of replacing

common words such as ‘COMPANY,’ ‘INCORPOiATE0,7

‘LIMITED,’ ‘FARMS,’ ‘BROTHERS,’ ‘ SALES, ‘ and

‘DISTRIBUTOR’ with standard spellings or abbreviations and replacing common variations of

first names such as ‘ROBERT,’ ‘BOB,’ ‘ROB,’

183
‘ROBT’ with standard spellings or abbreviationa.

The standardization ia typically done using

lookup tablea that contain previously identified

spelling variations. Such lookup tables are

easily updated when new spelling variationa are

encountered. Lookup tables are In uae at

USDA/SRS (1979), the U.S. Department of Commerce

(1978b, 1981), the Energy Information Administration (EIA) (Winkler, 1984), and Statistics

Canada (1982).

Formatting of name fields aaaociated with

individual involvea manually identifying the

aubfields FIRST NAtiE, MIDDLE INITIAL(S), and

SURNAME and either placing them in fixed locations (USDA/SRS, 1979) or in fixed order (U.S.

Dept. of Commerce, 1981). If NAME subfielda are

in fixed order, then software can be used to

Identify individual subfielda.

4.3. Formatting and Claanup of the Straet/

Nailing Address Field

Cleanup of the street/mailing address involves

replacing such commonly occurring vorda aa

‘STREET,’ ‘PO BOX,’ ‘RUML ROUTE,’ ‘ DRAWER,‘

‘AVENUE,’ and ‘HIGHWAY’ with standard spellings

or abbreviatlona. Such standardization

typically involves lookup tablka that are eaaily

updated as new spelling variationa are encountered.

Varioua spellings of large cities in the CITY

field can alao be standardized using lookup

tables. Such standardization may only be partially effective because of the large differences in spelling and abbreviationa used for

core cities and suburbs in large metropolitan

areas.

Formatting can alao involve placing aubfielda

such aa STREET NAME, STREET NUMBER, PO BOX

mm, RURAL ROUTE in fixed locationa

(USDAISRS, 1979; U.S. Dept. of Conmerce, 1978b;

Statistlca Canada, 1982).

ZIPSTAN software (U.S. Dept. of Coamerce,

1978b) has been developed to identify pertinent

subfielda of the STREET field in files of individuals. The following examples show representative EIA records before and after ZIPSTAN

processing:

Figure 1. -- Before ZIPSTAW

1. EXCH ST

2. HWT17 S

3. 1435 BANK OF THE

4. 2837 ROE BLVD

5. MAIN & ELN STs

6. CORNER OF MAIN & ELN

7. 100 N COURT SQ

8. 100 COURT SQ SUITE 167

9. 2589 WILLIAMS DR AFT 6

10. 15 RAILROAD AVE

11. 2ND AVE NWT 10 W

12. MAIN ST

13. 184 N DU PONT Pw

14. 1230 16TH ST

15. BOX 480

184

Figure 2. -After ZIP6TAN

Pra- S f

No. Eouse fixes Street Name f:x:s Unit

Ho. 12 1 12

1.

2. NW

3. 1435

4. 2S37

5.

6.

7. 100 M

8. 100 CT SQ

9. 25S9

10. 15

11.

12.

13. 184 N

14. 1230

15. 480

SXCE ST

17TH s

BANK OF THE

ROE BL

MAIN ELFl STS

CORNER OF MAIN CLJY

COURT SQ

•~~ NO ~~ ● ** RM 167

WILLIAMS DRAP6

RAILROAD AV

2ND AV NW 10

MAXN ST

DU PONT Pw

16TH ST

●PO BOX*

ZIPSTAN is able to identify accurately subfields in 13 of 15 casea. The two exceptions

are casea 2 and 8. In case 2, ‘WY’ ia moved to

a Prefix position and ’17’ is placed in the

STREET NAME position. In case 8, ‘COURT,’ the

STREET NAME, ia placed in a prefix location.

Although ZIPSTAN accurately identifies the

subfields associated with interaectiona (caaea

5, 6, and 11), such identification may not allow

accurate delineation of duplicate In comparison of varioua lists. Some Msta may

contain STREET ADDRESS in the following forma,

none of” which la readily comparable with the

forms in examples 5, 6, and 11.

5. 34 Main St

5. EIm and Main Streeta

11. Nwy low

11. 7456 Richmond Hwy

5. METHODSOF STRING COMPARISON

If comparable strings have been identified

(ace sections 3.4, 4.2, and 4.3), then it la

useful to compute a distance between them in

blocked paira of records. If properly devised,

string comparators can overcome minor spelling

● rrora.

5.1. Abbreviation Methods

Abbreviation methods (see e.g., Bourne and

Ford, 1961) are intended to maintain some information needed for identifying a record while

alleviating problems due to spelling variationa.

Aa an example, the SOUNDEX abbreviation method

will be described and Illustrated.

The SOUNDEX abbreviation of an alphabetic

word consists of four characters. The firat

SOUNDEX character agrees with the firat

character in the word. All nonleadfng vowels

and tha letters H, W and Y are deleted. Similar

sounding consonant are mapped into integer

codes aa follows:

B, F, P, V -> 1,

C, G, J, K, Q, S, X, Z -, 2,

D, T -, 3,

L -, 4,

M, N -~ 5, and

R -, 6.
.

Repeating Integer codes are deleted and

SOUNDEX abbreviations of less than four

characters are zero filled on the right.

Comparison of SOUNDEX abbreviations of words

induces a metric in which agreeing SOUWDEX

abbreviations are assigned distance O and disagreeing 1.

5.2. General String Comparators

Aa common abbreviation methods (section 5.1)

are not able to deal with typical coding errors,

more exotic methods for string comparison have

been introduced.

An early comparator is the Damerau-Levenstein

(D-L) metric (see e.g., Hall and Dowling, 1980,

pp. 388-390). The basic idea of the metric ia

as follows. Any string can be transformed into

another string through a sequence of changes via

substitutions, deletions, insertions, and possibly reversals. The smallest number of such

operationa required to change one string into

another is the measure of the difference between

them.

The minimum value that the D-L metric can

aasume ia O (character-by-character agreement)

and the maximum is the maximum number of letters

in the two words being compared. For Instance,

the D-L distance between ‘ABCDEFG’ and ‘WXTZ’ is

7.

Using the Damerau-Levenstein metric or

various straightforward extensiona of it (see

●.g., Hall and Dowling, 1980) is difficult

because: (1) the dynamic programming necessary

for computing the metric is cumbersome and (2)

neighborhoods of given strings contain too many

unrelated strings (i.e. , the metric does not

have good distinguishing power, see section

5.3).

5.3. Jaro’s String Comparator

Jaro (see e.g., U.S. Dept. of Commerce,

1978a, pp. 83-108-) introduced a string comparator that is more straightforward to implement

than the Damerau-Levenstein metric and more

closely relates to the type of decisions a human

being would make in comparing strings.

The string comparator ia a weighting function

for pairs of atrings denoted as reference file

strings and data file strings. It is defined as

follows (U.S. Dept. of Commerce, 1978a, p. 108):

W = wgt_cd*c/d + vgt_rd*c/r +

wgt_tr*(c-tr) /c

where

wgt —cd = weight associated with characters in

wgt rd =

wgt tr =

d=

r=

tr =

cthe data file string but not in the

reference file string;

weight associated with characters in

the reference file string but not in

the data file string;

weight associated with

transposition;

length of the data file string;

length of the reference file string;

number of transpoaitions of

characters; sod

number of characters in coamon in

the two stringa.

TWO character are considered in common only

if they are no further apart than~- 1)

where m = max(d,r). Characters in common from

two strings are said to be assigned. Other

characters from the two strings are unasai greed.

Each string has the same number of assigned

characters because each asaigned character

representa a match.

The number of transpositions are computed as

follows: The first assigned character on one

string ia compared to the first assigned

character on the other string. If the

characters are not the same, half of a tranapoaition has occurred. Then the eecond asstgned

character on one string is compared to the

second aasigned character on the other etring,

etc. The number of mismatched characters is

divided by two to yield the number of transposlt ions.

If two strings agree on a charscter-bycharacter basis, then the Jaro weight, W, is aet

equal to wgt_cd+wgt_rd+wgt_tr, which is the

maximum value that W can assume. The minimum

value that the Jaro weight, W,, can assume is O,

which occurs when the two stringa being compared

have no characters in common (subject to the

above definition of common).

5.4. Manual Comparison

The purpose of different string comparators

is to asaign a value to the quality of comparison in a manner that mimics how a human

being might make a decision. Because of this,

it is useful to describe how manual review

decisions can be quantified. In section 5.5,

the manual review decisions will be compared to

results obtained using the string comparators of

sections 5.1-5.3.

Quantification of manual review decisions can

be performed as follows:

1. have a number of individuala compare pairs

of corresponding substrings such as

SURNAMEa;

2. score comparisons using the scale: l-no

match, 2-likely falae match, 3-possible

true match, 4-likely true match, and

5-true match; and

3. average results of the comparisons over

individuals and compute the corresponding

coefficients of variation.

5.5. Comparison of String Comparators

Table 1 !movides a comparison of the measures

of agreement using the SOUNDEX abbreviation, the

Damerau-Levenstein metric, Jaro’s string comparator, and a weight based on manual review.

To make the values in the table easier to

compare, all meaaures were transformed to a

scale from O to 1. A value of O represents

nonmatch and a value of 1 represents match.

The transformations are performed as follows:

1. SOUNDEX=l-SOUNDEX;

2. D_L =(5-D_L)/5;

3. JARO =JARO/900; and

4. MAN =(MAN-1)/4.

In equations 1-4 the measures on the righthand side (as defined in sections 5.1-5.4) are

replaced by the scaled measures. As the basic

Damerau-Levenstein metric D-L (section 5.2) on

the right-hand side of equation 2 varies from O

(total agreement) to 5 (substantial disagreement) for the examples in Table 1, the scaled

185
D-L metric is transformed into a weight in which

O and 1 represent nonmatch and match, respect ively.

In computing the Jaro weight, JARO, the

weights wgt_cd, wgt_rd, and wgt_tr (section 5.3)

are each given the values 300 which are the

same as the default values given in the Census

software (U.S. Dept. of Commerce, 1978a, p. 88).

As the baaic JARO weight on the right hand aide

of equation 3 varies between O and 900, dividing

by 900 changes the scale from O to 1.

In Table 1, with the exception of example (h)

(completely different words), all examples

represent similar character atrings that

disagree because of minor transcription/ke~unch

errors. Each pair of surnames Is taken from EIA

files. With the exception of example (h), the

aurnemes represent the same entity.

Overall, we can aee that the SOUN’DEXweight

is high for only 5 of 9 matching surname pairs;

D-L weights are generally moderately high to

high for 8 of 9; Jaro weights are consistently

high; and the manually estimated weights vary

significantly with no apparent consistency. It

is important to note that, with the exception of

example (h), all waights should be consistently

high.

In comparing the D-L metric and the Jaro

weight, we see that the Jaro weight gives additional weight to longer, but similar, strings.

For instance, with short strings in which one

character disagreea (examples (f) and (i)), the

D-L and Jaro weighta are about the same. With

longer strings in which one character disagraes

(examples (d) and (e)), the Jaro weight Is

higher than the D-L weight.

For example (g), it la interesting to note

that the manually estimated weight of 0.88 la

lower than the weight of 1.0 provided by each of

the other string comparators. Human beings are

able to make use of the auxiliary information

that “Smith” la a cowunonly-occurring word and

downweight their judgments accordingly. Such

downweighting ts inharent in the application of

the Fellegi-Sunter model which utilizes frequency of occurrence of character strings (see

e.g., Rogot, Schwartz, O’Conor, and Olsen, 1983,

p. 324).

6. NEEDED FUTURI WORX

Nthough it is intuitive that preprocessing

can both identify information that should

correspond and make such information more

consistent, few, if any, studies have been aet

up to determine its effectiveness. We do not

know how much different types of preprocessing

reduce matching error rates, nor do we know the

extent to which they lower amounts of manual

processing.

Effective evaluation may require the creation

of data bases with all matches identified and

suitably connected to entities used for mailing

purposea. Fellegi and Sunter (1969) indicate

that error rates obtained using samples are

subject to substantial variability unless the

samples are very large. Winkler (1984) provides

examples of ratea of erroneous nonmatches baaed

on samples of size 1,800 for which the estimated

sampling error exceeds the estimated error rate.

A key issue that needs to be addressed is

whether the results obtained by empirical evaluation of methodologies on one data set are

likely to be relevant to a different data set.

Specific research problems follow.

6.1. Effects of Spelling Standardization

How much does standardization of the mellinu

of worda such as *COMPANY,l ‘CORIORATIOli,’ ‘i;

BOX ,‘ ‘STREET,’ and ‘EAST’ reduce the error

rates associated with a given matching strategy?

What arrors can certain types of standardization

induce?

Some matching strategies consist of blocking

files of individuals using the SOUNDEX or New

York State Intelligence and Identification (for

NYSIIS, see Lynch and Arends, 1977) abbreviation

of surnames. When compared with blocking using

surname, how much does blocking using abbreviated surnames reduce the rate of erroneous

nonmatches and can such abbreviations provide

information useful for delineating matches and

nonmatches within the set of blocked pairs?

Some matching strategies consist of blocking

files of businesses using the ZIP code and first

few characters of the NAME field. How much

effort is involved in cleaning up ZIP codes and

how much do the cleaner ZIP codes reduce ratea

of erroneous nonmatches? Should the ZIP codes

in a given metropolitan area all be mapped into

one sort key used for blocking records?

How much can the delineation of true and

false matches be improved if the spelling end

formatting of the CITT field are made more

consistent? What are the best strategies for

correcting inconsistencies in the CITY field?

6.2. Effect of Formettfng of Subfielda

How much does the identification of SURNAME,

FIRST NAME, HOUSE NUMBER, STREET NAME, and PO

BOX help reduce error rates? What subfields

prwide the greateat reduction? Are the subfields providing the greatest reduction different in files of bualnessea than in files of

individuals?

6.3. Abbreviation Methods Used in Blockin~

What are the best methods for blocking files

of individuals? Blocking on surnames ‘abbreviated using methods such as SOUNDEX and NYSIIS

will usually designate as nonmatches those

matches containing errors due to miskeying,

fnaertiona, deletions, and transpoaltions.

In comparing methods of abbreviation and

blocking, we need to consider rates of erroneous

nonmatches, total number of pairs in all blocks,

and computing requirements if some blocks are

large. Given these evaluation criteria, are

there methods of abbreviation and blocking that

would perform better than SOUNDEX or NYSIIS?

6.4. Effect of String Comparison

How much doea the string comparator of Jaro

(section 5.3) that is used for computing agreement weights for corresponding subfields such as

SURNAME, FIRST NAME, and STREET NUMBER (U.S.

Dept. of Commerce, 1978a) help reduce rates of

erroneous matches? Are there better algorithms

for string comparison? What measures should be

used In comparing the effectiveness of two

atring comparators?

186
ACKNOWLEDGMENTS

The author wishes to thank Yvonne M. Bishop,

Thomas Jabine, and Nancy J. Kirkendall for a

number of helpful comments.

REFERENCES

Bourne, C. P., and Ford, D. J. (1961), “A Study

of Methoda for Syatematlcally Abbreviating

English Words and Names,” J. ACM ~, 538-552.

Damerau, F. J. (1964), “A Technique for Computer

Detection and Correction of Spelling Errors,”

Communication of the ACM. ~, 171-176.

Fellegi, I. P., and Sunter, A. B. (1969), “A

Theory for Record Linkage,” JASA~,

1183-1210.

Hall, P. A. V. and Dowling, G. R. (1980),

“Approximate String Matching,” Computing

Surveys ~, 381-402.

Lynch, B. T. and Arends, W. L. (1977),

“Selection of a Surname Coding Procedure for

the SRS Record Linkage System,” U.S.

Department of Agriculture, Statistical

Reporting Service.

Morgan, II. L. (1970), “Spelling Correction In

Systems Programs,” Ccnmmmicatlons of the ACM,

~, 90-94.

Newcombe, H.B. and Kennedy, J.M. (1962), “Record

Linkage,” Communications of the ACM, ~,

563-566.

Rogot, E., Schwartz, S., O’Conor, K.,and Olsen,

c. (1983), “The Use of Probabilistic Methods

in Matching Census Samplea to the National

Death Index.” ASA 1983 Proceedings of the

Section on Survey Research Methods, 319-324.

Statistics Canada/ Systems Development Division

(1982), “Record Linkage Software.”

U. S. Department of Agriculture/ Statistical

Reporting Senice (1979), “List Frame

Development: Procedures and Software.”

U. S. Department of Commerce, Bureau of the

Census/Agriculture Division (1981), “Record

Linkage for Development of the 1978 Census of

Agriculture Mailing List.”

U. S. Department of Connnerce, Bureau of the

Census/Survey Research Division (1978a),

“UWIMATCH: A Record Linkage System.”

U. S. Department of Commerce, Bureau of the

Census/Suney Research Division (1978b),

“ZIPSTAN: Generalized Address Standardizer.”

U. S. Department of Conmerce, Office of Federal

Statistical Policy and Standards (1980),

“Statistical Policy working Paper 5: Report

on Exact and Statistical Matching

Techniques.”

Winkler, W. E. (1984), “Issues in Developing

Frame Matching Procedures: Exact Matching

Using Elementary Techniques.” Presented to the

ASA Committee on Energy Statistics in April

1984. A summary appeared in Statistics of

Income and Related Administrative Record

Research: 1984 U.S. Department of the

Treasury, Internal Revenue Senice, Statistics

of Income Division, 171-176. The summary also

appeared in the ASA 1984 Proceedings of the

Section on Survey Research Methods, 327-332.

Table 1: Comparison of String Comparator Metrics Using

Surnames that are Generally Similar

Maximum

surnames string I

SOUNDEX D-L Jaro Manual

length E

(a)

(b)

(c)

(d)

(e)

(f)

(9)

(h)

(i)

(j

Tranisano

T’raivsano

Alexander

Aleander

Nuzinsky

Newzinski

Smthfield

Smithfeld

Bachman

Bahcman

Dixon

Nixon

Smith

Smith

Smith

Jones

Ouid

Ovid

Boc

Boco

9

9

9

9

8

5

5

5

4

4

Number of values NA

above 0.5

0.00

0.00

1.00

1.00

1.00

0.00

1.00

0.00

0.00

1.00

5

0.60

0.80

0.40

0.60

0.80

0.80

1.00

0.00

0.80

0*80

8

0.93

0.96

0.81

0.93

0.96

0.87

1.00

0.00

0.83

0.92

9

0.35 40.3

0.63 15.1

0.42 39.2

0.63 20.2

0.63 30.9

0.13 35.1

0.88 24.0

0.00 0.0

0.55 13.2

0.32 29.3

5 NA

~ Coefficient of variation associated with estimate based

on manual review by nine individuals.

187
WEIGHTS IN COMPUTER MATCHING: APPLICATIONS AND AN INFORMATION THEORETIC POINT OF VIEW

Nancy J. Kirkendall, Energy Information Administration

This paper summarizes the historical development

of computerized matchfmerge procedures and

describes the test statistic used to clasaify

record pairs as a match or nonmatch in terms of

its information theoretic interpretation. Current matchfmerge software procedures are compared and contrasted based on their differing

approaches to estimation.

INTRODUCTION

The match/merge procedures discussed in this

paper are those which are intended to perform

exact matching. Exact matching has been defined

(U.S. Department of Commerce, 1980) as the

linkage of records from two or more files

containing units from the same population. The

intention of exact matching is to link data for

the same unit (e.g., person) from different

files. If units which do not represent the same

individual are linked, the result fs a false

match or type 2 error. If units which do

represent the same unit are not linked, the

result is a missed match, or type 1 error.

There are many different purposes in exact

matching. Examples range from obtaining more

data elements for an individual by merging

information from different surveys, to creating

a more comprehensive name and address list by

merging the names and addresses from many

sources. In the first case, it is important to

make sure that matching is done accurately so

that the merged data constitute a multivariate

observation from a single individual (see

Kelley, 1983). In the second case, the merging

is intended to ensure as complete a list as

possible while eliminating duplication.

The most significant paper on the theory and

practice of matching is by Fellegi and Sunter

(1969) . Their paper documents the derivation of

a test statistic and a critical region for

deciding whether or not a pair of records is a

match. In addition, it discusses some of the

assumptions necessary for practical application

and describes approaches for estimating the

probabilities which are used to calculate the

test statistic. Most of the probabilistic

matchlmerge procedures in use today are based on

an application of the techniques described in

the Fellegi-Sunter paper.

Although the Fellegi-Sunter paper was the first

publication of the theoretical background for

matchfmerge procedures, many of the ideas and

techniques embodied in the methodology had been

used since the late 1950’s by Howard Newcombe

et al. Newcombe’s papers from that time period

describe the use of the test statistic for which

the derivation was later presented by Fellegi

and Sunter. (See Newcombe et al., 1959 and

Newcombe and Kennedy, 1962.)

THEORETICAL BACKGROUND

Assume that two files, A and B, are to be

merged. Each file contains at least one record

for each unit (person or establishment) in the

file. Each record contains a set of attributes

for that unit. These attributes may include:

numerical identifiers with very good identifying

characteristics such as the social security

number; standard identifiers such as name and

address; characteristic information such as sex

or date of birth; or any other data which might

be available on survey files or administrative

record files.

In the matching process, each record in file A

can be compared to each record in file B. The

comparison of any such pair of records can be

viewed as a set of outcomes, each of which is

the result of comparing a specific attribute

from the record in file A with the same attribute in the record from file B. Outcomes may be

defined as specifically as desired. For example, one might define an outcome of a comparison

to be simply that the attributes agree or that

they disagree. Or, one might define the agreement outcome more specifically, baaed on the

possible values that attribute can take. For

example, one outcome might be that the surnames

agree and equal “Smith,” while another might be

that the surnames agree and equal “Zebra,” etc.

“Comparison of attributes” is usually interpreted to mean that the same attribute is

recorded on each record and that they can be

compared directly. However, it is possible to

“compare” different attributes which are known

to be correlated or to use information from only

one record in conjunction with general information from the other file. An example is given

in Smith, Newcombe, and Dewar (1983). In their

application, records from a file of patients

diagnosed as having cancer are linked with

records in a death file. The variable “cause of

death” in the death file is used in conjunction

with general statistics concerning the cause of

death among cancer patients and the cause of

death among the general population to provide a

different sort of “comparison of attributes.”

In the above, it was implied that every record

from file A is compared to every record from

file B. In practice, with large files this

would require an extremely large number of

comparisons, the vast majority of which would

not be matches. To make the size of the problem

more manageable, files are generally “blocked”

using one or more of the available attributes,

and record pairs are assumed to be a possible

match and subject to the detailed attribute

comparison only if they agree on the blocking

attribute. In using a blocking procedure, there

is necessarily a higher rate of unmatched

189
do represent the same unit, but disagree on the

blocking attribute, are automatically rejected

as possible matches. However, the gains in the

form of reduced processing are significant. See

Kelley (1985) for a probabilistic approach to

selecting blocking strategies.

THE PROBLEM

Probabilistic test procedures are based on

evaluating record pairs one at a time and

subjecting each pair to a decision as to its

match status. The procedure does not consider

the expected number of matches or nonmatches in

a merging of two files, and does not make use of

the result of the classification of any previous

record pairs.

In this section the test statistic and the

critical region are described based on an

information theoretic argument. Details of the

derivation are presented in the Appendix. The

resulting test statistic and critical region are

exactly the same as those derived by Fellegi and

Sunter. One advantage of the information

theoretic approach is that the inclusion of the

log of the prior odds of a match, as described

by Howe and Lindsay (1981) and by Newcombe and

Abbatt (1983) can be directly related to the

methodology. Calculation of this test statistic

yields a value which is commonly referred to as

the “weight” for or against a match.

Given any pair of records, we want to make a

decision as to whether they match (H -- the

null hypothesis) or do not match (H: -- the

alternative hypothesis). This decision will be

based on the observed comparison of the attribute items on the two records. The set of all

outcomes resulting from this comparison is the

random variable, xi, which takes values according to the outcomes which were specified for all

of the attributes.

The discrete random variable, xi, can take any

of n different values. The number n can be very

large, either because a large number of attributes are compared, or because a large number of

outcomes are possible for any one attribute

comparison. The probabilities with which xi

takes any of the n values under both H and H

are assumed to be known. The ques%ion 04

estimating these probabilities is addressed

later. The decision process is formalized by

considering the following two hypotheses:

Ho:

‘1:

The event that two records represent the

same unit (i.e., a match). Under Ho, the

frequency function of the random variable,

xi, is denoted P(xi/Ho) = poi for i=l, .,,

n.

The event that the two records represent

different units (i.e., a nonmatch.) Under

H,3 the frequency function of the random

v;riable, x , is denoted P(xi/Hl) = pli for

i=l, . . . n.i

Assume that two records are being compared and

that a decision will be made as to their match

status based on a comparison of three attributes: surname, first name, and sex. For each

attribute there will be two possible outcomes:

either they agree or they do not agree. Thu S ,

the comparison set can take any of 2**3 = 8

(n=8) possible values. For simplicity we also

assume that the probabilities of agreement or

disagreement of the attributes are independent

under both Ho and H

1“ Thus, given the following

table of probabilities, the frequency function

of the comparison vector can be calculated under

both hypotheses.

TABLE I

PROBABILITIES OF AGREEMENT

Attribute Under H

I

Under HI

o

Surname .90 .05

First name .85 .10

Sex .95 .45

In the following let x=(al,a2,a3), where ai = O

if item i disagrees, and ai=l if item i agrees.

The comparison of surname ia represented by al,

the comparison of first name by a2, and the

comparison of sex by a3. Thus, the random

variable, xi, has the frequency functions given

by poi (under Ho) and pli (under HI) in the

following table.

TABLE 11

PROBABILITIES FOR COMPARISON VARIABLE

1 1 I

i

‘i I

eo~ Pli

1

2

3

4

5

6

:

(0,0,0)

(1,0,0)

(0,1,0)

(0,0,1)

(1,1,0)

(1,0,1)

(0,1,1)

(1,1,1)

.0008

.0068

.0043

.0143

.0383

,1283

.0808

.7268

.4703

.0248

.0523

.3848

.0028

.0203

.0428

.0023

THE TEST STATISTIC

As shown in the Appendix, the test statistic

T(xi) = 10g(poi/pli) = I(o:l;xi). (1)

is a sufficient statistic for discriminating

between Ho and H

1“ The number log (poi/pli) is

an information number. It provides a meaaure of

190
.

.

the information for discriminating for Ho and

against Hl which waa gained by observing the

random variable, xi.

T(xi) is the log of the ratio of the probability

of the outcomes, denoted by xi, under Ho to the

probability of the same set of outcomes under HI

(the log of the likelihood ratio.) Note that if

these probabilities are the same then T(xi)=O,

and this set of outcomes has no discriminating

power for identifying whether records represent

the same unit. If poi is larger than pli, then

T(xi) will be positive for that category. The

larger T(xi), the stronger is the possibility

that observation of this set of outcomes indicates that the records represent the same unit.

If poi ia smaller than pli, then T(xi) is

negative. The smaller T(xi), the stronger is

the possibility that this set of outcomes

indicates that the records do not represent the

same unit.

DETERMINING THE CRITICAL REGION

The final part of the matching problem is to

determine cut-off values, c1 and C2, so that Hl

is rejected if T(xi) is greater than C2 and Ho

is rejected if T(xi) is less than cl. If T(xi)

falls between these two valuea, the test is

inconclusive and the record pair may be subject

to manual follow up.

In standard applications of testing simple

hypotheses, there are only two outcomes: accept

the null hypothesis or reject it. Here, the

three region test comes from the union of two

tests. First, consider a test of Ho vs. HI.

For a test with significance level alpha, this

leads to the critical region defined by cl.

Next, consider the test of Hl vs. Ho with

significance level beta. This leads to a

critical region defined by C2. Individually,

according to the Neyman-Pearson Lemma, these

tests are the best tests at their respective

significance levels. The first test rejects Ho

if T(xi) is less than cl. The second test

rejects H

1

if T(xi) is greater than c

2“

Since c1 is generally less than C2, the union of

these two tests yields the three region test

described above.

This is illustrated below with our previous

example. In Table 111 the column labeled T(xj)

is the log of the ratio of p

Oj and Plj ‘rem

Table II, but here the table is arranged so that

the T(xj) are in ascending order. The next to

last column presents the cumulative probability

under Ho of observing T(xi) less than or equal

to the given T(xj). It is used to specify cl.

In this example, if alpha is equal to .05, then

c1

is equal to -1.9. The last column is the

cumulative probability under HI of observing

T(xi) greater than or equal to the given T(xj).

It is used to specify C2. In this example, if

beta is equal to .05 then C2 is equal to 2.7.

TABLE III

THE DISTRIBUTION OF THE TEST STATISTIC

j n

-j ‘j

T(xj ) poj ‘lj ~1x Pok glk

1 (0,0,0)

2 (0,0,1)

3 (0,1,0)

4 (1,0,0)

5 (0,1,1)

6 (1,0,1)

7 (1,1,0)

8 (1,1,1)

-9.2

4.8

-3.6

-1.9

.9

2.7

3.8

8.3

.a308

.0143

.0043

.0063

.08@

.1283

.0383

.7268

.4703

.3848

.0523

.0248

.0428

.0203

.0028

.~23

.ax$

.0151

.0194

.0262

.1070

.2353

.2736

1.WM

1.CXW

.5301

.1453

.0930

.0682

.0254

.0051

.0023

Thus, if alpha and beta both equal .05, we would

classify a pair as a match if we observe vectors

(1,0,1), (1,1,0), or (1,1,1). We would classify

pairs as a nonmatch if we obssrve (0,0,0),

(0,0,1), (0,1,0), or (1,0,0), If we observed

(0,1,1): agreement on sex and first name, but

disagreement on surname, we would be unable to

classify the pair as either a match or a nonmat ch.

The test statiatic and critical region defined

in this way are the same aa those developed by

Fellegi and Sunter (1969), although that paPer

also included a discussion of randomization to

achieve the type 1 and type 2 error levels

exactly. They develop the decision rule for

accepting H or HI based on minimizing the

o

probability of not making a decision. That is:

minimizing the probability that T(xi) falla

between c1 and C2 for a given alpha and beta.

THE POSTERIOR ODDS RATIO

The development presented here and in FellegiSunter (1969) use the test statistic defined in

equation (l). However, equation (A2) can be

rewritten as

logp(Ho/xi)/p(Hl/xi) =lo8Poi/P1i + hpmo)@@l). (2)

Here the log of the posterior odds ratio is

written as the sum of the information number and

the log of the prior odds ratio. Howe and

Lindsay (1981) call equation (2) the “total

weight” for a match, but acknowledge that the

prior odds ratio is difficult to evaluate. The

most recent papers by Newcombe and Smith include

191
procedures for estimating the prior odds ratio

in some unique situations (see Newcombe and

Abbatt, 1983 and Smith, Newcombe, and Dewar,

1983) . Note that the prior odds ratio reflects

any information available regarding the match

status of a given record pair before the attribute comparison. If the prior odds of a match

were the same for each record pair then the test

statistic and critical region for the comparison

of attributea would both be shifted by the same

value. In such a case the inclusion of the

prior odds ratio would not change the outcome of

the statistical test. However, the posterior

odds ratio has the advantage that it can be

interpreted directly aa the odds that the record

pair matches.

In the Smith, Newcombe, and Dewar paper, the

prior odds ratio is calculated based on a life

table analysis of the severity of cancer diagnosed, an attribute available in the search

file, and the year of the death file being

searched. In their example, the prior probability of a match is different for each individual in the search file and instead of applying specifically to a record pair, it applies

to the individual record initiating the search

and to an entire one year death file.

INDEPENDENCEOF ATTRIBUTES -- A SIMPLIFYING

ASSUMPTION

In the original pages of this discussion, x. was 1.

defined to be a discrete random variable which

was the intersection of m attribute comparisons.

If the result of each attribute comparison is

denoted as t

j

for j=l, . . . . m, then xi can be

written as the intersection of the t :

j

Xi= tlnt20... ntm.

If tl, . . . . tm are statistically independent,

then equation (1) can be written as:

m

I(o:l;xi) = I(o:l;t ).

j:l ~

Thus, if the set of attribute variablea, tj, are

statistically independent, the weights (i.e.,

the information) for each t

j

can be calculated

separately, and the overall weight (the information contained in the intersection of the t ) is

just the sum of the weights for each tj. j

In the previous example, the three attributes

were assumed to be independent. Hence, the

weight for any observed vector can be calculated

as the sum of the information associated with

agreement or disagreement on esch attribute.

For example, for xi=(O,l,l) the weight can be

calculated as the sum of the information associated with disagreement on surname,

T(al=O) = log (.1/.95) = -3.25;

192

the information associated with agreement on

first name,

T(a2=l) = log (.85/.1) = 3.09;

and the information associated with agreement on

sex,

T(a3=l) = log (.95/.45) = 1.08.

The sum of these weights is .92, aa shown in

Table III for the weight (the value of T(xj))

associated with the observation (0,1,1). Thus ,

if it is reasonable to assume that the outcomes

of sttribute comparisons for different attribute are statistically independent, then the

calculation of the test statistic is simplified

because the weights can be calculated separately

and summed.

In this example, it ia reasonable to assume that

agreement on surname is independent of agreement

on either first name or sex. However, if there

is agreement on first name, it ia likely that

there will be agreement on sex. Hence, in this

example, the assumption of independence does not

really hold. To incorporate this dependence,

one would need to consider the probabilities

associated with the bivariate random variable.

AN EXAMPLE OF A MULTIPLE OUTCOMECOMPARISON

The following is a vastly simplified example of

defining the specific outcomes of attribute

comparison by making use of the values they can

assume. This type of “frequency” argument

resulta in lower weights for agreement on common

items and higher weights for agreement on rare

items. It is a simplified veraion of the

treatment of frequencies and error structures

presented h the 3?ellegi-Sunter paper, pages

1192 and 1193 (PP. 60 and 61 in this volume).

Here, assume that surnames are being compared in

a pair of records. Assume that there are only

two frequently occurring names in the file,

llsm~thlr and llJonesf~; the other names (m of thed

all occurring with roughly the same low

frequency. Thus, we define the following set of

outcomes of the comparison of surname:

/

“%dth” fftitwvariableesgn?esndbothequll

II=*,,!

“Jones” ifthetwovariablesegm?andbothequal

x= “Jcms,”

“other” ifbothvdatlesagmebutdomtequal

~~$~~th)j ~ll-,l?

“diaegree’’ifthaitem -.

(Note that the set of outcomes defined for item

comparison must specify a partition of the set

of all possible results into mutually exclusive

and exhaustive subsets.)

Further assume that: 1) surnames in the two

files under consideration are both random

samples from the same population, and that in

this population, ,,Smith!! occurs with probability

pa, “Jones” occurs with probability pb, and each
of the other m error-free names in the file

occurs with probability Po; and 2) the only

errors in the name fields are keypunch errors,

which occur at the same rate, 1%, in both files,

independent of the particular name.

Under H :

Under Hl:

A pair of records is a match. Names

agree unless there is a keypunch

error. Thus , the probability of

agreement on Smith is Po~ =

pa*(.99)**2 (the probability of

observing “Smith” times the probability that the value was keypunched

correctly on both files). Similarly,

the probability of agreement on Jones

po2 = pb*(.99)**2S and the probability

of agreement on one of the other nemea

fs Po~=Po *(.99)**2. The probability

of disagreement on name when the

record pairs represent the same

individual is Po~= 1-P01-P02 03

_rn*p

= (l-(.99)**2)*(pa+pb+m*po)

= 1-(.99)**2=.02.

The records do not represent the same

individual and any ajzreement on name

occurs at random. fi~ probability of

agreement with name “Smith” is

(.99*p )**2; the probability of

agreem~nt with name “Jones” ie

(.99*pb)**2; the probability of

agreement with some other name is

(.99*p )**2; and the probability of

disagr~ement

l-.99**2*(pa**2+pb~2+m*po~~ . (:

have assumed that the probability that

a keypunch error results in some valid

name is negligible.)

Thus, from equation (1) the weight fOr the

various outcomes is:

If x*=Smith,

T(x*)=log(.99**2*pa/ .99**2*Pa**2)=lo6(l/Pa) ●

~*=Jones,

T(x*)=log(.99**2*Pb/‘99**2*pb**2)=10g(1/pb)“

x*=other,

T(x*)=log(.99**2*Po/. 99**2*Po**2)=10g(1/po) “

xh+~sagree,

T(x*)=log

(.02/(l-*.99**2*(Pa**2+Pb**2+m*Po**2) )).

Newcombe, Kennedy, Axford, and James (1959)

noted that in frequency based matching, if an

item, a, is found in a master file with probability pa, and if the two files being matched

can be viewed as a sample from that master file,

then, when a record pair Is a match, the probability that the items agree and equal “a” iS

proportional to pa. When the record pair is a

nonmatch the probability is prOpOrtiOnal to

pa**2 with the same constant of proportionality.

Thus, the weight for a match when item a is

observed is log(pa/pa**2) = log(l/pa). This is

illustrated in the example above. Most of the

Smith and Newcombe papers describe calculation

of the weights for agreement on a particular

Item as the log of the inverse of the frequency

of occurrence of that item.

The Fellegi-Sunter paper presents a derivation

of the frequency based weights for specific

agreement in the presence of several types of

errora. Their procedure still leads to weights

for agreement of log(l/pa) because, as in the

above exemple, the error terms impact the

probability of agreement under H and the

probability of agreement under HI is the same

way.

VARIATIONS IN PRACTICE

Probabilistic matching techniques (based on the

Fellegi-Sunter paper) have been implemented in

ma”ny software systems, including the Generalized

Iterative Record Linkage System (GIRLS) from

Statistics Canada (see Smith and Silins, 1984)

which is now called the Canadian Linkage System

(CANLINK); UNIMATCH from the U.S. Bureau of the

Census (see Jaro, 1972); the Statistical Reporting Service’s (SRS) Record Linkage System from

the U. S. Department of Agriculture (USDA); and

the California Automated Mortality Linkage

System (CAMLIS) from the University of

California at San Francisco. Work by Rogot

et al. (1983) at the National Center for Health

Stetistice has also used probabilistic matching

techniques.

The two major references for this section are a

paper by Howe and Lindsay (1981), which describes a vereion of the GIRLS system, and a

number of unpublished papers by Richard Coulter,

Max Arellano, William Arends, Billy Lynch, and

James Margerson dated 1976 and 1977, which

describe the SRS Record Linkage System. These

two systems were included in this review because

they are applications of a modified FellegiSunter approach and because the available

documentation was thorough.

The GIRLS system was developed to SupPort

epidemiologlcal research. Thus, it is primarily

intended to link records for a cohort group to

morbidity or mortality data. Attributes available for comparison usually include first name,

surname, middle initial, sex, date of birth,

place of birth, parents’ names and places of

birth. Some of the application-specific items,

such as blocking attribute and definition of

outcomes for attribute comparison, are not fixed

in the system. They can be specified by the

user. In the following, the specific applications by Howe and Lindsay are described.

The SRS record linkage system , is intended to

support development and maintenance of statelevel sampling frames for agricultural surveys.

Here, the primary intent of the linkage system

iS to unduplicate a list created by merging

193
multiple lists. The most commonly available

attributes are surname, first name, and address.

In addition to the probabilistic matching

procedure, record pairs which have identical

address fields are reviewed manually to identify

matches. This system is not a general-purpose

matching system. It was developed and is used

solely to maintain the USDA frames.

Blocking

In these applications, both systems block first

on surname code -- a variation of the New york

State Identification snd Intelligence System

(NYSIIS) code. A surname code is an alphabetic

code designed so that the most similar names and

the names with the most frequently encountered

errors of misreporting will have the same code.

See Lynch and Arends (1977) for a description of

surname codes and the rationale used by SRS to

select the NYSIIS code for their system. If the

resultant block size is too big, SRS uses

secondary blocking on first initial and tertiary

blocking on location code. The Howe and Lindsay

application blocks first on NYSIIS code, then on

sex. In neither case are the weights changed to

reflect the impact of blocking.

Weights for Agreement

Both systems make extensive use of frequencybased weights, and both systems use the files

being matched to calculate the frequencies.

Both systems also assume that these frequencies

include keypunch errors, recording errora, and

legitimate name changes. This is different from

the Fellegi-Sunter approach, which assumed that

the frequencies were based on an error-free name

file.

The SRS approach handles partial agreements by

calculating a weight for agreement on specific

surname and a weight for agreement on specific

NYSIIS code with disagreement on surname. The

Howe-Lindsay paper extends the accounting for

partial agreement by specifying agreement on

specific first seven characters of surname;

agreement on specific first four characters with

disagreement on the next three characters; and

agreement on specific NYSIIS code with disagreement on the first four characters of surname.

In both systems, pairs with disagreement on

NYSIIS code will never be considered because of

the blocking.

Estimation of Error Rates

Both systems use an iteration scheme to provide

final estimates for the required error rates.

First, initial estimates are provided, a sample

of records is processed through the matching

algorithm, and a preliminary set of matched

record pairs is identified, These pairs are

assumed to be true matches and are used to

estimate the error rates, as discussed below.

These revised estimates for the error ratea are

input to the system; the sample is processed

again and the newly matched pairs are used to

reestimated the error rates. The iteration is

continued until the estimates for the error

rates converge.

194

-.The errors are handled in the Howe-Lindsay paper

as transmtsaion rates:

‘1 =

‘2 =

‘3 =

the probability that the first seven

characters of surname are equal to the

“true” value;

the probability thst the first four

characters are equal to the “true” value

but the next three characters are

different; and

the probability that the surname code is

equal to the “true” surname code, but

that the surnames disagree in the first

four characters.

These transmission rates can be estimated from a

sufficiently large set of pairs which represent

true matches by using the following counts: the

number of pairs which agree on the first seven

characters; the number of pairs which agree on

the first four characters not on the next three,

and the number which do not agree on the first

four characters. The assumption is made that

this aet of matched pairs is representative of

all possible matched pairs. Note that t3 will

be underestimated because of the blocking.

In the SRS system, the error rates used are:

e= the probability that a name’ is

misreported or misrecorded

‘T = the probability that in a record pair

which does represent the same unit, the

names are correct but different.

These definitions of the error rates are the

same aa those used in the Fellegi-Sunter paper.

The overall weights for specific agreement are

different because the frequencies themselves are

derived under different assumptions, as mentioned above. In the SRS system, the error

rates are estimated from the set of pairs which

represent true matchea by using: the number of

pairs which have the same name; the number which

have different names; and the number which have

similar names (where “similar” was not defined).

Here,

‘T

will necessarily be underestimated

because the blocking procedure assures that

records will be compared only if they agree on

NYSIIS code.

The Critical Region

Both systems use an empirical procedure to

determine the critical region. That ia, a

frequency distribution of the weights for a

sample of record pairs is plotted, and the

critical values are selected based on the ahape

of the curve. As an alternative, the SRS system

also calculates an initial lower critical region

as the sum of the weights for agreement of the

most common surname, first name, and location.

The initial upper critical region is estimated

as the initial lower critical region plus the

weights for agreement on the most common middle

name, route and box number. These calculated

upper and lower regions are used during the
iteration to estimate error rates. They are

conservative since both are positive.

System Considerations

In the Howe-Lindsay approach, an initial blocking and comparison are done before the frequency

based agreement weights are calculated. At this

stage, only weights for disagreement are summed

and as the accumulated weight becomes too

negative, the record pair can be rejected as a

possible match before all attributes have been

compared. With this approach the order of

adding in attributes is important, with those

having the greatest negative weight for

disagreement entering first. If the total

disagreement weight is above the threshold, the

record pair is a possible match. A separate

file is created containing those possibly

matched pairs. For each such pair, this file

contains one record with the identification

numbers of the two records, the results of the

comparison of attributes, and the values taken

(if needed for the weight calculation). This

potential linked file is then sent to a separate

subroutine for calculation of the weights.

Groupin~

Both systems create groups consisting of all

records which have been linked with each other.

(Here linked means that the calculated test

statistic is above the upper critical value.)

As described in the Howe and Lindsay paper. the

group is formed by first taking a single record

and adding to the group any records which have

been linked to it, then adding all records which

were linked to those records, and so on.

Additional subgroupings are considered when two

records from different groups have a weight

between the two critical values.

Interpretation of the groups depends on the

application. In the SRS application, members of

a group could all be duplicates to each other.

In the SRS system, subgroups are analyzed

manually. In some of the applications described

by Howe and Lindsay, neither input file has any

duplication, and there is at most one matched

record for a given record in the search file.

In this case the groups are analyzed to pick the

pair which represents the most likely match,

usually the pair with the highest weight.

SUMMARY

This paper has described the probabilistic

matching procedures discussed by Fellegi and

Sunter (1969) from an information theoretic

point of view. This approach gives additional

insight into the calculation of the posterior

odds ratio as mentioned by Howe and Lindsay, and

as implemented in the recent work of Newcombe

and Smith. Additionally, it has described some

of the differences between two of the major

systems which have been implemented based on the

Fellegi-Sunter paper. Major differences between

systems are in accounting for partial matches,

the definition of the error rates, and in the

handling of groups of record pairs which are all

linked to each other. The major differences

between these systems and the Fellegi-Sunter

approach are 1) that these systems base their

frequency counts on files which are acknowledged

to contain errors, and 2) that they use an

empirical procedure to determine the critical

region for the statistical test.

REFERENCES

Arellano, Max and Arends, William, “The Estimation of Component Error Probabilities for

Record Linkage Purposes,” Statistical Reporting

Service, U.S. Department of Agriculture, Unpublished, May 1976.

Arellano, Max, “Optimum Utilization of the

Social Security Number for Matching Purposes,”

“Weight Calculation for the Place Name Comparison,” “Calculation of Weights for Partitioned Variable Comparisons (Trailing Blanks

Case),” “Estimation of Component Error Probabilities for Record Linkage Purposes,” “Development of A Linkage Rule for Unduplicating

Agricultural Lists,” Statistical Reporting

Service, U.S. Department of Agriculture, Unpublished papers, 1976 and 1977.

Arellano, Max and Coulter, Rfchard, “Weight

Calculation for the Given Name Comparison,”

“Weight Calculation for the Middle Name Comparison,” “Weight Calculation for the Surname

Comparison,” Statistical Reporting Service, U.S.

Department of Agriculture, Unpublished papers,

1976 and 1977.

Coulter, Richard, “An Application of a Theory

for Record Linkage,” Statistical Reporting

Service, U.S. Department of Agriculture, Unpublished, March 1977.

Coulter, Richard and Mergerson, James, “An

Application of a Record Linkage Theory in Construction of a List Sampling Frame,” Statistical

Reporting Service, U.S. Department of Agriculture, Washington, D.C., April 1977.

Fellegi, Ivan and Sunter, Alan, “A Theory for

Record Linkage,” Journal of the American Statistical Association, 1969, pp. 1183-1210.

Howe, G. R. and Lindsay, J., “A Generalized

Iterative Record Linkage Computer System for Use

in Medical Follow-up Studies,” Computers and

Biomedical Research 14, Academic Press, 1981,

pp. 327-340.

Jaro, Matthew, ll~l~TcH_A cOIUpUtersystem ‘or

Generalized Record Linkage Under Conditions of

Uncertainty,” AFIPS Conference Proceedings, Vol.

40 for Spring Joint Computer Conference, May

1972, pp. 523-530.

Jaro, Matthew, “UNIMATCH--Generalized Record

Linkage Applied to Urban Data Files,” Proceedings of the American Statistical Association.

195
Kelley, Patrick, “A Preliminary Study of the

Error Structure of Statistical Matching,”

Proceedings of the American Statistical Association, Social Statistics Section, 1983.

Kelley, Patrick, “Advancea in Record Linkage

Methodology: A Method for Determining the Best

Blocking Strategy,” Record Linkage Techniques--

1985, Internal Revenue Service.

Kullback, Soloman, Information Theory and

Statistics, Dover Publications, Inc., New York,

New York, copyright 1968.

Lynch, Billy and Arends, Williams, “Selection of

a Surname Coding Procedure for the SRS Record

Linkage System,” Sample Survey Research Branch,

Research Division, Statistical Reporting Service, U. S. Department of Agriculture, Feb 1977.

Newcombe, Howard, “Reccird Linking: The Design

of Efficient Systems for Linking Records into

Individual and Family Histories,” American

Journal of Human Genetics, Vol 19, No. 3,

Part I, (Msy) 1967.

Newcombe, Howard, and Kennedy, James, “Record

Linkage: Making Maximum Use of Discriminating

Power of Identifying Information,” Communications of the Association for Computing Machinery

5, 1962, pp. 563-566.

Newcombe, H., Kennedy, J., Axford, S., and

James, A.,’’Automatic Linkage of Vital Records,”

Science, 130, 1959, pp. 954-959.

Newcombe, H., and Abbatt, J., “Probabilistic

Record Linkage in Epidemiology,” Report Prepared

for Eldorado Resources, Ltd., Oct. 1983.

Rogot, Eugene, Schwartz, Sidney, O’Connor, Karen

and Olsen, Christina, “The Use of Probabilistic

Methods in Matching Census Samples to the

National Death Index,” Proceedings of the

American Statistical Association, Section on

Business and Economic Statistics, 1983.

Smith. Martha, Newcombe, Howard, and Dewar, Ron,

“Proposed Procedure for the Alberta Cancer

Registry Death Clearance,” Health Division,

Statistics Ganada, (OEHRU-NO. 1), March 1983.

Smith, Martha, Newcombe, Howard, and Dewar, Ron,

“The Use of Diagnosia in Cancer Registry Death

Clearance,” Health Division, Statistic Canada,

(OEHRU-NO. 2), April 1983.

Smith, Martha and Silins, John, “Generalized

Iterative Record Linkage System,” (An excerpt),

Statistical Uses of Administrative Records:

Recent Research and Present Prospects, Department of the Treasury, Internal Revenue Service,

July 1984.

U. S. Department of Commerce, Office of Federal

Statistical Policy and Standards, Report on

Exact and Statistical Matching Techniques,

Statistical Policy Working Paper 5, 1980.

APPEND1X

This appendix presenta a derivation of the test

statiatic for determining whether a record pair

is a match or a nonmatch using an information

theoretic approach (see Kullback, 1968).

WHAT IS AN INFORMATION NUMBER?

Given the prior probabilities associated with a

match and a nonmatch, P(H ) and P(H1), we use

Bayes theorem to calculate”the posterior probabilities of H and H~ baaed on the observed

attribute comp~rison, xi:

P(Ho/xi) = p(Ho)*Poi/(p(Ho)*Poi + p(Hl)*P1i)

P(H1/xi) - P(H1)*pli/(P(Ho)*poi + P(Hl)*pli).

Dividing these gives the posterior odds ratio:

P(Ho/xi)/P(H1/xi) = P(Ho)*Poi/(p(Hl)*Pli),

and taking the logarithm (to any base) gives:

@ p@ohi)D@l/Q = @ P*/Pli + 1.% P(HO)/p@l) .

(Al)

This is the log of the posterior odds ratio or

equivalently, the log of the posterior likelihood ratio. It can be rearranged to get:

% Poi/P1i = M pmolx~)~m~ix~) - @ p@o)@q) “

(A2)

This number is the difference between the log of

the posterior odds ratio and the log of the

prior odds ratio. Thus, it provides a measure

of the information for discriminating in favor

of H- againat H, which was gained by observing

the ~andom vari&ble xi.

For this reason, the information

set of outcomes of the attribute

is defined to be:

I(o:l;xi) = log poi/pli.

THE MEAN INFOR14ATXO14

gained by the

comparison, xi,

(A3)

The mean information for discriminating in favor

of Ho against HI is the expected value of

I(o:l;xi) under Ho, or

1(0:1) = Eo(log Poi/Pli)

n

. z poi * log poifpli. (A4 )

i= 1

Here E. represents the expectation under Ho.

Note that the mean information is simply the

expected value of the log of the likelihood

ratio under H

o“

196
One useful mathematical fsct is that I(o:l) is

always greater than or equal to zero, with

equality only when poi = P~i for all i = 1,

.... n. This gives an approach to selecting

between the two hypotheses. Given any sample,

it is possible to evaluate the sampling distribution under both hypotheses, and to calculate

the mean information between the sampling

distribution and the hypothesized distribution.

The hypothesized distribution which was closer

to the sampling distribution, as measured by the

mean information, would be preferred.

THE TEST STATISTIC

When we compare the attributes associated with

any two records, the result is one of the n

possible values taken by xi. We denote this

observed random variable as x*. The probability

of observing X*=X f

is poi under Ho and pli under

‘1”

Thus, the asmpling distribution of x* is

s imply;

pi=l if x

* = x , p =0 if x* ne xi.

ii

We can write the mean information between the

sampling distribution and Ho as

I(X*:HO) =

and the mean

10g(l/poi) for X*=XiS

information between the samDline

distribution and Hl as

.-

I(x*:H1) = log(l/pli) for x*=xi.

The decision rule, as described in Kullback

(1968, chapter 5), is to pick the hypothesis

which haa the smallest mean information relative

to the sampling distribution. That is, we

accept the hypothesized distribution which is

closest to the sampling distribution.

Thus , the procedure would be to accept H if

o

I(x*:H1)-I(x*:HO) is positive (or “sufficiently

large.”) and accept HI if it ia negative (or

“sufficiently small.”)

This yields the test statistic, T(x*), where

T(x*) = I(x*:Hl)-I(x*:Ho)

= @3(poi/pli) for X*=Xi. (A5)

T(x*) is the log of the ratio of the probability

of the set of outcomes, x*, under H to the

probability of x* under HI. Note that”if these

probabilities are the same then T(x*)=O, and

this set of outcomes haa no discriminating power

for identifying whether records represent the

same unit. If Poi is larger than pli, then

T(x*) will be positive for that category. The

larger T(x*), the stronger is the possibility

that observation of this set of outcomes indicates that the records represent the same unit.

If Po~ is smaller than pli, then T(x*) is

negative, The smaller T(x*), the stronger is

the possibility that this set of outcomes

indicates that the records do not represent the

same unit.

Since T(x*) = log(poi/pli) with probability Poi

under H and with probability Pli under HI, the

o’

ratio of the probability that x*=x and the

i

probability that T(x*) = T(xi) is equal to 1.

Since the ratio of the probability

‘i and the probability function of

not depend on the poi or pli, T(xi)

cient statistic for discriminating

and H .

1

function of

T(xi) does

is a suffibetween H

o

197
ADVANCES IN RECORD LINKAGE METHODOLOGY:

A METHOD FOR DETERMINING THE BEST BLOCKING STRATEGY

R. Patrick Kelley, Bureau of the Census

I. INTRODUCTION

The term record linkage, as it will be used in

this paper, is a generic term for any process by

which the set of reporting units common to two or

more files of data is determined.

Historically, government agencies have been

the primary users of record linkage techniques.

The reasons such agencies carry out record linkage projects are as varied as the purpose and

scope of the agencies themselves. Consider the

following examples:

a) The United States Department of Agriculture uses record linkage to update mailing

lists (see Coulter and Mergerson, 1977).

b) Statistics Canada uses record linkage as a

tool in epidemological research (see Smith,

1982).

c) The United States Census Bureau uses record

linkage as a tool in coverage and content

evaluation (see Bailar, 1983).

For a more detailed discussion of the history

and and use of record linkage by United States

government agencies see U.S. Department of

Commerce (1980).

As an area of study, Record Linkage, with

its associated statistical problems, is a special

case of a larger area of concern. This area

makes use of various mathematical and statistical

techniques to study the problems involved In the

classification of observed phenomena.

Discriminant analysis, discrete discriminant

analysis, pattern recognition, cluster analysis

and mathematical taxonomy are some of the specific

fields which study various aspects of the classification problem. While record linkage contains

its own specific set of problems it also has a

great deal in common with these other fields.

The basic unit of study in the linking of two

files F1 and F2 is F1XF2, the set of ordered

pairs from F1 and F2. Given F1XF2, our job iS tO

classify each pair as either matched or unmatched.

This decision will be based on measurements taken

on the record pairs. For example, ifwe are linking person records, a possible measurement would

be to compare surnames on the two records, and

assign the value 1 for those pairs where there is

agreement and O for those pairs where there is

disagreement. These measurements will yield a

vector, r, of observations on each record pair.

The key fact which will allow us to link the

two files is that r behaves differently for

matched and unmatched pairs. Statistically we

model this by assuming that r is a random vector

generated by P( s I M) on matched pairs and

P( . I U) on unmatched pairs. Thus, ther value

for a single randomly selected record pair is

generated by PP( ● I M)+(l-P) p( o I U) where P

is the proportion of matched records.

This model for the record linkage problem is

the same as the one used in discriminant analysis.

In particular, as r is almost always discrete,

the literature on discrete discriminant analysis

is extremely useflul (see for example Goldstein

and Dillon, 1978). There are, however, several

areas of concern th~t seem to be a great deal

more important for record linkage than for the

other classification techniques.

Our topic of discussion in this paper, blocking, arises from consideration of one of these

problem areas. That area concerns the extreme

size of the data sets involved for even a relatively small record linkage project. The size

problem precludes our being able to study all

possible record pairs. So, we must determine

some rule which will automatically remove a large

portion of record pairs from consideration. Such

a rule is referred to as a blocking scheme since

the resulting subset of record pairs often forms

rectangular blocks in F1XF2.

The literature on the blocking problem is not

extensive. Brounstein (1969), Coulter and Mergerson (1977) and U.S. Department of Commerce

(1977) contain discussions of the practical aspects of choosing a blocking scheme; however,

they provide no general framework within which to

make such a selection. ,Jaro (1972) provides a

framework for the selection of a blocking scheme

but doesn’t discuss the errors induced by blocking. Many other papers, particularly those on

clerical matching, contain implicit information

on blocking. But so far there has been no systematic study of this area.

To provide such a study we begin with the

following three questions:

1) What are the benefits and costs involved

in blocking and how do we measure them?

2) How do we select between competing

blocking schemes? Is there a best scheme?

3) How do the various computing restrictions

effect our blocking scheme selection?

These three questions will serve as a guideline

for our investigation of the blocking problem.

But, before we begin this investigation, we need

to consider some background material on record

linkage.

II. BACKGROUND

Again, our job in linking the two files F1 and

F2 is to classify each record pair as either

matched or unmatched. In practice, however, we

usually include a clr?rical review decision for

tricky cases. So, our set of possible decisions is

Al: the pair is a match

A2: no determination made - clerical

review

A3: the pair is not a match.

Now, consider the class of decision functions

D( . ) which transform our space of comparison

vector values, elements of which we will denote

by Y, to the set of decisions {A1,A2,A3}. Given

199
two or more decision functions in this class, what

criterion will we use to choose between them?

In Fellegi and Sunter (1969) the argument is

put forward that, as decision A2 will require

costly and error prone clerical review, we should

pick a decision procedure which will minimize the

expected number of AZ decisions while keeping a

bound on the expected number of pairs which are

classified in error. Since the unconditional

distribution of the comparison vector is the same

for any randomly chosen pair, this reduces to

picking that decision procedure which will minimize P(A2) subject to P(AIIU)<= u and P(A31M)<=A.

Given that you know P( . IM) and P( . Ill),

Fellegi and Sunter prove that the decision procedure which solves this problem is of the form

{

A3if t(y) <=tl

(1) D(Y) = A2 if tl < t(y) <t2

Al if t(y) >=t2

where J?(Y)= P(Y lM)/P(Y IU), tl is the largest

value in the range of t(.) for which P(A31M)<= k

and t2 is the smallest value in the range oft(.)

for which P(A1/U) <=11 .

It is this decision procedure that forms the

basis for our study of the blocking problem.

III. MEASUREMENT OF THE COST AND

BENEFIT OF BLOCKING

In the past sections we have outlined the more

general aspects of record linkage and defined the

blocking problem. In this section we will discuss

blocking in the context of the decision procedure

given in section II.

We base our general blocking strategy on the

fact that the proportion of matched pairs in F1XF2

is small. So we will concentrate on blocking

rules in which the pairs removed by the rule will

be assigned the status of unmatched.

Fellegi-Sunter (1969) provides a formal model

for blocking. This model defines a blocking

scheme to be a subspace, say r*, of the comparison space. Kelley (1984) provides a preliminary

study of selected methods of measuring cost and

benefit. The method found to have the most

intuitive appeal is one that is based on the

following amended decision procedure:

{

A3 if J?(Y)<= tl ory c r*c

(2) o’(Y) = A2 if tl < l(y) < t2 andy c r*

Al if i(y) >= t2 and y e r*

A Venn diagram of this situation is given by

1----S3-----1-----S2---1----S1---------1

tl t2

where S3* is represented by the shaded region.

In this design Si and Si* are the regions of r

values for which we make decision Ai under

decision functions given by (1) and (2), respectively.

The error levels for this amended decision rule

are given by

P(S3* I M) = P(S3 I M) + P(S3* - S3 I M)

=1 + P(s3* -s3 I M).

and

P(S1* I u) =P(sl I u) - P(slfls3* [ u)

. P - p(slns3* I u).

These eauations give us a means to compute a cost

incurred by blocking on the subspace I?*, namely,

P(S3* - S3 I M), the increase in probability of

a false nonmatch. The benefit gained from blocking on r* takes the form of a decrease in the

number of pairs which will have to be processed.

We will measure this benefit by the unconditional probability that a randomly chosen record

pair yields a r vector in the block.

Now, given two blocking schemes which both

have cost less than or equal to a fixed amount,

the preferred scheme is the one with greatest

benefit. Thus, we define the best blocking

scheme to be that scheme which minimizes p(r*)

subject to P(S3*-S31M) <= w, where w is an independently determined upper bound on blocking

costs.

IV. COMPUTING THE BEST BLOCKING SCHEME -

THE ADMISSIBILITY CONCEPT

Since the comparison vector is discrete, the computation of the best blocking scheme will require

a comparison of all competing schemes. so, it’s

in our best interest to reduce the number of

competing schemes. To make this reduction we note

that if rl* and r2* are two conpeting schemes

such that rl* is a subset of r2* then F1* +s

uniformly better than r2*. So, we can remove

r2* from the set of competing blocking schemes.

The following definition formalizes this example:

r* will be said to be an admissible

blocking scheme at w = WO if

a) P(S3* - S3 I M) <=wO and

b) for every r** that is a subset of r*

P(S3** - S31M)>W0.

The concept of an admissible blocking scheme

given by this definition is analogous to the concept of an admissible decision procedure. It

serves to reduce, hopefully to a reasonable size,

the number of blocking schemes competing for

best. But, unfortunately, when actually applied

to the task of conputing the set of admissible

blocking schemes, this definition is very cumbersome. The following lemma gives necessary and

sufficient conditions for admissibility which are

more favorable to algorithm development:

Lemma 1:

r* is admissible at w = WO if and only if

r*o S3.~and

P(YIM) >wO - P(s3*-s31M) ~ o for all Y in r*.

200
Proof:

If r* is admissible then P(S3*-S31M) <= wO.

~u;;~e~LtfoJ3~;= r*- {y] we have P(S3** - S31M)

-s3= (S3* - S3)U({Y}-S3). so,

P({Y}-s31Mj + P(s3* - S31M) > wO.

From this relationship we see that if Y is in

S3then P(S3*-S31M) J wO; thus, I’*llS3 = 0. So

we have P(YIM) >wO - P(S3*-S31M) for all Y in r*.

Conversely, we first note that P (S3*-S31M)

<= Wo. Next, let r’ be a pro er subset of r*

then r’ is a subset of r*- r}Y for some y .

so, P(S3’-S3IM)>

\

= P(s3*-s31M) + P({y]-S3 M).

Thus, we have P(S3’-S3IM) >= P(s3*-S31M +

p(YIM) Jw(). Hence, I’*is admissible.

Now, in theory, we can use the result of lemma

1 to compute all admissible schemes. However,

since the minimum number of dimensional r vector

values is 2**n, we would have to generate and

classify on the order of 2**(2**n) subsets.

For n=5 this yields 4,294,967,300 subsets,

which is clearly too large for practical consideration. So, while the admissibility concept is

helpful in reducing the number of competing

schemes, it hasn’t served to provide us with a

practical algorithm for the computation of the

best blocking scheme. In the next section, “-

will give more attention to the development

such an algorithm.

v. IMPLEMENTATION CONSIDERATIONS

we

of

The previous section provides a general framework for studying blocking; however, it doesn’t

give us much insight into the practical side of

determining a block of records for possible

linkage. If we keep in mind that 1/0 and computing the comparison vector are the biggest

consumers of time in the linkage operation we see

that admissible blocking schemes that require the

computation of a r vector value for each record

pair are not practical. Thus, though a scheme

might be theoretically admissible it might not be

feasible.

One solution for this problem is to block by

using certain fields on the record (such as soundex code of surname or address range) as sort

keys. The blocks would be determined by those

record pairs with equal keys. Thus, the match

status of unmatched pairs would be implicitly

assigned to all record pairs with unequal keys.

Restricting our study to blocking schemes

which are determined by sort keys implies that

the comparison vector we want to use will consist

of dichotomous components measuring agreement on

the record identifier fields. We will further

assume that the components of the comparison

vector are stochastically independent for both

matched and unmatched record pairs.

Now, letting mi = P(ri=llM), ui=P(ri=llU) and

I’*be the blocking scheme determined by sorting

on components i],...,ik we have the following

result:

Lemma 2:

Suppose that mi>l/2 and ui<mi for all i then r*

is admissible at WO if and only if

a) WO - P(S3*-S31M) >= O

b) P(Y*IM) >Max {tlP( Y*IU),

w()- P(S3*-S31M)],

where Y* is such that yil* = 1,..., yik* = 1,

yik+l* = O, .... yip* = O.

Proof:

First suppose that r* is admissible at WO

then conditions a) and b) follow directly from

lemma 1 and the fact that P(YIM) > tl P(YIU)

for all Y in S3c.

Now, to establish the converse we first note

that, since mi > 1/2 fOr all i, P(Y*IM) =

min P(YIM). So P(YIM) JWO - P(S3*-S31M) >= O

yer*

for all Y in r*. Next we need to prove that r*n

S3 = 0. To prove this we note that ui < mi

implies thatmi/ui > (1-mi)/(1-ui). So, P(~~:/

P(YIU) > P(Y*lM)/P(Y*lU) for all Y in r*.

r*fiS3 . ~. The converse follows from lemma 1:

In comparing lemma 2 with lemma 1, we see that

lemma 2 has a definite computational advantage

above and beyond the reduction in competing

schemes gained hy restricting attention to those

schemes based on sorting. That advantage lies

in the requirement t.ocheck for admissibility at

only one point in the blocking scheme, namely

y*. This results in tremendous savings in col?Iputing time and simplifies algorithm construction and coding considerably. In the next

section we apply lemma 2 to a simple numeric

example.

VI. AN EXAMPLE

As an example, let’s consider matching two

files of records based on the identifiers surname,

first name, and sex.

Suppose we have determined beforehand that,

for surname ml = .90 and U1 = .05,

for first name m2 = .85 and U2 = .10,

and for sex m3 = .95 and U3 = .45.

Retaining the assumption of the previous

section our discriminant function is given by

3

L(y)= ln2(l(y)) =1 [Yi ln2 (mi/Ui)

i=1 +(1-yi) ln2 ((1-mi)/

(l-ui))].

To compute the Fellegi-Sunter decision procedure we first compute Lfor each agreement pattern

and then we order the patterns on increasing L.

The following table gives the results of this

operation:

i I

One minus

Pattern Sum of P(*IM) sumof P(cIU) L

I I I

(0,0,0) .00075

(0,0,1) .01500

(0,1,0) .01925

(1,0,0) .02600

(0,1,1) .10675

(1,0,1) .23500

(1,1,0) .27325

(1,1.1) 1.00000

.52975

.14500

.09275

.06800

.0?525

.00500

.00225

0.00000

-9.29

-4.76

-3.62

-1.87

.92

2.67

3.79

8.34

201
Using this table it is clear how one would

compute tl and t2 for given A and M .

For example, if we let A = .05 andv = .05

then tl = -1.87 and t2 = 2.67. The actual values

of k and u are .026 and .02525, respectively.

We will use this decision procedure to discuss

the blocking problem.

Consider our space of admissible blocking

schemes based on sorting. We note that since no

single component blocking scheme is admissible,

we have a total of four schemes to test. Now,

for convenience let 61 denote blocking on surname

and first name, B2 denote blockinq on surname and

sex, B3 denote blocking on first name and sex,

and B4 denote blocking on all components.

The following table gives the information

necessarv to determine the admissibility of FJi:

values of wO for

Bi P(S3*-S31M) p(Y*l~) which Bi is

admissible

B1 .209 .03825 .209 <w() < .24725

B2 .119 .12825 .119ZW0 < .24725

B3 .1665 .08075 .1665–< WO < .24725

B4 .24725 .72675 .2472~’wO < .974

Before we go on it is interesting to note that

the minimum WO value for which any of the Bi is

admissible is .119. Thus, the minimum 10SS we

can incur by blocking is an increase in false

non-match probability of .11!?.

Looking at the admissible blocking schemes as

a function of wO, we have the following:

1. For .119 < WO c .1665 82 is admissible.

2. For .1665~w0 < .209 62 and B3 are admissible.

3. For .209 < WO < .24725 Bl, B2, B3 are

admissible:

4. For .24725 ~ WO < .974 B4 is admissible.

Now, to compute the best admissible blocking

scheme we must determine which of the competing

schemes has the smallest probability of occurrence. The probability of occurrence of schemes

Bi, say P(Bi), is given by pP(BilM)+(l-p)P(Bilu),

where p is the proportion of matched record

pairs. Thus, in general, the best admissible

scheme will be a function of p.

To compute the best blocking scheme for cases

2 and 3 consider the following table:

P(BilM) P(BilU)

B2

I

.855 .0225 I

63 I .8075 .045 I

So, for case 2, 62 is the best blocking scheme

for values of p <= .3214 andB3 is the best blocking scheme for p j .3214. For case 3, 61 is

uniformly the best blocking scheme.

At this point, we have demonstrated how to

select the best blocking scheme for a fixed value

of Wo. But it still is unclear how one would use

this information to actually make a decision about

which scheme to use. To study this question let’s

consider the nature of such a decision. To select

a blocking scheme we need to balance the cost with

the overall benefit. Let’s redo our example this

time for several different values of WO and compare the benefits for the resulting schemes.

The following is the first part of the list of

the best blocking schemes for all values of wO.

This list is presented in increasing order of wO.

The expected benefit, in terms of the percent of

F1XF2 that would be examined, is given for each

scheme. TO compute this benefit the approximate

sizes of F1 and F2 are required. We used F1 size

= 200,000 and F2 size = 100,000 in this example.

1.

2.

3.

4.

Admissible blocking schemes at wO=O.049Z501

are as follows:

The scheme determined by sorting on sex.

The expected percent of the cross product of

this blocking scheme would examine is

bounded above by 45.00005%.

Admissible blocking schemes at wO=O.0992500

are as follows:

The scheme determined by sorting on surname.

The expected percent of the cross product

this blocking scheme would examine is bounded above by 5.00009%.

Admissible blocking schemes at wO=O.1442501

are as follows:

The scheme determined by sorting on surname

and sex.

The expected percent of the cross product

this blocking scheme would examine is bounded

above by 2.25008%.

Admissible blocking schemes at wO=O.149250

are as follows:

The scheme determined by sorting on first

name.

The scheme determined by sorting on surname

and sex.

Of these, the best blocking strategy, as a

function of the proportion of matched pairs,

is as follows:

For p=O.000000000 to p=0.939394700 sort on

components surname and sex.

For p=0.939394700 to p=l.000000000 sort on

components first name.

The expected percent of the cross product

this blocking scheme would examineis bounrl

ed above by 2.25008%.

To use this list for decision-making purposes

one would have to have some idea about how much

data they can afford to look at and how larqe a

false non-match rate they could tolerate. For

example, in lookinq at the scheme determined by

sortinq on sex, we have a small (thouah maybe

not small enough) WO value but the number of

record Dairs we would have to look at would be

around 9x1O**1C, which is clearly not feasible.

Sortino on surname has a sliqhtly hiqher WO

value, but reduces the number of records to

1()**l(-J. If we are willin~ to acceot an even

hiqher wO, then we can sort on surname and sex,

which further reduces the number of record Dairs

to 4.5X1O**9.

Another important piece of information that we

shouldn’t overlook is the number of record pairs

we can hold in memory at any one time. We don’t

want to select a blocking scheme for which the

individual block sizes are too large. So not

only is the total number of pairs in the block

important but so is the number of states of the

sorting variable and the distribution of that

202
variable over those states.

VII. SUMMARY

The blocking problem is intrinsic to record

linkage. As such, before a link between files is

attempted a decision must be made concerning the

appropriate blocking method.

In this paper we study this decision, along

with its costs and benefits, through the record

1inkage methodology developed in Fellegi and

Sunter (1969). This methodology applies classic

decision theory techniques to the record linkage

problem, constructing the optimum classifer under

a loss function analogous to that of hypothesis

testing.

The result of our study is a method which can

be used to balance the cost and benefit of blocking. This method involves maximizing benefit

subject to an upper bound on cost. The measurement of cost and benefit is based on the FellegiSunter method and, as such, makes use of a

similar loss function.

NOTES AND REFERENCES

Bailar, Barbara A. (1983), Counting or Estimation

in a Census -- A Difficult Decision, Proceedin s

of the American Statistical Association, +

Statistics Section, pp. 42-49.

Brounstein, S. H. (1969), Data Record Linkage Under Conditions of Uncertainty, delivered at the

Seventh Annual Conference of the Urban and Regional Information Systems Association.

Coulter, Richard W. and Mergerson, James, W.

(1977), An Application of a Record Linkage Theory

in constructing a List Sampling Frame. List

Sampling Frame Section, Sample Survey Research

Branch, Statistical Reporting Service, U.S. Department of Agriculture.

Fellegi, Ivan and Sunter, Alan (1969), A Theory

for Record Linkage, Journal of the American

Statistical Association, vol. 64, PP. 1=

210.

Goldstein. Matthew and Dillon. William (1978).

Discrete Discriminant Analysis: Wiley.

.

Jaro, M. A. (1972), UNIMATCH - A Computer System

for Generalized Record Linkage Under Conditions

m;3f;;:? - conference ‘roceed’ngs’

Kelley, Robert Patrick (1984), Blocking Consideration for Record Linkaae Under Conditions of

Uncertainty, Statistics if Income and Related

Administrative Record Research: 984 Department of the Treasury, Internal Revenu~ Service,

pp. 163-165.

Smith, Martha E. (1982), Value of Record Linkage

Studies in Identifying Population at Genetic

Risk and Relating Risk to Exposures. Progress

in Mutation Research, vol. 3, pp. 85-98.

U. S. Department of Commerce, National Bureau

of Standards (1977), Accessing Individual

Records from Personal Data Files Using NonOnlque dentlflers.

Us. Department of Commerce, Office of Federal

Statistical Policy and Standards (1980), Statistical Policy Working Paper 5 - Report on

Lxact and Statistical Matching Techniques.

203
Eli S.

WINKLER

This paper discusses Bill Winkler’s

presentation on “Preprocessing of Lists and

String Comparison.”

Key factors in “Preprocessing of Lists” are:

DISCUSSION

Parks, Consultant

1. The objectives of the system and the

costs of various levels and types of

matching error.

2. Costs of attaining a given matching

accuracy level by preprocessing vs.

other alternatives (e.g., suitably

tailored “tolerances”).

3. The nature of the matching system--

manual, computerized, “mixed,” etc.

4. How preprocessing is performed.

1. Objectives

The objectives of the system and the costs of

matching error are intimately related. For

example, if the objective is to estimate undercoverage of the U.S. census in each state,

city, county, township, place, etc. for

purposes of allocation of representation in

Congress and state legislatures, city/county

councils, etc. and for allocating federal and

state funds to state and local jurisdictions, a

uniform level of matching error everywhere is

more important than the absolute level of

matching error. Thus, preprocessing may have

little value if its effect is to reduce the

different types of matching errors by the same

percentages in all jurisdictions. On the other

hand, if preprocessing reduces urban matching

error more than rural, it may be desirable or

undesirable, depending upon whether the level

of urban matching error ~“thout preprocessing

is greater or less than the level of rural

matching error without preprocessing.

2. Alternative Techniques

The objective of preprocessing (i.e., reduction of matching errors) can be attained by

other means (e.g., the prescription of matching

“tolerances”): and these techniques may cost

less than preprocessing. For example, soundex

coding is a form of “matching tolerance.” That

is, al1 disagreements of vowels and some

disagreements of consonants are ignored in

determining whether a pair of records match on

the soundexed “identifier.” One can, in fact,

combine some preprocessing with tolerances

(and, perhaps, other error-reducing techniques)

to get a more efficient matching system than

either can give alone. For example, one can

prescribe standard abbreviations for the

address suffixes “Avenue,” “Street,” “Road,”

“Drive,” “Place,” “Boulevard,” etc., but also

provide that an address match whew the

suffixes differ will be accepted unless there

is another

agree. For

address

examDle.

“~utton Road” uriless

match where the suffixes

“Sutton Drive” would match

either file contains both

“Sutton Road” and “Sutton Drive.”

Standard spelling of name and address may be

achieved more accurately and more cheaply by

controlling data collection, recording and

“keying” (to put the data in machine readable

form) than by preprocessing. This would, for

example, avoid most of the errors of preprocessing by ZIPSTAN exhibited by the examples

shown in the paper. Preprocessing errors can

also be reduced or eliminated by other means,

such as the clerical insertion of distinctive

symbols to designate components of name and

address, as outlined in Section 4 below.

It should be noted that selection of an

“optimum matching strategy” is heavily

dependent upon the type(s) of matching

system(s) considered and that the choice of

type of matching system is a vital part of the

determination of “optimummatching strategy.”

3. Kind of Matching System

The paper by Winkler notes that matching

systems can be manual or computerized and

implies that preprocessing is largely unnecessary for manual matching systems. I think

his suggestion that individuals can usually

determine accurately whether a pair of name and

address records is actually a match or nonmatch

is somewhat optimistic. Individuals can make

this determination (so can a computer system),

but how accurately deDends on the kind of

system. The grea~ ad<antage of a com etent

human matcher operating in a properl,+

matching system is th f

YlexibilitY. vrovided. ofeco~r~e.ohe ~~ ~~nh~s

good judgment”and the-matching rules permit him

(her) to use that judgment (and I have seen

many sets of matching instructions which do

not). The great disadvantage of a welldesigned manual matching system with competent

matchers is the human matcher’s slowness and

the inevitable drop in efficiency in operating

in a system which requires examining large

masses of records; and not in lack of clear

decision rules, inconsistency of application of

decision rules, and nonreproducibility of

results. All of the latter do occur, but can

be adequately controlled in a well-designed

matching system (although it is not easy!).

However, humans cannot match the forte of the

computer--its speed in examining large masses

of data.

The solution to this problem is to let the

computer do what it does well and let humans do

what they do well. That is, design a mixed

computer-human system, in which the computer

handles the large mass of cases which can be

classified as positive links or positive

nonlinks, on a mechanical, routine basis.

Carefully trained and well+votivated humans

could then try to match the remaining cases,

205
using a “computer-interactive” system, where

the human would specify a small c~ass of

possible matches and the computer woIJld display

the records in this class, until a positive

link was found or there was adeauate evidence

that no such link existed.

4. _Techniques of Preprocessing

Certain elements of preprocessing ~ff11

unquestionably be valuable in any computerized

matching system. In particular, it is

important to develop some method so that the

computer can auickly and accurately identify

the various elements of the name and address:

surname, house number, street name or number,

first name, and the conventional prefixes and

suffixes to name and address. If this involves

elaborate manual rearrangeme~t and keying of

the name and address, substantial error is

likely to be introduced, possibly as much as

the preprocessing removes. The exampl~s in the

paper suggest that unaided computer formatting

is also likely to introduce as much error as it

removes. A solution may be something used in

one of the earliest (1956) computerized

matching systems, where clerks inserted a

distinctive and computer-readable symbol in

front of the components of name and address to

be used in the matching; e.g., * before

surname, # before house number, % before street

name, $ before P. O. box number, @ before

title, etc. After appropriate codes were

placed in fixed fields, the symbols were

deleted from the computer records.

206
DISCUSSION

Benjamin J. Tepping, Westat, Inc.

*

The papers by Kirkendall and Kelley contain

much interesting material, with some of which I

must take issue.

The Fellegi-Sunter model, on which these

. papers are based, recognizes that there are

three possible outcomes, but (it seems to me)

uses the wrong utility function. To simply

minimize the probability of subjecting a case

to clerical review conditional on bounds on the

probabilities of erroneous matches and erroneous nonmatches ignores important facts:

(a) the value of an erroneous match is, in

many (or perhaps most) applications,

suite different from the value of an

erroneous nonmatch;

(b) the cost and the probability of

misclassification associated with the

clerical review should be taken into

consideration.

We do not necessarily want to minimize the

number o~clerical reviews. We do want to

maximize the va1ue of the record linkage

operation. This implies that one must not only

determine the costs of the various components

of the operation, but must also set values on

the possible outcomes. An illustration of this

approach is the application of a theoretical

model of record linkage to the ChandrasekarDeming technique for estimating the number of

vital events on the basis of data from two

different sources. This was published in the

Bureau of the Census Technical Notes No. 4, in

1971 [1].

It appears that neither author is aware of my

paper [2] in JASA in 1968 in which is presented

a model for the optimum linkage of records.

The authors treat the problem as an exercise

in the testing of hypotheses. I think it is

preferable to regard it as a problem of

decision making, subject to a utility function

which depends upon the state of nature. In

these applications, the three possible decisions are to call the pair of records being

compared a match or a nonmatch, or to make some

kind of further investigation before deciding

on a classification. That investigation may

consist simply of subjecting the records to

personal scrutiny or may involve seeking

additional data. The utility function would

specify a gain or loss for each of the possible

decisions, conditional on whether the pair is

in fact a match or a nonmatch.

Kirkendall’s examples also ignore the problem

of fixing the values of the probabilities of

errors of the first and second kinds. Those

probabilities should not be arbitrary. Any

solution of the problem should depend upon

evaluation of the loss or gain of alternative

decisions as well as on the cost of nondecisions--e.g., resort to other means of

arriving at a decision.

Kirkendall’s first illustration assumes independence, both under Ho and under HI . In the

real wmrld, this assumption may be far from

true. For example, under either of the

hypotheses Ho or H1 , an agreement on first

name would increase the probability of an

agreement on the item sex--two records both

giving the first name as “Nancy” are not likely

to indicate different sexes. Presumably the

lack of independence could be treated as in her

example of cancer patients, essentially by

dividing the First Name item into two items:

one for cases in which both records show the

sex as male and one for cases in which both

records show the sex as female. This comment

also applies to Kelley’s numerical example, in

which independence of these components is

assumed.

As is pointed out by Kelley, the literature

that gives advice on the choice of blocking

schemes is not extensive. Yet practical

problems make blocking of the files being

compared essential, and Kelley’s work should

contribute to the improvement of blocking

designs. He does take account of costs, by

considering both the decrease in operational

costs , because blocking reduces the number of

comparison pairs, and the increase in the

probability of an erroneous nonmatch as a

result of blocking. (1 note, however, that he

does not use the fact that the probability of

an erroneous match decreases as a result of the

blocking.) His numerical examples illustrate

that the choice among competing admissible

blocking schemes involves the implicit assignment of relative values to an increase in the

probability of erroneous nonmatches and a

decrease in the number of comparisons. In

practice, no doubt, a similar implicit assignment of values to an erroneous match, an

erroneous nonmatch and a case referred to

personal review is made in order to fix the

values of the parameters A and ‘+ of the

Fellegi-Sunter model.

I think there is difficulty with the application of Kelley’s Lemma 2 to the determination

of a suitable blocking scheme even after

dealing with the lack of independence of the

components of the comparison vector. It seems

that a choice must depend, among other things,

on a knowledge of the probability, given that

the pair is a match (or a nonmatch), that there

is agreement between the units of the pair on

specified components of the comparison vector.

Estimates of such probabilities must ultimately

depend upon extensive empirical investigations,

although such est;mates seem often to be made

on the basis of assumed models.

RFFEPENCES

[1] Tepping, B.J., “The application of a

linkage model to the ChandrasekarDeming technique for estimating vital

events,” U.S. Bureau of the Census,

Technical Notes No. 4, Washington,

D.C., 1971, pp. 11-16.

r21 Tetmina. B. J.. “A model for oDtimum

linkag~’ of records,” Journal of the

American Statistical Association, 63,

968, PP. ~3zl~332.

207
REJOINDER

William E. Winkler, Energy

Eli Marks’ comments provide a valuable perspective to the overall objectives of matching

procedures.

Just as the Fellegi-Sunter matching procedure

contains computerized (automatic designation of

matches and nonmatches) and manual (review of

records designated for further manual followup)

components, so does preprocessing contain computerized (minor reformatting, spelling

standardization, string comparison) and manual

(keypunch/transcription, major reformatting)

components.

The respective roles of the two components

are best exemplified by Newcombe et al. (1983,

1959, 1962). Newcombe’s view is that computer

procedures should be devaloped for the most

routine and repetitive tasks. Aa knowledge of

the characteristics of address files and coding

techniques Lncreasea, computerized procedures

can replace greater proportions -- possibly all

-- manual components.

It is my experience that reasonably designed

manual procedures ara difficult and expensive to

implement. This is becauae of high turnover

rates and the neceaaity of training and constantly supervising personnel performing manual

processing. Computerized procedures can have

the benefit of being more cost-effective, consistent, and reproducible.

Both Marka and I note that the Census

Bureau’s ZIPSTAN software -- which is designed

for files of individuals -- induced minor errors

In files of businesses. In Winkler (1985), 1

show that ZIPSTAN’S identification of address

subfields can yield substantial improvements in

the discriminating power of the Fellegi-Sunter

matching procedure.

The coat in using ZIPSTAW was a few days of

my time installing it. The alternative would

have been to do nothing or develop manual procedures, set up computer files suitable for

manual review, train individual in computer

login and manual review procedures, and have the

individuals perform the review. Marka notea, if

Identifying individual subfielda of the name and

Information Administration

addresa involves “elaborate manual rearrangement

and keying . . . . substantial error is likely to

be introduced, possibly as much as preprocessing

removes.”

I strongly agree that our understanding of

“matching tolerances” needs to be improved. The

purpose of my discussion of string comparators

was to show the limitations of tolerances such

as SOUNDEX, particularly SOUNDEX abbreviations

of surnames used as sort keys during the

blocking stage of matching. For files of

businesses, I show (Winkler, 1985) that individual sort keya are generally not suitable for

creating blocks containing most matched pairs.

My solution is to apply independently multiple

sort keys.

String comparison metrics, such as Jaro’s

string comparator, can only be efficiently used

during the discrimination stage because they

involve the comparison of corresponding strings

from pairs of racorda. In my view, they offer

the best opportunity for developing tolerances.

How such tolerances fit in the framework of the

Fellegi-Sunter model needs to be described and

quantified.

REFERENCES

Newcombe, H.B., Kennedy, J.M., Axford, S.J., and

James, A.P. (1959), “Automatic Linkage of Vital

Records,” Science 130, 954-959.

Newcombe, H.B. and ~nedy, J.M. (1962), “Record

Linkage,” Communications of the ACM 5, 563-566.

Newcombe, H.B., Smith, M.E., Howe, G.R., Mingay,

J Strugnell, A., and Abbatt, J.D. (1983).

“liability of Computerized Veraus Manual

Searches in a Study of the Haalth of Eldorado

Uranium Workers,” Comput. Biol. Med. 13,

157-169.

—

Winklar, W. E. (1985), “Exact Matching

Lists of Buainessea: Blocking, Subfield

Identification, and Information Thaory,” Paper

presentad at the 1985 ASA Annual Meeting,

Section on Survey Research Methods, August 4-8,

1985, Las Vegas, Nevada, (pp. 227-241 in this

volume) .

209
REJOINDER

R. Patrick Kelley, U.S. Bureau of the Census

Let me start my rejoinder by saying that I

find Dr. Tepping’s comments both interesting

and helpful. The main criticism of my paper

given by Dr. Tepping is my choice of the

Fellegi-Sunter model as a basis for blocking

research. AS such, this exchange is simply

another in a long debate over the handling of

clerical costs and errors.

I have been aware of, and admired, Dr.

Tepping’s work on record linkage for quite,some

time. From a theoretical point of view, the

utility theory approach is a fascinating one;

however, clerical operations are hard to control and empirical investigations of clerical

error rates and costs are data dependent. This

makes estimates of the parameters in Dr.

Tepping’s model hard/expensive to obtain and

highly variable.

Due to these facts, it is my opinion that the

Fellegi-Sunter model provides the best general

foundation for record linkage research and

development. Methods which account for

clerical costs should be used only after there

have been several linkage projects run on data

frcnn the same source, using the same record

linkage system.

Dr. Tepping also commented on the assumption

of independence between comparison vector

components, the difficulty of estimating, the

difficulty of estimating model parameters, and

the potential sensitivity of linkage error

rates to errors in those parameter estimates.

These ccmments are well placed, and I am continuing work on the blocking problem in an

attempt to strengthen the results of this paper.

.

211
PROPERTIES OF THE SOCIAL SECURITY NUMBER RELEVANT TO ITS USE IN RECORD LINKAGES

Thomas B. Jabine, Consultant,

Linkage of records frcintwo data systems is

aided greatly by the presence in both systems

of the same numeric identifier, for example,

the social security number (SSN) for persons or

the employer identification number (EIN) for

businesses. When matching variables for two

records are compared, agreement on such numeric

identifiers is usually given a large weight in

deciding whether a true match exists.

Because of their importance for record

linkage, it is important to have ccinplete and

current information on the relevant properties

of each of these numeric identifiers. Such

properties include: coverage, general structure

and method of issuance, information content,

and appropriate methods of validation. Properties relevant to sample selection using numeric

identifiers are also of interest, since many

record-linkage studies are based on a sample

from one of the data systems.

This paper provides a description of the

properties of the social security number (SSN)

that are relevant to its use in record linkages. The description should be regarded as a

first draft and readers are urged to suggest

corrections and additions.

If this description of the SSN proves useful, it is suggested that the Administrative

Records Subcommittee of the Federal Committee

on Statistical Methodology make arrangements

to: (1) prepare and disseminate descriptions,

using the same format, of other commonly used

numeric identifiers, such as the EIN and the

unemployment insurance number, and (2) update

the descriptions periodically and whenever

significant changes occur.

Special thanks are due to Richard Wehrly of

the Social Security Administration for providing information used in developing the SSN

description. However, any errors are the sole

responsibility of the author and readers are

cautioned that the description of the SSN has

not been officially reviewed by the Social

Security Administration.

NUMERIC IDENTIFIER DESCRIPTION

1. Name of identifier

The social security number (SSN).

2. Administrative uses

Ns were Issued initially so that earnings

of persons in jobs covered by the soci~l

security retirement program could be reported,

by their employers, to the Social Security

kkninistration (SSA) and credited to the

persons accounts for subsequent use in determining benefit eligibility and payment amounts.

An early decision was made to use SSNS as

identifiers in the State-operated unemployment

insurance programs. No other significant uses

developed until 1961 when the Internal Revenue

Service, after discussions with .SSA,decided to

use the SSN as a taxpayer identification number.

After implementation of this decision, other

Cotmnitteeon National Statistics

uses by Federal and State governments followed

rapidly, and the SSN is now widely used as an

identifier for workers, taxpayers, drivers,

students, welfare beneficiaries, civil servants, servicemen, veterans, pensioners and

others (HEW Secretary’s Advisory Committee,

1973).

Legal justification for use of the SSN as

an identifier by Federal agencies comes from

Executive Order 9397, issued in 1943, which

directed Federal agencies to use the SSN when

establishing a new system of permanent account

numbers. The Privacy Act of 1974 placed some

restrictions on use of SSNS by Federal, State

and 1ocal government agencies, but uses

formally established prior to January 1, 1975

were not affected and these restrictions have

had only a minor effect on widespread administrative use of the SSN by governments and

private organizations (Privacy Protection Study

Commission, 1977).

-s .--SSNS are issued to persons.

b. -coverage provisions.--An SSN will

be issued to any Unlted States citizen upon

application and presentation of acceptable

evidence of identity. Foreign nationals

legally present in the United States will be

issued SSNS if legally entitled to work or if

they have an acceptable “nonWork reason” for

needing an SSN, e.g., the need for a taxpayer

identification number.

All persons with Federally taxable income

and their spouses are required to obtain SSNS

for use as taxpayer identification numbers.

SSNS are also required for many types of

benefits and for other purposes: social security, driver’s license, welfare benefits, voter

registration, participation in scholastic

aptitude testing programs, etc. For some of

these, requirements vary by State.

Volume and characteristics of issuance

to ;;te.--SSNs were first issued in November

By the end of 1975, over 235 million

SSNS had been issued and there were an estimated 180 million living SSN holders (Social

Security Administration, 1981b). As of the

close of 1983, approximately 287,083,000 SSNS

had been issued. It is estimated by SSA that

there were 204,760,000 living SSN holders at

the end of 1981. When SSN holders die, their

SSNS are not reissued to other applicants.

The t~e in Attachment A shows the number

of SSNS issued annually, by sex of applicant,

through the end of 1979. Following the large

number of issuances in the first 14 months

(November 1936 to December 1937), the volume of

annual issuances has fluctuated for a variety

of reasons, with a tendency to increase in

recent years as coverage of SSA benefit programs and the use of SSNS for non-SSA programs

has expanded. Today most of the SSNS are

issued to applicants under 20 years of age. In

1979, 62.8 percent of the SSNS were issued to

persons under 15 and another 26.2 percent to

213
persons between 15 and 19 (Social Security

Administration, 1981b).

Fran time to time, surname counts based on

the first six characters of the surname are

made frcm SSA’S account number files. Kilss

and Tyler (1574) show the rankings of common

surnames based on 1964 counts. Based on a 1974

tabulation, the ten most common surnames were:

Smith

Johnso(n)

Williamson)

Brown

Jones

Miller

Davis

Martin(ez)(son)

Anders(on)

Wilson

The letters in parentheses following some

names are intended to show the more common surnames that have these first six characters.

d. Uniqueness, stability.--Until 1972,

applicants for MS were not asked if they had

already been issued numbers, nor were-they

asked for proof of identity. As a result many

persons now have more than one SSN (Privacy

Protection Study Commission, 1977). As of

1973, it was estimated that 4.2 million persons

had two or more SSNS (HEW Secretary’s Advisory

Committee, 1973). More recent estimates are

not available. Today, intentional issuance of

multiple numbers to the same person is permitted only in exceptional circumstances,

generally involving national security or the

protection of the person in question.

In most cases where a person is known to

have more than one SSN, SSA’S computerized SSN

files contain a record for each of his or her

SSNS and cross references linking all of the

SSNS.

Sometimes more than one person uses the

same SSN. Some reasons why this happens are

discussed in item 8b. Estimates of the frequency with which this occurs are not readily

available, but it is believed to be much less

prevalent than issuance of multiple numbers to

the same person (HEW Secretary’s Advisory Ccmmittee. 1973).

4. Ge~eral structure and information content

The social security number has nine digits

arranged as follows: 000-00-0000. The first

three digits are called the area number, the

next two are the group number, and the last

four are the serial number. There are no check

digits. The serial number provides no information about the person to whom an SSN has been

assigned; however, the area and group numbers

do contain a limited amount of information.

The area number, digits one to three of the

SSN, carries some information either about the

SSN holder’s occu~ation or his or her Dlace of

residence at the” time the number was” issued.

For the ranges of area numbers used to date,

the information content is as follows:

(1) Area numbers 001 to 626. With a few

exceptions, each of these area numbers has

been assigned to a single State, one or

more to a State. For most SSNS, the area

number indicates only the SSN holder’s

State of residence at the time of issuance,

as derived from the mailing address on the

SSN application. For SSNS issued in t

early days of social security, the ar

number indicated the specific SSA fie

office from which the number was issue

regardless of where the applicant lived.

(2) Area numbers 700-728. These numbe

were assigned to railroad workers throu

1963. Since then, railroad workers ha

been assigned SSNS with the same are

numbers as other applicants.

The group number, digits four and five, 

combination with the area number, provides 

rough indication of when the SSN was issue

In particular, it is possible to tell wheth

an SSN was issued before or after another S

having the same area number but a differe

group. Within an area number, the gro

numbers are always used in the followi

sequence:

- Odd numbers from 01 to 09

- Even numbers from 10 to 98

- Even numbers from 02 to 08

- Odd numbers from 11 to 99

The group number 00 has never been use

Only the first two sets of group numbers in t

above sequence were used through 1965. Sin

then the third and fourth sets have been us

with some area numbers. Current information 

the last group number assigned for each ar

number can be obtained frcm SSA (see Secti

9a.).

5. Issuance procedures

Ns are issued by the Social Securi

Administration. Prior to July 1, 1963, t

Railroad Retirement Board issued SSNS (in t

700 series) to all railroad employees.

A single application form, Form SS-5, A

plication for a Social Security Number Card, 

used for initial applications, requests f

replacements for lost cards and correction

such as name changes. A copy of the applic

tion form is shown in Attachment B. Appl

cations must be accompanied by evidence of ag

identity and U.S. citizenship or lawful ali

status. They may be submitted either in perso

or by mail, except that aliens and persons 

or older making initial applications . must appl

in person. -

Most SSN applications are submitted to SS

field offices. In 37 States, applications fo

new welfare applicants needing””SSNs are de

veloped by the State welfare agencies an

submitted by the State directly to SSA’S Offic

of Central Records Operations. SSA distric

offices sometimes make arrangements wit

schools for “mass enumerations” in which SS

and school officials collaborate in obtaini

and reviewing applications from all studen

who wish to obtain SSNS.

The application forms (SS-5) and accompany

ing evidence submitted to district offices ar

screened for completeness and accuracy b

district office personnel, who make furthe

contacts with applicants when necessary. Th

SS-5 information is then keyed in the distric

office for direct transmission to SSA centra

operations.

The central processing of the applicatio

consists of validation (which is essentially 

matching operation) against existing SSN files

followed by appropriate actions. The exac

214
nature of the validation depends on the type of

application. For example, if an initial applicant alleges that he or she has not been issued

an SSN previously, the purpose of the validation is to confirm that allegation. Validation

procedures are discussed further in item 9b.

The final step depends on the results of

the validation. The main possibilities are:

assigning an SSN and mailing a card to a new

applicant, mailing a replacement card to an

applicant, correcting information (such as

name) about the applicant in the SSN computerized files, or asking the field office to

supply additional information.

When a new SSN is assigned, the next

available number for the State from which the

application was submitted is used. The

sequence of availability proceeds from the

lowest area number used in a given State

through the highest area number for that State,

using the same group number. For example, in

New Hampshire, which has been assigned area

codes 001, 002, and 003, the last available

number in group 001-52 would be followed by the

first available number in group 002-52, and the

last avaflable number in that group would be

~~_~d by the first available number in group

.

6. Sampling properties

In theory, a ~robability samDle could be

selected using digi’talpatte~ns based on any of

the nine digits of the SSN or combinations

thereof. However, consideration of the information content of the first five digits, as

described in item 4, makes it clear that use of

any of those digits should be avoided. It

would be most inconvenient to select a sample

that turned out to include only persons who

were railroad workers at the time their SSNS

were issued and had all been issued their SSNS

not later than 1963!

The serial number part of the SSN, however,

does not have this kind of problem and consequently is frequently used for digital sampling

from a file of records that includes SSNS.

Assuming a uniform distribution of 9,999

possible serial numbers (SSNS ending in 0000

have never been issued), ft is possible to

choose a digftal sampling pattern that will

approximate any desired sampling fraction.

There are usually several alternatives. For

example, to select a sample of approximately 5

percent (1 in 20) of the records, one could use

(1)

(2)

(3)

(4)

and so

selected

witbout

5 of the 100 possible combinations of

the 8th and 9th digits;

50 of the 1,000 possible ccfnbinations

of digits 7, 8 and 9;

500 of the 9,999 combinations of

digits 6, 7, 8and 9;

5 of the 100 possible combinations of

the 7th and 8th digits

forth. The combinations of digits

may be chosen at random with or

replacement (the latter would be

preferable) ‘ or systematically with a random

start. In the latter case, for exmple, we

might choose the pair 73 at random and include

with it the pairs 93, 13, 33 and 53.

The use of selected digits or combinations

of digits for sampling is actually a form of

cluster sampling. In the illustration used

above, we could describe a population of

records as consisting of 100 clusters, each

consisting of all records with SSNS having a

particular pair of 8th and 9th digits. Five of

these clusters are selected by an appropriate

probability sampling mechanism.

In practice, samples of this kind,

especially when only the 8th and 9th digits are

used, behave pretty much like randcan samples,

chosen without replacement. In particular,

reasonably accurate estimates of sampling error

can be calculated as though the data were from

a simple random sample.

In selecting samples based on the serial

number portion of the SSN, the following points

should be considered:

(1) The serial number 0000 is not used.

The effect of this, which is quite small, on

the expected sample size can easily be calcu1ated.

(2) The digital patterns used for any

particular sample determine only the ex ected

sampling fraction or size. The samp+ e size

realized by using a particular set of digits or

~ion of digits will, in general, differ

somewhat from its expected value. If precise

control of sample size is important, this can

be achieved by oversampling initially and then

subsampling units at random or systematically

from the initial sample.

(3) As discussed in item 3d, some persons

have been issued more than one SSN. Such

persons may have multiple chances of selection

in a sample of persons obtained by selecting

SSN3, depending on what record sets are being

used. If the number of SSNS that each sample

person has can be determined, appropriate

adjustments can be made in estimates based on

the sample. Because the phenomenon is infrequent, however, it is usually ignored in

practice.

(4) Various studies (Hawkes and Harris,

1969; Page and Wright, 1979) have shown that

the distributions of SSNS by ending digit in

selected record sets is essentially uniform.

However, studies conducted with various record

sets in the late 1960s and early 1970s (Hawkes

and Harris, 1969 ; Internal Revenue Service,

1973) showed a negative linear relationship

between the ascending sequence of digits in

positions 6 and 7 and the number of SSNS in

these record sets having those digits. This

probably resulted from the fact that, until

1972, SSNS in each area-group canbination were

issued consecutively by serial number, from

0001 to 9999. Since then, they have been

issued in a randomized order, largely to avoid

issuing consecutive numbers to persons with the

same surname. Because of the new issuance procedure, one would expect this relationship to

disappear gradually. However, to be on the

safe side, it is recommended that: (1) digital

sampling patterns use only the 8th and 9th

digits whenever requirements can be met in that

way, and (2) whenever multiple combinations of

two or more digits are used, they should be

selected systematically rather than at random

from the range of possible canbinations.

7. Links with other numeric identifiers

At the Federal levels there are two kinds

of links between SSNS and employer identifica215
tion numbers (EINs). For employees, the link

occurs in the W-2/W-3 annual wage and tax

reporting system (prior to 1978, reporting was

quarterly). For many years SSA has used this

link for statistical purposes, in the Continuous Work History Sample system, to add employer

locations and industry data to records of

earnings and demographic characteristics for

sample persons. More recently, the Statistics

of Income Division of IRS has used the same

link to obtain employer indust~ codes to use

as an aid in coding occupations reported by

individual taxpayers on their returns.

The second link between SSNS and EINs

applies to persons who operate businesses as

sole proprietors. This link applies primarily

to sole proprietors with employees; those with

no employees are not, in general, required to

obtain and use EINs. The link occurs in two

ways: on income tax returns of sole proprietors, and on new applications for EINs. On

income tax returns, the business schedules (C

and F) call for entries of both the EIN (if the

taxpayer has one) and the SSN. On EIN application forms (Form SS-4), applicants who are

sole proprietors are asked to enter their SSNS.

There are undoubtedly several links between

the SSN and other numeric identifiers at the

State and local levels. One obvious one is the

link between SSNS and employer unemployment

insurance (UI) identification numbers, which is

necessary for the operation of the UI program.

The precise nature of the linkage varies by

State and, for the minority of States which

operate under the “wage request” system, it may

not exist in any readily accessible sense.

8. Reporting formats and problems

a. Formats.--Many dlfferent administrative

and statistical forms include spaces for recording SSNS, either by the holders or by

someone else completing the form. There is no

standard format for this purpose. The particular format used may have some effect on the

accuracy with which SSNS are entered on the

forms and read from the forms for purposes of

manual transcription or data entry.

Format features that vary include: width

and height of the space provided for the

number; separators used for the area, group,

and serial numbers; use of boxes for individual

digits; and the label used to indicate what

should be entered. Some examples of these

features appear below. All of them show the

actual size of the entry space on the form.

Example 1. Department of State, Passport

Application, berm SDP-11 (7-79)

●lnTwMcE (cm,sum or ●mlIlncq Ceuntw) ●IRTMOATS

MO* * vJ

uREDATE [ HEIGHT lCOLOROFHAIR COLOROFEYEE

—- —In.

IEN7RESIOENCE (SmaaddW Clw,StaqZIPCoda) SOCIALSECURIW

NO. tNet ~orf)

1

FATHEWENAME ●lnln

Of several formats examined, this one

provided the narrowest space for entering the

SSN, with a width of 1 1/4 inches. Most others

were in the range of 1 1/2 to 2 inches.

Empl~iholF~m~llow~~~nu~r~~f~~~

FonnW-4 (10-79)

albTasewr-~~s-J-

:hholdlng Allowmm Ccrtifkato

IvEIWuaEalib wabEf● ii

This format allowed the smallest vertical

distance of those examined, 5/32 inch. It uses

vertical dotted lines as separators for the

three parts of the SSN.

plicInternal Revenue Service, Apor Employer Identification Number,

Form SS-4, (8-76).

.S $ndinsmen !hofsccwntinsnw

I

This format also uses the dotted vertical

lines as separators. In this case, the spaces

for the three portions of the SSN are all the

same length, 5/8 inch. Other forms using

separators make the lengths of the three spaces

roughly proportional to the number of digits to

be entered, i.e., 3, 2, and 4.

8ureau of the Census/Department

of w~dtiuman Services Income Survey

Development Program, 1978 Re~arch Panel-July

Questionnaire, Form ISDP-403.

Last

Fwst Mddle “ @

ocIal Secur!ty Number

‘@llll_l@llll

[

Last

This format i1lustrates the use of separate

boxes for each digit of the SSN. The three

parts of the SSN are separated by horizontal

dashes. The ciriled numbers are sou~e codes

for data entry.

(Ori=;plai~ll ~;”~;~ecf~z~ g:

SS-5 (5-84) (see Attachment B).

This item is completed only for persons who

already have SSNS and are applying for a replacement or correction. This format uses a

box for each digit, with intervening spaces,

and horizontal dashes to separate the three

parts of the SSN. The wording of the iter

1abel ref1ects the fact that the form is

216
sometimes ccmpleted by someone other than the

“applicant.”

Example 6. Internal Revenue Service, Form

1040 Income Tax Return for Single Filers

with no Dependents.

Pleaseprintyournumbers Iikethis.

123+5678q0

%cii security number

m-mm

This format is used for handwritten entries

by taxpayers that wi11 be read automatically by

optical character reading equipment. On the

actual form, the boxes for the individual

digits are in light blue. The boxes for the

area, group and serial parts of the SSN are

separated.

Example 4 above comes frcnna questionnaire

that is completed by trained Census Bureau

interviewers. The other examples are all from

forms that are filled by members of the general

public. No experimental research on alternative formats for recording SSNS has been

identified. Sane other research has suggested

that the use of individual character separators

may actually reduce legibility of entrieS

(Wright, 1980).

. Reporting and processing errors. --Most

errors In Ns in data files occur for two

reasons: (1) the person completing the form or

answering the questions gave an SSN for the

wrong person, or (2) the SSN is for the right

person, but it was reported, recorded, transcribed or keyed incorrectly.

The first type of error can occur, for

example, when a widow reports the number under

which she is receiving benefits, rather than

her own. Another example is what SSA calls the

“pocketbook number.” The number 078-05-1120

appeared on a sample account number card

contained in wallets sold nationwide in 1938.

Several thousand people mistakenly reported

this number to their employers as their own!

By the 1970s there were over 20 different

pocketbook numbers (HEW Secretary’s Advisory

Committee, 1973, p. 112).

People who lose their social security cards

can apply for replacement cards bearing the SSN

already issued to them. In cases where they

are not able to give their SSN on the application, SSA must determine the correct SSN based

on other identifying information. Occasionally

a mismatch occurs and the person will be issued

a replacement card bearing someone else’s SSN.

The second type of error is usually an

error in a single digit or a transposition of

digits, types of errors that could be easily

corrected if a check digit were used.

Cobleigh and Alvey (1974) describe errors

detected when SSNS reported in the Current

Population Survey were validated against Social

Security Administration files. About three

percent of the reported SSNS were clearly in

error. Roughly two-thirds of these were found

to have transposition or single-digit errors.

Another one-sixth were SSNS belonging to other

members of the same household, and the remainder could not be located in SSA’S files.

9. Validation procedures

a. Intra-record validation.--When undertaking record llnkages based on SSNS, it is

usually desirable to- start by identifying SSNS

that are clearly invalid. A first step might

be to look at the SSN itself and determine

whether it is within the range of numbers

issued to date. SSA will make available, on

request, up-do-date information on the area

numbers that have been issued so far and, for

each of those numbers, the “highest” group

number issued. “Highest” must be intewreted

in terms of the standard. sequence for use of

group numbers within an area number, as

explained in item 4 above.

Attachment C provides this information as

of January 2, 1985. As of that date, the only

area numbers used were those in the ranges 001

to 587, 589 to 595, 600 and 601, and 700 to

728. Also, group number 00 and serial number

0000 are never used. Current information on

highest group numbers may be obtained from the

director of the OASDI Statistics Division;

Office of Research, Statistics and International Policy; Social Security Administration.

If records to be linked have information on

date of birth or age, the SSN can be checked

for consistency with age. The operating rule

is that a person whose SSN was issued x years

ago must be at least x years old. Since

virtually all numbers issued through 1961 were

issued to employed persons, only a few errors

would be made by requiring that persons with

numbers issued in this period be at least x +

15 years old. For SSNS issued from 1951 onwards, the SSA can provide fairly precise

information about the years in which numbers

with specific area-group combinations were

issued (contact the source given in the preceding paragraph). For numbers issued prior to

1951, only rough estimates of issuance periods

for area-QrouD combinations are DOSSiblr2.

b. ~alidation against SSA”records.--Validation is defined broadly here as a ~rocess in

which SSN information ‘for individuals from

sources external to SSA records is checked

against those records to determine its validity. Specifically, if the external record

includes an SSN, it is desired to know whether

the SSN is the correct one for that person and,

if it is not correct, what the correct SSN, if

any, is for that person. If the external

record for a person has no SSN, it is desired

to know whether that person has an SSN and, if

so, what it is. This kind of validation

requires matching external records to SSA

records and should be thought of in that

context.

Validation of SSN information is done

routinely by SSA for program purposes. Somewhat less frequently it is undertaken for

statistical purposes. Some examples of the

latter are:

(1) Validation of SSNS collected in pretests for the 1970 Census of Population (Ono et

al., 1968).

217
(2) Validation of SSNS collected in the

March 1973 Current Population Survey, as a

preparatory step before adding SSA and IRS

administrative data to the survey records

(covered in several reports and articles, e.g.,

Cobleigh and Alvey, 1974; Social Security Administration, 1981a).

(3) Validation of SSNS collected in panel

surveys as part of the Income Survey Development Program (Kasprzyk, 1983).

(4) In various mortality followup studies,

as a preparatory step before determining which

members of an externally identified study population have died, according to SSA records.

Attachment D provides a summary description

of SSA’S current validation procedures for

program operations. A combination of computerized and manual procedures is used, and

unresolved cases are returned to district

offices with an instruction to seek additional

information from the applicant or claimant.

The SSN files maintained by SSA are now fully

computerized and a more sophisticated computer

validation system is being developed.

A variety of validation procedures have

been used in statistical applications; some of

them are described in the references cited

above.

The circumstances under which SSA will

validate SSN information for administrative or

statistical purposes are limited by law and by

SSA regulations and policies. Anyone wishing

to validate SSN information for statistical or

research purposes should contact SSA’S Office

of Research, Statistics and International

Policy.

10. U<e as a matching variable

Arellano (n.d.) discusses use of the SSN in

record linkages based on the model proposed by

Fellegi and Sunter (1969). He recommends that

the SSN not be used for blocking, because of

the possibility that some individuals in the

files to be linked may not have been issued

SSNS. To use the SSN as a component of the

comparison vector, Arellano reccunmendsthat the

9 digits of the SSN be partitioned into four

elements on a 2,2,2,3 basis. He identifies 17

possible configurations of the SSN component of

the comparison vector, covering the possible

realizations of agreements and disagreements in

the four elements, plus the case in which no

SSN is available for one or both members of the

comparison pair. He then suggests procedures

for assigning conditional probabilities to

these configurations for the matched and unmatched sets. These probabilities are based on

assumptions about the kinds of errors that can

occur in the matched set and on observed frequencies of realizations of the first three

elements of the partitioned SSNS in the files

to be linked (realizations of the fourth element are assumed to be uniformly distributed).

Rogotfe~al~h:1983) report on linkages of

records Census Bureau’s Current

Population Survey with the National Death

Index, using each person’s name, SSN and date

of birth as key matching variables. Based on

the results of an evaluation study in which

“truth” (match or non-match) was based on a

consensus of three raters using all available

information for a set of “possible matches,”

they concluded that whenever SSNS agreed, it

was appropriate to classify the pair of records

as a positive link, provided there was agreement on sex. The use of probabilistic matching

procedures was restricted to cases for which

the SSNS did not agree or were missing on one

or both records.

REFERENCES

Arellano, M.

(n.d.) Optimum utilization of the social

security number for matching purposes.

No further identification available.

Cobleigh, C. and Alvey, W.

1974 Validating reported social security numbers. American Statistical Association

Proceedings, Social Statistics Section,

5-.150

Fellegi, I. and Sunter, A.

1969 A theory for record linkage. Journal of

the American Statistical Association,

-. ;

Hawkes, T. and Harris, R.

1969 An analysis of social security numbers in

the SMI actuarial sample. Actuarial Note

No. 62. Social Security dmlnlstratlon,

U.S. Department of Health, Education, and

Welfare.

HEW Secretary’s Advisory Ccmmittee on Automated

Personal Data Systems

1973 Records, Computers and the Rights of

lltlzens. Washington, :

Department of Health, Edu;a~ion, and . .

Welfare.

Chapt. VII. The Social Security Number

as Standard Universal

Ident~fier

Chapt. VIII. Recommendations Regarding Use

of the Smial Security Number

Internal Revenue Service

1973 Evaluation of the randomness of the four

ending digits of the social security

numbers on the master file, 1971. Unpublished report by Mathematical Statistics

Branch, Statistics Division. U.S. Department of the Treasury.

Kasprzyk, D.

1983 Social security number reporting, the use

of administrative records, and the multiple frame design in the Income Survey

Development Program. Pp. 123-144 in

Technical, Conceptual and Administrative

Lessons of the ISDP (M. David, cd.) Washington, D.C.: Social Science Research

Council.

Kilss, B. and Tyler, B.

1974 Searching for missinq social securitv

numbers.- American Sta~istical Associatio~

Proceedings, Social Statistics Section,

31-.144

One, M., Patterson, G. and Weitzman, M.

1968The quality of reporting social security

numbers in two surveys. American Statistical Association Proceedings, Social

Statistics Section, 197-2(J5.

Pacie.W. and Wrlaht. G.

19?9-A Statisti~al’ Study of the VA Annual

?atlent Census Sampling Procedure, 1915-

977 Controller Monograph Technical

~;s, No. 1. Veterans Administration.

218
Privacy Protection Study Commission

1977 Personal Privacy in an Information

Society. Washington, D.C.: U.S. Government Printing Office.

Chapt. 16. The Social Security Number.

Rogot, E., Schwartz, S., O’Conor, K. iiOlsen, C.

1983 The use of probabilistic methods in matchina census samDles to the National Death

InGex.- Pp. 75~80 in Statistics of Income

and Related Administrative Record Research: 1983. Internal Revenue Service.

Sacia7 Securitv Administration

1981a Method< of Estimation for the 1973

Exact Match Study. Studies from

interagency data linkages, Report No.

10. Washington, D.C.: Department of

Health and Human Services.

Social Security Administration

1981b Social securitv numbers issued.

1937-79. Rese~rch and Statistics

Notes, No. F

Washington. D.C~: UYS. ;epar~w;~e;+

Health-and-Human Services.

Wright, P.

1980 Strategy and tactics in the design of

forms. Visible Language 14(2): 151-193.

219
ATTACHMENT A

Table 1.--Social Security Numbers Issued, By Sex of Applicants, 1937-79

(In thousands)

Year I

Total I

Male I

Female

37,139

6,304

5,555

5.227

6.678

7,637

7.426

4,537

3,321

3.022

2.728

2.7m

2,340

2,891

4,927

4,363

3.464

2.743

4.323

4,376

3,639

2,920

3,388

3.415

3,370

4.519

8,617

5,623

6.131

6,506

5,920

5,862

6,289

6.132

6.401

9.564

10.038

7,998

8,164

9.043

7,724

5,260

5,213

26,981

4,010

3.291

3,080

3.702

3.547

2.905

1.830

1.506

1.432

I,299

I,305

1,113

I.406

2.420

2,292

1,664

1.299

2.304

2,391

1,793

1.384

1.645

1.663

1,665

2,109

3.739

2.707

2.746

2.894

2,855

2.856

3.105

3,004

3.122

3.948

4.849

3,950

3,992

4.507

3,872

2,682

2,649

10.158

2.294

2,264

2,147

2.976

4,090

4,521

2,707

1.815

{.~~

1,429

1,415

1,227

1.485

2.507

2.071

1,800

1.444

2.019

1.985

1.846

1.536

1,743

1.752

1.705

2.410

4.878

2,916

3.385

3.612

3.065

3.006

3.184

3.128

3.279

5.616

5.189

4.048

4.172

4.536

3.852

2.578

2.564

lIncludes issuances in November and December 1936.

Source: Social Security Administration, 1981b.

220
ATTACHMENT B

Form SS-5.--Application for a Social Security Number Card

.

.

DEPARTMENTOFHEALTHANOHUMANSERVICES

SOCIALSECURIN ADMINISTRATION

FormAppmvsd

OMBNoOSW00S4

FORM SS-5 - APPLICATION FOR A

SOCIAL SECURITY NUMBER CARD

(Orlglnd, R*placomont or Corrootion) MICROFILM REF NO (SSA USE ONLY)

I

...-.— —— —.——— .—— ,

19therequestedInformathisprovfded,we frtsynotbeebtetolaw ● SootatSoourffyNumber(20CFR 422-103(b))

RUCTIONS Sefore completingthisform.pleasereedtheinstructions ontheoppositepsge.Youcm typeorprint,ueing

PPLICANT pen with clerk blue or black ink. 00 not use pencil.

!TO First Lul

* I

-

NAME

BESHOWN

ONCARr

FULLNAl

BIRTH(IFOTHER

THANABOVE)

OTHER

P&g(s)

u 1

MEAT First Middl@ I Last

1

1 I

---—

MAILING (SImt/Apt. No.,P.O. Box. Ru181Rout@No.)

AODRESS

cmv

[ml

STATE ZfP ZIP COOE

❑ d Otfwr(Ssomrruamson Psw2) I

SEX

❑

MALE

•1

FEMALE

, 1 ,

~ IRACE/ETHNICDESCRIPTION(Chsckonsonly)(Voluntarjl

5 ❑

•1

● A*n, Asiwr-tiw!sa or PscWIcIslmdsf(Includmp.rsonsof Chimss,

Filipino,J@aISES,Korssn,Ssmosn,.sc.,●wmstIvof dcss.nt)

b, tiispsnic(includesp.rsonc of Chissno.Cuban.AS.xicmor M.xicuIAm.rican.Pu.no Rissn,Southof C+nlrslAmorism.or oth.r Sp@nish

●tceslfv ord.sc.nt)

c. Nagmor Blsck(notHiapsnic)

d NonhsrnAmwic4nIndianor AtssksnNStiVS

I I [ I

DATE

h,, ,-

MONTH; OAY ; VEAR PRESENT MS

#h

CITY I Pa

1, AGE PLACE

STATEOR FOUEIGNCOUNTRY;

OF 7 8 S&”

1

t,

BIRTH 1, 1

1, ~n

MOT++Ea S Pm Mlddm Last (NW - num)

NAMEAT ;

NSRSlfWH I

F,mt I

FATHER’S M-M I L#m

NAMEI

~

1.Has● SocialS.curitynumb.rcard.vor ❑ YES(2} ❑ NO(I) ❑ Ocmtknow(l) ~ _~

MOWN i vEAm

bNn r.au.st.d forIhap.rsonlistedinifom ,

,.

,,

b Wss● cardrscaiwdforthep.rmnIislod in •1 YES(3) n NO(1) ❑ Don’tknow(1) ff YOUshosk,d~ksaorb,mmpfsks

item1? Itam*c Sflmtqhw,OmwwlsDgOtOftsEnll.

c. EINWtf!oSocialSecuritynumbsr●ssignedtothe

p.fsonIicl.dinitem1.

d. nt.rthenameshownonthemostrecentSocOalSecuritycard e DEteof

~

MONTH , OAY ; YEAR

i~~u.dforth. PWsonhst.dinitem1, birthcormstion 1 1

(S.0 Instruction10 I

ongag. 2) I 1

f 1 . ,

TOOAVS MONTH OAV ; YEAR Tetephonenumberwherewe HOME ; OTNER

OATE : t ,

#

1

, 1121%‘&2%w’%uwcodO )

WARNINO DofbrskslyfumMfns (or ssustnsto bs Eumlshsd)fdss fmformsflamonEhkE~fs~e—~brfkmw~,arbokfl.

IMPORTANTREN}NOER:SSS●AOE1PDRREOUIREDEVIOCNTfARY 00CUM!NTS.

rouR SIGNATURE YOURRELATIONSHIPTO PERSONIN ITEM1

1141❑ S.,r❑ Ortwr,.soec,f.,

I

WITNESS(Nosd.donlydsqnsdbymsrk,X) WITNESSP40dedOnlyifsignedby mwkX)

00 NOTwRITEBELOWTHISLINEIFOR55A USEONLY} OTC SSARECEIPTOATE

aIC3NE0Cl •l ❑-0 ❑l-n ❑ •l •l ‘w

Blc SIGNATUREANOTITLEOF EMPLOYEE(S)REVIEWING

EVIOENCEANO/ORCONDUCTINGINTERVIEW

m~ NC C~AN

TYPE(S)OFEvIOENCESUB~lTTEO MANOATORY

❑ l’%%%: ‘

OATE

CONOUCTED DATE

I I ION IITV 10CL

Form 8S-5 (5-84) Destroy prior editions

221
ATTACHMENT C

Distribution of Social Security Numbers as of

Number Issued Within Each

January 2, 1985: Highest Grw

Area Number*

001 68

002 68

003 66

004 82

005 so

006 80

007 80

008 “66

009 64

010 66

011 66

012 64

013 64

014 64

015 64

016 64

017 64

018 64

019 64

020 64

021 64

022 64

023 64

024 64

025 64

026 64

027 64

028 64

029 64

030 64

031 64

032 64

033 64

034 64

035 54

036 52

037 52

036 52

039 52

040 76

04i 76

042 76

043 76

044 76

045 76

046 76

047 76

048 76

049 74

050 68

051 68

052 68

053 68

054 68

055 68

056 68

057 68

058 68

059 68

060 68

061 68

062 68

063 68

064 68

065 68

066 68

067 68

068 68

069 68

070 68

071 68

072 68

073 68

074 60

075 68

076 68

077 68

078 68

079 68

080 68

081 68

082 68

083 68

084 68

085 68

086 68

087 68

088 68

089 68

090 68

091 68

092 68

093 68

094 68

095 68

096 68

097 68

098 68

099 68

!00 6S

101 68

102 68

103 66

104 68

105 68

106 68

107 68

108 68

io9 68

110 68

111 68

112 68

113 68

t14 68

115 68

116 68

117 68

118 68

119 68

120 68

121 68

122 66

123 66

+24 66

125 66

126 66

127 66

128 66

f29 66

130 66

131 66

132 66

133 66

134 66

135 78

t36 78

137 78

$38 76

139 76

140 76

141 76

142 76

%43 76

144 76

145 76

146 76

147 76

148 76

149 76

150 76

f51 76

152 76

153 76

154 76

155 76

156 76

157 76

158 76

159 64

160 64

161 64

162 64

t63 64

164 64

165 64

166 64

i67 64

166 64

169 64

170 64

171 64

172 64

173 64

174 64

175 64

176 64

177 64

178 64

179 64

180 64

18t 64

182 64

183 64

184 64

185 64

186 64

187 64

188 64

189 64

190 64

191 64

192 64

f93 64

194 64

195 64

198 64

197 84

198 64

t99 64

200 62

201 62

202 62

203 62

204 62

205 62

206 62

207 62

208 62

209 62

210 62

211 62

212 06

213 06

214 06

215 06

216 06

217 06

218 06

219 06

220 04

221 68

222 66

223 33

224 33

225 33

226 33

227 33

228 33

229 33

230 3f

231 31

232 27

233 27

234 27

235 25

246 25

237 45

238 45

239 45

240 43

241 43

242 43

243 43

244 43

245 43

246 43

247 59

248 59

249 59

250 57

251 57

252 49

253 49

254 49

255 49

256 4S

257 47

258 47

259 47

260 47

261 99

262 99

263 99

264 99

265 99

266 99

267 99

268 62

269 82

270 82

271 82

272 82

273 82

274 82

275 82

276 82

277 82

278 82

279 82

280 82

281 82

282 82

283 82

284 82

285 82

286 82

287 82

288 82

289 82

290 80

291 80

292 80

293 80

294 80

295 80

296 80

297 80

298 80

299 80

300 80

301 80

302 80

303 92

304 92

305 92

306 92

307 92

30S 92

309 92

310 92

311 92

312 92

313 92

314 92

315 92

316 92

317 92

318 74

319 74

320 74

321 74

322 74

323 74

324 74

325 74

326 74

327 74

328 74

329 74

330 74

331 74

332 74

333 74

334 74

335 74

336 74

337 74

338 74

339 74

340 74

34+ 74

342 72

343 72

344 72

345 72

346 72

347 72

348 72

349 72

350 72

351 72

352 72

353 72

354 72

355 72

356 72

357 72

358 72

359 72

360 72

361 72

362 94

363 94

364 94

365 94

366 94

367 94

368 94

369 94

370 94

37i 94

372 94

373 94

374 94

375 94

376 94

377 94

378 94

379 94

380 94

381 94

382 94

383 92

384 92

385 92

386 92

387 92

368 92

389 92

390 92

39~ 92

392 92

393 92

394 92

395 92

396 92

397 92

398 92

399 92

400 25

401 25

402 25

403 25

404 25

405 25

406 23

407 23

408 45

409 45

4fo 45

4if 45

412 45

4i3 45

414 45

415 43

416 19

417 19

4i8 t9

4i9 19

420 f!ii

421 19

422 19

423 19

424 17

425 51

426 51

427 49

428 49

429 57

430 57

431 55

432 55

433 w

434 55

435 55

436 55

437 55

438 55

439 53

440 84

441 84

442 84

443 84

444 84

445 84

446 82

447 82

448 82

449 69

450 69

451 69

452 69

453 69

454 69

455 69

456 69

457 69

458 69

459 69

460 69

461 69

462 69

463 69

464 69

465 69

466 69

467 69

468 04

469 04

470 04

471 04

222
Distribution of Social Security Numbers as of January 2, 1985 (cent’d.)

472 04

473 04

474 02

475 02

476 02

.!77 02

478 06

479 06

480 06

48f 06

482 06

483 06

484 04

405 04

486 90

487 90

480 90

489 88

490 88

49f 08

492 ~8

493 88

494 80

495 88

4S6 88

497 88

498 88

499 88

500 88

5ot 04

502 02

503 04

504 04

505 13

506 13

507 lt

508 11

509 88

510 88

511 88

512 88

513 88

514 86

51s 86

516 04

517 04

518 11

519 11

520 04

521 43

522 43

523 43

524 43

525 53

526 99

527 99

528 49

529 49

53o 08

531 96

532 96

533 96

534 96

535 94

536 94

537 94

538 94

539 94

540 11

541 11 564 8i

542 ii 565 81

543 11 566 81

544 1~ 567 81

545 83 568 81

546 83 569 81

547 83 570 81

548 83 571 81

549 83 572 81

550 a~ 573 8f

BBI et 574 76

552 81

553 81

57!5 27

554 81

576 27

555 81

577 11

556 81

578 08

579 08

557 81 580 19

558 81 581 99

559 61 582 99

560 81

561 81

583 99

562 81

584 97

585 51

563 8$ 586 78

587 49

588 00

589 30

590 30

591 30

592 30

593 30

594 28

595 28

596 00

597 00

598 00

599 00

600 16

601 14

602 00

603 00

604 00

605 00

606 00

607 00

608 00

602 00

610 00

611 00

612 00

613 00

614 00

615 00

616 00

617 00

618 00

619 00

620 00

621 00

622 00

623 00

624 00

625 00

626 00

700 18

701 t8

702 18

703 18

704 18

70s 18

706 18

707 18

708 18

709 18

710 18

711 18

712 18

713 18

714 f8

715 18

716 18

717 18

718 18

719 18

720 la

721 18

722 18

723 18

724 28

725 18

726 18

727 10

728 14

*First three digits of the social security number are area numbers; second

two digits are group numbers.

Group 00 is not a valid group -- it is for program purposes only.

223
Validation and

VALIDATION OF SSN’S

Excerpt from

Screening Techniques for Social Security Numbers

Minimum information needed to validate an SSN

is the person’s name, sex, date of birth and

the alleged SSN. Validation occurs only when

the information on a current transaction exactly matches or can be reconciled with the

information on the Alphident/Numident data

bases or the microfilm subfiles of these systems. In certain circumstances, additional

matching information is needed before validation can occur. If earnings are reported

without an SSN or with an SSN or name that does

not agree with these files and the correct .SSN

cannot be determined through internal screening

operations, the employer or the worker is asked

to furnish additional information to identify

the record. The Internal Revenue Service (IRS)

uses a similar system to validate SSN’S of

taxpayers.

MANUAL SCREENING OF DUPLICATE

AND ORIGINAL SSN APPLICATIONS

The electronic screening operation to which

every application is subjected is capable of

processing roughly 85 percent of all applications input by field offices. Through a

sophisticated series of screening grids, the

computer makes a decision: is this applicant

already represented in the Alphident data

base? If the decision is yes, the previously

assigned SSN is identified and a replacement

card is prepared and mailed. If the decision

is no, a number is assigned and a card is

printed and mailed.

However, the decision-making capability of

the system is deliberately limited because some

applications have identifying information common to others or conditions exist which should

receive a clerical review. These applications

produce worksheets which are processed manually

by OCRO.

Worksheets to be screened are checked against

the Alphident Microfilm File and the Alphident

Microfiche File, using the name and date of

birth shown on the application. If an SSN is

not located for the name and date of birth

shown, another search is made using dates of

birth somewhat different from the one given on

the application. If an SSN is still not located, certain other variations are checked,

including name at birth or on the signature

line if different from the name in item 1;

acceptable variations of common first names;

dropping middle name shown; substituting

different middle initials; substituting maiden

surname for middle given name for married

females; substituting initials only in place of

ccmplete given names; etc. Once a “possible”

SSN is located, verification can be made immediately since full identifying information is

available on the Alphident “files. See RM

00204.020 for procedures for handling “UTL” and

“Investigate” items.

THE ALPHIDENT MICROFILM AND

MICROFICHE FILES

The electronic Alphident file is updated

daily. If an SSN holder loses the social

security card within the first days after it was

issued, the number can be located and verified

electronically.

The Alphident Microfilm File is an alphabetical file based on the Russell Soundex

coding system. It contains essentially the

same information as the electronic file.

Because the Alphident Microfilm File is

updated only every 3 months, each week an

accretion file is prepared on microfiche. This

file contains all SSN assignments and corrections to our records processed during the

preceding 12 weeks. This file is referred to

when there is reason to believe that there was

a recent SSN action for an individual.

Each record entry on both the Alphident

Microfilm and the Alphident Microfiche Files

consists of the following:

DATA I POSITIONS

Blank ..............................

Soundex ............................

Blank ..............................

Applicant’s Surname ................

Applicant’s Given Name .............

Applicant’s Middle Name ............

Month of Birth ........O............

Blank ..............................

Day of Birth .......................

Blank ..............................

Century of Birth ...................

Year of Birth ......................

Blanks ............................,

SSN ................................

Blank ..............................

Mother’sSurname ...................

Mother’s Given Name ...............

Mother’s Given Initial .............

Blank ..............................

Sex/Race ...........................

Blank ..............................

Father’s Surname ...................

Father’s Given Name ...............

Father’s Middle Initial ............

Blank ..............................

City/CountyofBirt.h ...............

State/Country of Birth .............

Blanks .............................

Form/Entry .........................

Blanks .............................

Reference Number ...................

Blank ..............................

2-;

7-2:

28-43

44-45

56-57

58

59-60

61

63-%

65-66

67-77

78

79-91

92-102

103

104

105-106

107

108-120

121-131

132

133

134-140

141-142

143-144

145-146

147-148

149-159

160

224
COMMON NAMES IN THE ALPHIDENT FILE

There are over 360 million records in the

Alphident File, representing over 277 million

SSN’S assigned. Many of the names in the file

are the same or are very similar, This is why

it is extremely important to get conmlete and

accurate identifying irIfO~atiOII on original

applications and on requests for duplicate SSN

cards. It is equally important to obtain

information that is consistent with that on the

original application. Applicants who have lost

their original cards should be questioned

closely to find out if any of the information

on the current application is now different

frcm that which they showed on their original

application.

The latest tabulation of ccmnmon surnames in

the SSN file was made in 1974. Some examples

of the number of times a common name could

appear in Alphident are given below.

I

NAME NUM8EROF ITEMS IN

ALPHIDENT

I

Smith ......................

Johnso(n) ..................

Williamson) ...........

Brown ......................

Jones ......................

Miller .....................

Davis ......................

Martin(ez)(son) ............

Anders(on) .................

Wilson .....................

2,382,509

1,807,263

1,568,939

1,362,910

1,331,205

1,131,861

1,047,848

1,046,297

825,648

787,825

THE RUSSELL SOUNDEX CODE

By using the Russell Soundex Code system,

searching for possible SSN’S on the Alphident

film and fiche in OCRO is accomplished quickly.

Here are the basic rules for using the

Soundex Code.

Use the first letter of the surname, then

code the remaining letters as follows:

LETTERS CODE SYM80LS

BPFV ............................ 1

CGJKQSX2 ........................ 2

DT .............................. 3

L ............................... 4

MN ..............................

R ............................... :

Vowels are not coded, nor are the letters W,

H, and Y. Two successive letters with the same

code numbers are coded only once.

Example:

“Mack” is coded M-200. The “a” is not coded

since it is a vowel. “c” falls under code

symbol 2. “k” also falls under code symbol 2,

but is not used since two successive letters

with the same code sumbol are coded only once.

Since the complete Soundex Code must consist of

the first letter of the name followed by three

numbers, we add enough zeros to complete the

3-digit code.

Here are some other examples:

1. Snyder - S-536

2. Way - W-ooo

3. Bear - B-600

4. Brown - B-650

LIMITATIONS IN OCRO SCREENING FOR SSN’S

When an applicant has indicated a previous

SSN in item 10 of the SS-5 and the correct

number cannot be found in the electronic or

OCRO screening operations, the data are

returned via form SSA-431O to the district

office. This is because studies show that many

such applicants are mistaken in stating they

previously applied for a number, and it is not

worthwhile spending additional time on the case

unless different information can be found.

When the district office receives a form

SSA-431O frcin OCRO, it should recontact the

applicant for any different information that

~{ be useful in screening. See RM 00204.020

Take appropriate action, but do not

r~t~rn the SSA-431O to OCRO.

Upon recontacting the applicant, the district

office may discover that a married woman

obtained her original SSN under a first

husband’s name, but is now ap~lying for the

duplicate in her second husband’s name; that a

man who calls himself “Winslow” obtained his

number earlier in life as “Buddy;” or that Mr.

~li~;s,, record was set up originally under

. There is also a possibility that the

applicant may be able to locate the previously

issued SSN on an old pay stub or by asking a

present or a past employer. This new information may enable OCRO to locate the original

SSN. If the applicant is unable to give any

information different from what was previously

given and is unable to locate the alleged’

number, the district office has no other choice

but to request assignment of an original SSN.

However, this should be done only as a last

resort, particularly if the person has earnings

under the original number which might not be

credited when the SSN holder applies for

benefits.

These facts point UP the need for obtaining

the most accurate information possible during

the initial interview with the applicant,

whether it be for an original or duplicate SSN

card; otherwise, multiple numbers may result.

Any reasonable assistance should be extended to

the applicant to help find out definitely what

the alleged prior SSN is. (See RM 00202.025

1.10.)

Source: “The Social Security Number,” Program

Operations Manual System, part I, _

Section -015, Sccial Security

Administration.

225
EXACT MATCHING LISTS OF BUSINESSES:

BLOCKING, SUBFIELD IDENTIFICATION, AND INFORMATION THEORY

William E. Winkler, Energy Information Administration

1. INTRODUCTION

.The purpose of this paper is to present an

evaluation of matching strategies for name and

address files of businesses. In evaluating

mstching methods, we wish to minimize erroneous

matches and nonmatches and the amount of manual

review.

This work and previous work by various authors

(Newcombe, Kennedy, Axford, and James, 1959;

Newcombe and Kennedy, 1962; Newcombe, Smith,

Howe, Mingay, Strugnell, and Abbat t, 1983;

Coulter, 1977; Coulter and Mergerson, 1977;

Rogot , Schwartz, O“Conor, and Olsen, 1983 ;

Kelley, 1985) rely on matching strategies based

on a theory of record linkage formalized by

Fellegi and Sunter (1969) and first considered by

Newcombe et al. (1959). The Fellegi-Sunter model

provides an optimal means of obtaining weights

associated with the quality of a mstch for pairs

of records. Linked pairs (designated matches)

and nonlinked pairs (designated nonmatches)

receive high and low weighta, respectively.

Pairs designated for further manual followup

receive weights between the sets of high and low

weights.

Early work by Newcombe et al. (1959, 1962)

showed the potential improvement (lower rates of

erroneous matches and nonmatches and of manual

followup) when weights were computed using

surname and date of birth in comparison to when

weights were computed using surname only.

Coulter (1977) provided an example of the

decrease in discriminating power as the

probability of identifiers (such as surnames,

first names, middle names, and place names) being

misreported (transcribed inaccurately) and/or

pairs of identifiers associated with individuals

being different but accurately reported

increases.

While the applied work referenced above

involved files of individuals only, this paper

provides an evaluation involving files of

businesses. Matching using files of businesses

is different from matching files of individuals

because business files lack universally available

and locatable identifiers such as surnames.

Matching consists of two stages. In the

——blocking stage, sort keys, such as SOUNDEX

abbreviation of surname, are defined and used to

create a subset of all pairs of records from

files A and B that are to be merged. Records

having the same sort key are in the same block

and are considered during further review.

Records outside blocks are designated as

nonmatches. In the ——.—— discrimination stage,

surnames and other identifying characteristics

are used in assigning a weight to each pair of

records identified during the blocking stage.

With the exception of Newcombe et al. (1959,

1962), little work has been performed in

evaluating how many erroneous noomatches arise

due to a given blocking strategy. The chief

reason that little work haa been performed is

that identifying erroneous nonmatches due to

blocking and accurately estimating error rates is

difficult (Fellegi and Sunter, 1969; Winkler,

1984a,b).

The key to identifying difficulties in

blocking files of businesses is having a data

base in which all matchea are identified and

which is representative of problems in many

business files. In section 2, the construction

of such a data base from 11 Energy Information

Administration (EIA) and 47 State and industry

files is described. Section 2 also contains a

summary of the Fellegi-Sunter model and the

criteria used in evaluating competing matching

strategies.

Section 3 is divided into two parts. The

first part contains results obtained by multiple

blocking strategies using a procedure in which

the numbers of erroneous nonmatches and matches

are minimized under a predetermined bound on the

number of pairs to be passed on to the

discrimination stage (for related work aee

Kelley, 1985). The results are related to

results obtained during the discrimination stage

and build on earlier work of Winkler (1984a,

1984b).

In the second part, the main results of the

discrimination stage are presented. The effects

of improved spelling standardization procedures

and identification of additional comparative

subfields are highlighted. Although the

deleterious effect of poor spelling

standardization is covered by the Fellegi-Sunter

theory and presented in the simulation results of

Coulter (1977), no concrete examples have

previously been presented.

The second part also contains results on the

variation of cutoff weights and misclassification

and nonclassification rates during the

discrimination stage. The results are based on

small samples used for calibration and obtained

using multiple imputation (Rubin, 1978; Herzog

and Rubin, 1983) and bootstrap imputation (Efron,

1979; Efron and Gong, 1983). Fellegi and Sunter

(1969, p. 1191) indicate that results based on

samples are unreliable.

Finally, the second part presents results

addressing the strong independence assumptions

necessary under the Fellegi-Sunter model and

conditioning techniques that can be used in

improving matching performance in some situationa

when direct application of the Fellegi-Sunter

model yields high misclassification andjor

nonclassification rates, The investigation of

independence uses the hierarchical approach of

contingency table analysis (Bishop, Fienberg, and

Holland, 1975). The conditioning argument uaea a

steepest ascent approach (Cochran and Cox, 1957).

Section 4 contains a summary and further

discussion of the results and problems for future

research.

227
2. EMPIRICAL DATA BASE, METHODS, AND

EVALUATION CRITERIA

This paper-s approach to developing more

effective matching strategies involvea:

1.

2.

3.

4.

A

constructing an empirical data base for

testing procedures;

employing the Fellegi-Sunter model of

record linkage;

defining evaluation criteria; and

refining procedures in response to

empirical results.

suitable data base should have all

duplicates identified and connected to their

respective parents (records used for mailing

purposes) and present problems that are

representative of similar data files (in this

caae, files of businesses). The identification

of al 1 duplicates allows determination of

erroneous nonmatchea during the blocking stage.

Evaluation criteria should be such that they are

suitable for adoption by others performing

research in matching methodologies.

2.1. —-—.——.—-—..-——- Creation of a Suitable Empirical Data Base

The empirical data base consists of 66,000

records of-sellers of petroleum products. It was

constructed from 11 EIA lists and 47 State and

industry lists containing 176,000 records.

Easily identified duplicates having essentially

similar NAMEand ADDRESS fields were deleted when

the melded file waa reduced from 176,000 to

66,000 records.

The data base contains 54,850 records

identified as headquarters or parents (records

-——

used for mailing purposes); 3,050 records

identified as duplicate= (records having names

and addresses similar to their parents-); and

8,511 records identified as associates (records

such aa subsidiaries and branches that have names

andjor addresses different from their parents-).

Duplicates were identified primarily through

elementary computer-assisted techniques ( see

Winkler, 1984a); associates were identified

through surveying and call-backs. Our evaluation

will only consider how well various strategies

perform in matching duplicates with headquarters.

The presence of unidentified associates, however,

can cause falsely higher error rates (see section

2.3.1).

2.1.1. —-

General~plicability of Results

-— -

Procedures developed for dealing w~~fi problems

in the main empirical data base would be

generally applicable to most EIA systems because

the data base:

1.

2.

3.

ia larger than any other master frame file

in EIA;

is involved with retail sales-- such frames

are often more difficult to work with than

files of individuals or files of headquarter

addresses of large corporations; and

had greater formatting and spelling

standardization difficulties-- it waa

constructed from many more sources than any

228

other EIA frame.

Because the main empirical date base is

constructed from many different lists and

contains many records associated with retailera,

results should be representative of the

difficulties encountered with similarly

constructed, non-energy files of businesses.

2.1.2. Z!!QE!3 sPe~liw Standardization

The original spelling standardization software

contained two basic loops. The first replaced

most punctuation with blanks and deleted multiple

blanks within a field. The second used lookup

tablea to replace a given spelling of a word with

a standardized spelling or abbreviation. Blanks

were generally used to delimit words within

fields.

Spelling atandarization software was updated

in two ways. First, the logic of the processing

was enhanced to cause changes in character

strings that are not easily updated because they

contain embedded punctuation or blanks. For

instance, “-S”’ ia replaced by “S”’ and ““MC NEELY”’

by ‘“MCNEELY.”

Second, standardization tables were updated

with a very large number of spelling variations

of words such aa ‘COMPANY,” ‘DISTRIBUTOR, ‘

‘SERVICE,- and “CORPORATION.” The key to

systematically identifying such Spellfng

variations was a program that created an

alphabetic listing and frequency count of every

word in a prespecified field such as NAME or

STREET ADDRESS. As more than 90 percent of

keypunch errors occur after the first character

(ace e.g., Pollock and Zamora, 1984), most

spelling variations of commonly occurring words

in the empirical data base have probably been

identified.

2.1.3. ——-—.—— Identification of Subfields

The identification of subfields was done in

two stages. In the first, ZIPSTAN software (U.S.

Dept. of Commerce, 1978b) was used to process the

STREET ADDRESS field. Although the Census Bureau

uses a UNIVAC computer system, we were able to

obtain an unsupported version of ZIPSTAN that had

been created for use on IBM systems.

The basic idea of ZIPSTAN waa to identify key

subfields of the STREET ADDRESS field for files

of individuals. Although ZIPSTAN assumes that

the street address begins with a numeric word,

which is the usual situation in the files of

individuals for which ZIPSTAN waa designed, it is

able to process other types of street addresa

subfields that typically occur in files of

establishments or businesses.

Although ZIPSTAN provided warning messages for

18 percent of the 66,410 records in the empirical

data base, it was still helpful for most cases.

Warning messages consisted of ‘MISSING STATE

NAMES- (records associated with non-US postal

addresses), “PLACE NAMES CONVERTED- (minor

conversion of the city field), ‘STREET NAMES

CONVERTED” (minor conversion of the street name),

“SYNTAX CONVERSION. (conversion of unacceptable

patterns of word characteristics) , and ‘POST

OFFICE BOXES” (containing PO BOX).

The following examples show aome

repreaentative EIA records before and after

ZIPSTAN processing.
Before ZIPSTAN

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

EXCH ST

HWY17S

1435 BANKOF THE

2837 ROE BLVD

MAIN & ELM STS

CORNER OF MAIN & ELN

100 N COURT SQ

100 COURT SQ SUITE 167

2589 WILLIAMS DR APT 6

15 RAILROAD AVE

2ND AVE HWY 10 W

MAIN ST

184 N DU FONT PKWY

1230 16TH ST

BOX 480

After ZIPSTAN

Pre- sufNo. House fixes Street Name fixes

No. 12 12

Unit

1.

2.

3. 1435

4. 2837

5.

6.

7. 100

8. 100

9. 2589

10. 15

11.

12.

13. 184

14. 1230

15. 480

EXCH ST

HW 17TH s

BANK OF THE

ROE BL

MAIN EIAl ST%

CORNER OF MAIN ELM

N COURT SQ

CT SQ *** NO NAME *** RM 167

WILLIAMS DR AP 6

WiILROAD AV

2ND AVHW 10

MAIN ST

N DU FONT Pw

16TH ST

*PO BOX*

ZIPSTAN is able to identify accurately

subfields In 13 of 15 cases. The two exceptions

are cases 2 and 8. In case 2, “HWY” is moved to

a prefix position and -17- is placed in the

STREET NAME position. In case 8, ‘COURT,- the

street name, is placed in a prefix location.

Although ZIPSTAN accurately identifies the

subfields associated with intersections (cases 5,

6, and 11), such identification may not allow

accurate delineation of duplicates in comparisons

of various lists. Some lists may contain STREET

ADDRESSes in the following forms, none of which

can be readily comparable with the forms in

examples 5, 6, and 11.

5. 34 Main St

5. Elm and Main Streets

11. Hwy 10 W

11. 7456 Richmond Hwy

In the second stage of subfield

identification, the following words in the NANE

field were identified:

KEYWORD1 Largest word in NAME field

KEYWORD2 2nd largest word in W field

(ties broken by alpha sort)

CON Concatenation of initials

The above three subfields were used for

comparison purposes because the NAME field in

lists of businesses generally does not contain

words such as SURNAMEand FIRST NAME that are

present in files of individuals. Based on a

sample of 1000 records, an upper bound of 27

percent at the 95 percent confidence level is

placed on the number of records containing a word

that could be identified as SURNAME.

The identification of SURNAMES was not

performed for three reasons: (1) it is difficult

to develop software that accurately identifies

records that contain SURNAME (see U.S. Dept. of

Agriculture, 1979); (2) it ia difficult develop

software to identify SURNAMES within the WANE

field (e.g., PAUL ROBERT or ROBERT PAUL- which is

the SURNANE?); and (3) the small number of

records to be compared and containing surnames

was not sufficient to justify such a development

effort.

The following provides examples of legitimate

variations associated with NAME field of one

company;

J K Smith Co

Smith Jonathon K

Smith Fuel Service Co

J K Smith Exxon Fuel Service

J K S Fuel

Fellegi and Sunter (1969, pp. 1193-1194)

provide an explicit theoretical model for how

much such legitimate spelling variations decrease

the accuracy with which matches and nonmatches

are delineated. Coulter (1977) provides an

empirical example of the decrease based on a

simulation.

Identifying and comparing the largest words in

the NAM8 field are only performed after spelling

standardization andfor abbreviation so’that the

chance of designating large words with little

distinguishing power is minimized.

For instance, if a character string such as

‘DISTRIBUTOR” appeared in the name field, it

would likely be the longest word. Replacing the

various spellings of “DlSTRIBUTOR- with an

abbreviation such as ‘DSTR- either allows it to

be deleted so that it is not considered by the

keyword-identification program or allows longer

words with pcssibly more distinguishing power to

be identified.

Although methods of identifying subfields

might be considered results, we are primarily

concerned with how their identification affects

the efficacy of various matching procedures.

Consequently, the identification can be

considered a preprocessing step ( see e.g.,

Winkler, 1985) that is used in creating the data

base used in evaluations.

2.1.4. Completeness—.-.——-.— of Identification—-_——of

!!s@@+E.s=

It is likely that few, if any, additional

erroneous nonmatches of duplicates are present in

the empirical data base for three reasons.

First, no additional duplicates were identified

in the set of headquarters records during a

manual review of all 1,500 records in a random

sample of 3-digit ZIP codes. Second, no

additional duplicates were identified during a

review of a sample of 20 pages (each containing

60 records) in a listing that was ordered

alphabetically using the NAME field. Third, no

additional duplicate were identified during the

229
discrimination stage (section 3.2).

Without further manual followup, it is

impossible to determine how many unidentified

asaociate records are in the set of headquarters

records. It is unlikely that surveying and

callbacks--because they were first-time

efforta --would have been able to identify them

all.

Even if more associates are identified, the

results of matching duplicates against

headquarter will not be aerioualy affected. The

main effect of identifying more associates will

be to lower the estimated rates of erroneous

matches. Some duplicates are now matched to

headquarters that are not identified as their

parent and that are actually associates of the

duplicates” parents. Each such match is

presently counted as an erroneous match.

2.2. Methods

2.2.1. ——---————z The Formal Probabilistic Model

The Fellegi-Sunter model (1969) usea an

information-theoretic approach embodying

principles first used in practice by Newcombe

(Newcombe et al., 1959). For a review of

existing techniques and their relationship to

classical information theory see Xirkendall

(1985).

In the Fellegi-Sunter model, agreements on

characteristics such as SURNAMBor ZIP code are

assumed to be more common among truly matched

pairs than among erroneously matched or unblocked

pairs. In practice, specific binit weighta of

agreement (or disagreement) are computed by,

W = log A/B

2

where

A= the proportion of a particular agreement (or

disagre~ment) defined as specifi~ally as one

wishes among matched pairs, and

B= the corresponding proportion of the same

agreement (or disagreement) among pairs that

are rejected as matches.

The following table will help us to understand

more specifically the computation of weights.

Table 1: Counts of True State of Affairs

Specified Match Nonmatch

Characteristic

Agree a b

Disagree c d

If we wish to compute the weight associated

with agreement on a specified characteristic,

then we take A=a/(a+c) and B=b/(btd); for

disagreement, we take A=c/(a+c) and B=d/(b+d).

For each detailed comparison of a pair of

records, the weights for appropriate agreement

and disagreements are added together, and the

total weight, TWT, ia used to indicate the degree

of assurance that the pair relates to the same

entity. The procedure asaumea that weighta

associated with individual agreements or

disagreements are uncorrelated with each other

(at least conditionally, see e.g., Fellegi and

Sunter, 1969, p. 1190).

Cutoffs UPPER and LOWER are chosen (using

empirical knowledge or educated gueasea) and the

following decision rule Ls used:

If TWT > UPPER, then designate pair aa a

match.

If LOWER <= TWT <= UPPER, then hold for manual

review.

If TNT < LOWER, then designate pair as a

nonmatch.

Given fixed upper bounds on the percentages of

erroneous nonmatches having TWT < LOWER and of

erroneous matches having TWT > UPPER, Fellegi and

Sunter (1969, p. 1187) show that their procedure

is optimal in the sense that it minimizes the

size of the manual review region.

In some caaes, either looking at disjoint

subsets of the aet of blocked pairs and/or

increasing or decreasing individual weights used

in computing the total weight, TWT, can improve

the efficacy of the above decision rule. For

instance, among a set of records that are blocked

into paira using the first six characters of the

STREET field, individual weights associated with

agreement and disagreements on characteristic

of the NAME field might be increased and

decreaaed, respectively.

A procedure that usea individual weights, that

have been varied in order to achieve greater

accuracy in the set of pairs designated as

matches and nonmatches andfor a reduction in the

set of records held for manual review, will be

referred to as a modified information-theoretic

procedure. An unmodified procedure will be

referred to as the basic information-theoretic

procedure.

2.2.2. Specific ~eight Computation

In addition to individual ~ights computed

using the subfields HOUSE NUMBER, PfiFIX, SiRBET

NAME, SUFFIX, UNIT DESIGNATOR, XEYWORD1,

XEYWORD2, and CO given in section 2.1.3, the

following subfielda were used in computing

individual weights:

Field

I

Subfield Columns

I

Designated as

NAME 14,5-10,11-20,21-30 Nl,N2,N3,N4

STREET 1-6,7-15,16-30 S1,S2,S3

z 1P 1-3,4-5 Z1,Z2

CITY 1-5,6-10,11-15 C1,C2,C3

STATE 1-2

TELEPHONE 1-3,4-6,7-LO T1,T2,T3

WL-NAME 1/ 1-4,5-10,11-20,21-30 w1,W2,W3,W4

~/ Sort words in NAME field by decreasing

order of wordlength. Break tiea with alpha

sort.

Generally, corresponding subfields were used

in computing individual weights. The exceptions

were comparison of the first and second keyworda

(section 2.1.3) in the NAME field.

It is important to note that if any weight

associated with a given SORT XEY, say TEL8PHONE,

230
used in blocking is computed only for records

within the subset of pairs having the SORT KEY

agreeing, then the comparison has no

discriminating power and the resulting weight is

zero. If, however, a weight is computed for a

ccaparison of a SORT KEY within a subset of pairs

which do not all agree on the SORT KEY, then the

weight could be nonzero. Also, it is intuitive

that some of the comparisons, say of the above

defined subfields of the NAME and KEYWORDs

(section 2.1.3) may not be independent.

2.2.3. Variances

As the tru~fi and falsehood of matches in the

set of blocked pairs were known for the

evaluation files, estimated error rates and their

variances were obtai~ed using multiple samples.

The basic procedure was to draw samples of

equal size, compute cutoff weights using each

sample (based on at most 2 percent of nonmatches

being classified as matches and at most 3 percent

of matches being classified as nonmatches) , use

each pair of cutoff weights on the entire data

base to determine overall error rates, and

compute the variances of the cutoff weights and

the overall error rates over the set of samples.

The multiple imputation procedure of Rubin

(1978) has been used for evaluating the effects

of different methods of imputing for missing data

but is applicable in our situation. Multiple

imputation entails obtaining several estimates

using different samples and then computing the

mean and variance over samples. In using Rubin”s

procedure, we sample without replacement.

The key difference from Efron”s bootstrap is

that sampling is performed with replacement. Our

application corresponds almost exactly to the

first example in the paper of Efron and Gong

(1983).

2.2.4. The Independence Assumption --——--——--

Fellegi and Sunter (1969, pp. 1189-90) state

that the independence assumption for the

comparisons of information contained in different

subfields is crucial to their theory but that the

independence assumption may not be crucial in

practice. They note that obtaining total weights

having a probabilistic interpretation only

necessitates that comparisons be conditionally

independent. The conditioning must be consistent

with the way total weights are computed.

There are several practical difficulties with

testing their independence assumption. First, it

must be tested separately for matches and

nonmatches. Newcombe and Kennedy (1962) provide

a method of approximating the weights for

nonmatches and show that accurately approximating

the weights for matches is difficult. The chief

reason is that the number of nonmatches ia close

to the number of pairs in the cross product of

two files A and B while matches represent a

relatively small subset (of all pairs) having

specific characteristics.

Second, the weights of nonmatches and matches

may vary substantially depending on what blocking

criteria are used. If, say, four independent

criteria are used, then it might be necessary to

examine as many as 15 (2**4-1) mutually exclusive

subsets of the set of blocked pairs (see sections

3.1 and 3.2).

Third, the collection of the information

necessary for contingency table analyses is

difficult because we have no strong control over

sampling design (Bishop, Fienberg, and Holland,

1975, pp. 36-39). Even with moderately large

samples, some of the subsets determined by

blocking criteria may be too small for adequate

analy”sis of the conditional independence of two

variables given two or more variables because of

the number of marginal constraints that are zero

(see section 3.2.8).

Fourth, if many different subfields and/or

different means of comparing them are considered

(we will consider 30; Newcombe and Kennedy,

(1962, p. 566), considered 200), then modelling

the conditional relationshipa using contingency

table techniques (Bishop, Fienberg, and Holland,

1975) can be cumbersome.

Even if dependencies occur, it may be possible

to vary weights associated with individual

comparisons (i.e., steepest ascent, see e.g.,

Cochran and Cox, 1957, pp. 357-369) to determine

whether the efficacy of the overall weighting

procedures can be improved, Our specific

steepest ascent method generally involved

choosing a few individual weights in disjoint

subseta determined by blocking criteria (sections

3.1 and 3.2) and varying them by+l- 0.5.

It is important to note that modifications to

individual weights may be heavily dependent on

the subsets determined by the blocking criteria.

2.3. ————--——— Criteria for Evaluation

2.3.1. Type I and II Errors

A Type I er~~r=-an erroneous nonmatch and a

——-

~1 error is an erroneous match. The Type I

rate ia U/D*100 where U is the number of

erroneous nonmatches and D is the number of

matches. The Type II error rate is F/M*100 where

M is the number of pairs designated as matches

and F is the number of erroneous matches.

As duplicates unmatched during the blocking

stage are considerably more difficult to identify

than false matches during the discrimination

stage, the primary emphasis in developing a new

strategy was minimizing Type I errors during the

blocking stage before minimizing Type II and Type

I errors during the discrimination stage.

It is important to note that if a pair of

files haa no erroneous nonmatches, then any

matching strategy applied will yield either no

pairs during the blocking stage or a Type I error

rate of O percent and a Type 11 error rate of 100

percent. Because the empirical data base is

relatively free of duplicates (as a result of

reducing the empirical database from 176,000 to

66,000 records), application of any matchi~

strategy will produce relatively high Type I

error rates during the blocking stage.

As we are primarily concerned with evaluating

methodologies for accurately matching pairs that

are not readily matched using elementary

comparisons (e.g., having major portions of key

fields agreeing exactly), the iata base of 66,000

records is more suitable for use than the

original aet of 176,000 records.

2.3.2. Overall Rate of Duplication _————

The number of erroneous nonmatches as a

percentage of the total number of records in a

file is also an important evaluation criteria.

We define the overall rate of duplication as

Q/(X+Q)*100 where Q is the number of erroneous

231
IIOOmatCheS and X is the number of parent records.

This additional evaluation criteria la

important because the Type 11 error rate criteria

will not provide a measure of how free of

duplicates a file is. The Type 11 error rate

doea not work well because, as the n~ber of

matches, D, in a file decreasea, the Type I error

rate (u/D*100, where U ia the number of erroneous

nonmatchea) will necessarily increase.

In the analysis of the empirical data base, D

is held constant so that the comparative

advantages of various strategies can be assesaed

Using Type I error rates. The overall rate of

duplication will not Work well for these

comparative evaluations because it is too

dependent on the nuber of parent records, X,

which does not change. That ia, if U1 and U2 are

the numbers of erroneous nonmatches under two

matching strategies and Ul~2<~, then U1/(Ul+X)

and U2/(U2+X) are approximately equal.

2.3.3. ———--.—-—---

bmount of Manual Review

The amount of manual review is a critical

feature in any matching procedure because manual

review is both time-consuming and expensive. If

one procedure requires one half as much manual

review aa another, yields Type I error ratea that

are only somewhat higher than the other, and

yields similar rates of erroneous nonmatchea

(section 2.3.2), then there is strong

justification for adopting the procedure

requiring less manual review.

3. RESULTS USING THB EMPIRICAL DATA BASE

Results of the empirical analysea for the

blocking stage and the discrimination stage are

presented in sections 3.1 and 3.2 respectively.

3.1. —-—— Comparison of Sets of Blocking Strate@_e~ ______

The following five criteria were used for

blocking filea into sets of linked pairs used in

the discrimination’ atage. The aet of five

criteria were developed by comparing a large

number of criteria. If the upper bound on the

overall rate of erroneous matchea during the

blocking stage ia aet at 65 percent, then this

set of five gave the largeat overall reduction in

erroneous nonmatches (see Winkler, 1984a).

BLOCKING CRITERIA

1. 3 digits ZIP, 4 characters NAME

2. 5 digits ZIP, 6 characters STREET

3. 10 digits TELEPHONE

4. Word length sort W field, then uae 1. *

5. 10 characters NAME

* This criterion also has a deletion stage

which prevents matching on commonly

occurring words such as ‘OIL,- “FUEL,-

“CORP ,- and “DISTRIBUTOR.-

3.1.1. Type I and 11 Error Rat~~_by Individual

Blocking Criteria

—

—-—--—_-

Table 2 presents counts and ratea of matches,

erroneous matches, and erroneous nonmatches for

each of the five matching criteria given above.

As we can ace, no single criterion providea a

significant reduction in the rate of erroneous

nonmatches. The best is criterion 4 (wordlength

sort) which leavea 702 (23 percent) duplicates

unlinked. The reason criteron 4 works best is

that the NAME field does not have subfields

(generally words) that are in fixed order or in

fixed locations. Consequently, criterion 4 links

NAME fields from headquarters and duplicate

having the following form:

John K Smith

Smith J K CO

Criterion 3 (TELEPHONE) provides the lowest

rate 8.7 percent (186/(186+1952)) of arroneoua

matchea and the second best rate 34.7 percent

(1057/3050) of erroneous nonmatches. Criterion 5

(10 character of the NAME) provides both tha

worst rate of erroneous matches, 58.6 percent

(1259/1259+889)), and the worst rate of erroneous

nonmatches, 63.3 percent (1932/3050).

Table 2: Ratea of Matches, Erroneous Matches,

and Erroneous Nonmatchea by Blocking

Criteria

lLink with[Link withl Not I Actual

1 1460 727

(66.8)

2 1894 401

(82.5)

3 1952 186

(91.3)

4 2261 555

(80.3)

5 763 4534

(14.4)

1387

(45.5)

1073

(35.2)

1057

(34.7)

702

(23.0)

1902

(62.4)

3050

3050

3050

3050

3050

I_/ Type II error rates are in parentheaea.

2_/ Type I error ratea are in parentheses.

3.1.2. Comparison of Seta of Criteria

In comparing subsets of the five blocki~

criteria, the primary concern ia in reducing the

number of erroneous nonmatchea. The nunber of

matches and erroneous matches in the set of pairs

created in the blocking stage is dealt with

primarily during the discrimination stage.

The comparison takes the form of considering

the incremental reduction in the nwsber of

erroneous nonmatchea aa each individual criteria

iS added. Although criteria 3 and 4 perfotm beat

on the empirical data baae, they are considered

later than criteria 1 and 2.

Criteria 1 and 2 are applicable to all EIA

files becauae all of them have identified NAM

and ADDRESS fields. As many non-EIA source liata

used in updating do not contain telephone

numbers, criterion 3 is not applicable to them.

As a number of EIA lists have conaiatently

formatted NAME fielda, criterion 4 will yield

little, if any, incremental reductions In the

number of erroneous matches during the blocking

stage.

232
Table 3: Incremental Decrease in Erroneous Nonmalches and

IncrementalIncreasein Mstches and Erroneous

Matches by Sets of Blocking Criteria

Set of Rate of Erroneous Matches/ Erroneous

Criteria Erroneous Nonmatches/ Incremental Matches/

Used !fonmatchesIncrementalIncrease Incremental

Decrease Increase

1 45.5 13871NA i460/ !iA 727/ NA

1,2 15.1 460/927 249511035 1109/ 289

1,2,3 3.7 l12f348 29081 413 1233/ 124

1,2,3,4 1.3 39/ 73 2991j 83 1494f 261

1 ,2,3,4,5 0.7 22/ 17 3007/ 16 5857/4363

NA- not applicable.

3.1.3. The Preferred ———Set of Blocking Criteria

The preferred set

--—.-

of blocking criteria are

criteria 1, 2, 3, and 4. Criterion 5 (10

characters of the NAME) was considered becauae it

yielded the greatest reduction in erroneous

nonmatches of any fifth blocking criteria while

keeping the overall percentage of erroneous

matches below 65 percent.

Criterion 5, however, ia not suitable for

inclusion because it incrementally adds 16

❑ atches and 4363 erroneous matchea while reducing

the number of erroneous nonmatches from 39 to 22.

As the discrimination stage (section 3.2)

delineates matches and nonmatches with an error

rate of 3 percent and 99.6 (4363/4379) of the

incrementally-added paira are false, inclusion of

criterion 5 would yield an overall increase in

the number of erroneous nonmatches.

Blocking 3050 duplicate with 54,850 parents

using the preferred set of blocking criteria

yielded 4485 pairs (2991 matches and 1494

nonmatches) for consideration during the

discrimination stage.

It is important to note that the 39 matches

not identified during the blocking stage are

never again considered. Erroneous matches

created during the blocking stage are considered

during the discrimination stage and still can be

correctly designated. These reasons led to our

emphasis on minimization of Type I errors during

the blocking stage prior to minimization of Type

I and 11 errors during the blocking stage.

3.2. Discrimination

The discrimination stage was divided into two

parts: (1) a part in which 2240 pairs were

designated as matches using an ad hoc decision

rule and (2) a discrimination stage in which the

remaining 2245 pairs were designated as either

matches, erroneous matches, or candidates for

manual review.

The ad hoc decision rule generally consisted

of designating those pairs as matches that had

been connected by two or more blocking criteria.

The exceptions were records connected by 1 and 4,

only (NAME and WL-NAME), and 2 and 3, only

(STREET and TELEPHONE). Slightly more than 98

percent of the 2240 records designated as matches

were actually matches.

Prior to use in the information-theoretic

discrimination procedure, the 2245 remaining

pairs were further divided into four mutually

exclusive classes using the preferred blocking

criteria (section 3.1.3):

Claas 1 (1021 records): Linked by 1, only, and

by 1 and 4, only.

Claas 2 ( 624 records): Linked by 2, only, and

by 2 and 3, only.

Class 3 ( 256 records): Linked by 3, only.

Class 4 ( 344 records): Linked by 4, only.

3.2.1. Overall Results

-—---

Table 4 presents a summary of results obtained

during the discrimination stage. It shows that

2148 (96 percent) of 2245 records are classified

as matches or nonmatches and that only 3 percent

(68/2148) of the classified records are

misclassified. Results are based on using the

entire data set for calibration (i.e., obtaining

cutoff weights) and evaluation. Variance results

(section 3.2.6) based on 25 different samples

used for calibration yield cutoff weights and

error rates that are consistent with results in

Table 4.

Two observations are that the cutoff weights

vary substantially across classes and that 100

percent of the records in classes 2 and 4 can be

classified. The varying cutoff weights indicate

that cutoff weights may vary with different types

of addreas lists. Thus , new calibration

information may be needed for each new file

encounted. Calibration information is based on

knowing the actual truth and falaehood of matches

within a representative set of blocked pairs.

Table4: ResultsfromUsinga ModifiedInformation-Theoretic

Model forDelineatingMatchesandErroneousMatches

(3 PercentOverallMisclassification Rate)

Misclassed Total

as Classedas

CutoffWeights Total

class

Total

I

WatchClassedRecords

LOWER UPPER Match Mstch

1 4.5 7.5 28 8 692 274 966 1021

2 2.5 2.5 5 3 379 2h5 624 624

3 -0.5 4.5 5 6 104

4

110 214 256

8.5 8.5 9 .4 266 78 344 344

Totals 47 21 L441 707 2148 2245

The largest group of misclassified records are

those erroneous matches that have the same

address and phone number aa the headquarters”

records. For example:

(a) Apex Oil 222 Columbia St NE Salem

OR 97303 503/588-0455

Jones Co 222 Columbia St N E Salem

OR 97303 503/588-0455

(b) AAOil Main St Smallsville TX

77103 713/643-2121

Smith J K Co Main St Smallsville TX

77103 713/643-2121

Example (a) repreaents two different companiea

located in the same office building. Example (b)

represents two different fuel oil dealers, one of

which has gone out-of-business.

Misclassified matches (erroneous nonmatches)

generally had typographical differences or

missing data in a number of subfields, as in the

233
examples below:

(c) Smith Oil

Hardsburg

Smith J K

Hardsburg

(d) Mcneely R

MPLS

R Mcden Neely

St Louis Par

W 31st St N Church St

PA 18207 713/643-2121

N Church St

PA 18207 missing

3312-14 Harris Ave

14N55246 612/929-6677

3312 Harris Ave

NN 55246 612/929-6677

Example (c) has a minor variation in the NAME

field, a major variation in the STREET field, and

a missing TELEPHONE field. Example (d) has major

variations in the NAME field and CITY fields and

a minor variation in the STREET field.

3.2.2. Improvement Due to New Spelling

Standardization

The ~rxnt due to the new sDsllinx

standardiza~ion waa quite minor as the reeults in

Figures 1 and 2 show. Figures 1 and 2 represent

plots of the numbers of matches and nonmatches

against total weight using the early and new

spelling standardizations, respectively.

The results are only shown for Class 2

(section 3.2 and section 3.1.3) because records

blocked using STREET ADDRESS only or STREET

ADDRESS and TELEPHONE only are intuitively among

the moat difficult to work with (see examplea in

section 3.2.1). Both figures will be compared

with other figures corresponding to Class 2 that

appear in sections 3.2.2, 3.2.3, and 3.2.4.

Although characteristic resulta for other claases

will be mentioned, no grapha will be presented

for them.

Figures 1 and 2 show the classic patterns in

matches and nonmatches (Newcombe et al., 1959;

Newcombe et al., 1983; Rogot et al., 1983). In

RWFtEtlMslWs@tVemua

colaltaofMatcheasndMnMstdM

morto New~8tsn&d2aNnft

Pfbrtoldenubhd~

140-

120-

100-

[

BO80-

40-

~

\

x:,,

20-

,&x

:{,:

#k

,,~

,,

0

-20 -lo

Y Matches

!, ,0 t’J’Jnmatches

!,1,

,,

!,

!,

!,

!,

t,

!,

1)

!,

!,

II

1,

!,

1,

,,

,,

!,

,,

!,

!,

!,

!,

.-. -.

20 30 4

Total~sight

mu?E21btsl wat@llvbfsua

cmmtad MatahsstirwsmMas

AftefNswm~

Prbrto MmtHkshofaMslds

t Legend

Matches

,! .’ Nonmotches

-20 -lo 0 20 30

Total‘~eight

40

both figures, -. the curves of matches almost

entirely overlap with the curves of nonmatches.

As the distinguishing power of the weighting

scheme improves, the curves move apart.

3.2.3. Improvement Due to Address Subfield

Identification

Figure 3 is a plot of t~e numbers of matches

FlwREa.mtalwaldltvafstn

cQlsttsofMstchassndmsmwlas

AfterNew spemg8tddkah

Mess SMle!d IdaMbUm

Legend

\ Matches

~0 ~onma!ches

-lo 0 10 20 30

TotalWeight

D

234
and nonmatches against total weight when the new

spelling standardization and address subfield

identification (section 2.1.3) is used.

Comparison with Figure 2 showa that the subfield

identification yields a moderate improvement

(i.e., the curves of matches and nonmatches

overlap less.)

Although not shown in this paper, examination

of similar sets of plots for other classes,

particularly those blocked using the NAME field,

show less improvement when ~ditional Weights

obtained using the ADDRESS subfields are used.

3.2.4. Improvement Due to Name Subfield

Identification

FiRUre f4 is a ulot of the numbers of matches

and n~nmatches agai~st total weight when the new

spelling standardization and name and address

subfield identification are used (see section

2.1.3 for a list of the subfield). Comparison

with Figure 3 shows that the NAME subfield

identification yields little, if any,

improvement.

Although not shown in this paper, examination

of similar sets of plots for other classes,

particularly those blocked using the NAMS field,

show greater imrmovement when additional weiahts

obtain~d using tie NAMS subfields are used. -

~4iTotdwd@tvamN

colmtaafMstoheaud~

NamssndAd&sss ““-’luE&i%%%%%2

I

ILegend

< Matches .- ..-. . . . .

0 Nonmatches

-20 -lo 0 20 30 440

Total‘&eight

3.2.5. Improvement Due to Conditioni.~

Figure 5 is a plot of the numbers of matches

and nonmatches against total weight when a

special conditioning (see section 2.2 and section

3.2.8) procedure in addition to the new spelling

standardization and name and address subfield

identification ia used. Comparison with Figure 4

shows that the conditioning yields a substantial

improvement in Class 2. Other classes (not

shown) show slight improvements.

FlaREtkmt#wsi@ltvsrsu

c4ultsofmtchesandNomatdm

tJsmesndMrkeasslMeklkhmkdon

Legend

Motches

Nonmotches

$

!

,’

.20 -to o 20 30

Total~eight

I

comparison of Figure 5 with Figures 1 or 2

show the significant improvements obtained using

the modified information-theoretic model that

includes all enhancements.

Table 5 shows the results from using the basic

information-theoretic model that are comparable

to the results in Table 4. The only difference

is that a modified information-theoretic

procedure is used in obtaining Table 4 results.

Overall comparison shows that the modified

information-theoretic procedure performs better

than the basic information-theoretic procedure.

Specifically, comparison of the two tables

shows that the total number of records classified

rises from 1526 (out of 2245) to 2148 while the

overall misclassification rate falls frcm 5

percent to 3 percent.

Comparison of Tables 4 and 5 also shows that

the main difference in the modified and basic

procedures is that the modified procedure allowa

classification of all 624 records in clasa 2

while the basic procedure allows classification

of only 215.

Table 5: Results frca Usina an Information-Theo ret Lc Model

for Oelineatinf-Mstches andErroneous Matches

(5 Percent Ove;allNisclaasification Rate)

Nisclassed Total

aB Classedas

CutoffWeights

class MatchNon- MatchClassedRecords

LOWER

1 0.5 6.5 39 14 674 264 93s 1021

2 -4.5 3.5 2 4 100 115 215 624

3 -4.5 6.5 2 1 55 42 97 256

4 2.5 11.5 11 2 254 46 300 346

Totals 54 21 1055 471 1526 2245

235
3.2.6. Variancea

Tables 6.. 7.. anti 8 Dresent estimates and their

coefficients of variation obtained using 25

calibration samples and Rubin”s multiple

imputation technique. For each calibration

sample, the sample sizes in Classes 1, 2, 3, and

4 were 240, 200, 120, and 160, respectively.

Cutoff weights and misclassification rates were

obtained for each sample. Estimates are the

average cutoff weights and average

misclassification rates over 25 replications

(samplea). Variances of the estimates are over

25 replications.

Overall, the reaul ts indicate that the

estimated cutoff weights and misclassification

rates vary significantly from calibration sample

to calibration sample. The variances are

functions of both the sample sizes on each

replication and the number of replications. When

the number of replications was held at 25 and the

sample sizes decreaaed to 120, 100, 80, and 90

for the four classes, estimated coefficients of

variation over 25 replication were approximately

30 percent higher on the average for

misclassified matches and about the same for

misclassified nonmatchea.

The fact that the coefficients of variation

decrease substantially as sample sizes increase

indicates that calibration asmples should be as

large as possible. As the total number of

records considered in these analyses was quite

small, taking substantially larger samples was

not practicable.

Examination of Table 6 shows that the

estimated coefficients of variation associated

with the cutoff weights using the modified

information-theoretic procedure range from 15.3

percent to 99.5 percent; and from 14.3 percent to

115.4 percent with the basic

information-theoretic procedure. The cutoff

weights are consistent with the cutoff weights

given in Table 4 and Table 5. Results in Tables

4 and 5 were obtained using the entire data aet

instead of samples.

Examination of Tables 7 and 8 show that the

misclassification and nonclaasification rates can

vary significantly. Coefficients of variation of

the estimated misclassification rates for the

modified information-theoretic procedure vary

from 33.2 to 109.9; for the basic procedure from

33.8 to 112.9.

Table6: EstimatedCutoffWeightsandTheir Variances

25 Replications,Withand WithoutConditioning

Varianceof Cvs of

Estimated Estimated Estimated

ClassStatusCutoffWeights CutoffWeights CutoffWeights

11—

LOWER UPPER LOWER UPPER LOWER

I

UPPER

1 c 2.66 7.72 7.02 2.05 99.5 18.5

2 c 1.44 1.44 0.62 0.62 54.9 54.9

3 c -3-39 5.82 8.74 2.08 87.2 24.8

4 c 6.89 1.92 1.11 7.57 15.3 23.1

1 Wc -1.92 8.05 4.90 1.50 115.4 15.2

2 Wc -5.04 4.56 0.52 1.41 14.3 26.1

3 UC -6.38 6.82 1.46 1.66 18.9 18.9

4 Wc 1.71 12.13 3.11 7.56 102.9 22.7

~/ C-Conditioning,WC-WithoutConditioning.

~

cl&ssStatusKecordsMatch N(,zI1 c 1021 10.4 27.L 75.2 260.7 647.2 .038 .041

2 c 624 9.7 3.0 0.0 244.0 367.3 .038 .008

3 c 256 3.0 3.5 94.2 85.2 70.0 .034 .048

4 c 344 1.4 10.2 23.5 54.3 254.6 .026 .039

Total 2245 24.5 44.1 ~92.9 644.21338.1.037 .03

1 Uc 1021 8.9 26.2 145.4 237.1 603.3 .036 .042

2 Wc 624 3.8 3.9 450.6 89.4 76.3 .040 .048

3 UC 256 1.6 2.3 178.8 38.1 35.1 .041 .062

Ii UC 344 1.3 9.6 57.7 38.8 236.6 .032 033

Total 2245 15.6 42.0 832.5 403.4951.3 .037 .04

1/ C-Conditioning, WC-WithoutConditioning.

Comparison of the modified and basic weighting

procedures shows that the modified procedure is

able to classify accurately significantly more

records, particularly in claases 2 and 4, than

the basic procedure. The resulta are consistent

with those presented in Tables 4 and 5.

Results obtained using Efron-s bootstrap

imputation with 25, 100, 200, and 500

replications are consistent with the results in

Tables 6, 7 and 8.

3.2.7. Overall Rate of Duplication

The overall rate of duplication (section

2.3.2) is 0.19 percent (100*102/(54850+102))

where the number of headquarters records is

54,850 and an estimated upper bound on the number

of erroneous nonmatches is 102).

The estimated upper bound, 102, on the n~ber

of erroneous nonmatches iS the nmber of matches

Table 8: Coefficients of Variation of Estimated

Counts of Misclassification and

Nonclassification —1/

25 Replications With and Without Conditioning

Misclassed as

Total Not

Class Status Records Match Non- Classed

2/ Match .

1 c 1021 69.5 47.4 54.7

2 c 624 64.6 81.1 0.0

3 c 256 96.6 84.1 40.9

4 c 344 109.9 33.2 60.8

1 Wc 1021 62.3 42.3 34.0

2 Wc 624 112.9 96.2 9.0

3 Wc 256 106.9 65.5 8.1

4 I.lc 344 99.6 33.8 34.3

~1 Units are percentages.

~1 C-Conditioning, WC-Without

Conditioning.

236
.

that are unblocked plus an upper bound on the the

number that are erroneously classified as

nonmatches during the discrimination stage.

Thirty-nine records (section 3.1.2) are unblocked

using the preferred set of blocking criteria.

The estimated upper bound consists of the sum

of the estimated upper bounds on the numbers of

automatically erroneously matched records in

classea 1-4 and an estimate of the number of

matches that are misclassified during manua 1

rev Lew. The upper bounds at the 95 percent

confidence level in classes 1-4 (using the

estimates in Tables 7 and 8) are 24.9, 22.2, 8.9,

and 4.5, respectively.

We assume that two percent of the estimated

124.3 matches in the estimated aet of 192.9

records (see Tablee 7 and 8) will be misclassed

during manual review. This yielde that 2.5

metchea will be misclassed as nonmatches.

Thus, the upper bound is 102

(=39+24.9+22.2+8.9+4.5+2.5).

3.2.8. The Independence Assumption

Independence of comparison noes not hold.

This is shown by the significant variation of the

lower and upper cutoff weights acroas Claesee 1

thru 4 in Tables 4, 5 and 6. If the comparisons

were independent, then individual weights and

cutoffs for the total weights would be reasonably

coneiatent across clasees. Individual weights

(not shown) vary more than the cutoff weights

across classea.

Independence of interactions within classee is

illustrated by Tables 9 and 10. They chow the

two-way itiependence of the interactl.ona of some

of the aubfields given in section 2.1.3 for

aubfields that ere generally not connected and

Table 9: Independence of Two-Way Interactions

for Selected Subfielda that are

Generally Not Connected with Blocking

Characteriatica, By Class &/

K11 Ill K22/H K1l/SN K22/sN

1 yes yes no ~j no ~1

2NANA yes yes

3 no ~f no ~j no ~/ yea

4 yes yea yea yea

NA- not applicable because one of two

variablee ia baaically the same as a

blocking characteristic due to small sample

size.

Kii is the comparison of KEYWORDI with

KEYWORDi, for 1=1, 2; H ia comparison

of HOUSE NUMSERwith HOUSE NUMBER;

SN ia the comparison of STREET NANE

with STREET NAMB.

Independent when H ia included in a

3way contingency table analysie.

Independent when Kll is included.

Independent when K22 is included.

and

Table 10: Independenceof Two-WayInteractionsfor

SelectedSubfieldsthatare SomewhatConnected

withBlockingCharacteristics,By Class

class Wlls; wl/s2 wl/s3 W21S1 w2/s2 w21S3 w3/sl w31S2 W31S3

1 yes yes yes yes yes yes yes yes yes

2 NA yes yes NA yes yes 3.4 yes yes

3 no~l n02_lno~l 11 o&/no —21 no~l no —51 no~l no .1/

4 NA NA NA yes yes no _1/ no _1/ no _21 no _1/

A ~j no no yes yes yes yes yes yes yes

NA- not applicable because one of two variables is usedas

a blocki~ characteristic.

IndependentwhenS2 is includedin a 3-uaycontingency

tableanalysis.

IndependentwhenS1 is included.

IndependentwhenW2 is included.

IndependentwhenW3 is included.

IndependentwhenS3 is included.

Aggregateof Clasaes1-6.

somewhat connected with blocking characteristic

respectively. The variables used in the

comparisons were defined in sections 2.1.3 and

2.2.2, respectively.

The Fellegi-Sunter model (1969, pp. 1189-1190)

does not require full independence of

interaction. It only requires that interactions

be conditionally independent.

In over half the entriee in Tables 9 and 10,

the two-way interactions are independent

unconditionally at the 95 percent confidence

level and the hierarchical principle (Bishop,

Fienberg, and Holland, 1975) assures that all

such two-way interactions are always

conditionally independent. In all cases in which

two-way interactions are not unconditionally

independent, a third variable was found so that

the two-way interactions were independent at the

95 percent confidence level given the third

variable.

It ia important to note two points. First,

some of the Interaction of variablea (not

presented in the tables) such as H and S1 or W1

and Kll are often not independent unconditionally

and it aeema likely that they will generally not

be independent conditionally. Second, building a

precise model, by mutually exclusive clasa, in

which only the minimal eet of variables necessary

for effective discrimination is included, and

which precisely models the conditional

relationships, is likely to be difficult and

heavily dependent on the empirical data base

used.

What we attempted to do in our approach waa to

find a superset of the minimal set of variables

needed for effective discrimination; apply them

all in creating the weights for each class;

perform minimal modification in the baeic

procedures for creating the weights; and show

that the failure of the Independence assumption

ie not too crucial.

237
4. CONCLUSIONSAND FUTUREWORK

This section contains a brief summary of the

results of this paper, a discussion of how the

results relate to previous applied work and

existing theory, and a set of problems for future

research.

4.1. ————.

Summary

The results of this paper imply that the keys

to delineating matches and nonmatches accurately

are: (1) good spelling standardization and (2)

accurate identification of corresponding

subfields. They also imply that the independence

assumption, required by the information-theoretic

model of Fellegi and Sunter (1969), is not

critical in practical applications of the type

performed in this paper.

A key advantage of the Fellegi-Sunter approach

is that it 1 ends itself to incremental

improvements, as knowledge of both file

properties and data manipulation techniques (via

software) increase.

4.2. Further Discussion of Results

. ..———.--.--—————--

4.2.1. —---..—-—— Independent Application of Multiple

________________

i!locti~ ——-—-—— Criteria

Newcombe~t al. (1962, pp. 563-564) provide an

example of applying multiple blocking criteria

independently. They blocked first on surname and

then on maiden name in files of individuals used

for epidemiological research. In their study of

a special sample of 3560 matches (linkages in

their terminology), 98.4 percent (3504) were

obtained using SOUNDEX coding of surname and an

additional 1.4 percent (to a total 99.8 percent)

were obtained using SOUNDEX coding of maiden

surname. The increase in the total number of

pairs considered for review when the second

blocking criterion was used was 100 percent.

The results of section 3.1 show that, within

the set of criteria considered, no single

blocking criterion can yield a subset of pairs

containing 8Cl percent of matches and no two can

yield subseta containing 90 percent. The work of

Winkler (1984a,b) provides a considerably more

exhaustive study of blocking criteria and shows

how the set of criteria used in this study work

reasonably well on two additional sets of files.

Kelley (1985) provides a theoretical

foundation for the simultaneous consideration of

several subfields which is consistent with the

Fellegi-Sunter model. In hypothetical examples,

he shows how best to apply simultaneously first

name, surname, and sex as blocking criteria.

Section 3.1 results show that criterion 5, 10

characters of the NAME, does not perform well

(62.4 percent of matches are not blocked and only

14.4 percent of the blocked pairs are matches)

while criterion 1, 3 digits of the ZIP and 4

characters of the NAM.E, performs considerably

better (45.5 percent of matches unblocked and

66.8 percent of the blocked pairs are matches).

Thus, our results serve as partial corroboration

of Kelley-s results.

It seems likely that independent application

of multiple blocking criteria such as done in

this paper will be necessary to identify matches

in other files of businesses. ~is is primarily

due to lack of identifiers such aa surnames.

4“2”2”E.P2!.LC%LYSY!!TIEZSL!!:

The comparison of Figurea 1 and 2 in section

3.2.2 showed that improved spelling

atandardization of commonly occurring words did

not yield any dramatic improvement in the ability

to distinguish matches and nonmatches. Results

for other classea (not shown) were similar. The

results, however, may not be representative

because the files had already been standardized

using a somewhat more elementary set of tables.

It is possible that improvements could be more

dramatic when results using totally

unstandardized files are compared with results

using well standardized files.

Additionally, consistent spelling of commonly

occurring words can allow their identification;

thus , making it eaaier to identify other

subfields having greater disthguiahi.ng power.

4.2.3. ———-—_——___

Subfield Identification

Section 3.2 results (particularly Figures 2-4)

showed improvements in the Fellegi-Sunter

weighting procedure-s ability to delineate

accurately matches and nonmatches and reduce the

size of the manual review region. The

improvements were due to the identification of

aubfields in the NAME and STREET fields using

ZIPSTAN and KEYWORDsoftware, respectively.

The improvements using ZIPSTAN in classes 1

and 4 (not shown) were quite substantial. They

were, however, not as dramatic as the

improvements in classes 2 and 3 when conditioning

procedures were used.

The results basically show ua that it may be

possible to delineate and compare subfields

(particularly within the NAME field) that yield

greater distinguishing power. In particular, if

such comparable subfields are distinguished, then

string comparator metrics (see e.g., Winkler,

1985) which allow assignment of weights of

partial agreement between stringa (rather than

just l-agree and O-disagree) could be used to

deal with subfields containing minor

keypunch/transcription errors.

4.2.4. ——-

Independence, Conditioning , and Steepest

Ascent

The results in section 3.2 (particularly

subsections 3.2.1 and 3.2.8) show that the

comparison of characteristics of varioua

subfields are generally not independent. Fellegi

and Sunter (1969, p. 1191) indicate that their

weighting scheme may work well in practice even

when the independence assumption is not met.

In an early analyais (not shown), weights were

computed uniformly over all pairs within the set

of blocked pairs, rather than separately in the

four subclasses. Analyses similar to those in

section 3.2 (particularly, using figures like

Figures 1-5) showed that weights computed

uniformly did not have aa much distinguishing

power. In particular, the curves of nonmatches

and matches never moved as far apart as the

curves moved apart in Figure 5. Results (not

shown) for other classes used in this paper were

quite similar to those in Figures 1-5.

We can conclude that, at leaat in our example,

dependence of comparisons leads to less

discriminating power. We should note, however,

that a large number of comparisons were

performed, some of which are likely not to .be

238
independent conditionally. It may be possible all caaes, the distinguishi~ power remained

.

that- subsets of the comparisons (they are likely

to vary significantly from class to class) may be

created in which the comparisons are

conditionally independent. For such subsets,

however, it is not clear whether the overall

discriminating power will increase.

It is important to note that, for those

procedures in which only one blocking criterion

is used (such as blocking on SOUNDEXabbreviation

of surname in files of individuals), it may be

possible to compute weights uniformly over the

entire set of blocked pairs. The four classes

which we considered were created using the

preferred set of four blocking criteria. Thus ,

our weight creation scheme is conditional on the

set of blocking criteria.

The conditioning argumenta in this paper

consisted primarily of the subdivision of the set

of blocked pairs into four classes based on the

four blocking criteria and steepest ascent

methods of weight variation. Both procedures are

cumbersome to apply, the second particularly so .

It may be possible to produce some algorithm for

conditioning or some other method which allows a

systematic approach to conditioning. Bishop,

Fienberg, and Holland (1975, Chapter 11) provide

a useful discussion of the difficulties with some

of the meaaures of association that have been

developed.

4.2.5. —..-.——-_——--

Legitimate Representation Differences and

Keypunch/Transcription Error

..———---—-

Fellegi and Sunter (1969, pp. 1193-1194)

provided a specific model which incorporate

error rates associated with legitimate

representation differences of the same entity

(see e.g., the name variations in section 2.1.3)

andjor keypunch/transcription error. Their

results (see also Coulter, 1977; Kirkendall,

1985) show that, in the presence of such errors,

agreement weights remain approximately the same

as agreement weights in the absence of such

errors, while disagreement weights (which are

generally negative) increase. The results have

substantial intuitive appeal.

Review of figures like Figures 1-5 for classes

1, 3, and 4 (not shown) and examination of pairs

that are either misclassified or not classified

in all 4 classes indicate that keypunch error

plays a substantially greater role Ln classes 1

and 3 than in classes 2 and 4. The results are

consistent with Table 4 results in which all

records in classes 2 and 4 are classified (none

held for manual review) while a moderate number

of records in classes 1 and 3 (55 of 1021 and 42

of 256, respectively) are held for manual review.

A partiai explanation of the differences is

that classes 1 and 3 contain a moderate number of

pairs of records having substantial variations in

the NAME andlor STREET fields while classes 2 and

4 do not. In class 1, many keypunch errors occur

after the first four characters of the NM.

Being able to block on TELEPHONE (class 3),

allows significant reduction in the number of

erroneous nonmatched because so many

kevmnch/transcriptions can occur in the NAME and

ST~kET fields (se: also Winkler, 1984a).

An additional series of steepest

variations were performed in classes 1 and

ascent

3. In

constant or became slightly “ - worse. “ In some

casess grapha such aa given by Figure 5 contained

curves of nonmatches and matches for which the

humps moved apart but for which the manual review

region remained constant or increased in height.

Thus, it seems unlikely that more conditioning in

the form presented in this paper will improve

procedures. Rather, it seems likely that

improvements will depend more on better

identification and comparison of subfields.

4.2.6. —.-.—-——_

Adaptability of the Fellegi-Sunter

——— Procedures

Newcombe et al. (1959, 1962) first showed that

the basic weighting procedure as presented in

Fellegi and Sunter (1969) could be improved by

adapting it to make use of additional comparative

information. Figures 1-5 in this paper

illustrate successive improvements which can be

obtained using spelling standardization,

additional comparisons of subfields of the NAME

and STREET fields, and conditioning argumenta.

Further improvements seem likely. They can be

obtained using techniques that are already

available. For instance, Statistics Canada

(1982) has developed sophisticated methods of

delineating subfielda within the NAME field for

uae on the Canadian Business Register.

Identifying subfields as Statistics Canada has

done could allow a number of less sophisticated

comparison (such aa firat four characters and

next six characters of the NAME field) to be

dropped and discriminating power to increase.

ZIPSTAN software (U.S. Dept. of Commerce, 1978b)

yielded subfields of the STREET field which

provided increased discriminating power.

Use of frequency counts of the occurrence of

substrings (e.g., Zabrinsky occurs less often and

has more distinguishing power than Smith) could

be incorporated in matching lists of businesses.

Presently, such matching using frequency counts

is applied to lists of individuals (e.g., U.S.

Dept. of Agriculture, 1979; Us. Dept. of

Commerce, 1978a). The theoretical justification

for procedures using frequency-based matchiqg are

explicitly described by Fellegi and Sunter (1969,

pp. 1193-1194).

Use of frequency-based matching involves use

of lookup tables for obtaining weights associated

with individual comparisons. Such lookups can be

performed efficiently using K-D trees (Friedman,

Bentley, and Finkel, 1977). EM presently uses

K-D trees for search of lookup tables during

spelling standardization.

String comparator metrics (see e.g., Winkler,

1985) allowing comparison of strings containing

minor keypunch errors could also be used in

adapting the weighting procedures.

4.3. Problems Remaining —-—

Effective evaluation of the efficacy of

various matching procedures requires having a

representative data base in which matches and

nonmatches have been identified and tracked.

Such data bases can be created during list

updating projects and

incremental improvements

made (see e.g., Coulter

Smith et al., 1983).

are necessary if

in procedures are to be

and Mergerson, 1977;

239
Effective evaluation also requires having

ccmmon terminology and measures that allow rough

comparison of results obtained using

significantly different data bases andlor

methodologies. The results of this paper and

others (see e.g., Newcombe et al., 1983; Rogot et

al., 1983) suggest a number of avenues for future

research that can be incorporated into existing

procedures in a straightforward manner.

4.3.1. —.—— Error Rates

Various authora (see e.g., Newcombe et al.,

1983; Rogot et al., 1983) have presented the

ratea of erroneous matches and nonmatches during

the discrimination stage but generally do not

mention the rates of erroneous nonmatches that

remain unlinked during the blocking stage. As

the Fellegi-Sunter model explicitly provides

measures of the Type I and Type II error rates,

it seems natural to extend investigation of such

rates to both blockiog and discrimination stages.

The results of this paper imply that error

rates occurring during both stages must be

investigated simultaneously. For instance,

during early stsges of the work at EIA no

effective methods existed for accurately

delineating matches and nonmatches during the

discrimination stage. Aa more effective methods

of delineating matchea and nonmatches during the

discrimination atage are developed, it seems

likely that additional blocking criteria (such as

criterion 5 in section 3.1) may be adopted

without increasing the rate of erroneous

nonmatchea.

Other measures, such aa the overall rate of

duplication given in this paper (see also

Winkler, 1984a,b), may provide additional insight

into how well a specific application is performed

and provide additional information comparable

with other applications.

Type I error rates based on samples (see e.g.,

Winkler, 1984a,b) have been ahown to yield

coefficients of variations of approximately 100

percent even with samples as large as 1800.

Although Fellegi and Sunter (1969) indicate that

estimating error rates based on samples yields

high variancea, they did not provide an example

showing the magnitude of the problem. There may

be better methods for obtaining such error rates

and their variances when samples are used.

4.3.2. General Applicability of Linkag.~

Mechanisms

Winkler (1984a,b) showed that the preferred

set of blocking criteria are reasonably

applicable to two other data bases having

different characteristics from the empirical data

base that was used for analyaes in this paper.

In those papers, however, blocking criteria were

investigated independent of the discrimination

stage.

Investigations of the efficacy of different

blocking strategies when both blocking and

discrimination stages are considered

simultaneously are necessary. The investigations

should be performed on files with significantly

different characteristic.

For instance, is the use of an abbreviation

method such as SOUNDEX (e.g., Bourne and Ford,

1961) or NYSIIS (e.g., Lynch and Arends, 1977)

abbreviation of SURNAMEthe only way to block

filea of individuals? If ao, why are such

blocking methods effective in reducing the rate

of erroneous nonmatchea? What methods were

investigated and why were they rejected? Should

files of individuals be blocked several different

ways Usiq significantly different blocking

criteria?

4.3.3. String Com~arators

-——--

If corresponding strings such aa SURNAMEare

identified, then it ia possible to define

distance or weighting functions that compare

nonidentical strings. Such weighting functions

(see e.g. Winkler, 1985, pp. 12-16) can be

derived using abbreviation methods such aa

SOUNDEX (e.g., Bourne and Ford, 1961), using the

Damerau-Levens tein metric (e.g., Hall and

Dowling, 1980, pp. 388-390), or the string

comparator of Jaro (e.g., U.S. Dept of Commerce,

1978a, pp. 83-101).

Each of the methods is intended to allow comparison of strings in which minor typographical

differences occur. What are the relative merits

of different weighting functions? Am there any

better algorithms for string comparison?

4.3.4. Tracki~True and False Matches

In linking pairs ‘~f—r;c=--i~- lists of

buaineases, many erroneous matches Wil~t~e~

similar NAMEs and/or STREET ADDRESSes.

may have different NAMEs andlor STREET ADDRESSes

(e.g., aubaidiaries, successors). Delineation of

moat such matches and nonmatches can require

manual followup which is both time-conaumi~ and

expensive.

If matches and nonmatches are tracked properly

and the weighting methodology for delineating

matches and nonmatchea is reasonably effective,

then many nonmatches that have similar NAMSS and

STREET ADDRESSes to previous nonmatches or

matchea having different NAMES and/or STREET

ADDRESSes from their true Parents will not

reqmire manual review.

To determine if it i.a coat-effective to track

matchea and nonmatches, research is needed to

show :

1.

2.

3.

4.

5.

how classes of matches and nonmatches of

records linked using various blocking

criteria should be set up to allow

tracking;

how effective weighting schemes should be

determined that allow maximum use of the

tracking system;

how pairs newly linked during an update

should be compared within equivalence

classes and acrosa equivalence (a record

can be linked truly once and falsely many

times);

how updating using the results of 1, 2,

and 3 should be perfotmed; and

how the results of the updating should be

evaluated.

ACKNOWLEDGMENT

The author wishes to thank Dr. Yvonne M. Bishop

for a number of helpful comments and conversations.

240
REFERENCES

Bishop, Y. M. M., Fienberg, S. E., and Holland,

P. W. (1975), —.—.———.-—.— Discrete Multivariate Analysia,

MIT Press, Cambridge, MA.

Bourne, C. P., and Ford, D. F. (1961), “’A Study

of Methods for Systematically Abbreviating

English Words and Names,” J. ACM, 8_, 538-552.

Coulter, R.W. (1977), ““An Application of a Theory

for Record Linkage,” Technical Report from the

Statistical Reporting Service of the U.S. Dept.

of Agriculture.

Coulter, R.W. and Mergerson, J.W. (1977), “’AII

Application of a Record Linkage Theory in

Constructing a List Sampling Frame,” Technical

Report from the Statistical Reporting Service of

the U.S. Dept. of Agriculture.

Cochran, W.G. and Cox, G.M. (1957)

Experimental Deaigna, J. Wiley and Sons,

New York.

Efron, B. (1979), ‘“Bootstrap Methoda: Another

Look at the Jackknife,” Ann. Stat., ~, 1-26.

Efron, B. and Gong, G. (1983), ‘“A Leisurely Look

at the BootstraD. the Jackknife. and

Cross-Validatio~:’” The American’Statistician ~,

36-48.

——-——.-——— $

Fellegi, I. P., and Sunter, A. B. (1969), “A

Theory for Record Linkage,”’ JASA, ~, 1183-1210.

Friedman, J. H., Bentley, J. L., and Finkel, R.

A. (1977), “~ Algorithm for Findimz Best Matches

in Logarithmic 8x~cted Time,” ACM trans. Math.

Software, ~, 209-226.

——--——

Hall, P. A. V. and Dowling, G. R. (1980),

““ApprOxirnateString Matching,” Computig

——-

Surveys, ~, 381-402.

Herzog, T. and Rubin, D. (1983), “Using Multiple ,

Imputations to Handle Nonresponse,” in

=lE-Dsn—szp&*cYeL!02Ps -?:

Theory and Bibliographies, edited by Msdow,

--——--—-_

W.G., Olkin, I., and Rubin, D.B. Academic Press,

New York, 210-245.

Kelley, R. P. (1985), “Advances in Record Linkage

Methodology: A Method for Determining the Best

Blocking Strategy,” Invited paper presented at

the Workshop on Exact Matching Methodologies in

ROSSIW, VA, on Msy 9-10, 1985.

Kirkendall, N. (1985). “Weights in Computer Matching: Applications and an Information Theoretic

Point of View,” Record Linkage Techniques--l985,

Internal Revenue Service.

Lynch, B.T. and Arends, W.L. (1977), “’selection

of a Surname Coding Procedure for the SRS Record

Linkage System,’” Technical Report frca the

Statistical Reporting Service of the U.S. Dept.

of Agriculture.

Newcombe, H.B., Kennedy, J.M., Axford, S.J., and

James, A.P. (1959), “Automatic Linkage of Vital

Records,” Science, 130, 954-959.

Newcombe, H.B. and Kennedy, J.M. (1962), “Record

Linkage,’”Communications of the ACM, ~, 563-566.

Newcombe, H.B., Smith, M.E., Howe, G.R., Mingay,

J Strugnell, A., and Abbatt, J.D. (1983),

“’~liability of Computerized Versus Manual

Searches in a Study of the Health of Eldorado

Uranium Workers,” Comput. Bio1. Md>, ~,

157-169.

Pollock, J. and Zamora, A. (1984), “Automatic

Spelling Correction in Scientific and Scholarly

Text,” CommuriLcations of the ACM, 27, 358-368.

Rubin, D. (1978), ‘“Multiple Imputat~ns in Sample

Surveys--A Phenauenological Bayesian Approach to

Nonresponse,” ASA 1978 Proceedings of the

---——---—

Section on Survey Research Methods, 20-28.

-—-— -—

Rogot, E., Schwartz, S., O“Conor, K., and Olsen,

C. (1983), “The Use of Probabilistic Methods in

Matching Census Samplea to the National Death

Index.” ——--

ASA 1983 Proceedings of the Section on -—————.--—--

—Surve~Reaearch Methods, 319-324.--— ——

Smith, M., Newcombe, H.B., and Dewar, R. (1983),

““Automated Nationwide Death Clearance of

Provincial Cancer Registry Files--The Alberta

Cancer Registry Study,”” ASA 1983 Proceedi~s of

--—— ----

the Section on Survey Research Methods, 300-305.

_—.——

Statist~s=~~~~a~ems Development Division

(1982), ‘iRecord Linkage Software.’”

U. S. Department of Agriculture/ Statistical

Reporting Service (1979), “’List Frame

Development: Procedures and Software.”

U. S. Department of Commerce, Burzau of the

Census/Agriculture Division (1981), “Record

Linkage for Development of the 1978 Census of

Agriculture Mailing List.”

U. S. Department of Commerce, Bureau of the

Census/Survey Research Division (1978a),

“UNIMATCH: A Record Linkage System.’”

U. S. Department of Commerce, Bureau of the

Census/Survey Research Division (1978b),

“ZIPSTAN: Generalized Address Standardizer.”

Winkler, W. E. (1984a), “Issues in Developing

Frame Matching Procedures: Exact Matching Using

Elementary Techniques.’” Presented to the ASA

Energy Statistics Committee in April 1984.

Winkler, W. E. (1984b), “Exact Matching Using

Elementary Techniques.”m —-—-——-——---— ASA 1984 Proceedings of

the Section on Survey Research Methods,

--———-

237-242.

—-——.—

Winkler, W. E. (1985), “’Preprocessing of Lista

and String Comparison,” Record Linkage Tech=

niques--l985, Internal Revenue Service.

.

.

.

241
.

.

.

Section IV:

Application

Case Studies
THE NATIONAL OEATH INDEX EXPERIENCE; 1981-1985

John E. Patterson and Robert Bilgrad, National Center for Health Statistics

The National Death Index (NDI) is a central,

computerized index to the death certificates

filed in each State vital statistics office.

This computer file contains a standard set of

identifying information for each person dying in

the U.S., beginning with 1979. The NDI was

established to assist health and medical

investigators in determining whether persons in

their studies may have died, and if so, to

provide the names of the States in which those

deaths occurred, the dates of death, and the

corresponding death certificate numoers. The NDI

user can then obtain copies of death certificates

from the appropriate State offices.

The NDI became operational in November 1981.

As of March 31, 1985, the NDI file contained 10.3

million death records for the five-year period

1979-1983. A total of 168 NOI file searches have

been performed, involving 2,352,001 records

submitted by 99 NDI users. This report gives a

brief overview of the NDI users and their

research activities, and descrioes recent

evaluations and planned revisions of the NDI

matching criteria. Procedures for using the NDI

are also presented.

1. OVERVIEW OF NDI USERS

The NDI has been used in a variety of health

and medical research projects which rely on the

successful ascertainment of the vital status of

their study subjects. The research projects of

the 99 NDI users have been grouped into five

oroad research categories in Table 1. These

categories are (1) exposure cohorts, involving

studies of the effects of being exposed to

potential risk factors in the workplace, the

environment, or as a result of diagnostic or

therapeutic procedures; (2) disease cohorts,

involving followup of persons diagnosed as having

cancer or other diseases; (3) life style/risk

fac~o~s! involving studies of the effects of

actlvltles such as smoking or drug abuse; (4)

clinical trials, primarily involving studies of

the potentially beneficial effects of various

therapies for specific diseases; and (5) general

population cohorts, involving followup of survey

participants not selected on the basis of a

specific diagnosis or exposure to risk factors.

Forty percent of the NDI users are conducting

occupational studies involving followup of

rosters of employees to determine whether there

have been any harmful effects resulting from

their exposures to potentially harmful

substances. Most of these studies are oeing

performed by the National Institute for

Occupational Safety and Health as well as by oil

and chemical companies. Another 28 percent of

the NDI users are involved in followup activities

on cohorts of persons diagnosed as having cancer

or other diseases.

Table 1 also shows the types of organizations

using the NDI. It should be noted that while

Federal agencies account for only 18 percent of

the NDI users, the Federal government is actually

providing the funding support for about threefourths of the studies oeing performed by

universities and consulting firms.

Many of the NDI users are either following

cohorts of under 2,500 persons or use the NDI

only to check on those study subjects which are

considered lost to followup. Almost threefourths of the users have submitted fewer than

10,000 names. The fewest records submitted for

an NDI file search were 7. The largest volume of

records was suomitted by the Census Bureau for

the National Longitudinal Mortality Study being

supported oy the National Heart, Lung and Blood

Institute. Thus far, this study has involved the

submission of a test file of 225,875 Census

Bureau records and the main study file of 994,195

records. The study’s methodology involves a

search of the NDI file every two years. The

second NDI search for the main study is scheduled

for around July 1985 and will involve

approximately 1.2 million Census Bureau records.

2. COMPLETENESS AND QIJALITYOF NDI AND USER DATA

The effectiveness of the NDI matching process

is dependent on the following three factors: (1)

the completeness and quality of the death

certificate data suomitted to the National Center

for Health Statistics (NCHS) by the State vital

statistics offices for use in creating the NDI

file, (2) the completeness and quality of the

data provided oy the NDI user, and (3) the

effectiveness of the NDI matching criteria.

The completeness of the NDI file is probably

well in excess of 99 percent. Data on virtually

all deaths occurring from 1979 to 1983 have been

submitted by the fifty States, the District of

Columbia, New York City, Puerto Rico and the

Virgin Islands. The NDI file now contains 10.3

million records. Table 2 shows that the

completeness of data for most data items exceeds

97 percent except for middle initial (71.7

percent), father’s surname (86.2 percent), and

social security numer (91.0 percent). Although

9.0 percent of the records do not contain social

security numbers (as shown in Table 3), only 6.0

percent of the records for persons 22 years and

older do not contain such numbers. As might oe

expected, death records for females have higher

percentages of social security numbers not

reported than records for males.

It is very difficult to assess the quality of

the data on the NDI file, but we have reason to

believe that it is probably quite good. The

quality of the NDI data is most affected by how

the death record information is reported to and

recorded oy funeral directors. The death

certificate is a legal document which must be

filed in the State where the death occurs. Most

States continually encourage funeral directors to

make every effort to obtain accurate information

from the person making the funeral arrangements.

Funeral directors have a strong incentive for

245
obtaining and accurately recording good

identifying information on each decedent. Their

clients would not be pleased if errors appeared

on the certificate, since this would very likely

delay settlement of claims for life insurance and

other survivor benefits. All States perform 100

percent verification of the coding and keying of

their records. NCHS also performs various

quality control checks as the States’ data are

received.

The completeness and quality of data submitted

by NDI users, on the other hand, vary greatly

depending on how the data were collected. The

complete and accurate collection of the NDI data

items listed in Taole 2 will, of course, enhance

the effectiveness of any subsequent searches of

the NDI file. This table summarizes the overall

completeness of the data suomitted by NDI users;

however, the completeness of each data item

varies greatly among the different users,

especially for such items as middle initial,

social security number, State of residence and

State of birth.

Because of the newness of the NDI program,

many users did not or could not insure the

collection of all of the NOI data items. NCHS

strongly encourages investigators who are or will

be planning studies to make every possible effort

to collect all of the NDI data items, even if the

investigators do not have specific plans to

conduct a followup of study subjects to ascertain

their vital status. Once a study is completed,

the same or other health investigators may decide

that future followup of the study group may

indeed De very useful. Internally, NCHS has

instituted a policy requiring each new survey to

collect all of the NDI data items, regardless of

whether the survey staff or others in NCHS plan

to use the NDI to followup on the survey

participants in the future.

3. RECENT REVISIONS IN THE NDI MATCHING CRITERIA

When the NDI retrieval program was first

designed and implemented, a fairly simple set of

seven matching criteria was developed (1) to use

most effectively the principal identifiers on the

death. record; (2) to satisfy the needs of the

ma.lorltyof potential users; (3) to make searches

against the NDI very routine, eliminating the

need for special programming for individual

users; and (4) to take into account the policy

concerns of the States. These concerns were very

significant and had a major impact on the

development of the initial matching criteria.

Many States felt that the NDI users should be

required to provide a fairly substantial body of

identifying information for their subjects. They

should not accept matching solely on the basis of

social security numbers, for example. A number

of States were also concerned about probabilistic

matching. They felt that their regulations would

prevent them from searching their files on a

probabilistic basis, and they did not believe

that they could delegate authority to NCHS to do

what they would not be permitted to do themselves.

For an NDI record to qualify as a possible

match with a given user record, under the initial

matching criteria, at least one of the following

246

seven combinations of data items must agree o

both records:

;:

3.

4.

5.

6.

7.

Social security number, first name.

Social security number, last name.

Social security number, father’s

surname.

If the subject is female: social

security number, last name (user’s

record) and father’s surname (NDI

record).

Month and exact year of birth, first

and last name.

Month and exact year of birth, first

name, father’s surname.

If the subject is female: month and

exact year of birth, first name, last

name (user’s record) and father’s

surname (NDI record).

Nine evaluations of the effectiveness of th

above matching criteria have been performed b

NCHS and by several NDI users. The results ar

summarized in Table 4. Each of these evaluation

involved study files of known decedents whic

were searched against the NDI file. In thos

evaluations where social security numbers wer

available for a large proportion of decedents

the resulting percentages of true matches (use

records which were correctly identified a

deceased) ranged from 92.1 percent to 98.

percent. The differences in these percentage

are attributed primarily to differences in th

quality of the users’ data sets. Thre

evaluations showed that, without the benefit o

~only 79.7 percent [8], 80.0 percent [10], an

social security numbers true matches amounte

81.9 percent [9], primarily because o

discrepancies in year of birth and names

However, two other users apparently had muc

better data on dates of birth and names becaus

they achieved true matches of 91.1 percent [1

and- 96.5 percent [3] without the benefit o

social security numbers.

Most of our advisers and users have stresse

that our first efforts to improve our matchin

criteria should be to maximize the number of tru

matches, even if this means a significan

increase in the false matches which may b

generated as a by-product. Our users hav

generally found that nearly all false matches ca

be eliminated easily by simply reviewing th

output of the ND1 search. This is especiall

true for small studies. For very large studie

computerized processing of the ND1 output i

necessary to identify true matches and to isolat

questionable matches which deserve close

inspection. Several users have developed thei

own computerized algorithms for this purpose.

As a result of the evaluations mentione

above, NCHS is planning to add five new matchin

criteria to the initial seven. The fiv

additional matching criteria are listed below an

are numbered 8 through 12 to distinguish the

from the initial seven. A possible NDI recor

match would be generated if any of thes

combinations of data items agree on an NDI and 

user record.

8. Month and ~ 1 year of birth, first and

last name.

9. Month and + 1 year of birth, first and

middle ini~ials, last name.
10. Month and exact year of birth, first and

middle initials, last name.

11. Month and day of birth, first and last

name.

12. Month and day of birth, first and middle

initials, last name.

Our evaluations have shown that by also

permitting matches on month and day of birth and

on month and + 1 year of birth, the percentage of

true matcher generated can be increased

significantly. One of the NCHS evaluations

mentioned previously, involving a cancer registry

file containing social security numbers on 85.9

percent of its 2,598 records, showed an increase

in true matches from 92.1 percent to 96.2 percent

with the addition of the five new matching

criteria [8] . The increase in matching

effectiveness is greatest, however, for study

files having very few or no social security numbers. Another NCHS evaluation involved a file

without social security numbers for 607 decedents

in the NCHS National Health and Nutrition Examination Study. This evaluation showed an increase

in true matches from 81.9 percent to 89.5 percent

[91. ---

The initial retrieval program permitted first

names, last names and fathers’ surnames to match

on the basis of either their exact spelling or

Soundex codes. Evaluations showed that the use

of Soundex codes often generated agreements on

names which were dissimilar, however, causing a

number of unnecessary false positives to be

generated, while adding very little to the number

of true positives. With the planned implementation of the revised matching criteria, the use of

Soundex codes will be eliminated. Phonetic matching will be performed only on last names and

fathers’ surnames and will be based on NYSIIS

codes (New York State Identification and Intelligence System). The NYSIIS coding system which

will be used was first modified abd tested by the

Us. Department of Agriculture [111 and was

subsequently adopted for use in Statistics

Canada’s Mortality Data Base. The ccxnputer

program which assigns the modified NYSIIS codes

was obtained by NCHS from Statistics Canada.

4. USING THE NOI

As mentioned above, health investigators

planning to use the NOI are encouraged to collect

as many of the NDI data items as possible and to

insure that the data are of good quality. To

become an NDI user, health investigators must

first complete and submit an NDI application

form. Each form is reviewed by the advisers to

the NDI program to insure that (1) the proposed

use of the NDI is solely for statistical purposes

medical or health research and (2) the

~~plicant provides adequate assurances that the

identifying death record information obtained

from the NDI and from the State vital statistics

offices will be kept confidential and will oe

used only for the proposed study.

Once the applicant is notified that the

application is approved, the NDI user may then

submit records for an NDI file search. The user

must submit records on a magnetic tape which

conforms with the NCHS tape specifications, file

format requirements, and coding instructions.

Users planning to submit under 300 records have

the option of using NCHS coding sheets. The

results of an NDI file search are sent to the

user (along with the user’s data) within three

weeks after the user’s records are received by

the NCHS computer facility.

The user must assess the quality of each

possible NDI ‘record match listed and determine

which NDI matches are worthy of further

investigation. A sample of the planned revision

of the NDI Retrieval Report is presented in Taole

5. The Retrieval Report lists all user records

involved in a match with one or more NDI records.

The State of death, death certificate number and

date of death are listed for each possible match,

along with an indication of which data items are

in agreement. Two changes in this report should

further assist NDI users in evaluating the

quality of possible matches. First, the revised

Retrieval Report will show which digits of the

social security numbers are in agreement. The

current report merely indicates whether or not

there was an agreement on the entire social

security numoero Second, the new report will

indicate the extent to which the years of oirth

disagree; e.g., +1 year, -1 year, -15 years, etc.

The current report simply indicates whether or

not there is exact agreement on the year of

oirth.

The user must decide which, if any, of the NDI

records are true matches and then ootain copies

of the death certificate from the appropriate

State vital statistics offices. Most users are

interested in ootaining the cause of death from

the death certificate. Some users also conduct

death record followback activities to the

hospitals, physicians, next-of-kin, and/or other

persons or establishments indicated on the death

certificates. Other users simply obtain copies

of certificates to assist in confirming whether a

questionable match is actually the person in the

study.

Once an application is approved, requests for

repeat searches of the NOI file (for additional

years of death or for different study subjects)

do not need to go through the formal review and

approval process again, as long as the

information provided in the initial application

remains essentially the same. Death records for

a particular calendar year are added to the NDI

file annually, approximately 12-14 months after

the end of that calendar year. Records for

deaths occurring in 1984 are scheduled to be

added to the NDI file around February 1986.

5. ADDITIONAL REFERENCES CONCERNING THE NDI

In addition to the NDI users’ articles and

studies cited above, several other articles have

been written describing the experience of NDI

users [12-151. There have also been articles

written regarding the potential use of the NDI

for various studies [16-18]. Finally, papers

have been written in which birth certificates

from the NCHS 1980 National Natality Survey were

searched against the NDI to produce infant

mortality rates [19-221. Copies of these four

unpublished papers can be obtained from NCHS [231.

Persons interested in receiving copies of the

NDI User’s Manual [241 and an NDI Application

24-?
Form should write or call: 

, 

y, 

Center for Health Statistics, 1984.

[1]

[2]

[3]

[4]

[5]

[6]

[71

[81

[9]

NATIONAL DEATH INDEX

Division of Vital Statistics

National Center for Health Statistics

3700 East West Highway, Room 1-44

Hyattsville, Maryland 20782

Telephone: (301) 436-8951

NOTES AND REFERENCES

Wentworth, Deborah N., et al., “An

Evaluation Social Se:~;;;~

AdministrationOf Mas~~~ Beneficiary

File and the National Death Index in the

Ascertainment of Vital Status,” American

Journal of Public Health~lJ~~. 73, ~

November 1983, pages .

Acquavella, J.F., Donaleski, D., and Hanis,

N.M., “An Analysis of Mortality Follow-up

through the National Death Index for a

Cohort of Refinery and Petrochemical

Workers,” (Accepted for publication in the

American Journal of Industrial Medicine,

985).

Stampher, Meir J., et al., “Test of the

National Death Index,” American Journal of

- ‘o’” 11” ‘0” 5“’ 1984’ ‘ages

Data are based on preliminary, unpublished

results of an evaluation of the NDI matching

criteria performed oy Nancy Fink, Johns

Hopkins School of Hygiene and Puolic Health,

1983.

Lubitz, J. and Pine, P., “Initial Findings:

Development and Use of a Linked MedicareNCHS Mortality File,” (Read oefore the l12th

Annual American Puolic Health Association

Meeting, Anaheim, California, November 11,

1984).

Curb, J. David, et al., “Ascertaining Vital

Status through the National Death Index and

the Social Security Administration,”

American Journal of Epidemiology, Vol. 121,

No. 5, 85, pages 754-766.

Davis, Kathryn B., et al., “A Test of the

National Death Index Using the Coronary

Artery Surgery Study (CASS),” (Accepted for

publication by Controlled Clinical Trials,

1985).

Patterson, John E., “Evaluation of the

Matching Effectiveness of the National Death

Index,”- American Statistical Association

Proceedings of the Social Statistics

Section, 83, pages 1-10.

Data are eased on unpolished results of an

evaluation of both the initial and revised

NDI matching criteria performed by Helen

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

248

Rogot, Eugene, et al., “On the Feasibility

of Linking Census Samples to the National

Death --—. Index for EDidemioloaic Studies: A

Progress”Report,” ~erican J~urnal of pUbliC

Health, Vol. 73, No. 1, November 1983,

Daaes .-4 1265-1269.

Lvnch. Billy T. and Arends, William L.,

S~lection of a Surname Coding- procedure for

the SRS Record Linkage System, Statistical

Reporting Service, U.S. Department of

Agriculture, February 1977.

Arellano, Max G., et al., “The California

Automated Mortality Linkage System (CAMLIS),”

American Journal of Public Health, Vol. 74,

No. 12, December 1984, pages 324-1329.

Lubitz, James, “The Cost of Cancer and the

Medicare Hospice Benefit,” Proceedings of

the 19th National Meetinq of the Puollc

Health Conference on Records and Statistics,

August 1983, pages 145-147.

Nelson, Nancy A. and Van Peenen, P.D.F.,

“RE: ‘Test of the National Death Index’”

(letter to the editor), American Journal of

-f~z”t~~l’fi~z5’auZ~ ~~~

Stampher).

Rogot, Eugene, et al., “The Use of

~~:~b~listic Methods in Matching Census

to the National Death Index,”

Statistics of Income and Re1ated

Administrative Record Research:

Statistics of Income Division, Interns;

Revenue Service, October 1983.

Beebe, Giloert, “Record Linkage Systems -

Canada vs. the United States,” American

Journal of Public Health, Vol. 70, December

80, pages 1246-1247.

Edlavitch, S.A., Feinleit), M. and Anello,

c “A Potential Use of the National Death

I~~ex for Postmarketina Drua Surveillance,”

Journal of the American”Medi~al Association,

vol. 3, No. 9, March 1, 1985, pages 1292-

1295.

MacMahon. Brian. “The National Death Index.”

(editorial), A&rican Journal of Publ;c

Health, Vol. 73 No. 11, November 1983,

pages 1247-1248. ‘

Placek, Paul, et al., “Methodology for the

‘1980 National Natality Survey/National

Death Index Match’ Project,” 1984

Proceedings of the American Statist~

Association, Section on Survey Research

Methods.

Keppel, Kenneth G., et al., “Infant

Mortality Rates Based on Linked Records from

the 1980 National Natality Survey,” (Read

before the 1985 Annual Meeting of the

Population Association of America, Boston,

Massachusetts, March 28-30, 1985).
[211 Kessel, Samuel S., et al., “Fetal,

Perinatal, and Neonatal Death Rates

According to Hospital, Maternal, and ;~~~~

Character sties: United States,

(presented at the 1985 Annual Meeting of tie

&nerican Statistical Association, Las Vegas,

Nevada, August 5-8, 1985).

[22] Placek, Paul J., “Record Linkage Methodologies in the 1980 National Natality Survey

(NNS) and 1980 National Fetal Mortality

Survey (NFMS),” (presented at the meetings

of the International Statistical Institute,

Amsterdam, The Netherlands, August 12-22,

1985).

[23] Copies of references [191, [20], [211, and

[22] can be obtained by writing to: Natality

Statistics Branch, Division of Vital Statistics, National Center for Health Statistics, 3700 East-West Highway, Room 1-44,

Hyattsville, Maryland 20782.

[24] User’s Manual: The National Death Index,

Us. Department of Health and Human

Services, National Center for Health

Statistics, DHHS publication numoer (PHS)

81-1148, Septenmer 1981.

,

.

,

.

.

.

.

.

249
Table 1

NATIONAL DEATH INDEX (NDI) USERS ANDRECORDVOLUMES

Users NDI Searches User Records

NDI User

Characteristics

Number Percent Number Percent Number Percent

Types of Research:

Total-----------------------

Exposure cohorts

Occupational-------------

Environmental------------

Diagnostic/therapeutic---

Disease cohorts

Cancer registries--------

Other--------------------

Life style/risk factors-----

Clinical trials-------------

General population cohorts--

Types of NDI Users:

Total-----------------------

Federal Government----------

State Government------------

University------------------

Private Industry------------

Hospital--------------------

Consulting firm-------------

Record Volume:

Total-----------------------

Under 2,500-----------------

2,500 - 9,999---------------

10,000 - 24,999-------------

25,000 - 99,999-------------

100,000 - 499,999-----------

500,000+--------------------

99

40

5

2

13

15

9

9

6

99

18

4

28

13

19

17

99

42

29

12

13

2

1

100.0

40.4

5.1

2.0

13.1

15.2

9.1

9.1

6.1

100.0

18.2

4.0

28.3

13.1

19.2

17.2

100.0

42.4

29.3

12.1

13.1

2.0

1.0

168

57

18

3

16

18

14

14

28

168

62

3!

17

22

24

168

45

38

31

33

7

14

100.0

33.9

10.7

1.8

1;:;

8.3

8.3

16.7

100.0

36.9

3.6

22.0

10.1

13.1

14.3

100.0

26.8

22.6

18.5

19.7

4.2

8.3

2,352,001

636,752

78,824

7,566

38,002

42,120

116,875

86,333

1,345,529

2,352,001

1,516,313

45,056

327,060

221,942

63,120

178,510

2,352,001

29,259

165,711

225,466

513,014

424,356

994,195

100.O

27.1

3.4

0.3

1.6

1.8

5.0

3.7

57.2

100.0

64.5

1;:;

9.4

2.7

7.6

100.0

1.2

7.1

9.6

21.8

18.0

42.3

250
Table 2

NUMBER OF RECORDS AND PERCENT COMPLETENESS

OF NATIONAL DEATH INDEX (NDI) AND USER DATA ITEMS

Data Items

I

NDI File I

User Files

No. of Records--------

Percent Complete:

Last Name-----------

First Name----------

Middle Initial ------

Social Security No.-

Birth Month ---------

Birth Oay-----------

Birth Year ----------

Father’s Surname----

Sex-----------------

Race ----------------

Marital Status ------

State of Residence--

State of Birth ------

Age at Death --------

10,290,730

99.9

99.9

71.7

91.0

98.8

98.7

99.4

86.2

99.9

97.9

99.4

99.9

99.5

99.9

1,131,931*

99.9

99.7

73.4

84.2

95.7

87.9

97.0

8.9

92.6

53.1

17.9

44.2

18.6

10.6

* The total number of user records shown excludes 1,220,070 records

associated with the National Longitudinal Mortality Study,

sponsored by the National Heart, Lung and Blood Institute and

involving both the Census Bureau and the National Center for Health

Statistics. This large volume of records was eliminated from this

table to give a more realistic presentation of the completeness of

the data items submitted by the other 98 NDI users.

251
Table 3

REPORTING OF SOCIAL SECURITY NUMBER ON NATIONAL DEATH INDEX (NDI) RECORDS;

BY SEX AND AGE AT DEATH

Percent not Reported

Number of NDI Records WITHIN Age/Sex Group

Age at

Death

Both Both

Sexes* Male Female Sexes* Male Female

All Ages-- 10,289,958 5,536,778 4,753,180 9.0 7.8 10.3

0-16------ 356,704 208,377 148,327 88.6 87.4 90.3

17-21----- 126,475 95,242 31,233 17.8 16.9 20.6

22-59----- 1,965,257 1,279,175 686,082 8.4 7.2 10.6

60+------- 7,841,522 3,953,984 3,887,538 5.3 3.6 7.2

* The record counts and percentages do not include 772 records for which sex was

not reported.

252
Table 4

EVALUATIONS OF THE EFFECTIVENESS OF THE NATIONAL DEATH INDEX (NDI)

MATCHING CRITERIA USING RECORDS OF KNOWN DECEDENTS

Percent

NDI Users and User Studies* Known True True

Decedents Matches Matches

University of Minnesota

School of Public Health

(Multiple Risk Factor Intervention

Trial (MRFIT) for coronary heart disease) [I]....

Exxon Corporation

Research & Environmental Health Division

(Mortality study update of Exxon workers) [2]....

Harvard Medical School

(Nurses health study) [3]........................

Johns Hopkins School of

“7

iene and Puolic Health

Health effects of low-level radiation

in shipyard -workers) [4]........................

tiealthCare Financing A&ministration

(Use and costs of Medicare services

by cause of death) [5]...........................

Universityof Texas at Houston

School of Public Health

(Hypertension Detection and Follow-up

Program post trial survey) [6]...................

University of Washington

(Coronary Artery Surgery Study) [7]..............

National Center for Health Statistics

Division of Vital Statistics

(Evaluation of NDI using cancer registry

records) [8]

INITIAL matching criteria: ......................

Using Social Security Nunber (SSN) ...........

Using birth month/year .......................

NEW matching criteria ...........................

Using SSN ...................................,

Using birth month/day —or birth month/~1 year.

National Center for Health Statistics

Division of Analysis

(First National Health and Nutrition

Examination Survey epidemiologic follow-uP) [9]

INITIAL matching criteria (without SIN) .........

NEW matching criteria (without SIN) .............

191

1,449

346

8,947

69,631

1,154

370

2,598

2,231

2,596

2,598

2,231

2,596

607

607

188

1,407

334

8,485

65,000

1,074

344

2,394

1,874

2,069

2,500

1,874

2,351

497

543

98.4

97.1

96.5

94.8

93.3

93.1

93.0

92.1

84.0

79.7

96.2

84.0

90.6

81.9

89.5

* Numbers in brackets refer to studies cited in the NOTES and REFERENCES Section.

253
Io- =

10+ =

a .

N .

I .

=V

.

i.

SXMUAS :((13ilNIlN03)

------- ----------------- ----------------------- -------------------------

x x S1- /.-xx

N x--- --x--

x 10+ x- xx

-- x-xxxxx

xx-- x-xxx x Lo+ x- xXxxxxxxxx Xxx xxx-xSIOWA

NIHlIl!(13Sfl 3N1 :318V1

q3eaa xapuI (ION) “pJOx.4

03 s,Jaq3eJ aumuns uo leuo!3eN

~SPl alUl?U UO JaSn pJOXJ paJQdUKS3

!30s =

80s =

‘w =

S3/=

M-109NIav3H :sNoIlvIA311aw

-------------------------- . ------ ..- . . . ----- ------- -----

I 10N vNV LS9869 12-CO - 6L NX

VNVISIllOl V VOEIZV OE-LO-LO X61

nol I s1 VNV ZV VOEb OSJ-LO-LO xx

* VINVAlASNN3d 860198 18-10-ZO X9X

30 H1V30 m3swnN I+1V30 1W4 3wvNuns S4 30S 33S ON OH AO tlA XIV X35 X)V21 w Uos 90s

31V1S LB33 31V0 40 3WVN SL13H1V4 /N1 HIZIIE 31V(l

SI31 1(IN (lWS33Zi S3H3LVW ) N1 03XNVM B30VI0 (

vN1938 S3NVH coo 10 6666 ZI 01 al - 4 - W Vd VI C18SII0

V4 Sd3Hl 3WVNIWVS 30S 33S ON Ow Aa 8A 3W X35 33VM Sw klos SOS Vlvo

HILIIE 31v0 u3sn

SSOd 3181 1N303330 3WVN

ls3n03u 0N033H SSLld) 31131 S3H3JVW = (* ION lddV SIN 668ZP8 1081NO3 ON LOS.V

------- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ----- ----- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ----- ----- ------ ----- ------

llV) ?Ul ! uo!3euLso4u u! H.KI s alduexa s! leo~~aqlod~q [”

lVA31ti13tl 180d3H ‘- (13SIA3U

alqel ~
AN IMPLEMENTATION OF A TWO-POPULATION FELLEGI-SUNTER PROBABILITY LINKAGE MODEL

Max G. Arellano, University of California, San Francigco

I. INTRODUCTION

The California Automated Mortality Linkage

System (CAMLIS) has been in operation at the

University of California, San Francisco, since the

fall of 1981. It was organized under the sponsorship of the Department of Epidemiology and International Health to facilitate the clearance of

study population files submitted by qualified

investigators againat mortality files for the

State of California.

The linkage of two independently generated data

files has long been thought to be the exclusive

province of highly trained clerks because of the

need to process the discrepancies which frequently

occur between sets of identifying information for

the same person on the two files.

A computerized approach to the record linkage

problem can adopt either deterministic or probabilistic decision criteria. Deterministic linkage

criteria require the formulation of a ‘match key’

to establish the relationship between records on

the two files to be linked. This match key

functions on an ‘either or’ basis, i.e., if an

identical value of the match key is found on both

files, the records with the identical values are

said to be matched. Otherwise, the records are

said to be unmatched. In order to perform its

required function with minimal error, this match

key must possees as many of the characteristics of

a unique identifier as possible. Match keys can

be constructed from any conceivable combination of

last name, first name, sex, social security

number, birth date (or portions thereof), or any

other identifying items present on the file.

Although it ie not a true unique identifier, the

ready availability of the social security number

has led to its widespread use as the match key of

choice in deterministic linkage applications.

Probabilistic linkage criteria are baaed on a

1inkage weight calculated for each pairwise

comparison between records on the two files to be

linked; theee linkage weights are the sum of

component weights calculated for each item of

identification contained on the two files. The

component weights are functions of occurrence

probabilities and of the reliability of the data

iteme. Probabilistic decision criteria provide an

attractive alternative to deterministic 1inkage

criteria as a means of computerizing the record

linkage activity primarily because: 1) they assign

weights in a manner that ia consistent with our

own human intuition and 2) they cam accommodate

partial agreements. On the debit side: 1) they

require the estimation of many paremetera, some of

which are inestimable, 2) they are much more

difficult to program and 3) they are more coatlY

to uee.

Our decision to adopt probabilistic decision

criteria waa baaed primarily on our conviction,

based on a careful analysis of the available

information, that the requirements of investigators in the health and medical care research

fields could not be met solely by deterministic

linkage criteria. Our experience over the last

four years has served to confirm the validity of

that decision.

II. THE FELLEGI-SUNTER WEIGHTING ALGORITHM

The Fellegi-Sunter [1] weighting algorithm

requirea the estimation of two probability distribution functions:

If we let,

pjA= P(Occurrence of the jth configuration

in population A)

pjB = P(Occurrence of the .ith configuration

pjA~=

Woj, =

moj, =

in population B) -

P(Occurrence of the jth configuration

in AIIB)

Probability linkage weight for the

.jthagreement configuration

P(Occurrence of the jth agreement

configuration~the record pairs are

associated with members of the

matched set)

= pojl(a,b)<M)

= pjA~(l-eA)(l-e~)(l-e~)

U(]j) =P(Occurrence of the jth agreement

configuration~the record pairs are

associated with members of the

unmatched act)

.

.

Poj\(a,b)4U)

pjApjB

log[moj)/uo’j)]”

obvious difficulties encountered in

Then, Woj, =

Among the

the implementation of this model are:

(A) It does not address the problem of estimating the e or eT terms. We generally

refer to these aa the 1’component error

probabilities. “

(B) The p B term requirea information whic~

can on9 y be obtained when the linkage

been completed in a satisfactory manner, if

then.

If the populations represented by the files

that are being linked can be regarded as s~plea

drawn from the same population, i.e., the onepopulation” model, some simplifications can be

introduced into the above expressions:

m(}j)=pj(l-e)2(l-eT)

Uoj, = P:

Woj, = 10g[m(lj)/uoj)l

= log[p]l (l-e)2(l-eT)]

Moreover, if the data are being collected continuously, as is generally the case under the

circumstances to which the one-population model is

255
applicable, procedures can readily be developed to

iteratively obtain’’good’’eatimates of the component error probabilities. This is, unfortunately , not the case for situations to which the twopopulation model would generally be applied. For

one thing, if the populations being linked do not

overlap, the PA term is meaningless. The model

alao requires e~imetes of component error probabilities specific to the files that are being

linked.

Prior information on the record-pairs that

correspond to the intersection of the two populations ia obviously desirable, if not absolutely

necessary, before probability linkage can be

initiated. However, since this is precisely the

information we are attempting to obtain by meana

of probability linkage, if it can be obtained by

other means~one may legitimately question the need

for probability linkage.

In this paper 1 will describe the approach that

has been adopted by the CANLIS project to the

problem of implementing a two-population FellegiSunter model.

III. THE CAMLIS IMPLEMENTATION OF THE

TWO-POPULATION FELLEGI-SUNTNR MODEL

Central Concepts

The CAMLIS approach is based on the following

central concepts:

(A)

(B)

(c)

(D)

A two-stage linkage process, consisting of

a deterministic first atage ( pr~im~

based on the social security

followed by a probabilistic second stage,

is necessary to achieve the desired perfo rmanc e characteristics. This strategy

has several benefita:

(1)

(2)

Each stage is capable of detecting

valid linkages which will eecape

detection by the other atage.

Since deterministic linkage is

carried out first, the correctly

matched records which it produces

can be used to derive estimates of

the component error probabilities

required by probability linkage.

A phonetic name encoding algorithm with

superior operating characteristics must be

used to form the baaic comparison groups

for probability linkage to minimize the

number of peirwise record comparisons that

must be carried out. We chose to adopt a

modified version of the New York State

Identification and Intelligence System

(NYSIIS) phonetic coding system for this

purpose. It is doubtful if CAMLIS could be

operated on a coat-effective basis without

the use of a phonetic name coding system

with the superior performance characteristics of NYSIIS.

A modification of the weighting algorithm

for the two-population Fellegi-Sunter model

ia necessary to compensate for the inestimable parameters.

Component error probabilities can be estimat~d from the ’’batched set” produced by

firet atage or deterministic linkage.

In this presentation, I will focus primarily on

points (C) end (D) above, i.e., on our approach to

the estimation of the parameters required by the

two-population Fellegi-Sunter weighting algorithm.

256

The Estimation of Relative Frequency Parameters

In CAMLIS application, a user file, which we

‘enote as ‘ile ‘A’ is linked to a California State

mortality file, which we denote as file LB. Since

the characteristics of most user files are significantly different from those of the California

mortality file, the two-population model is

obviously called for. However, many of the parameters required by the two-population model, e.g.,

are inestimable.

‘~~~~~~inized

We therefore carethe expressiona for the two

probability distribution functions to determine

whether a simplification was possible. We first

made the Observation that the characteristics of

the user file are always subsets of the characteristics of the mortality file; we alao observed

that, for those components that are independent

of mortality, pA “ P

. A~~:

These observations

resulted in the elimm Ion of the PA term from

the weighting algorithm and served to Justify the

use of relative frequencies derived only from the

mortality files. Since these relative frequencies

can change over time, files have been developed

which contain the necessary relative frequencies

at five-year intervals; CAMLIS procedure retrieve

them as necessary.

The component for which the assumption ie not

tenable is tdrth year; an entirely different

approach to might computation for the birth year

component has, therefore, teen developed.

The Estimation of Component Error Probabilities

Within the context of a mortality clearance

system, it is not possible to derive separate

estimates of component error probabilities for

files LA and LB; there is just, not enough information available. We therefore made the simplifying assumption that the corresponding component

error probabilities in the two files were identical, i.e., we aasume that:

e=e= A ‘B

Estimates of e and eT are derived from the

matched record-pairs produced by first atage

deterministic linkage. To eliminate spurious

matches, we require a high concordance among the

identifying elements on the two files that are not

incorporated into the match key.

The basic algorithm that we utilize to

calculate agreement configuration weights ia

therefore:

‘(Jj) = pjA(l-e)2(l-eT)

u(~j) = pjApj~

W(]j) = 10g[Zloj)/u(lJ)l

= log[Pj~l (l-.e)2(l-e~)]

IV. CONCLUSION

The Fellegi-Sunter model requires an assumption

regarding the independence of the components of

the comparison vector; this assumption is

frequently a major concern in linkage applications. It is nqt my intention to minimize the

importance of this assumption. The real concern,

however, must be the extent to which violationa of
this assumption affect the reeulte produced by the

model.

(A) The component of the comparison vector

should be carefully chosen. Only one of

several highly dependent components should

be incorporated into the model.

(B) Although it is possible to correct for the

effect of dependence, for moderately

dependent components, these efforts are

hardly ever worth the small gain in precision that can be realized.

(C) Me have done a great deal of difference

analysis. Our conclusion is that the estimated component error probabilities and

relative frequencies must differ considerably from the appropriate values to significantly affect the computed weights.

(D) For matches that achieve a linkage weight

significantly greater than the upper

threshold value, a bias in the weight is

obviously of no consequence. Similarly,

for matches that achieve a linkage weight

significantly below the lower threshold

value, a bias in the weight is alao of no

consequence. The vast majority of recordpairs achieve either very low or very high

linkage weights.

(E) Record-pairs which achieve a linkage weight

between the lower and upper threshold

values are subject to manual review. Since

record-pairs fall into this category

because they either contain ambiguous or

aparae identifying information, it is

extremely doubtful whether they would

differ significantly if the weighta were

computed according to a more precise model.

In any case, comparable results could be

obtained by redefining the upper and lower

threshold values.

The major advantage of probability linkage is

that it permits a meaningful ranking of matched

record-pairs. The ranking makes it possible to

focus review efforts on the comparisons which have

been assigned borderline weights. It can readily

be shown that the gain achieved by verifying the

probability linkage decisions above a certain

threshold value and below a certain threshold

value is negligible.

Our experience with the Fellegi-Sunter

probability linkage criteria has been uniformly

favorable. It is our considered opinion, however,

that probabilistic linkage and deterministic

linkage are best utilized as complimentary procedures and that both are necessary to achieve

optimum results.

REFERENCES

[1] Fellegi, 1., and Sunter, A: (1969) “A Theory

for Record Linkage,lt Journal of the

American Statistical Association, 64, 1183-

1210.

257
DERIVING LABOR TURNOVER RATES FROM ADMINISTRATIVE RECORDS

MalcolmS.Cohen,Universityof Michigan

,

U.S. nonagricultural establishmentswillhireworkers new

to theirfirmsan eathmted 64 milliontimesduring1985.

These hiring transactions probably will involve only 12-16

million workers who changed their primary jobs.

An econometric model was constructed using administrative records from Social Security files, and estimates

of new hires were made by industry, state, age, race, and

sex. When this study was done, Social Security records

were available only through the mid- 1970s. Wage records

used in the administration of the unemployment insurance

system were available in sixteen states to verify the accuracy of the econometric estimates. Because wage

records were available only for sixteen states, and because

of differences in state laws and data processing procedures,

wage records could not be used for obtaining national estimates,

Organizationally, this paper is divided into two main sections. In the first, the methodology employed is described.

The second presents examples of the various results, as well

as some general comments about the usefulness of these

administrative records.

METHODOLOGY

Social Security data from a one-percent Sam Ie of a

continuous work history file for the period 1971- Y 6 were

used to construct labor turnover measures. Instructions for

using the methodology were given to three government

agencies, who then did the matching and provided tabulations for different years. These agencies were the New

York Department of Labor, the Social Securit Administration, and the Bureau of Economic Analysis. Trl e provisions

of the 1976 tax reform act require the Internal Revenue

Service to screen the data for possible confidentiality disclosures prior to release, All analyses of social security

records were from tabulations provided by the government

agencies. No Social Security data were released on individual workers or firms.

Employee records were matched with employer records.

If a worker’s identification number appeared in a firm’s file in

a given quarter, but dld not appear in the file in the previous

quarter, the worker was classified as an accession to the

firm [1]. If a worker classified as an accession did not work

for the firm for the prior four quarters, that worker was

classified as a new hire. The decision to use four quarters

as a determining factor was somewhat arbitrary. That period

of time was chosen because it was long enough to identify

workers who return to a firm seasonally, although it would

not exclude workers who may have worked for a firm

sometime in the more distant past. The higher degree of accuracy that might be attained by matching records several

years back, however, was not considered great enough to

justify the substantial increase in cost of matching data for

more than four quarters [21.

Itis also possible to generate other turnover measures

using the pattern of employment within the firm. For example, if a worker is present in a given quarter and absent in

the next quarter, this is a separation. If a worker is a new

hire who continues to work for a period of, say, an additional two quarters, this is a permanent new hire. If a

worker is an accession (not employed in previous quarter)

who did work for the firm sometime in the previous four

quarters, this is a recall. If a worker is an accession and

separation in the same quarter, this is a short-term accession. Various turnover measures were developed based on

these definitions.

Data were constructed for new hires from quatierly Social Security records from the second quarter of 1972 to

the second quarter of 1975. A special tabulation for 1975-

76 was used for special analyses but not included in the

quarterly analyses used to generate current estimates.

A model was developed to predict new hires. The

model’s derivation begins with a tautology:

(1) AE = NH + Recalls- Quits- Layoffs- OS

where AE is change in employment; NH is new hires; and OS

ia other separations.

From this we obtain:

(2) NH= AE-Z

where Z = Recalls - Quits - Layoffs - OS

To obtain rates, both series were divided by E. It was

assumed that the unemployment rate would be a good proxy

for Z. It was assumed that there was a negative correlation

between Z and the unemployment rate.

When the equation was estimated, data from the Bureau

of Labor Statistics (BLS) 790 series were used for employment, and data from the monthly Current Population Survey

were used for unemployment rates and seasonal dummy

variables. The final equation was

(3} NHRt = a. + a, %AEt + a2URt_, +

+ c@l + a4S2+a5S3 + 016D+El

where NHR is the new hke rate; %AE k the percentage

change in BLS 790 employment; UR is the unemployment

rate; S, ,S2 and S3 are seasonal dummies fOrthefirsthree

quarters of the year; D is 1 in the first quarter of 1974; and

E, is a random term.

The dummy variable was used because of a data error in

the first quarter of 1974 in the data provided. The coefficient a, is expected to be positive, tile U2 is predicted to

be negative. The equations were estimated for each atate

with a total of thirteen observations. The results of the

model for fiscal 1975 were simulated to determine goodness of fit.

Figure 1 provides the %AE and UR~_1 parameters,the . .

proportion of variation explained by the model (R2), actual

new hire rate, and percent error in the forecast for all 50

states. All parameterssignificant at the .05 levelare indicatedby an asterisk.

One of thedifficulties withthismodel isthatdataforthe

dependentvariablecannotbe obtainedfrom SocialSecurity

databeyond 1977 on a quarterlybaais.Onlyannualnew hire

ratescan be computed. These can only be obtained by special arrangements with the Internal Revenue Service and the

Social Security Administration. To verify the model in

selected states, however, wage records were obtained using

similar concepts for workers covered by unemployment insurance. These data can be generated quarterly on a current

basis in wage records states. Over 40 states are wage

records states. Special arrangements must be made,

however, in each state to obtain these data The arrangements require considerable data processing to match

workers and firms over at least four quarters.

Our estimates were compared with the wage records

data in sixteen states. The results of the comparisons are

shown in Figure 2. The errors are generally relatively small

except in Florida. Here, however, the Florida data provided

were probably more prone to error than our estimates. The

significantly lower repotied new hires in Florida probably

represents an undercount in the state’s processing. The

*

259
otherstates.

We simulatedour model snd obtained new hire estimates

for 1975-85 [3].

RESULTS

Figure 3 shows the predicted number of new hires from

1975 through 1985 using our model. Fi ure 4 illustrates the

five states with the Iar est number o ? new hires. These

states accounted for 4 !!!% of all new hires in the United

States. Converting the new hires into rates, Figure 5 shows

the parts of the United States with the highest and lowest

ratea. The higheat rates are weat of the Mississippi. A

prominent exception is Florida.

It is also possible to compare new hire rates by industry.

Figures 6 and 7 ahow the industries with the highest and

loweat rates, respectively.

In 1985 it is unlikely that social services would be among

the high new hire rate industries. This reflects than es in

government priorities over the decade. It is pro % able,

however, that the other industries are high and low turnover

industries in 1985.

Individuals versus Transactions

One of the difficulties in interpreting our measures is

reconciling the incredibly high turnover(e.g.,80% in 1985)

with our knowledge of how often workers change jobs.

The number of turnovertransactionsincludeinstances where

one worker changed jobs more than once, so the total does

not reflect the actual number of workers who changed jobs.

Thus, when turnover is expressed as a percentage of

employment, the result should not be interpreted as the percentage of workers who changed jobs. To gain some insight into reconciling this apparent dilemma, we developed

some special tabulations from 1975-76 Social Security

files, Firat we computed an annualized 84% new hire rate

for 1976 by multiplying the rate obtained in the second

quarter of 1976 by 4. This is certainly comparable to the

rates we had been obtaining for other years. A different

analysis was carried out where workers were assigned to

their primary jobs, where they earned the most money

during 1976. Only 16% of the workers were new hires in

thek primary jobs, based on the second quarter of 1976.

Some of these workers could have accounted for several

new hire transactions. Similarly, workers who were not new

hires in their primary jobs could be new hires in secondary

jobs. Thus, we estimated that of the 64 million new hires,

about 14 million workers were new hires in their primary

jobs. In another quarter we estimated a ratio which would

suggest that slightly under 16 million workers were new

hires in their primary jobs. An estimate of 12-16 million

seemed appropriate due to the limited number of quarters

on which we could base our ratio.

was the average number of employers for whom employees

worked in different industries. We assigned workers to the

employer from whom they received the majority of their

earnings and tabulated the number of different employers.

Four nonagricultural industries--heavy construction contractors, water transportation, eating and drinking places,

and motion pictures--had an average of two or more

employers per’ worker. Water transportation (Iongshore)

averaged 2.5 emplo ers per worker. The industries with an

average of 1.25 or 7ewer employers (with at least 100,000

persons in the industry) included primary metals, communic~lons, and public Wllities.

Areas for Further Research

The information obtained from Social Security records

and state unemployment insurance records represent about

the only currently comprehensive source of labor turnover

data. Our model permits obtaining current estimates from

these data. It would be useful to tabulate annual Social

Securit files to determine labor turnover from more recent

Social 1 ecurlty fdes. It would also be useful to forecast the

turnover rates by industry, age, and sex. The 1975-76

special tabulations by person and transaction provide

detailed characteristics by state, SMSA, industry, age, wage

class, sex, and race. Additional analyses of these data

remain to be carried out, as well as additional analyses of

separations and short-term new hires. Finall

cient forecast estimates can be made by com k“mmg ‘ore cross- ‘ffi

section and time-series turnover data.

NOTES AND REFERENCES

[1] A worker’s identification number smears in the file if

the worker had wages greater th& zero in a given

quarter.

[21

[31

Using California wage records from the Unemployment Insurance system, the California Employment

Development Division did a test of how many fewer

new hires there would be if seven auarters were used

as a cut-off instead of four, and fo~nd only about 2%

fewer new hires. (Glen Siebert, Employment Service

Potential: Indicators of Labor Market Activity, pp. 48-

Sacramento, CA Employment Development

Department, 1977.)

For a more complete description of the simulation

methodology, see Malcolm S. Cohen and Arthur

R. Schwartz. “A New Hires Model for the Private Nonfarm Economy,” Economic Outlook for 1984, Department of Econom[cs, Unwerslty of

bor, 1984.

Mlchlgan, Ann Ar260
Figure 1. New HireRates by State,Fiscal1975,

% Error,R2, Selected Coefficients

State

Alabama

Alaska

Arizona

Arkansas

California

Colorado

Connecticut

Delaware

D.C.

Florida

Georgia

Hawaii

Idaho

Illinois

Indiana

Iowa

Kansas

Kentucky

Louisiana

Maine

Maryland

Massachusetts

Michigan

Minnesota

Mississippi

Missouri

Montana

Nebraska

Nevada

New Hampshir~

New Jersey

New Mexico

New York

N. Carolina

N. Dakota

Ohio

Oklahoma

Oregon

Pennsylvania

Rhode Island

S. Carolina

S. Dakota

Tennessee

Texas

Utah

Vermont

Virginia

Washington

W. Virginia

Wisconsin

Wyoming

1975

New

Hire

Rate

19,1

42.0

24.9

22.4

23.4

28.3

15.0

15.9

20.8

26.3

20.2

20.9

26.3

16.8

15.5

18.7

23.1

17.7

26.3

18.2

18,2

16.5

14.5

17.3

19.5

18.2

23.5

20,6

33.2

17.5

17.1

28.3

15.7

16.9

22.2

15.0

24.8

23.3

13,9

17.8

17.6

\;::

27.1

23.9

18.0

18.0

22.4

15,7

14.8

33.4

I975

Y.

~rror

-.3

5.2

.3

-::

1.6

-::

-3.5

-1.3

-1.1

.9

.1

-:;

:?

-1.2

1.7

-3.0

-,1

-1.9

-4.1

-.1

.2

-1::

1,7

-.5

-2.0

-.1

-2.3

-1.7

-1.5

2.0

-.3

-.1

1.1

.2

-1.9

-1.9

-2.4

-.6

-.3

.0

-::

-2:<

-.1

4.4

R2

.943

.941

.978

.966

.930

.951

.984

.828

.822

.973

.982

.819

.898

.988

.992

.951

.944

.980

.890

.943

.982

.976

.935

.958

.938

.989

;:%:

.975

.917

.978

.916

.959

.970

.902

.996

.944

.925

.980

.960

.918

.968

.978

.977

.967

.821

.970

.953

.964

.988

.899

%E

51.94

165.85*

148.44*

.44::

97:2*

97.25*

-64.85

89.90

178.70*

11 8,40*

122,97

68.38

11 1.27*

83.49*

25.81

63.74

107.40*

-15.77

105.95

162.01*

126.06*

73.53*

62.99

96.48*

99.74*

191 .26*

74.97

165.36*

135.78*

121.4W

103.08

109,77*

112.58*

229.05*

91 .35*

13144

103.60

134.31*

72.75*

69.73*

133.96*

::.:;*

109:57

161.11

107.94*

14 1.46*

145.31*

72.78M

21.54

%E = percentage change inemployment

URLAG = unemdovment rateintxevious auarter

URLAG

-1 .5*

2.34

- 1.65*

::.;?

-2:75*

-1.03*

-3.25*

-1.49

-2.2%

-~:g

-.52

-1.19*

- 1.56*

-1.61*

-1.33

-1.07*

-1.37

::::

-.8 1*

-1.48*

-1 .30*

-1.36

-1.13*

-.20

-.86

- 1.42*

-2.02*

-1 .20*

-1.61*

-1.16*

-2.03*

.72

-1.33*

-1.08

-1.22

-.96*

- 1.84*

-1.77*

-.50

-1.38*

-1 .57*

-1.20

-.18

-1 .66*

::;;

-1.39*

-1,22

= coeff’icie’nt significantat the .05 ievel

;= 13 for each state

261
State

Arkansas

Pennsylvania

South Dakota

Idaho

Cal ifornia

North Dakota

North Carolina

Nevada

South Carolina

#da i ne

Illinois

New Mexico

Missouri

Iowa

Mississippi

Florida

Period

Fiscal 1979

Fiscal 1976

Fiscal 1979

Fiscal 1980

Fiscal 1981

Fiscal 1976

Fiscal 1976

Fiscal 1977

Fiscal 1978

Fiscal 1979

Fiscal 1976

1979 - 4th Q.

Fiscal 1976

Fiscal 1979

Fiscal 1980

Fiscal 1981

1979 - lst-3rd Q.

1981 2nd-4th Q.

Fiscal 1978

1979 3rd-4th Q.

Fiscal 1979

Fiscal 1980

1979 -3rd-4th Q.

Calendar 1981

Fiscal 1981

i981 4th Q.

Calendar 1980

Calendar 1981

New Hires

Reported

by State

Employment

Agencies

583.990

2,051,553

177,433

f42,795

f34, 109

238,989

6,142,625

6,625,804

7,523,644

8,366,534

147,081

392,663

309, 100

452,679

464,348

438,880

611,324

550,619

263, f75

1.436,475

4f0,927

378,288

718,946

1,073,31+

587,016

101,921

2,673,019

2,918,487

262

Predicted

New Hires

603,500

2,147,100

155,800

137,500

142,900

241,000

5,796.000

6,506.800

7,640,400

8,226,400

144,300

370,300

298,300

476,800

466,600

477,600

627,700

522,900

268,900

4,593,500

412,000

386,200

670,400

1,204,900

582,500

107.400

3,790,500

3,729,700

%

Difference

+3.34

+4.66

-12.19

-3.70

+6 .57

+0.84

-5.64

-1.80

+f.5!5

-1.67

-1.88

-5.7f

-3.48

+5.32

+o.48

+8.95

+2 .68

-5.03

+2.17

+10.93

+0. 26

+2. 10

-6.75

+12.26

-0.77

+5.40

+41.81

+27.80
Figure 3. Number of New Hires In the private Nonfarm EconoIuy by State

(annual totals In thousands)

State

A 1abama

Alaska

Arizona

Arkansae

Caltfornfa

Colorado

Connecticut

O.c.

Delaware

Flor!da

Georg 1a

Hawa 1i

Idaho

Illlnois

Indtane

1owe

Kansas

Kentucky

Louisiana

Maine

Maryland

Massachusetts

Michigan

Mtnnesota

Miss4ssiPP

M issour 1

Montana

Nebraska

Nevada

New Hampsh

New dersey

New Mexfco

New York

North Care’

North Oako<

Ohio

Ok Iahoma

Oregon

Pennsvlvan

re

ina

a

a

Rhode- Island

South Carollna

South Oakota

Tennessee

Texas

Utah

Vermont

Virginia

Washington

West Virginia

Wlsconaln

Wyoming

U.S. Total

1975 I f976

610.9 693.3

178.8 112.3

514.5 624.0

378.8 443.0

5219.2 6059.2

656.3 813.2

530.9 653.1

183.9 164.2

126.4 182.4

2006.5 2567.0

103$.3 1226.7

179,6 205.6

207.4 250.0

2195.1 2718.2

876.1 1080.2

485.4 547.4

507.6 564.0

563.8 647.9

868.4 +019.4

201.8 24i.O

774.1 859.4

1181.1 $416.4

1377.7 ~639.4

713.2 823.4

403.6 460.4

947.7 ~096.5

167.2 213.5

3f8.7 366.4

255.1 319.4

153.5 208.2

1396.3 1648.9

269.6 312.5

3211.6 3568.6

1035.5 1225.3

134.8 t40.5

1702.0 2077.8

632.1 715.4

562.8 661.6

1664.6 2214.4

187.4 223.7

501.3 594.0

119.1 139.9

838.4 975.2

3369.7 3886.5

287.7 337.3

96.3 !13.2

848.0 *014.2

828.6 952.7

26’7.3 288.8

702.2 030.5

123.9 147.2

82794.9 50296.0

f977

765.6

92.8

735.7

498.7

6811.6

951.6

719.2

t74.4

203.2

2968.8

1388.1

219.9

261.4

2813.5

1192.6

602.0

612.7

733.1

*092.7

247.6

981:1

1535.9

1862.6

905.2

517.6

~201.8

208.6

381.3

380.8

236.8

t793.3

358.1

3809.7

t377.6

~38.7

2324.2

775.4

744.8

2330.8

244.6

649.8

*47.9

1091,6

4228,4

366,9

123,7

1~24.8

1034.8

307.0

922.1

~66.3

;5356.0

1978 I 1979 I i980

878.3 895.3

*O2 .6 109.2

903.1 1005.0

580.6 606.2

7838.4 8294.0

1148.8 1242.7

829.0 852.6

196.3 205.8

243.4 259.2

3614.6 3884.4

1667.6 1720.3

263.0 268.2

289.9 282.7

3178.2 3172.0

1393.0 1351.4

691.4 718.5

694.4 726.4

826.1 785.0

1239.0 1308.0

278,0 277.4

lllf.2 fo77 .0

1707, * 1768.3

2~~0.8 2036.3

4065.5 ~i23.6

566.2 577.7

1347,0 1366.0

24t.4 219.7

4i8.9 44~.6

473.4 488.6

277.1 292.2

2038.4 2069.0

399.3 4*4.6

4285,6 4391.2

1622.5 1707.0

*60.2 t6i.8

2632.2 2622.1

904,6 933. t

839.5 884.7

27i7.4 265i.9

279.3 286.8

764.0 797.9

159.8 155.1

1231.3 1223.3

4909.2 5327.6

425.1 434.8

136.9 133.7

1308.3 1378.2

~174.8 ~212.2

337.4 353. !

fo75. 1 1121.9

193.0 209.8

13768.0 65824.0

808.1

llt.7

891.5

534.0

7770.4

f140.8

795.4

187.0

220.3

3790.5

~547. 1

254.5

260.9

2826.4

ilj5.6

633.8

655.,0

689.9

1298.7

261.7

1008. ~

~697.5

~676.4

993.8

507.2

i184.6

2Qf.2

399.7

459.4

255.4

19+7.8

378.9

4072.8

174f.9

14*.I

2204.4

942.4

750.3

2285.8

258.4

719.9

134.2

fo79 .2

5266.0

395.6

125.3

1234.8

1085.9

324.6

932.2

210.6

60108.0

1981 I 1982

770.0 620.8

126.8 136.2

889.8 730.0

503.7 392.4

7760.8 6700.8

1087.3 888.8

757.8 642.0

180.8 147.8

208.1 140.3

3729.7 3104.6

+437.8 1143,7

238.6 213.0

260.3 247:2

2639.7 2074.3

1078.8 799.8

580.4 453.0

646.4 532.3

659.4 541.6

1295.5 1167.1

247.4 222.8

976.8 872.3

1652.0 1422.0

f574.2 +177.4

950.0 766.8

501.5 406.8

1204.9 iot9.6

224.6 183.8

394.3 336.2

470.8 400.0

253.0 f83.3

f878.5 1572.4

384.8 334.4

4015.6 3356.4

f378.6 1027.8

f59.9 f45.9

2i52.4 ~653.7

972.8 820.1

697.0 592.8

2289.0 1628.0

244.0 188.8

684.2 536.7

~41.2 f22.7

t072.2 869.2

5359.2 4772,0

402.6 350.8

f26.O f23,0

1151.6 953.5

1072.0 1026,9

299.3 259.6

898. ~ 683.0

212.2 192,7

38904.0 48876.0

1983

598.7

171.3

789.2

364.2

6743.6

913.8

654.6

151.3

138.5

2983.2

1223.9

221.4

274.5

2241.8

844.7

405,5

521.9

633,7

1084.5

237,4

927.6

1525.1

1292, 1

766.2

429,4

959.4

200.5

317.7

442.6

202.5

1628.0

338.0

3296.5

1072,5

~71.8

1595,4

836.3

625.4

1840.2

i88.5

508.5

i4f.o

850. E

4376.8

359.1

143.0

912.0

1156.8

237.7

667.8

189.0

19396.C

$984 I 1985

744.9 829.9

162.5 172.3

921.3 10+9.3

472.8 541.5

8001.6 8532.8

1190.4 1371.7

781.2 862.5

174.2 186.5

183.7 211.3

3778.2 4162.8

1464.5 i634.2

265.2 278.5

290.2 303.1

2632.6 2761.8

1062.0 lf53.4

518.4 596.0

623.2 680.3

687.9 739.6

~240.2 9369.5

267.8 272.5

1007.8 1053.0

~7i7.6 *812.5

i574.6 ~728.Q

958.9 fo74 .3

514.0 558.6

1158.4 1251,6

24i.8 249.0

381.7 406.1

575.5 637.0

256.6 278,6

~887.O 2041,0

407.4 454,4

3719.8 40~4.8

i366. 1 1512,6

185.3 t90.o

2062.7 2247.7

975.2 1072.2

748.8 8*3.8

2118.2 227i.6

236.0 265.1

654.4 725.6

155.8 163.8

1063.3 li35.4

5!32.4 5760.4

434.4 475.6

149.4 152.7

~172.3 ~303.f

*313.9 1364.2

272.8 294.8

856.0 973.9

222.8 235.9

58984.0 64196.0

263
l%#lW040’sWOiO&Wl b Highest IULmbWof

CAL TEX FLA NY ILL

IITotal

ElPermanent

Source Institute of Laborand Industrial Relations,Universityof Michigan,November 1983

Ftgure5. ProjectedQuarterlyNewHireRates,1984.

Qusrtorly Nat HireSatas A

(Mewhlr.spor100q“loyee$)J

L

15- 17

IY

18- 19 E

Lessthsn15

m \

Q

a

20-25

•ill

~r”t.rthsn25 ~ .

y

w

Instituteof Laborand IndustrialReletions,Unlversltyof tlichigan.

Moveaber1983.

264
Fi ure 6. Industries with Highest New Hire Rates,

18 75 2nd Quarter

50

45

40

35

30

w

s

; 25

2

20

15

10

5

0 —

83

11- !l!l73 15-17 70

Two-digit industry code

c1New hire rate

H

Permanent new hire rate

83 - Socialservices

73 - Businessservices

15-17 - Construction

58

70 - Hoteland otherlodging

58 - Eatingand drinkingestablishments

Source: Instituteof Labor and IndustrialRelations,Universityof Michigan

265
Fi ure 7. Industries with Lowest New FlkeRates,

1i!75 2nd Quarter

I

10

9

8

7

6

w

‘4

3

2

1

0 k

33 48-49 26 15 35-38 60 82

Two-digitindustrycode

II New hirerate

N

Permanent new hirerate

33 - Primarymetalmanufacturing

48-49 - Communicationsandpublicutilities

26 - Papermanufacturing

45 - Airtransportation

35-38 - Machin~~y+ transportation + instrument

manufacturing

60 - 8anking

82 - Educationalservices

Source: Instituteof Labor and IndustrialRelations,Universityof Michigan

266

!
DISCUSSION

Norman J. Johnson, U.S. Bureau of the Census

I would like to present my discussion of

these three papers in terms of points which we

have encountered in an application of matching

from our project. I have been working on

developing the data base for The National

Longitudinal Mortality Study (NLMS). This

study is being conducted jointly by the

National Heart, Lung, and Blood Institute, the

National Center for Health Statistics and the

U.S. Census .Bureau. The primary objectives of

the NLMS are to analyze socioeconomic, demographic and occupational differentials in

mortality within the United States. A major

interest of our analysis will be to compare

survival rates of different subsets of the

cohorts.

The study population consists of eight

cohorts of selected Census sa~les. Deaths in

this population are identified through periodic

matching to the National Death Index (NDI), the

index discussed in the first paper by Mr.

Patterson. As pointed out in that presentation, in terms of number of records submitted

for matching, our project is a major user of

the National Death Index. The National

Longitudinal Mortality Study currently consists

of approximately 1 million records from eight

cohorts. One match has been made to the NDI,

which at the time consisted of approximately 6

million records. We intend to conduct followUP matches approximately every two years.

The process we used to obtain the final

matched records was completed in two steps.

First, our files were matched to the NDI using

the NCHS criteria. Then, an extensive screening was made of the resulting match using some

of the methodologies discussed in presentations

given earlier in these sessions to determine

the final true match status. This second step

involved both computer and manual matching.

Our approach in the ccmputer matching phase was

similar to that used in the CAMLIS project of

Mr. Arellano, the presenter of the second paper

of this section. A link was made deterministically for all matches in which there was an

exact agreement on social security nunber.

Records not matched deterministically were then

matched probabilistically using a modified

Newcombe model. Weights for this model were

estimated from a subsample of records frc+nthe

NCHS match which had been reviewed manually to

establish correct match status. Three categories of records from the probabilistic match

resulted: true, false and questionable matches.

Questionable matches were decided on the basis

of a manual review. This process and the final

results have been schematically diagramed in

Figure 1. From the initial one million

records, approximately 12,900 links occurred.

The information in the figure also indicates

the substantial difference in the true match

rate between the deterministic and the

probabilistic steps.

Figure 1. -- National Longitudinal Mortality Study

1 Million Survey Records

~

NDI MATCH (6!lillion Records)

55,000 Hits

Removed 15,000 Invalids - Oate of Death Preceded

Pate of Survey

—Removed 500 Alias Duplicates

11,100

DETERMINISTIC

39,?11 FINAL HITS

28,166

(SSN Agrees) PROBABILISTIC

/ \

10,500 1,911 1,300

Sure True Questionable Sure False

24,go@

Sure True Questionable Sure False

150 450 850

True False True False

k

. .

s12,911 True Hits -

.

267
PATTERSON AND BILGRAD

As I mentioned in my introduction, our project ,is a major user of the National Death

Index. Deaths in our cohorts are determined by

linking our records to records in this Index.

The NDI matching algorittm is, in a sense,

deterministic. It uses combinations of five

major variables in seven criteria to determine

a link. These criteria are soon to be expanded

to twelve. A link is made if any one of the

seven criteria is satisfied. As other studies

continue to match using this index, the NDI may

wish to incorporate some probabilistic components into their matching procedure based on

the experience of their users. Results frcnn

our project may be helpful in this regard.

Five major categories of users were summarized in the presentation. The major users

identified are in health-related fields. In

many health studies, analysis is done by

comparing survival of cohorts, as is the case

in our study. Rare events are often of

interest and small counts may be greatly

affected by match rates. For this reason, in

our study, we feel that matching algorithms

should put emphasis on detecting true matches,

with willingness to manually review more

questionable matches, in order to rule out

false positives. The additional criteria made

available in the new NCHS matching algorithm

are a step in the right direction. The expanded criteria will generate more true links

as well as more false positives.

The paper presents results of studies to

measure the improvements in the match rate to

the NDI due to the replacement of the Soundex

Code for matching of names by the NYSIIS code.

If the NCHS studies of the effects of this

change are true, that is, 18 percent fewer true

matches and 31 percent fewer false matches

could be expected, then, in view of the comments which I made earlier, the Soundex Code

would be preferable to us.

ARELLANO

I will focus my discussion on the three

points mentioned in the conclusion sectfon of

the paper. The paper deals with the use of the

Fellegi-Sunter approach in the CAMLIS project

to link user files to death certificates from

the state of California. The ffrst point

discussed concerns the potential for making

estimates of error terms in the Fellegi-Sunter

model. The estimation of error terms is a

m~”or difficulty encountered in application of

the theory. In some applications, making simplifying assumptions is the only way to obtain

estimates of errors. The similarity of the

CAMLIS study and the National Longitudinal

Mortality Study may enable us to exchange estimated parameter values once they are obtained.

The conclusion on the robustness of erro

probability estimates is important and poten

tially very useful. This quality of th

estimates would allow the use of approximat

values without great risk of poor matching

results and pennit a more frequent borrowing of

parameter values from other studies. A nic

collection of results in the literature demon

strating this robustness would be very useful.

The third point covered in the conclusio

deals with the effects of bias. We hav

observed a positive bias in our scoring al

gorithm. It would be helpful for us to know i

the CAMLIS project has identified any consis

tent bias in their procedure. If so, what

explanation do they have for it?

COHEN

The findings of this particular study are

based on the results of a match of two files

performed by a Government agency. The match

was based on an apparently deterministic match

procedure using a certain identificatio

number, The provider of such match result

should advise clients of error rates and

nonmatch results of similar studies. Error

rates of such matches should be required as

part of publications and presentations in order

to give the reader a chance to determine if any

biases have resulted due to the matching procedure. This is similar to documenting which

computer and software were used when publishing

papers based on computer simulation. In this

paper, matching determines the study and data

base. What is the error rate in the identification number in both files? Errors in

deterministic match variables are more important than in probabilistic match variables.

The paper does compare the finding of this

study with those of other sources to demonstrate that the match was effective.

The question of what impact effective matching algorithms have on the confidentiality of

person records was mentioned in the paper. The

law provides specific statements on this

subject. Some confidentiality problems were

discussed in an earlier session. By linking

data from several sources, individual records

can be identified more easily. In the case of

data collection at the Census Bureau, there is

an additional concern. The Bureau is a passive

collector of data. Cooperation of the respondent is of crucial importance in obtaining

reliable information. As the public becomes

aware of our ability to link records from

several Governmental agencies, response rates

to our questionnaires may decrease, become

biased, and possibly inaccurate due to the fear

of person-record identification. This is in

spite of the potential to provide more beneficial information than would exist without the

linked records.

268
ON MATCHING WITH PERSONAL NAMES

J. T. Kagawa, Cancer Research Center of Hawaii

M.P. Mi, Urdveraity of Hawaii, Honolulu

In the record linkage process, personal

names are important matching criteria for

comparing documents to identify information

belonging to the same individual or family. The

discriminating power of the surname, given name,

and middle name for linkage varies depending on

the frequencies of various possible

configurations in the population. Although the

total number of possible configurations of

personal names is extremely large, the

distribution of these configurations are not

uniform,

Due to the many people of different

nationalities in Hawaii, the name structure has

become very diverse and therefore, offers a good

opportunity to study the name configurations

that are available in the population. Higratory

waves of contract laborers and others seeking

new opportunities introduced many new names to

Hawaii. Often times, names written in Chinese

or Japanese characters had to be phonetically

translated and Anglicized by insnigration

officers who had little or no knowledge of these

languages. This process created further

heterogeneity and inconsistencies within names.

It is not uncomnon to find two or more different

names derived from the same character or to find

that one surname was actually derived from two

completely different characters. Names were

also shortened or modified if they were too

difficult to pronounce.

In an attempt to develop an optimal strategic approach for computerized linkage of various

documentary sources, studies are being conducted

to elucidate the variation in personal names in

the population. Some pertinent questions to be

answered are: 1) how many possible configurations for surname, given name, and middle initiala there are in each racial group? 2) how

are these configurations distributed in the

population? and 3) is there any evidence of

time trends in these distributions or name

patterns? Preliminary results from the analysis

of the 1942-43 llawaii Population Registration

are presented in this report.

HATERIALSAND HETHODS

The Population Registration was conducted

in Hawaii during 1942-1943 under martial law.

There were a total of 439,601 residents

registered and fingerprinted. Eight major

racial groups were selected including Caucasian,

Hawaiian, Portuguese, Chinese, Filipino,

Japanese, Puerto Rican, and Korean. The

description of each of these racial groups in

Hawaii was given previously by Adams (1937), and

Lind (1955),

Recorded configurations for surname, given

name and middle intials were tabulated

separately by sex and race directly from the

1942-1943 population. For each of the eight

racial groups, the name configurations were

grouped into four types based on the relative

frequency in the registration file. The first

type was for unique configurations. The next

type was for configurations with a relative

frequency less than 0.1 percent. The third type

was for configurations of fairly frequent

appearance equal to or greater than 0.1 percent

but less that 1 percent. Lastly, any

configuration with a relative frequency of 1

percent or greater waa considered in the fourth

group. Since the number of configurations was

tabulated directly from the data, which were

subject to errors in reporting and recording,

possible errors could have been included.

Errors could have occurred by insertion,

substitution, deletion, and switching of one or

more alphabetic letters and such an alteration

could or could not be a valid configuration. It

was therefore assumed for this analysis that

most errors are made accidently, presumably at

random, and the altered configuration should be

unique,

The relative frequency for each of the

configurations for surname, first name, and

middle initials was calculated. The relative

frequency of the i th configuration is

pi=mi/H, where n is the total number of

individuals in the population and mi the

number of individuals having the ith

configuration. The probability that two

individuals randomly sampled from the population

would match on the ith configuration is Pi2.

This also approximates the probability of a

chance match for the ith configuration when two

documentary sources of vital events from the

population are brought together for linkage.

The sum of these probabilities over all

configurations, that is xPi2, is the

probability of a chance match on any

configuration for a given criterion. Therefore,

the greater the total probability, the less

discriminating is the linkage criterion among

individuals.

RESULTS AND DISCUSSION

Table 1 gives the number of males and

females in each racial group. These groups

represented 83 percent of the total population

in 1942. The Japanese group was the largest,

accounting for 37 percent, and larger than any

other two groups combined. The Caucasian group

ranked second, followed by the Filipino,

Portuguese, Chinese, Hawaiian, Puerto Rican, and

Korean. These groups and outcrosses among these

groups have contributed to the ethnic diversity

of Hawaii’s present population.

The surname distributions are shown in

Table 2. Data on females were not used because

of the possible inclusion of their married

surname. The total number of surnames varied

greatly from one race to another. There were

only 241 configurations in the Korean group,

269
while the Filipino group

times more configurations.

names in the Filipino

relative frequency of 1

had approximately 60

There were no common

group based on the

percent or greater.

There were a total of only five common names

representing only a very small proportion of

individuals in the Caucasian, Hawaiian, and

Japanese groups. Conversely, a large number of

individuals shared more than 12 common names in

the Korean and Chinese groups. The total

probability of chance match also differed

markedly among the eight racial groups. The

probability of match between two individuals

randomly selected from the population was

approximately 6 in 10,000 for the Filipinos as

compared to the estimate of 850 in 10,000 for

the Koreans. In the Korean group, about

one-half of the subpopulation shared four common

surnames, namely: Kim (22.4%), Lee (15.2%),

Park (6.8%), and Chung (4.5%). A high

probability equal to 293 in 10,000 was also

found for the Chinese group. There were 25

common surnames shared by 68 percent of the

Chinese population. The most consnon Chinese

surnames being Wong (8.1%), Lee (6.3%), Chung

(5.2%), Ching (5.1%), and Chm (5.3%).

The distribution of the given name for each

racial group is shown in Table 3. The ratio of

the number of surname configurations to the

number of given names varied from race to race.

For the Caucasian, Portuguese, and Hawaiian

groups, there were a greater number of surname

configurations than given names. This

relationship was completely reversed for the

Chinese and Koreans. The Japanese and Puerto

Rican groups had approximately the same number

of surnames and given names. As shown in the

table, there were very few common given names.

However, these common names accounted

collectively for a significant portion of each

of the subpopulations, For males, the

percentage of the population sharing common

names was 65 for the Portuguese, 62 for the

Hawaiian, 49 for the Puerto Rican, and 46 for

the Caucasian. Among the females, the

percentage estimates were lower, varying from 25

to 43. In the Chinese, Japanese, and Korean

groups the common given names for males and

females were of Western origin. Yoshiko, being

a common given name of Japanese origin among the

Japanese females was the only exception. As

shown with surnames, the probability of chance

match for the given name as a matching criterion

also varied from race to race. The highest

value was 323 in 10,000 for the Portuguese males

and the lowest was 33 in 10,000 for the Japanese

females. The Portuguese and Hawaiians showed

the highest probabilities of chance match for

both the male and female given names.

The possibility of time trends of selecting

given names was also tested” based on the 1942

population file. The recorded given names were

tabulated by sex and age for each of the eight

racial groups. The age groups were 0-19, 20-49

and 50-99. Except for native Hawaiians,

individuals with birth years between 1843-1892

were mainly those whO immigrated to the

islands. The other two age groups were

comprised of a mixture of later arriving

insnigrants and individuals born in Hawaii. A

given name was determined popular if the

relative frequency was 1.0 percent or greater

of the total number of individuals in each

race. The distributions based on age groups

also showed variations among the different

racial groups.

The majority of the given names of the

oldest age groups were the names from their

native country. With the influence of Western

culture, the given names of the younger age

groups showed the trend towards adopting the

popular English names of the times. It was also

observed that the names in the 20-49 age group

of the Japanese continued to be largely

Japanese. Although still of Japanese origin,

the names were quite distinguishable from those

of the older generation. Also the selection of

Spanish names for the Filipino group prevailed

over the three age groups. The popular English

male given names among the racial groups

remained unchanged throughout the years. The

popular female names showed more distinctive

periods of rise and decline, which may be

attributed to the influence of literary

characters and famous people.

Two middle initials were recorded for

individuals registered in the 1942 population

file. The middle initials distributions are

shown in Table 4. The blank configuration

represented 44 percent in the males and 37

percent in the females of the eight racial

groups analyzed. The blank response indicated

either missing information or a valid

configuration. Hany immigrants to Hawaii from

China, Japan, and Korea did not have middle

names. Out of the total possible configurations, the Chinese had the largest number of

different combinations for both males and

females. Hiddle initials for the Chinese and

Korean groups, mostly comprised of double

initials, generated a large number of possible

configurations. The frequency of uncommon

middle initials was reflected in the lower

probability of chance match for both of these

groups. The frequencies of common middle

initials were high in the remaining racial

groups.

The observed variations in name patterns

among the different racial groups in Hawaii

provides a unique testing ground for the study

of record linkage methodology, The analysis of

the 1942 Hawaii Population Registration file

showed that the distributions of the

configurations for surnames, given names, and

middle initials were definitely nonuniform.

Personal names for the different racial groups

maintained varying degrees of discriminating

power. A study is being planned to analyze the

neme structure of the present Hawaii

population. There has undoubtedly been many

more new names introduced into the population.

REFERENCES

Hawaii

2. Lind,

Honolu:

121.

1. Adams. R. 1937. Interracial Harriage in

New York: Hacttillan. pp. 353,

A.W. 1955. Hawaii’s People.

u: University of Hawaii Press. pp

270
Table 1. Size of Subpopulations

Sex Racial Groupsl

CAU I

PTG

I

HAW CHI FIL JAP

I

POR K13R

No. individuals

Males 34566 15790 7752 16118 40323 84298 4372 3786

Females 25988 15886 7321 12426 10946 78669 3385 2738

.

lCAU = Caucasian; PTG = Portuguese; HAW = Hawaiian; CHI = Chinese; FIL =

Filipino; JAP = Japanese; POR = Puerto Rican; KOR = Korean.

Table 2.--Distribution of Surnames by Racial Groups

Racial GrouDsl

Sex / Type2 CAU I

PTG HAW CHI I

FIL IJAP

I

POR I

KOR

Number of Configurations

.

Males

Unique 8548

Rare 4658

Fair 79

Common 1

Al1 13286

866 896 240 8960 1111 553 101

546 943 205 5341 3831 199 48

167 231 76 73 192 157 74

16 1 25 0 3 15 18

1595 2071 546 14374 5137 924 241

Zpi

Males

Common 0.01

Other 0.99

Males

Al1 0.07

0.29 0.01 0.69 0.00 0.03 0.32 0.72

0.71 0.99 0.31 1.00 0.97 0.68 0.28

Zpi2 X 10-2

0.83 0.15 2.93 0.06 0.20 1.20 8.50

lSee Table 1.

‘Unique = single count in the population; Rare = 0.01% - 0.09%; Fair =

0.10% - 0.99%; Common = 1% or greater.

271
Table 3.

Distribution of Given Names by Racial Groups

Racial Groupsl

Sex / Type2 CAU

I

PTG HAM CHI

I

FIL I

JAP I

POR

I

KOR

Number of Configurations

Males

Unique

Rare

Fair

Common

Al1

432

239

81

23

775

723

412

136

15

1286

0.65

0.35

0.32

0.68

619

217

71

21

928

3798

1054

99

15

4966

2971

1266

219

7

4463

4883

3795

153

9

8840

467

16!3

98

22

755

1664

253

86

14

2017

1512

905

113

20

2550

Females

Unique

Rare

Fair

Common

AlJ

1866

869

165

14

2914

680

235

116

19

1050

2030

570

137

17

2754

1486

656

206

5

2353

1963

1882

228

4

4077

393

108

138

18

657

730

99

147

13

989

Males

Common

Others

0.46

0.54

0.62

0.38

0.23

0.77

0.13

0.87

0.13

0.87

0.49

0.51

0.20

0.80

Females

Common

Others

0.25

0.75

0.43

0.57

0.24

0.76

0.09

0.91

0.04

0.96

0.36

0.64

0.23

0.77

2pi2 X 10-2

Males, all types

Females, all types

1.69

0.77

3.23 2.82 0.51 0.49 0.40

1.80 1.59 0.57 0.40 0.33

1.96

1.39

0.43

0.71

1

CAU = Caucasian; PTG = Portuguese; HAW = Hawaiian; CHI = Chinese; FIL =

Filipino; JAP = Japanese; POR = Puerto Rican; KOR = Korean.

2Unique = single count in the population; Rare = 0.01% - 0.09%; Fair =

0.10% - 0.99%; Conrnon= 1% or greater.

272
Table 4.--Distribution of Middle Initials by Racial Groups

Racial Groupsl

Sex / Typez CAU I

PTG HAW

I

CHI

I

FIL

I

JAP

I

POR KOR

Number of Configurations

Males

Unique

Rare

Fair

Commmon

Al1

Females

Unique

Rare

Fair

Common

Al1

Males

Blanks

Common

Others

Females

B1anks

Commmon

Others

122

134

1

20

277

118

107

3

20

248

0.17

0.81

0.02

0.14

0.83

0.03

64

22

1;

107

84

59

7

15

165

0.39

0.58

0.03

0.30

0.64

0.06

50

22

13

11

96

47

37

16

9

109

Epi

0.38

0.55

0.07

0.23

0.70

0.07

72

219

120

8

419

91

179

137

18

425

0.46

0.10

0.44

0.20

0.32

0.48

Zpi2 X 10-2

96 52 15

24 8 2

10 7

1;

144 ;: ::

96 80 18

31 78 2

7 11

17 12 1:

151 181 42

0.34 0.60 0.54

0.63 0.36 0.43

0.03 0.04 0.03

0.39 0.49 0.43

0.57 0.45 0.52

0.04 0.06 0.05

73

59

92

222

73

29

89

20

211

0.61

0.06

0.33

0.31

0.39

0.30

Males

Blanks

Common & Others

Al 1

Females

Blanks

Common & Others

Al1

2.83 15.35 14.67 21.16 11.57 35.36 28.60 37.13

4.12 2.35 10.46 0.28 2.92 1.60 1.54 0.19

6.95 17.70 25.13 21.44 14.49 36.96 30.14 37.32

1.81 9.12 5.25 3.81 15.34 23.79 18.30 9.89

5.25 3.54 14.88 0.96 2.36 2.12 2.69 1.02

7.06 12.66 20.13 4.77 17.70 25.91 20.99 10.91

lCAU = Caucasian; PTG = Portuguese; HAW = Hawaiian; CHI = Chinese; FIL =

Filipino; JAP = Japanese; POR = Puerto Rican; KOR = Korean.

2Unique = single count in the population; Rare = 0.01% - 0.09%; Fair =

0.10% - 0.99%; Common= 1% or greater.

273
SURNAME BLOCKING FOR RECORD LINKAGE

F. Quiaoit, Cancer Research Center of Hawaii, and

M.P. Ni, University of Hawaii, Honolulu

.

.

.

%

In the linkage between two documentary

sources, each record from one source is compared

wi”th all the records in the other source. For

one-file linkage involving a single source, each

record is compared with all other records except

itself. In either case, the number of such

pair-muse comparisons becomes extremely large

even if the size of the documentary source is

moderate. The fact that only a small fraction

of these comparisons are meaningful emphasizes

the need for the grouping of records based on

one or more selected items of identifying information. This is known as blocking. Once

blocks are formed, the comparison of records is

only made between the two corresponding blocks

for two-file linkage or within the block for

one-file linkage.

In principle, any identifier may be used as a

blocking criterion. Surname is often selected

for this purpose. Blocking may be made on the

whole or part of the surname configuration.

The use of a phonetic code on the surname for

blocking has become popular in many applications. The objective of the present study was

to evaluate the performance of several blocking

methods based on prevalent name patterns in various racial groups in a multi-ethnic population,

and to test the effects of blocking on linked

pairs in which one or both records had known

reporting or recording errors in the surname

field.

MATERIALS AND METHODS

Data on surnames from the conplete 1942-43

Population Registration in Hawaii were used.

There were a total of 439,601 individuals

registered and fingerprinted under martial

1aw. Eight major racial groups were selected

including Caucasian, Portuguese, Hawaiian,

Chinese, Filipino, Japanese, Puerto Rican, and

Korean. All recorded surname configurations

for male subjects were analyzed in the present

study. Two methods, namely: the New York State

Identification and Intelligence System (NYSIIS)

and the Russell’s Soundex system were chosen to

pre-code surnames phonetically. Under each

method, records were blocked with the same code.

These two systems were compared specifically to

the other five methods of blocking, namely, by

the whole surname, first character of surname,

first two, three, or four characters of surname, respectively. Criteria such as the total

number of blocks formed, distribution of block

size, and surname information in matching were

used for evaluation.

A set of known linked record pairs was obtained from the linkage project between the

1942 Population Registration file and the death

file (1942-79) in Hawaii. It consisted of all

male subjects aged 60 and over in the 1942

population who died during the 38-year period

from 1942 to 1979. A total of 11,367 linked

pairs were established by computer as well as

by manual search (Mi et al,, 1983). Pairs, in

which recorded surname and first name ,were

switched, were excluded. There were 672 pairs

with various error conditions in surname. The

concordance rate of each method, which is the

percentage of record pairs that were properly

placed in the same block regardless of these

errors, was used for comparison.

RESULTS AND DISCUSSION

The number of male subjects in the 1942 Population Registration is shown for each racial

group in Table 1. The total number of recorded

configurations for surname varied greatly among

racial groups ranging from only 241 in the

Korean group to 14,374 among the Filipino. The

average number of individuals possessing the

same surname varied from 2.6 for the Caucasian

group to 29.5 for Chinese men. The value for

each racial group was also the average block

size when blocking was based on the whole surname of twelve characters. Most of the surname

configurations wew unique, having only a

single representation in the population. These

unique configurations included rare spelling

variations, and errors in reporting and recording. When a part of the surname was used for

blocking, records having the same leading

characters in their surname fields were grouped

together. As shown in Table 1, the number of

blocks increased from an initial maximum of 26,

based on the first character of the surname,to

several hundreds or thousands using more leading characters for blocking. However, the

magnitude of increase was not linear for each

additional character used, and varied from one

race to another. The distribution of blocks by

size also changed. When the whole surname was

used for blocking, most blocks were small with

10 or less records. If blocking was based on

the first character of surname, the block size

increased tremendously. If MOre leading

characters were used, the number of records in

each block decreased as expected. The performance of the first four characters of surname

for blocking was comparable to the NYSIIS and

Soundex method in the percentage distribution

of blocks by size in all groups except the

Chinese and Koreans. The NYSIIS and Soundex

method produced a much higher percentage of

large blocks of over 50 records in the Cfiinese

and Korean groups. This was because almost all

the Chinese and Korean surnames were five characters or less in length.

It should be emphasized that block size is an

important consideration in the choice of a

blocking method for linkage. Since the number

of pair-wise comparisons is equal to the product of the size of two corresponding blocks in

two-file linkage and to the product of the

block size and block size minus one in one-file

275
linkage, a larger block size will greatly affect

the cost of a linkaqe.

The other criterion which deserves attention

is the loss “of surname information in matching

by blocking:-.Suppose that there is no blocking

and the whole documentary source or file is

used as a giant block for pair-wise comparison. The amount of information provided by

surname in’matching is approximately l-Zpi2

where pi is the relative frequency of the ith

surname configuration and Zpi = 1. The squared

term represents the probability of chance match

on the ith configuration. When summed over all

configurations, the squared term gives the

total probability of chance match in surname.

The exact probability of chance match is 1 -

Xpipi‘ in the two file linkage where pi’ is

the relative frequency of the ith configuration

in the second source. If all individuals have

the same surname, that is, pi = 1, every record

pair must agree on surname and the total probability of chance match reaches the maximum of

1. Under this special condition, surname

clearly provides no information. On the other

hand, if each individual record has a different

surname, the probability of chance match is

minimal and the amount of information provided

by surname reaches the maximum. When blocking

is made based on surname (a part or whole), the

newly structured block consists of records of

one or more surnames, each with the relative

frequency of pij,, the jth surname within the

itll block. The relative frequency of the ith

block is qi, and the probability of chance

match for records with the ith blocking criterion is qi2. The probability of chance match

on surname within newly structured blocks is

ZEpid2/Zqi2, and the amount of information of

surname in matching is estimated by 1 -

EZpij2/Zqi2. Suppose that the whole surname is

used for blocking. Because each block is

characterized by a different surname, obviOUSIY ZZpfd2/Zqf z = 1, therefore surname is no

longer informative and provides no discrimination among records within any block in which

pair-wise comparisons are made.

The average and maximum number of surnames

per block and the estimates of surname information in matching under various blocking

methods are given in Table 2, When blocking is

based on the first character, the amount of

surname information was generally high except

for the Korean group. The probability of

chance match on surname was estimated to be

0.085, the highest among the eight racial

groups studied (Kagawa and Mi, 1985). The

amount of information decreased rapidly, particularly in the Chinese group, as the number

of leading characters for blocking increased.

When blocking is based on the NYSIIS and

Soundex codes, the amount of information was

close to those estimates derived from the

blocking based on the first four characters in

several racial groups. These phonetic coding

methods seemed to be desirable especially for

the Chinese and Korean groups, but not for the

Japanese. The concordant rate was defined as

the percentage of total pairs in which both

members were blocked concordantly by a given

method. Table 3 gives the estimates of the

concordant rate for the four selected methods.

The rate over all racial groups was 56.7, 43.9,

56.4, and 64.9 pe~ent, respectively, for blocking based on the first three characters, first

four characters, NYSIIS code, and Soundex code

of surname. Both NYSIIS and Soundex methods

consistently produced a high concordant rate in

all racial groups. 8ecause Chinese and Korean

surnames are generally short (composed of three

to five characters), errors would have to occur

in the first few characters. It was anticipated that blocking based on the first three

and four characters would not be highly desirable. Among the 672 linked pairs, 176 linked

pairs were found to be concordant by all four

methods. Erroneous conditions at the end of

the surname were not detected even by the

modified NYSIIS system. There were 87, 106,

98, 86 and 119 record pairs in which errors

occurred in the first, second, third, fourth,

and between the fifth and eighth positions,

respectively. Therefore, it may be concluded

that in a population where spelling variations

or errors in rer)orting and recording usually

occur after the fourth position of the surname,

these four methods would perform equally well

for blocking. Othewise, NYSIIS and Soundex

should be more promising than methods which are

based on the use of leading characters.

REFERENCES

Mi, M.P., J.T. Kagawa, and M.E. Earle. 1983.

An operational approach to record linkage.

Meth. Inform. Med. 22:77-82.

Kagawa, J.T. and M.P. Mi. 1985. On matchinq

with personal names, CIP.269-273 in this volume.

276
Table 1. Block Characteristics by Methods

Item I

Racl al Groupsl

CAU I PTG I HAW I CHI I FIL I JAP I PUR I KOR

Number of

Male Subjects 34566 15970 7752 16118 40323 84298 4372 3786

Number of Blocks

Block Size

Distribution, %

- 10

1: - 50

51 - 100

101 - 500

501 - 1000

> 1000

Blocking by Complete Surname

13286 1595 2071 546 14374 5137 924 241

96.7 85.1 93.4 77.5 96.6 73.8 92.3 80.1

3.0 10.5 6.4 14.6 3.0 19.9 6.5 13.7

0.2 2.6 0.1 2.o oe2 ~Cl 008 4.6

0.1 1.6 0.0 5.5 001 3,1 o*4 ().8

0.0 0.2 0.0 0.0 0.2 0.0 0.8

0.0 0.0 0.0 ;:: 0.0 0.0 0.0 0.0

Average Size 3 10 30 3 5 16

Maximum Size 397 550 9; 1313 289 10;: 288 848

Blocking by First Character of Surname

Number of Blocks 26 26 23 24 26 25 24 22

Block Size

Distribution, %

1 - 10 3.9 11.5 17.4 12.5 3.9 16.0 8.3 31.8

11 - 50 3.9 19.2 26.1 12.5 3.9 4.0 25.0 27.3

51 - 100 3.9 3.9 21.7 3.9 8.0 9.1

101 - 500 15.4 15.4 17.4 4::: 23.1 12.0 5::: 18.2

501 - 1000 15.4 23.1 13.0 16;7 15.4 8.3 9.1

> 1000 57.7 26.9 4.4 12.5 50.0 5;:: 0.0 4.6

Average Size 1329 614 337 672 1551 3372 182 172

Maximum Size 3474 1922 4214 4157 4539 11229 811 1055

Blocking by First 2 Characters of Surname

Number of Blocks 280 155 142 113 232 178 144 82

Block Size

Distribution, %

- 10 34.3 36.1 62.0 39.8 35.8 32.6 58.3 65.9

1: - 50 21.8 26.4 24.7 27.4 17.2 18.0 24.3 15.9

- 100 10.0 12.3 4.2 12.1 10.1 9.7 12.2

1;; - 500 28.6 18.7 7.8 1::; 26.3 18.5 7.6 2.4

501 - 1000 5.0 5.8 0.7 3.5 4.7 6.7 0.o 3.7

> 1000 0.4 0.7 0.7 2.7 3.9 14.0 0.0 0.0

Average Size 123 103 54 143 174 474 30

Maximum Size 1008 1128 2869 4153 2809 6321 422 8;;

See note at the end of the table.

277
Table 1. Block Characteristics by Methods (Continued)

I

Racial Groupsl

Itern CAU

I

PTG

I

HAW

I

CHI FIL

I

JAP

I

PUR

I

KOR

Blocking by First 3 Characters of Surname

Number of Blocks 2212 655 491 354 1880 835

Block Size

Distribution, %

1 - 10 68.6 68.8 75.6 68.1 66.5 50.1

11 - 50 24.5 19.1 18.3 19.5 23.7 24.9

51 - 100 3.8 6.6 3.1 3.1 4.9 7.3

101 - 500 3.1 4.9 3.1 6.8 4.6 12.7

501 - 1000 ‘ 0.0 0.6 0.O z.5 o.? 2.9

~ 1000 0.0 0.0 0.0 0.9 0.0 2.2

471 179

77.1

14.5

5.6

1.7

1.1

0.0

21

849

229

79.0

14.9

4.4

0.9

0.9

0.0

17

848

89

68.5

14.6

10.1

4.5

2.3

0.0

43

965

84.1

12.3

2.3

1.3

0.0

0.0

Average Size 16 24 21 101

Maximum Size 471 575 4:; 13;; 740 3879 30:

Blocking by First 4 Characters of Surname

Number of Blocks 6941

Block Size

Distribution, %

1 - 10 90.6

11 - 50 8.2

51 - 100 0.9

101 - 500 0.3

501 - 1000 0.0

> 1000 0.0

Average Size 5

Maximum Size 401

1112 974 490 5719 1818 709

79.9 82.3 75.9

13.1 15.4 13.9

4.1 1.4 2.7

2.6 0.8 5.9

0.3 0.0 1.0

0.0 0.0 0.6

85.9

11.9

1.5

0.6

0.0

0.0

61.1

24.5

5.9

6.9

0.7

0.8

89.0

9.0

1.4

0.6

0.0

0.0

14 9 33

554 255 1322

46

3838

6

300

Blocking by NYSIIS

Number of Blocks

Block Size

Distribution, %

- 10

1: - 50

51 - 100

101 - 500

501 - 1000

> 1000

7293 1025 631 209 6526 1922 649

91.7

7.1

0.8

0.4

0.0

0.0

79.4

12.5

4.6

3.2

0.3

0.0

80.0

13.8

4.3

1.9

0.0

0.0

71.8

12.4

3.3

7.7

2.9

1.9

87.6

10.7

1.2

0.6

0.0

0.0

55.8

26.4

6.8

10.0

0.8

0.2

88.4

9.2

1.5

0.8

0.0

0.0

Average Size

Maximum Size

5

414

16

586

13

406

77

2311

6

366

44

1114 30:

See note at the end of the table.

278
Table 1. Block Characteristics by Methods (Continued)

laclal GroupsL

Itern CAU I

PTG HAW ICHI FIL I

JAP I

PUR I

KOR

Number of Blocks

.

Block Size

Distribution, %

- 10

1: - 50

51 - 100

101 - 500

501 - 1000

> 1000

Average Size

Maximum Size

2864

72.9

22.1

3.6

1.5

0.0

0.0

12

449

Blocking by Soundex

813 441 161

73.8 77.1 60.9

16.0 15.7 16,2

5.8 3.6 4.4

4.1 3.0 13.0

0.4 0.7 3.7

0.0 0.0 1.9

20 18 100

587 774 2275

2779 948 555 86

66.8 43.1 85.8 62.8

26.8 26.9 11.5 16.3

4.8 9.5 1.6 12.8

1.6 15.5 1.1 5.8

0.0 4.3 0.0 2.3

0.0 0.6 0.0 0.0

15 89 8 44

352 1395 300 885

.

lCAU = Caucasian; PTG = Portuguese; HAM = Hawaiian; CHI = Chinese; FIL =

.

Filipino; JAP = Japanese; PUR = Puerto Rican; KOR = Korean.

279
Table 2. Surname Characteristicswithin Blocks

Racial Groupsl

Blocking Criterion CAU I

PTG HAk/ CHI I

FIL IJAP I

PUR

I

I(OR

First character

First 2-characters

First 3-characters

First 4-characters

NYSIIS

Soundex

First character

First 2-characters

First 3-characters

First 4-characters

NYSIIS

Soundex

First character

First 2-characters

First 3-characters

First 4-characters

NYSIIS

Soundex

Average Number of Surnames Per Block

511 61 90 23 553 206

48 10 15 5 62 29

6 2 4 2 8 6

2 1 2 1 3 3

2 2 3 3 2 3

5 2 5 3 5 5

Maximum Number of Surnames Per Block

1407 184 961 1553 834

352 100 632 ;: 962 376

178 31 118 12 269 210

37 10 60 117 89

51 13 71 3; 52 70

68 16 136 24 74 71

Surname Information in Matching

0.99 0.89 0.99 0.81 0.99 0.98

0.94 0.70 0.99 0.70 0.97 0.94

0.75 0.32 0.93 0.20 0.85 0.84

0.40 0.14 0.78 0.07 0.57 0.79

0.48 0.17 0.90 0.57 0.46 0.43

0.64 0.20 0.95 0.54 0.61 0.64

39 11

6 3

2 2

1 1

1 1

2 2

113 31

48 22

23 23

10 10

9

15 15

0.$36 0.47

0.63 0.29

0.34 0.08

0.18 0.02

0.20 0.25

0.27 0.14

‘CAU = Caucasian; PTG = Portuguese; HAW = Hawaiian; CHI = Chinese; FIL =

Filipino; JAP = Japanese; PUR = Puerto Rican; KOR = Korean.

280
Table 3. Concordant Rate of Blocking

Racial Groupsl

Blocking Method Total 1

CAU J

HAN CHI FIL 1 JAP I

PUR I

KOR I OTH

Number of Linked Pairs with Errors in Surname

672 167 77 28 78 222 54 10 36

Concordant Rate (%)

First 3-characters 56.7 56.3 62.3 32.1 48.7 54.5 79.6 50.0 63.9

First 4-characters 43.9 50.3 52.0 14.3 32.1 41,4 59.3 20.0 44.4

NYSIIS 56.4 60.5 57.1 57.1 59.0 51.4 70.4 40.0 44.4

Soundex 64.9 66.5 53.3 71.4 71.8 65.3 75.9 50.0 44.4

‘CAU = Caucasian; PTG = Portuguese; HAW= Hawiian; CHI = Chinese; FIL =

Filipino; JAP = Japanese; PUR = Puerto Rican; KOR = Korean; OTH = All Others.

281
Section V:

Application

Case Studies II

<
1979 SOLE PROPRIETORSHIP EMPLOYMENT AND

Nick Greenia, Internal

I. BACKGROUND

As the result of an interagency agreement

between the Internal Revenue Service (IRS) and

the Small Business Administration (SBA), IRS

Statistics of Income (S01) Division is augmenting its tabulations of business financial

data (income statement, and balance sheet, when

possible) with two additional data items,

Payroll and employment, from e 10 ment tax

returns, Form 941 and Form 943. “lmpl’oymentis

also ta be used as an additional table

classifier. The Small Business Administration

(SBA) expects that the tabulations will prove

useful in the continuing development of its

Small Business Data Base in fulfillment of its

Congressional mandate (P.L. 96-302 Title IV) to

evaluate public policy and economic trends that

affect small businesses without thereby placing

any additional data collection burden on small

businesses [1].

To produce these enhanced data, SOI is

linking its perfected [2] sample files of

business information and tax records for

corporations (Form 1120 series), partnerships

(Form 1065), and sole proprietorships (Schedules

C, F, or Form 4835 appended to Form 1040) to

their respective Forms 941 (Employer’s Quarterly

Federal Tax Return) and/or Forms 943 (Employer’s

Annual Tax Return for Agricultural Employees) in

order to abstract employment and payroll from

the latter two types of records. The linkage is

effected through the Employer Identification

Number (EIN).

These studies commence with Tax Year 1979 and

will be repeated for all three types of business

entity for Tax Year 1982 to coincide with the

Economic Censuses. Thereafter, they will be

undertaken annually for corporations and

quinquennially for partnerships and sole

proprietorships [3].

For the Tax Year 1!?7? Sole Proprietorship

Employment and Payrol1 Study, the process

entailed attempting to (a) link the 108,335

business Schedules C and F and Forms 4835

appended to Forms 1040 on the SOI Individual

sample file to possible counterpart employment

and payroll records in the population files of

some 5 million Forms 941 and 943 for all types

of business entity; (b) resolve multiple matches

and mismatches for matched sole proprietorship/

employment and payroll records; and (c) reweight

for false unmatched sole proprietorship records.

II. SOURCE FILES

Each of the business employment and payroll

studies will add employment and payroll data to

the financial data already available from the

IRS SOI business statistics series by matching

SOI sample files of business income and tax

returns with the corresponding quarterly or

annual Employer’s Tax Returns reporting Federal

income tax withheld and Social Security (FICA)

taxes (Forms 941 and Forms 943).

PAYROLL: PROCESSING METHODOLOGY

Revenue Service

Processing for the 1979 Sole Proprietorship

Study consisted of linking by EIN sole proprietorship business records associated with the

SOI-perfected Tax Year 1979 Form 1040 sample

file [4] to Census-perfected extracts of their

corresponding Form 941 (Employer’s Quarterly

Federal Tax Return) and Form 943 (Employer’s

Annual Tax Return for Agricultural Employees)

records. Sole proprietorship business records

were appended to the sole proprietor’s Form 1040

and for this study were one of the following

three types:

(1) Schedule C (Profit or Loss from Business

or Profession),

~;{ ~~1f8~5(Fa(~a#c0~e;;: ‘x~n~oe~)’ and

Expenses and Summary of Gross Income from

Farming or Fishing).

File extracts containing EIN, payroll, and

employment were provided by Census for the

population of some 5 million Forms 941 and 943

(Census deleted Form 943 employment due to its

unreliability as a consequence of the March 12

reporting requirement, seasonality of farm

employment, and exclusion of certain employee

groups not under Social Security) for Calendar

Years 1978, 1979, and 1980. The

Census-perfected extracts of Form 941 and Form

943 data were themselves derived from tape

extracts originally produced on a contractual

basis by IRS (initial processor of the complete

data set for tax administration purposes) as

authorized by Internal Revenue Code section 6103

for Census as part of Census’ ongoing effort to

update annually its Standard Statistical Establishment List (5sEL).

Generally, problems of access to data were

minor for SOI since all source documents were

IRS-related and originally filed with IRS.

While data access posed little difficulty for

SOI, however, SBA could receive only tabulations

of aggregated data--no files of microdata

records--due to the restrictions IRS places on

the disclosure of confidential taxpayer data

under sections 7213 and 7431 of the Internal

Revenue Code.

III. MATCH/MERGE METHODOLOGY

Foremost among the challenges presented by

the 1979 Sole Proprietorship Study were those

relating to the matching variable itself, the

EIN, and the sole proprietorship’s filing period.

Each of these factors directly affected linking

procedures and strategies regarding the Form 941

and Form 943 data.

While the EIN was a required entry for a Form

4835 if Form 943 was filed, it was required for

a Schedule C or Schedule F if the sole proprietor had a Keogh plan (self-employed deferred

compensation plan) or was required to file an

employment (Form 941 or Form 943), excise, or

alcohol, tobacco, and firearms tax return.

Matters were complicated for Schedule C and

Schedule F, however, by the Keogh p?an provision

285
as follows. Prior to 1978, employers maintaining Keogh plans were required to have an EIN in

order to complete Form 5500-K (Annual Return/

Report of Employee Pension Benefit Plan for Sole

Proprietorships and Partnerships with Fewer than

100 Participants and At Least One OwnerEmployee), even if the only participants were

oWner-eInPloYees (sole proprietors and certain

partners). In 1978 and 1979, owner-employee

Keogh plans without common-law employee participants (i.e., with only owner-employee participants) were no longer required to file Form

5500-K, but Schedule C and Schedule F instructions for EIN completion still read as described

above; that is, Keogh plans without common-law

employees were not excluded explicitly. Of the

more than 650,000 Forms 5500-K filed for Plan

Year 1977, some 450,000 were for plans without

common-law employees. Therefore, while it is

unclear what the impact of such a situation was

for 1979 Schedules C and F, it is apparent that

the potential for problems in the 1979 Sole

Proprietorship Employment and Payroll

(false matches to Forms 941 and Forms 943~t;~

considerable.

The EIN potential problem was compounded by

the fact that while sole proprietorship Forms

941 and 943 were processed by IRS and posted by

EIN to the IRS Business Master File (the computer

data storage system from which the original Form

941 and Form 943 file extracts were produced for

Census processing/perfection), the sole proprietorship records (Schedules C and F, Form 4835)

were processed with the appropriate Forms 1040

and posted to the IRS Individual Master File

(IMF) by the Form 1040’s Social Security Number

(SSN). Little testing or perfection was performed for the sole proprietorship’s EIN, and

thus, the potential for false matches as well as

false non-matches--due to incorrect and even

missing EIN’s on the IMF side--was significant.

If the sole proprietorship’s EIN posed a

problem for the link operation, so did its filing

or accounting period. Since (a) no such item

existed on the business records themselves (it

was abstracted from the one Form 104C to which

multiple sole proprietorship records could be

appended), (b) a Form 1040 whose accounting

period ended in other than December was presumed

to have a full-year fiscal accounting period,

and (c) 98.6 percent of the 92,694,302 Forms

1040 processed for Tax Year 197? had Calendar

Year 1979 accounting periods, SOI decided that

part-year records and other possibly out-of-scope

records (e.g., certain prior-year returns) would

not be excluded from processing. Instead, the

assumption was made that all sole proprietorship

records should be treated as full-year calendar

1979 accounting period records. Accordingly,

significant savings of both time and money were

realized by disregarding the accounting period

from the S(!1 Form 1040 sample file and using

only the 1979/1980 Census Form 941/943 file for

this study (instead of both the 1978/1979 and

1979/1980 files, as was done for the 1979

Partnership Employment and Payroll Study).

Since EIN generally was required as an entry

on the business schedule only in the event of

payroll taxes (Forms 941 and 943) or a Keogh

plan, EIN-linkages could be contemplated for

just a subset of the sole proprietorship sample.

In fact, of the 108,335 Schedules C and F and

Forms 4835 on the SOI Sole Proprietorship sample

file, only 31,008 had EIN’s and, therefore, could

be viewed as potential initial matches with the

Forms 941 and 943. By type of record, the

sample counts were the following.

Form 4835:

Schedule F: 2,6;!

Schedule C: 28,356

IV. PROBLEMS AND RESOLUTIONS

Of the 31,008 records with EIN’s (see Figure

1), 24,153 matched on EIN with Forms 941 and/or

Forms 943 on the 1979/80 Census extract (EIN was

unique for each Form 941 or Form 943 but could

have been shared by a Form 941 and a Form 943).

Of these 24,153 matches, 4,503 were multiple

matches, meaning an SOI sole proprietorship

record matched to a Form 941 or Form 943

matching either another SOI sole proprietorship

record, an SOI partnership record, or an SOI

corporation record. Of the inter-business

entity (instead of intra-business entity)

multiple matches, 117 were for sole

proprietorships matching Forms 941/943 with

records on either the SOI Partnership sample

file or the SOI Corporation sample file.

Consequences would have been dire indeed had all

these multiple matches not been individually

reviewed (an operation to be treated as obligatory, given the size of the largest possible

sole proprietorship weight--over 2,000--and the

simply astronomical amounts of payroll, hundreds

of millions of dollars per Form 941 for a number

of cases, reported for what were probably large

corporations).

Figure 1. 1979 Sole Proprietorship Employment

and Payroll

Preliminary Unweigh@d Processing Counts

(Pre-Reweighting)

Number of Businesses

Itern (Schedule C and F, Form

4835)

Statistics of Income

Sample................. 108,335

Without EIN........... 77,327

With EIN.............. 31,008

Initially matched on

EIN to 1979/80 Form

941 and/or

Form 943............ 24,153

Initially unmatched

on EIN to 1979/80

Form 941 and/or

Form 943............ 6,855

All multiple matches were manually reviewed

using one-line record listings containing the

following data items: EIN; sole proprietorship

industry code; sole proprietorship code (to

distinguish between Schedules C and F and Form

4835); Form 1120/1065 code (to identify inter2B6
business multiple matches, but only those from

SOI sample files); sole proprietorship business

receipts, business deductions, and proxy payroll

(salaries and wages plus cost of labor); Form

941 calendar 1979 payroll; Form 941 calendar

1980 payroll; Form 943 calendar 1979 payroll;

and Form 943 calendar 1980 payroll.

At least two factors (other than the

questionability of the sole proprietorship’s

EIN) are responsible for exacerbating the

multiple match (as well as the false non-match)

situation. The first is the sole proprietorship/corporation “connection” and helps explain

at least some of the sole proprietorship/

corporation multiple matehes and mismatches.

Apparently, sometimes a corporation such as a

large department store will subcontract work to

a sole proprietorship, say, for aPPliance rePair

or upholstery cleaning, and the sole proprietorship will incorrectly report the corporation’s

EIN instead of its own. The second factor concerns multiple sole proprietorships run by the

same sole proprietor, even in different business

activities. The sole proprietor might legitimately file several different business returns--

each with the same EIN (when EIN is necessary)--

and either one Form 941 or Form 943 for all

businesses or one for each (also using only one

EIN). Regardless, IRS would end up processing

several business returns but only one consolidated (by either the proprietor or IRS) Form

941/943 containing all employment and payroll

data for the sole proprietor. This latter consideration turned out to be quite significant

due to the high number of “multiple matches”

which were of this variety.

Resolution of multiple matches was accomplished first by “transcribing to unmatched

status” sole proprietorship records with nonzero proxy payroll (the sum of salaries and

wages p?us cost of labor) which matched to a

Form 941 or Form 943 whose payroll was

egregiously greater than the sole proprietorship’s proxy payroll (often sole proprietorship/

corporation matehes probably). Second, the

assumption was made that for purposes of this

processing stage, records with zero proxy

payroll generally should become unmatched

records. Finally, within each group of both

like SSN’S and EIN’s (to ensure that ‘like” sole

proprietorships also belonged to the same sole

proprietor or Form 1040), the remaining matches

of sole proprietorship records with non-zero

proxy payroll were “perfected” by reapportioning

the Form 941/943 payroll and employment data

among the sole proprietorship records based on

their share of the like group’s total proxy

payroll. When possible, this reapportionment

scheme was applied according to the type of sole

proprietorship record best corresponding to the

Form 941 or Form 943. For example, if a Form

941 and a Form 943 matched a Schedule C and a

Schedule F, the Form 941 data were accorded to

the Schedule C and those of the Form 943 to the

Schedule F. If a Form 941 or a Form 943 matched

both a Schedule C and a Schedule F, the Form 941

or Form 943 was reapportioned among both

schedules.

Comparison listings were used after resolution to ensure that all problem matches had, in

fact, been remedied. Subsequent to multiple

match processing, the final stage in mismatch or

false match testing was performed: scrutiny and

resolution of matches in which Form 941 or Form

943 payroll exceeded the business record payroll

or proxy payroll by at least $1,000 (see Figure

2). Manual review of one-line listings for these

records identified only 45 matches worth retaining; the remainder were dispatched to unmatched

status via an algorithm which required Form 941/

943 payroll to be strictly less (no tolerance)

than the sole proprietorship’s business deductions (business deductions was chosen in case

proxy payroll had been reported or was “hidden”

in deduction items other than cost of labor and

slaries and wages) in order for the match to be

kept. (The tolerance was dropped for this resolution process due to the large weights observed

for a number of sole proprietorships and also

because business deductions was sometimes zero.)

Comparison listings were again used to verify

that no anomalies slipped through processing [5].

Figure 2. 1979 Sole Proprietorship Employment

and Payroll

Unweighed Match-Processing Counts

(Pre-Reweighting)

I

Sole Proprietorship Records

Category In? t~al EIN

Matches Retained Rejected

to Form

941/943 M;;ch Mg;ch

TOTAL........ 24,153 22,279 1,874

Multiple business record

matches..... 4,503 3,612 891

Form 941/943

payroll exceeded business deductions by

$1,OOO*. . . . 737 45 692

Records with

zero 1979

Form 941/943

employment

and payroll* 291 0 291

Other

matches..... 18,622 18,622 0

* NOTE: Matched records meeting this condition

but resolved as unmatched during other

processing stage are excluded from this

count.

The intent underlying both multiple match and

mismatch processing was that only matches with

almost certain probabilities of being “good” were

to remain as matches. That is, the assumption

was that possibly marginal matches were to be

treated during these processing phases as “truly

false” matches. The goal was to produce a solid

reweighing base of good matches so that

287
reweighing for false non-matches based on their

characteristics would be as accurate as possible.

It was thought that any marginal cases would be

more suitably accounted for later by those

characteristics which allied them more closely

with either true matches or true non-matches as

a result of reweighing analysis.

V. REWEIGHING

On a weighted basis, only 11.1 percent of the

12,329,982 sole proprietorships in the SOI 1979

population matched a Form 941/943 after resolution of multiple matches and mismatches. Since

82.3 percent of sole proprietorships did not have

an EIN and only 7.4 percent of all unmatched

records had EIN’s, however, this statistic is

not as discouraging as it might first appear.

In fact, the match rate was 63.0 percent when

only records with EIN’s are considered.

Final problem adjustments consisted of

reweighing for false non-matches [6], based on

analytical tables of matched and unmatched

frequencies classified by industry, Form 1040

adjusted gross income, business receipts, and

proxy payroll (cost of labor plus salaries and

wages). Unmatched frequencies were further

broken down according to whether sole proprietorship records were moth or without EIN, since

imputation factors might differ considerably for

these two sets.

Reweighing was more significant in terms of

impact for the 1979 Sole Proprietorship Study

than the 1979 Partnership Employment and Payroll

Study [7] largely due to the sole proprietorship

EIN problem (the EIN’s potential absence and

other complications as discussed above) and the

distribution of unmatched proxy payroll. Of the

~~~i4 billion reported ~mg;j:y payroll by all

proprietorship (matched and

unmatched), only $28.8 billion or 67.9 percent

was accounted for by matched records. If proxy

payroll is a good indicator of “true matchability” (97.7 percent of matched records also

reported proxy payroll), it seemed that a significant portion of true matches remained to be

“found,” given that 27.6 percent of unmatched

records with EIN’s and 22.2 percent of unmatched

records without EIN’s also reported proxy payrol1. Of course, to the extent that proxy payroll consists of contract labor or other “nontrue” payroll components, it might not be such a

good indicator for certain sole proprietorships--

especially for proprietorships filing Schedules

F but not requfred to file Form 943 for

employees not under Social Security (see Data

Limitations below). Imputation for “missing”

data rather than reweighing for false

matches might be more the issue then.

Reweighing was based upon a file of

defined differently in terms of matched

unmatched status from that of the 1979 Par’

ship Employment and Payroll Study. For the

Partnership Study, a matched record was clef’

Drimarilv for reasons of simplicity

nondata

and

ner1979

ned,

and

expedien~y (it was also the first “of th; business employment and payroll studies to be

undertaken and, consequently, the first to

encounter new obstacles and the attendant

deadlines and cost restrictions in surmounting

them), as any Form 1065 matching on EIN with a

1978, 1979, or 1980 Form 941 or Form 943 containing either employment or payroll for 1978,

1979, or 1980. This definition unfortunately

allowed into tabulations some records with both

zero employment and zero payroll for 1979, since

they contained data for either 1978 or 1980.

While this definition is being discontinued for

future business employment and payroll studies,

it also was not used for the 1979 Sole Proprietorship Study, even though a file containin two

years (1979 and 1980) of Census Form 94Y/943

data was used for matching purposes. In fact,

only records matching on EIN to a 1979 Form 941

or Form 943 containing employment or payroll

data are considered matches--and these criteria

must have been met even after multiple match and

mismatch problem resolution. That is, records

initially “matched” but later transformed to

unmatched status as a result of resolution

processing are not considered matched for

reweighing and table purposes.

VI. DATA LIMITATIONS

Following are qualifications necessary to

better understand the data in terms of conceptual limitations posed by slightly different

terminologies employed across return forms as

well as differences in data reporting requirements:

(a) Sole proprietorship proxy payroll was

defined as the sum of salaries and wages plus

cost of labor in order to be consistent with the

definition of proxy payroll used for the 1979

Partnership Employment and Payroll Study. While

this item was used primarily for purposes of

comparison with Form 941/943 payroll during

multiple match and mismatch processing,

definitional differences between these two

versions of payroll also warrant aggregate

comparisons to ascertain what effect not only

actual but also perceived differences had on the

data.

Salaries and wages and cost of labor were

available from Schedule C as the items wages

(form instructions required the reporting of

both salaries and wages) and cost of labor but

from Schedule F and Form 4835 only as the item

labor hired. All of these items should have

excluded compensation of the proprietor, but

since the Sole Proprietorship Study required

gross payroll, they included amounts deducted

for jobs or WIN credits,

Overstatement of proxy payroll may have

occurred due to inclusion of payments for

contract labor, such as certain janitorial,

secretarial, or agricultural employees not

reportable on Forms 941/943 but deducted on the

business schedule, probably under cost of labor.

On the other hand, understatement of payroll may

have occurred if payroll were reported as commissions, legal and professional fees, repairs,

other costs of sales and operations, or other

business deductions. Additionally, for certain

businesses in the Retail and Services industry

groups, tip income would have been reportable on

Form 941 but not claimed as a deduction on the

Schedule C. Finally, a definition of payroll

conforming more closely to the concept of total

compensation might also contain contributions to

both pension and profit-sharing plans and

288
employee benefit programs (such as health and

prepaid dental insurance), though the proPrfetor’s contributions to the latter were not specifically excluded by Schedule C instructions.

(b) For payroll, Form 941 appears to have

required as reportable compensation virtually

what was required in the counterpart Form W-2

and Form W-3 items; i.e., income which was

taxable but not necessarily tax “withholdable.”

Form 943 required the reporting of all taxable

cash wages to employees subject to FICA taxes,

but excluded th, value of non-cash items, such

as food and lodging--potentially significant

components of compensation for agricultural?

employees and also reportable on Schedule F as a

deduction under labor hired. A further limitation was that reportable taxable wages were only

required for workers under Social Security (thus,

excluding many non-resident alien agricultural

workers) and were not to exceed the FICA maximum,

a little more than $22,000 for 1979 and for purposes of this study probably not too detrimental.

In addition to taxable wages, Form 941

required the reporting of all tips and other

compensation to employees even if income or FICA

taxes were not withheld and specifically

excluded only annuities, supplemental unemployment compensation benefits, and gambling winnings--even if income taxes were withheld on

these.

(c) While the Form 941/943 March 12 reporting

date for employment was an obvious data limitation, it was exacerbated by the possibility of

employment double-counting due to employees who

worked two or more jobs with different employers

filing different employment tax returns.

(d) While testing was conducted to identify

possible mismatches in which Form 941/943 payroll was abnormally high, none was attempted

(primarily due to time and other cost constraints) for possible false matches or mismatches in which it was too low. For the 1982

study, it might be possible to establish acceptable ranges for payroll/proxy payroll ratios by

industry, geography, and certain size classes,

but any such operation should be excessively

circumspect, given “hidden” proxy payroll, as

well as the problem with EIN’s previously discussed. (For other recommended enhancements,

see also section 10, Greenia, Nick, Match Group

Case Study #00002, “1979 Sole Proprietorship

Employment and Payroll.”)

ACKNOWLEOGMENTS

For their thoughtful review of material in

this report, the author thanks Tom Jabine

(National Acade~ of Sciences), Carol Utter

(Bureau of Labor Statistics), and Doug Sater

(Bureau of the Census). Appreciation is also

extended to Wendy Alvey and Beth Kilss for their

*

help in editing the manuscript and to Dawn

Nester and Rodney Turner for typing its many

drafts.

NOTES AND REFERENCES

[11

[21

[31

[41

[5]

[61

[7]

For further information on the Small

Business Data Base, see Kirchhoff, Bruce

A. and Hirschberg, David A., “Smal1

Business Data Base: Progress and

Financial Statement Dab” to Evaluate the

Status of Smal1 Business,” 1982

Proceedings: American Statist~

association. -= Section on Survev Research

- ;: an Ro.s~~ f ‘Paui-”a;d

------.-..

T 1

-i’Size of Employment in Stat~t~~~

of Income: A New Classifier.” 1982

Proceedings: American Sta~istTT5T

Association, Section on Survey Research

Methods.

~erfection essentially consisted of

testing and resolving obvious math errors

as well as data inconsistencies in each

file record. Errors could have been made

by the taxpayer or during a data

processing stage.

A more comprehensive treatment of small

business employment and payroll, forthcoming from David A. Hirschberg and Bruce

Phillips of SBA, will folow the conclusion of the Tax Year 1979 corporation and

sole proprietorship studies. Final tabulations for these two studies were

provided to SBA in July 1985.

For a detailed account of the sampling

scheme involved in selecting this sample,

as well as other information--including

tabulations--concerning this file, see

Statistics of Income--l979/8O, Sole

?%oprletorshlp Returns.

)or more details on the false match

resolution phase, see Problems and

Resolutions, Greenia, Nick, Match Group

Case Study #00002, “1979 Sole Proprietorship Employment and Payroll.”

Fora complete discussion of the reweighing process, including its assumptions,

see Day, Charles, “Imputation Methodology,

1979 Forms 1040/941/943 Link Study,” June

1985. This unpublished report is available upon request by writing to Director,

Statistics of Income Division, D:R:S,

Internal Revenue Service, 1111 Constitution Avenue, N.W., Washington, DC

20224.

See Greenia, Nick, Match Group Case Study

#00006, “1979 Partnership Employment and

Payroll.”

289
THE DEVELOPMENT OF

David Hirschbe~,

●

.

As part of its data base developmental

effort, the Office of Advocacy, Small Business

Administration (SBA), has developed a Master

Establishment List (MEL) with over 8.1 million

businesses. In creating the list, two

ccxmnerciallyavailable lists were merged. The

first, the Dun’s Market Identifier file,

contained over 4.6 million records; the second,

the Market Data Retrieval file--a “yellow-page”

listing--contained over 7 million records.

The MEL provides direct statistics on the

number and geographic distribution of America’s

small businesses. It also facilitates communication with the small business sector and is a

vital tool for conducting surveys and mailings

to selected industrial sectors regarding governmental policy.

This paper describes the development of the

Master Establishment List. First, some background is provided on existing Small business

files. Then the MEL is discussed, some of itS

uses are described and some on-going validation

efforts are mentioned. The paper concludes by

raising some of the policy implications of

concern to SBA.

BACKGROUND

Although major progress has been made, the

small business sector remains poorly documented

in the Federal statistical system. Most existing Federal statistical data and administrative

record sources are not adequate for assessing

the impact on small business in a variety of

policy analysis and decisioninaking areas. It

is interesting to note that of the 124 pages of

statistical tables appearing in the Economic

Report of the President, 1985, only one is

relevant to small business activity, “8usiness

Formation and Business Failures, 1940-84.” [11

(The source of this business formation and

THE MASTER ESTABLISHMENT LIST

Small Business Administration

business failure data is Dun and Bradstreet.)

Two other sources of information on business

formation are the Bureau of Economic Analysis

and the Internal Revenue Service. However,

there are obvious problems in using their data

as well. For example, the Index of Net Business

Formation, published by the Bureau ot Economic

Analysis, is 114.8 for 1983 (with 1967 = 100).

This growth level is sharply at variance with

the number of business tax returns reported by

IRS, as shown below. Furthermore, the number

of enterprises has increased from 3.3 million

in 1976 to 4.4 million in 1982.

The Small Business Administration, Office of

Advocacy’s Smal1 Business Data Base was

designed to provide more reliable information

on the scope and contribution of the small

business sector. This data base is drawn from

commercially available data and places little

additional paperwork burden on the business

conununity. It permits the maintenance of

confidentiality and provides policy-relevant

data.

The first project, which is now complete, was

the development of the United States Establishment and Enterprise Microdata (USEEM) files for

1976, 1978, 1980 and 1982. These files are

based on Dun and 8radstreet’s Market Identifier

(DMI) files, which are collected for credit and

insurance purposes. They have been edited,

cleaned and reformatted, and are the basic

centerpiece of the Small Business Data Base.

These four files contain information on

business organizations that reported business

activity in any one year. Each record which

identifies an establishment has the following

information: (1) Dun’s number--this is a

number assigned by Dun and Bradstreet that

uniquely identifies each establishment and can

be used to merge with prior-year files; (2)

geographic location -- city, county, SMSA,

state and zip code; (3) year business started;

(4) number of employees; (5) annual sales

volume; (6) Standard Industrial Classification

(SIC) code; (7) parent and headquarter’s city

and state; (8) Dun’s number of parent and

ultimate parent; (9) subsidiary indicator; (10)

status indicator -- single location, headquarters, establishment or branch; and, (11)

manufacturing indicator --- indicates if

manufacturing takes place at the location.

Table 1. IRS Business Tax Returns by Legal Form of Organization

(in millions)

Year Total Proprietorships Partnerships Corporations

1967 8.5 6.1 .9 1.5

1976 11.3 8.1 1.1 2.1

1982 14.6 10.2 1.5 2.9

Source: Statistics of Income Division, IRS.

291
The USEEM files now contain data for the

estimated 8 million business establishments

which existed during the period 1976-82. For

each year, annual files include approximately 5

million records. These records provide estimated employment and industry classification

for establishments and firms, the start date

(age), organizational statuS and geographic

data for each firm.

These USEEM files have been linked into a

longitudinal sample file, the Unitea States

Establishment Longitudinal Microdata File

(USELM), enabling researchers to follow the

same establishments over time. This is a

primary and necessary requirement to address

policy-relevant research issues. The 1984

files are currently being developed; they will

laterbe merged with the USELM 1976-82 files.

The second project involves working with Dun

and Bradstreet’s raw financial statement file

(FINSTAT). The FINSTAT file contains about

150,000 financial statements for 1975, but for

the past few years the number has iftCreaSedto

almost 500,000 per year. To preserve the confidentiality of cooperating companies, al1

identifying Information has been removed by Dun

and Bradstreet. Although the file includes the

major U.S. corporations, approximately 95 percent of the firms have fewer than 100 employees

and 74 percent have fewer than 20 employees.

By comparing these data With other sources, we

are beginning to resolve the question of how

well these data represent the small business

community.

Finally, a major effort is underway to have

data available on small business from the

various statistical and administrative agencies

of the Federal Government. Together with the

Internal Revenue Service (IRS), for examPle,

the Small Business Administration is supporting

an effort to link IRS’ business Statistics of

Income files for partnerships, proprietorshiPs

and corporations with that agency’s tax reports

of employment and payrolls. This overcomes:

significant shortcoming in the IRS files.

rich as they are for analytical purposes, there

is no employment reported on business tax returns. Other projects include organizing the

IRS Corporate Source Book [2] into machinereadable form and examining disclosure and

confidentiality issues, particularly as theY

relate to business data from IRS and Census

sources, so as to develop disclosure strategies

for the release of microdata (data on individual firms).

THE MASTER ESTABLISHMENT LIST (MEL)

A universe list of firms and establishments

is the core element of a statistical program.

The Bureau of the Census uses the annual IRS

business tax returns, combined with emPIOYer

withholding/social security reports and multiestablishment company surveys, to develop their

list of businesses with employees--the Standard

Statistical Establishment List (SSEL). Multiestablishment companies of the Company Organization Survey enable the SSEL data to provide

linkage between establishments and their Parent

firms. The total number of establishments in

the SSEL in 1977 was approximately 4.3 million,

compared with the 15.6 million business tax

returns. Most of this difference is made up of

firms without employees.

The Bureau of Labor Statistics (tlLS) also

Prepares lists of establishments or, more

correctly, tax units. Administrative records

from each of the State unemployment insurance

systems are compiled annually. Linkages between the establishments and their enterprises

are not available. Other agencies have developed lists to meet their needs as well. An

example is the Post Office/Survey Research

Center Sample of Nonhousehold Mailers.

Unfortunately, Advocacy cannot use the Census, IRS, or 8LS lists as the basis of its

sampling frame. By law, the information in

these sources cannot be disclosed. Therefore,

Advocacy undertook to develop a Master Establishment List based on merging two publicly

available private sources: (1) the Dun and

Bradstreet’s Market Identifier (DMI) file and

(2) a “yellow-page” listing from Market Oata

Retrieval, Inc. (MDR) for the year 1981. The

MDR file is compiled from 9 million entries,

including duplicates, in the nation’s telephone

directory yellow pages. The MDR covers many of

the establishments in the DMI file and also

many small establishments and persons who do

not have credit ratings.

Merging the DMI and MDR files involved a considerable effort, given the enormous size of

these files and the absence of unique identifiers. [3] About 3.5 million unduplicated

records in the MDR file were identified as not

having a matching record in the DMI file. The

resulting MEL file contains a total of 8.1

million firms and establishments for 1981. [4]

The coverage of the MEL is important. It is

useful to compare with comparable tabulations

of employment from the Census Bureau’s County

Business Patterns (CBP). Table 2 does this for

the DMI components of the Master Establishment

List.

The first two columns of Table 2 list the

number of establishments identified in the OMI

and CBP. As mentioned previously, the DMI file

covers all establishments with Dun and Bradstreet credit ratings. This includes a small

number of establishments with no employees, as

well as an undetermined number of small establishments with employees. In contrast, the CBP

includes only establishments with employees.

Given these coverage differences, it is noteworthy that there is a basic similarity in the

total number of establishments.

Several reasons exist for the differences by

industry, but they are difficult to quantify.

Discrepancies may result from differences in

industrial classification between the DMI and

the CBP. The extent to which the OMI file

includes firms with no employees, as well as

establishments which are no longer in business,

is not known.

Given these classification and coverage problemS, the employment estimates are remarkably

similar at the major industry division level,

as shown in Table 3. Total employment in the

DMI file is 6 percent less than that of BLS and

2 pe~ent more than that of C8P. For minin9,

contract construction, manufacturing, and services, the DMI reports slightly more employment

292

.-
!

Table Z. Establishment Counts by Major Industry Division:Dun’s Market

Identifier (DMI) and County Business Patterns (CBP), 1981

(Establishments in Thousands)

Ratio

Industry DMI CBP DMI/CBP

All Industries, Total 4,635 4,587 1.01

Agriculture, Forestry & Fishery 120 804 .15

Mining 42 359 .12

Construction 612 626 .98

Manufacturing 441 336 1.31

Transportation, Communications

& Public Utilities 182 162 1.12

Wholesale Trade & Retail Trade 1,846 1,887 .98

Finance, Insurance & Real Estate 372 387 .96

Services 1,019 1,445 .71

Note: Components may not add to total due to rounding.

Source: Tabulations from the DMI and County Business Patterns, U.S. Bureau of

the Census (selected years).

Table 3. Employment by Major Industry Division: Dun’s Market Identifier (DMI),

Bureau of Labor Statistics (BLS) and County Business Patterns (CBP), 1981

(Employment in Millions)

r

Ratio

Industry DMI BLS CBP

CBP/DMI BLS/DMI BLS/CBP

All Industries, Total 74.7 75.1 74.8 1.001 1.005 1.004

Agriculture, Forestry

& Fishery .8 NA .3 .38 NA NA

Mining 1.3 1.1 1.1 .85 .85 1.00

Construction 5.9 4.2 4.3 .73 .71 .98

Manufacturing 21.2 20.2 20.4 .96 .95 .99

Transportation,

Communications,

& Public IJtilities 4.1 5.2 4.6 1.12 1.27 1.13

Wholesale Trade

& Retail Trade 16.7 21.6 20.3 1.22 1.29 1.06

Finance, Insurance

& Real Estate 4.6 5.2 5.4 1.17 1.15 .98

Services 19.0 18.6 17.9 .94 .98 1.04

Note: Components may not add to total due to rounding.

Source: Preliminary Report on the Development of the Master Establishment List, 1982, Social and

Scientific Systems, Inc.

293
Table 4. Dun’s Market Identifier (DMI) and Market Data Retrieval (htlR)Files

as Components of the Master Establishment List, 1981

Number of Establishments in Thousands

Ratio

Industry DMI MDR MEL MDR/IIMI

All Industries, Total 4,635 3,488 8,123 .75

Agriculture, Forestry

& Fishery 120 49 169 .40

Mining 42 10 52 .25

Construction 612 215 828 .35

Manufacturing 442 82 524 .19

Transportation,

Conmnunications&

Public Utilities 182 84 267 .46

Wholesale Trade

& Retail Trade 1,846 1,054 2,900 .57

Finance, Insurance

& Real Estate 372 407 779 1.09

Services 1,019 1,577 2,595 1.54

Note: Components may not add to total due to rounding.

Soume: Preliminary Report on the Development of the Master Establismnem

List, 1982, Social and Scientific Systems, Inc.

than the CBP or BLS files. However, there is

significant undercoverage for wtiolesale and

retai1 trade; transportation, communications

and public utilities; and finance, insurance

and real estate.

Unfortunately, employment is not available

from the MOR file, but the number of establishments added to the DMI file is shown in Table

4. It was apparent from the detailed industry

tabulations that the added MOR firms were

mostly professionals, such as doctors and

lawyers, as well as taxi operators, truckers,

insurance agents, and real estate brokers --

businesses that generally do not use credit.

These sectors are basic to small business

aCtlVltY and it is important that they be

included in lists of small businesses.

In contrast to the 15 million tax returns

filed with IRS, the Master Establishment List

contains 8.1 million. firms and establishments.

It does not follow that there is a deficiency

in the MEL. Inspection of,the sales distribution reported in IRS’ proprietorship files

suggests that they include persons with other

occupations and do not truly reflect full-time

business activity. Of the 12.7 million proprietorship reports in 1980, almost half have

business receipts below $5,000.

The analysis of the DMI file and the business

units added by the MDR file indicate that, for

mOSt purposes for which the file will be used,

the MEL is representative of the full-time

business population with employees.

294

USES OF THE MEL

The Master Establishment List has been used

for a variety of purposes. Users studying

specific problems relating to small business

have requested that the Small Business Administration make specialized tabulations from the

MEL, draw samples based on those tabulations,

and provide mailing lists for the sample cases.

In some cases the requests have asked for firms

by industry and size for a specific State or

designated SMSAS or even particular counties.

Although some users have been concerned with

the broad spectrum of business units, other

users’ interests have been highly specialized.

An example of the use of the MEL to create a

specialized data base was its use in analyzing

the proposed legislation on enterprise zones.

Because the establishments in the MEL have addresses, it is possible to examine the existing

location of business activity in central cities

and non-central cities in relation to the proposed enterprise zones. Some measure of the

magnitude of potential costs and benefits of

the legislation can be obtained by analyzing

projected changes in business activity and employment.

In another application, using a three percent

sample of the MEL’s businesses, an OwnershiP

Characteristics Survey was initiated in January

of 1984. It asked respondents for the legal

form of ownership as well as for the sex, race

and veterans status of the business owner.
Summary results are available in the

the President on the State of Small

1985.” [5]

VALIDATION EFFORTS

“Report of

Business,

The exact matching of the 4.6 million DMI

records and the 9 million MDR records to produce 8.1 million Master Establishment List

records was considerably more successful than

might have been expected, and the resultin9 MEL

file has had wide use. As the tabulations of

MEL show, the DMI data were augmented in Precisely those areas where it was known that

coverage was incomplete (i.e., services and

trade). Although there are undoubtedly

additional small businesses that are without

Dun’s credit ratings and are not listed in the

yellow pages, it is not clear that further

efforts to extend the MEL would be worthwhile.

Validation studies have been carried out

analyzing the MEL’s coverage, consistency, and

completeness. One such study involved matching

the establishments in the area samples of the

University of Michigan’s Survey Research Center

with the establishments listed in source areas

in the Master Establishment List. Another

study is comparing State unemployment insurance

data with DMI files.

The former study revealed important differences in the MEL list and the list compiled

by Michigan. However, recent research has

indicated that these lists are subject to obsolescence. Turnoverlfis ,~gt~t one percent a

month; therefore, “ compiled for

different time periods are compared, a large

number of nonmatches should be expected. This

and other experience has snown that a lar9e

proportion of nonmatches occurs when business

lists are matched using different sources of

information. [6]

In the latter study, unemployment insurance

microdata files and DMI files were matched for

a recent time period for Texas and Pennsylvania. When the comparisons are completed,

they will yield information of considerable

value in evaluating the DMI file. It can be

noted that only about 40 percent of the firms

in the files were matched.

FEDERAL POLICY IMPLICATIONS

Using the January 1985 DMI and MDR files, an

updated MEL is being created. We are asking

support from the various statistical agencies

to provide resources to continue this effort,

to improve its quality and help make it generally available to the statistical community.

There is a clear need throughout the Federal

establishment for a consistent and reliable

business universe frame for a variety of research and sampling purposes. Each Federal

agency now operates its own system, virtually

oblivious to the activities and requirements of

others. Employment differences between systems

are explained as due to classification, reporting and coverage procedures. In this time of

considerable budgetary restraint, cooperation

in the development of databases such as the MEL

is absolutely necessary.

ACKNOWLEDGMENTS

The development of FINDIT and the actual computer matching were carried out by Social and

Scientific Systems, Inc., a Maryland based 8(a)

firm. The contributions of Dennis Abels, Itendy

Alvey, Sam George, and Pal Khera are gratefully

acknowledged.

NOTES AND REFERENCES

[11

[21

[3]

[4]

[51

[61

Council of Economic Advisors. (1985)

Economic Report of the President, 1985,

table B-91, p. 337.

The Internal Revenue Service’s Statistics of Income Division produces a

Corporate Source Book annually, which

Presents detailed income and balance

sheet data classified by industry and

size of total assets. For more information, contact the Corporation Returns

Analysis Section, Statistics of Income

Division (D:R:S:C) at IRS.

For a detailed description of the

methodology and computer algorithm, see

“File Matching Utilizing Automated

Heuristic Techniques (FINDIT),” by

Social and Scientific Systems, Inc.,

Bethesda, MD, January 1983.

See “Preliminary Reports on the Development of the Master Establishment

;:;t,” by Social and Scientific Systems,

., Bethesda, MD.

Small Business Administration. (1985)

The State of Small Business: A Report

of the President, 1985.

Converse, Muriel and Heeringa, Steven

G. (1984) “An Evaluation of the

Accuracy and Current Utility of the

1981 Master Establishment List (MEL),”

Institute for Social Research, University of Michigan, Ann Arbor, MI.

295
ENllANCINGPATA FROM THE SUPVEY OF INCOME AND PROGPAM PARTICIPATION WITH DATA FI?OP ECONOMIC

CENSUSES ANO SURVEYS--A BRIEF DISCUSSIO}J OF MATCtlING METHODOLOGY

Doualas K. Sater, Rureau @f the Census

This discussion involves the enhancement

of data from the Survey of Income and Program

Participation (SIPP) with data from econanic

censuses and surveys. This is a pilot project and is still in the development stages.

This discussion focuses on the matching methodology, problems, and problem resolution.

1. INTRODUCTION

The Survey of Income and Program and Participation is a new Census Bureau Survey

designed to collect a host of information

on the social, demographic, and economic

situation of the nation’s individuals and

families.

The data will be extremely valuable to

labor market analysis, but they have one major

shortcoming--they do not include characteristics of the employer for which the sample

persons worked. This gap can be bridged by

the addition of information on enployers

that is collected in the econcmic censuses.

The addition of economic data to the SIPP

will enable researchers to obtain improved

estimates of the impact of economic and

institutional forces which have been intensively studied but are only partially understood or measured. Some of the areas in

which the matched file can yield new insights

are: the relationship between capital and

wage rates, structural unemployment, the

transition from a goods to a service econany,

unions and the labor market, productivity

analysis and numerous other studies. For

some of the studies, data at the establishment

level are appropriate, and for others, enterprise level data are needed.

II. DEFINITIONS

An establishment is defined as a single

physical location where business is conducted

or where services or industrial operations

are performed. Where separate activities

are performed at a single physical location,

each activity is treated as a separate

establishment. The legal entity is an organizational unit which is assigned an employer

identification number (EIN) by the IRS for

tax reporting purposes. The leaal entitv

represented by “the EIN may compr;se one o;

more establishments. The enterprise is the

entire economic unit consisting of one or

more establishments or legal entities under

ccmmon ownership or control. The following

figure (Figure 1) shows a partial example of

these definitions.

We will be conducting the matching activity for about 20,000 persons in Wave 6 of the

SIPP -- the first annual “round-up.” In

addition to the demographic and economic

Figure 1.--A Partial Example of Basic Definitions

ENTERPRISE

3-- SEARS,

ROEBUCK ‘>

AND CO.

LEGAL

ENTITIES ESTABLISHMENTS

‘Et::::

SEARS,

E

LOCATION 1

+ ROEBUCK LOCATION 2

AND CO.

(SALES) LOCATION N

information, the Wave 6 questionnaire also

asks for the employer name, address, and

employer identification number for up to three

employers.

The first step in this process was to examine the available econcmic data sources.

The Census Bureau conducts numerous economic

censuses and surveys, such as the Census

of Manufactures, which contain the needed

economic data. For linkage purposes, the

economic census records also contain a census

file number (CFN) which uniquely identifies

the establishment. They also contain the

establishment name and the establishment

address, but they do not contain the EIN.

The first option would be tomatch the SIPP

directly to each economic census needed.

(Figure 2 shows a simplified diagram with

Figure 2.--Simplified Diagram of Direct Match to

Three Economic Censuses

m

297
only 3 possible econcmic data sources.)

This would involve numerous matches on

employer names and addresses. Since we are

only trying to match about 20,000 cases, the

development and testing of programs and

the sorting of the economic files were more

than we wanted to tackle in this pilot project. Further, the economic censuses do not

cover all establishments. That is, they do

not cover some “out-of-scope” establishments nor do they cover small establishments.

Since about half of all establishments have

less than 5 employees, this is a serious

shortfall for our purposes.

Amoreattractive approach would be to conduct the match through an intermediate data

set and program system, namely the Standard

Statistical Establishment List (SSEL) and the

Census Control System or CCS (Figure 3).

The SSEL is a centralized multipurpose computerized name and address file of all known

Figure 3.--Simplified Diagram of Match to Three

Economic Censuses Using the

SSEL and the CCS

El M4TCH

SIPP > USING

+4

SSEL

Ccs

PTi$!iE%

CENSUS OF

MANUFACTURES

CENSUS OF

“n

CENSUS OF

RETAIL TRADE

DISTRIBUTIVE

TRADE

employer firms and nonemployer agricultural

finns. (This includes the out-of-scope and

small establishments as well as establishments covered by the economic census.) The

CCS is an interactive random access name

search program and series of files derived

from the SSEL. It contains the establishment name and address, the EIN and the

census file number. The file also contains

selected search keys: ZIP Code from the

address, a name search key and the EItJ.

Further, these files also contain selected

data such as the number of employees and the

annual payroll. In essence, the CCS is a

computer assisted manual search program, and

it seems to fit our needs quite nicely.

Thus, the approach taken is to use the CCS

to match to the SSEL to pick up the CFN and

selected bits of data. The CFN will then be

used to match to the economic censuses. The

CFIJ has another nice property, it allows us

to match at the establishment or the enterprise level.

The CCS oDerates in two basic modes:

1.

2.

In the EIN mode, one provides the

system with the EItland it return

an abbreviated SSEL record for tha

EIN.

In the name search mode, one pro

vides the system with the name. The

system compresses the name, select

the search key, locates the bloc

of records corresponding to thi

name key, and returns all record

in this block. Additiona

screening is performed based on

other data (such as ZIP Code) i

it’s provided to the system. Th

selection of the correct record i

then done manually.

For multi-establishment enter

prises, located in either the EI

or the name search mode, a secon

search is done which lists all

establishments within the legal

entity or enterprise, as appropriate. The selection of th

correct establishment record i

then done manually.

A hypothetical example would be as follows

Suppose one wanted to locate American Ar

Supplies, 1235 Main Street, 20735. We woul

provide the system with “American Art Sup

plies, 20735”.

It would return, for example, the followin

three records frointhe Block:

1. &nerican Art Supplies

2. American Fabricates

3. American Farm Products

We then select record (1) and it provide

a second listing containing, for example, th

following two records:

1. American Art Supplies-Hqt.

1235 Main Street.

2. Pmerican Art Supplies-Sales

425 Canal Street.

We then extract the CFN associated wit

record 1. This is an oversimplification o

the system but it gives a general idea o

the process.

To make the process as efficient as pos

sible, a stage-by-stage process has been de

signed which maximizes the amount of compute

work and minimizes the amount of manual re

view. For example, well-considered sortin

of the SIPP file can greatly speed the pro

cess. That is, assembling the same employe

names into groups will allow one search fo

many records with the same name. Employer

of 250 or more employees account for les

than 1 percent of all employers, but accoun

for 31 percent of all employees.

III. MATCHING PROBLEMS

There are numerous problems with name

matching. First, there are reported name

variations due to abbreviations, misspellings, etc. For a household interview survey

such as the SIPP, there are several thing

298
that must occur to get a correct name spelling. The interviewer must hear the response

and spell the name when filling in the form.

The data keyer must be able to read the

written entry and key the name. This, in itself is more than ample opportunity for the

introduction of errors. Plus, there are errors introduced through phonetic problems.

Names such as KROEHLER, BEALLS FLORIST,

BURROUGHS, and PFEIFFER BREWERY would pose

such problems.

Also, the SSEL, as good as it is, does

contain some typographic errors. At any

rate, most of these cases are expected to be

resolved through the computer assisted manual

search process using the reported address and

“judgment.” For example, if we are trying

to locate “KRAYLER, 75 Ely Street, Binghamton,

N.Y.” we might decide that this is really

“Kroehler Manufacturing Co. of Binghamton.”

We are referring to this process of decision

as “judgement” because some degree of uncertainty may exist. If the level of uncertainty seems excessive, the case will be

referred for further review. However, care

must be exercised in the implementation of

“judgment.” It implies a lack of uniformity

and nonempirical matching criterion.

Another problem is the reported name

variations for franchises and “Doing Business As” vs. legal name. As an example,

an establishment may be commonly known as

“Wendy’s,’’butin actuality, it is a franchise

using the Wendy’s name and whose legal name

is John Smith Enterprises. The match process

does not have, in its design, an a priori

process to resolve these problems, but

the professional review process may be able

to identify and resolve such cases.

A potential problem is the presence of

mailing address on the SSEL rather than the

physical address. Although every effort is

made to obtain the physical address for the

SSEL file, there are occurrences where the

address on the SSEL is the address of the

lawyer, accountant, or the administrative

office. Depending on the particular circumstances, the problems may be solved or may

be intractable.

Also, multiple establishment names on SSEL

records may cause problems.

These are occutrencesof different establishments having the same name. A hypothetical

example would be as follows:

Clinton Aluminum (Hdqts.)

1235 Main Street

Clinton Aluminum (Mfg)

751 Ash Street

Clinton Aluminum (Sales)

755 Ash Street

This, in itself, poses no major problems,

unless the address is not reported in the

SIPP. Thus, the first question is whether

there is sufficient name detail reported in

the SIPP to match sucha case without address?

That is, are division or group names reported

in the SIPP? Given the amount of space on

the form, I think not. A typical SIPP entry

for this example would simply be “Clinton

Aluminum.” In this event, other matching criteria need to be implemented. If each establishment is in a different part of the country, the selection of the establishment within the same SMSA as the SIPP respondent’s may

be a reasonable criterion. Another possibility would be to use the SIPP respondent’s

occupation. For example, if the occupation

were salesman, a reasonable criterion would

be to assign the case to Clinton Aluminum -

Sales Division.

Suppose, in the Clinton Aluminum example,

we have located the correct legal entity,

but cannot match to the correct establishment.

This case should not be hastily written

off as a nonmatch. We already know slot

about it. We know the enterprise, the legal

entity, and we know that it is one of three

establishments. It seems that a conditional

allocation process will maximize the amount

of information. There are several ideas

for performing this allocation. One approach

would be to use an average value for all three

establishments. Another would be to randomly

assign the case to one of the three establishments or to do the assignment according to

a probability function based on employment

size. The probability of correct match is

that dependent on the probability function

and, for mismatches, data utility is dependent

on the degree of homogeneity of the three

establishments. In the Clinton Aluminum example, suppose that all three establishments

are the same size. Then the chance of a

correct match is one in three. In this same

example, the wage structure and degree of

unionization, etc. are likely to be quite

different between the establishments. Thus,

a mismatch will distort the data. In a case

such as Wendy’s or McDonald’s,such data distortion would be minimal.

I have not considered this allocation

process in depth, but will in the next few

months. At any rate, I will need to assign

two sets of flags to keep track of what was

done and how well the record was matched.

The first w“ll identify the type of match.

The second will apply to allocated matches

and will provide an assessment of the probability of correct match.

Iv. PF!E-TEST RESULTS

A small-scale familiarization test of

this computer-assisted manual search process

using the Census Control System was conducted. The sample was comprised of 166

employer names reported in the Waves 1 and 2

of the 1984 SIPP. These cases were drawn

from a sample of Primary Sampling Units

(Psu). These PSU’S were not scientifically

sampled, but were arbitrarily chosen to

include (1) a variety of PSU’S (by size and

region), and (2) a variety of manufacture rs. Because this is not a scientific

sample and only manufacturers are included,

the results cannot be generalized and are

included only as an approximate indicator.

The purpose of this exercise was primarily

educational; that is, to see how the process

works with real data.

299
Waves 1 and 2 asked for the name of the

employer for which the person worked during

the reference period. Although the employer

address and Employer Identification Nmber

were not collected in these waves, we tried

to obtain the employer addresses for these

cases from a variety of reference materials,

such as the Major Employer Lists from the

1980 census, telephone directories, and

Standard and Poor’s Index of Corporations.

Table 1 shows the different levels of

employer information and the proportion of

Table 1 .--Results of Address Search Operation

Itern I I

Total . . . . . . . . . . . . . . . . . . . . . . . . I !:: &80

With Corp. Hdqts . . . . . . . . . . .

1

94 j 56.6

No Corp. Hdqts............. 72 1 43.4

1

With Estab. Address.......... ~ 72 ~ 43.4

Wjth COrp. HrJqts . . . . . . . . . . . 1 44 1 26.9

t@ COrp. Hdqts . . . . . . . . . . . . . 1 281 16.9

No Estab. Address............ ~: / ::.;

With Corp. Hdqts...........

NoCorp. Hdqts............. 44 26;5

cases at each of these levels. Table 2

shows selected results of this test.

Even though an establishment address was

found for only 43 percent of the cases, the

employer name in the SIPP was matched to the

correct enterprise 78 percent of the time.

The similar match rate is 78 percent for legal entities and 51 percent for establishments. For those cases where there was an

establishment address, the match rates are:

88 percent for enterprises, 88 percent for

legal entities, and 81 percent for establishments. (Note that the lines “Matched to

Enterprise” and “Matched to Legal Entity” are

not equivalent. As an example, if a person

reported he/she worked for Sears, Roebuck

and Company, the person can be matched to

the enterprise, but not to the legal entity.

That is, which of the following would be the

correct legal entity: Allstate Insurance,

Coldwell Banker & Co., Dean Witter Financial

Services, or Sears Merchandise group? As it

turns out in this very small-scale test, we

did not encounter any cases of this type.

Hence, the number matched to legal entity is

130 and the number matched to enterprise is

130.)

1. Type 1 -- These nonmatches represent cases

where there were more than one establishment with the same name all at different

addresses. If the address was reported

in the SIPP, we would have been able to

match these cases. Thirty-one of the 46

nonmatch cases were Ty~e 1’s.

Table 2.--Results of Matching Test

SIPP-SSEL Natch Status Total

Number Percent

I

Total...................................... 166 “ 100.0

Matcbectto Enterprise...................... 130 78.3

Matched to Legal Entity (EN)............. 130 78.3

Matched to Establishment................. 84 50.6

Uniquely Identified by Name............. 75 45.2

Uniquely Identified by Nan!e & Address. . . 9 5.4

Not t-latched to Establishment . . . . . . . . . . . . . 46 27.7

Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 18.7

Type 2.................................. 9 5.4

Type .~.................................. 6 3.6

Type d..................................

h!otNatched to Legal Entity (EN)......... 3:

0

21.7

Not Matched to Enterprise.................. 36 I ?1.7

X -- Data cell does not aPPly.

With Establishment

Ac

Number

72

63

63

58

49

9

5

x

5

0

0

9

9

Iress

Percent

100.0

87.5

87.5

80.6

68.1

12.5

6.9

x

6.9

.0

.0

12.5

12.5

No Establishment

Addr

Number ~

94

67

67

26

26

x

41

31

4

6

0

27

27

55

Percent

100.0

71.3

71.3

27.7

27.7

x

43.6

33.0

4.3

.0

.0

28.7

28.7

Type 1 -- These nonmatches”represent cases where more than one establishment was found in the SSEL,

all at different addresses (but part of the same company) and the company name matched the name

reported in the SIPP.

Type 2 -- These nonmatch cases represent more than one establishment at the same address in the

SSEL; that is, we would need more information than just the address (such as plant or division

name or SIPP occupation) to identify the correct establishment.

Type 3 -- These are cases where the SSEL contains mixed types of entries, some Type 1 and Somf?

Type 2.

Type 4 -- These are cases where we could not identify any establishments in the enterprise by name.

There were no Tyt)e4’s in the test.

(See text for more details on the definitions of the nonmatch types 1-4.)

300
2.

3.

4.

Type

more

name

need

and

name

2--These are cases where there are

than one establishment with the same

and at the same address that is, we

more information than just the name

address (such as plant or division

or SIPP occupation). Nine of the 46

nonmatch cases were of this type.

Type 3--These are cases where the SSEL contains mixed types of entries, some Type 1

and some Type 2.

Type 4--These are cases where we could not

identify any establishments within the

enterprise by name. There were no Type

4’s in the test.

There were 36 cases for which we could not

locate the enterprise on the first pass. A

large part of this is due to the lack of

address for these cases. For the 16 of these,

the location was apparently outside the search

area we tried (PSU of SIPP respondents address). An address reported in the SIPP will

pennit us to match most of these. Also, we

were able to locate an additional 12 through

further research. These were, fn general,

very small comoanies. The remaining 8 are, as

yet, unresolved. Given the nature of this

test, these results were most encouraging.

The 130 SIPP-SSEL matched cass were also

matched to the Census of Manufacturers (CM).

Of these, 100 matched exactly 26 matched to

the enterprise, but the establishment was

non-manufacturing and not in the CM, 3 very

small and out-of-scope for the CM, and the

remaining case was a true nonmatch.

v. OTHER ISSUES

There are a number of other issues to be

faced in this project, some of which are:

1. Adjustment for nonmatches--allocation or

reweighing. Nonmatch rates will be significantly different between large and

smal1 employers. Since much of the

analysis will be affected by this, some

sort of allocation or reweighing will be

necessary.

2. Development of match status flags and

probability of correct match status.

3. Development of a process of computing

match error rates.

4. Errors in EIN’s.

5. Differences in reference periods between

the Economic Censuses, SSEL, and the

SIPP.

6. Suppression issues in data releases.

We will be investigating these issues in

the next few months as work on this pilot project progresses.

1.

2.

3.

4.

5.

6.

BIBLIOGRAPHY

Sater, Douglas K., “Enhancing Data from

the Survey of Income and Program

Participation with Data from Economic

Censuses and Surveys,” Unpublished paper, July, 1985.

Haber, Sheldon E., et al., “Matching

Economic Data to the Survey of Income

and Program Participation: A Pilot

Studv.” Merican Statistical Association

Proc~~dings, Soclal s tatlstlcs Section,

984.

U.S. Department of Commerce. Bureau of

the Census, The Standard ‘Statistical

Establishment List Program, Technlcaf

Paper 44, January, 19/9.

Kasprzyk, Daniel, and Roger A. Herriot,

“The Survey of Income and Program Particitation,’’-llnericanStatistical Association Proceedings, Social tatlstlcs

Xectlon, 1984.

U.S. Department of Commerce, Office of

Federal Statistical Policy and Standards, Statistical Policy Working Paper

2 -- Report on s tatistical Disclosure and

Disclosure Avoidance Techniques, May

Kasprzyk, Daniel, “Social Security

Number Reporting, the Uses of

Administrative Records and the Multiple

Frame Design in the Income Survey

Development Program,” Technical,

Conceptual and Administrative Lessons of

th e Income Survey Development Program,

30C1al Science Research Council, New

York, New York.

301
PISCUSSIOPJ

Joseph Steinberg, Survey Resign, Inc.

INTRODUCTION

The three papers presf?nted illustrate three

of a number of varyina objectives of exact

matching:

(1) addition of data from second file to host

(2

(3

Th

file for the same IRS business tax unit;

construction of a more comprehensive

frame by combining files; and

addition of variables on establishment

economic data to data for individuals in

the Survey of Income and Program Participation (SIPP).

s discussion Drimarily comments on earlier

drafts of these papers. -

These papers describe the files used and how

the matching was done in fine detail. I leave

it to those more expert to comment on these

matters; I will not try to comment on that.

PERSPECTIVE OF COMMENTS

The point of view taken in preparing these

comments was:

(1)

(2)

(3)

(4)

(5)

(6)

How does the quality (or likely results)

of the exact matching conform to

statistical standards used to judge a

statistical study or to ,j udge

completeness of a frame?

After reading or listening to the paper,

what is known about factors (and their

magnitudes) affecting the nonsampling

error component of the results?

What additional information should be

made available to judge the nonsampling

error?

What more (should) might possibly be

tried to reduce the nonsampling errors?

Further, if a sample reinterview program

is considered useful in measuring

coverage and content (net and gross)

differences in a sample survey or census.,

why not use a sample reinterview program

for evaluation and calibration in

matching studies?

Is the matching aiwr’each oDtimal or is it

better to coliect ’data through a survey

process?

In view of the review approach, you will see

that this discussion provides some comments and

a series of auestions for the presenters.

GREENIA

Nick Greenia has an interesting problem, even

though both files come from IRS forms. The

supplementary forms for individuals (C, F, and

4835), which are of interest, may not show the

EIN or, if EIN is shown, it may be incorrect.

What is known (if anything) about false

nonmatches or false matches as a result (since

only the 1979/1980 files of the Forms 941/943

were used, and not 1978/1979)? What is known

about the false nonmatch rate which resulted?

It is interesting to ohserve that many

identifier systems have similar problems --

here it is the “sole propietorship/corporation

connection” re the EIN. There used to be (and

may still be) the problem in the SSN: multiple

people gave an identical SSN as a result of the

purchase of a wallet that had a valid SSN on a

specimen identification card.

I noted that matched cases were dropped when

the 941/943 payroll was greater than the sole

proprietorship’s business deductions. Was any

effort made to contact any sole proprietorship

when this was found? Wouldn’t it be of

interest to know, for a small sample, at least,

under what circumstances this situation arose?

May not treating such cases as unmatched

eliminate an important class of novel

situations? Why do you think, Nick, that

reweighing overcomes the problem?

Given the assertion in the paper “... that a

significant portion of true matches remained to

be found ...” (Section V), would the analytic

objectives be served if the tabulations of

“matched” data are based on not much more than

the original set of matches? Would the

nonsampling error of the results be too large?

I have often wondered whether information on

the Forms W-3 was available on any accessible

file. Since the Form 941 employment is only

for employees for the pay period ending March

1?, would a more useful source of employment

and payroll be:

(1) ~hj ~fiber of statements--counts of Forms

(2) total payroll for the year from the

summary W-3 process?

Incidentally, if any of these ouestions

suggest a need for contact with a business (as

941/943 payroll greater than business

~ductions), a statistical study (perhaps

conducted by a third party) should be

considered the vehicle, with results available

to IRS only in tabulations (screened for

disclosure problems). Consider, a statistical

reinterview program may be a useful means for

evaluating overall quality and not just for

special issues.

HIRSCHBEPG

Now I turn to Dave Hirschberg’s paper. In

the paper, I found the interesting points:

(1)

(2)

that the Master Establishment List (MEL)

is uniaue in its representativeness of

small businesses of all size categories,

and

that the total number of businesses

included in the MEL exceed more than half

of the population or universe of all

(small and large) businesses reporting to

the Internal Revenue Service.

303
My question is: How complete is MEL? The

tables show the relation of the Duns Market

Identifiers (or DMI) to County Business

Patterns. l-lowdo the distributions of MEL

compare with some standard? And, by Standard

Industrial Classification (SIC code) and

employment size?

At another point, the author indicates that

businesses not represented in the MEL are

mostly smaller businesses or individuals that

might be located in their homes or who, due to

limited activities, wuld not appear in the

credit markets nor advertise in the yellow

pages.

In view of this, what problems are there in

the Small Rusiness Administration (SBA) use of

MEL? AlSO , what is known about the rate of

inclusions in MEL files of firms no longer in

existence (given the slowness of purge of the

DMI and Market Data Retrieval, Inc’s “yellowpage” listings)? What is the duplication rate

still in the file? (One source paper says “...

hopefully relatively few.”) Further, what is

known of the proportion of false matches --

discards from one file or the other that really

didn’t match? This is not to suggest that

“Findit” as a match program has any discernible

problems -- at least to my knowledge.

Now, I turn to another matter. This project,

creation of MEL, was initiated since there was

essentially no single file available to SBA

which satisfied its needs--and it is understandable why various agencies have statutes (Census)

or regulations which require confident~ality of

frames, ~rivacy being deemed more important than

government-wide efficiency.

What is the confidentiality status of MEL?

Does SBA have a regulation which prohibits

disclosure? What are any other possible public

uses - could another government agency, say,

Department of Energy, or could a research ffrm

doing a study for a government agency have

access? At what price? How does this compare

to your costs?

On another matter -- what improvements in

file completeness would there be from access to

the UI files in the 25 states willing to share

their files? Has anyone explored the

possibility that uniform files for these 25

states may exist in a Federal agency’s hands --

the Bureau of Labor Statistics (BLS)? And what

cooperation can be wrked out between SBA and

BLS, given written agreement by these 25 states

to permit SBA access?

The paper recognizes that data collection is

“non-rigorous” and, therefore, employment, and

possibly SIC codes, too, t-nay be inaccurate.

What, if anything, can be said about the

effects of possible inaccuracies on the use of

subsets of MEL as survey frames? Consider the

value of a sample reinterview program to check

on quality.

Finally, the paper mentions that some checks

were planned, e.g., MEL vs. University of

Pfchigan, Survey Research Center’s sample of

their nonhousehold establishment list. Are

there any results of such checks available?

What do they show about the completeness of MEL?

SATER

Now concerning Doug Sater’s paper; first, I

turn to the SIPP information collection to be

used for the match. Has Census considered the

desirability of expanding the questions being

asked (name of employer, address, employer

identification number)? PerhaDs, in addition

to address (or, if not available), they coul

consider getting nearest street intersection

asking for telephone nwnber at place of

employment -- for possible use, when no EIN i

given, for calling the employer; or, if n

address, calling to establish an address?

AlSO , w SIPP-collected data -- what. step

are taken to assure that SIPP-collected EIN i

consistent with SIPP-collected information o

employer name and address?

The paper discusses a prospective matchin

project, and it is interesting to read abou

the decision process that leads to the decisio

concerning the source file and matchin

method. It will be interesting to hear, in th

future, what actually took place: the degre

of manual effort and the various costs

Incidentally, what is the relative budge

planned for this matching activity compared t

the SIPP data collection phase? It would b

interesting to know, both here and in othe

matching projects, about relative budgets fo

matching vs. data collection of source surveys.

In view of the author’s contention that the

expect to obtain (in the SIPP) valid EINs abou

40 percent of the time and that there is a

need to use a variety of methods to try to

determine the EIN in the remainder, how wil

the match validity be tested? (The paper say

error measurement will be the subject of futur

development. And evaluation strategies will b

the subject of future development.) What abou

considering a sample reinterview program a

part of the evaluation strategy?

The paper describes a smal1 scal

familiarization test. Admittedly, it was not 

true test, since address and EIN had not bee

collected in the nonprobability set of unit

used for the test.

How secure are you, Doug, in the rates o

exact matching cited in the paper? Do you hav

plans for another, truer, test, using 

subsample of the SIPP that you plan to use

before mounting the full-scale matchin

project? Suppose the results are not as goo

as in the small-scale familiarization test

what if the results suggest a 60-70 percen

match rate. Would you recommend the projec

move forward?

The paper notes that adjustments are planne

for matching problems. What order of magnitud

of matching problems do you believe are likel

to occur, for which allocation or reweighing

is the preferred solution? What do you anti

cipate will be the net effect on the level o

nonsampling error in some principal result?

304
REJOINDER

Nick Greenia, Internal Revenue Service

The discussant’s observations are, of

course, most appreciated and exhibit a grasp of

the Sole Proprietorship Link Study’s fundamental

problem: as a first time study, it had to cope

with how much was simply unknown.

The decision to employ the 1979/1980 file

of Form 941/943 records and omit the 1978/1979

file as well as the fiscal filing period

possibility was due to two factors: higher

processing costs and the 1979 calendar filing

period assumption. Higher costs of additional

linkage processing for files not originally

designed for the link studies per se (i.e., the

SOI-perfected sole proprietorship sample file

and the Census-perfected Form 941/943 population

file) were deemed unwarranted primarily because

(a) for Tax Year 1979 some 99% of all Forms 1040

had calendar year 1979 filing periods and (b) of

those which had fiscal or non-1979 filing

periods, many were probably filed for members of

partnerships.

Other than what is known of false matches

obtained from match processing as well as the

increase in aggregate data resulting from

reweighing for false non-matches (increases of

16% for number of businesses, 10% for payroll,

and 11% for employment), nothing is known of

this processing decision’s direct impact on

false matches and non-matches. Probably it had

little impact since match problems in general

were thought to be attributable primarily to the

Employer Identification Number (or lack of it)

on the sole proprietorship’s business schedule.

The second Sole Proprietorship Link Study (Tax

Year 1982) is expected to benefit from the 1979

experience in this regard primarily because such

tradeoff decisions as necessitated for the 1979

Study will be precluded by the 1982 sample file

format design.

No sole proprietorships were contacted

during the study‘S match processing phase

primarily due to resource constraints. Although

the payroll/deductions discrepancy was designed

to catch “hidden payroll” on the business

return, the 1982 study probably will compare

payroll to proxy payroll. This change is suggested by the 1979 experience which has led us

to believe that hidden payroll is less of a

potential problem than the overstating of proxy

payroll--primarily due to its inclusion of

contract labor payments as well as payroll not

reportable on Form 941/943 for certain employee

classes. Again though, it is important to err on

the conservative side (particularly when examining the payroll/deductions relationship) by

building a sound match base, due to the large

weights on some sample business records.

Reweighing is thought to overcome potential

problems of omission by compensating for any

marginal matches missed through groups of solid

match records with similar characteristics.

Further, it was a desirable step in order to

provide the Small Business Administration (SBA)

with as full a data set as possible to meet

SBA’S own analytic needs.

The discussant’s suggestion to replace the

Form 941 file with W-3 file counterpart information (total compensation for payroll, number of

W-2’s attached as an employment proxy) would be

desirable if control problems currently confronting the W-2/W-3 tapes--annually provided to IRS

by SSA for the Combined Annual Wage Reporting

Agreement Form 941/943 reconciliation effort--

could be overcome. SSA is planning to overhaul

its current computer processing system in 1987,

which might be a more appropriate time to

reconsider such an approach. In the meantime,

however, it might be worthwhile to pursue this

idea with the thought of supplementing Form 943

information--weakened in the past by reporting

qualifications as well as the general problem of

reporting employment only for the March 12 pay

period,

305
REJOINDER

David Hirschberg, Small BIISineSsAdministration

Joseph Steinberq’s questions regarding the

Master Establishment List’s (hEL) quality and

conformity to statistical standards lie at the

heart of the matter, once the major issue of

mechanically merging files is solved.

Limited opportunity exists here for full

discussion of the quality issues raised by

Joseph Steinberg. However, there are several

studies and reports which provide the interested researcher with such information. Discussions of the overall quality of the Dun’s

DMI file can be found in ‘D&B, DMI: Data User

Conference.” [11 ,,Another publication of interest includes, A Comparison of Employment

Data From Several Sources: County Business

Patterns, UI and Brooking’s USEEM,” by Candee

Harris. [2] That report provides a fairly

extensive examination by industry of the small

business population.

Generally the nonsampling errors which are of

concern can be examined from the information

presented. The impact of the matching on the

overall quality of the MEL is more complicated. From a statistical point of view,

little is known about how completely the

“yellow pages” cover the universe of business.

Definitive efforts to evaluate the Master

Establishment List are hampered by the lack of

uniform numerical identifiers in the various

systems. Even when numerical identifiers, such

as Federal employer identification numbers, are

available, the matching of files from different

systems is not a straightforward task, as Nick

Greenia has pointed out in his paper. [3]

A great deal of work is needed in this area,

and access to administrative records from State

and Federal agencies is necessary. In addition,

a requirement exists to more carefully define a

small business for statistical purposes.

The overall documentation of the Small Business Data Base work can be found in the appendices to the “State of Small Business: A

Report of the President” for each year beginning with 1982. [4] A more comprehensive guide

to information relating to specific issues can

be found in “The Development of the Small

Business Data Base of the U.S. Small Business

Administration: A Working Bibliography” by

Bruce D. Phillips. [5] Most of these publications are available from the Office of

Advocacy. Methodological and quality issues

raised by Steinberg are directly addressed.

Steinbe@ also raised the issue of the MEL’s

confidentiality status. This is now under

discussion with the firms producing the files,

and a formal statement on this issue should be

forthcoming.

As mentioned previously, the inability to

match files of business firms, along with a

large turnover rate, plagues any attempt to

develop independent verification of the MEL.

The University of Michigan Survey Research

Center report, although vigorous in its aPproach, was not able to ovemome these prob-

~~~ ~~~urr~!n, it‘inferences was difficult between to determine the two

precisely what the problem was.

One final comment with regard to the State

unemployment insurance data is in order. The

potential use of these files was explored with

the States and the Bureau of Labor Statistics;

because of confidentiality provisions, access

could not be Drovided. Althouah a few States

did decide to”make their fi’

research purposes, the cost

grating them into the MEL prec’

[11

[2]

[3]

[4]

[51

[61

REFERENCES

AdviSOW Consnission

e; available for

nvolved in inteuded their use.

on Intergovernmental ‘Relations. (1979) “DiBj DMI:

Data Users Conference,” Washington,

D.C. (mimeographed).

Harris, Candee S. (1981) “A Comparison

of Employment Data for Several Business

Data Sources: County Business Patterns

on Unemployment Insurance, and the

Brookings U.S. Establishment and Enterprise Microdata File,” The Brookings

Institution, Washington, D.C.

Greenia, Nick (1985) “1979 Sole Proprietorship Employment and Payroll:

Processing Methodology,” Record Linkage

Techniques--l985, Internal Revenue

Service, Washington, D.C.

Small Business Administration. (Annually, 1982 to 1985) The State of Small

Business: A Report of the President.

Phillips, Bruce D. (1985) “The Development of the Small Business Data Base of

the U.S. Small Business Administration:

A Working Bibliography,” Record Linkage

Techniques--l985, Internal Revenue Service, Washington, D.C.

Conve~1~841uriel and Heeringa, Steven

G. “An Evaluation of the

Accuracy and Current Utility of the

1981 Master Establishment List (MEL),”

Institute for Social Research, University of Michigan, Ann Arbor, MI.

307

,.
Section VI

Computer

■

■

Software

1

m- —’—
PROJECT LINK-LINK: AN INTERACTIVE DATABASE OF ADMINISTRATIVE RECORD LINKAGE STUDIES

Jane L. Crane, National Center for Education Statistics

Douglas G. Kleweno, U.S. Department of Agriculture

l!uch information exists on linkage studies

using administrative records and, in some cases,

survey data. A database called LINK-LINK

illustrates the electronic retrieval of linkage

study information. This paper is a guide for a

prospective user of LINK-LINK. It will briefly

describe the database and potential uses of the

system, explain how one searches the database

for general or specific linkage project

information, outline procedures for obtaining

copies of the database and address the future

direction of the project.

The database is the end-product of a pilot

study by the statistical Dolicy committee formed

from the Matching Group of the Administrative

Records Subcommittee, a standing comnittee of

the Federal Cotnnittee on Statistical

Methodology. The coo?nittee encourages use of

the database and solicits comnents and

suggestions from all users.

A DESCRIPTION OF LINK-LINK

LINK-LINK is an interactive information

database devoted to administrative record and

survey data linkage studies. The initial

database contains 30 studies which were selected

for complexity, originality, and diversity of

record linkages. Appendix A provides a list of

these studies by title.

Information for each study in the database

was obtained “

self-administered

questionnaire. T~~l~~est~onnaire, designed b.y

the statistical policy comnittee, was completed

for each linkage study. Respondents for the

pilot study were contacted by telephone and

letter before receiving the questionnaire.

After the information was COlleCted, it was

edited for clarity and completeness and then it

was keyed into the database.

The database is comprised of a series of

menu-type prompts to direct the inquirer during

the interactive information search. The menu

allows the user to choose the search category

from a list that appears on the screen. There

is considerable flexibility in the database

because of a variety of search categories. [n

addition, the prompts also allow selection of

a particular area of user interest.

LINK-LINK was written using a dBASEIII

software program. The database, which was

developed on an IBM PC/XT personal computer, is

on a 5%” floppy disk.

Equipment requirements for LINK-LINK include:

an IBM PC/XT or any other fully compatible

personal computer with the MS-DOS or PC-DOS

Version 2.0 or greater operating system; a minimum of 256K bytes of memory; two 360K floppy

disk drives or one 36CU floppy disk drive and a

hard disk drive; and a printer with at least an

80 column capacity.

Objectives for Developing LINK-LINK

The primary objectives of the database are as

follows:

1) inform and educate data users about

record linkage activities;

2) identify and describe major record

linkage data files;

3) illustrate procedures to meet

confidentiality requirements associated

with a particular record file;

4) demonstrate linkage methorJo:~/~‘including

software limitations, quality

concerns and linkage solutions; and

5) identify a knowledgeable contact person

for further linkage information.

Type of Information Available

Each study in the database can be referenced

to obtain a broad spectrum of linkage study

information including: the linkage purpose;

linkage methodology including software used;

linkage data files; methods used to meet legal

requirements for matching; type of dissemination

of the linked data; names of cooperating

institutions and their contact person; and

titles of supporting linkage publications. A

more detailed description of the database

contents is given in Tables 2 and 3.

Potential Uses of LINK-LINK

LINK-LINK is a reference source for people

seeking information on record linkage studies

involving administrative records and/or survey

data. The database is a useful tool to:

1) identify new and significant linkage

programs using administrative records and

survey data, or discover the most recent

research activity involving linkage of

records;

2) identify the potential uses of linkages

involving administrative andior survey

data records;

3) identify the complexity and limitations

of data linkages as dictated by public

policy;

4) keep abreast of research “

administrative record and survey da~~

linkages and avoid redundancy of research

efforts; and

5) use as a basis for additional research.

LINK-LINK’S MAIN MENUS

There are two main menus which provide the

user with a largeselectionof information to

investigate record linkage studies contained in

311
p

database to identify all linkage studies for a

certain characteristics such as the linkage

purpose or linkage method. It is also possible

to search a specific project for detailed study

information. The logic of the system flow is

from general categories to specific

detail.

study

Table 1 shows t$e system’s two main menus

with the initial user selection categories.

Based on the user’s interest, the appropriate

menu selector value is entered.

Main Menu I is an exploratory menu to give

the user a listing of linkage studies by general

cateyor.y. Main Menu II provides detailed data

specific to a study in the database. A series

of submenus direct the user to the appropriate

information of interest within the main menu.

Main Menu I

The user, uoon entering the database, keys

“do explore” to display the Main Menu I selection categories. As the user responds to

additional menu prompts, the search for

information narrows unti1 a list of record

linkage studies is identified. The format for

the list of studies is a five-digit database

reference number, a project title, and a brief

statement of the study description. The listing

is displayed on the computer monitor and is also

routed to a printer for hard copy.

Main Menu I selection categories. For example,

to obtain a list of linkage studies used for the

construction of a sampling frame, the user keys

a “1” in the Main Menu I and a “l” in the

submenu. The end point of the Main Yenu I is a

list of database linkage studies satisfying the

conditions as defined by the user in one or more

menus.

At the end point of a path search in Main

Menu I, the system prompts the user 1) to return

for further exploring using major categories in

the Main Menu I; 2) to request specific

information for one or more studies listed using

Main Menu 11; or 3) to leave the system entirely

with a series of “O” or quit prompts.

Main Menu II

Main Menu II provides the user access to

detailed information on a specific linkage

study. The user must know the five-digit

database reference number which is provided when

the listing of studies is printed at the end of

Main Menu I. Only one study can be searched at

a time. The user can request information on

additional studies by entering each reference

number as requested. All information displayed

on the monitor is again routed to the Drinter

for hard coPy.

Table 1: Main Menu Selection Categories in LINK-LINK

Menu Selector Category

MAIN MENU I

(1) Identification of Linkage Purpose

(2) Restrictions on Access of Files for

Linkage Purposes

(3) Linkage Methods and Related Issues

(4) Data Files Used in Linkages

(5) Subjects and Respondents on Files

(6) Title and Short Description of Linkage

Project

(7) Type of Dissemination

(8) Documentation of Linkage Studies by

Title and Author

MAIN MENU 11

(1) Access to Files for Linkage Purposes

(2) Linkage Methodology

(3) Data File Description

(4) Titles/Authors of Written Documentation

(5) Contact Person for Study Information

312
Table 2: Description of Selection Categories for Main Menu I

Selector Category Description of Contents

1.

2.

Identification of

Linkage Purpose

Restrictions on

Access to Files

3. Linkage Methods

4.

5.

6.

7.

8.

Data File Used

in Linkages

Subjects and

Respondents

Title and

Description

Type of Dissemination

Documentation of

Linkage Studies

Ten linkage purposes are identified. The

user selects a category for a list of

studies.

A submenu with two options are available

to the user to identify general study

safeguards:

1) studies where access to linkage records

is permitted when respondent permission

is obtained, and

2) studies where agency policy or legal

authority restricts disclosure (general or specific statutes).

Four options in the submenu permit the

user to investigate how database study

files were linked:

1) software used for data preparation;

2) software used for matching;

3) data quality problems; and

4) linkage problems.

Each submenu prompts the user to select

a category of interest.

Datasets used in all linkage studies

contained in the database are listed.

Number and title of a study are listed

first, followed by the dataset(s).

Four general categories of subject/respondent interest are available.

List of linkage studies with database

reference number, title, and study

description is available.

Four dissemination categories in the

submenu are available for the user to

obtain a list of linkage studies:

1) real eased in aggregate form;

2) public use microdata file;

3) restricted use microdata file; and

4) no dissemination.

List of linkage studies with any published

documentation by author, title, and

date is available.

313
after exuloring for information in Main Menu I.

The user simplv enters Main Wenu 11 with the

five-digit database reference number for which

additional information is requested. Table 3

describes the five selection categories

available.

It is possible, if the database reference is

known, to skip Main Menu I and go directly to

Main Menu II by keying “do lnktomn2.” This

co~and will place -you at the beginning of Main

Menu 11 where you will be asked to select from

the categories identified in Table 3.

THE FUTURE OF LINK-LINK

At this time, the future of LINK-LINK is

uncertain. The Matching Group of the

Administrative Records Subcommittee is searching

for an individual or Agency to assume

responsibility for t?e database. Because the

current version of LINK-LINK is a pilot effort

still in the development stage, an evaluation of

the database design is in order. In addition,

the mechanics for updating current 1inkage

studies and adding new studies to the database

must be addressed. It is also necessary to

support users who request a copy

database.

of the

Copies of the LINK-LINK database may be

obtained by mail. Send two formatted flopPy

disks for each copy of the database requested

and a pre-addressed mailer to return the disks.

Table 3: Description of Selection

5%” flexible disk

Double Sided

Double Density

40 tracks

Send correspondence and floppy disks to:

Fritz Scheuren, Ph.D.

Chairperson, Administrative Records

Subcommittee, Federal Committee on

Statistical Methodology

c/o Statistics of Income Division

Internal Revenue Service D.R.S

1111 Constitution Avenue, N.W.

Washington, DC 20224

ACKNOWLEDGMENTS

The success of this project is due to the

contributions of members of the Matching Group

of the Administrative Records Subcommittee.

Group members were: Jane L. Crane, chairperson,

National Center for Education Statistics; Mary

Bentz and Beth Kilss, Internal Revenue Service;

Douglas G. Kleweno, U.S. Department of

Agriculture; Wendel Thompson, U.S. Department

of Energy, and Tom Reilly, Bureau of the Census.

Fritz Scheuren, Internal Revenue Service,

provided leadership as the chairperson of the

standing subcommittee. Thomas B. Jabine,

Consultant, Contnittee on National Statistics,

made many valuable contributions including the

identification of the studies for inclusion in

the LINK-LINK database.

Categories for Main Menu II

Selector Category Description of Contents

1. Access to Files for Specific information on:

Linkage Purpose

parties to the

transaction; incentives; how legal requirements

were met; how records were obtained;

procedures to protect identifiable records

during linkages; type of dissemination, if

any; and steps taken to prevent disclosure

after records have been linked.

2. Linkage Methodology Specific study information on: software used

to prepare data files and to link records;

problems in data quality; and problems

encountered during the linkage process are

listed.

3. Data File Specific linkage study data set names and

Description key variables are listed from each data set.

4. Titles/Authors of References of publications by title,

Documentation author, and date for specific linkage study

are provided.

5. Contact Person for Specific linkage study resource person

Study Information including individual’s title, employer,

address and telephone number are identified.

314
APPENDIX A: DATABASE STUDIES BY TITLE

Tax Year 1979 Sole Proprietorship

Employment and Payroll

Residential Energy Consumption Survey

Developing A Sampling Frame

Of Petroleum Sellers

IRS/Census Direct Match Study

Tax Year 1979 Partnership

Employment and Payroll

Employer Reporting Unit Match

study (ERuMS)

SRS/ASCS Data Exchange

Intergenerational Wealth Study

Enhancing Data From the SIPP

With Economic Data

IRS 1979 Occupational Coding Study

Linked IRS-SSA Data File

Updating of the SSEL

IRS 1982 Estate Collation Study

Deriving Labor Turnover Rates From

Admin Records for U.S. and 30 States

Mail List Development for 1982 Census

Of Agriculture

High School and Beyond--Third FO11OW-UP

Student Financial Aid Record Component

National Health and Nutrition Exam

Survey, Epidemiologic Follow-up Study

Census/IRS Link Study

1982 Partnership Employment and Payroll

Link Study

1982 Sole Proprietorship Employment

and Payroll Link Study

Continuous Wage/Benefit History Project

IRS Mortality Statistics Study

Current Population Survey/ National

Death Index Match Study.

Forward Trace Study

Continuous Work History Sample System

Wage and Tax Statement Extract

Information Returns Program Match

IRS/SSA/DOD Match

Special Frame Study

Master Employment List-Unemployment

Insurance Records of Texas and

Pennsylvania

315
CURRENT RECORD LINKAGE RESEARCH

Matthew Jaro, U.S. Bureau of the Census

This paper discusses problems involved in the

design and implementation of record linkage

algorithms for file matching under conditions of

uncertainty. Current research activities in this

area are summarized, along with a brief survey of

some underlying theoretical considerations. This

paper stresses techniques that might be used for

obtaining confidence in the match decision and

algorithm validation. The research being conducted for the 1985 pretest in Tampa, Florida is

discussed.

1. SIJMMARYOF RESEARCH ACTIVITIES

Record linkage is the process of examining

two computer files antilocating pairs of records

(one from each file) that agree (not necessarily

exactly) on some combination of identifiers (or

fields). For the Census FJureauthis process is

typically executed on two files containing individual names, addresses and demographic characteristics. Specifically, record linkage is important for census undercount determination, address

list compilation and general census evaluation.

Record linkage research is focused on the

development of an algorithm and accompanying

manual procedures that will accomplish the ahove

goals in a statistically justifiable manner.

To this end the following major activities must

be initiated:

il.

B.

c.

r).

development of a statistical foundation for

the record linkage process;

construction of a data base that can be

used for calibration, validation and testing of the characteristics of the linkage

process;

development of methods to obtain information on the discriminating power of the

various identifiers and their associated

error rates (discriminating power is a

measure of an identifier’s usefulness in

predicting true match pairs); and

design and implementation of computer algori~hms to pefform the actual linking.

The results of this research will he:

A.

B.

c.

D.

more accurate unclercount determination

and coverage analysis;

reduction of costly clerical procedures by

use of automated methods;

a statistically valid process which can

replace previous ad hoc techniques; and

algorithms that will be useful for overco;erage determination and address list

compilation.

2. AREAS OF INVESTIGATION

There are several areas of investigation that

must be pursued in order to design and implement

a successful matching system. These areas are

currently the focus of attention for the Record

Linkage Research Staff.

2.1 Blocking and Other Search Restricting Technloues

The set of records that will be examined to

find a match for a given record is called a

block. Obviously, if an entire file were

searched for a match for each record, the probability of finding a true match would be highest,

since no records are excluded from consideration.

However, the cost of such a process would be

prohibitive. As we restrict our search, we

exclude records and increase the probability

that the “true match” record would be excluded--

but the cost of searching decreases.

The ideal blocking identifier would be one

which nearly always agrees in “true match” record

pairs but nearly always disagrees between pairs

which are not valid matches. This ideal blocking

identifier nwst have a large enough number of

possible values to insure that the file will be

partitioned into many (and therefore smaller)

blocks. R. Patrick Kelley of our staff has

developed a method for computing an optimal

blocking strategy, considering the tradeoffs of

COMpUtat.iOfl cost against errors introduced by

restricting the search for matches. See [4].

2.2 Weights

The terms “identifier” or “component” represent fields on a computer file (and are used

interchangeably). Typical components are street

name, street type (e.g., Street, Avenue, etc.)

surname, given name, etc. The discriminating

power of a component (or identifier) is a measure

of how useful that component is in predicting a

match. Consider a component such as surname.

Common values of surname (such as “Smith”) have

greater chances of accidental agreement than do

rare values (such as “Humperdinck”). Consequently, the frequency of occurrence of a particular

value of an identifier is one determinant of the

weight or importance of that value as an indicator of matched or unmatched records. Another

determinant of the weight is the error rate

associated with the value of that component.

High error rates diminish the predictive usefulness of an identifier or its values.

Fellegi and Sunter, in [1], presented a

general theory of record linkage, including discussions of weight calculations and the development of optimal decision rules. Their basic

idea for weighting is summarized below.

The two files (A and B) to be linked consist

of a number of components (identifiers) in

common. Consider all possible pairs of records.

A particular pair is either truly a matched

pair (an element in the set M of all matched

pairs) or an unmatched pair (an element in the

set U of all unmatched pairs).

For all pairs (p) and each component (or

component-value state) i let:

317
mi= Pr (component agrees I

pc?l)

Ui= Pr (component agrees pc u).

Weight for the ith component = log2 (mi/ui).

The above computation would be the same if we

were considering specific values of components

(such as “Smith” Or “Humperdinck”) rather than

the component as a whole (surname). Similar

weights can be computed for disagreements. mi

is computed by examining all matched pairs; ui

is computed by examining all unmatched pairs.

For the two files A and B,

{U}=[AX B} -{M} .

Since the cartesian product A x B is O(n2) and

M is O(n) (where n is the number of records in

the smaller file), then { U } is much greater

than { M ) and the ui can be computed by

taking the frequency counts of the components

in both files.

The calculation of m requires a prelinked set

of records M. This fact presents the greatest

practical difficulty because of the large sample

size necessary, the cost of producing such samples and the inherent error in manual processes.

Fellegi and Sunter, in [1], suggest a method

of weight calculation that does not require

prelinked pairs. It uses an assumption of the

statistical independence of the components and

requires the solution of a non-linear system

of equations. We plan to investigate the use

of this method, which to our knowledge has never

been tested.

Another method of weight calculation that we

will consider is that of iterative refinement.

We propose this method to avoid the construction

of costly samples. If there were no errors in a

given component, the value “m” for that component

would be 1 and the weight for the component

could be calculated from the frequency of occurrence of the component value states.

These initial weights can be refined as

follows: !4henever a record pair disagrees on a

component, that pair would be presented to an operator by the matching program. The operator can

then make a decision as to whether the pair is a

match or not. This places the pair in either

the set M or U and the weights can now be updated

(since m is now less than 1 -- because of the detected error -- if this pair is placed in {M}).

The program can obtain information regarding

the error rates of each component in this manner,

updating the probability as records are processed. The operator supplies the “truth” regarding each record in question (does this pair

belong to set { M } or to set { U } ?). This

teaches the program to make similar decisions to

those of the operator.

The operator can set the level of errors that

will control the display of candidate record

pairs. IrI this way, records can be matched

automatically despite small errors in components.

As confidence is gained, the thresholds for

manual intervention can be moved. After all

records have been processed, the entire file can

be rematched using the new weights and the process can be continued until consecutive iterations produce small differences.

An investigation into this technique is required to determine whether such iterations will

converge to a stable set of weights and to determine the amount of bias introduced by such

estimation techniques.

A third method of weight calculation that

might be explored would involve automatically

making the “M” or “U” decisions, instead of relying on human operators. This would be accomplished by considering pairs of records “that

match on all fields except a specified number.

Those pairs could be assigned a match status

if the composite weight ( z wi) for the pair

was sufficiently greater than the cut-off

threshold. The distance from the cut-off would

leave room for weight estimation error without

effecting the “M” or “U” decision, and hence,

the “M” decision could be made automatically

with some degree of confidence. These cases

would be used to tabulate the error rate probabilities.

Since the cut-off threshold for a match decision is dependent upon the weights of each field,

this threshold would move as weights are revised.

The effect of this concomitant variation on the

weight estimation must be investigated.

2.3 Composite Weights

If the components are assumed to be statistically independent, then the composite weight

is equal to the sum of the individual component

weights. Adding the weights is equivalent to

multiplying the conditional probabilities.

Weights for disagreements can be computed similarly to weights for agreement. Disagreements

are generally given negative weights, whereas

agreements receive positive weights.

We know that some dependencies exist (such as

sex and given name) but the extent to which

dependence changes the matching decision rules

must be analyzed. For example, “Robert” is

principally a male given name, but “Stacy” could

be either male or female. Such dependencies

could have an effect on the probabilities of

agreement given unmatched pairs. If the errors

in the fields are dependent, then the probabilities of agreement given matched pairs could

change. The disagreement weights would also

change proportionally.

We are currently designing simulation experiments to study the effect of covariance on the

decision results. It is hoped that a regression

analysis will provide information concerning

this relationship after a number of runs with

differing covariance configurations.

2.4 Error Rates

If a plot were to be made of numbers of observations versus composite weight, a hi-modal

distribution would result. Since most pairs

are elements of { U } , the disagreement mode

is much larger than that for agreement.

For each pair, one of three decisions is

made. The pair is said to match if the weight

is greater than a threshold P, or not to match

if the weight is less than a second, lower

threshold A . Pairs having weights between

these thresholds are classed in the “don’t know”

category. These pairs must be followed-up using

a computer-assisted manual approach.

Once the thresholds are set, bounds on the

318
.

probabilities of false matches and false nonmatches can be computed by integrating the portions of the distribution tails lying beyond the

threshold values. ~y tabulating weights of

candidate pairs, the matcher program could provide information on the error rates associated

with the component values. These error rates

are useful for verification. The success of

this technique will depend upon our ability to

fit a curve to the observed tails of each mode

in order to perform the integration.

2.5 Component Values

The matcher algorithm will use a table of

weights derived from investigations on weight

methodologies (see 2.2). One weight would be

associated with each predetermined component or

identifier value. The algorithm would store the

most frequent values of components from tables

prepared by other programs and component values

not in this list would be given a relatively

high weight. Thus, popular names (which have

low discriminating power) would receive lower

weights than comparatively rare ones, without

requiring the construction of exhaustive lexicons. Value tables would only be used if successful results could not be obtained by considering a component to have a single weight.

The weight tables for the program will include

expected frequencies of occurrence of component

values, error rate information and number of

records processed for past data. Information

from the current data could be used to update

the weight tables as the program gains experience

matching.

?.6 Flayesian Adjustment

In addition to keeping records of expected

frequencies (based on earlier observed frequencies), the program will also keep observed frequencies of a block for a specific file. If

there is much deviation between observed and

expected frequencies, temporary modification to

the weights can be considered. For example, in

a Spanish-speaking area, the name “GONZALEZ”

might occur relatively more frequently than it

does on the average for the United States.

Missing data values could also result in the

reduction of discriminating power of a field

within a block.

We have incorporated a Bayesian adjustment

technique into our experimental matcher. We

have assumed a Beta prior distribution and are

investigating parameter estimation techniques

for this distribution.

2.7 Distance Metrics

Simple agreement/disagreement patterns of

component pairs are not adequate for character

strings and numeric data. We are investigating

prorating the weight on the basis of degree of

agreement.

A number of character-string comparison routines for component values which do not agree

completely are available, including the routine

designed by Jaro and Corbett, which has been

used for 12 years in the UNIMATCH system [3].

Through the use of such a routine, words can be

matched despite spelling errors. The UNIMATCH

algorithm is an information-theoretic comparator

which takes into account phonetic errors, transpositions of characters and random insertion, replacement and deletion of characters. These approaches will be tested in the matcher algorithm.

2.8 Assignment

After blocking, the program uses the various

techniques described above to construct a composite weight for each pair in the block. These

weights are stored in a cost matrix and the

assignments can be made by solving the problem:

n

Maximize z=; Z Cij Xij

i=l j=l

Subject to

n

E ‘ij = 1

j =1

n

Z Xij = 1

i=1

j=l,Z,...,n

where Cij is the cost (weight) of matching record

i with record j. X is an indicator variable. The

matrix is made square by the use of dummy weights.

This problem is the linear sum assignment

problem, which is a degenerate transportation

problem that can be solved efficiently using

only additions and subtractions. Once an

optional assignment set is obtained, the

Fellegi-Sunter decision procedure is applied to

determine whether an assignment represents a

match, a clerical review case or a non-match.

3. MATCHER IMPLEMENTATION PLANS

An experimental program has been implemented

that incorporates the techniques discussed in

this paper so that controlled tests can be conducted without undue difficulty. This program

is operating on an IBM Personal Computer.

For production matching it is anticipated

that not more than two passes will be required

to match nearly all records not requiring professional review. Records failing to match on

blocking components in the first pass would have

a second chance to match on different blocking

components during a second pass. By selecting

two high discrimination/low error rate sets for

blocking, the probability of intersecting errors

is minimized. The high discrimination/low error

rate property for a component means there is a

high probability that the component can accurately predict a matching record pair. BY using two

such components, the chance of a successful

match is relatively good, since errors on both

components would be required to reject a record.

We plan to utilize experience gained by Statistics Canada (the Generalized Iterative Record

Linkage System [2]) and others in our investigation into the problems of record linkage. It is

our intent to’have an operational

use with the 1985 Census pretest.

most important applications will

evaluation for the Decennial Census.

program for

One of the

be coverage

319
tions of Uncertainty. Spring Joint Computer

[1] Fellegi, I.P. and Sunter, A.B., A Theory Conference, 1972, AFIPS -- Conference Profor Record Linkage, Journal of the American ceedingsl Vol 40, 19~

Statistical Association, Vol 64,

1183-1210

PP

---

1.4J Kelley, F

[2] Generalized Iterative Record Linkage Systems for Recor..

(GIRLS), Institutional &Agriculture, Survey

-,,,--7- .,,

certainty. Proceedir

Methods Oivision, Statistics Canada, Inter- Statistical As~nriati

nal Documentation, Oct. 1978 Xectlon, h’1

=ns 3 l~a 4 were prepared with the

[3] Jaro, Matthew~ UNIMATCH - A Computer System assistance of 1’).Childers.)

R. Patrick. Blocking Considerations

PII Iinkana Ilnder conditions of Unngs of the American

.---.-.ion, Social Statistics

ldelpia, 19 ~4s PP. 602-605.

320
RECORO-KEEPING MD EMTA.PREPARATION PRACTICES TO FACILITATE RECORD LINKAGE

Martha Smith, Statistics Canada

Lack of adequate personal (or “entity”) identifying information and appropriate documentation on

what is contained in historical files can be major

stumbling blocks i.n carrying out long-term followup studies. Over the past few years, considerable

experience has been gained in the use of existing

administrative (e.g., industrial emp.Toyee, mortality, hospital, cancer, marriage, birth) survey and

census data Files for record linkage studies in

Canada [1-J].

The purpose of this paper is to give some practical pointers for agencies and individuals

involved in implementing future linkage projects,

particularly those where large historical files

are treing’used, md where no unique identity numbers are available. Specific examples will be

given here which relate to occupational md environmental health studies, but many of the record

linkage problems and their solutions apply also to

other areaa of statistical research.

Organizationally, the present paper is divided

into six main sections. The firat section gives

the main results and conclusions. The second section outlines the kinds of data files required for

occupational ad environmental health studies. The

third section describes the role that various

broad categories of records can play in the linkage process. The fourth section gives examples of

the practical problems in the preparation of

existing files for linkage, along with the methods

and some of the software developed to cope with

these problems. The fifth aectim deals with the

probabilistic matching technique end the art of

designing an efficient linkage operation. The laat

section makes recommendations for future record

keeping and data preparation practices to facilitate record linkage.

1. NAIN RESULTS tM)CONCLLJSItMIS

A generalized record linkage system has been

developed based on the concepts of probability a~d

the use of ‘weighted’ record comparisons [4-7].

The probabilistic methods developed have several

desirable featurea:

records can be linked which lack unique numerical identity numbers;

records are able to link despite diacrepanciea

which may exist between identifying particular;

‘weights’ can be assigned for agreement, disagreement, and partial agreement; and

- the technique discriminates between rare and

common values of a given identifier.

On the basis of fairly extensive experience

with computerized record linkage of a probabilistic kind, using the generalized iterative record

linkage system (GIRLS), it seems unlikely that the

technology and the software will be major limiting

factors in the future. The major costs, which can

limit the application of the approach, are often

likely to be associated with the need to do data

entry for additional identifiers in a standard

fashion, if these have not already been captured

in machine readabJe form. For historic data files,

lack of appropriate documentation and standard

data entry rules can cause problems. Some software

has been developed to aid in the preprocessing of

such files. It ia therefore recommended that if

the files are to be used for record linkage, sufficient identifying items be captured at the time

of the initial data entry. Compromises whereby the

amount of identifying information is restricted in

order to reduce costs will be reflected in reduced

accuracy of the linkages, and of the kinds of uses

that can be made of the files.

Certain files may serve in the role of intermediate files that facilitate the linkage of other

filee.

Procedures to evaluate the quality of the linkage should be planned early. For example, it may

be possible to incorporate known alive cases in a

mortality search; to carry out independent manual

follow-up on a sample of the file ad compare with

the computer reeults; or to carry out an alive

foJ low-up to complement the death search.

Improvement of present data sources and the

development. of new sources wuld seem to be necessary if further demands for occupational and environmental health statistics are to be met. A

checklist of data items to be collected has been

described elsewhere [3-4].

Collaboration and co-operation among individuals md sgenciea are often required to complete

studies. Suitable communication networks among

investigators must be established, particularly if

there is a long geographic distance between the

interested groups.

11. KEY ELENENTS IN A TYPICAL FOLLOW-UP STUDY

TNE KINOS ff CMTA FILES REQUIRED

Certain general principles shape whatever epidemiological studies for long-term health effects

are mdertaken and influence the nature of the

procedures for data gathering and anaIysis. The

data gathering could include examining data systems already available which could facilitate the

study. The requirements for identifying information are similar whether one is looking for

changes to the exposed individual, or for inherited changea affecting the offspring frcm such

individuals.

The key elements for data collection that

shcruld be included in any such study are described

in [4]. A typical follow-up study often requires

some knowledge of work histories, dose histories,

health outcomes and the personal identification of

the individuals involved. The software available

must be capable of bringing all the various relevant files together at appropriate times.

The kinds of linkages involved may be a series

of internal linkages to identify data pertaining

to the same individual (e.g., to create individual

work histories) ae well as two-file linkages (e.g.

321
to match a wrk record against a death record).

The matching techniques can use individual identification numbers (e.g., Social Insurance Numbers),

probabilistic matching techniques, or a combination of the two.

III. THE FINCTIONS OF BROADCATEGORIES ff

SOURCE RECORDS

The kinds of source records required for studies of delayed health effects may serve one or

more of four possible functions in the follow-up

process.

First, such records may identify an individual

as belonging to an “at rick” population (or to a

“control” population with which the other is to be

compared). In this case they are referred to as

“starting point” records which initiate the

follow-up process.

Alternatively they may identify an end effect,

such as cancer or death in an individual wlnois a

member of a study population, in which case they

are referred to as “endpoint 1, records. One examPle

of an endpoint file is the Canadian Mortality Data

Base consisting of the records of all deaths in

that country dating back to 1950. Follow-up thus

will consist of using a file of starting-point

records to search a file for potential end-point

records, and of linking those records from the two

files which relate to the same individuals.

The third possible function of a record file is

that of an intermediate file which facilitates the

aesrching and the linkage process. For example,

where a starting-point record carries the maiden

surname of a woman *O later married, and the endpoint record contains her married surname, the

search of the endpoint file may be more productive

and accurate where reference can be made to another file, such as a marriage file or the Social

Insurance Nunber Index which contains both of

these names.

The fourth function of record files is as a

source of the detailed statistical variablea

required for the analysia. For exaple, linkage

may be required to bring together individual work

histories, dose histories and smoking histories.

In considering the possible uses of various

available files, all four functions must be kept

in mind.

IV. PREPARING THE INPUT FILES

Prior to linkage of any kind, the records being

used need to be brought into the formats that are

required for making the necessary comparisons, and

into the sequences that are appropriate for the

linkages. The quality of the identifiers needed

for linkage may also be tested by looking for

blank fields and for values of the identifiera

that are not permitted (such aa day of birth .

32). If data collection and data entry have not

been done with record linkage initially in mind,

this phaae can be quite time consuming and costly.

We have found the Statistical Analysia System

(SAS) very helpful at this atage, and as a routine

we systematically scrutinize the values of fields

in files to be used in linkage. These are compared

with any available documentation regarding coded

values and their meanings. One can check how many

fields have non-missing values, valid values,

ranges, codesets, or invalid characters or values.

Whereas blank fields can only be filled from other

sources, fields which have unacceptable values

may sometimes be corrected.

One may wish to create a new field for each

record to indicate the !lavallability~l and validity of fields on the same record. For example the

value w120112001M could indicate “present and with

the valid code range” (l), “present, but with an

invalid code” (2), or “absent” (0). A SAS distribution of this word facilitates one’s assessment

of the likelihood that one will be able to link

the files.

It ia necessary to obtain copi.ea of the forms

of the original source docunents, the record layout-a and any file documentation, along with detailed information regarding how the ad+ninistrative system works.

Some problems one may expect to encounter have

to do with the quality of the records, and mme

methods which have been used to deal with the problems are as follows:

(1) Lack of a standard format - particularly for

the name and address fields

If name fields have been entered in string format

and if a variety of delimiters have been used to

separate surnames from forenaes, it may be necessary to put the values of the fields into a standard fixed format. It is particularly difficult to

separate the components in a name field if blanks

have been used as the delimiter. A simple NAMESCAN

routine has been developed, which changes all

alphabetic characters to lrA!~ and leaves all other

characters intact. A SAS distribution can then be

made to look at the various patterna on tlw file.

When standardizing name fields, titles should

be put in a separate field e.g., Mrs, Jr, Sr. Twopart surnames can be concatenated (SMITH-JONES to

SMITH30NES) and retained along with alternate entries for SMITH and for 30NES, special characters

may be eliminated (O’CONNOR to OCONNOR) and prefixes concatenated (VAN DYK to VANDYK). A prefix

list is ahown in Table 1. Geographic and disease

codes will usually have changed over time. It may

be necessary to recode fields so that all records

share a common system of codes, or to use ranges

of codes that sre comparable.

Table 1. —List of Surnme Prefixes

BON

D

DA

DE

DEL

DEN

DER

DES

DI

Do

DU

EL

FITZ

L

LA

LAS

LE o

LES ST

LI STE

LU VAN

LOS VANDEN

M VANDER

MAC VON

MC VONDER

(2) Spelling errors

To get around spelling errors in surnames, a

phonetic encoding scheme can be used. We currently

use the modified New York State Identification

and Intelligence System (NYSIIS) surname code [8].

In the 1950-79 Mortality Data Base file, there

were about 200,000 unique surnames which mapped

into about 40,000 NYSIIS codes. Based on evalua322
tion studies OF esrlier linkage projects, we are

currently considering msking modifications to this

coding scheme based on some of the phonetics

involved with Csnsdisn names (particularly French

names).

(3) Incomplete files

Due to the rules regarding cutoff dstes for preparation of statistics from certain files, one may

find that records are missing due to Late registrations. If the files are assigned nwnbers in an

orderly fsshion, a sequence and continuity check

of the nwnerically sorted file can be carried out,

missing gaps listed, as well as the first and last

record nunbers of the files. We have done this for

the Pbrtality Data Base file. Where exposure data

files have been maintained separately from the

Msater Identification file, some utilites can be

used to match files for “orphan” records i.e. an

exposure record with no corresponding record on

the master identification file or vice versa.

(4) Missing identifiers

These can be assessed from SAS output of individual fields, as well as using the availability

word for a number of variables. It is advisable to

split a field into its component parts - for example, for birth date use yesr, month and day. Sometimes sex code has been found missing from files.A

list of all forenames appearing on the kbrtality

Data Base has been created. This has been used to

impute a sex code e.g., I=male only, 2=female

only, 3=either male or female forename. Sex code

is required so that appropriate weights can be

assigned for forensmes in the frequency weighting.

(5) Lack of documentation of old historicalfiles

Here we have found SAS output very helpful, and

created documentation regarding the contents of

each field.

(6) Possible correlateddata items

Certain data fields may be correlated, therefore

caution has to be taken when assigning weights to

these items e.g., birth place of father, mother,

and a child. In certain instances the information

relates to identical items (e.g., an address and

postal code); in other cases it maY reflect rnultipls wrong guesses (e.g., a birthdate being incorrectly reported).

(7) Duplicaterecordsmt properlyidentified

It is important that for a two-file linkage, all

records that are known in advance to relate to the

same individual be properly identified. This is to

ensure that any groups to which either record of

such a pair may belong can be combined by the linkage system. Typical examples are records relating

to wmen *O have both a maiden name and a married

surname. One is unlikely to want to discard one

record and keep the other, because there may be

records on the other file that relate to either

surnane. A field can be added to the record to

contain a value of 1,2,3 etc. to indicate whether

this is the first, second or third “duplicate”

entry for this record. If no duplicate exists, the

value of the field can be set to zero. Such duplicate records must all be assigned the same unique

nunber (in the GIRLS system this is referred to as

the SEQUENCE number).

If an intermediary file is used, alternative

entries can be put in with different versions of

the identifying information. These may be either

entries from both files separately or in hybrid

form (i.e. certsin items from one file and other

items from the other file).

(8) An internal linkage should have been done

firat

Any file that is going to be used for a two-file

linkage, should first be examined to determine

Aether an internal linkage is required to bring

together all records which refer to the sane individual (or entity). If one is uncertain about

whether there are duplicates, sometimes a fairly

inexpensive first check may be to sort the file by

surname, first forename, and birth date and to

create a microfiche copy of the file for visual

examination. A great deal of work in a two-file

linkage can be saved by first unduplicating in

this fashion the two files that sre to be linked.

(9) Length of data fields

If two fields are to be compared, the lengths of

the dsta fields need to be compatible. For

example, as a standard, we encode ten letters of

the surnsne into the NYSIIS code. If the nunber of

letters in one file is less than ten characters,

problems can arise when the codes are compared. It

is therefore advisable to use a surname field that

is ten characters or greater. If special characters were originally used, the data entry of the

field should be large enough to allow for the elimination of these special characters in the preprocessing.

(10) SePrating out values tiere the same field

was used for more than one purpose

As an example, the same field on some files may be

used for maiden as for alias surname. l%e may wish

to try to separate out the two types of surnames

that have been entered, so that during the linkage

step appropriate rules can be used.

(11) Several mique nusbering systems used over

time

In certain files, several numbers may have been

used over time to refer to the same individual. In

administrative sytems, there may be a rather different problem; one often needs to clarify whether

such numbers have ever been reassigned to other

individuals.

In certain cases, one may wish to chain all the

various numbers that were used by the same person

over time and use this as a pocket identifier

within Mich a probabilistic match could be made.

v. PRDEABILISTIC RECORD LINKAGE TECHNIQU3

The Basic Principle

There are three major difficulties to be overcome in order to achieve efficient record linkage.

The personal identifying items are often inadequate to discriminate between the person to whom a

record truly refers, and other persons in the population who have similar names. A second difficulty arises because when people report personal

identifiers they frequently make mistakes. The

third difficulty srises becauae of the large volume of records involved in record linkage. Some

related difficulties include the setting of appropriate threshold vsluea for acceptance and rejection of linkages, deciding how most efficiently to

carry out a multi-step operation, deciding on the

number of partial agreements to use and the selection of pocket identifiers.

The objective of the Generalized Iterative

Record Linkage System was to make it possible for

computer procedures to efficiently carry out the

data processing involved in the probabilistic

323
matching of data files, and to do so easily for a

wide variety of diverse data requests. The GIRLS

system has involved optimizing four major tasks:

~ the search operation, (2) the decisionnrnaking

step, (3) the grouping of records, and (4) the retrieval of information.

In the searching step, the sequencing information is used as a means of avoiding the many unprofitable pairings that would hsve to be exsmined

if every record initiating s search were compared

with every other record in the file being

searched. Generally for searches of the Fbrtality

Data Base, comparison pairs are created only where

both the sex and the phonetically coded form of

the surname agree.

For other applications, the sequencing may be

by one of several systems of nunerical identifier

or by phonetically coded surname. Regardless of

the means by which the record pairs are brought

together, the next step will be a detailed comparison of the remaining identifiers. This is necessary even where the nuneric identifiers agree,

because such identifiers are occasionally used

improperly by persons to whom they do not belong,

and sometimes even by a relative of the rightful

owner ho has the same surname.

At the present time, a test is being made to

provide a measure of the usefulness of employing

personal identifiers from the Social Insurance

Number (SIN) index file to supplement those from

the work records, for the purposes of carrying out

automated death searches. Not only are the names,

birth dates and such more likely to be recorded on

the SIN record, they are also more likely to be

complete, snd as well they will frequently include

the mother’s maiden surname, which carries considerable discriminating power and is quite unlikely

to be available from my work record.

In the decisiommaking step, each of the remaining identifiers is compared in turn, wherever

it ia represented on both members of the comparison pair of records.

The odds associated with any specified outcome

from the comparison of any identifier are:

freq of specific outcome in linked pairs

odds =

freq of specific outcome in unlinked pairs

This applies equally to agreement, disagreements

snd to any degree of similarity or dissimilarity

no matter how it is defined (as long as both definitions are identical above and below the line).

When pairs are sorted in descending order of

total weight, a Point 1S reached at which the

record pairs should be judged mlinkable or borderline. To calculate where this threshold should

be, two further values are required to be weighted

for a two-file linkage. These are:

(1) the likelihood thst the individual is represented in the file being searched, so that there

is a potential for linkage, and (2) the size of

the file being searched, since the opportunity for

fortuitous sgreement increases in proportion to

the file size.

The logarithms of both of these values will be

negative. When added in with the weight from the

identifier comparisons, the resultant sum is known

as the “absolute total weight!!.

W* . W +log2 Na(L) + log2 1

tiere, Na K

W* = log2 of the absolute odds in fsvour of a correct linkage;

W = log2 of the relstive odds in favour of a correct linksge ❑ WI + W2 + W3 . . . . where these are

each logs to the base 2 of the odds ratioa for the

successive identifier comparison outcomes;

Na and Na(L) are respectively, the total nunber

of records in the file initiating the aearchea and

the nunber out of these that will be linked with

matching records in the file being searched (or a

reasonably close estimate of Na(L)/Na may be used

initially); and

Nb . the size of the file being searched.

TO calculate wI, w2. . . . . for reasons of convenience it is desirable to treat separately the

data derived from linked pairs and that which

applies to unlinked pairs. If w is the net weight

for the particular identifier comparison outcome,

log2 (frequency in linked paira) is the negative

component of this net weight, and log2 (1/ frequency in unlinked pairs) is the positive component of the net weight.

Because the negative components of weight vary

with the quality of the file initiating the

searches i.e. with the reliability of the identifiers as recorded on that file, these negative

components need to be recalculated for each new

linksge before the final weighting is done. The

dsta may be obtained initially from preliminary

machine linkage, rwunerical linkages Mere svailable, or from manual linkages. Examples of how the

weights are obtained are discussed in refersnce

[9].

The pxmitive components tend to be stable where

the files being searched are the sane on succeaaive occasions (e.g., the death file) and can usually be calculated frca the frequencies of the

identifier values in that file.

Ths Art of Record Linkage

The art of designing an efficient computerized

linkage operation depends less won theory than an

intuitive perception of how best to carry out the

comparisons and what outcomes from these are most

likely to be revealing, so that they ought to be

recognized by the computer.

Some of the intuitively obvious refinements

that have actually been put to use in Statistics

Canada’s death searches have to do with:

(1) Recognition of partial a9reement outc~es>

e.g., of

surnames (three levels of agreement/disagreement);

given names (eight levels of agreement/disagreement, including agreement truncation where the

initials agree);

birth year (uP to 6 levels of a9reement/disagreement);

birth month (3 levels);

birth day (4 levels).

(2) Recognition of cross-agreement, e.g., of

initials (where there is no straight agreement);

month snd day of birth - as for initials.

(3) Recognition of degreea of compatibility/incompatibility e.g., in

last known alive year versus death date (up to 4

levels);

marital status (up to 4 levels for each status

on a search record).

(4) Comparison of place of work versus place of

death.

324

— —.—
(5) Calculation of age at the time of the matching

death to determine the likelihood OF death in a

particular year using life-table data.

(6) Use OF death File size for that same year as

influencing the odds for a fortuitous similarity

of the identifying particulars.

A potential refinement may be judged worth

retaining as a part of the Iinkage procedure where

it is used often enough in doubtful matches, and

makes a large enough difference in the final decision to link or not to link, to justify the possible added complexity in the programming. The GIRLS

system makes it possible to gather such data aFter

a preliminary linkage and sgain after a final production run.

The best tactic when designing a linkage procedure for a specific operation is to gather such

empirical data after s preliminary linksge so that

the procedure csn be revised before the final

weighting. The information needed earliest has to

do wit+ the frequencies among linked pairs of the

different comparison outcomes recognized by the

preliminary linkage procedures. The tabulations

(“m fo outcomes”) should recognize all the

comparison outcomes likely t.o be useful in the

decision process.

We often find that. what. one learns by looking

at some manual linkages first can be very helpful

in plannlng a study. This aids in working out the

appropriate methods to use and in preparing cost

estimates. @e may have to decide *ether there is

enough identifying Information avsilable to do the

linkage. To get an overall estimate of this, one

can first imagine how strongly unfavorable the

odds would be if one dld not know whether any of

the items agreed or disagreed, and were linking to

a file of a given size. Then, as one compares each

item, in turn, and assumes they agree, this will

demonstrate the possible extent of the increaae in

likelihood favouring correct linkage. One can use

a global overall weight. for the items employed in

this exercise, snd hence get a ballpark impression of whether or not there are enough items

available to make it work (see Tables 2 and 3 for

an example).

After the linkage status decision has been

made, the system can identify groups of records

which potentially refer to the same entity and it

can indicate where conflicts exist. A conflict

exists where groups do not fit your requirement

e.g., one record relating to more than one death

record. In the GIRLS system there are two ways OF

resolving these conflicts - automatic resolution

by the system based on the ‘best’ linkage, or by

manual resolution. A combination of the two often

mrks best.

The retrieval of information operation of the

system ia designed to quickly and concisely aid

the user in making decisions regarding the future

direction of the linkage process. The GIRLS system

can produce reports at the detailed level on

weight sets, linked pairs, group reports, information about the linked pairs, and it can also produce estimates for updating the weights. One msy

wish to produce reports based only on links for

which a given condition is true (e.g., all links

above a given *ight) or for *ich a condition

using vsriables on the source records may be true

(e.g., all knom dead caees aa knovm earlier on

the worker’s nominal roll file).

VI. FIJTWIE DIRECTIONS

There are three main directions for our future

endeavors:

(1) The improvement and expansion of existing

search d linkage facilitieswhich could include

further development and enrichment of our current

files (e.g., addition of occupation and industry

on the death file). The NYSIIS code routine needs

to be evaluated more fully taking into account the

kinde of names found in Canada. A dictionary of

accredited comparison procedures needs to be

developed from past linkage studies that could

serve as a guide for future studies. Results from

earlier studies need to be carefully evaluated,

Table 2. —Ex=ple of a PossibleCensus-to-DeathLinkage— Likelihoodof FortuitouslySelectingthe

Correct Ckath Record,Using no IdentifiersOther than Ssx (Aeamee enweration in 1971 at

age 42, death in 1979 at age 50, and male sex)

WEIGHT CUMULATIVE

WEIGHT

COMPARISON ITEMS ODDS CUMULATIVE NOTES

ODDS

(lo x log*)

Random chance of finding death in 1979

male death file, assuning it is there 1/96,532 1/96,532 -166 -166 1

Likelihood of dying in that year,

if alive st the beginning of it 1/131 1/12,645,692 - 70 -236 2

Likelihood of being alive at the beginning

of 1979 if enumerated in 1971 1/1.04 1/13,151,520 - 1 -237 3

Note: (1) From death file size, For males dying in 1979.

(2) From life tables for likelihood of death in a 12 month period, for a male of age 50.

(3) From life tables, For the likelihood of survival to age 50 among a cohort of males still

alive at age 42.

325
Table 3. --hmnple of a PossibleCensus-to-Dsath Linkage -- Cumulative Effect of Successive Agreements on the Odds in Fsvourof a CorrectMatch, when all Identifiersare Presentmd Agree

WEIGHT CLWJLATIVE

WEIGHT

IDENTIFIER AGREEING ODDS CUMULATIVE

ODDS

(lox 1092)

(Random chance) 1/13,151,520 -237 -237

Surname 2,287/7 1/5,745 +112 -125

First initial 14/1 1/410 + 38 - 87

Second initial 14/1 1/29 + 38 - 49

Rest of first name 87/1 3/1 + 64 + 15

Marital status 26/1 8/1 + 14 + 29

Year of birth 56/1 437/1 + 58 + 87

Month of birth 12/1 5,242/1 + 36 +123

Birth prov/country 8.6/1 45.078/1 + 31 +154

Ethnicity 3.5/1 157,773/1 + 18 +1 72

Parental birthplaces 1. 2/1 189,328/1 +2 +174

Industry, major 6/1 1,135,968/1 + 41 +21 5

Occupation, major 11/1 12,495,648/1 + 31 +246

Residence province 4.4/1 54,980,851/1 + 21 +267

Residence city 72 [1 3,958,621,272/1 -r. 62 +32 9

particularly with respect to the use of intermediate files and the use of alive follow-up procedures as were used in the Ontario miners study

[10]. Further refinements are needed in developing

a file of non-links to get weight estimates, particularly kere the comparisons are fairly complex

(e.g., weighting of forensmes).

(2) The development of new and much needed data

bases tiich would identify, in a more systematic

fashion than heretofore, the occupational and environmental circumstances of people, and which

could be used as startinq point files, to initiate

the searches for subsequent health histories. Here

data collection rules and Forms need to be more

clearly developed which could be used by industry. Use of new files such as census of agriculture, farm registers, and census of population

files can be exploited. The use of existing files

for alive and morbidity follow-up need to be explored.

(3) The exploration with other agencies of any

collaborationsthat would be productive for generation of the required statistics, and for setting

up the necessary cormnmication network and financial support to implement such recommendations.

ACKNOWLEDGMENTS

The author would like to thank Dr. Newcombe for

his contribution to this paper. The opinions expressed in this paper are those of the author and

do not necessarily represent the views of Statistics Canada.

REFERENCES

[1] Smith, M.E. and Newcombe, H.B., “Automated

Follow-up Facilities in Canada.” AJPH Vol.

70, No. 12, pp. 1261-1268, 1980.

[2] Smith, M.E., “Long-term Medics] Follow-up in

Canada.” It): Pete, R., schneidermerr, M. eds.

[31

[41

[5]

[6]

[7]

[61

[9]

Quantification of Occupational Cancer.

Banbury Report 9, Cold Spring Harbor Laboratory, pp. 675-688, 1981.

Smith, M.E., “Development of a National

Record Linkage Program in Canada.” American

Statistical Association - 1982 Proceedings of

the Section on Survey Research Methods, pp.

303-308, 1982.

World Health Organization$ “Guidelines for

the Study of Genetic Effects in Human Popu

lations.” Environmental Health Criteria 46

(in press).

Howe, G.R. end Lindsay, J., “A Generalized

Iterative Record Linkage Computer System for

Use in Medical Follow-up Studies.” Computers

and Biomedical Research, Vol . 14, pp. J27-

540, 1981.

Smith, M.E. and Silins, J., “Generalized

Iterative Record Linkage System.” American

Statistical Association - 19BI Proceedings of

the Social Statistics Section, pp. 128-137,

1981.

Hill, T., “Generalized Iterative Record Linkage System: GIRLS.” Research and Genera] Systems, Informatica Services and Development

Division, Statistics Canada, Ottawa, 1985.

Lynch, B.T. and Arends, W.L., “Selection of

a Surname Coding Scheme for the SRS Record

Linkage System.” Statistical Reporting Services, U.S. Department of Agriculture,

Washington, D.C., 1977.

Newcombe, H.fl. and Abbatt, J.D., “Probabilistic Record Linksge in Epidemiology.” Red Book

Series Report No. 5. Eldorado Resources Limited, Suite 400, 255 Albert Street, Ottawa,

Ontarioo, KIP 6A9, November 19Fj3,

[10] Muller, J., Wheeler, W.C., Gentleman, J.F.,

Suranyi, G., Kusiak, R., “Study of Mortality

of Ontario Miners.’! International Conference

on Occupational Radiation Safety in Mining,

VOJ. I, pp. 335-343, 1984.

326
GENERALIZED ITERATIVERECORD LINKAGE SYSTEM

Ted Hill and Francis Pring-Mill, Statistics Canada

The Generalized Iterative Record Linkage System (GIRLS)

project was initiated at Statistics Canada in 1978. This paper

outiines the concepts behind the system, and summarizes how

these have been implemented to provide a Powerfd tool suitable for a variety of record lhtkage applications.

1.0 RECORD LINKAGE AND GIIU.S

Record hkage is the process of identifying two or more

records which refer to the same entity. An entity could be a

person, or a business, for example.

In the case where records have unique identifiers (for

example, social insurance number), the process of linking is

relatively simple as it involves matching on only one field.

However in cases where records do not have unique identifiers,

information from several fields typically has to be compared to

estimate Use likelihood that a potential link is a ‘true’ one.

For these cases record linkage is a probabilistic process, and it

is for this situation that GIRLS was designed.

GIRLS stands for the “Generalized Iterative Record Linkage System” which has been developed at Statistics Canada,

starting in 1978. Since then, the system has been systematically

maintained and enhanced on a regular basis.

GIRLS provides a command language in which you can

write YOUI own rules for comparing records. statisticallydenved weights are attached to potential links according to the

outcomes of these comparisons. Your GIRLS commandsare

automatically translated into PL/1 (a high-level programming

language), compiled, link-edited and executed on the input files

to generate an online project database of potential links and the

records involved in them. Using other GIRLS commands, you

can then query this database to see the results. If these are

not satisfactory, you cart update the &tabase in various ways,

or simply change your comparison rules and try again.

To this end, GIRLS breaks the linkage process into a

sequence of distinct phases. Each phase involves deciding on

vahses for system parameters, examining their effec~ and

adjusting the values as necessary before going on to the next

phase, Results from later phases often suggest adjustments to

earlier phases. Because phases are distincL YOU~n =@’ retrace your steps, run an earlier phase again with new adjustments, run intermediate phases as they are, and quickly catch

up to where you were. This is why GIRLS is called an ‘iterative’ record linkage system.

The principal aims of GIRLS are:

1. To enable you to develop the best comparison rtdes

and statistical weights for the purpose of your linkage

prqjecL

2. To provide a convenient framework for this development

3. To encourage iterative refinement through a sequence

of phases and reports.

4. To make the final linkage fa% cheap, and accurate,

Examples of GIRLS applications include:

1. ‘Follow-up’ studies

Health Division at Statistics Catta& currently runs linkage projects with files provided by employers of individuals exposed to potential health hazards in the

cow of their work (e.g. uranium miners). These are

linked with the Canadian Mortality Database to check

that the proportion of matches found is not above normal.

Such studies can detect rials to health associated with

particular occupations, thus pointing the way to causes

of disease. They can also aid in testing the long-term

effects of curative measures.

2. Building ‘case histories’

Separate records referring to the same person often

accumulate in a system. For example, a new record is

often made each time an individual is admitted to a

hospital. GIRLS cart conveniently bring rhese records

together, enabling larger composite records to be made

representing ‘case histories’ for individuals.

2.0 FEATURES OF GIRLS

In the past, record linkage systems have usually been tied to

methodologies suited to particular application requirements.

GIRLS provides a general solution to developing particular

linkage systems.

Its principal features are:

1.

2.

3.

4.

5.

6.

7.

A sequence of phases encourages iterative refinement of

the linkage process.

The full power of database mamgement technology is

provided. This includes: automatic maintenance of data

integrity across separate files, checkpointing facilities for

project recovery, as well as back-up and restore procedures.

BOrh ‘one-’ and ‘two-’ tile linkages can be performed.

(One-tile, or internal, linkages can be useful for unduplicating a file or creating composite records.)

A variety of samples of records from the input files

can be extracted for the purposes of experimenting.

A simple but powerful GIRLS command language is

provided to write comparison rules, update the project

database, and obtain a wide variety of reporm at many

levels of detail.

The commands provided for writing comparison rules

can detect full agreement, various levels of partial

agreement, disagreement, and missing values. They m

also specify cross comparisons of different fields, as

well as rules to be executed conditional on the outcomes of previous comparisons.

For special purposes you can also write your own PL/1

code and have it included in the Compare program

automatically generated from your GIRLS commands.

327
8.

9.

10.

11.

Statistically-derived weights are generated and attached

to links to reflect the probability that the records being

compared refer to the same entity.

Potential links are automatically classified as: rejected,

possible, or definite, by comparing link weights against

threshold values. You specify these threshold values,

and you can easily adjust them. You can also reclassify links manually.

Records which refer to the same entity are grouped.

Where conflicts exist within groups, tltese can be

resolved either automatically by the system, or manually

on a record-by-record basis. (For example, a conflict

wotdd exist when records are expected to link to at

most one record on the ‘other’ file, but a group contains some which have linked to several records.)

Both batch and online modes are available. Online

enablesfast iterative adjustment of system parameters

by providing quick feedback as to the current state of

the project database.

3.0 BASIC OPERATIONS

The phases of the GIRLS system can be grouped into three

main operations.

1. searching,

2. Decision Making.

3. Grouping.

This is shown below:

Figtue 1: Basicoperations

OPERATION PRINCIPAL PHASE uSER

Searching

B - ‘+

~,,,on [1=11> I ;

● USER ●

‘LB ~ :

Grouping

m L ‘ ‘E

3.1 Searching

In this operation, pairs of records are compared field by field

according to comparison rukes You specify. Theoretically, every

possible pair of records should be compared. However the

number of possible pairsin even a small file is very large, So

for practicak reasons, records are first blocked into smaller

328

‘pockets’ in such a way that it is realistic to look for links only

within pockets.

You use GIRLS commands to define your input files, indicate which fields define your pockets, select your sample of

records, and specify rules according to which your records are

to be compared. Your GIRLS commands are then automatically translated into a PL/1 program, called the Compare program, which is executed on your input files to produce tie

project database of potential links,

You can write rules to compare fields with values that are:

character (e.g. surname), numeric (e.g. birthyear), or coded (e.g.

for fields with a small number of discrete values such as birthplace). Your rules can be made conditional on particular outcomes from previous comparisons. You can also specify cross

comparisons of different fields (for example, first given name

with second given name, in the event that straight comparisons

of each field have not already produced an agreement). If

your rules do not fit conveniently into the firmat of the

GIRLS command language, you can also write them yourself in

PIJ1 and have them included in the Compare program.

The outcome of having executed a comparison rtde can be:

agreemen~ one of various levels of partial agreemen~ disagreemen~ or mi~ng. You can Wecify a ‘dobal’ weitit to be

-.

attached in the event of each one of these possible outcomes,

3.2 Decision Making

In this operatio~ the potential links generated by the Compare

program are evaluated. This involves updating link weights

and comparing them against threshold values to decide which

to keep and which to reject. Link weights are updated with

‘frequency weight sets’ which reflect the probability of particular a~eemems happening by chance. These weights are

derived according to formtdae developed by Geoff Howe’,

Mike Eagen, and David Binder from methodologies proposed

by Howard Newcombe’, Ivan Fellegi and Alan StmteP.

After weight update, the status of links k determined by

comparing their total weights agahtst two threshold values.

Links with we@hts above the upper ae classified as ‘definite’,

those with weights below the lower threshold are ‘rejected’,

those with weights between the two are ‘possible’. This is

shown in Figure 2, which is explained as follows:

Figure 2 Link thresholds classify links into three statuses

Link thresholds: Lowe r Upper

Number

of

record

pairs

REJECTEO

● *

●

●

●

●

●

●

9

●

,

true

unmatched

a

● +

.-I-I-H

POSSIBLE

● .

.* . . . .

. ..0.....

. . . . . . . . .

. . . . ...0.

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

DEFINITE

*.*

.

●

true ●

matched 8

●

●

. . .

** .

false $ uncerta i n

unmatched

+ false

ma Cched

TOTAL WE I GHT . . . ...>
Let all possible record pairs be divided into two poptdations: those rword pairs which are ‘truly matched’, and those

which are ‘truly unmatched’. The goal of the linkage project

is then to find the members of the ‘truly matched’ population.

Because it represents all possible record pairs which do not

match, the true unmatched population will be far greater than

the uue matched one. This is shown on the left The smaller

u’ue matched population is shown on the righL The problem

is the overlap in the middle, because for these record pairs it

is not obvious to which distribution they belong.

The two tlreshold lines show how GIRLS handles this

problem area. Links to the right of the upper threshold are

considered ‘definite’, those to the left of the lower are cortsidered ‘rejected’, those between tie two are considered ‘possible’.

While permitting flexibility, this approach allows two types of

error which arty linkage project should aim to minimize.

First is the ‘false unmatched’ area on the lefL These are

the record pairs which have been rejected even though they

were part of tie true matched population, This can happen

when information is incomplete or imccurate on records which

‘should’ have matched. Second is the ‘false matched’ area on

the right,Thesewe therecordpairswhichhave beenaccepted even though they were part of the true unmatched poptdation. This can happen when records lmk very similar even

though they refer to different entities, e.g. the different members of the same family. At first glance, these two areas can

be minimized simply by setdng the thresholds far ap~ However this makes for many possible links in bemveett,which will

have to be resolved latel. By adjusting the thresholds and

inspecting various samples of links, however, you can choose

the best thresholds for your purposes.

3.3 Grouphtg

In thisoperation,tierecords are grouped according to the status of the links between them. Records may have just one

link to another record, or they may have several links to several xecords. Records joined either by possible or definite links

Me arranged into ‘major’ groups - which can be large. Within

major groups, lecords joined by definite links are furthef

arranged into‘minor’ groups. A major group may therefore

contain several minor groups, and it is the minor groups that

contain the best links.

At this stage, ‘conflicts’ may arise, typically when groups

are larger than you want them to be. The system identifies

conflicts for you based on your linkage reqtdremen$ e.g. oneto-one (i.e. groups are to contain pairs of records only, one

from each file). Resolving the conflicts can be done in either

of two ways, or both:

1. You can let the system resolve conflicts automatically.

This is called ‘automatic resolution’. In this case all

you specify is your linkage requirement e.g. one-toone, many-to-one, or one-to-many.

2. You mrt resolve the conflicts yourself manually. This

is called ‘manual resolution’.

You can also use both methods, automatic resolution first followed by an ezarnimtion of the results and some manual rearrangement where necessary.

4.0 ENVIRONMENT

4.1 Flowchart

Figure 3 shows a flowchart overview of the system. At the

top, two files of records (File ‘A’ and File ‘B’) are preprocessed for input to GIRLS. In the middle, records are

compared according to your comparison rul~ and an online

project database is created on the nghL This consists of

potential links (LINK), the records involved in them (DATA

and DATB), together with other files for use later.

On the lefL the user is shown interacting with the system

via GIRLS commands in the light of the linkage project

requirements and feedback from reports as to the ctment state

of the project database. At the bottom, two files of ‘matches’

are produced. On each output tile, each original input record

that has been linked is identified by a unique sequence number

and has a number identifying the group to which it has been

assigned.

Figrue 3: Flowchart overview of the system

~fi,’- >

..““-’’’’;’;’”’<- ..............

I

,“,”,lO,,,s,+ qT:n.

v 4

T

....................... LINK “

Comuand, >.

. .

,.

● uSER ● GIRLS

~;

&

. .

rape.rts .—.

:pJ:

.<—.

. . . . . . . . . . . . . . . . . . . . . . . . . . .

I

O“’’’”” @ @ :: I

L-Ji‘“”’”””’””

;

................

Post-Proc**s .

. . . . . . . . . . . . . . . .

4.2 Iteration

Iterative refinement of the linkage process can include adjustments to:

1. COMPARISON RULES

From the very many possible links which exist between

all possible record pairs, these rules determine which

are to be considered the ‘potential’ links to be written

to the project database. These rules cart be written,

re-writte~ ordered and re-ordered, so as to produce

enough suitable links as eflkiently as possible.

2. WEIGHTS

These are attached to links via the comparison rules

which applied to the records when the links were

formed. It is easy to modify these weights, and thereby select the best ones for YOUIpurposes.

329
3. THRESHOU VAWES

These determine the proportion of definite, possible,

and rejected links. The best mixture depends on the

aim of a pardcular linkage project, and is determined

by experimenting wifi the thresholds, and seeing the

types of groups which are formed.

For example, for a statistical study it may be satisfactory to find %. of the links. While for other types of

study, it may be necessary not to miss any of them.

4.3 GIRLS ProjectFiles

Making the iterative concept work in practice requires maintaining dam integri~ across several files when any one of them

is being updated, For this reason, an integrated database

approach has been taken using the RAPID Database Mattagement System developed at Statistics Canada.’The principal

RAPID files are:

1.

2,

3.

4.

WEIGHT FILE (IVGHT)

For eachfieldto be weighted,thiscontainsthevalues

forthefieldand thefrequencyweightforeachvalue.

LINK FILE (LINK)

For each ‘potential’ link between a pair of records, thk

file contains: - the outcomes (agree, disagree) for

each comparison rule - the current total weight of the

link - the current status of the link (definite, possible,

or rejected) - other system control information

DATA FILES (DATA, DATBj

These contain the records involved in potential links.

MAJOR GROfJP FILE (MJGR)

This contains information for each group, enabling

reports to be made according to type of group, e.g.

“display all groups having more than six records”.

4.4 Typical Scenario

A typical (abbreviated) scenario for a GIRLS linkage project

might be:

1. Write rules specifying how fields are to be compared.

2. Calculate frequency weight sets (a SAS function is provided to do this job).

3. Use sampling facilities to select a sample of records

from the pre-processed input files.

4. Adjust appropriate system parameters, both in batch

mode andlor online, until satisfactory results are

obtained.

5. Run the full lkkage in batch.

Using the system online greatly speeds up the iterative adjustment of linkage parameters. The result can be a linkage process uniquely adapted to the purposes of your lirtkage project.

Favorable reports from current users include:

● The system is ‘comfortable’ to use because you remain in

control at all stages.

● The command language enables boti updates to be made

easily, and reports to be obtained to verify intended

results.

● Iteration can be continued for as long as it takes for You

to be satisfied.

5.0 PHASES

This section briefly outlines the various

system, Further details are given in the

the User Guide.

5.1 Pre-Process

F’qwse: to get tiles ready for linking

phases of the GIRLS

Strategy Guide and in

●

●

●

●

●

●

●

●

●

standardize names and addresses

validhy check

decide on PGCKET

assign SEQUENCE numbers. (These uniquely identify

each record.)

make duplicate records, when you know records match

although they look different. 13g. a record for an individual using her maiden name, and anofher record for the

same individual using her married name.

recode, e.g. from different codes to common code. (For

example, from one hospital coding system to anotJter.)

encode, e.g. from surname to NYSIIS code

split files, e.g. by sex, year

sat files by PGCKET

5.2 WeightCreatiorI

Purpose;to create global and frequency weights

● use the provided SAS fimetion to:

calculate frequency weights themselves

generate GIRLS weight update commands

calculate global weights (optional)

“The rarer the value, the higher the FREQ weight.”

The frequency weight formula used is:

FW-IOXI09( total nunber of racords )

I 2( )

( No. occurrences of field va)ue i )

where “FWi” is the frequency weight for value “i”. For

example, the value “SMITH” for the SURNAME field could

have a frequency weight of “40”.

330
5.3 Analysis

Pwpose: to specify comparison rules

● define input files

● choose fields to compare

character e.g. surname

numeric e.g. birthyear

coded e.g. marital status

conditional and cross comparisons

your own PL/1 code

choose possible outcomes to weight

fully, partially agree

disagree

missing

● your rules are then translated into a PL/1 rmmattt called

the ‘Compare’ program

. .

Figwe 4: The Analysis phase

file definitions

+

compar i son rules

.. . ... . . .. . . .

@III--’

.

:

.

‘“’’’”s

.. ... . ... .

!

.

“ m

I

ti

. . . (PL/1 source (linkage

code) tables) e

S.4 Compare

Pwpo.w to build the linkage database

b set thresholds: upper, lower, and cutoff so as to reject

obvious non-matches quickly

● select a sample of pockets with whiclr to experiment

● execute the Compare program

The comparison rtdes start assigning global weights to

potential links, which are rejected as soon as either current total weight falls below cutoff or if final total weight

will be less than the lower threshold.

The linkage database of potential links and all records

involved in them is created.

Figwe 5; The Corrtpare phase

Input to GIRLS (linkage

tables)

‘F”””””””+ I

. . .. . . .. . ...

~ ; ‘“~’R’ +’ H . . . .. . . .. . . .

I

(Records Involved in potential 1 inks) (Potential 1 inks

5.5 WeightUpdate

Pwpose: to apply snd/or modify the weights

● look at link weights ‘before’

● apply weights

You attach frequency weight sets to comparison rules.

The system finds all links to which each rule applies and

updates the link weights accordingly.

● look at link weights ‘after’

5.6 Lti

Purpose: to assign statuses to the links

● set a lower and an upper threshold

The system classifies links by comparing their total

weights against these thresholds and assigning a status of

definite, possible, or rejected (as explained in Section 3.2).

● inspect results

5.7 Group

Purpose: to build groups of records

● the system builds ‘major’ and ‘minor’ groups of records

based on their link status.

major groups have both definite and possible links

minor groups have definite links only

i.e. minor groups contain the best links.

● the system combines groups which share duplicated

records. For example, combining a group which contains

Mary Smith (maiden) with a group which contains Mary

Brown (married).

331
● resolve group conflicr5,either automatically or manually

● output final versions of groups

The Weight, Link, and Group phases are represented below.

Figure 6: The Weight, Link, and Group phases

Project Database

/.. . . .. .. ... . . . .. .. . ... . ... .. . . . .. . .. . . ... .. .. . .. . . .. . .\

[

LINK DbTA DATE WGHT

I

MJGR

. . . . . . . . . . . . . .

~: Uir :—m

. . . . . . . . . . . . . .

I

EEil-””t’”’ ‘r””““’4zi&El

5.8 Post-Process

Purpose: to use the resultsof GIRLS

● e.g. for an internal linkage, prepare composite records to

represent case histories

● e.g. “for a two-file linkage, for each group, generate one

record to represent all the members

● create summary files

6.0 EXAMPLE

Thisisa simpleexample to show how the GIRLS linkage process works for a two-file linkage.

Part 1 of Fiqure 7 represents the contents of two

files to be linked by GIRLS. File DATA contains 6 records

which are to be matched against the 9 records of fde DATB.

Let the pocket identifier be the SURNAME field (which means

that records are compared only if SURNAMEagrees on the

two records). ROW specifiestie row numberof the recordon

the files,and the “...” representsmissingdata,

Part 2 of Figure 7 shows examples of frequency

weights on the WGHT file for the fieids SURNAME, MARST

and BIRTHYR. (For example, the weight for the surname

“Quigley” is “1OO”,) We will be using these weights later to

calculate the total weights of links.

Figure 7: Example: two input files and a Weight file

%rt 1--- OhTA and OATB fi le

File I Rm I SURNAME { MARST I B IRTHYR

1

D 2

A 3

T 4

A 5

6

1

2

D 3

4 4

T 5

B 6

7

8

9

Barnes

Barnes

Jones

Jones

Qulgley

Quigley

Barnes

Barnes

Barnes

Jones

Jones

Jones

Jones

Qulgley

Quigley

01 1950

. . 1950

03 . . . .

02 1960

03 . . . .

02 19s0

01 1950

. . 1960

02 1960

03 . . . .

02 1960

. . 1960

02 1960

02 1970

03 1970

?ti 2.— WGHT file

SURNAME MARST BI RTHYR WEIGHT

Barnes 40

Jones 10

Quigley 100

01 10

02 20

03 30

1950 10

1960 20

1970 30

The table below shows the links we have on the project

database LINK tile after executing the Compare phase and

applying the frequency weights in the WGHT fde. The columns in the table are explained below.

Figwe 8: Example: the resulting Link fde

SURNAME@SURNAMEMARST BIRTHYR QBIRTHYR TOTWGHT STATUS

OUTCOME RESULT

D(-2G) D[-40)

1

p Barnes L

;;1

A ~950 Eio

A

Pos

Barnes M A 1950 50 Pos

~q~ L Jones 03 M 40 Pos

4 ~ & Jones 02 k

:

!360 50 Pos

L Jones 02 A

6 : :3

1960 50 Pos

O“lgley o

7

M 80 DEF

55 A U.rgley 03 M !30 DEF

858 A Qwgley 02 80 OEF

96? A D<,tgley D : 40 Pos

● THREs H=(40.75~ ●

Nues:

1. “LINK ROW” identifies (he record number of each

link. This identifies the link in subsequent reports.

2. “DATA ROW” and “DATB ROW” indicate the File

‘A’ and File ‘B’ records that are involved in a link.

3. ‘SURNANO? and “BIRTHYR” are fields containing

the outcomes of comparison. These are “A” (agree),

“D” (disagree), “M” (missing on one or both records).

4. For agreement the “@SURNAME” and *@?BIRTHYR”

fields contain the result on which the fields agreed.

5. The “MARST” field contains the outcome of the comparison if it is “D” (disagree) or “M” (missing), or the

332
result on which the fields agreed if the outcome was

agreemenL

6. For disagreement the weights sre specified under

SURNAM13 MARST,and BIRTHYR. Eg. for disagreementon BIRTHYRthe weightadded is “-40”.

7. “TOTWGHT” (total weight)is the sum of the relevant

agreementand disagreementweightsfor each link.

8. “STATUS” shows the link status for each link. This is

baaed on the total weight (TOTWGHT) for the link

and the current threshold values (THRESH). In MS

example, the lower threshold is “40”, and the upper

‘75”. “POSS” corresponds to ‘powible’ and “DEF’” to

‘deftite’. (In this example, comparisons resulting in a

total weight less than the lower threshold (40) are

excluded from further processing.)

For example, for Link 8 we calculate the total weight

(TOTWGHT) from the information on the L3NK fde, and the

weights on the WGHT file, as follows:

Figtue 9 Example: calculating the weight for Link 8

Comparison I Va1ue I Weight

SURNAME QUIGLEY 100

MARST 02 20

B I RTHYR disagree -40

TOTWGHT = 80

The tlml table below shows the group numbers assigned to the

records after grouping. Records with the same group number

refer to the same individual. Records having no group number

have no matches on the ‘other’ file. These groups are based

on the DATA ROW, DATB ROW, and STATUS values shown

on the LINK file.

For example, Group 1 contains three “Barnes”records:

A(l), A(2), and B(l), i.e. two File ‘A’ records have been

groupedwith one File ‘B rezord. If our linkage requirement

is one-to-one, then this group contains a ‘conflict’ which will

have to be resolved.

Figure 10: Bxample: group numbers show the linked records

File ROW SURNAME MARST B IRTHYR GROUP

1 Barnes 01 1950 1

2 Barnes ... 1950 1

DATA 3 Jones 03 . . . . 2

4 Jones 02 1960 3

5 Quigley 03 .... 4

6 Quigley 02 1960 4

1 Barnes 01 1950 1

2 Barnes 1960 ...

3 Barnes i; 1960 ...

4 Jones 03 . . . . 2

DATB 5 Jones 02 1960 3

B Jones .. 1960 ...

7 Jones 02 1960 3

6 Quigley 02 1970 4,

9 Quigley 03 1970 4

7.0 GIRLS TRAIN3NG

As theGIRLS system is relatively complex, we strongly recommend participating in the introductory Seminar, followed by

experimenting with an Example Reject that has been set up

for training purposm.

7.1 GIRLS Seminar

This is a one-day seminar which covers all aspecrs of the

GIRLS system. It is given by the GIRLS system staff on an

ad hoc basis. lt requires the use of an overhead projector and

can be presented at Statistics Canada or elsewhere. This Seminar is a valuable introduction to the system.

7.2 Exampleproject

This is a miniature GIRLS linkage project witi two small files

of teat data. It consistsof a sequence of batch jobs contining

examples of the typical use of GIRLS commands. Submitting

these jobs one at a time produces a sequence of listings showing the sages by which the records from the two files become

linked. You are also encouraged to make a copy of these jobs,

change the cmnmanda, and then re-subrnit the jobs to see the

effeet of your changes. This Example Reject is a valuable

learning tool.

8.0 HARDWARE AND SOFI’WARE

REQUIREMENTS

GIRLS requirestiefollowing hardwareandsoftware:

IBM 370 compatible hardware with at least two million

bytes of storage (red or virtual).

The OS MVS or MVT operating syWem.

The RAPID database management system.

The IBM PIA compiler.

Direct access storage devices (3330, 3350, 3380 etc.)

The following are not mandatory but are highly desirable:

SAS (Statistical Analysis System) in order to use the

Weight Cn.ation function. TSO or ISPF.

NOTES AND REFERENCES

Howe, G.R. and Lindsay, J.( 1981). A generalized iter$tiV

record linkage system for use in medical follow-up studies.

Computers and Biomedical Research, VOI14.327-340.

Newcornbe, H.Il. (1%7). Record linking: the design of

eff]aent systems for linking records into individual and family histories. American Journal of Human Genetics, vol 19,

335-359.

Fellegi, I.P. and Sunter, A.B. (1%9). A theory of record

linkage. Journal of the American Statistical AaaociatiotLvol

64, 1183-1210.

RAPID Database Management System. Informatics Systems

Division, Reseach and General Systems Subdivision, Statistics Canada.
Appendix A:

Selected Bibliographies of

Exact Matching Methodologies

and Applications
UPDATED BIBLIOGRAPHY OF WORK ON EXACT MATCHING

Compiled through 1s?85by

Wendy Alvey, Internal Revenue Service

This bibliography is one of the products which

grew out of the Workshop on Exact Matching

Methodologies, held in Arlington, Virginia, May

9-10, 1985. It draws on references from papers

presented and suggested citations provided by

participants who attended that conference. The

aim was to round out the other bibliographic

materials on matching included here, making them

more current and filling in some of the

historical gaps. The starting place for the

effort was an earlier collection. which focused

on U.S. linkage techniques dur;ng the period

1950-1974:

Scheuren, Fritz and Alvey, Wendy. (1974)

“Selected Bibliography on the Matching of

Person Records from Different Sources.”

Proceedings of the American Statistical

Assoclatlon, Social tatlst~cs SectJon,

pp. 151-154 (pp. 347-356 in this volume).

SCOPE AND LIMITATIONS

The primary emphasis of the present bibliography is on major methodological developments

and applications involving exact matching during

the past eleven years. Many of the citations

document recent linkage efforts involving

matches of administrative and survey records for

statistical purposes. The references are

believed to be less complete in other areas,

especially in epidemiological applications. For

citations in that area, see:

Wagner, G. and Newcombe, t!.B.(1970) “Record Linkage: Its Methodology and Application in Medical Oata Processing,” Methods

of Information in Medicine, vol. 9,=

PP. 121-138 (PP. 357-374 in this volume).

While this bibliography concentrates on

matches of individuals, some establishment

studies are referenced, as well. However, time

constraints prevented us from covering this area

completely. For documentation of some of the

earlier literature pertaining to matching of

businesses see:

Phillips, Bruce D. (1985) “The Development

of the Small Business Data Base of the

Us. Small Business Administration: A

Working Bibliography,” Record Linkage

Techniques--l985, Internal Revenue Service,

PP. 375-379 (in this volume).

It is important to note that the present

bibliography deals only tangentially with the

confidentiality and disclosure issues which are

so vital a part of many matching studies. In

particular, just some of the more important

recent references are cited. TW excellent

bibliographies on privacy and confidentiality

are:

Flaherty, David H.; Hanis, Edward H.; and

Mitchell, S. Paula. (1979) Privacy and

Access to Government Data for Research:

An International Blbllography, ManseTT

Publlcatlons, London, U.K; and

Flaherty, David H. (1985) Privacy and Data

Protection: An International Bibliow~h~~~j:;~, ~!t:stry ‘(’bllcatlOns’

See also:

Us. Department of Commerce, Office of

Federal Statistical Policy and Standards.

(1978) Report on Statistical Disclosure

and Disclosure Avolriance Techniques, Statlstlcal Policy Working Paper 7 Government Printing Office, Washington, ‘D.C.

Further, it should also be pointed out that no

attempt has been made to cover the literature on

synthetic or statistical matching. For a

summary of the recent work in this area, see:

Paass, Gerhard. (1985) “Statistical Record

Linkage Methodology,” a paper presented at

the 45th fleetingof the International Statistical Institute, Amsterdam, August 1985;

Rodgers, Willard. (1984) “An Evaluation of

Statistical Matching,” Journal of Business

and Economic Statistics, American StatlsHcal Assoclatlon, vol. 2, no. 1, pp. 91-

102; and

Rubin, Donald. (1986) “Statistical Matching Using File Concatenation with Adjusted

Weights and Multiple Imputation,” Journal

of Business and Economic StatAmerican Statlstlcal Association, vol. 4,

no. 1, pp. 87-94.

Finally, for a bibliography which focuses mainly

on issues only indirectly related to exact

matchinq, see also:

Smith, Wray. (1985) “Bibliography of

Methodological Techniques Related to Exact

f.latching,; Recorcl Linkage Techniaues--

19E’5,Internal Revenue Service, pp. ?P1-?82

~this volume).

ACKNOWLEDG14EP!T

This bibliography could not have been compiled

without the extensive contributions of the

Matching Group, Administrative Records Subcommittee, Federal Committee on Statistical

Methodology. Thanks to them and to the participants of the Workshop on Exact flatching

Methodologies for their suggestions and

reconwnendations.

337
CITATIONS

Abels, Dennis. (1982) “File Matching (Itilizing

Automated Heuristic Techniques (FINDIT),” an

unpublished working paper, Social and Scientific

Systems, Inc., 7101 Wisconsin Avenue, Bethesda,

MD 20814.

Acheson, E. P. (1968) Record Linkage in Meti

tine, E. and S. Livingstone, Edinhurgh.

Alvey, Wendy and Aziz, Faye. (1979) “Qualit.yof

Mortalitv ReDortino in SSA Linked Data: Some

Prelimin~ry r Resul~s,” Proceedings of the

American Statistical Association, Section on

Survey Research Methods, pp. 715-279.

Alvey, Wendy and Scheuren, Fritz. (1982) “Background for an Administrative Record Census,”

Proceedings of the American Statistical Association, Social Statistics Section, pp. 13/-146.

Arcangeli, Sam. (1985) “Pilot Test of a ProtoType Unemployment Insurance Wage Report and

Occupational Information System,” an unpublished

paper, State Joh Training Coordinating Council,

Tallahassee, FL 32301.

Arellano, Max G. (1976a) “Application of the

Fellegi-Sunter Record Linkage Model to

Agricultural List Files,” an unpublished working

paper, Statistical Reporting Service, Us.

Department of Agriculture, Washington, PC 20250.

Arellano, Max G. (1976b) “Calculation of Weights

for Partitioned Variable Comparisons,” an unpublished working paper, Statistical Reporting

Service, U.S. Department of Agriculture,

Washington, DC 20250.

Arellano, Max G. (1!?76c) “The Development of a

Linkage I?ulefor Unduplicatina Agricultural List

Files,” an unpuhlisbed working paper, Statistical Peporting Service, U.S. Department of

Agriculture, Washin@on, PC ?0?50.

Arellano, flax G. (1976d) “The Estimation of

P(M),” an unpublished working paper, Statistical Reporting Service, U.S. Department of

Agriculture, Washington, DC 20250.

Arellano, Max G. (l?76e) “Optimum Utilization of

the Social Security Number for Matchin?

Purposes,” an unpublished working paper, Statistical Reporting Service, IJ.S. Department of

Agriculture, Washington, DC 20250.

Arellano, Max G. (1976f) “Weiaht Calculation for

the Place Name c’omparison,-”an unpublished

working paper, Statistical Reporting Service,

U.S. Department of Agriculture, Washington, DC

20250.

Arellano, Max G. (1!?85)“An Implementation of a

Two-Population Felleai-Sunter Probability Linkage ‘hdel,” Pecord” Linkaae Techniques--l985,

Internal Revenue Service, pp. 255-?57 (in thfs

volume).

Arellano, Max G. and Arends, William L. (1976)

“The Estimation of Component Error Probabilities

for Pecorc! Linkage Purposes,” an unpublished

working paper, Statistical Reporting Service,

U.S. Department of Agriculture, Washington, O.C.

20250.

Arellano, Max G. and Coulter, l?ichardW. (1976a)

“Weight Calculation for the Civen Name

Comparison,” an unpublished working paper,

Statistical Reporting Service, U.S. Department

of Agriculture, Washington, D.C. 20250.

Arellano, Pax G. and Coulter, Pichard W. (1976h)

“Weight Calculation for the Middle Name

Comparison,” an unpublished Wrking paper,

Statistical Reporting Service, U.S. Department

of Agriculture, Washington, D.C. 20250.

Arellano, Max G. and Coulter, Richard W. (1976c)

“Weight Calculation for the Surname Comparison,”

an unpublished working paper, Statistical

Reporting Service, U.S. Department of

Agriculture, Washington, D.C. 20250.

Arellano, Max G. et al. (1984) “The California

Automated Mortality Linkage System (CAJ?LIS),

American Journal of Public Health, vol. 74.,no.

~$ PP. -330.

Aziz, Faye and Buckler, Warren. (1980) “Mortality and the Continuous Work History Sample,”

Proceedings of the American Statistical Association, Section on Survey Research ~~etho~s, PP.

461-m

Aziz, Faye: Kilss, Beth: and Scheuren, Fritz.

(1978) “1973 Current Population Survey -

Administrative Record Exact Hatch File Codebook

Part I -- Code Counts and Item @efinitions,~

Studies from Interagency Data Linkages, report

no. 8, Social Security Admlnlstration.

Baldwin, John A. and Acheson, E. Donald (Eds.).

(1984) A Textbook of Medical Record Linkag

Oxford University Press.

Beebe, Gilbert W. (1980) “f?ecord Linkage

Systems--Canada vs. the United States,” American

Journal of Public Health, vol. 70, pp. 1246-124/.

Beebe, Gilbert W. (1981) “Record Linkage and

Needed Improvements in Existinq Data Resources,”

Banbupy Report, no. 9, Cold Sp;ing Harbor Laboratory, Cold Spring Harbor, NY, pp. 661-673.

Beebe, Gilbert W. (1985) “Why Are Epidemiolo

gists Interested in Matching Algorithms?” Record Linkaae Techniques--l985, Internal Rev~e

Service, pp. 139-143 (in this volume).

Bentz, Mary. (1985) “Intergenerational Wealth

Study: Prospects for Data Analysis and Methodological Research,” a paper presented at the

Conference on Tax Modelling, Lac Ste. Marie,

Quebec, Canada, September 17-19, 1985. (T

appear in the Proceedings of that conference

now in progress.)

338
Rercini, Deborah; Sirken, Monroe: and Mathio.

Wetz, Nancy. (1985) “Results of a Field Test

Linking the National Survey of Family Growth to

the National Health Interview Survev.” a naDer

to appear in the Proceedings of the Ame~ican

Statistical Association, SectIon on Survey

Research Methods.

Boruch, Robert and Cecil, J.S. (197!?)Assurina

the Confidentiality of Data in Social Ftlnlversltyof Pennsylvania Press, Phlladelphla. -

Boruch, I?obert and Stromsdorfer, Ernst. (1985)

“Exact Matchina of Microdata Sets in Social

Research: Benef~ts and Problems.” Record Linkaae

Techniques--l985, Internal P;venue Sirvice~

pp. 145-153 (in this volune).

Bourne, C.P. and Ford, C.F. (1961) “A Study of

Methods for Systematically Abbreviating Enalish

Words and Parries,”Journal-of the Association of

Computing Machinery, vol. 8, pp. 538-552.

Bristol, Ralph B. (1!!85) “Age and Tax Filing,

1981,” SOI Bulletin, vol. 5, no ?, Internal

Revenue Service, k!ashington,DC.

Brown, Rachel. (1984) “Research on the Uses of

Administrative Records for the 1990 Census,”

Proceedings of the American Statistical Association, Soclal Statlstics Section, pp. 443-4f18.

Buckler, Warren. (1985) “Fmployer Peporting Unit

Match Study (ERUPS): A Progress Peport,” a paper

to appear in the Proceedings of the American Statistical Association, Section on !jurveypesearch

f4ethods.

Childers, Daniel and POgan, Howard. (1084)

“Matching IRS Records b Census Records: Some

Problems and Results,” Proceedings of the American Statistical Association, Section an Survey

Research Methods, pp. 3C1-306.

Clark, Phil. (1985) “Estimating After-Tax

Income Using Matched Il?S-CensusData,” a paper

presented at the Conference on Tax Modelling,

Lac Ste. Marie, Quebec, Canada, September 17-19,

1985. (To appear in the Proceedings of that

conference, now in progress.)

Cohen, Malcolm. (1985) “Deriving Labor Turnover

Rates From Administrative Records,” Record

Linkage Techniques--l985, Internal Revenue

Service, pp. 259-266 (in this volume!.

Coulter, Richard W. (1975) “Sampling size in

Estimating Component Error Probabilities,” an

unpublisheduwasrking paper, Statistical Reporting

Service, Department of Agriculture,

Washington, DC- 20250.

Coulter, Richard W. (1976a) “Processing of

Comparison Pairs in Which Place Names I?isaqree,”

an unpublished working paper, Statistical

Reporting Service, U.S. Department of

Agriculture, Washington, DC 20250.

Coulter, Richard W. (1976b) “A Weight for

‘Junior’ vs. Missing,” an unpublished working

paper, Statistical Reporting Service, U.S.

Department of Agriculture, Washington, DC 20250.

Coulter, Richard. (1985) “An Application of a

Theory fOr Record Linkage,” Record Linkage

Techniques--l985, Internal Revenue Service,

PP. ?9-96 (in this volume). (This paper is one

selection in a series of papers produced by the

Department of Agriculture. It contains

annotated references to the rest of the papers.

~OUl ter, Richard W. and Mergerson, James W.

(1977) “An Application of a Record Linkage

Theory in Constructing a List Sampling Frame,”

an unpublished mrking paper, Statistical

I?eporting Service, U.S. Department of

Agriculture, Washington, DC 20250.

Cox, Lawrence. (1980) “Suppression Methodology

and Statistical Disclosure Control,” Journal of

the American Statistical Association, vol. 75,

no. 370, PP. 377-385.

Cox, Lawrence and Boruch, Robert. (1985) “Emerging Policy Issues in Record Linkage and Privacy,”

a paper presented at the 45th Neeting of the

International Statistical Institute in

Amsterdam, The Netherlands, August 12-22, 1985.

Cox, Lawrence; Johnson, Bruce; McDonald, SarahKathryn; Nelson, Dawn; and Vazquez, Violets.

(1985) “Confidentiality Issues at the Census

Bureau,” First Annual Research Conference,

Bureau of the Census, Washington, DC, pp.

199-218.

Crane, Jane and K1eweno, Douglas. (1985)

“Project LINK-LINK: An Interactive Data Base of

Administrative Record Linkage Studies,” Record

Linkage Techniques--l985, Internal Pevenue~

vice, pp. 311-?15 (in this volune).

Damerau, F. J. (1!?64)“A Technique for Compllter

Detection and Correction of Spelling Errors,”

Communications of the Association for Computing

flachlnery,vol /., PP. 171-176.

Del13ene, Linda. (1979) “1973 Current Population

Survey - Administrative I?ecordExact Match File

Codebook, Part 11 -- Companion Datasets and

Other Supplementary Information,” Studies from

Interagency Data Linkages, report no. 9, .SociaT

Security Admlnlstratlon.

PelBene, Linda and Aziz, Faye. (1982) “Further

Investigation into Mortality Coverage in Social

Security Administration Data,” Amerjcan Statistical Association Proceedings, Section on Survey

Research Methods, pp. ?92-291.

Duncan, George T. and Lambert, Diane. (1986)

“Disclosure-Limited Data Dissemination,” Journal

of the American Statistical Association-

, no. .

Ericksen, Eugene P. and Kadane, Joseph B. (19.!33)

“Using Administrative Lists to Estimate Census

Omiss;ons: An Example,” Proceedings of the American Statistical Association, SectIon on Survey

Research Hethods, pp. 361-366.

Fellegi, Ivan P. (1985) “Tutorial on the

Fellegi-Suntir Model for Record Linkage,” Record

Linkage Techniques--l985, Internal Revenue=

vice, pp. 127-138 (in this volume).

339
Fett, Michael J. (1984) “Matching: The Develop- Howe, G. R. (1985) “The Use of Comt)uteriz

ment of Matching Criteria for ‘Epidemiological

Studies Using Record Linkage Techniques,”

International Journal of Epidemiology, vol. 13,

no. 33 PP. 351-355’.

Fink, Nancy. (1984) “Estimated Sensitivity and

Specificity of the i!ationalDeath Index,” School

of Hygiene and Public Health, Johns Hopkins

University.

Flaherty, David t!. (1978) “The Bellagio

Conference on Privacy, Confidentiality and the

Use of Government Microdata,” New Directions in

Program Evaluation, vol. 4, pp.~-30.

Flaherty, David H. (1979) Privacy and Government

Data Banks: An International Perspective,

llansellPubllcatlons, London, U.K.

Flaherty, David H. (1985) Privacy and Oata

Protection: An International Bibliography,

Rnowledge Industry Publlcatlons, Inc., WhT@

Plains, NY.

Flaherty, David H; Hanis, ------- Fdwarrl .H.: ...- a,.- ncl

Mitchell, S. Paula. (1979) Privacy and Access to

Government Data for Researcl: An International

Bibliography, Mansell Publlcatlons, London, U.K.

Greenia, Hick. (1985) “1979 Sole Proprietorship

~m;;~ent and Payroll: Processing Methodology,”

Linkage Techniques--l985, Internal

Revenue Service, pp. 2F5-28° (in this volume),

Haber, Sheldon E. (1985) “Applications of a

Matched File Linking the Bureau of the Census’

Survey of Income and Program Participation and

Economic Data,” SIPP Working Paper series, no.

8502, Bureau of the Census, Washington, DC.

Haber, Sheldon; Ryscavage, Paul; Sater, Doug;

and Valdisera, Victor. (1984) “Matching Economic Data to the Survey of Income and Program

Participation: A Pilot Study,”

the American Statistical Assocla%=%%#ion,

Ytatlstlcs Section, pp. 529-533.

tiartigan, J.A. (1981) “Consistency of Single

Linkage for High-Density Clusters,” Journal Of

the American Statistical Association, “vol. fb,

PP. 3~~-394.

Hill, Ted. (1983) “Generalized iterative Record

Linkage System (GIRLS),” Documentation, Special

Resources Subdivision, Systems Development

Division, Statistics Canada, Tunney’s Pasture,

Ottawa, Ontario, KIA OT6, Canada.

Hill, Ted and Pring-!!ill, Francis. (1935)

“Generalized Iterative Record Linkage System,”

Record Linkage Techniques--l985, Internal

Revenue SerViCe, pp. 327-333 (in this volume).

Hirschberg, David. (1985) “The Developfnent of

the Master Establistnnent List,” Record Linkage

Techniques--l985, Internal Revenue Service,

pp. 291-295 (in this volume).

Record Linkage in Follow-up Studies of Cance

Epidemiology in Canada,” Journal of the Nationa

Cancer Institute (in press).

Howe, G. R.; Fraser, D.; Lindsay, J. (1983)

“Cancer Mortality (1965-77) in Relation to

Diesel Fume and Coal Exposure in a Cohort of

Retired Rai1way Workers,” Journal of the

National Cancer Institute, vol. 70, no. 6s PP.

Howe, G. R. and Lindsay, J. P. (1981) “A Generalized Iterative Record Linkage Computer

System for Use in Medical Follow-up Studies,”

Computers and Biomedical Research, vol. 14, pp.

-.

Howe, G. R. and Lindsay, J. P. (1983) “A

Follow-up Study of a Ten Percent Sample of the

Canadian Labor Force: 1. Cancer Mortality in

Males, 1965-73,” Journal of the National Cancer

Institute, vol. 70, no. 1, pp. 3/-44.

HOW, G. R.; Lindsay, J.; Coppock, E.; and

Miller, A. B. (1979) “Isoniazid Exposure in

Relation to Cancer Incidence and Mortality in a

Cohort of Tuberculosis Patients,” International

Journal of Epidemiology, vol. 8, no. 4* PP.

-a

Jabine, Thomas. (1985) “Properties of the

Social -... Securitv Number Relevant to Its Use in

Reco~d Linkag~,” Record Linkage Techniques--

1985, Internal Revenue Service, PP. 213-225 (In

m-volume).

Jabine, Thomas and Scheuren, Fritz. (1985)

“Goals for Statistical Uses of Administrative

Records: The Next Ten Years,” and accompanying

discussion conunents by William Butz, John J.

Carroll, Janet Norwood and Charles Waite,

Journal- of Business and Economic Statistics;

vol. , no. 4) PP. 380-404”

Jaro, Matthew. (1984) “Record Linkage Research

and the Calibration of Record Linkage Algorithms,” Proceedings of the Anerican Statistical

Association, Social tatlstlcs Section, pp. !JY9

1.

Jaro, Matthew. (1985) “Current Record Linkage

Research,” Record Linkage Technicwes--l985,

Internal Revenue Service, pp. 317-320 (ln this

volume).

Jensen, Poul. (1983) “Towards a Register-Based

Statistical System--Some Danish Ex~erience,”

Statistical J~urnal of the United Nations

Iconomlc Conmsslon for kurope, vol. 1, no. 3,

m-3bb.

Jensen, Poul and Thygesen, Lars. (1985) “Linkage

Of Records on Objects of Different Kinds:

Methodological Problems and Practical Experience,” a paper produced at Denmarks Statistik,

Copenhagen, Denmark.

340
Johnson, David P.; Liss, Teri L.: and Witt,

Cecilie A. (1984) “1980 AHA Hospital and

National Natality/Fetal Mortality Survey Linkaoe

Methodology,” Proceedings of the American St~-

tistical Association, Section on Survey Research

~ethods, pp. 411-414.

Johnson, Norman; Glover, Claudia; and Rogot,

Eugene. (1985) “General Mortality Among Selected

Current Population Survey Cohorts for 1979-1981.”

a PaPer to appear in the Proceedings of the

American Statistical Association, SectIon on

survey Research Methods.

Kagawa, J.T. and Mi, M.P. (1985) “On Matching

With Personal Names,” Record Linkage Techniaues--

1985, Internal Revenue Service, pp. 269-273 (In

TliiTvolume),

Kasprzyk, Daniel. (1983) “Social Security ll~ber

Reporting, the Use of Administrative Records,

and the Multiple Frame Design in the Income

SurveY OeVelOment Program,” Technical, Conceptual and Administrative Lessons of the 1~

Martln llavld (Ed ) s 1 Science Researc;

Council, Washingto;, ’DC,~~.a123-144.

Katz, Arnold; Teuter, Klaus: and Sidel, paul.

(1984) “Comparison of Alternative Ways of

Oeriving Panel Data from the Annual Demographic

File of the Current Population Survey,” Review

of public Data Use, vol. 12, pp. 35-44.

Kelley, R. Patrick. {1984) “Blocking Considerations for Record Linkage Under Conditions of

Uncertainty,” Proceedings-of the American Statistical Association, Social Statlstlcs SectIon,

PP. ~02-605.

Kelley, R. Patrick. (1985) “Advances in Record

Linkage Methodology: A Method for Determining

the 8est Blocking strategy,” Record Linkage

Techniques--l985, Internal Revenue Service,

pp. l!X-203 (in this volume).

Kestenbaum, Bert. (1985) “The Measurement of

Early Retirement,” Journal of the American

Statistical Association, vol. 90 , no. 389s PP.

-.

Kilss, Beth and Alvey, Wendy (Eds.). (1985) Record Linkage Techniques--l985, Internal F?ev~e

3ervlce.

Kilss, Beth: Oh, H. Lock; and Scheuren,

Frederick. (1977) “1964 Current Population

Survey - Administrative Record Pilot Link File

Codebook,” Studies from

m;ati~yort ‘“O 7’ l~;::nc<ec:y:~

9

Kilss, Beth and Scheuren, Frederick. (1978) “The

1973 CPS-IRS-SSA Exact Match Study,” Social

Security Bulletin, Department of Health, -

tion, and Welfare, vol. 41, no. 10, pp. 14-22.

Kilss, Beth; Scheuren, Fritz; and Buckler,

Warren. (1980) “Goals and Plans for a Linked

Administrative Statistical SamDle.” Proceedings

of the American Statistical Association, Sectlon

on Survey Research Methods, pp. 450-455.

341

,

Kirchhoff, Bruce A. and Hirschberg, David A.

(1981) “Small Business Oata Base: Progress and

Potential,” Proceedings of the American Statistical Association, Sectlofi on Survey Research

Rethods, pp. 533-531.

Kirkendall, Nancy. (1985) “Weights in Computer

Matching: A~plications and an Information Theoretic Point.-ofView,” Record Linkage Techniques--

1985, Internal Revenue Service, pp. 189-?97 (in

lTiTSvolume).

Lubitz, J. and Pine, p. (1984) “Initial Findings: Development and Use of a Linked Medicare/

NCHS Mortality File,” a paper presented at the

annual meeting of the American Public Health

Association in Anaheim, CA, November 1984.

Lynch, B. T. and Arends, W.L. (1977) “Selection

of a Surname Coding Procedure for the SF!SRecord

Linkage System,” Sample Survey Branch, Research

Division, Statistical Reporting Service, U.S.

Department of Agriculture, Washington, D.C.

MacMahon, Brian. (1983! “The National Death

Index,” “~rican Journal of Public Health, VO1.

73, no. n, PP. 1241-1248.

Madigan, F.C. and Wells, H.B. (1976) “Report on

Matching Procedures of a Dual Record System in

the Southern Philippines,” Demography, vol. 13,

pp. 381-395.

Mi, M. (1967) “Record Linkage and Other Genetic

Studfes itl Hawaii,” Proceedings of the 111

International Congress of Human Genetics, J.A.

hOW, and J.V. Nee1 (tds.), PP. 489-496.

Mi, M., Kagawa J., Earle, M. (1983) “An Operational Approach to Record Linkage,” I?ethods of

Info~ation in Medicine, vol. ?2, pp. //-82.

Miettinen, 0.S. (1970) “Matching and Design

Efficiency in Retrospective Studies,” hnerican

Journal of Epidemiology, vol. 91, no. 2, ~

I.

Morgan, H.L. (1970) “Soelling Correction in

Systems Programs,” Communications of the Association of Computing Machinery, vol. 3s PP. g~-

.

Nelson, 0.0. (1976) “on the Solution Of a

Polynomial Arising During the Computation of

Weights for Record Linkage Purposes,” an

umublishedu~rlcing Paper, Statistical Reporting

Service, Department of Agriculture,

Washington, 6C. 20250.

Newcombe, H. B. (1967) “Record Linking: The

Design of Efficient Systems for Linking Records

into Individual and Familv Histories,” American

Journal of Human Genetics,”vol. 19, pp. 3~

Newcombe, H. B. (1969) “The Use of Medical

Record Linkaqe for Population and Genetic

Studies,” Met~ods of In~ormation in Medicine,

vol. 8, pp. 7-11.

Newcombe, H. B. and Abbatt, J.O. (1983)

“Probabilistic Record Linkage in Epidemiology:

Principles Employed,” Eldorado Resources Ltd.

—.
Newcombe, H. B., Smith, H.E., and Abbatt, J.D. Redfern, Phillip. (1!H23) “A Study of the Futu

(1982) “Linkage Procedures for the Eldorado of the Census of Population:

Mortality Searches--ENL-Link 2,”

Alternative A

Eldorado preaches,”

Nuclear Limited.

a report conmnissionedby the Stati

tical Office of the European Communities.

Newcombe, H. B.; Smith, MOE.; Howe, G.R.; Rodgers, Willard. (1984) “An Evaluation o

Mingay, ,].; Strugnell, A.; and Abbatt, J.D. Statistical Matching,” Journal of Business an

(1983) “Reliability of Computerized Versus Economic Statistics, Anerlcan Statlstlca

Manual Death Searches in a Study of the Health Association, vol. 2, no. 1, pp 91-102.

o! Eldorado Uranium Workers,” Computers and

Biomedical Research, vol. 13, no. 3, pp. lbl-m Rogot, E.; Feinleib, M.; Ockay, K.A.; Schwartz

s.; Bilgrad, R.: and Patterson, J. (1983) “o

P?ass, G. (1 085a) “Disclosure Risk and the Feasibility of Linking Census Samples to th

Disclosure Avoidance for Microdata,” a paper National Death Index for Epidemiologic Studies

presented at the International Association for A Progress Report,” American Journal of Publi

~oc~+l Service Information and Technology, May Health, vol 73, pp. 1~269.

---- .

Paass, Gerhard. (1985b) “Statistical Record

Linkage Methodology,” a paper presented at the

45th meeting of the International Statistical

Institute, Amsterdam, August 1985.

Patterson, John. (1980) “The Establishment of a

National Death Index in the United States,”

Cancer Incidence in Defined Populations, Cairns,

Lyon, and kolnlck (kd ) C ld Spring Harbor

Laboratory, Banbury Repo~~ ~, p;. 443-447.

Patterson, John. (1983) “Evaluation of the

Matchinq Effectiveness of the National Death

Index,”-Proceedings of the American Statistical

Association, Social Statistics Section, pp. 1-10.

Patterson, John and Bilgrad, Robert. (1985) “The

National Death Index Ex~erience: 1981-1985.”

Record Linkage Techniques-’-l985, Internal Revenue Service, pp. 245-254 (in this volume).

F’etska, Th~mas. (19&.5) “Record Linkages Used in

SOI Studies of the Business Sector,” a paper

presented at the Conference on Tax Modellin

$’ l--5Ste. Marie, Quebec, Canada, September 17-1.,

. (To appear in the Proceedings of that

conference, now in progress.)

Rogot, Eugene; Schwartz, Sidney; O’Conor, Karen

and Olsen, Christina. (1983) “The Use of Proba

bilistic Methods in Matching Census Samples t

the National Death Index,” Proceedings of th

American Statistical Association, Section on Sur

vey Research Methods, pp. 319-3.24.

Rogot, Eugene; Sorlie, Paul; Glover, Claudia

and Johnson, Norman. (1985) “Mortality by Caus

of Death Among Selected Current Population Sur

vey Cohorts for 1979-1981.” a naper to appear i

the Proceedings of the &e’ricanrStatistical Asso

ciation, SectIon on Survey Research Methods.

Rubin, Donald. (1986) “Statistical Matchin

Using File Concatenation with .. . . Adjusted Weiqht

and ‘Multiple ImpUtaiiOri~” Journal of Busifie

and Economic Statistics, American Statlstlca

Assoclatlon. vol. 4. no. 1. DS. 87-94.

Sailer, peter. (7985) “The Longitudinal Sampl

in the Study of Tax-Related Issues,” a pape

presented at the Conference on Tax Modelling

i_;_5Ste. Marie, Quebec, Canada, September 17-19

. (To appear in the Proceedings of tha

conference, now in progress.)

.-

Sater, Phillips, Bruce. (1985) Douglas K. (1985) “Enhancing Data fro

“The Development of the the Survey of Income and Program Participatio

Small Business Data Base of the U.S. Small l?usiness Administration:

with Data from Economic Censuses and Surveys--A

A Working Bibliography,”

Record Linkage Techniques--l985, Internal Reve- Brief Discussion of Matching Methodology,” Record Linkage Techniques--l985, Internal Rev~e

nue Service, pp. 375-379 (in this volume). tervlce, pp. 2!’7-301(In this volume!.

Plewes, Thomas. (1985) “Confidentiality Principles and Practice,” First Annual Research

Conference, Bureau of the Census,

2T9-226.

Washington,

c, PP.

Pollock, J. and Zamora, A. (1984) “Automatic

Spellinq Correction in Scientific and Scholarlv

Text,“ ‘Communications of the Associ~ijOri-”O~

Computing Machinery, vol. z f, PP. 358-~

Prochaska, Dean; Des, Jane; and Gaulden, ToMMy.

(1979) “Record Linkage for Development of the

1978 Census of Agriculture Mailinq List.” Proceedings of the American Statistical Assoc-iatfi,

~us;ness and Lconomlc Statlstlcs Section, pp.

]11-.182

Quiaoit, F. and !Ii, pl.p. (~L?85) “Surname

Blocking for Record Linkage,” Record Linkage

Techniques--l985, Internal Revenue Service,

pp. 275-281 (in this volume).

Scheuren, Frederick; Bridges, Benjamin; and

Kilss, Beth. (1973) “Subsampling the Current

Population Survey: 1963 Pilot Link Study,”

Studies from Interagency Data Linkages, report

no. 1, Social Securlty dmlnlstratlon.

Scheuren, Frederick; Herriot, Roger; Vogel

Linda; Vaughan, Denton; Kilss, Beth; Tyler,

Barbara; Cobleigh, Cynthia; and Alvey, Wendy.

(1975) “Exact Match Research Using the March

1973 Current Population Survey--Initial Stages,”

Studies from Interagency Data Linkages, report

no. 4, Soclal Security Admlnlstratlon.

Scheuren, Frederick; Kilss, Beth; and Cobleigh,

Cynthia. (1975) “1973 Current Population Survey

- Summary Earnings Record Exact Match File

Codebook, Part II -- Sucmlernental Information,”

Studies from Interagency’ Data Linkages, report

no. 6, Social Security Admlnlstratlon. .

342
Scheuren, Frederick.; !(ilss, Beth; and oh, H.

Lock. (1973) “Coverage Differences, Noninterview

Nonresponse, and the 1960 Census Undercount:

1963 Pilot Link Study,” Studies from Interagency

Data Linkages, report no. 2, SOC7al Security

Admlnlstratlon.

Scheuren, Frederick J. and Oh, H. Lock. (1975)

“Fiddling Around with Nonmatches and Mismatches,” Proceedings of the American Statistical Association, Soclal Statistics SectIon,

PP. ~27-63~.

Scheuren, Frederick; Oh, H. Lock; Vogel, Linda;

and Yuskavage, Robert with Kilss, Beth and

13elBene, Linda. (1981) “Methods of Estimation

for the 1973 Exact Match Study,” Studies from

Interagency Data Linkages, report no. 10, SociaT

3ecurlty Admlnlstratlon.

Scheuren, Frederick; Vaughan, Denton; and Alvey,

!J~J~y (1975) “1973 Current Population Survey -

Earnings Record Exact Match File

Codebook, Part I -- Basic Information.” Studies

from Interagency Data Linkages, report ~

30C1al Security Admlnlstratlon.

Scheuren, Fritz. (1983) “Design and Estimation

for Large Federal Surveys Using Administrative

Records,” Proceedings of the American Statistical Assoclatlon, SectIon on Survey Research

]Iethods,pp. 3//-381.

Scheuren, Fritz. (1985a) “Evaluating Manpower

Training: Some Notes on Data Handling Issues,” a

paper commissioned by the Job Training Longitudinal Survey Panel.

Scheuren, Fritz. (1985b) “Methodologic Issues in

Linkage of Multiple Data Bases,” a paper commissioned for the National Academy of Sciences’

Panel on Statistics of an Aging Population. AlSO

in Record Linkage Techniques--l985, Internal

‘Revenue‘jervi~e,pp. 155-17P (in tfi~v~~ume).

Scheuren, Fritz. (1985c) “Role of Government

Statistics in a Democratic Society: A Federal

Producer’s Point of View,” a paper presented at

the Annual Meeting of the Society of Actuaries

and the American Academy of Actuaries, New

Orleans, Louisiana. October 14. 1985. (To

appear in an abbreviated form in The Record.)

Scheuren, Fritz and A.lvey, Wendy. (1974) “Selected Bibliography on the Matching of Person

Records from Different Sources,” Proceedings of

the American Statistical Association, SoclaT

Statistics Section, pp. 151-154.

Scheuren, Fritz; Oh, H. Lock; and Alvey, Wendy

with Kilss, Beth; and DelBene, Linda. (1980)

“Matching Administrative and Survey Information:

Procedures and Results of the T963 Pilot Link

study,” Studies from Interagency Data Linkages,

report no. 3, Social Security dmlnlstratlon.

Smith, M. E. (1982) “A Development of a

National I?ecord Linkaqe Proqram in Canada.”

Smith, M.E. (1985) “Record Keeping and Data

Preparation Practices to Facilitate Record

Linkage,” Record Linkage Techniques--l985,

Internal Revenue Service, pp. 327-326 (in this

volume).

Smith, M. E. and Newcombe, H. B. (1975) “Methods

for Computer Linkage in Hospital Admission -

Separation Records into Cumulative Health Histories, “ Methods of Information in Medicine,

vol. 14, pp. 118-lzb.

Smith, !!. E., and Newcombe, H. B. (1979) “Accuracies of ComDuter Versus Manual Linkages of

Routine Health Records,” Methods of Information

in Medicine. vol. 18. DD. W-97. -,.

Smith, M.E., Newcombe, H.B., and Dewar, R.A.D.

(1983) “Automated Nationwide Death Clearance of

Provincial Cancer Reqister Files--The Alberta

Cancer Registry Stu~y,” Proceedings of the

American Statistical Association, Section on

Survey Research Methods, pp. 300-305.

Smith, M. E. and Silins, J. (1984) “Generalized

Iterative Record Linkaqe System,” (an excerDt)

Statistical Uses of ‘Administrative Records:

Recent Research and Present Prospects, vol. ?

Wash:

ington, OC.

Smith, Wray. (1984) “background Research and

Issue Identification in Exact Matchina of Microdata,” Task 5 Report, Mathematic~ ‘Policy

Research, Inc., Washington, OC.

Smith, Wray. (1985) “Bibliography of

Methodological Techniques I?elated to Exact

Matching,” Record Linkage Techniques--l985,

Internal Revenue Service, pp. ?O1-3P2 (in thfs

volume).

Smith, Wray and Scheuren, Fritz. (1985a)

“Multiple Linkage and Measures of Inexactness:

Methodology Issues,” a paper presented at the

Workshop on Exact Matching Methodologies,

Arlington, VA, May 9, 1985.

Smith, Wray and Scheuren, Fritz. (1985b) “some

New Methods in Statistical Disclosure Avoidance,” a paper presented i% the Annual Meeting

of the American Statistical Association, Section

on Survey Research Methods, Las Vegas, August

1985.

Social Security Administration, Office of t7esearch and Statistics. (1979)

Notes, nos. 1-7, Beth Kilss, Wendymne, Faye Aziz, and Fritz Scheuren (Eds.).

(A series of working papers on the feasibility

of creating Linked Administrative Statistical

Samples for epidemiological research.)

Spruill, Nancy. (1983) “The Confidentiality and

Analytic Usefulness of Masked Business Microdata,” Proceedings of the American Statistical

Association, Section on Survey Research Methods,

PP. 602-607.

Proceedings of the - Amer{can Statistical Spruill, Nancy.

Association, Section on Survey Research

(1984) Protecting ConfidenMethods, tiality of Business Microdata

PP. 303-308.

by Masking, Tfie

?ubllc Research Institute, Alexandrla, VA.

343
State of California, Employment Development

Department. (1979) Technical Development

Issues: Employment Service Potential, vol. 11.

Steinberg, J. and Pritzker, L. (1967) “Some

Experiences with and Reflections on Data Linkage

in the United States,” Bulletin of the International Statistical Institute, vol. 42, PP. 18~-

Taft, l?. (1970) “Name Search Techniques,” Project Search (System for Electronic Analysis and

Retrieval of Criminal Histories) Special Report

No. 1, New York State Identification and Intelligence System (NYSIIS), Bureau of Systems Development, Albany.

Tepping, Benjamin J. (1971) “The Application of

a Linkage Model b the Chandrasekar-Oeming Techniaue for Estimating Vital Events,” Technical

Notes 4, Bureau of the Census.

Thygesen, L. (1983) “Methodological Problems

Connected with a Socio- DemoqraDhic Statistical

System Based on Administrati~e Records,” Bulletin of the International Statistical Inst_,

VO1. 50, book 1, Madrld, PP. 2zf-242.

Us. Department of Agriculture, Statistical

Reporting Service. (1975-1977). Series of

working papers (for the most part unpublished)

on the development of a record linkage system.

(Individual contributions are listed separately.)

Us. Department of Agriculture, Statistical

Reporting Service. (1976) “Partitioned Variable

Comparison/Algorithm for Identifying Configurations,” an unpublished working paper.

U.S. Department of Commerce, National Rureau of

Standards. (1977) “Accessing Individual Records

from Personal Data Files Using lJon-UniqueIdentifiers,” NBS Special Publication 500-2.

U.S. Department of Commerce, Office of Federal

Statistical Policy and Standards. (1978a) A

Framework for Planning U.S. Federal Statistic~

Tor the 1980‘s, Washington, DC.

U.S. Department of Commerce, Office of Federal

Statistical Policy and Standards. (1978b)

Report on Statistical Disclosure and Disclosure

Avoidance Techniques, Statistical Pollcy Working

Paper 2, Government Printing Office, Washington,

DC.

U.S. Department of Commerce, Office of Federal

Statistical Policy and Standards. (1980) Report

on Exact and Statistical Natching Techn~

statistical Pollcy Working Paper 5, Government

Printing Office, Washington, DC.

U.S. Department of Health and Human Services,

SOCial Security Administration. (1980) Economic

and Demographic Statistics, Wendy AlveRllss and }rltz Scheuren (Lds.).

U.S. Department of Health, Education, and Welfare, Social Security Administration. (1973-1980)

Series on Studies ftim Interagency Data Linkages:

reports numbers 1-11. (Indlvldual contributions

are listed separately.)

U.S. Department of Health, Education, and Welfare, Social Security Administration. (1973)

“Some Observations on Linkage of Survey and

Administrative Record Data,” Studies from

Interagency Data Linkages, DHEW Pub. No.

4-1175U.

U.S. Department of Health, Education, and Welfare, Social Security Administration. (1978)

Policy Analysis With Social Security Research

U.S. Department of Health, Education, and Welfare, Social Security Administration. (1979)

Statistical Uses of Ahinistrative Records with

Emphasis on Mortallty and Dlsablllty Research,

Linda DelBene and Fritz Scheuren {Lds.).

Us. Department of Health, Education, and

Welfare, SociaJ Security Administration. (1980)

“Measuring the Impact on Family and personal

Income Statistics of Reporting Differences

Between the Current Population Survey and Administrative Sources,” Studies from Interagency

Data Linkages, report no. Il.

U.S. De~artment of the Treasury, Internal Revenue Service. (1981-1984) Series on Statistics

of Income and Related Administrative Record

Research, Wendy Alvey and Beth Kllss (Eds.).

U.S. Department of the Treasury, Internal Revenue Service. (1984) Statistical Uses of Administrative Records: Recent Research and Present

‘m;. )’’.O’s‘ and 2’ ‘eth ‘-

11ss and Wendy

Wagner, G., and Newcombe, H.B. (1970) “Record

Linkage: Its Methodology and Application in

Medical Data Processing (A Bibliography),”

Methods of Information in Medicine, vol. 9, no.

~

Wentworth, D.; Neaton, J.; and Rasmussen, w.

(1983) “An Evaluation of the Social Security

Administration Master Beneficiary Record File

and the National Death Index in the Ascertainment of Vital Status,” Anerican Journal of

Public Health, vol. 73, pp. lZIO-1214.

Winkler, williarn. (1984a) “Issues in Developing

Frame Matching Procedures,” a paper presented to

the American Statistical Association’s Committee

on Energy Statistics, April 1984.

344

-— -.
Winkler, William.(1984b) “Exact Matching Using Winkler, William. (1985b) “Preprocessing of

Elementary Techniques,” Proceedings of the Lists and String Comparisons,” Record Linkage

Itnercan Statistical Association, SectIon on Techniques--l985, Internal Revenue Service,

3urvey Research Methods, pp. 23!-242. pp. lP1-1E!7(in this volume).

Winkler, William. (1985a) “Exact Matching Lists

of Businesses: Blocking, Subfield Identification

and Information Theory,” Record Linkage Tech- Ziegler, Martin. (1977)

niwes--l985, Internal Revenue

“Efforts to Improve

Service, PP. 221- Estimates of State and Local Unemployment,”

L41 (In this volume). Monthly Labor Review, November, pp. 12-18.

345
SELECTED BIBLIOGRAPHYON THE MATCHING OF PERSON RECORDS FROM DIFFERENT SOURCES

Compiled through 1974 by

Fritz Scheuren and Wendy Alvey, Social Security Administration

Tba referenceslisted here are restricted

essentiallyto published information on

the results and methodologyof matching

person records. No material relating to

establishment matching is included.

Several compilationsof articles exist on

the confidentiality Issues raised by

record linkages. As a rule, therefore,we

have excluded these citations from the

bibliography. l’wo recent such ccmpilationa are:

Report of the Pres&3!entt8 C@nmkh on Federal t%Ut~8t~C8,

VO1. 1, 1971, pp. 246-254.

u. s. Department of Health, Education, and Welfare. Records,

CU?lpUteF8, and the R@ht8 of

citizens : Report of the Secre-

~ ‘8 Advhory C&ttee on

Autmated p@80?UZ~ Data Sy8t~13,

1973, pp. 298-330.

Some other limitations should also be

mentioned:

1. The listing does not contain

citations to studies which began

with an administrativerecord and

then drew a sample of casea to be

interviewed. (Excluded from the

bibliography, therefore, are

basically all reverwe record check

studies of financial characteristics, as well aa proep8ctiVe

epidemiologicalstudies.)

2. only references to “exact”

matching are included. Synthetic

or “statistical”matching studies

are not shown. (For citations

to the literature on synthetic

matches, see Hadner, D. B. The

Statiat;cal Matchtng of Wmw&zta

set8 : the Bureau of Eccncmic

Analya&3 1964 Clmrent Populat&m

Survey--Tax ModeZ Match, Yale

Univ., New Haven, 1974. See also

the hmaz8 ofEc&c and Sociul

Mea8urenk?nt, vol. 3, 1974, where

several artfcles on synthetic

matching are presented.)

3. Studies of matching for use in

Dual System Estimation are also

not included. For a recently

published source of informationin

this area, see Marks, E.S.,

Seltzer, W. and Krotki, K.J.

Popuktwn Growth E8t7%?at~cn: A

Handbook of vital StCZti8t{C8

Meaeumment, The Population

Council, New York, 1974.

4. Studies involving record linkage

in medicine are covered only

partially. For additional references in this area the reader

might consult Acheson, E.D. Med-

<cal Record Linkage. Oxford

University,London, 1967 or Record

Linkage in Med~c~ne, hoc. Int.

Sym.. Oxford, July 1967, W~lMsm.e

and Wilkins, Baltimore,1968.

COMPLETENESS OF COVERAGE

The bibliography’s coverage of m~”op U. S.

studies involvinglinkagesbetween eurvey

(or census) schedulesand administrative

records is believed to be reasonably

complete for the period 1950-1974.

However, only a few references are given

to work done outside the United States and

to research engaged in bafore 1950.

SOURCES

Many of the citations shown here were

selected from the folldwing three reference works:

U. S. Bureau of the Census. lndaxes to survey methodology literature, Technical Paper No. 34,

1974.

Forsythe,J. L~8* of Referenoe8 c??

Reeult8 and Methcobzogy

Matohing Studie8, 1966 (Unpu;~

lished Census Bureau Memorandum).

u. s. Public Health Service.Use

of vital and health records in

epidemiologic research, V~tal and

Health Statistic, series 4, no.7,

1968.

ACKNOWLEDGMENTS

Special thanks are due to Irene Jacobs,

Office of Research and Statistics

Library, Social Security Administration, and Patricia Fuellhart and her

staff, Statistical Research Division,

Bureau of the Census, for their assistance in this effort.

347
[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[91

[10]

[11]

Ac!heeon,E. D. Zhe Qxfml 

XliI&aU -iRFWQ=m Ll&S=QrUl

X$?@? Oxford Regional

Hospital Board, Ox;ord, 1963.

Ache80n, E. D. Oxford Record Linkage

Study, ~ a m ~ -

~, VO1. 18, 1964, pp. 8-13.

Ache80n, E. D. The Oxford Record

Linkage Study, a rwiew of the

method with some preliminary re8UltS, ~. ~. &. MA., VO~.

57, 1964, pp. 11 ff.

Acheeon,E. D., end mans, 3. G. The

Oxford Record Linkage Study, Biometrics, vol. 2, 1963, pp. 367 f~

Anderson, O. W., and Feldman, J. J.

Famfly Medical Costs @ Voluntary

Ilealth =n~~ &-

VX, Mc(lraw-Hill, NEWYork, 1956.

Anderson, O. W., and Sheatsley, P.

H. $OmDrehensive Medical Insurance,

Health Information Foundation Redearch Series, No. 9.

Anderson, R., and Anderson, O. W. ~

IkQadf&&Uealkh Serxkes, Univ. of

Chicago, Chicago, 1967.

Bachi, R., Beron, R., and Nathan, G.

Methods of record-linkage and applifitihM in 18rael, Bul 1etln qf *

vol. 42, Proc. of 36th

Sydney, 1967, pp. 751-765.

Bahn, A. K. Methodological

population of out-patient

atric clinics, Maryland,

W W -raD h ~.

Pub. no. 821, 1961.

Session,

study Of

psychi1958-59,

W, PHS

Balemuth, E. Health interview responses compared with medical recorde, U. S. Public Health Sezwice,

V5tal ~d Health $tatisti~, serfes

2, no. 7, 1965.

Bancroft,G. ~ American Labor

Force: Its Growth ‘~~and

Composit=, Wiley, N=York, 1958,

PP● 151-175.

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

348

Belloc, N. B. Validation of morbid

ity survey data by comparison with

ho;pital ‘records; ~. ‘Amer. Stat.

Assn., vol. 49, 1954, pp~2-8~

Binder, S. Present possibilitiesand

future potentlaltties, The Use ~

Vital and Health Records=r ~etic

—Eix— and Studies,‘— United

~tions, New York, 1962, pp. 161-

170.

Bixby, L. E. Income ot people aged

65 or older: overview rrom the 1968

survey of the aged, Social Securit~

Bulletin,report no. 1, 1970, pp.

28-34.

Brounstein,S. H. Data Record Linkage Under Conditionsof Uncertainty,

7th Annual Conferenceof———the Urban

~ Re~ional Information SYeteme

Association, Los Angeles, California, 1969.

Chase, H. C. A etudy of infantmortality from linked records: method

of study and registrationaspects,

U. S. Public Health Service, Vital

and Health Statistics, seriee,20,

x 7, 1970.

Christenson, H. T., Culturalrelativism and premarital sex norms,

&!fX. &X. w., vol. 15, 1960.

David, M., Gates, W., and Miller, R.

Linkage and retrieval ~f micro-

~ —w. Heath, Lexi~n,

Mass., 1974.

Davidson, L. Retrieval of misspelled

names in an airline pu~denger

records system, g~nicati on~ a

theACM, —— vol. 5, 1962, pp. 169-171.

Densen, P. M., and Shapiro, S.

Researchneeds for record matching,

1.K?f2*-* W* k. W* W*

Sac., 1963, pp. 20-24.

DuBoi8 , Jr., N. S. D. On the

problem of matching documents with

missing and inaccurately recordad

Items (preliminary report), Annals

!X~~* Vo=

1964, P. 1404.

_——- .—
[22] DuBois, Jr., N. S. D. A document

linkage program for digital

computers, Behavioral Science, vol.

10, 1965, pp. 312-319.

[23] DuBois, Jr., N. S. D. A solution to

the problem of linking rnultfvariate

documents, Jour. Amer. Stat. Assn.,

vol. 64, 196~P.~-1~ —

[24] Dunn, H. L. Record linkage, American

=lUMU, VO1O 36,

.

[25] Dunn, H. L., and Grove, R. D.

Completeness of birth registration

in the United States, Estadistica,

vol. 1, 1943, pp. 3-17.

[26] Elinson, J., and Trussell, R. E.

Some factors relating to degree of

correspondence of diagnostic information obtained by household interviews and clinical examinations,

me rican ~ ti W M,

vol. 47, 1957, Pp. 311-321.

[27] Fellegi, I. P., and Sunter, A. B. A

theory for record linkage, Jour.

~. ~. ~., vol. 64, =9,

PP. 1183-1210.

[28] Ferber, R. The Reliability——of Consumer ReDOrtS—& ~ @set8

& Debts, University of Illinois,

Urbana, 1966.

[29] General Register Office.

1951 Census of Endand ~ -, .— —

General Report, London, 1958, pp.

41-45, 5056.

[30] Goldberg, I. D., Goldstein, H.,

Quade, D., and Rogot, E. The use of

vital records for blindness

research,Amer. Jour. ~. Health, —.

VO1. 54, 1964, pp. 278-285.

[31] Goldberg, S. Discussion of data

storage and linkage, Bullet%n .—of the

International Statistical Instftute,

Proc. of 36th Session, Sydney, vol.

42, 1967, pp. 806-808.

[32] Grove, 1?. D. Studies in the completeness of birth registration, U.

s. Bureau of the Census, Vital

Statistics -- Special Reports, ~

27, no. 18, 1943.

[33] Guralnick, L., and Nam, C. B.

Census-NOVS study of death certificates matched to census records,

The MilbankMemor ial~ guarterly,

vol. 37, 1959, pp. 144-153.

[34] Haber, L. I?. Evaluating response

error in the reporting of the income

of the aged: benefit income, Proc.

Amer. Stat. Assn. Sot. Stat. ~,

l~~c.

[35] Haenszel, V., Loveland, D. B., and

Sirken, M. G. Lung-cancer mortality

as related to residence and smoking

histories. I. white males, Journal

~f && National Cancer Institute, .—

vol. 28, no. 4, 1962, pp. 947-1001.

[36] Hambright, T. Z. Comparability of

age on the death certificate and

matching census record, U. S., MeyAug. 1960, U. S. Public Health

Service, YQal ARfl Xi@k.11

~, series 2, no. 29, 1968.

[37] Hansen, M. E. The role and feasibility of a national data bank,

based on matched records and

~nterviews? _ - t= ~-

dent’s Commission ~ Federal

Statistic S, vol. 2, PP. 1-63.

[38] Hauser, P. M., and Kitagawa, E. M.

Social and economic mortality

differentials in the Un~ted States,

1960: outline of a research project.

Proc. &r. Assn. Sot. Sta~. Sec.,

=0, pp. 116-= — — —

[39] Hauser, P. M., and Lauriat, F.

Record matching-theory and practice

(abstract),Proc. Amer. Stat. Assn.

~. ~. K, l~pm. —

349
Rhoads, F. D. Comparison of birth

tests by several methods in Georgia

and Maryland, U. S. Bureau of the

Census, Vital Statistics -- M

Reports, v~7, no. 60, 1939, pp.

681-695.

[41] ”Hoal, P. G., and Peterson, R. P. A

solution to the problem of optimum

classification, Annals of

Mathematical Statistics, vol. 2T

1949, pp. 433-438.

[42] Hogben, L., Johnstone,M. M., and

Cross, K. L. Identification o~

medical documents, British Med. ~.,

1948, pp.- ‘—

[43] Horn, W. Reliability Survey: a survey on the reliability of responses

to an interview survey, Het PTT- .—

bedrijf, vol. 10, 1960, pp. 105-156.

[44] Horn, W. Nonresponsein an inteniew

survey: some specific phenomena (a

case-study), Het PTT-bedri.jf,vol.

12, 1963, pp. ~-19~

[45] Jaro, M. Unimatch--a computer system

for generalizedrecord linkage under

conditions of uncertainty, Spring

Joint Computer Conference, 1972,

AFIPS-Conference Proceedings,vol.

~1972, pp. 523-530.

[46] Uohnson, Jr., C. E. Consistency of

reporting of ethnic origin in the

Current Population Survey, Technical

PaDeg&. ~, 1974, pp. 23-27.

[47] Kaplan, D. L., Parkhurst, E., and

Whelpton, P. K. The comparabilityof

reports on occupation from vital

records and the 1950 census, Vital

Statistics -- -1 R~%=

53, no. 1. 1961, pp. 1-27.

[48] Kelly, T. F. Factors affecting

poverty: a gross flow analysis, ~_e

President’s Commission ~ Income

Maintenance Pro~r~ :

Studies, 1970, pp. 1-82.

tudinal data from cress-section surveys: an illustration from the Current Population Survey, Annals ~

Economic and Social Measurement,

vol. 2, 197~pp. 209-214.

[50] Kelly, F!. H. Methods and Resources

——for the ConstructIonan~~aintenance

o_f & Navalo Population Re!gister, a

report prepared for the National

Cancer Institute by the Bureau of

Ethnic Research, Department of

Anthropology, University of Arizona,

Tucson, 1964.

[51] Kennedy, J. M. Linkage ~f Birth and

Marriage Record Using a Digital

Computer, Atomic Ener~ ~ Canada,

~., Chalk River, Ontario, l=

[52] Kennedy, J. F!. The use of a digital

computer for record linkages, The

@ ~f Vital and Health Statist= ——

for Genetic and Radiation Studies,

United Nations, New York, 1962, pp.

155-160.

[53] Kitagawa, E. M., and Hauser, P. M.

Methods u~?d in a current study of

social ~.id economic differences in

mortalfty, Emerging Techniques in

Population Research, Milbank Memo=

ial Fund, New York, 1963, pp. 250-

266.

[54] Kltagawa, E. M., and Hauser, P. M.

ferential Mortality Q t~-United

States: 2&!+Q’ & Socioeconomic

Evfdemiology Haward Univ., Cambridge, 1973: pp. 183-227.

[55] Klebba, A. J., Maurer, J. D.,and

Glass, E. J. Mortality trends: age,

color, and sex, U. S. 1950-69,U. S.

Public Health Semite, Vital &

Health Statistics, series 20, no.

15, 1973.

[56] Livingston,R. Evaluation of the

reporting of public assistance

income in the spectal census of Dane

County, Wisconsin (MeY 15, 1968).

Proc.-- Ninth Worl&h;p Oil Publii

Welfare Resea rch ad Stati~ics, l!ew

Orleans, 1969.

350
[57]

[58]

[59]

[60]

[61]

[62]

[63]

[64]

Livingston, R. Revaluating the

reporting of public aseiatance

income in the 1966 Survey of

Economic Opportunity, Proc. Tenth

Workshop on Public Welf~Res= ——

and Statis~c~O.

Lloyd, J. W. Discussion of matching

of census and vital records in

social and health research: problems

and results, -c. Amer. Stat. Assn.

Sot. —.Stat. Sec., 19Tp.71. —

Loeb, J. Weight at birth and

survival of the newborn by age of

mother and total-birth order, U. S.,

early 1950, u. s. Public Health

Service, Vital and Health Statistics, serial, =5~.—

Mandel, B. J., Wolkstein, I., and

Delaney, M. M. Coordinationof oldage and survivors insurance wage

records and the post-enumeration

survey, Studies Q Income qd

Wealth: ~n Appraisal ~f t~ ~0

=s IQ3Q2Z!ff Qf@, Princeton, vol.

23, 1958, pp. 169-178.

Marks, E. S., Mauldin, W. P., and

Nisselson, H. The post-enumeration

survey of the 1950 censuses: a case

history in eurvey design, &r.

Amer. *. lX!E!2*Yus PP” 220-

E

Marks, E. S., and Waksberg, J.

Evaluation of coverage in the 1960

Census of Population through caseby-case checking, Proc. ~. ~.

Aasn. Sot. Stat. S= 1966, pp. 62-

K——’

Mssi, A. T., Sartwell,P. E., and

Shulman, L. E. The use of record

li~e to determine familial occurrence of disease from hospital records (Hashf.moto’sdisease),&. ~.

U. Health, vol. 54, 1964, PP.

1887-1894.

McCarthy, M. A. Comparison of the

classificationof place of reeldence

on death certificatesand matching

census records, U. S. Public Health

Service, Vital ~ Health ~s-

~, series 2, no. 30, 1969.

[65]

[66]

[67]

[68]

[69]

[70]

[71]

[72]

[73]

Meltzer, J. W., and Hochstim, J. R.

Reliability and validity of survey

data on physical health, Publi&

Eealth ReDOrtS, vol. 85, 1970, pp.

=086 .

Miller, H. P., and Paley, L. R.

Income reported in the 1950 census

and on income tax returns, ~tudies

~Incom~&-: ~ ~

&f * ~- M ~,

Princeton,vol. 23, 1958, pp. 179-

201.

Moriyama, I. M. Uses of vital records for epidemiological research,

J. m. Qj&., vol. 17, 1964, pp.

889-897.

Morrison, F. S., and Blen, A. L.

Method of testing in the 1950 census

the completeness of birth registration~~, volo 7, 1549s

pp. 185-193.

Murray, J. H., and Raber, L. D.

E!ethodologyand validation,Appendix

A of the -d Potxlation ——of the

ynfted States: t~ ~ Social

$ecurfty Survey ——of the ~d,~

A. Epstein and J. H. Murray, SSA/ORS

research report no. 19, 1967, pp.

193-219.

Nathan, G. On optimal Matching ~oCase Inst5tute of

%%%ogy, Cleveland, 1964.

Nathen, G. Outcome probabilitiesfor

a record matching process with

complete invariant information,~.

k“ -t” -“s Vol” 629 196?$

pp. 454-469.

Neter, J., Meynes, E. S., and

Ramanathan,R. The effect by mismatching on the measurement of response errors, -e 4XK* &f&*

Assn. ~. ~. &&., 1964, pp. 2-

r

Neter, J., Maynes, E. S., and

Ramanathan,R. The effect of mismatching on the measurement of response errors, J. b. ~. h.,

vol. 60, 1965, pp. 1005-1027.

351
[74] Newcombe, H. B. Detectlonof genetic

trends in public health, Effect ~

Racliatlon ~ Ihnzan Xered~World

Health Organiza= Geaeva, 1957,

pp. 157-168.

[75] Newcombe,H. B. Environmental versus

ganetic interpretations of birthorder effects, Eugenics Quarterl.;

vol. 11, 1964, p. 36ff.

[76] Newcombe, H. B. Panel discussion,

seeaion on epidsmiological studiae,

Second International Conference on

~aital Mel

— .

formation, The International Medical Congrese, Ltd., New

York, 1964, pp. 345-349.

[77] Newcombe, H. B. Pedigrees for population studies, a progress report,

Cold Srming Harbor SymDosiaon~-

ki&8&&8~, VO1. 29, 1964, p.

21ff.

[78] Newcombe, H. B. Population genetics,

population records, *

IhIman Genetics, Holden-Day, New

~ 19-92-113.

[79] Newcombe, H. B. Risk of fetal death

to mothers of different ABO end RR

blood type, ~. ~. Human Genet.,

vol. 15, 1963, pp. 449-X —

[80] Newcombe, H. B. Screenfng for efect6 of maternal age and birth order

in a register of handicapped children, Ann. Human Genat., vol. 27,

1964,%. 3~2.—

[81] Newcombe, H. B. Untapped knowledge

of human populations, action OS

tkRQxal$QAm!Q&wwkh vol.

56, series 3, isection3, 1962, pp.

173-180.

[82] Newcombe, H. B., Axford, S. J., and

Jamee, A. P. A plan for the study of

fertility of relatives of children

suffering from hereditary and other

defects, Atomic Energy of Canada,

~., report no. 511, ChaiZXr,

Ontario, 1957, p. 50.

[83] Newcombe, H. B., James, A. P., and

&ford, S. J. Family linkage of

vital and health recorde, Atomic

Ener~ ~ Canada,J@., report no.

470, Chalk River, Ontario, 1957.

[84] Newcombe, H. B.S and Kennedy, J. M.

Record linkage making maximum use of

the discrimination power of identifying information, $~ ications

Q the ~ vol. 5, 1962, pp. 563-

566.—

[85] Newcombe, H. B., Kennedy, J. M.,

&ford, S. J., end James, A. P.

Automatic linkage of vital records,

Science, vol. 130, 1959, pp. 954-

959.

[86] Newcombe, H. B., and Rhynas, P.O.

Child spacing following stillbirth

and Infant death, Eu&lmicS ~-

terly, vol. 9, 1962, pp. 25-35.

[87] Newcombe, H. B., end Rhynas, P. O.

Family lhlqe of population records, ~ Use ~ Vital —— and —Health

Statistics——f=Genetic ——and Radiation

Studies, United Nations, New York,

1962, pp. 135-1540

[88] Newcombe, H. B., and Tavsndale, O.

G. Effects of father’s age on die

risk of child handicap or death, ~.

~. HuhwtnGenet., vol. 17, 1965, pp.

163-178.

[89] Newman, S. M. Problem in mechanizing

the search in examining p8t811t

‘ applications,~ -~

and Develomen %-MB 3, 1956.

[90] Nicholson, W. Tha income data series

in the graduated.work Incentive

experiment: an analysie of their

differences,Chapter 6, Part C, The

~ Jersey Graduated W- Intent=

@meriment, 1974.

[91] Nitzberg, D. M., and Sardy, H. The

methodology of computer linkage of

health and vital records, ~.

Amer. Stat.-. ~. ~. SSS.,

1965, pp. 100-106.

352
[92]

[93]

[94]

[95]

[96]

[97]

[98]

[99]

[100]

[101]

Ohlsson, I. Merging of data for

statistical uses ~ & &

ternational Statistical Institute,

vol. 42, Proc. of 36th Sessfon,

Sydney, 1967, pp. 751-765.

One, M., Patterson, G. F., and

Weitzman, M. S. The quality of

reporting social security numbers in

two sumeye, ~. ~. ~. ~.

~= ~tc~c~ 1968$ PPs 197-205-

Perkins, W. M., and Jones, O. D.

Matching for census coverage checks,

Proc. Amer. &t@. -n. &c. ~.

~, l= pp. 122-139.

Phillips, Jr., W., and Bahn, A. K.

Experiencewith computer matching of

names, Proc. Amer. Stat. Assn. Sot.

-t. ~~ l= pp~6-2~ —

Phillips, Jr., W., Bahn, A. K., end

Miyasaki, M. Person+natching by

electronic methods, Ions

@ t~e Am, vol. 5, 1962, pp. 404-

407.

Phillips, Jr., W., Gorwitz, K., and

Bahn, A. K. Electronic maintenance

of caee registers, - &fLlklI

~, VO1. 77, 1962, pp. 503-510.

Pollack, E. S. Use of census matching for etudy of psychiatric admission rates, Proco Amer. Stat. Asen.

SQ& w. m7 19wppno7==

Presidentts Ccxmnittee to Apprafse

Etnploymentand Unemployment Statistics. Meaaurin~ Eumlovment.—and Unemployment, 1962, pp. 386-394.

Rogers, P. B., Council, C.

Abernathy, J. R. Testing

refzietrationcompletenessin

R ., and

death

of-premature inf&s, Publi& a~g~~

=49 vol. 76, no. 8, 1961, pp.

-G

Schachter, J. Matched record comparison of birth certificate end cenaua

information:’ United States, 1950,

U. S. Public Health Senfce, Vital.

statistics —~~, Vol

47, no. 12, 1962, pp. 365-399.

[102]

[103]

[104]

[105]

[106]

[107]

[108]

[109]

[110]

Scheuren, F. J., Bridges, B., and

Kilss, B. Report no. 1: subsampling

the Current Population Survey: 1963

Pilot Link Study, Studies m

Interagency Data Linkages, Social

Security Admin~ration, 1973.

Scheuren, F. J., and West, G. 1966

& ~ Survey&f Xconomic Opv=

tunity: Computer Consistency Checks,

Office of Economic Oppor=Y,

1971, pp. 113-124 (Proces&d). ‘-

Schneider, P., and Knott, J. Accuracy of census data ae measured by

the 1970 CPS-Censue-IRS k%tchfng

Study,me -C ~= -“ m.

Stat. Sec., 1973, pp. 152-159.

Shapiro, S. Estimating birth registration completeness,~. ~. ~.

~., VO1. 45, 1950, pp. 261-264.

Shapiro, S. Recent testing of birth

registration completeness in the

United States, Population Studies,

vol. 8, 1954, pp. 3-21.

Shapiro, S., and Densen, P. Research

needs for record match%ng, Proc.

w. m. &!Ml* SQ!L.m. ~,

1963, pp. 20-24.

Shapiro, S., and Schechter, J. Birth

registration completeness, 1950,

?-, _ ReDOIW3, vol. 67,

1952, pp. 513-524.

Shapiro, S., and Schachter, J.

Methodology and summary results of

the 1950 birth registration test in

the United Statea, @tadietic~, vol.

10, no. 37, 1952, pp. 688-699.

Siegel, J. S. .—1970 Censue —of PoPulatiQU@HRUainE EUQa&@naRd &EalX!2UU& Eat&M&W QfQUU-

~e ~f Population & ~, &g &

~ DemoEraDhiC~, U. S. Bureau of the Census, PHC(E)-4, 1974.

[111] Siegel, J. S., and Zelnilc,M. An

evaluation of coverage in the 1960

censue of population by kechniquea

of demographic analysis and by

composite methods, Proc. Amer. Stat.

Aa8n. Sot. ~t. ~, ~6,<,

=5. —

353
[112] Silver, J. Discussionof matching of

census and vital records in social

and health research: problems and

results, Proc. Amer. Stat. Assn.

~. ——Stat~ec.,~65, p~40. —

[113] Simpson, J. E., and Von Arsdol, Jr.,

M.D. The matching of census and

probation department record systems,

~. Ur. Stat. Aesn. &. Stat.

~.., 1965, pp. 116-121. —

[114] Sirken, M. G. Hospital utilization

in the last year of life, U. S.

Public Health Service, Vital and

Health Statistics,series ~. ~

1965.

[115] Sirken,M. G. Research uses of vital

records in vital statisticssurveys,

earth Methods in Health Care, ed.

by J. B. McKin=y~d~ New

York, 1973, pp. 39-46.

[116] Sirken, M. G., Maynes, E. S., and

Frechtling, J. A. The survey of

consumer finances and the census

quality check, —.Studies in Income —. and

Wealth: ~ Appraisal ~f the —— 1950

@!!U!?. Income Data, VO~. 23,

Princeton, 1958$ p~27-168.

[117] Sirken,M. G., Pifer, J. W., and

Brown, M. L. PSQfi?2 02 surveys

Linked to Death Records, 1962.

[118] Steinberg, J. Some aspects of statistical data linkage for individuals, Data-Bases,Computers,—.and the

Social Sciences, Wiley, New York, .—

1!$70,pp. 238-251.

[119] Steinberg, J. Some observationson

linkage of survey and administrative

record data, Studies ~ Interagency Data Linkages, Social Secur-

~ty Ati~stration, 1973, pp. 1-14.

[120] Steinberg, J., Hearn, S., and

Deutch, J. Social Security Administration’s evaluation and measurement system, Proc. Amer. Stat.

Assn. &. stir ~~966~ .

X268.

[121] Steinberg,J., and Pritzker, L. Solne

experienceswith and reflections on

data linkage in the United States,

Bulletin of—— the International

tic al Institute, vol. 42,

1967, pp. 786-805.

[122] Sunter, A. B. A statistical approach

to record linkage, record linkage in

medicine, Proc. of.— the International

Svmv , E. & S.

Livingstone, Ltd., London, 1968.

[123] Tepping, B. J. Study ~ ~tching

T~ & @bscriDtions

Fulfillment,Philadelphia, 1955.

[124] Tepping, B. J. Progress Report on

the —— 1959 Matching Study, Nationz

Analysts Inc., Philadelphia, 1960.

[125] Tepping, B. J. A model for optimum

linkage of record, ~. ~. ~.

~n., vol. 63, 1968, pp. 1321-1332.

[126] Tepping, B. J. The applicationof a

linkage model to the ChandrasekarDeming technique for estimating

vital events, Technical Notes &

u. s. Bureau of the Census,

Washington, D. C., 1971.

[127] Tepping, B. J., and Bailar, B. A.

Enumerator variance in the 1970

census, Proc. Amer. Stat. Assn. Sot.

~. &T19~pp. 160-169. —

[128] Tepping, B. J., and Chu, J. T. ~

~a~~;,~Matching Rules Applied to

-t Data, l?atio~~

Analysts, Inc., Phil~phia, 1958.

[129] Turner, Jr., M. L. A new technique

measuring household change, Demography, vol. 4, 1967, pp. 341-3-

[130] u. s. Bureau of the Census.

Infant enumeration study: 1950

completeness of enumeration of

infants related to: residence, race,

birth month, age and education of

mother, occupation of father,

?rocedural studies

w, no, 1, 1953.

Qf*l~

354
[131]u. s. Bureau of the Census.

The post-enumeration survey: 1950,

Technical P-r, Lo. ~, 1960.

r1321 . . U. s. Bureau of the Census.

Evaluation and Research Program ~f

3&M. s. C=uses ~f Population-d

Housing, 1960: Record Check Studies

~f Population Coverage, series HR60,

no. 2, 1964.

[133] u. s. Bureau of the Census.

Evaluation and Research Program @

-E. ~. C~uses ~f Population a~d

Housing, 1960: ~ ~f Eata ~n

PoDulatio7 Characteristi~ ~

~ 12YCPS-Censw -, series

ER60, no. 5, 1965.

[134] u. s. Bureau of the Census.

Evaluation ~ Research Pro~ram ~f

WIJ. ~. Censuses Q Population a~d

Housing, ~: ~e Employer ,RCheck, series ER60, no. 6, 1965.

r1351 u. s. Bureau of the Census.

. .

Evaluation @ Research Proiwam o~

~ ~. ~. Censuses w Pormlation ~d

Housfn8, _1960: Effects ~ Interviewers and Crew Leaders, series

ER60, no.~ l=

[136] U. S. Bureau of the Census.

Evaluation and Research Program o~

t& g. ~. ~opulation and Housing,

~: -- ~ =ura~ &

Income Reporting, series ER60, no.

8, 1970.

[137] IJ. s. Bureau of the Census.

l%!Q QQSJMJ ~f povulation& =-

- Evaluation a~ Research Program:

T~t of Birth RegistrationComDletenese fi6=QQ, PHC(E)-2, 1973.

r1381 .——. u. s. Bureau of the Census.

.—1970 Census .of Population and Houe-

& Evaluation——and Researc~Pro~:

the Coverage & Rousing ~ t~ ~0

CZnsus, PHC(E)-5, 1973.

[139] u. s. Bureau of the Census.

——1970 Census —of Population and Housi= Evaluation——and Researc=ro~a:

Qg Medicare Record Check: ~

Evaluation @ t~ Coverafie &

—Persons 65.— Years of.- &e ~Qyy ~

the 1970 Census> PHC(E)-7, 1973.

[140] u. s. Bureau of the Census.

——1970 Census of. Population and Hous-

~ Evaluation~ Researc=ro~~:

@ ~PS-CensW l@!&lh PHC(E)-11,

1975.

[141] U. S. Public Health Service. --

tics of ~ United States, 1950,

vol. 1, 1954, pp. 108-112.

[142]U. S. Public Health Service.Weight

at birth and its effect on survival

of the newborn in the United States,

early 1950, Vital Statistics —

w Reports? vol. 39, no. 1,

.

[143]U. S. Public Health Service. Reporting of hospitalizationin the Health

Interview Survey, Public Health Service Publicationno. 584-D4. Vital

and Healtl. tatistics,series 2, no.

~1~

[144] U. S. Public Health Service. Comparison of hospitalizationreporting in

three survey procedures,Vital —— and

Health Statistics,series 2, No. 8,

1965.

[145] u. S. Public Health Service. Interview response on health insurance

compared with insurance records,

Vital and Health Statistics, series

~.~8, 1966.

[146] U. S. Public Health Service.A study

of infant mortality from linked

records, Vital and Health

Statistics,series 20, nos. 12, 13,

and 14, 1972-1973.

355
[147] U.S. Social Security Administration.

Workers covered under Social

Security: 1963 administrativeinformation and pilot link results

compared, Studies from Interagency

Data Linkages, repor=. 3.

[148]U.S. Social Security Administration.

XY!S?QQQ Policies and Procedures

for EstablishingInit= Entitlement

to RSDI Benefits, series 1, nos. 21-

.—

24, 1974.

[149] U.S. Social Security Administration

ml% Sample Longitudinal BnployeeEmpl~er Data 1971

(Processed).

[150]Wells, B. Optimum Matching Rules,

Univ. of North Carolina, 1974.

356
Record Linkage

\

ItsMethodology and Application in MedicalData Processing*

A bibliography compiled by

G. WAGNER and H. B. NEWCOMBE

For the further development of medical data processing the method of record linkage is of utmost importance.

It is obvious that two or more items of information about

persons or person-groups, recorded at different times and

at different places, are of much greater significance when

available together than when isolated from each other

and inaccessible at the same time. The process of linking

together information of medical interest pertaining to the

same person is called Medical Record Linkage. Today

the linkage procedure can be profitably carried out automatically by computers.

Within the last decade a fast growing literature on

this subject has been published, so that it has become

more and more difficult for the individual scientist to keep

abreast with the wealth of publications.

The references given here have been grouped according to the following topics:

A. General Remarks on Record Linkage

B. Methodology of Record Linkage

c.

D.

E.

F.

The Identification Problem

The Privacy Problem

Application of Medical Record Linkage in

1.

2.

3.

4.

5.

6.

Patient Care and Medical Data Processing

Epidemiology

Vital Statistics, Demography

Genetics

Public Health Services

Other and Non-medical Fields

Costs of Record Linkage.

We hope that this bibliography will be of some help

for more detailed studies in this important field of medical

data processing.

G. WAGNER (Heidelberg,Germany)

H. B. NEWCOMBE (ChalkRiver,Ontario/Canada)

.

*Reprinted with permission from Methods of Information in Medicine - Methodik

der Information in der Medizin, Vol. IX, No. 2, Copyright o1970 by F.K.

Schattauer, pp. 121-138.

*

357
A. GeneralRemarkson RecordLfnkage

ACHESON, E. D.: Medical Record Linkage.

(Oxford University Press, London-New York-Toronto 1967).

ACHESON, E. D.: Some remarks on contemporary British

medical statistics.

J. roy. statist. Sot., Series A, 131: 9—12, 1968.

ACHESON, E. D.: Linkage of medicalrecords.

Brit.med. Bull.24:206—209, 1968.

ACHESON,E.D.:Computers and medicalrecordlinkage.

WHO-Seminar on thepublichealthusesofelectroniccomputers,London,1968.

(WHO Document Euro 3092/6,WHO RegionalOfficefor

Europe,Copenhagen 1968).

ACHESON,E.D. (Edit.):Record Linkagein Medicine.

(E.&S. LivingstoneLtd.,Edinburghand London 1968).

ACHESON,E.D.:Medicalrecordlinkage.

Meth. Inform.Med. 8: 1—6, 1969.

ACHESON,E.D.:PersonalRecord Linkage.

In E, Fritzeund G.Wagner (Hrsg.):Dokumentation des

Krankheitsverlaufs. Bericbtiiberdie 13.Jahrestagungder

GMDin Bochum,30.9.-2. 10.1968.

(F.K, SchattauerVerlag,Stuttgart-NewYork 1969).

ACHESON,E,D. and FOIIBES,J.A.:Experimentintheretrievalofinformationingeneralpractice.A preliminaryreport.

Brit.J.prev.SOC.Med. 22:105—109,1968.

Ad Hoc Committee on the ImplicationsofRecord Linkage

forHealth-RelatedResearch:HealthResearchUses of RecordLinkagein Canada.

(Med.Res.Count. of Canada,Ottawa 1968).

BAHN,A. K.:Mental healthclinicstatistics: Needs,

sources,methods.

Publ.Hlth.Rep.69:619—625, 1954.

BAHN, A. K.: Experienceand philosophywith regard to

caseregistersin healthand welfare.

Commun. ment.Hlth J.3:245—250, 1965.

BEJEROT,N.: Sjukvardensjournal-ocharkivproblem.

Svenska Lak.-Tidn.62:1346—1359,1965.

BILLETED,B.M.: Anforderungenan Konzeptionund Aufbau

einerDatenbank.

ADL-Nacbr. No.58, 652—656, 1969.

B.MA. PlanningUnit:Computers inMedicine.

Report of the Working Partyon Computers in Medicine.

(B.M.A.House, London 1969).

BOTHWELL,P, W.: Routine,recordsand research.

Med. Rec.5: 359—365, 1961.

BOTHWELL, P. W.: Conceptual and organizational problems

in medical and particularly epidemiologic researd

Publ, Hlth 76: 360—367, 1962.

CHEESEMAN, E. A.: Medical record linkage in Northern ireland — reconnaissance and proposals with particular reference to problems of identification.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. ?0—?6.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

COHEN, B. M.: Methodology of record follow-up studies on

veterans.

Amer. J. publ. Hlth 43: 1292—1298, 1953.

DAVIES, M.: Toward a medical data bank for a total population.

Datamation 15: 257—260, 1969.

DOLL, R.:Documentazioneospedalieranell’epocadei calcolatorielettronici.

Appl.bio-med.talc.elettr.4:61—72, 1969.

DOUGLAS,J.W. B. and BLOMFIELD,J.M.: The reliability of

longitudinalsurveys.

Milbank mem. Fd Quart.34:227—252, 1956,

DUBOXS,N. S. and D’ANDREA,J. R.:A document linkage

program fordigitalcomputers.

Behav.Sci.10:312—319, 1965.

DUNN, H. L.:Record Linkage,

Amer. J.publ.Hlth 36:1412—1416,1946.

DUNN, H. L.:Elements of a coordinationsystem of vital

recordsand statistics.

Publ.HIth Rep. 68:793—801, 1953.

DUNN, H. L,: A national identity registration system to

synthesize social statistics.

Estadist. J, Int.-Amer. Statist, Inst. 11: 605-615, 1953.

DUNN, H. L. and GILBERT, M.: Publichealthbeginsin the

family.

Pub].Hlth Rep. (Wash.)71:1002-1010,1956.

FARR,W.: in >>Reporton Army MedicalStatistics,, by Lord

HerbertrSirA. Tullochand Dr.W. Farr.

(BritishParliamentaryPaper,No.366,1861).

FRITZE,E. und WAGNER, G. (Hrsg.):Dokumentation des

Krankheitsverlaufs. Probleme der Erfassungdes zeitlichen

Krankheitsablaufesund des MedicalRecord Linkage.

Verb.Bericht13.JahrestagungGMD, Bochum 30.9.— 2.10.

1968,(F.K. SchattauerVerlag,Stuttgart-NewYork 1969).

GODBER,G.:Opening Remarks.

In E. D, Acheson (Edit.):Record Linkage in Medicine,

pp. 1—4.

(E.& S.LivingstoneLtd.,Edinburghand London 1968).

HEASMAN,M. A.:Recentdevelopmentsinhospitalin-patient

statisticsin Scotland.

Med. Rec. 8: No.3,2-6, 1967.

KEMPF,B.:Le traitementde I’informationclansIesh6pitaux.

Maroc mold.45: 15—17, 1966.

KEMPF, B.:Contribution& l’etudede l’automatisationdes

dossiersmedicaux.

Maroc med. 45:45—52, 1966.

KILPATRICK,S.I.,MATHERS,J.D. and STEVENSON,A. C.:The

importanceof populationfertilityand consanguinitydata

beingavailablein medico-socialstudies.

Ulstermed. J.24:113—122,1955.

MARON, M. E.:Largescaledatabanks.

Spec.Libr.60:3—9, 1969.

MARSHALL,J, T,:Canada’snationalvitalstatisticsindex.

popul.Stud.1:204—211, 1947.

MASSfi, L. et REYNAUD, J.:Les banques d’informationssanitaireset la >>symnemonique<<.

Rev. frang.aff.SOC.23:20—30, 1969.

MOSBECH,J.:Medicalrecordlinkage,(Dan,)

Ugeskr.Laeg.129:1733—1734,1967.

NEWCOMBE, H. B.:Untapped knowledge of human populations.

Trans.roy.Sot.Can.,Sect.III,56: 173—180,1962.

NEWCOMBE, H, B.:Record linkage:conceptsand potentialities.In MedicalResearchCouncil:Mathematicsand ComputerScienceinBiologyand Medicine,pp.4349.

(H.M.S.O.,London 1965).

358

\
NEWCOMBE, H. B.: Products from the early stages in the

development of a system of linked records.

In E. D. Acheson (Edit,): Record Linkage in Medicine,

pp. 7—33.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

OHKURA, K.: Use of family registration in the study of

human genetics in Japan.

Jap. J, hum. Genet. 5:61-68, 1960.

C)xford Record Linkage Study: Alphabetic list of data

abstracted — correct 10th September 1969 (mimeograph).

Oxford Regional Hospital Board, Oxford 1970.

PAYNE,L. C.: An Introduction to Medical Automation.

(Pitman Med. Publ, Co., Ltd., London 1966).

RANDALL,H. B.: Strengths and limitations of the cumulative

health record.

J. Sch. Hlth 37:86-89, 1967.

Royal Commission on Population: Reports and selected

papers of the Statistics Committee, Vol. 11, pp. 29—48.

(H.M. S.O., London 1950).

SHAPIRO, S. and DENSEN, M.: Research needs and record

matching.

(Proc. Amer. Statist. Ass.r Sot. Statist. Sect. pp. 20—24,

1963).

SMtTH, A.: Linkage of child health records.

WHO-Paper Euro 0215(7), Copenhagen, 19. Aug. 1969.

STOCKS, P.: Measurement of morbidity.

Proc. roy. Sot. Med. 37:593-608, 1944.

Study Group on Record Linkage: Progress Report of the

Public Health Conference on Records and Statistics.

U.S. Department of Health, Education and Welfare, Document No. 603.5—5/31/66.

TENNMON,P.: Passport to immortality: Sir Macfarlane Burnett: Behind and ahead.

The Bulletin (Sydney) 8?: 23—24, 1965.

THOMAS, D. S.: Continuous register system of population

accounting.

In National Resources Committee (Edit.): The problems of

a changing population, pp. 276-297.

(U.S. Govt. Print. Off., Washington, DC. 1938).

WAGNER, G.: lnternationales Symposion uber Medical Record Linkage, Oxford 17./18. 7. 1967.

Meth. Inform. Med. 4: 187—190, 1967.

WAGNER, G.: Medical Record Linkage — Einfuhrung in das

Thema.

In E. Fritze und G. Wagner (Hrsg.): Documentation des

Krankheitsverlaufs. Bericht uber die 13. Jahrestagung der

GMD, Bochum, 30.9. bis 2.10.1968.

(F. K. Schattauer Verlag, Stuttgart-New York 1969).

WEm, R. D.: Development of Medical Records in Scotland.

Med. Rec. 8: 7—10, 1967.

WHO: The public health use of electronic computers.

Report on a Seminar convened by the Regional Office for

Europe of the WHO, London, 17—21 June 1968.

WHO-Document EURO 3092, WHO Regional Office for

Europe, Copenhagen 1969.

WITTS, L. J.: People in confidence; the expanding circle.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 333—338.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

YELLOWLEES, H,: interface problems in medical and administrativepatientmanagement,

Proc.Intern.Meet. Automated Data Processingin Hospitals,Elsinore1966.

Editorial:Order in the medicalrecord.

Lancet 1965,H, 675-6?6.

Editorial:Record Linkage.

WHO Chron.21:441-442, 1967.

Editorial:Rethinkingmedicalrecords,

Lancet196?,H, 925—926.

Editorial:Medicalrecordlinkage.

Canad. med. Ass.J.98:607—608, 1968.

Editorial:Record linkage.

Brit.med. J. 1968,111,116-117.

Editorial:Record linkage.

Brit.J.prev.SOC.Med. 23:203—204, 1969.

Editorial:Record Linkage Conference.

Note on Meeting held at the ScottishHospitalCentre on

Friday,13thDecember 1968.

Hlth Bull.(Edinb.)2?:31—37, 1969.

B. Methodologyof Record Lfnkage

ACHESON,E. D.: The Oxford Record Linkage Study: Report

on the second year’s operations,

(Oxford Regional Hospital Board, Oxford 1963].

ACHESON, E. D.: Oxford Record Linkage Study: A central

file of morbidity and mortality records for a pilot population.

Brit. J. prev. SOC.Med. 18:8-13, 1964.

ACHESON,E. D.: The structure, function and cost of a file of

linked health data.

In Medical Research Council: Mathematics and Computer

Science in Biology and Medicine, pp. 61-69.

(H. M. S.O., London 1965).

ACHESON,E. D.: Medical record linkage — the method and

its applications.

Roy. SOC. Hlth J. 86: 12—16, 1966.

ACHESON, E. D.: Some potentialities of the computer in

national health services.

Proc. Intern. Meet. Automated Data Processing in Hospitals, Elsinore 1966.

ACHESON, E. D.: Medical Record Linkage.

(Oxford University Press, London-New York-Toronto 1967).

ACHESON,E. D.: Record linkage techniques in studies of the

etiology of cancer.

Proc. roy. Sot, Med. 61:726-730, 1968.

ACHESON,E. D. (Edit.): Record Linkage in Medicine.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

ACHESON, E, D.: The Oxford Record Linkage Study; the

first five years.

In E. D, Adeson (Edit.): Record Linkage in Medicine,

pp. 40-49.

(E, & S. Livingstone Ltd., Edinburgh and London 1968),

ACHESON,E. D. and EVANS,J. G.: The Oxford Record Linkage Study: A review of the methods and some preliminary

results.

Proc. roy. Sot. Med. 57:269-274, 1964.

359
ACHESON, E. D. and WATTS, S. P.: Using Computers.

Brit. med. J. 1968, I, 316.

Ad Hoc Committee on the Implications of Record Linkage

for Health-Related Research: Health Research Uses of Record Linkage in Canada.

(Med. Res. Count. of Canada, Ottawa 1968).

BAHN, A. K.: The development of an effective statistical

system in mental illness.

Amer. J. Psychiat. 116: 798-800, 1960,

BAHN, A. K,: Methodological study of a population of outpatient psychiatric clinics. Maryland 1958--59.

Publ. Hlth Monogr, No. 65. (U.S. Govt. Print. Off,,

Washington, D.C., 1961).

BAHN, A. K.: Person matching by electronicmethods.

Comm. Ass.comput.Mach. 5:404—407, 1962.

BINDER, S.: Information storage, retrieval and processing:

Present possl%ilities and future potentialities.

In: The Use of Vital and Health Statistics for Genetic and

Radiation Studies, pp. 161—167.

(U.N. Publication, Sales No. 61, New York 1962).

BDTHWELL,P.W.: Routine,recordsand research.

Med. Rec.5:359—365,1961.

BOTHWELL,P.W.: A New Look at PreventiveMedicine.

(PitmanMedicalPublishingComp.,London 1965).

CHSUSTENSEN,H. T.:The method of recordlinkageapplied

to familydata.

Marr.Fare.Living20:3843, 1958.

CoPE,C. B.:A centralizednation-widepatientdatasystem.

[n E,D, Acbeson (Edit.):Record LinkageinMedicine,

pp. 34-38.

(E.& S.LivingstoneLtd.,Edinburghand London 1968).

D’ANDREA, J. R. and DUBOIS,N. S.:On the problem of

matching documents with missing and inaccuratelyrecordeditems(Abstract).

Ann. math. Statist.35: 1404-1405,1964.

DAVIDSON,L.:Retrievalof misspellednames in an airline

passengerrecordsystem.

Comm, Ass.comput.Mach. 5: 169—171,1962.

DAVIS,L.S.,COLLEN,M. F.,and RUBIN,L.:Computer-stored

medicalrecord.

Comp. biomed.Res.1:452-469,1968.

DEMNG, W. E. and GLASSER, G, J.:On the problem of

matchinglistsby samples.

J.Amer. statist.Ass,54:403-413, 1959.

DOLL,R.:Hospitalrecordsin the computer age.

.Proc.roy.Sot.Med. 61:709--715,1968.

DOLL,R.:Record linkagetechniquesin studiesofthe etiology of cancer.

Proc.roy.Sot.Med. 61:731—732, 1968.

DUBOIS,N. S. and D’ANDREA,J. R,:A document linkage

program fordigitalcomputers.

Behav.Sci.10:312—319, 1965.

FRITZE,E. und WAGNER, G. (Hrsg.):Documentation des

Krankheitsverlaufs. Probleme der Erfassungdes zeitlichen

Krankheitsablaufesund des MedicalRecord Linkage.

Verb. Bericht13.JahrestagungGMD,

Bochum 30.9.— 2.10.1968.

(F.K. SchattauerVerlag,Stuttgart-NewYork 1969).

GALLOWAY,T. McL.: Computers.Theiruse in localhealth

administration.

Roy. SOC.Hlth J.86:213—216, 1966.

360

GALLOWAY,T. McL.: The use of an electronic computer in

a health department.

Canad. J. pubL Hlth 57: 331—332, 1966.

GEDDA, L. and MILANI-COMPARETTI,M.: Computerization of

a permanent twin register: a basic tool in scientific

researd.

Acts Genet. med. (Roma) 15: 333—344, 1966.

GURALNICK, L. and NAM, C. B,: Census — NOVS study of

death certificates matched to census records.

Milbank mem, Fd Quart. 37:144-153, 1959.

HALL, P.,MELLNER, Ch. and DANIELSON,T.:J 5 — A data

processingsystem formedicalinformation.

Meth. Inform.Med. 6: 1—6, 1967.

HAMBFUGHT,T. Z.:Comparabilityof age on the death certificateand matchingcensusrecord.

Vitaland HealthStatistics, Publ.Hlth Publ.No. 1000

Series2-No.29.

(U.S.Dept.H. E.W., Washington,D.C.,1968).

HAUSER,P.M. and LAURIAT,P,:Record matching— theory

and practice(Abstract).

(Proc.Amer. Statist.Ass.,Sot.Statist.Sect.,p. 25,1963)

HUBBARD,M. R.:Computer Systems forMedicalRecord

Linkage.

(Thesissubmittedforthe degreeof Bachelorof Sciencein

the Universityof Oxford 1969).

HUBBARD,M. R. and FISHER,J.E.:A computer system for

medicalrecordlinkage.

In E.D. Acheson (Edit.):Record LinkageinMedicine,

pp. 15?—1?0,

(E.& S.LivingstoneLtd.,Edinburghand London 1968).

JAHN,E.:Probleme der Zusammenfuhrung zeitlichund ortlichdifferenterGesundheitsdaten.

In E. Fritzeund G, Wagner (Hrsg.):Dokumentation des

Krankheitsverlaufs. Berichtiiberdie 13.Jahrestagungder

GMD in Bochum, 30, 9. — 2. 10. 1968.

(F. K. Schattauer Verlag, Stuttgart-New York 1969).

JAINZ,M.: OrganisationeinerDatenbank auf Magnetbandern furdieHautklinikKiel.

Meth. Inform.Med. 8: 190—192,1969,

JAMESON,M. J.:A system of recordingthe familyhistory

in generalpractice.

J.roy.CoIl.gen.Pratt.16:135—143,1968.

KENNEDY, J. M.: Linkage of birthand marriage records

usinga digitalcomputer.

Atomic Energy of Canada Ltd.,ReportNo. 1258,

Chalk River,Ontario1961.

KENNEDY J.M.: The use of a digitalcomputer forrecord

linkage.

Proc.Seminar on the Use of Vitaland Health Statisti

forGenet.and Radiat.Studies,pp. 155—159.

(U.N, Publication,SalesNo. 61,New York 1962).

KENNEDY, J. M.: Filestructuresfor the automaticmanipulationof linkedrecords.

In E.D. Acheson (Edit,):Record LinkageinMedicine,

pp. 109—1 18.

(E.& S.LivingstoneLtd.,Edinburghand London 1968).

KENNEDY,J.M.,NEWCOMBE, H. B.,OKAZAKI, E. A. and SMITH,

M. E.: List processing methods for organizing files of

linked records.

Atomic Energy of Canada Ltd., Report No. 2078,

Chalk River, Ontario 1964.

\
KENNEDY, J. M., NEWCOMBE, H. B., OKAZAKI, E. A. and SMITH,

M. E.: Computer methods for family linkage of vital health

records.

Atomic Energy of Canada Ltd., Report No. 2222,

Chalk River, Ontario 1965.

MADow, W. G.: Interview data on ~ronic conditions compared with information derived from medical records.

Vital and Health Statistics, Publ. Hlth Publ. No. 1000-

Series 2-No. 23.

(U.S. Dept. H. E.W., Washington, D.C., 1967),

NEWCOMBE, H. B.: Products from the early stages in the

development of a system of linked records.

In E. D, Acbeson (Edit.): Record Linkage in Medicine,

pp. 7—33.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

NEWCOMBE, H. B.: The use of medical record linkage for

population and genetic studies.

Meth. Inform. Med. 8: ?—11, 1969.

NEWCOMBE, H. B.: Die Anwendung des Medical Record

Linkage fur Bevolkerungs- und genetiscbe Studien.

MABKSTEINER,A. und WOLF, Ch.:Die Normierung inhomo- In E. Fritzeund G. Wagner (Hrsg.):Dokumentation des

gener medizinischerDatenbestandemittelseinerEDVA. Krankheitsverlaufs. Berichtuber die 13.Jahrestagungder

In K. Fellinger(Hrsg.):Computer inder Medizin. GMD inBochum, 30.9. — 2. 10. 1968.

Probleme, Erfahrungen, Projekte. (F. K. Schattauer Verlag, Stuttgart-New York 1969).

(Verlag Bruder Hollinek, Wien 1968). NEWCOMBE, H. B., JAMES, A. P., AXFORD, S. J.: Family

MCCARTHY, M. A.: Comparison of the classification of

Linkage of Vital and Health Records:

place of residence on death certificates and matching (1) For population studies of hereditary influences on

census records. health,

Vital and Health Statistics, PubL Hlth Publ. No. 1000- (2) For verification of status for welfare programmed and

Series 2-No. 30. other purposes,

(U.S. Dept. H.E.W., Washington, D.C., 1969). Atomic Energy of Canada Ltd., Report No. 470,

Chalk River, Ontario 1957.

MCKUSICK,V, A.:Some computer applicationstoproblems

in human genetics.

Meth. Inform.Med. 4: 183—189,1965.

MOORE, F.:Development of a regionalhealthinformation

center.

Proc.4.IBM Medical Symp., Endicott, N. Y., pp. 225—239.

(Yorktown HeightdN.Y. 1962).

MOORE, F.: Health Information Systems.

(Univ. of South. Calif. School of Med., Los Angeles 1963).

MOORE, F. J.: Mechanizing a large register of first order

patient data,

Meth. Inform. Med. 4: 1—10, 1965.

NATHAN, G.: Outcome probabilities for a matching process

with complete invariant information.

J. Amer. statist. Ass. 62: 454-469, 1967.

NEWCOMBE, H. B. and KENNEDY, J. M.: Record linkage.

Making maximum use of the discriminating power of identifying information.

Comm. Ass. comput. Mach. 5: 563—566, 1962.

NEWCOMBE, H. B. and KENNEDY, J, M.: Demographic analysis and computer programs.

In: U.N. World Population Conference, Belgrade 1965,

Vol. III, pp. 251—253.

(United Nations, New York 1965).

NEWCOMBE, H. B., KENNEDY, J. M., AXFORD, S. J. and JAMES,

A. P.: Automatic linkage of vital records.

Science 130:954-959, 1959.

NEWCOMBE, H. B. and RHYNAS, P. O. W.: The cost of individual follow-up studies of large populations,

Atomic Energy of Canada Ltd., Report No. 1255,

Chalk River, Ontario 1961.

NEWCOMBE, H. B.: Population genetics: Population records. NEWCOMBE, H. B. and RHYNAS, P. O. W.: Family linkage of

In: Methodology in Human Genetics, pp. 92—1 13. population records.

(Holden-Day Inc., San Francisco 1962).

Proc. Seminar on the Use of Vital and Health Statistics for

Genet. and Radiat. Studies, pp. 135—153.

NEWCOMBE, H. B.: Screening for effects of maternal age (U.N. Publication, Sales No. 61, New York 1962).

and birth order in a register of handicapped children.

Ann. hum. Genet. 27: 367—382, 1964.

NITZBERG, D. M.: Results of research into the methodology

of record linkage.

NEWCOMBE, H. B.: Pedigrees for population studies. In E. D, Acheson (Edit.): Record Linkage in Medicine,

A progress report. pp. 187—202.

Cold Spr. Harb. Symp. quant. Biol. 29: 21—30, 1964.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

NEWCOMBE, H, B.: Record linkage: concepts and potentiaNITZBERC, D. M. and SARDY, H.: The methodology of comlities. In Medical Research Council: Mathematics and Computer linkage of health and vital records.

puter Science in Biology and Medicine, pp. 43-49.

(Proc. Amer. statist. Ass., Sot. Statist. Sect. 1965].

(H.M.S.O., London 1965). PHXLLIPS,W., jr.:Record linkagefora chronicdisease

register.

NEWCOMBE, H. B.:Use of vitalstatistics. In E.D. Acbeson (Edit.):Record LinkageinMedicine,

In:U.N. World Population Conference, Belgrade, 1965, pp. 120—151.

Vol. H, pp. 494--497. (United Nations, New York 1965). (E. & S, Livingstone Ltd., Edinburgh and London 1968).

NEWCOMBE, H. B.: Record Linking: The design of efficient PHILLIPS, W., jr. and BAHN, A. K.: Experience with comsystems for linking records into individual and family

histories.

puter-matching of names.

(Proc. Amer. statist. Ass., Sot. Statist. Sect.

Amer. J. hum. Genet. 19: 335—359, 1967. pp. 26—37, 1963).

361
PHILLIPS,‘AJ.,jr.,BAHN, A. K. and MIYASAKI, M.: Personmatching by electronic methods.

Comrn. Ass. comput. Mach. 5: 404-40?, 1962.

PHXLLIPS, W., jr., GORWITZ, K. and BAHN, A. K.: Electronic

maintenance of case registers.

Publ. Hlth Rep. ?7: 503—510, 1962.

Royal Commission on Population: Reports and selected

papers of the Statistics Committee, Vol. II, pp. 2948.

(H.MS.O., London 1950).

SHAPIRO, S. and DENSEN, M.: Research needs and record

matching.

(Proc. Amer. Statist. Ass., Sot. Statist. Sect.

pp. 20—24, 1963) .

SHAPIRO,S. and SCHACHTER,J.: Methodology and summary

results of the 1950 birth registration test in the United

States.

Estadist. J. Int.-Amer. Statist. Inst. 10:688-699, 1952.

SHAPIRO,S., WE] NBLA~, E., FRANK,C., SAGER,R. and DENSEN,

P.: The H.LP. study of incidence and prognosis of coronary

heart disease: Methodology.

J. dron. Dis. 16: 1281—1292, 1963.

SMrTH, A. E.: Automatic linkage of medical and vital registration records.

Brit. J. SOC. prev. Med. 1?: 185-190, 1963,

SMITH, M. E., SCHWARTZ, R. R. and NEWCOMBE, H. B.: Computer methods for extractingsibshipdata from family

groupingsof records,

Atomic Energy of Canada Ltd.,ReportNo. 2520,

Chalk River,Ontario1965.

SMYTHE,M.: Record Numbering.

In E.D. Acheson (Edit.):Record LinkageinMedicine,

pp. 179-183.

(E.& S.LivingstoneLtd.,Edinburghand London 1968).

SPICER,C. C.:Practicalproblems of recordlinkageon a

nationalscale.

In MedicalResearchCouncil:Mathematicsand Computer

ScienceinBiologyand Medicine.(H.M.S.O.,London 1965).

Study Group on Record Linkage:ProgressReport of the

PublicHealth Conferenceon Records and Statistics.

U.S.Department of Health,Educationand Welfare,

Document No. 603.5-5/31/66.

SUNTER, A. B.: A statistical approach to record linkage.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 89—107.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

SUNTER, A. B. and FELLIGI, J. P.: An optimal approach to

record linkage.

(Proc. 36th Sess. Int. Statist. Inst., Sydney, Australia 1967).

TEPPING, B. J.: Study of matching techniques for subscriptions fulfillment.

(National Analysts Inc., Philadelphia 1955).

TEPPING, B. J. and CHU, J. T.: A report on matching rules

applied to Reader’s Digest Data.

(National Analysts Inc., Philadelphia 1958).

THATCHER,R. A.: Medical records in the computer age.

Med. J. Aust. 2:234-236, 1968.

WATTS, S. P, and ACHESON, E. D.: Computer method for

deriving hospital in-patient morbidity statistics based on

the person as the unit.

Brit. med. J. 196?, IV, 476-477.

WEIR, R. D.: The introduction of record linkage in northeast Scotland,

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 55-60,

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

YODER, R, D.: Preparing medical record data for computer

processing.

Hospitals 40: ;5-76, 1966.

C.The Identification Problem

ACHESON, E. D.: Some potentialities of the computer in

national health services.

Proc. Intern. Meet. Automated Data Processing in

Hospitals, Elsinore 1966.

ACHESON,E. D.: Medical Record Linkage.

(Oxford University Press, London-New York-Toronto 1967).

ACHESON,E. D.: Linkage of medical records.

Brit. med. Bull. 24:206-209, 1968.

ACHESON,E. D. (Edit.): Record Linkage in Medicine.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

ACHESON,E. D.: Medical record linkage.

Meth. Inform. Med. 8: 1-6, 1969.

ACHESON,E. D.: Personal Record Linkage.

In E. Fritze und G. Wagner (Hrsg.): Documentation des

Krankheitsverlaufs. Bericht iiber die 13. Jahrestagung der

GMD in Bochum, 30.9. — 2, 10. 1968.

(F. K. Schattauer Verlag, Stuttgart-New York 1969).

ACHESON,E. D. and BARR, A.: Multiple spells of in-patient

treatment in a calendar year.

Brit, J. prev. SOC. Med. 19: 182—191, 1965.

Ad Hoc Committee on the Implications of Record Linkage

for Health-Related Research: Health Research Uses of

Record Linkage in Canada.

(Med. Res. Count. of Canada, Ottawa 1968).

ArbeitsausschuB Medizin in der i2GD: Ein dokumentationsgerechter Krankenblattkopf fiir stationary Patienten aller

klinischen Father (sog. Allgemeiner Krankenblattkopf).

Med. Dok, 5: 57—71, 1961.

BAHN, A. K.: Person matding by electronic methods.

Comm. Ass. comput. Mach. 5:404-407, 1962.

BAHN, A. K. and BAHN, R.: Considerations in using social

security numbers on birth certificates for research

purposes.

PubL Hlth Rep. (Wash.) 79: 937—938, 1964.

BENNETT, A, E, and HOLLAND,W. W.: Towards the development of electronic data-processing systems for medical

records.

Lancet 1965, II, 1176—1 178.

BILLETER, B. M.: An forderungen an Konzeption und Aufbau einer Datenbank.

ADL-Nachr. No. 58, 652-656, 1969.

362
BJARNASON, O., FRIDRIKSSON,S. and MAGNUSSON,M.: Record

linkage in a self-contained community.

In E, D. Acheson (Edit.): Record Linkage in Medicine,

pp. 62—68.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

BIWNNER,H., PAUMGARTNER, G., GRABNER,G., GRABNER,H.,

MARKSTEINER,A. und WOLF, Ch.: Erfahrungen mit der Documentation von Krankengeschichten einer internen Klinik.

In K. Fellinger (Hrsg.): Computer in der Medizin.

Probleme, Erfahrungen, Projekte.

(Verlag Briider Hollinek, Wien 1968).

CHEESEMAN,E, A.: Medical record linkage in Northern Ireland — reconnaissance and proposals with particular reference to problems of identification.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. ?0-67.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

CoPE, C. B.: A centralized nation-wide patient data system.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 34-38.

(E. & S. Livingstone Ltd., Edinburgh and London 1968].

DAVIDSON,L.: Retrieval of misspelled names in an airline

passenger record system.

Comm. Ass. comput. Mach. 5: 169—171, 1962.

FASSL, H.: Das Risikopatientenregister der Universitatskliniken Mainz.

Meth. Inform. Med. ?: 214—218, 1968.

FRITZE, E. und WAGNER, G. (Hrsg.): Dokumentation des

Krankheitsverlaufs. Probleme der Erfassung des zeitlichen

Krankheitsablaufes und des Medical Record Linkage.

Verb. Bericht 13. Jahrestagung GMD,

Bochum 30.9. — 2.10. 1968.

(F. K. Schattauer Verlag, Stuttgart-New York 1969).

GtEBSDORF,P., GUNTHER,O., KREUZ,B. und SCHNEIDER,W.:

Zum Entwurf eines einheitlichen und dokumentationsgerechten Krankenblattkopfes fur Patienten in stationaren

Einrichtungen.

Dtsch. Ges.-wesen 21:462-469, 1966.

HEASMAN,M. A.: Recent developments in hospital in-patient statistics in Scotland.

Med. Rec. 8: No. 3, 2—6, 1967.

HOGBEN,L. and CROSS,K. W.: The statistical specificity of

a code personnel cypher sequence.

Brit. J. SOC.Med. 2: 149—152, 1948.

HOG~EN, L,, JOHNSTONE, M. M. and CROSS, K. W.: Identification of medical documents.

Brit. med. J. 1948, I, 632435.

HOPKINS,R, A. and GARDNER, E. A.: Development of a

flexiblecontrolsystem in the maintenance of a patient

case register.

In Enslein,K. (Edit.):Data Acquisitionand Processing,

vol.3,pp. 191—198.

(Pergamon Press,Ltd.,Oxford-London1964).

JAHN,E.:Problemeder Zusammenfuhrung zeitlichund ortlichdifferenterGesundheitsdaten.

In E. Fritzeund G. Wagner (Hrsg.):Dokumentation des

Krankheitsverlaufs. Berichtuber die 13.Jahrestagungder

GMD inBochum, 30.9.— 2,10.1968.

(F.K. SchattauerVerlag,Stuttgart-NewYork 1969).

JUNGNER,G.: Health Screening.

Proc. Intern. Meet. Automated Data Processing in

Hospitals, Elsinore 1966.

KALLEN,B. and WINBERG,J.: A Swedish register of congenital malformations. Experience with continuous registration during 2 years with special reference to multiple

malformations.

Pediatrics 41: ?65—?’76, 1968.

KEMPF, B,: Contribution a l’etude de

dossiers medicaux,

Maroc med. 45: 45—52, 1966.

KENNEDY,J. M., NEWCOMBE,H. B., OKAZ

automatisation des

KI, E. A. and SMITH,

M. E.: Computer methods for family linkage of vital health

records.

Atomic Energy of Canada Ltd., Report No. 2222,

Chalk River, Ontario 1965.

LEDLEY, R. S. and LUSTED,L. B.: The use of electronic computers in medical data processing. Aids in diagnosis, current information retrieval, and medical record keeping.

IRE Trans. med. Electron. ?: 31-47, 1960.

LINDBERG,D. A. B.: Electronic retrieval of clinical data.

J, med. Educ. 40: 753—759, 1965.

MAASS, W,: Zum Problem der Bildung einer einheitlichen

deutschen Bevoikerungsnummer.

ADL-Nachr. H. 25, 254-266, 1963.

MAASS, W.: Noch einmal — Zur Frage der Bevolkerungsnummer oder Personalkennzif fer,

ADL-Nacbr. H. 29, 528—531, 1963.

MARCUSSON,H.: Zur Erprobung eines neuen Gesundheitsbogens des Jugendgesundheitsschutzes,

Z, ges, Hyg. 11: 123—140, 1965.

MARTHALER, T. M.: A standardized system of recording

dental conditions.

Helv, odont, Acts 10: 1—18, 1966.

MASSk, L. et REYNAUD,J.: Les banques d’informations sanitaires et la >symnemoniquea.

Rev. franc, aff. SOC. 23: 20—30, 1969.

Ministry of Health — Central Health Services Council:

The standardisation of hospital medical records.

Report of the Sub-Committee of the Standing Medical Advisory Committee. (H.M. S.O., London 1965).

MOORE, F. J.: Mechanizing a large register of first order

patient data.

Meth. Inform. Med. 4: 1—10, 1965.

NEWCOMBE, H. B.: Record linkage: concepts and potentialities.

In Medical Research Council: Mathematics and Computer

Science in Biology and Medicine, pp. 4349,

(H.M. S.O., London 1965).

NEWCOMBE,H. B.: The use of medical record linkage for

population and genetic studies.

Meth. Inform. Med. 8: 7—11, 1969.

NEWCOMBE,H. B.: Record linkage and hospital discharge

abstracts.

(Working Paper for the Conference on Hospital Discharge

Abstracts, Airlie House,

Warrenton/Va., June 1969; Med. Care, in press).

363
y

Linkage of Vital and Health Records:

(1) For population studies of hereditary influences on

health,

(2) For verification of status for welfare programmed and

other purposes.

Atomic Energy of Canada Ltd., Report No. 470,

Chalk River, Ontario 1957,

NEWCOMBE, H. B.,and KENNEDY,J.M.: Record linkage.

Making maximum use of the discriminatingpower of

identifyinginformation.

Comm. Ass.comput.Mach. 5:563—566, 1962.

NIELSEN,H.:The centralpopulationregisterand the prospectiveassignmentof a person-number(Dan.).

Ugeskr.Laeg.129:1734-1736,1967.

NIELSEN,H.:The personalnumbering system in Denmark.

In E.D. Acheson (Edit.):Record LinkageinMedicine,

pp. 173—177.

(E.& S.LivingstoneLtd.,Edinburghand London 1968).

“NITZBERC,D. M.: Resultsof researchintothemethodology

of recordlinkage.

In E.D. Acheson (Edit.):Record LinkageinMedicine,

pp. 187—202.

(E.& S.LivingstoneLtd.,Edinburghand London 1968).

PHILLIPS,W., jr.:Record linkagefora chronicdiseaseregister.

InE, D. Acbeson (Edit.):Record LinkageinMedicine,

pp. 120—151,

(E.& S.LivingstoneLtd.,Edinburghand London 1968).

POSTEL,H. J.:Die KiilnerPhonetik.Ein Verfahren zur

Identifizierung von Personennamen aufder Grundlageder

Gestaltanalyse.

IBM-Nachr.19:925—931, 1969.

SCHENTAL,J.E.,SWEENEY,J.W,, NETTLETON,W. J.,jr.,and

YODER, R. D.:Clinicalapplicationof electronicdata processingapparatus.HI. System for processingof medical

records.

J.Amer. med. Ass. 186: 101—105, 1963.

Scottish Home and Health Department — Scottish Health

Services Council: Hospital medical records in Scotland.

Development and standardisation.

(H.M.S.O., Edinburgh 1967).

SELMER, E, S.: Registration numbers in Norway: some

applied number theory and psychology.

J. roy. statist. Sot., Sect. A, 130:225-231, 1967.

SMITH,A. E.: Preservation of confidence at the central

level.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 338-345.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

Study Group on Record Linkage: Progress Report of

Public Health Conference on Records and Statistics.

U.S. Department of Health, Education and Welfare,

Document No. 603.5—5/31/66.

SUNTER, A. B.: A statistical approah to record linkage.

In E. D. A&eson (Edit.): Record Linkage in Medicine,

pp. 89—107.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

the

TURNER, J. G. and SHANNON, P. D.: Mechanical identification

of hospital records saves time and money.

Canad. Hosp. 33:44-50, 1956.

364

deutscbenBevolkerungsnummer.

ADL-Nachr.H. 27,411-414, 1963.

WAGNER, G.:The developmentofthe standardizedmedical

recordin Germany,

Med. Rec.7: 183—188,1965.

WAGNER, G.:MedicalRecord Linkage— Einfuhrunginalas.

Thema.

In E. Fritzeund G. Wagner (Hrsg.):Dokumentation des

Krankheitsverlaufs. Berichtiiberdie 13.Jahrestagungder

GMD inBochum, 30.9.— 2.10.1968.

(F.K. SchattauerVerlag,Stuttgart-NewYork 1969).

WAGNER, G.,IMMICH,H. und KOHLER,C.:Der Krankenblattkopf der HeidelbergerKliniken.

Meth. Inform.Med. 7: 17—25, 1968.

WAGNER, G. und STUTZEH,G.: IJberdie Selektivitatder

sog.I-Zahlim *AllgemeinenKrankenblattkopfund die

BrauchbarkeitihrereinzelnenKomponenten.

Meth. Inform.Med. 2: 148—155, 1963.

WHO: The publichealthuse of electroniccomputers.

Report‘ona Seminar convened by the RegionalOfficefor

Europe of theWHO, London,17—21 June 1968.

WHO-Document EURO 3092,WHO RegionalOfficefor

Europe,Copenhagen 1969.

WTSTBOLDT,S.:MED-Kortet,sjukv~rdshandlingen,filmMlkortetocb ADB.

Svenska Lak.-Tidn.62:3369-3374, 1965.

YODER, R. D., DREYFUS, R. H. and SALTZBERG,B.: Identification codes for medical records.

Health Serv. Res, 1:53-65, 1966.

YODER, R. D., SWEARINGEN,D, R., SCHENTHAL,J. E., SWEENEY,

J. W. and NETTLETON, W, J., Jr.: An automated clinical

information system.

Meth. Inform. Med. 3; 45—50, 1964.

D. The PrivacyProblem

ACHESON, E. D,: Medical Record Linkage.

(Oxford University Press, London-New York-Toronto 1967).

ACHESON,E. D,: Linkage of medical records,

Brit. med. Bull. 24:206-209, 1968.

ACHESON,E. D. (Edit.): Record Linkage in Medicine.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

ACHESON,E. D.: Medical record linkage,

Meth. Inform. Med. 8: 1-6, 1969.

ACHESON,E. D.: Personal Record Linkage.

In E. Fritze und G. Wagner (Hrsg.): Dokumentation des

Krankheitsverlaufs. Bericht iiber die 13. Jahrestagung der

GMD in Bochum, 30.9. — 2. 10. 1968.

(F. K. Schattauer Verlag, Stuttgart-New York 1969).

ARMER, P.: Social implications of the computer utility.

Dec. P-3642, Rand Corp., Santa MonicdCalif. 1967.

BAHN, A. K., GOLDBERG,1. D. and GORWITZ,K.: Longitudinal

studies using psychiatric case registers.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 226—249.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).
BARAN, P.: Remarks on the question of privacy raised by

the automation of mental health records.

Dec. P-3523, Rand Corp., Santa Monica/Calif. 1967.

BEJEROT, N.: Sjukvardens journal-och arkiv problem.

Svenska Lak.-Tidn. 62: 1346-1359, 1965.

B.M.A. Planning Unit: Computers in Medicine.

Report of the Working Party on Computers in Medicine.

(B.M.A. House, London 1969).

BRUYN, H. D.: Confidentiality in the use of health records.

J. Sch. Hlth 37: 161—165, 1967.

BUTLER,R.N.:Privilegedcommunicationsand research.

Arch.gen.Psychiat.8: 139—141, 1963.

CURRAN, W. J., STEARNS, B. and KAPLAN, H.: Privacy, confidentiality and other legal considerations in the establishment of a centralized health-data system.

New Engl. J. Med. 281: 241—248, 1969.

DuNN, E. S., Jr.: The idea of a national data center and the

issue of personal privacy.

Amer. Statist. 21: 21—27, 1967.

FREED,R, N.:A legalstructurefora nationalmedical

datacenter.

Proc.AFIPS FallJointComputer Conference,

Vol.33,Part1,pp. 387—394.

(Thompson Book Comp., Washington,D.C, 1968).

GLAZEBROOK,P. R.:Medical confidences,researchand the

law.

In E.D. Acheson (Edit.):Record LinkageinMedicine,

pp. 323—333.

(E.& S.LivingstoneLtd.,Edinburghand London 1968).

HAGMAN, D. G.:The non-litigantpatient’srightto medical

records:medicinevs.law.

J.forens.Sci.14:352—369, 1969.

HALL, P.,ENGKVIST,O., MELLNER, C. and DANXELSSON,T.:

The problem of secrecyin mechanicalstorageof records

(Swed.).

Svenska Lak.-Tidn.62:2934-2936,1965.

HARRISON,A.:The problem ofprivacyinthecomputerage:

an annotatedbibliography.

Dec.RM-5495-PR/RC,Rand Corp.,SantaMonica/Calif.1967.

HARTMAN, J.D.:Medical recordsand privilege.

Med.-leg.Bull.178:1—3, 1968.

HEASMAN, M. A.: Manual of hospital morbidity statistics.

WOH-Paper WHO/HS/Nat. Corn./l 47 — 11. June 1963.

HOFFMAN,L. J.: Computers and Privacy: A survey.

Comput. Surv. 1: 85—103, 1969.

KEMPF, B.:Contributiona l’etudede l’automatisation des

dossiersm6dicaux.

Maroc. med. 45:45-52, 1966.

KEUNE H. G.: Zu einigenrechtlichenFragen bei der Benutzungvon Krankenunterlagenfurwissenschaftli~e

Zwecke.

Z. arztl.Forth.61:887—890, 1967.

KOHLHAAS,M.: Zur WeitergabearztlicherBefunde an

fremde Personenoder Patienten.

Dtsch.med. Ws&r. 94:1503-1504,1969.

LEDLEY,R. S.and LUSTED,L.B.:The use of electroniccomputersin medicaldata processing.Aids in diagnosis,current information retrieval, and medical record keeping.

IRE Trans. med. Electron. ?’: 3147, 1960.

LETOUnNEAU,C. U.:Erosionof the professionalsecret.

Hosp. Mgmt 99:53—55, 1965.

MCCARTHY, J.: Information.

Scient. Amer. 215: 65—72, 1966,

MICHAEL, D. N.: Speculations on the relation of the computer to individual freedom and the right to privacy.

George Washington Law Rev. 33: 270—286, 1964-65.

RINnAN1,T, H., ALEMEIDA, V. B. and KABE, N. G.: Medical

record and the law.

J. Ass. Phycns India 13; 154-t59r 1965.

SMITH, A. E.: Preservation of confidence at the central level.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 338—345.

(E. & S. Livingstone Ltd., Edinburgh and London 1968),

SNEDEKER, L.: On confidentiality and data banks.

New Engl. J. Med. 281:269-270, 1969,

TAEUBER, C,: Invasion of privacy.

Eugen, Quart. 14: 243—246, 1967.

U.S. Congress, House Committee on Government Operations: Privacy and the national data bank concept.

(U.S. Govt. Print, Off., Washington, D.C., 1968).

WHITTIER, J. R.: Research on Huntington’s chores:

Problems of privilege and confidentiality.

J. forens. Sci. 8: 568—575, 1963.

WITTS, L. J.: People in confidence; the expanding circle.

In E. D. Acheson (Edit.): Record Linkage in Mediciner

pp. 333-338.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

ZACHARY, M, C.: Confidentiality of medical records. Role

of the nurse.

Arch. environm. Hlth 19: 282—285, 1969.

E.Application of Medical Record Lfnkage in

L) Patient Care and Medical Data Processing

ACHESON, E. D.: Association between ulcerative colitis,

regional enteritis and ankylosing spondylitis.

Quart. J. Med. 29:469-499, 1960.

ACHESON,E, D.: The Oxford Record Linkage Study: Report

on the second year’s operations.

(Oxford Regional Hospital Board, Oxford 1963).

ACHESON, E. D.: Hospital morbidity in early life in relation

to certain maternal and foetal characteristics and events

at delivery,

Brit. J. prev. SOC. Med. 19: 164—1?3, 1965.

ACHESON,E. D.: Medical record linkage — the method and

its applications.

Roy. SOC. Hlth J. 86: 12—16, 1966.

ACHESON,E. D.: Medical Record Linkage.

(Oxford University Press, London-New York-Toronto 1967).

ACHESON, E. D.: The Oxford Record Linkage Study; the

first five years.

In E, D. Acheson (Edit.): Record Linkage in Medicine,

pp. 4049.

(E. & S. Livingstone Ltd.r Edinburgh and London 1968).

365
ACHESON,E, D.: Linkage of medical records.

Brit. med. Bull. 24:206-209, 1968,

ACHESON,E. D. (Edit.): Record Linkage in Medicine.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

ACHESON,E. D.: Medical record linkage.

Meth. Inform, Med. 8; 1—6, 1969.

ACHESON,E. D,: Personal Record Linkage.

In E. Fritze und G, Wagner (Hrsg.): Dokumentation des

Krankheitsverlaufs. Bericht uber die 13. Jahrestagung der

GMD in Bochum, 30.9. — 2.10. 1968.

(F. K. Schattauer VerIag, Stuttgart-New York 1969).

ACHESON,E, D. and BARR, A,: Multiple spells of in-patient

treatment in a calendar year.

Brit. J. prev. SOC, Med. 19: 182—191, 1965.

ACHESON,E. D. and EVANS,J. G.: The Oxford Record Linkage

Study: A review of the methods and some preliminary

results.

Proc, roy. Sot. Med. 57: 269—274, 1964.

ACHESON, E. D. and FELDSTEIN, M.: Duration of stay in

hospitals for normal maternity care.

Brit. med. J. 1964, H, 95—99.

BABIGIAN,H. M., GARDNER,E. A., MILES, H. C. and ROMANO,

J.: Diagnostic consistency and change in a follow-up study

of 1215 patients.

Amer. J. Psychiat. 121: 895—901, 1965.

BAHN,A. K.: Methodological study of a population of outpatient psychiatric clinics. Maryland 1958—59.

Publ. Hlth Monogr. No. 65.

(U.S. Govt. Print. Off., Washington, D.C., 1961).

BAHN,A. K., GOLDBERG,1, D. and GOINVITZ,K.: Longitudinal

studies using psychiatric case registers,

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 226-249.

(E. & S, Livingstone Ltd., Edinburgh and London 1968),

BENJAMIN,B.: Assessment of medical care.

Proc. roy. Sot. Med. 60:809-813, 1967.

BENNETTA. E, and HOLLAND,W. W.: Towards the development of electronic data-processing systems for medical

records.

Lancet 1965, II, 1176—1 178.

BROTHERSTON,J,: General discussion and summing up.

In E. D. Acheson (Edit.): Record Linkage in Mediciner

pp. 379—381 .

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

CoPE, C. B.: A centralized nation-wide patient data system.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 34-38.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

CRONKHXTE,L. W.: Patient location control as a first step

toward a total information system.

Hospitals 41: 107—1 12, 1967.

DAVIES,M.: Toward a medical data bank for a total population.

Datamation 15: 25?—260, 1969.

DOLL, R.: Hospital records in the computer

Proc. roy. Sot. Med. 61: ?09—715, 1968.

age.

FAIRBAIRN,A. S,: Comparison of diagnosis on successive

hospital admissions.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 21 5—225.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

FAIRBAIRN,A. S. and ACHESON,E. D.: The extend of organ

removal in the Oxford area.

J. chron. Dis, 22: 11 1—122, 1969.

FRITZII, E. und WAGNER, G. (Hrsg.): Dokumentation de

Krankheitsverlaufs. Probleme der Erfassung des zeitlichen

Krankheitsab]aufes und des Medical Record Linkage.

Verb. Bericht 13, Jahrestagung GMD, Bochum

30.9. — 2. 10. 1968.

(F. K. Schattauer Verlag, Stuttgart-New York 1969).

HALI,, P,, MELLNER, Ch. and DA~IELSSON,T.: J 5 — A data

processing system for medical information.

Meth, Inform. Med. 6: 1—6, 196?.

HEDLEY, A. J., SCOTT, A. M. and DEBENHAM, G.: A computer

assisted follow-up register,

Meth, Inform. Med. 8: 6?’-77, 1969.

HOBBS, M. S. T. and ACHESON, E. D,: Secondary sex ratio

following bleeding in pregnancy,

Lancet 1966, 1, 462A63.

KALLE~, B. and WINBERG, J.: A Swedish register of con

genital malformations. Experience with continuous regi

stration during 2 years with special reference to multiple

malformations.

Pediatrics 41: 765—776, 1968.

MaCMAHON, B. and NEWILL, V. A.: Birth characteristics o

children dying of malignant neoplasms.

J. nat. Cancer Inst. 28; 231—244, 1962.

MASI, A. T., SARTWELL,P. E. and SHULMAN,L. E.: The use o

record linkage to determine familial occurrence of disease

from hospital records. (Hashimoto’s disease).

Amer. J, pub], Hlth 54: 1887—1894, 1964,

MOORE, F.: Health Information Systems,

(Univ. of South. Calif. School of Med., Los Angeles 1963)

MOORE, F. J.: Mechanizing a large register of first order

patient data.

Meth. Inform. Med. 4: 1—10, 1965.

MOSBECH,J.: General discussion and summing up.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 381—383.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

NEUWIRTH, A. A., REDMOND,M. M. and WILSON, D.:

A >womb to tomb< public health record.

Health (New Haven) 79: 2—7, 1952.

NEWCOMBE, H. B.: Record linkage and hospital discharge

abstracts. (Working Paper for the Conference on Hospital

Discharge Abstracts, Airlie House,

WarrentonfVa., June 1969; Med. Care, in press).

PHILLIPS, W., jr.: Record linkage for a chronic disease

register.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 120—151.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

366
RANG, E. H., ACHESON,E. D. and O’CONNOR, B. T.: Clinical

significance of deaths after discharge from hospital unrecorded in the hospital notes.

Lancet 1968, II, 906-910.

TOTTERMAN,L. E,, FOFNSTROM,E., RAMO, P, et al.: Completion

of the delivery record of the central hospital with data

obtained from the maternity card, follow-up examination

and auto-anamnesis form (Finn.).

Suom. Laak. 21:2437-2443, 1966.

WHO: The public health use of electronic computers.

Report on a Seminar convened by the Regional Office for

Europe of the WHO, London, 17—21 June 1968.

WHO-Document EURO 3092, WHO Regional Office for

Europe, Copenhagen 1969.

YODER, R. D., SWEARINCEN, D. R., SGHENTAL, J. E.: SWEENEY,

J. W. and NETTLETON, W. J., Jr.: An automated clinical

information system.

Meth. Inform. Med. 3: 45—50, 1964.

2.) Epidemiology

ACHESON,E. D.: Oxford Record Linkage Study: A central

file of morbidity and mortality records for a pilot population.

Brit. J. prev. SOC.Med. 18: 8—13, 1964.

ACHESON, E, D.: Record linkage techniques in studies of

the etiology of cancer.

Proc, roy, Sot. Med. 61:726-730, 1968.

ACHESON, E. D. and BACHFIACH,C. A.: The distribution of

multiple sclerosis in U.S. veterans by birthplace.

Amer. J. Hyg. ?2: 88—99, 1960.

ACHESON,E. D., TRUELOVE,S. C. and WrTTS, L. J.: National

Epidemiology.

Brit. med. J. 1961, I, 668.

Ad Hoc Committee on the Implications of Record Linkage

for Health-Related Research: Health Research Uses of

Record Linkage in Canada.

(Meal, Res. Count. of Canada, Ottawa

BAHN, A. K.: The development of arl

system in mental iilness.

Amer. J. Psychiat. 1’16:79&-f300, 1960.

968).

effective statistical

BAHN, A. K.: A new psychiatric epidemiology.

Israel Ann. Psychiat. 2: 11—18, 1964.

BALODIMOS,M. C. and HURXTHAL, L. M.: The remote prediabetic state: effect on infant size, fetal and perinatal

mortality.

Geriatrics 21: 119—127, 1966.

BIbSTRUP,P, L. and” EASE, R. A. M.: Carcinoma of lung in

workmen in bichromates-produ~ ing iridttstry in Great

Britain.

Brit. J. indtwtr. Med. 13: 260—264, 1956.

BIARNASON,O., FRIDRnrSSON,S. and MAGtWSSON,M!: Record,

linkage in a self-contained community.

In E. D. Acbeson (Edit.): Record Litikage in Medicine,

pp. 62-68.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

BOTHWELL, P. W.: A New Look at preventive Medicine.

(Pitman Medical Publishing Comp., London 1965).

367

BRAGG,R. L.: Risk of admission to mental hospital following

hysterectomy or cholecystectomy.

Amer. J. publ. Hlth 55:1403-1410, 1965.

CASE, R. A. M. and LEA, J.: Mustard gas poisoning, chronic

bronchitis and lung cancer.

Brit. J. prev. SOC, Med. 9: 62—72, 1955.

CASE, R. A, M.: Cohort studiesin assessingenvironmental

hazards.

In E.D. Acheson (Edit.):Record LinkageinMediciner

pp.207—213.

(E.& S.LivingstoneLtd.rEdinburghand London 1968).

CASE, R. A. M. and HOSKER, M. E.: Tumours of the urinary

bladder as an occupational disease in the rubber industry

in England and Wales.

Brit. J. prev. SOC, Med. 8: 39—50, 1954.

CASE, R. A. M., HOSKER,M. E., MCDONALD,D. B. and PEARSON, J. T.: Tumours of the urinary bladder in workmen

encrarred in the manufacture and use of certain dvestuff . .

intermediates in the British Chemical Industry,

Brit, J. industr. Med. 11: 75—104, 1954.

CHRISTOPHERSON, W. M. and PARKER,J. E.: Relation

vital cancer to early marriage and childbearing.

New Engl. J. Med. 273: 235—239, 1965.

of cerCtocco, A.: On the mortality in husbands and wives.

Hum. Biol, 12; 508-531, 1940.

CONTERIO, F. and CAv.4r,r.r-SFoRz& L. L.: Evolution of the

human constitutional phenotype: an analysis of mortality

effects.

Ric. scient,, Suppl. 29, 71—78, 1957.

COURT-BROWN,W. M. and DOLL, R.: Mortality from cancer

and other causes after radiotherapy for ankylosing spondylitis.

Brit. med. J. 1965, II, 1327—1332.

COURT-BROWN,W. M., DOLL, R. and HILL, A. B.: Incidence

of leukaemia after exposure to diagnostic radiation in

utero.

Brit. med. J. 1960, 11, 1539--1545.

DA SILVA, H. J., ABBATT, J. D., DA MOTTA, L. C. and RORIZ,

M. J.: Malignancy and other late effects following administration of thorotrast.

Lancet 1965, II, 201—205.

DAVIES,M.: Toward a medical data bank for a total population.

Datamation 15: 257—260, 1969.

DAVIES,M. A., PRYWES,R., TZUR, B., WEISKOPF,P. and

STERK, U. V.:

The Jerusalem perinatal study. 1. Design and Organization of a continuing, community-based, record-linked

survey.

Israel J. Med. ScL 5: 1095—1 106, 1969.

DOLL, R.: Record linkage techniques hr studies of the etiology of cancer.

Proc. roy. Sot. Med. 61: 731—732, 1968.

DOLL, R., JONES, F. A., PYGOTT,F. and STUBBE,J. L.: The risk

of gastric cancer after medical treatment for gastric ulcer.

Gastroenterologia 88: 1—12, 1957.

DUNGAL,N.: The specific problem of stomach cancer in

Iceland.

J. Amer. med. Ass. 178: 789—798, 1961.
Brit, J. prev. SOC,Med. 21: 9?—107, 196?.

HEASMAN, M. A.: The use of record linkage in long-term

prospective studies.

In E. D. Acbeson (Edit.): Record Linkage in Medicine,

pp. 251—256.

(E. &S. Livingstone Ltd., Edinburgh and London 1968).

HILL, A. B,, DOLL, R,, GALLOWAY,T. M. and HUGHES,J. P. W.:

Virus diseases in pregnancy and congenital defects.

Brit. J. prev. SOC.Med. 12: 1—7, 1958.

HIRAYAMA, T. and WYNDER, E. L.: A study of the epidemiology of cancer of the breast — 11. The influence of hysterectomy<

Cancer 15:28-38, 1962.

HOSBS, M. S. T, and ACHESON, E. D.: Perinatal mortality

and the organization of obstetrics services in the Oxford

area in 1962.

Brit. med. J. 1966, 1, 499-505.

HOBBS, M. S. T. and ACHESON,E. D.: Secondary sex ratio

following bleeding in pregnancy.

Lancet 1966, I, 462-463.

KELLER, A, Z.: The epidemiology of lip, oral and pharyngeal cancers, and the association with selected systemic

diseases.

Amer. J. publ. Hlth 53:1214-1228, 1963.

KILPATRICK,S. I., MATHERS, J. D. and STEVENSON, A. C.: The

importance of population fertility and consanguinity data

being available in medico-social studies.

Ulster med. J. 24:113-122, 1955.

LENZ,. W.: Malformations caused by drugs in pregnancy.

Amer. J. Dis. Child. 112:99-106, 1966.

MACCACARO, G. A.: Elaborazione elettronica per lamedicinapreventive.

AppL bio-med.talc.elettr.3:259-2?9,1968.

MacKENzm, I.: Breast cancer following multiple

fluoroscopes,

Brit. J. Cancer 19: 1-8, 1965.

MOORE, F,: Health Information Systems.

(Univ. of South. Calif. School of Med., Los Angeles 1963)

MOORE, F.: Metropolitan health information systems.

Proc. 11. Internat. Congr. Med. Rec. Libr., Chicago 1963

,NEWCOMBE, H. B.: Panel discussion. Sessionon epidemiologicalstudies.

In:2.Int.Conf.on CongenitalMalformations, pp.345-349.

(lntemational MedicalCongressLtd.,New York 1964).

NEWCOMBE, H. B.: Present state and long-term objectives

of the British Columbia population study.

In J. F. Crow, J, V. Neel (Edits): Proc. 3. Internat. Congr.

Hum. Genet., Chicago 1966, pp. 291-313.

(Johns Hopkins Press, Baltimore 1967).

NEWCOMSE, H. B.: Die Anwendung des Medical Record

Linkage fiir Bevolkerungs- und genetiscbe Studien.

In E. Fritze und G. Wagner (Hrsg.): Dokumentation des

Krankheitsverlaufs. Bericht iiber die 13. Jahrestagung der

GMD hr Boctmm, 30.9. — 2.10.1968.

(F. K. Schattauer Verlag, Stuttgart-New York 1969).

368

population and genetic studies.

Meth. Inform. Med. 8: 7—11, 1969,

NEWCOMSE, H. B.: Pooled records from multiple sources

for monitoring congenital anomalies.

Brit. J. prev. SOC.Med. 23; 226-232, 1969.

NEWCOMBE, H. B.: The value of Canadian hospital insurance records for detecting increases in congenital anomalies.

Canad. med. Ass. J, 101: 121—128, 1969.

NEWCOMSE,H. B. and RHYNAS,P. O. W.: The cost of individual follow-up studies of large populations.

Proc. Internat. Population Conf., New York 1961;

Atomic Energy of Canada Ltd., Report No, 1255,

Chalk River, Ontario 1961.

PAFFENBARGER,R. S., WOLF, P. A., NOTKIN, J. and THORNE,

M. C.: Chronic disease in former college students.

1. Early precursors of fatal coronary heart disease.

Amer. J. Epidem. ’83: 314-328, 1966.

PELL, S.: Epidemiologic studies in a large company based

on health and personal records.

Publ. Hlth Rep. 83: 398-405, 1968.

PHILLIPS, W., jr., GORWITZ, K. and BAHN, A. K.: Electronic

maintenance of case registers.

PubL Hlth Rep. 7?: 503-510, 1962.

RENWICK, D, H.: The combined use of a central registry

and vital records for incidence studies of congenital

defects.

Brit. J. prev. SOC. Med. 22:61-67, 1968.

SHAPIRO,S., WEINBLATT, E., FRANK,C., SACER, R. and DENSEN,

P.: The H. L P. study of incidence and prognosis of coronary heart disease: Methodology.

J. cbron. Dis. 16: 1281—1292, 1963.

SLONE, D., GAETANO,L. F., LIPWORTH,L,, SHAPIRO,S., LEWIS,

G. P. and JICK, H.: Computer analysis of epidemiologic

data of effect of drugs on hospital patients,

PubL Hlth Rep. 84: 39—52, 1969.

SMITHELLS, R. W.: Incidence of congenital abnormalities

in Liverpool, 1960-64.

Brit. J. prev. SOC. Med. 22: 36-3?, 1968.

STEWART, A. and HEWITT, D.: Leukaemia incidence in

children in relation to radiation exposure in early life.

In Ebert, M. and Howard, A. (Edits): Current Topics in

Radiation Research, Vol. 1, pp. 223—253.

(North-Holland Publishing Co., Amsterdam 1965).

STOCKS,P.: Measurement of morbidity.

Proc, roy. Sot, Med. 37: 593—608, 1944.

U.S. National Committee on Vital and Health Statistics:

Epidemiologic Uses of Vital and Health Statistics. A report

of the Sub-Committee on Vital and Health Statistics for

Epidemiologic Purposes.

(U.S. Govt. Print. Off., Washington, D.C., 1966).

WATTS, S, P. and ACHESON, E. D.: Computer method for

deriving hospital in-patient morbidity statistics based on

the person as the unit.

Brit. med. J. 196?, IV, 47=77.

WELLS, J, and STEER, C. M.: Relationship of leukaemia in

children to abdominal irradiation of mother during pregnancy.

Amer. J. Obstet. Gyrrec. 81: 1059—1063, 1961.
WHO: The use of electronic computers in health statistics

and medical research.

Report on a Symposium convened by the Regional Office

for Europe of the WHO, Stockholm, 6-10 June 1966.

WHO Document EURO-341, WHO Regional Office for

Europe, Copenhagen 196?.

WHO: The public health use of electronic computers.

Report on a Seminar convened by the Regional Office

for Europe of the WHO, London, 17—21 June 1968.

WHO-Document EURO 3092, WHO Regional Office for

Europe, Copenhagen 1969.

YOUNG, M., BENJAMIN, B. and WALLIS, C.: Tkie mortality of

widowers.

Lancet 1963, II, 454-456.

3.) Vital Statistics, Demography

ACHESON,E. D.: Medical Record Linkage.

(Oxford University Press, London-New York-Toronto 1967).

ACIIZSON, E. D. (Edit.): Record Linkage in Medicine.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

Ad Hoc Committee on the Implications of Record Linkage

for Health-Related Research: Health Research Uses of Record Linkage in Canada.

(Med. Res. Count. of Canada, Ottawa 1968).

ALDEMON, M. and MEADE, T.: Accuracy of diagnosis on

death certificates compared with that in hospital records.

Brit. J. prev. SOC. Med. 21: 22—29, 1967.

BARRAI, I., MORONI, A. and CAVALLI-SFORZA, L. L.: Further

studies on record linkage from parish books.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 270—280.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

BENJAMIN, B.: Computers: Their use in medical statistics

and health services.

ROY. SOC. Hlth J. 86: 205—213, 1966.

BOYCE, A. J., KUCHEMANN, C. H. and I-LWUUSON,G. A.: Neighborhood knowledge and the distribution of marriage

distances.

Ann. hum. Genet. 30: 335—338, 1967.

CAVALLI-SFORZA, L. L.: Demographic attacks on genetic

problems: Some possibilities and results.

Proc. Seminar on the Use of Vital and Health Statistics

for Genet. and Radiol. Studies.

(U.N. Publication, Sales No. 61, New York 1962).

CHRISTENSEN,H. T.: Studies in child spacing. I. Premarital

pregnancy as measured by the spacing of the first birth

from marriage.

Amer. SOC. Rev. 18: 53—59, 1953.

CHRISTENSEN,H. T. and BOWDEN, O. P.: Studies in child

spacing. H. The time-interval between marriage of parents

and birth of their first child, Tippecanoe County, Indiana.

Social Forces 31:346-351, 1953.

CHRISTENSEN, H. T. and MEISSNER,H. H.: Studies in child

spacing. 111. Premarital pregnancies as a factor in divorce.

Amer. SOC. Rev. 18:641-644, 1953.

CHRISTENSEN,H. T. and RUBENSTEIN,B. B.: Premarital preg

nancy and divorce.

Marr. Fare. Living 18: 114-123, 1956.

DOUCLAS, J. W. B. and BLOMFIELD,J. M.: The reliability of

longitudinal surveys.

Milbank mem. Fd Quart. 34: 227—252, 1956,

DUNN, H. L.:Elements of a coordinationsystem of vital

recordsand statistics.

Publ.HlthRep.68:793-801,1953.

HAMBRIGHT,T, Z.: Comparability of age on the death certificate and matching census record, Vital and Health

Statistics, Publ. Hlth PubL No. 1000-Series 2-No. 29,

(U.S. Dept. H. E. W., Washington, D.C., 1968).

HENM, L.: Problemes de la recherche demographique

moderne.

Population 21; 1093—1 114, 1966.

HUBBARD,M, R. and ACHESON, E. D.: Notification of death

occurring after discharge from hospital.

Brit. med. J. 196?, HI, 612-613,

KEYFITZ, N.: Utilisation des machines electroniques clans

les calculs demographiques,

Population 19: 673-682, 1964.

KUCHEMANN,C, H., BOYCE, A. J. and HARRISON,G, A.:

A demographic and genetic study of a group of Oxfordshire villages.

Hum. Biol. 39: 251—276, 1967.

LESLIE, G. R., CHRISTENSEN, H. T. and PEARMAN, G. .~.:

Studies in child spacing. IV, The time interval separating

all children in completed families of Purdue University

Graduates.

Social Forces 34: 77-82, 1955.

LUNDE,A. S. (Edit.): The 1970 Census and Vital and Health

Statistics. A study group report of the Public Health Conference on Records and Statistics.

Vital and Health Statistics, PubL Hlth PubL No. 1000-

Series 4-No. 10.

(U.S. Dept. H. E. W., Washington, D. C., 1969).

MADOW,W. G.: Interview data on chronic conditions compared with information derived from medical records.

Vital and Health Statistics, Publ. Hlth Publ. No. 1000-

Series 2-No. 23,

(US. Dept. H, E. W., Washington, D.C,, 1967).

MARSHALL,J, T.: Canada’s national vital statistics index.

popul. Stud. 1:204-211, 1947.

MATHER, K.: Genetical demography.

Proc. roy. Sot. B 159: 106—125, 1963.

MCCARTHY,M. A.: Comparison of the classification of place

of residence on death certificates and matching census

records. Vital and Health Statistics, Publ. Hlth PubL No.

1000-Series 2-No. 30.

(U.S. Dept. H. E. W., Washington, D.C., 1969).

MINET, P. L.: Fertilite precoce dune cohorte de mariages

clans une province Canadienne.

Acts genet. (Basel) 14: 186—196, 1964.

MOORE, F.: Development of a regional health information

center.

Proc. 4. IBM Medical Syrnp., Endicott, N.Y., pp. 225—239.

(Yorktown Heights/N.Y. 1962).
MOIrONI, A.: Sources, reliability and usefulness of consanguinity data, with special reference to Catholic records.

Proc. Seminar on the Use of Vital and Health Statistics

for Genetic and Radiation Studies, pp. 109—118.

(U.N, Publication, Sales No. 61, New York 1962).

NEWCOMBE, H. B.: Radiationr genetics and vital statistics:

A pilot study in demographic genetics.

(Brochure prepared for the exhibit of Atomic Energy of

Canada Ltd. at the 10th Int. Congr. Genet., Montreal 1958).

NEWCOMSE, H. B.: The uniqueness of Canadian vital statistics for studies in population genetics.

Canad. J. Genet. Cytol. 1: 13-15, 1959.

NEWCOMBE, H. B.: Use of vital statistics.

In: U.N. World Population Conference, Belgrade, 1965,

Vol. II, pp. 494-497. (United Nations, New York 1965).

NEWCOMBE, H. B.: Couplage de donn6es pour les etudes

d6mographiques.

Population 24: 653—684, 1969.

NEWCOMBE, H. B., JAMES, A. P., AXFORD, S. J.: Family

Linkage of Vital and Health Records:

(1) For population studies of hereditary influences on

health,

(2) For verification of status for welfare programmed and

other purposes.

Atomic Energy of Canada Ltd., Report No. 470,

Chalk River, Ontario 1957.

NEWCOMBE, H. B. and KENNEDY,J. M.: Demographic analysis and computer programs.

In: U.N. World Population Conference, Belgrade, 1965,

Vol. III, pp. 251—253. (United Nations, New York 1965).

NEWCOMBE, H. B. and SMITH,M. E.: Changing patterns of

family growth: The value of linked records as a source

of data.

Popul. Stud. (in press).

RANG, E. H., ACHESON,E. D. and O’CONNOR, B. T.: Clinical

significance of deaths after discharge from hospital unrecorded in the hospital notes.

Lancet 1968, II, 908-910.

Royal Commission on Population: Reports and selected

papers of the Statistics Committee, Vol. 11, pp. 29-48.

(H.M.S.O., London 1950).

SHAPIRO, S. and SCHACHTER,J.: Methodology and summary

results of the 1950 birth registration test in the United

States.

Estadist. J. Int.-Amer. Statist. Inst. 10:688-699, 1952.

SMITH, A. E,: Automatic linkage of medical and vital

registration records.

Brit. J. SOC. prev. Med. 17: 185-190, 1963.

SPICER, C. C.: Practical problems of record linkage on a

national scale.

In Medical Research Council: Mathematics and Computer

Science in Biology and Medicine.

(H.M. S.O., London 1965).

SUTTER, J., COUX, J.-M. et MUGNIER, M.: Organigrammes

pour l“etude mecanographique de la parente et de la

fecondite clans une population.

Population 21: 75—98, 1965.

THOMAS,D. S.: Continuous register system of population

accounting.

In National Resources Committee (Edit.): The problems of

a changing Populationr pp. 276-297.

(U.S. Govt. Print. Off., Washington, D.C., 1938).

4.) Genetics

ACHESON,E. D.: Hospital morbidity in early life in relation

to certain maternal and foetal characteristics and events

at delivery.

Brit. J, prev. SOC. Med. 19: 164-173, 1965.

ACHESON, E. D.: Medical Record Linkage.

(Oxford University Press, London-New York-Toronto 1967).

ACHESON, E, D, (Edit.): Record Linkage in Medicine.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

Ad Hoc Committee on the Implications of Record Linkage

foI HeaIth-Related Research: Health Research Uses of

Record Linkage in Canada.

(Med. Res, Count, of Canada, Ottawa 1968),

BARR, A. and STEVENSON, A. C.: Stillbirth and infant mortality in twins.

Ann. hum. Genet. 25: 131—140, 1961.

BARRAI, I., CAVALLI-SFORZA, L. L. and MAINAItDI,M.: Studio

pilota per la determinazione degli effetti dells consanguineita su caratteri esaminati alla visita di leva.

(Atti V. Riun. Scient, A.G.I,, pp. 317—331, 1959).

BARRAI, L, CAVALLESFORZA,L. L. and MORONI, A.: Record

linkage from parish books,

In Medical Research Council: Mathematics and Computer

Science in Biology and Medicine, pp. 51-60.

(H.M.S.O., London 1965).

BARRAI, I., MORONI, A. and CAVALLI-SFORZA,L. L.: Further

studies on record linkage from parish books.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 270—280.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

BOYCE, A. J., KtiCHEMANN, C. F. and HARRISON, G. A.: The

reconstruction of historical movement patterns.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 303—317

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

CARTER, C. O.: General discussion and summing up.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 383-385.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

CAVALLI-SFORZA, L. L.: Some notes on the breeding patterns

of human populations.

Acts Genet. med. (Roma) 6:395-399, 1957.

CAVALLI-SFORZA, L. L.: Some data on the genetic structure

of human populations.

Proc. 10. Int. Congr. Genet. Darwin Centenn. Symp, Genet.

Evolut., Vol. I, pp. 388-407.

(Univ. of ToIonto Press, Toronto 1959).

CAVALLI-SFORZA, L. L.: Demographic attacks on genetic

problems: Some possibilities and results.

Proc. Seminar on the Use of Vital and Health Statistics

for Genet. and Radiol. Studies.

(U.N. Publication, Sales No. 61, New York 1962).

370
CHRISTENSEN, H. T,: The method of record linkage applied

to family data.

Marr. Fare, Living 20:3843, 1958.

CHUNG,C. S.: Applications of digital

genetics.

Meth. Inform, Med. 3: 6%—72, 1964,

CROW, J. F.: Some possibilities for

intensities in man.

Hum. Biol, 30; 1—13, 1958.

DUNN, H. L. and GILBERT,M.: Public

family.

computersin human

measuring selection

healthbeginsin the

Publ. Hlth Rep. (Wash.) 71: 1002—1010, 1956.

EDWARDS,J. H.: The interpretation’of pedigree data.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 282—292,

(E. & S. Livingstone Ltd., Edinburgh and London 1968].

EDWARDS,J. H.: Linkage studies of whole populations.

In J. F. Crow and J. V. Neel (Edits): Proc. 3. Int. Congr.

Hum. Genet., Chicago 1966, pp. 479-482.

(Johns Hopkins Press, Baltimore 1967).

FALCONER,D, S.: The inheritance of liability to certain

diseases, estimated from the incidence among relatives.

Ann. hum. Genet. 29: 51—76, 1965.

FRITZE, E. und WAGNER, G. (Hrsg.): Dokumentation des

Krankheitsverlaufs. Probleme der Erfassung des zeitlichen

Krankheitsablaufes und des Medical Record Linkage.

Verb. Bericht 13. Jahrgang GMD, Boclmm 30.9. — 2, 10.

1968.

(F. K. Schattauer Verlag, Stuttgart-New York 1969).

GEDDA, L. and MILANI-COMPARETTI, M.: Computerization of

a permanent twin register: a basic tool in scientific research.

Acts Genet. med. (Roma) 15: 333—344, 1966.

HIGGINS,J. V., REED, E. W. and REED, S. C.: Intelligence

and family size: a paradox resolved.

Eugen. Quart. 9: 84—90, 1962.

HOBIJS, M. S. T.: A study of birthweight and other factors

in sibships.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 358—367.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

KENNEDY, J. M., NEWCOMBE, H. B., OKAZAKI, E. A. and

SMITH, M. E.: Computer methods for family linkage of

vital health records.

Atomic Energy of Canada Ltd., Report No. 2222,

Chalk River, Ontario 1965.

KUCHEMANN,C. H., BOYCE, A. J. and HARRISON,G. A.: A

demographic and genetic study of a group of Oxfordshire

villages.

Hum. Biol. 39: 251—276, 1967.

LABERGE,C.: Prospectus for genetic studies in the French

Canadians, with preliminary data on blood groups and

consanguinity.

Bull. Johns Hopkins Hosp. 118: 52-68, 1966.

MANGE, A. P.: Fortran programs for computing Wright’s

coefficient of inbreeding in human and non-human pedigrees.

Amer. J. hum. Genet. 16:484, 1964.

MASI, A, T., SARTWELL, P. E. and SHULMAN, L. E.: The use

of record linkage to determine familial occurrence of

disease from hospital records. (Hashimoto”s disease).

Amer, J, publ. Hlth 54: 1887—1894, 1964,

MATHER, K.: Genetical demography.

Proc. roy. Sot. B 159:106-125, 1963.

MCKUSICK,V. A.: Some computer applications to probiems

in human genetics.

Meth. Inform. Med. 4: 183—189, 1965,

MCKUSICK,V. A.: Genealogic and bibliographic applications

of computers in human genetics.

In J. F. Crow and J. V. Neel (edits): Proc. 3. Int, Congr.

Hum. Genet., Chicago 1966, pp. 483488.

(Johns Hopkins Press, Baltimore 1967).

MCKUSICK,V. A.: Family-oriented follow-up.

J. chron. Dis. 22: l—?, 1969.

MCKUSICK, V. A. and CROSS, H. E.: Genealogical linkage

of records in two isolate populations.

In E. D. Acbeson (Edit.): Record Linkage in Medicine,

pp. 263—269.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

MCKUSXCK,V. A., EGELAND,J. A., ELDRIDGE,R. and KRUSEN,

D. E.: Dwarfism in the Amish. 1. The Ellis-van Crefeld

Syndrome.

Bull, Johns Hopkins Hosp. 115:306-336, 1964.

MCKUSICK,V. A., ELDRIDGE,R., HOSTETLER,J. A., RWANGWIT,

U. and ECELAND,J. A.: Dwarfism in the Amish. IL Cartilagehair hypoplasia.

Bull. Johns Hopkins Hosp. 116: 285—326, 1965.

MCKUSICK, V. A., HOSTETLER, J. A. and EGELAND, J. A.:

Genetic studies of the Amish. Background and potentialities.

Bull. Johns Hopkins Hosp. 115: 203—222, 1964.

MCKUSICK, V. A., HOSTETLER,J. A. et al.: The distribution

of certain genes in the Old Order Amish.

Cold Spr. Harb. Symp. quant. Biol. 29: 99—1 14, 1964.

MI, M. P.: Record linkage and other genetic studies in

Hawaii.

In J. F. Crow and J. V. Neel (Edits): Proc. 3. Int. Congr.

Hum. Genet., Chicago 1966, pp. 489496.

(Johns Hopkins Press, Baltimore 1967).

MILHAM8 S.: Increased incidence of anencephalus and spina

bifida in siblings of affected cases.

Science 138: 593—594, 1962.

MILHAM,S.: Random distribution of affected birth ranks in

anencephalic and spina bifida sibships with two affected

cases.

Nature 200:480-481, 1963.

MILLER, J. R.: Human genetics in public health research

Canad. J. publ. Hlth ?: 1-8, 1966.

MORONI, A.: Sources, reliability and usefulness of consanguinity data, with special reference to Catholic records.

Proc. Seminar on the Use of Vital and Health Statistics

for Genetic and Radiation Studies, pp. 109—1 18.

(U.N. Publication, Sales No. 61, New York 1962).

MORTON,N. E., CROW, J. F. and MULLER, H, J.: An estimate

of the mutation damage in man from data on consanguineous marriages.

Proc. nat. Acad. Sci. 42:855-863, 1956.

371
NEWCOMBE, H. B.: Radiation, genetics and vital statistics:

A pilot study in demographic genetics.

(Brochure prepared for the exhibit of Atomic Energy of

Canada Ltd. at the 10th Int. Congr. Genet., Montreal 1958).

NEWCOMSE, H. B.: The uniqueness of Canadian vital statistics for studies in population genetics.

Canad. J. Genet. CytoL 1: 13—15, 1959.

NEWCCIMBE, H. B.: Feasibility of estimating consequences

of an increased mutation rate.

In Gardner, L. I. (Edit.): Molecular Genetics and Human

Disease, pp. 186—203.

(C. C. Thomas, SpringfieldiIIL 1960).

NEWCOMBE,H. B.: Genetics, radiation and people,

Canad. J. Genet. Cytol. 2; 220—223, 1960.

NEWCOMBE, H. B.: Population genetics: Population records.

In: Methodology in Human Genetics, pp. 92—1 13.

(Holden-Day Inc., San Francisco 1962).

NEWCOMBE, H. B,: Untapped knowledge of human populations.

Trans. roy. Sot. Can., Sect. III, 56: 173—180, 1962.

NEWCOMBE, H. B.: Genetic effects in populations, with

special reference to studies in man, including ABCC results.

Radiat. Res. 16: 531—545, 1962.

NEWCOMBE, H. B.: Screening for effects of maternal age

and birth order in a register of handicapped children.

Ann. hum. Genet. 27: 367—382, 1964.

NEWCOMBE,H. B.: Pedigrees for population studies. A progress report.

Cold Spr, Harb. Symp. quant. Biol. 29: 21—30, 1964.

NEWCOMBE, H. B.: Use of vital statistics.

In: U.N. World Population Conference, Belgrade, 1965,

vol. II, pp. 494-497.

(United Nations, New York 1965).

NEWCOMBE, H. B.: Environmental versus genetic interpretations of birth order effects.

Eugen. Quart. 12: 90—101, 1965.

NEWCOMBE, H. B.: The study of mutation and selection in

human populations.

Eugen. Rev. 57: 109—125, 1965.

NEWCOMBE,H. B.: Radiation protection in Canada, Part VI:

Problems in the assessment of genetic damage from exposure of individuals and populations to radiation.

Canad. med. Ass. J. 92: 171—176, 1965.

NEWCOMBE, H. B.: Familial tendencies in diseases of children.

Brit, J. prev. SOC. Med. 20: 49—57, 1966.

NEWCOMBE, H. B.: Present state and long-term objectives

of the British Columbia population study.

In J. F. Crow, J. V. Neel (Edits): Proc. 3. Internat. Congr.

Hum. Genet., Chicago 1966, pp. 291—313.

(Johns Hopkins Press, Baltimore 1967).

NEWCOMBE,H. B.: Risks to siblings of stillborn children.

Canad. med. Ass. J. 98: 189—193, 1968.

NEWCOMBE, H. B.: Products from the early stages in the

development of a system of linked records.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 7—33.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

372

NEWCOMBE, H. B.: Multigeneration pedigree from linked

records.

In E. D. Acheson (Edit,): Record Linkage in Medicine,

pp. 295—301 .

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

NEWCOMBE, H. B,: Die Anwendung des Medical Record

Linkage fiir Bevolkerungs- und genetische Studien.

In E, Fritze und G. Wagner (Hrsg.): Dokumentation des

Krankheitsverlaufs. Bericht iiber die 13. Jahrestagung der

GMD in Bochum, 30.9. — 2.10.1968.

[F. K. %hattauer Verlag, Stuttgart-New York 1969).

NEWCOMBE, H. B.: The use of medical record linkage for

population and genetic studies.

Meth. Inform. Med. 8: 7—11, 1969.

NEWCOMBE, H. B.: Value of Canadian hospital insurance

records in detecting increases in congenital anomalies.

Canad. med. Ass. J. 101: “121—128, 1969.

NEWCOMBE, H. B.: The use of medical record linkage for

monitoring congenital anomalies.

Brit. J. prev. SOC. Med. 23:226-232, 1969.

NEWCOMBE, H. B., AXFORD, S. J. and JAMES, A. P.: A plan

for the study of fertility of relatives of children suffering

from hereditary and other defects.

Atomic Energy of Canada Ltd., Report No.511,

Chalk River, Ontario 1957.

NEWCOMBE, H. B., JAMES, A. P., AXFORD, S. J.: Family Linkage of Vital and Health Records:

(1) For population studies of hereditary influences on

health,

(2) For verification of status for welfare programmed and

other purposes.

Atomic Energy of Canada Ltd., Report No. 470,

Chalk River, Ontario 1957.

NIIWCOMSIE, H. B. and RHYNAS, P. O. W.: Child spacing

following stillbirth and infant death.

Eugen. Quart. 9: 25—35, 1962.

NEWCOMLIE,H, B. and TAVENDALE,O. G.: Maternal age and

birth order correlations: Problems of distinguishing mutational from environmental components.

Mut. Res. 1:446-467, 1964.

NEWCOMBE, H, B. and TAVENDALE, O. G.: Effects of father’s

age on the risk of child handicap and death.

Amer. J, hum. Genet. 17: 163—178, 1965.

OHIiURA, K.: Use of family registration in the study of

human genetics in Japan.

Jap. J. hum. Genet. 5:61-68, 1960.

REED, E. W. and REED, S. C.: Mental Retardation: A Family

Study.

(W. B. Saunders, Philadelphia 1965).

SLATIS,H. M., REIS, R. H. and HOENE, R. E.: Consanguineous

marriages in the Chicago region.

Amer. J. hum. Genet. 10: 446-464, 1958.

SMITH M. E., SCHWARTZ,R. R. and NEWCOMBE, H. B.: Computer methods for extracting sibship data from family

groupings of records.

Atomic Energy of Canada Ltd., Report No. 2520,

Chalk River, Ontario 1965.
WHO Expert Committee on Human Genetics: Human

Genetics and Public Health.

WHO Technical Report Series No. 282.

(World Health Organization, Geneva 1964).

WOOLF, C. M., STEPHENS,F. E.r MULAIK,D. D. and GILBERT,

R E.: An investigation of the frequencies of consanguineous

marriages among the Mormons and their relatives in the

United States.

Amer. J. hum. Genet. 8:236--252, 1956.

YANASE,Y.: The use of the Japanese family register for

genetic studies.

Proc. Seminar on the Use of Vital and Health Statistics

for Genetic and Radiation Studies, pp. 119—133.

(U.N. Publication, Sales No. 61, New York 1962).

YERUSHALMY, J.: Neonatal mortality by order of birth and

age of parents,

Amer. J. Hyg. 28: 244—270, 1938.

Editorial: Genetic studies by record linkage.

Lancet 1965, I, 1056—1057.

5.) Public Health Services

ABRAMS,M. E.: An integrated medical records service at

Thamesmead.

In E, D. Acheson (Edit,): Record Linkage in Medicine,

pp. 370—373.

(E, & S. Livingstone Ltd., Edinburgh and London 1968).

AIrRAMS,M, E., BOWDEN,K. F., CHAMBERLAIN,J. O. P. and

MACCALLUM,I. R.: A computer-based general practice and

health centre information system.

J. roy. CoIl. gen. Pratt. 16:415-427, 1968.

ACHESON,E. D,: Some potentialities of the computer in

national health services.

Proc. Intern. Meet. Automated Data Processing in Hospitals, Elsinore 1966.

ACHESON, E. D.: Computers and medical record linkage.

WHO-Seminar on the public health uses of electronic

computers, London, 1968.

(WHO Document Euro 3092/6, WHO Regional Office for

Europe, Copenhagen 1968).

ACHESO~, E. D.: Record Linkage in Medicine.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

ACHESON,E. D.: Linkage of medical records.

Brit, med. Bull. 24:206-209, 1968.

ACHESON,E. D.: Medical record linkage.

Meth. Inform. Med. 8: 1-6, 1969.

ACHESO~, E. D. and FORBES, J, A.: Experiment in the

retrieval of information in general practice. A preliminary

report.

Brit. J. prev. SOC. Med. 22: 105—109, 1968.

BAHN, A. K.: Experience and philosophy with regard to

case registers in health and welfare.

Commun. ment. Hlth J. 3: 245—250, 1965.

BAHN, A. K.: Some research tools for community mental

health planning and evaluation with particular reference

to psychiatric case registers.

(Proc. Internat. Res. Seminar Commun. Mental Hlth Progr.,

May 1966).

BALDWIN,J. A., lNNES, G., MILLAR, W. M., SiiARP, G. A. and

DORBICOTT, N.: A psychiatric case register in north-east

Scotland.

Brit, J. prev, SOC.Med. 19:38-42, 1964.

BENJAMIN,B.: Integrated community health records.

Med. Rec. 10:4147, 1969,

DAVIES,M.: Toward a medical data bank for a total population.

Datamation 15: 257—260, 1969.

FBITZE, E. und WAGNER, G. (Hrsg,): Dokumentation des

Krankheitsverlaufs. Probleme der Erfassung des zeitlichen

Krankheitsablaufes und des Medical Record Linkage.

Verb. Bericht 13. Jahrestagung GMD, Bochum 30.9. — 2.10.

1968.

(F. K. Schattauer Verlag, Stuttgart-New York 1969).

GALLOWAY,T. McL.: Management of vaccination and immunization procedures by electronic computers.

Med. Offr 109: 232—233, 1963.

GALLOWAY,T. McL.: Computers. Their use in local health

administration.

Roy. SOC. Hlth J. 86: 213—216, 1966.

GALLOWAY,T. McL.: The use of an electronic computer in

a health department.

Canad. J. publ. Hlth 5?: 331—332, 1966.

GALLOWAY,T. McL.: Medical records and record linkage in

local government.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 80-81.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

GARDNER,E. A.: The use of a psydiatric case register in

the pianning and evaluation of a mental health program.

Psychiat. Res. Rep., Amer. psychiat. Ass. 22: 259—281, 1967.

GORWITZ, K., BAHN, A. K., KLEE, G. and SOLOMON,M.:

Release and return rates for patients in state mental

hospitals of Maryland.

Publ. HIth Rep. 81: 1095—1 108, 1966.

HANNAFORD,R,: Automating tbe administration of public

health.

Comput. Dig. 2: 17, 1967.

HOBBS,M, S. T. and ACHESON,E. D.: Perinatal mortality and

the organization of obstetrics services in the Oxford area

in 1962.

Brit. med. J. 1966, I, 499--505.

HOBBS, M. S. T. and ACHESON,E. D.: Obstetric care in the

first pregnancy.

Lancet 1966, I, 761—764.

MOORE, F.: Health Information Systems.

(Univ. of South. CaIif. School of Med., Los Angeles 1963).

MOORE, F.: Metropolitan health information systems.

Proc. 11. Internat. Congr. Med. Rec. Libr., Chicago 1983.

NEUWIRTH,A. A., REDMOND,M. M. and WILSON, D.:

A swomb to tomb<< public health record.

Health (New Haven) 79: 2—7, 1952.

RAMSAY,J. D.: Electronic SAeduling of an Immunization

Program — the Weyburn, Saskatchewan, Pilot Project.

Canad. J. pub]. Hlth M: 459-464, 1969.

373
g

health record.

J. Sch. Hlth 37:86-89, 196?.

ROSNErL L. J.: Applications of automatic data processing

to a public health agency’s operations.

Publ. Hlth Rep. 80: 625—632, 1965.

SAUNDERS, J. and SNAITH,A. H.: Cervical cytology: a computer assisted population screening programme.

Med. Offr 11?: 299—302, 1967.

WEIrr, R. D.: The introduction of record linkage in northeast Scotland.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 55—60.

(E, & S. Livingstone Ltd., Edinburgh and London 1968].

6.) Other and non-medical fields

Ad Hoc Committee on the Implications of Record Linkage

for Health-Related Research: Health Research Uses of Record Linkage in Canada.

(Med. Res. Count. of Canada, Ottawa 1968).

BINDER, S.: Information storage, retrieval and processing:

Present possibilities and future potentialities.

In: The Use of Vital and Health Statistics for Genetic and

Radiation Studies, pp. 161—167.

(U.N. Publication, Sales No. 61, New York 1962).

BOYCE, A. J., KtiCHEMAN~, C. F. and HARRISON,G. A.: The

reconstruction of historical movement patterns.

In E. D, Acheson (Edit.): Record Linkage in Medicine,

pp. 303—31?’.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

CHRISTENSEN, H. T.: Cultural relativism and premarital sex

norms.

Amer. SOC, Rev. 25: 31—39, 1960.

CROXFOFID,A. A.: Record linkage in education.

In E. D, Acheson (Edit.): Record Linkage in Medicine,

pp. 351—356.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

DAVIDSON, L.: Retrieval of misspelled names in an airline

passenger record system.

Comm. Ass. comput. Mach. 5: 169—1?1, 1962.

DUBOIS, N, S. and D’ANDREA, J. R.: A document linkage

program for digital computers.

Behav. Sci. 10: 312—319, 1965.

TEPPING, B. J.: Study of matching techniques for subscriptions fulfillment.

(National Analysts Inc., Philadelphia 1965).

374

, pg 

applied to Reader’s Digest Data.

(National Analysts Inc., Philadelphia 1958).

Editorial: Checking the Bouncers.

Time Msg. Jan. 4, 1963.

F. Costs of Record Linkage

ACHESON, E. D.: The structure, function and cost of a file

of linked heaIth data.

In Medical Research Council: Mathematics and Computer

Science in Biology and Medicine, pp. 61-69.

(H,M.S.O., London 1965),

ACHESON, E. D,: Some potentialities of the computer in

national health services.

Proc. Intern. Meet. Automated Data Processing in Hospitals, Elsinore 1966.

ACHESON, E. D.: Medical Record Linkage.

(Oxford University Press, London-New York-Toronto 1967).

ACHESON, E. D. (Edit.): Record Linkage in Medicine.

(E, & S. Livingstone Ltd., Edinburgh and London 1968).

ACHESON, E. D. and FORBES, J. A.: Experiment in the

retrieval of information in general practice. A preliminary

report.

Brit. J. prev. SOC. Med. 22: 105—109, 1968.

Ad Hoc Committee on the Implications of Record Linkage

for Health-Related Research: Health Research Uses of Record Linkage in Canada.

(Med. Res. Count. of Canada, Ottawa 1968).

NEWCOMBE, H. B. and RHYNAS,P. O. W.: The cost of individual follow-up studies of large populations.

Proc. Internat. Population Conf., New York 1961;

Atomic Energy of Canada Ltd., Report No. 1255,

Chalk River, Ontario 1961.

WEIR, R. D.: The introduction of record linkage in northeast Scotland.

In E. D. Acheson (Edit.): Record Linkage in Medicine,

pp. 55—60.

(E. & S. Livingstone Ltd., Edinburgh and London 1968).

Addresses of the Authors: Prof. Dr. med. G. Wagner,

Institut fiir Documentation, Information und Statistik am

Deutschen Krebsforschungszentrum, 6900 Heidelberg,

Berliner Straf3e 27;

Dr. H. B. Newcombe, Head, Biology Branch, Biology and

Health Physics Division, Atomic Energy of Canada

Limited, Chalk River, Ontario/Canada.
THE DEVELOPMENT OF THE SMALL BUSINESS DATABASE

OF THE U.S. SMALL BUSINESS ADMINISTRATION: A WORKING BIBLIOGRAPHY

Ccmpiled through 1985by

Bruce D. Phillips, Small Business Administration

The Small Business Data Base (StltJB)1s an

integrated effort for developing and organizing

data on the role of small business lflthe U.S.

economy. It is desiqned to serve two purposes: (1) to comply with tne mandate of the

Congress and (2) to meet the needs of the

research community for analyzing cause and

effect relationships of small business problems

and progress. Because ot the aiversity ot

small business, no one file or collection of

statistics is adeauate to meet those needs.

The Congressional mandate as stated in Public

Law 96-302 instructs the Office of Advocacy of

the Small Business Administration to develop a

data base to be used for historical description

and policy analysis. The specifics of the law

are divided into two parts: the “indicative”

data base for creating mailing lists and the

“external” data base for descriptive StdtlStlCS

and policy analysis.

The SBOB effort is unique because it stresses

maximum use of business microdata, the files of

individual firms. Therefore, the tile provides

information of the “indicative” data base and

overcomes the problem of non-comparability of

longitudinal data for firms of small and large

size classes, as well as other major difficulties of establishing the “external” data

base.

The “indicative” data base 1s the Master

Establishment List (MEL) of more than 8.9

million 1984 establishment recoras. It provides the names, addresses and industry and

geographic codes for 3.4 million establishments, and adds employment, sales, age of firm

and enterprise linkage for an additional 5.5

million firms and establishments. The 1984

MEL, witn information current as of January 1,

1985, will be completed November 30, 1985.

The “external” data base is based upon

proprietary files of the Oun and Bra(lstreet

Corporation. The USEEM (United States

Establishment and Enterprise Microdata) files

are cross-sectional files of about 5 million

records each for 1976, 1978, 1980, and 1982.

(The 1984 files will be available during the

Spring of 1986.) Tne USELM (United States

Establishment Longitudinal Microdata) files are

longitudinally linked files covering the same

years as the USEEM files. However, USELM files

are based on a representative weighted sample

of verified and linked establishment records

which have been eaited for consistency over

time. The USELM is an approximate 50 percent

of the USEEM files.

There are three methods of accessing data in

the Small Business Data 8ase: by obtaining

aggregate data which has been published ifl

sumnary form, by purchasing detailed data at

the U.S. or sub-state levels, and by ordering a

customized tabulation on a cost reimbursable

basis from the Oata Base Branch of the Office

of Advocacv (202-634-7550).

The papers in the enclosed bibliography provide a record of the progress to date in the

development of the Small Business Data Base

including details on increasing the access to

it. Both methodological and applications

papers are included in the hope that persons

interested in small business research wi 11

consult both the enclosed studies for reference, as well as design new research applications in areas of importance to the small

business community.

INTRODUCTION

The studies below describe the Creation,

documentation, and applications of the Small

Business Data Base of the Office of Advocacy of

the U.S. Small Business Administration. Tne

lists below, while comprehensive, examine OnlY

the most relevant studies during the years

1980-1985; the bibliography 1s therefore

representative but not necessarily exhaustive.

The studies below do, however, provide a recent

chronological history on the development of the

Small Business Data Base, and examples of some

applications using the available data.

Two types of studies have generally been

included. First, one collection of papers

describes the detailed creation of the three

major files of the Small Business Oata Base:

the USEEM (United States Establishment and

Enterprise Microdata) file, the MEL (Master

Establishment List), and the USELM (Unites

States Establishment Microdata file). These

files contain approximately 5 million, 8

million, and 20 million records, respectively,

and their development is described in the

papers in this bibliography.

In general, the USEEM file is available on

both an enterIwise and establishment basis by

size class, while the MEL includes USEEM, Plus

an additional 3 million businesses appearing in

yellow page type commercial listings. The MEL

is only an establishment file. The USELM file

is available for

Both USEEM j~~US!ti

establishments

enterprise size class).

files are availa~le for 1976, 1978, 1980, and

1982. (The 19B4 files will be available

during the Spring of 1986.) The MEL file for

1984 (with data current as of January 1, 1985)

will be available on November 30, 1985.

The second group of studies detailed in this

bibliography are research applications either

using the data files directly, or comParln9

them with other government data sources, such

as from the Bureau of the Census, the Internal

Revenue Service, or the Bureau of Labor Statistics. The papers contained in these sections

are both by staff members of the Office of

Economic Research, as well as by SBA contractors.

It is hoped that the source materials listed

below will be periodically expanded and updateu

*

375
as new contracts are completed, and as additional years of data become available. For

example, some of the Papers in the section

describing the Dun and Bradstreet Financial

Statistics File are quite preliminary, and are

the result of previous attempts to assess the

overall quality of the data on an industry

specific basis. Ongoing contracts are creating

a finanClal encyclopedia out of this source on

an aggregate basis. In still other ongoing

research, tne development ot a longitudinal

enterprise file, using tne USEEM database, is

exPected to be completed during the Spring of

1986 for the years 1976-1984. Finally, several

papers from ongoing interagency agreements

between SBA and other agencies, particularly

the Internal Revenue Service, are described

which are augmenting the Small Business Data

Base.

I. 1984/1985

A. Methodological Papers and File Descriptions

Richard Boden and Bruce D. phillips, “Uses and

Limitations of USEEM/USELM Data.” Office ot’

Advocacy, U.S. Small Business Administration,

October 1985.

C.D. Day, “1979 Corporation, partnership and

Sole ProPrietorshiP Employment and Payroll

Studies: An Initial Look at the Relative

Efficiency of Small and Large 8usiness.”

Prepared under an Interagency Agreement between

the Statistics of Income Division, Internal

Revenue Service, U.S. Department of the

Treasury, and the U.S. Smal1 Business

Administration, Office of Advocacy, draft

October 1985. (This project entailed a match

of SOI files with employment enhancements.)

Nick Greenia, “1979 Sole Proprietorship Employment and Payroll: Processing Methodology,”

Record Linkage Techniques--l985, Internal

Revenue Service, 1985. Prepared under an

interagency agreement with the Office of

Advocacy, U.S. Small Business Administration.

Lou Jacobsen, “Analysis of the Accuracy of

SBA’S Small Business Data Base.” Prepared by

the Hudson Institute of the Center for Naval

Analysis under contract to the Office of

Advocacy of the Sma11 Business

Administration, August”.~~85. (This was a

matc~ing study between UI and SBDB data.)

Steven Lustgarten and Stavros Thomadakis, “Firm

Size and Resource Mobility.” (Progress reports

available, final report due December 1985).

Prepared under contract SBA-7156-OA-83 for the

Office of Advocacy of the U.S. Small Business

Administration.

Social and Scientific Systems, Inc., “Review of

Work Performed During 1984 and Projections for

1985,” Washington, D.C., January 1985. Prepared for the Office of Advocacy of the U.S.

Small Business Administration under contract

number 3-84-6666.

376

Robert F. Teitel, “The Development Process for

the Creation of the SBA Small Business Database

Containing the U.S. Establishment and Enterprise Microdata (USEEM). Prepared under

contract 9182-OA-83 for the Office of Advocacy

of the U.S. Small Business Administration,

September 1984.

U.S. Small Business Administration, Office of

Advocacy, Office of Economic Research, Data

Base Development Division, “Constructing a

Business Microdata Base For The Analysis of

Small 8usiness Activity,” November 1984.

U.S. Small 8usiness Administration, Office of

Advocacy, Office of Economic Research, Data

Base Development Division, “The Derivation of

the U.S. Establishment Longitudinal Microdata

(USELM) File: The Weighted Integrated USEEM

1976-1982 Sample,” December 1984.

B. Research Applications

Faith Ando, “Distribution of Business Loans,

Credit and Investment Capital to Selected

Sub-Categories of Smal1 Business.” Final

report expected November 1985. prepared by tne

JACA Corporation for the Office of Advocacy of

the U.S. Small Business Administration under

contract 6061-OA-82.

Aram Research Associates, “Informal Investor

Survey in the Eastern Great Lakes.” In

Preparation for the Office of Advocacy of the

Us. Smal1 Business Administration under

contract number SBA-7187-OA-83. Final report

expected December 1985.

Jack Faucett, “Procurement Share vs. Industry

Share.” Final report October 1985. Prepared

for the Office of Advocacy of the U.S. Small

Business Administration under contract

S8A-8566-OA-84.

North River Associates, “Small Business Use of

Slack Resources and Service to New and Minor

y9;pts.” Final report expected November

. Prepared under contract number

S8A-7185-OA-83 for the Office of Advocacy of

the U.S. Small Business Administration.

Bruce D. Phillips, Hyder Ali Lakhani, and

Samuel L. George, “The Economics of Metric

Conversion for Small Manufacturing Firms in the

United States.” Technological F~recasting and

Social Change: 25 ), April 84s PP. 109-121.

Bruce D. Phillips, “The Effect of Industry

Deregulation on the Small Business Sector.”

Business Economics: 20(1), January 1985, pp.

28-39.

Willard Risdon, “Developing A Key Financial and

Income Statements Data Base for Veteran @neci

Business.” Final report August 1985. Prepared

for the Office of Veterans’ Affairs of the U.S.

Small Business Administration under contract

#7215-VA-83.
.

David Rothenberg, “i.differences Between Veteran

and Non-Veteran owned Businesses.” Final

report August 1985. Prepared for the Office of

Veterans’ Affairs of the U.S. Small Business

Administration under contract #7215-VA-83.

David Rotnenberg, “Firm Size and Profitability.” Work in progress under contract

86-AER-84-280 for the Office of Advocacy, U.S.

Small Business Administration. Final report

expected January 1986.

Social and Scientific Systems, Inc., “Review of

Work During Last Twelve Months and Work Projection for Next Twelve Months.” Washington,

D.C., March 1984. Prepared for the Office of

Advocacy of the U.S. Small Business Administration under contract no. SBA-4-O-8(a) - C-2136.

II. 1983

A. Methodological Papers and File Oescriptions

Candee Harris, “U.S. Establishment and

Enterprise Microdata Database Description.”

Business Microdata Project, The Brookings

Institution. Funded under contract to the

Sma11 Business Administration, Office of

Advocacy, April, 1983.

Candee Harris, “Comparison of County Busines~

Patterns and USEEM Employment Figures,

Business Microdata Project, The Brookings

Institution. Funded under contract to the

Smal1 Business Administration, Office of

Advocacy, 1983.

Candee Harris, Handbook of Small Business

Data: A Sourcebook for Researchers and PolicyMakers, U.S. Small Business Administration,

Office of Advocacy, August, 1983.

IJavid A. Hirschberg, “The Development of a

Small Business Data Base: A Proqress Report.”

Appendix B of The State of Smal_l Business: A

Report of the President, 9 3)$ PP.

271-301.

Hyder Lakhani, “Preliminary Final Report:

Validity of the SBA’S Master Establishment

List, April, 1983,” prepared by Social and

Scientific Systems, Inc. Funded by the Small

Business Administration.

Marjorie Odle and Catherine Armington,

“Weighting the 1976-80 and the 1978-80 USEEM

Files for Dynamic Analysis of Employment

Growth,” Business Microdata Project, The

Brookings Institution. Funded under contract

to the Small Business Administration, Office of

Advocacy, Revised April 1983.

B. Research Applications

Catherine Armington, “Further Examination of

Recent Sources of Employment Growth: Analysis

of the USEEM Data for 1976-80,” Business

Microdata Project, The Brookings Institution.

Funded under contract to the Small Business

Administration, Office of Advocacy, March 1983.

Maureen C, Glebes, “An Economic Profile of the

State of Indiana,” Office of Advocacy, ~cl 1

Business Administration, Washington, . .,

January 4, 1983.

Thomas A. Gray, with Maureen Glebes and Edward

Starr, “Small Business in the U.S. Economy,”

Chapter 2 in The State of Small Business: A

Report of the president, Washington, O.C., GPO,

~arch 83, PP. 27-58.

Bruce D. Phillips, with William Scheirer,

“Smal1 Business Dynamics and Methods for

Measuring Job Generation.” Chapter 3 in The

State of Small Business: A Report of ~

~resident,” Washington, D.C., GPO, March 1983,

PP. 61-86.

Thomas A. Gray and David L. Hirschberg, “Shifts

in the Employment Status of Proprietors.

1960-1975,” Office of Economic Research, presentation for the Eastern Economics Association, March 10-11, 1983.

Hyder Lakhani, “Econometric Analysis of

Profitability of Finns by Size in Retail Trade

and Service Industries in 1980,” Social and

Scientific Systems, Inc. Final report prepared under contract to the Smal1 Business

Administration, Office of Advocacy, April 1983.

Social and Scientific Systems, Inc., “Financial

Analysis of Firms by Size in Manufacturing,

Services and Retail Trade Industries, 1977-

1981: Final Report.” Prepared under contract

to the Small Business Administration, Office of

Advocacy, February 1983.

III. 1982

A. MethoclologicalPapers ana File Descriptions

Catherine Armington and Marjorie Odle, “small

Businesses -- How Many Jobs?” The Brookings

Review, Winter 1982, prepared under contract to

mall Business Administration.

Candee S. Harris, “A Comparison of Employment

Data for Several Sources of Business Oata:

County Business Patterns, Unemployment

Insurance and U.S. Establishment and Enterprise

Microdata,” Working Paper No. 5, Business

Microdata Project, The Brookings Institution,

Revised March 1982. Prepared under contract to

the Office of Advocacy of the Small Business

Administration.

Richard Hayes, Kevin Hollenbeck, and Marjorie

Odle, “Development of an Enterprise Based

Longitudinal Data File.” The Policy Research

Group, November 1982. Prepared under contract

to the Office of Advocacy, Small Business

Administration.

Bruce D. Phillips, “The Small Business Data

Base and Other Sources of Business Information:

377
g

A Report of the president, Washington, D. C.,

GPO, 1982, Pp. 247-281.

Bruce D. Phillips arm Davia A. Hirschberg,

“Longitudinal Data for Small Business Analysis”

in Development and Use of Longitudinal

Estab~ishment L)ata, U.S. Dept. of Commerce,

Bureau of the Census, Economic Research Report

ER-4, (GPO, 1982) pp. 93-109.

Paul Rose and Linda B. Taylor, “Size ot Employment in SOI: A New Classifier” in U.S. Dept.

of tne Treasury. Inr.ernal Revenue Service.

Statistics of In~ome and Related Administrativ~

Record Research: . . . Department of the

Treasury, Internal Revenue Service, pp. 35-41.

Social and Scientific Systems, Inc., “Preliminary Report on the Development of the Master

Establishment List,” November 2, 1982. Funaed

under contract to the Office of Advocacy, Small

Business Administration.

Social and Scientific Systems, Inc., “Technical

and Analytical Support provided to SBA During

Fiscal year 1982,” November 12, 1982. prePared

under contract for the Office of Advocacy,

Small Business Administration.

Nancy L. Spruill, “Measures of Confidentiality,” Statistics of Income and Related

Administrative Record Research: 1982, U.S.

~epartment of the Treasury, Internal Revenue

Service, 1982, pp. 131-137.

Dun and Bradstreet Financial Statistics File

Papers

Alan Unger, “The Finstat Project phase I:

Descriptive Statistics and Quality Assessment

of Financial Data on the Services Industries.”

Prepared by Group Operations, Inc. , under

contract for tne Office of Advocacy, Small

8usiness Administration, March, 1982.

Applied Systems Institute, “Development and

Implementation of Automated Finstat Imputing

Algorithms Phase I.” Prepared under contract

for the Office of Advocacy, Small Business

Administration, March 1982.

Delta Research Corporation, “Finstat File

Retail Sector (SIC Codes 5200-5999): Editing

and Analysis Report.” Prepared under contract

for the Office of Advocacy, Small Business

Administration, March 1982.

System Sciences Incorporated, “Phase I Final

Report on the Investigation of the Dun and

Bradstreet Finstat File Agricultural Sector.”

prepared under contract for the Office of

Advocacy, Small Business Administration, March

1982.

ESR Associates, “Analysis of Finstat Construction File.” Prepared under contract for the

Office of Advocacy, Small Business Administration, March 1982.

pp

David L. Bircn and Susan McCracken, “The Small

Business Share of Job Creation: Lessons

Learned from the Use of a Longitudinal File.”

MIT Program on Neighborhood and Regional

Change, Cambridge, Mass. Prepared under

contract for the Office of Advocacy, Sma11

Business Administration, November 1982.

Maureen C. Glebes, “Economic Profiles for

Selected States: Connecticut, Florida, Hawaii,

Illinois, New York, Pennsylvania, Tennessee,

Texas, West Virginia, Wisconsin, Wyoming,”

Office of Advocacy, Small Business Administration, November, 1982.

David Hirschberg and Bruce D. Phillips, “Using

Financial Data to Evaluate the Status of Small

Business,” in Statistics of Income and Related

Administrative~ 198 , U.S.

Department of the Treasurv, Internal Revenue

Service, pp. 71-75.

. .

Bruce D, phi]lips, with William Whiston, Alice

Cullen, and David Hirschberq. “Small Business

in the-U.S. Economy,” Chapte~-l in The State of

Small Business: A Report of the President,

‘Washington, ~ . .,

Bruce D. Phillips, with William Whiston, Alice

Cullen, and David Hirschberg, “Current and

Historical Trends in the Smal1 Business

Sector,” Chapter 2 in The State ot Small

Business: A Report of the President,

Washington, U.L., bP(.1,March, 198Z, PP. 53-1U5.

Bruce D. Phillips and William Knight, “Tne

Davis-Bacon Act Reconsidered: A New Small

Business Tax,” The Restructuring Economy:

Implications for Small Finns, Bentley College,

Waltham, Mass., August, 1982, pp. 330-352.

Proceedings of the 1982 Small Business Research

Conferencii.

Bruce D. Phillips and Hyder Laktlani, “A Study

of Profit by Asset Size Class: TWO hypotheses,” Small Business Administration, Office

of Economic Research, draft, September, 1982.

IV. 1981

A. Methodological Papers and File Descriptions

Catherine Armington and Marjorie Odle, “Associating Establishments into Enterprises for a

Microdata File of the U.S. Business Population,” Statistics of Income and Related

Administrative Record ReseaKh, U.S. Department of the Treasury, internal Revenue Service,

1981, pp. 71-76.

Candee Harris, “Creating a Business Data Base

from Dun and Bradstreet Oata Files.” Workin9

Paper No. 3. Business Microdata Project, Tne

Brookings Institution, March 1981. Prepared

under contract for the Office of Advocacy,

Small Business Administration.

378
Bruce A. Kirchhoff and David A. Hirsctlberg,

“Smal1 Business Data Base: Proqress and

Potential,” Statistics ot Income aid

Administrative Record Research, U.S.

ment of the Treasury, Internal Revenue

1981, pp. 61-67.

Relatea

DepartService,

Constance Mitchell, Documentation of the

Employment Imputation for the IUSBDE UsinQ

County Business Patterns Employment

99regates. Business Microdata Project, The

Brookinqs Institution. Januarv 1981. pregared

under contract for the Off;ce of Aavocacy,

Small Business Administration.

Constance Mitchell and Matthew Lynde,

Documentation of the Imputation of B~anch

Records for the IUSBDB Business Microaata

ProJect, The Brookings Institution, July 1981.

Prepared under contract for the Office of

Advocacy, Small Business Administration.

Bruce D. Phillips, “A Comparison of Three

Establishment-Based Data Sources: The Dun and

Bradstreet Market Identifier File, County

Business Patterns, and Unemployment Insurance

(U.I.) Data, 1977-1978.” Draft, Office of

Economic Research, Sma11 Business Administration, March 1981.

Marjorie Odle, Creating an Interim U.S. Business Data Base IUSBDB): Documentation of the

Match Process Linking the Dun and Bradstreet

Data Files, Business Mlcroaata ProJect, The

Brookings Institution, January 1981. Prepared

under contract for the Office of Advocacy,

Small Business Administration.

B. Research Applications

David L. Birch and Susan MacCrack@n, “CorPOrate

Evolution - A Micro-Based Analysis.” MIT

Program on Neighborhood ana Regional Change,

Cambridge, Mass. Prepared for the Office of

Advocacy, Smal1 Business Administration,

January 1981.

Bruce D. Phillips, “Recent Trends in the

Distribution of Employment by Business Size and

Industry,” Statistics of Income and Related

Administrative Record Research, U.S. Oepartment ot he Treasury. Internal Revenue Service,

1981, pp. 77-87. ‘-

v. 1980

Methodological Papers and File Descriptions

Catherine Armington, “The Brookings MultiEstablishment Enterprise File,” Working PaPer

No. 1, Business Microaata Project, The

Brookings Institution, August 1980. Preparea

under contract for the OffIce of Advocacy,

Small Business Administration.

Maureen C. Glebes, “An Economic Profile of the

State of Missouri,” Office of Advocacy, Small

Business Administration, Washington, D.C.,

November 2, 1980.

Maureen C. Glebes, “An EconcnnicProfile of the

State of New Hampshire,” Office of Advocacy,

Smal1 Business Administration, Washington,

D.C., October 26, 1980.

VI. 1979

Methodological Papers and File Descriptions

David L. Hirschberg and Vernon Renshaw, “Access

to Administrative Records on Establishments and

Individuals for Public Policy Analysis,” Bureau

of Economic Analysis, draft, 1979, prepared tor

the 1979 Annual Meetings of the American Statistical Association.

379
BIBLIOGIGWHY OF METHODOLOGICALTECHNIQUES RELATED TO EXACT MATCHING

Compiled through 1984 by

Wray Smith, Harris-Smith Research, Inc.

This supplementary bibliography provides

references to sources that are not found in most

lists of publications in the field of exact

matching but which may be useful to the

investigator who is looking for additional tools,

especially in the areas of contingency table

techniques, cluster analysis, pattern recognition,

and sequence comparison theory.

AITCHISON, J. and DUNSMORE, 1.:1.,dg~1975),

Statistical Prediction Analysis. UK,

and New York, NY: Cambridge University Press:

BARR, A. and FEIGENBAUM, E.A., eds. (1982), The

Handbook of Artificial Intelligence: Volume II.

LOS Altos, CA: William Kaufmann, Inc.

BARR, R.S. and TURNER, J.S. (1982), “Microdata

File Merging Through Large-Scale Network

Techniques,” in D. Klingman and J. Mulvey, eds.,

Network Models and Associated Applications.

Amsterdam: North-Holland Publ. Co.

BELLMAN, R. (1961), Adaptive Control Processes: A

Guided Tour. Princeton: Princeton University

Press.

BERGER, J.O. and WOLPERT, R.L. (1984), The

Likelihood Principle: A Review, Generalizations,

and Statistical Implications. IMS Lecture Notes -

Monograph Series, Vol. 6. Hayward, CA: Inst. of

Math. Statistics.

BISHOP, Y.M.M.

Multidimensional

27, 545-562.

BISHOP, Y.M.M.,

(1975), Discrete

(1971), ItEffects of Collapsing

Contingency Tables,’t Biometrics,

FEINBERG, S.E., and HOLLAND, P.W.

Multivariate Analysis: Theory and

Practice, Cambridge, MA: MIT Press.

BOURNE, C.P. and FORD, D.F. (1961), “A Study of

Methods for Systematically Abbreviating English

Words and Names,” Journal of the Association for

Computing Machinery, 8, 538-552.

BREIMAN, L., FRIEDMAN,J., OLSHER, R. and STONE,

C. (1984), Classification and Regression Trees.

Belmont, CA: Wadsworth International Group.

CHVATAL, V. and SANKOFF, D. (1975), l~Longest

Common Subsequences of Two Random Sequences,”

Journal of Applied Probability, 12, 306-315.

CORMACK, R.M. (1971), !1A Review of

Classification,l!Journal of the Royal Statistical

Society, Ser. A, 134, 321-367.

COWAN, C.D. and FAY, R.E., III (1984), “Estimates

of Undercount in the 198o Census,” 1984

Proceedings of the American Statistical

Association, Section on Survey Research Methods.

DEKEN, J. (1979), !~someLimit Results for Longest

Common Subsequences,” Discrete Mathematics, 26,

17-31.

DEMPSTER, A.P., LAIRD, N.M., and RUBIN, D.B.

(1977), !r~ximum Likelihood From Incomplete Data

via the EM Algorithm,” Journal of the Royal

Statistical Society, Ser. B, 39, 1-38.

DOLBY, J.L. (1970), !IsomeStatistical Aspects of

Character Recognition,“ Technometrics, 12,

231-245.

DUBOIS, D. and PRADE, H. (1980), Fuzzy Sets and

Systems: Theory and Applications. New York:

Academic Press.

DURAN, B.S. and ODELL, P.L. (1974), Cluster

Analysis: A Survey. (Lecture Notes in Economics

and Mathematical Systems, 100. Operations

Research.) New York: Springer-Verlag.

EVERITT, B. (1974), Cluster Analysis. London:

Heinemann.

EVERITT, B. and HAND, D. (1981), Finite Mixture

Distributions. London: Chapman and Hall.

FATTI, L.P. (1983), “The Random-Effects Model in

CliscriminantAnalysis,” Journal of the American

Statistical Association, 78, 679-687.

FIENBERG, S.E. (1977), The Analysis of

Cross-Classified Categorical Data. Cambridge, MA:

MIT Press.

FOWLKES, E.B. and MALLOWS, C.L. (1983), “A Method

for Comparing Two Hierarchical Clusterings” (with

discussion), Journal of the American Statistical

Association, 78, 553-584.

GOLDSTEIN, M. and DILLON, W.R. (1978), Discrete

Discriminant Analysis. New York: Wiley.

GORDON, A.D. (1981), Classification. London:

Chapman and Hall.

GUPTA, M.M., RAGADE, R.K., and YAGER, R.R., eds.

(1979), Advances in Fuzzy Set Theory and

Applications. Amsterdam: North-Holland Publ. Co.

HALL, P.A.V. and DOWLING, G.R. (1980),

“Approximate String Matching,” Computing Surveys,

12, 381-402.

HAND, D.J. (1981), Discrimination and

Classification. New York: Wiley.

HARTIGAN, J.A. (1975), Clustering Algorithms. New

York: Wiley.

HARTIGAN, J.A. (1978), tlAsyrnptotic DistributiOne.

for Clustering Criteria,w Annals of Statistics, 6,

117-131.

381
NY: Chapman and Hall.

JAZWINSKI, A.H. (1970), Stochastic Processes and

Filtering Theory. New York: Academic Press.

KANDEL, A. (1982), FUZZY Sets and Pattern

Recognition. New York: Wiley-Interscience.

KRISHNAIAH, P. and KANAL, L., eds. (1982),

Handbook of Statistics, Vol. 2: Classification,

Pattern Recognition, and Reduction of

Dimensionality. New York: ElsevierlNorth-Holland.

KU, H.H., VARNER, R.N. and KULLBACK, S. (1971),

~~onthe Analysis of Multi-dimensional Contingency

Tables,” Journal of the American Statistical

Association, 66, 55-64.

LANCASTER, H.O. (1969), The Chi-squared

Distribution. New York: Wiley.

LITTLE, R.J.A. (1976), !!InferenceAbout Means from

Incomplete Multivariate Data,” Biometrika, 63,

593-6o4.

MATHAI, A.M. and RATHIE, P.N. (1975), Basic

Concepts in Information Theory and Statistics:

Axiomatic Foundations and Applications. New York:

Halsted Press/Wiley.

NEGOITA, C.V. and RALESCU, D.A. (1975),

Applications of Fuzzy Sets to Systems Analysis.

New York: Wiley.

OJA, E. (1983), Subspace Methods for Pattern

Recognition. New York: Wiley (Research Studies

Press).

OKNER, B.A. (1974), !rDataMatching and Merging: An

Overview,!! Annals of Economic and Social

Measurement, 2, 347-352.

OKUDA, T., TANAKA, E. and KASAI, T. (1976), “A

Method for the Correction of Garbled Words Based

on the Levenshtein Metric,” IEEE

Computers, C25, 172-177.

RODGERS, W.L. (1984), llAn

Statistical Matching,” Journal

Economic Statistics, 2, 91-102.

Transactions on

Evaluation of

of Business &

(with discussion), Bi,ometrika,63, 581-592.

RUKHIN, A.L. (1984), ‘fAdaptive Classification

Procedures,” Journal of the American Statistical

Association, 79, 415-422.

SANKOFF, D. and KRUSKAL, J.B., eds. (1983), Time

Warps, String Edits, and Macromolecules: The

Theory and Practice of Sequence Comparison.

Reading, MA: Addison-Wesley,

SEBER, G.A.F. (1984), Multivariate Observations.

New York: Wiley.

SELLERS, P.H. (1980) “The Theory and Computation

of Evolutionary Distances: Pattern Recognition,”

Journal of Algorithms, 1, 359-373.

SHAFFER, J.P. (1981), !!Complexity: An

Interpretability Criterion for Multiple

Comparisons,!!Journal of the American Statistical

Association, 76, 395-401.

ULAM, S.FI. (1972), I!Some Combinatorial Problems

Studied Experimentally on Computing Machines,” in

Applications of Number Theory to Numerical

Analysis, ed. S.K. Zaremba, New York: Academic

Press, 1-3.

VAN RYZIN, J., ed. (1977), Classification and

Clustering. New York, NY: Academic Press.

WAGNER, R.A. and FISCHER, f.1.J. (1974), ‘fThe

String-to-String Correction Problem,” Journal of

Association for Computing Machinery, 21, 168-173.

WEGMAN, E.J. and SMITH, J.G. (1984), Statistical

Signal Processing. New York: Marcel Dekker.

WHITE, D.J. (1969), Dynamic Programming. San

Francisco: Holden-Day.

WOLFF, D. and PARSONS, M.L. (1983), Pattern

Recognition Approach to Data Interpretation. New

York: Plenum Press.

WOODWARD, W.A., PARR, W.C., SCHUCANY, W.R., and

LINDSEY, H. (1984), “A Comparison of Minimum

Distance and Maximum Likelihood Estimation of

Mixture Proportions,” Journal of the American

Statistical Association, 79, 590-598.

382
Appendix B:

Workshop Particulars —

Workshop Program

List of Attendees

List of Sponsors

.-.4 /
THE WASHINGTON STATISTICAL SOCIETY

AND THE

FEDERAL COMMITTEE ON STATISTICAL METHODOLOGY

Workshop on Exact

Matching Methodologies

MAY 9-10, 1985

ROSSLYNWESTPARK HOTE

1900 1!,FORT MYER DRI:

ARLINGTON,VIRGINIA ,

;E

Z2209

-—
WORKSHOP ON

AGENDA

EXACT MATCHING METHODOLOGYES

May 9-10, 1985

Sponsored by

The Washington Statistical Society

and the

Federal Committee on Statistical Methodology

THURSDAY, MAY 9, 1985

8:30 a.m. - 9:30 a.m. Promenade, 2nd Floor

REGISTRATION/COFFEE

9:30 a.m. - 11:30 a.m. Rosslyn A, 2nd Floor

OVERVIEW OF APPLICATIONS AND INTRODUCTION TO THEORY

Chair: FRITZ SCHEUREN, Internal Revenue Service

( 9:45) “Tutorial on the Fellegi-Sunter Model for Record Linkage,” IVAN

FELLEGI, Statistics Canada

(10:15) “Why Are Epidemiologists Interested in Matching Algorithms?”

GILBERT W. BEEBE, National Cancer Institute

(10:45) “Exact Matching: The Products of Record Linkage -- Productive,

Misleading, and Otherwise,” ROBERT F. BORUCH, Northwestern

University, and ERNST STROMSDORFER, Washington State University

(11:15) Floor Discussion

11:30 a.m. - 1:00 p.m.

LUNCHEON (Prepaid Event)

Westpark Cafe, 1st Floor

387

.—
WORKSHOP ON EXACT MATCHING METHODOLOGIES

THURSDAY, MAY 9, 1985 -- continued

1:00 p.m. - 3:00 p.m. Rosslyn A, 2nd Floor

CURRENT THEORY AND PRACTICE

Chair: THOMAS B. JABINE, Consultant, Committee on National Statistics

(1 :10) “Multiple Linkage and Measures of Inexactness:Methodology

Issues,” WRAY SMITH, MathematicalPolicy Reseamh, and FRITZ

SCHEUREN, Internal Revenue Service

(1:25) “An Information Theoretic Approach to Weights in Computer

Matching,” NANCY I(IRKENDALL,Energy Information Administration

(1:40) “Advances in Record Linkage Methodology: A Method for Determining

the Best Blocking Strategy,” PATRICK KELLEY, Bureau of the Census

(1:55) “Preprocessing of Lists and Substring Comparison,” WILLIAM E.

WINKLER, Energy Information Administration

(2:10) Discussant: BENJAMINTEPPIIIJG,Westat, Inc.

(2:25) Discussant: ELI MARKS, Consultant

(2:40) Floor Discussion

3:00 p.m. - 3:30 p.m. Promenade, 2nd Floor

COFFEE BREAK

3:30 p.m. - 5:00 p.m. Rosslyn A, 2nd Floor

APPLICATION CASE STUDIES I

Chair: MARIA ELENA GONZALEZ, Federal Committee on Statistical Methodology

(3:35) “The National Death Index Experience: 1982-1985,” JOHN E. PATTERSON

and ROBERT BILGRAD, National Center for Health Statistics

(3:50) “An Implementationof a Two-Population Fellegi-Sunter Probability

Linkage Model,” MAX G. ARELLANO, University of California, San

Francisco

(4:05) “Deriving Labor Turnover Rates from Administrative Records,”

MALCOLM S. COHEN, University of Michigan

(4:20) Discussant: NORMAN JOHNSON, U.S. Bureau of the Census

(4:35) Floor Discussion

5:00 p.m. - 7:00 p.m. Dogwood, 2nd Floor

INFORMAL RECEPTION (With Cash Bar)

388
FEDERAL

Members of the

COMMITTEE (M STATISTICAL METNOOOLOGY

(May19B5)

Maria Elena Gonzalez (Chair)

Office of Mamgement and Budget

Barbara Bailar

Bureau of the Census

Norman Beller

National Center for Education Statistics

Yvonne Bishop

Energy Information Administration

Edwin Coleman

Bureau of Economic Analysis

John Cremeans

Bureau of Industrial Economics

Zahava Doering

Defense Manpower Data Center

Daniel Garnick

Bureau of Economic Analysis

C. Terrence Ireland

Department of Defense

Charles Jones

Bureau of the Census

397

*U.S. GOVERNMENT PRINTING OFFICE: 1986-491.487:45034

Daniel Kasprzyk

Bureau of the Unsus

William Kfbler

Department of Agriculture

David Pleme

Federal Resewe Bank

Thomas Plews

Bureau of Labor Statlstlcs

Jane Ros$

Social Security Atiinistration

Fritz Scheuren

Internal Revenue $ewice

MonPoe Sirken

Natfonal Center for Nealth Statistics

Thomas Staples

Socfal $acurfty A&inistration

Robert Tortor8

Department of Agriculture

—.

.-
WORKSHOP ON EXACT MATCHING METHODOLOGIES

FRIDAY, MAY 10, 1985

8:30 a.m. - 9:00 a.m. Promenade, 2nd Floor

COFFEE

9:00 a.m. - 10:30 aom. Rosslyn A, 2nd Floor

APPLICATION CASE STUDIES II

Chair: DANIEL H. GARNICK, Bureau of Economic Analysis

(9:05) “The 1979 Partnership and Sole Proprietorship Employment and

Payroll Link Studies,” NICK GREENIA, Internal Revenue Service

(9:20) “Creating the Small Business Administration’sMaster Establishment

List,” DAVE HIRSCHBERG, Small Business Administration

(9:35) “Enhancing Data from the Survey of Income and Program Participation

with Economic Data,” DOUGLAS K. SATER, Bureau of the Census

(9:50) Discussant: JOSEPH STEINBERG, Survey Design, Inc.

(10:05) Floor Discussion

10:30 a.m. - 10:45 a.m. Promenade, 2nd Floor

COFFEE BREAK

10:45 a.m. - 12:15 p.mo Rosslyn A, 2nd Floor

COMPUTER SOFTWARE AVAILABLE

Chair: HERB MILLER, Social and Scientific Systems, Inc.

(10:50) “Project LINK-LINK: An Interactive Database of Administrative

Record Linkage Studies,” JANE L. CRANE, National Center for

Education Statistics, and DOUGLAS G. KLEWENO, Department of

Agriculture

(11:05) “Design and Implementationof a Generalized Record Linkage System,”

MATTHEW JARO, U.S. Bureau of the Census

(11:20) “Recordkeepingand Data Preparation Practices to Facilitate Record

Linkage,” MARTHA E. SMITH, Statistics Canada

(11:35) Discussant: TED HILL, Statistics Canada

(11:50) Floor Discussion

389
WORKSHOP ON EXACT MATCHING METHODOLOGIES

FRIDAY, MAY 10, 1985 -- continued

12:15 p.m. - 1:30 p.m.

LUNCH BREAK

1:30 p.m. - 4:30 p.m.

COMPUTER SOFTWARE WORKSHOP (Three Continuous Concurrent Sessions)

(1:30)

(2:30)

(3:30)

;;::;;

(3:30)

(1:30)

(2:30)

(3:30)

4:30 p.m.

ADJOURN

JANE L. CRANE, National Center Rosslyn A, 2nd Floor

for Education Statistics,

will demonstrate LINK-LINK.

MATTHEW JARO, Bureau of the Gnsus, Rosslyn A, 2nd Floor

will demonstrate the Census Bureau’s

Matching System.

TED HILL, Statistics Canada, will Shenandoah A, 2nd Floor

present a seminar on Statistics

Canada’s Generalized Iterative

Record Linkage System (GIRLS).

390
Llsl OF A1-IENUEES

WORKSt@P ON EXACT 14ATCtiINLltiTtIOUOLklLS

MAY 9-lU, 1985

Ralph Bristol

Department of the Treasury

James Alder

Bureau of the Census

Warren L. Buckler

Social Security Administration

Lois Alexander

Social Security Administration

Paul Burke

Department of tiousingand Urban Development

Bernard Altschuler

Bureau of Labor Statistics

Willian P. Butz

Bureau of the Census

Wendy L. Alvey

Internal Revenue Service

Charles Byte

The Brookings Institution

Keith Mnick

Bureau of the Census

Helen Choi

Internal Revenue Service

John C. Angle

Internal Revenue Service

IlalcolrnS. Cohen

University of Michigan

Jonathan G. Ankers

Bureau of the Census

SartlArcangeli

State of Florida

Sherry Courtland

Bureau of the Census

l~laxArellano

University of California

Charles Cowan

Bureau of the Census

Catherine Armington

Applied System Institute

Lawrence H. Cox

Bureau of the Census

Barbara Bailar

Bureau of the Census

Pat Crabbe

Internal Revenue Service

Erma Barren

Social Security Administration

Jane Crane

National Center for Education Statistics

Lester CUrtin

National Center for health Statistics

Gilbert Beebe

National Cancer Institute

Richard Bell

Social Security Administration

John Czajka

Mathenatica Policy Research, Inc.

William Bennett

Bureau of Labor Statistics

Robert Dalryrnple

Office of Analysis and Evaluation

IdaryBentz

Internal Revenue Service

Ramesh Dandekar

Energy Information Administration

Charles Day

Internal Revenue Service

Paul P. Biemer

Bureau of the Census

Robert Bilgrad

National Center for Health Statistics

Linda DelBene

Social Security Administration

Charles Eastlack

Westat, Inc.

Robert F. Boruch

Northwestern University

Alan Eck

Bureau of Labor Statistics

Chet Bowie

Bureau of the Census

James E. Bozik

Bureau of the Census

biarleneEinstein

Bureau of Labor Statistics

Frank Elsen

Energy Information Administration

Bertie Brame

Internal Revenue Service

391

.-
Ivan P. Fellegi

Statistics Canada

Kathleen Jablonski

Capital System Group

Wayne Finegar

Social Security Administration

l.latthewJaro

Bureau of the Census

Barbara Garner

Bureau of the Census

Robert Jewett

Bureau of the Census

Daniel H. Garnick

Bureau of Economic Analysis

Norman Johnson

Bureau of the Census

Tomy W. (iaulden

Bureau of the Census

David Judkins

Bureau of the Census

William Gerber

Internal Revenue Service

Kenneth Kaplan

Bureau of the Census

Claudia Glover

Bureau of the Census

l,~ronKatzoff

Bureau of the Census

Patrick Kelley

Bureau of the Census

I.lariaE. Gonzalez

Office of Management and Budget

Berdj Kenadjian

Internal Revenue Service

Mildred Gray

University of the District of Colunbia

Bertran M. Kestenbam

Social Security Administration

Beth Kilss

Internal Revenue Service

Wayne B. Gray

National Bureau of Economic Research

Brian Greenberg

Bureau of the Census

Nancy Kirkendall

Energy Information Administration

Nick Greenia

Internal Revenue Service

Douglas Kleweno

Department of Agriculture

Richard Wess

University of Utah

Lynn Kuo

Department of Agriculture

Lisa Gross

Internal Revenue Service

Jim tlarte

Internal Revenue Service

Enrique Lams

Bureau of tileCensus

Willian LaPlant

Bureau of tileCensus

Beth Hill

Bureau of the Census

Ted Hill

Statistics Canada

Terry Lotz

University of Utah

Dr. Joseph L. Lyon

University of Utah

David Hirschberg

Small Business Ackninistration

Eli S. Harks

Consultant

K. P. Ho

Atomic Energy Control Board

Paul tiolland

Educational lesting Service

Reggie D. Hasano

Bureau of the Census

Carlyle haw

National Center for Education Statistics

Geoffrey howe

University of Toronto

Terry Ireland

Department of Defense

Jim lkBride

Response Analysis Corporation

Philip llcClain

Centers for Disease Control

Richard Irwin

Bureau of the Census

Nelson llcClung

Department of Treasury

Thmas Jabine, Consultant

Comittee on National Statistics

392
Robert J. Nclntire

Bureau of Labor Statistics

Douglas K. Sater

Bureau of the Census

Fritz Scheuren

Internal Revenue Service

Nichael L. Idersch

Bureau of the Census

Mary Ellen Schiller

Social Security Administration

M. P. Ni

University of Hawaii

JackSchmulowitz

Social Security Administration

Herbert J. Miller

Social & Scientific Systems, Inc.

Deborah Moore

Bureau of the Census

I.larvinL. Schwartz

Internal Revenue Service

Jeffrey Moore

Bureau of the Census

Sidney Schwartz

Bureau of the Census

Ann J. Nakamura

Revenue Canada Taxation

Daniel F. Skelly

Internal Revenue Service

Dwaine Nelson

Department of Agriculture

hlarthaSmith

Statistics Canada

Elizabeth Nelson

internal Revenue Service

Wray Smith

MathematicalPolicy Research

Karen V. O’Conor

Bureau of Labor Statistics

Joseph Steinberg

Survey Design, Inc.

H. Lock Oh

Internal Revenue Service

B. J. Stone

National Cancer Institute

Bill Page

National AcadeW of Sciences

Tim H. Tang

Pacific Gas & Electric Company

David P. Paris

Internal Revenue Service

Benjamin Tepping

Westat, Inc.

John Patterson

National Center for Health Statistics

Kathryn Thonas

Bureau of the Census

John D. Pearson

Energy Information Administration

John Thompson

Bureau of the Census

Thomas Petska

Internal Revenue Service

Wendel L. Thompson

Energy Information Administration

Henry Power

Department of Agriculture

James Tonascia

Johns IlopkinsSchool of Hygiene & Public health

D. Dean Prochaska

Bureau of the Census

Barbara Tyler

Social Security Administration

Judith N. Rayner

Mestat, Inc.

Carol M. Utter

Bureau of Labor Statistics

Eugene Rogot

National Iieart, Lung, and Blood Institute

Richard Wehrly

Social Security Administration

Steve Repel

Department of Agriculture

Oliver Milson

Internal Revenue Service

Jeffrey Roth

National Research Council

Robert Uilson

Internal Revenue Service

Peter Sailer

Internal Revenue Service

Barry R. Windheim

Internal Revenue Service

William E. Winkler

Energy information Administration

Jean Salter

Bureau of Economic Analysis

393
President

g 

Board of Directors

WASHINGTON STATISTICAL SOCIETY

(1984-1985)

C. Terrence Ireland

Department of Defense

President Elect

Mary Grace Kovar

National Center for Health Statistics

Past *resident

Maria Elena Gonzalez

Office of Manawnent and Budget

Secretarv

Rich Allen

Department of Agriculture

Treasurer

Bennie Clemmer

Social Security Administration

Representatives-at-Large

Daniel Kasprzyk

Bureau of the Census

Nancy Kirkendall

Energy Information Administration

Susan Miskura

Bureau of the Census

Richard Taeuber

National Center for Education

Statistics

Methodology Section Chair

Michael Cohen

National Academy of Sciences

Representative of the ASA Board

Jerry Coffey

Office of Management and Budget

Representative to Council on Chapters

Don King

King Research, Inc.

395

—–. —. .