[GRAPHIC] \SW6CVR.GIF
Statistical Policy
Working Paper
Report on
Statistical Uses Of
Administrative Records
Prepared by
Subcommittee on Statistical Uses of Administrative Records
Federal Committee on Statistical Methodology
U.S. DEPARTMENT OF COMMERCE
Philip M. Klutznick, Secretary
Luther H. Hodges, Jr., Deputy Secretary
Courtenay M. Slater, Chief Economist
Office of Federal Statistical Policy and Standards
Joseph W. Duncan, Director
Issued:   December 1980
Statistical Policy Working Papers are a series of technical
documents prepared under the auspices of the Office of Federal
Statistical Policy and Standards.  These documents are the product
of working groups or task forces, as noted in the Preface to each
report.
These Statistical Policy Working Papers are published for the
purpose of encouraging further discussion of the technical issues
and to stimulate policy actions which flow from the technical
findings and recommendations.  Readers of Statistical Policy
Working Papers are encouraged to communicate directly with the
Office of Federal Statistical Policy and Standards With additional
views, suggestions, or technical concerns.
Office of                                                 W. Duncan
Federal Statistical                                        Director
Policy and Standards
           For sale by the Superintendent of Documents,
                  U.S. Government Printing Office
                      Washington, D.C. 20402
                   Office of Federal Statistical
                       Policy and Standards
                    Joseph W. Duncan, Director
     Katherine K. Wallman, Deputy Director, Social Statistics
      Gaylord E. Worden, Deputy Director, Economic Statistics
                  Maria E. Gonzalez, Chairperson,
               Committee on Statistical Methodology
                              Preface
      This working paper was by the members of the Subcommittee on
Statistical Uses of Administrative Records, Committee on
Statistical Methodology.  The Subcommittee was chaired by Daniel H.
Garnick, Bureau of Economic Analysis, Department of Commerce.  The
members of the subcommittee are the authors of this report, their
names are listed below.
     The first portion of this report provides a review of major
administrative report files pertaining to individuals and to
businesses.  Major statistical uses of administrative records are
outlined, including: (1) direct use of the records to obtain
statistics and to supplement existing data via expanding coverage
or content; and (2) technical uses of the data for constructing
sampling frames, quality control, improving on procedures, and data
evaluation.  New developments in data from business establishment
reporting and a number of potential uses of administrative records
for data linkage are described.  Technical problems in the
statistical use of administrative records, including coverage,
comparability, error and timing of data are discussed.  the final
section of the report covers various in accessing administrative
records for statistical purposes.
     While much statistical use of administrative records is
currently made in Federal agencies, this report is intended to
inform managerial and technical staffs of the vast potential as
well as difficulties entailed in augmenting current uses of
administrative records for statistical purposes.  The Office of
Statistical Policy and Standards hopes to organize, with the help
of Subcommittee members, seminars with Federal employers to
disseminate the findings of this report. The implementation of the
recommendations in report will be explored by the Office of
Statistical Policy and Standards.
            Members of the Subcommittee on Statistical
                  Uses Of Administrative Records.
                            (June 1980)
Daniel H. Garnick* (Chair)
Bureau of Economic Analysis (Commerce)
Lois Alexander
Social Security Administration (HHS)
Paul A. Armknecht
Bureau of Labor Statistics (Labor)
David V. Bateman
Bureau of the Census (Commerce)
Lawrence A. Blum
Bureau of the Census (Commerce)
Warren L. Buckler
Social Security Administration (HHS)
David W. Cartwright
Bureau of Economic Analysis (Commerce)
John DiPaolo
Internal Revenue Service (Treasury)
Maria E. Gonzalez* (ex officio)
Office of Federal Statistical Policy & Standards (Commerce)
John A. Gorman
Bureau of Economic Analysis (Commerce)
David A. Hirshberg
Small Business Administration
Beth A. Kilss
Social Security Administration (HHS)
J. Knott
Bureau of the Census (Commerce)
Bruce Levine
Bureau of Economic Analysis (Commerce)
Nash J. Monsour
Bureau of the Census (Commerce)
Allan Olson
Economic Development Administration (Commerce)
Elizabeth H. Queen
Bureau of Economic Analysis (Commerce)
Vernon Renshaw
Bureau of Economic Analysis (Commerce)
Fritz J. Scheuren*
Social Security Administration (HHS)
Daniel F. Skelly
Internal Revenue Service (Treasury)
Hyman Steinberg
U.S. Postal Service
Additional Contributors to the Report on Statistical Uses of
Administrative Records
Jeanne E. Griffith
Office of Statistical Policy and Standards (Commerce)
Daniel Kasprzyk
Assistant Secretary for Planning and Evaluation (HHS)
Susan Miskura
Bureau of the Census (Commerce)
* Member, Committee on Statistical Methodology
                                ii
                          Acknowledgments
The body of this report is the collective effort of the
Subcommittee on Statistical Uses of Administrative Records.
Although the subcommittee members reviewed and commented on all
parts of this report, specific individuals were responsible for
preparing the various sections.  In the case of Chapter VI, the
subcommittee benefitted from the expertise and contribution of
several additional persons in preparing the case studies.  The
authors of the chapters appear below:
Chapter                       Authors
I         Daniel Garnick, Maria Gonzalez, Vernon Renshaw, Lois
          Alexander, David Hirschberg, Fritz Schuren
II        Vernon Renshaw, David Hirschberg, Daniel Garnick
III       Joseph Knott, Lawrence Blum, Waken Buckler, Vernon
          Renshaw, Fritz Scheuren
IV        Vernon Renshaw, David Cartwright, Nash Monsour, Lawrence
          Blum, John Gorman, Daniel Skelly, John DiPaolo, Warren
          Buckler, Elizabeth Queen
V         Lawrence Blum, Paul Armknecht, Warren Buckler, David
          Cartwright, Vernon Renshaw
VI        Fritz Scheuren, Beth Kilss, Jeanne Griffith, Daniel
          Kasprzyk, David Bateman, Sue Miskura, Maria Gonzalez
VII       David Cartwright, Vernon Renshaw, Bruce Levine, Warren
          Buckler, Fritz Scheuren
VIII      Lois Alexander
     Maria Gonzalez worked with the subcommittee throughout its
two-year study.  Members of the Federal Committee on Statistical
Methodology and the Office of Statistical Policy and Standards
provided additional assistance and encouragement.  Critical reviews
of earlier draft versions by Thomas Jabine, Barbara Bailar, and
Tore Dalenius were particularly helpful in the development of this
report.
     Discussion by Richard Ruggles on papers by Daniel Garnick and
Joseph Knott, David Cartwright and Paul Armknecht, David Hirschberg
and Vernon Renshaw, and Lois Alexander at the Statistical Uses of
Administrative Records Session of the 1979 American Statistical
meetings aided in sharpening the focus of this report.
     Others who contributed to the work of the Subcommittee
include: Yoshio Akiyama, Leroy Bailey, Robert Berney, J. Robert
Brown, Morris M. Kleiner, Lillian Madow, Harriet Orcutt, and Max
Shor.
                                iii
                Members of the Federal Committee on
                      Statistical Methodology
                            (June 1980)
Maria Elena Gonzalez (Chair)
Office of Federal Statistical Policy and Standards (Commerce)
Barbara A. Bailar
Bureau of the Census
Norman D. Beller
Economics, Statistics, and Cooperatives Service (Agriculture)
Barbara A. Boyes
Bureau of Statistics
Edwin J. Coleman
Bureau of Economic Analysis (Commerce)
John E. Cremeans
Bureau of Economic Analysis (Commerce)
Marie D. Eldridge
National Center for Education Statistics (Education)
Daniel H. Garnick
Bureau of Economic Analysis (Commerce)
Thomas B. Jabine
Energy Information Administration (Energy)
Charles D. Jones
Bureau of the Census (Commerce)
William E. Kibler
Economics, Statistics, and Cooperatives Service (Agriculture)
Alfred D. McKeon
Bureau of Labor Statistics (Labor)
Raymond C. Sansing
Internal Revenue Service (Treasury)
Fritz J. Scheuren
Social Security Administration (HHS)
Lincoln E. Moses
Energy Information Administration (Energy)
Monroe G. Sirken
National Center for Health Statistics (HHS)
Wray Smith
Office of the Assistant Secretary for Planning and Evaluation (HHS)
Thomas G. Staples
Social Security Administration (HHS)
                                iv
                         Table of Contents
                                                               page
Preface                                                           i
Acknowledgments                                                 iii
List of Figures                                                  ix
List of Tables                                                    x
Abbreviations                                                    xi
Chapter I.     Findings and Recommendations                       1
     A.   Statistical Standards                                   1
     B.   Access                                                  2
     C.   Other Government-Wide Program Coordination and Support  3
Chapter II.    Introduction and Summary                           5
     A.   Introduction                                            5
     B.   Summary                                                 6
          1.   Chapter III                                        6
          2.   Chapter IV                                         7
          3.   Chapter V                                          8
          4.   Chapter VI                                         8
          5.   Chapter VII                                        8
          6.   VIII                                               9
Chapter III.   Major Administrative Record Files                 11
     A.   Scope of Study and Survey Conducted                    11
          1.   Scope of Study                                    11
          2.   Survey Conducted                                  12
     B.   Survey Results                                         12
          1.   Files Pertaining Mainly to Individuals            12
               a.   Universe                                     12
               b.   Geographic Information                       17
               c.   Demographic Information                      17
               d.   Reporting Unit                               17
          2.   Files Pertaining Mainly to Businesses             18
               a.   Universe                                     18
               b.   Geographic Information                       18
               c.   Economic Data                                18
               d.   Reporting Unit                               18
     C.   Continuous Work History Files                          18
     D.   The Evolution of Statistical Uses of Administrative
          Records                                                19
     E.   Appendix III.1 The Survey Questionnaire                20
     F.   Appendix III.2 The CWHS Data System                    23
          1.   Data Sources                                      23
          2.   Processing Procedures - Administrative Records    23
          3.   Processing Procedures - Statistical Records       24
          4.   Sample Design                                     24
          5.   Data Files                                        25
               a.   One percent Sample Annual Employee-Employer
                    (Ee-Er) File                                 25
               b.   One percent Sample Annual Self-Employed (SE)
                    File                                         25
                                 v
                                                               page
               c.   One percent Sample Longitudinal Employee-
                    Employer Data (LEED) File                    25
               d.   One percent 1937 to Date CWHS File           26
               c.   One-Tenth of One percent 1937 to Date CWHS
                    File                                         26
Chapter IV.    Major Statistical Uses of Administrative          27
     A.   Defining Administrative and Using Them Statistically   27
     B.   Internal Revenue Service                               28
     C.   Social Security Administration                         29
     D.   Bureau of Economic Analysis                            29
     E.   Census Bureau                                          32
          1.   Economic Censuses                                 32
          2.   Census Of Agriculture                             33
          3.   Survey of Minority-Owned Businesses (SMOBE)       33
          4.   Current Economic Indicators                       33
          5.   The Standard Statistical Establishment List       34
     F.   The Small Business Administration                      34
     G.   Appendix IV.1 Data from IRS and SSA                    35
          1.   Data from IRS                                     35
          2.   Data from SSA                                     39
Chapter V.     Developments in Data from Business Establishment
               Reporting                                         43
     A.   Standard Statistical Establishment list                43
          1.   File Construction                                 44
          2.   Multiestablishment Firms                          44
          3.   Single Establishment Firms                        44
          4.   File Maintenance                                  45
          5.   Confidentiality                                   45
     B.   W-2 and W-3 Records                                    45
     C.   Unemployment Insurance System                          47
          1.   Master List of Employers                          47
          2.   Employers' Quarterly Tax Report                   47
          3.   Individual Wage Records                           49
          4.   Improving Data Quality                            49
Chapter VI.    Potential Uses of Administrative Records for Data
               linkages: Selected Case Studies                   51
     A.   Introduction                                           51
     B.   Case Study 1:  Linked Administrative Statistical Sample
                         (LASS) Project                          51
          1.   Background and Initial Project Goals              52
               a.   LASS Data Elements                           52
               b.   LASS Goals                                   53
          2.   Pilot Activities and Feasibility Issues           53
               a.   Resolving Privacy Concerns                   53
               b.   Examining SSA-NCHS Death Reporting Differences54
               c.   Adding Data From Death Certificates to the
                    CWHS                                         54
               d.   Usability of IRS Occupation Information      54
               c.   Upgrading CWHS Industry and Place of Work Data55
               f.   Evaluating W-2 Residence Data                55
          3.   Operational Implementation Issues                 56
          4.   References                                        56
     C.   Case Study 2:  The Use of Administrative Records in the
                         Survey of Income and Program
                         Participation                           57
          1.   Objectives and Description                        58
                                vi
                                                               page
               a.   Site Research                                58
               b.   1978 Panel                                   59
               c.   1979 Panel                                   61
          2.   Major Difficulties                                61
          3.   Uses of Administrative Files                      62
          4.   Quality of Results                                63
          5.   Bibliography                                      63
     D.   Case Study 3:  Use of IRS/SSA/HCFA Administrative Files
                         for 1980 Census Coverage Evaluation     64
          1.   Introduction                                      64
          2.   Objectives of the Program to Estimate the Census
               Undercount                                        64
          3.   Matching Techniques                               65
               a.   Matching of Survey Housing Unit and Person
                    Records to Census Records                    66
               b.   Matching of CPS and Census Enumerated Housing
                    Unit and Person Records to Administrative File
                    Records                                      66
          4.   Administrative matching                           66
          5.   Research Conducted for Match Study                67
               a.   1978 CPS/IRS Match Study                     67
               b.   IRS Census Match Study (Involving Richmond
                    Virginia and Southwest Colorado Dress
                    Rehearsal Censuses)                          68
          6.   Estimation                                        68
          7.   Anticipated Cost and Timing of Administrative Match
               Study                                             70
          8.   References                                        70
     E.   Case Study 4:  Record Linkage in the Nonhousehold      70
          1.   Introduction                                      70
          2.   Results from the Travis County, Texas and Camden New
               Jersey Pretest                                    71
          3.   Plans for the 1980 Census Nonhousehold Sources    75
          4.   Summary and Future Considerations                 76
          5.   Sources of Further Information                    77
          6.   References                                        77
          7.   Appendix-Matching Instructions                    78
     F.   Concluding Comments                                    78
Chapter VII.   Technical Problems in the Statistical Use of
               Administrative Records                            81
     A.   Coverage                                               81
     B.   Comparability                                          83
     C.   Reporting and Processing Errors                        85
          1.   Reporting Problems                                86
          2.   Processing Problems                               86
          3.   Extent of Errors                                  97
          4.   Related Problems with Other Data                  88
          5.   Errors in Other Information                       98
     D.   Problems with Timing of the Data                       89
     E.   Conclusion                                             89
Chapter VIII:  Legal Issues in the Statistical Use of
               Administrative Records                            91
     A.   Legal and Administrative System                        91
          1.   Factors Precipitating the Shift Toward Greater
               Statistical Use of Administrative Records         91
                                vii
                                                               page
          2.   Concept of Functional Separation                  92
          3.   A Language Framework for Legal Issues             93
          4.   Options:  Legislative Approaches to Functional
                         Separation                              95
     B.   Dynamics of Functional Separation                      96
          1.   Dimensions and Characteristics of the Legal
               Framework                                         96
               a.   Disclosure Within the Agency, a Broader View 96
               b.   Disclosure to Agency Contractors             97
               c.   Disclosure Among Federal Agencies            98
               d.   Use By Non-Statisticians of Statistical Files
                    Compiled From Administrative Source Records  99
          2.   A Closer Look At Some Federal Statutes Affecting
               Statistical Use of Administrative Records and
               Protection of Statistical Records from
               Nonstatistical Use                               100
     C.   Summary and Directions for the Future                 102
     D.   Notes and References                                  102
References                                                      104
                               viii
                          List of Figures
Figure                                                         page
III.1     Major Administrative Files Surveyed by the Subcommittee
          on the Statistical Uses of Administrative              11
V.1       Forms W-2 and W-3                                      46
V.2       Statistical Uses of Unemployment Insurance Administrative
          Records from Establishments                            48
VI.4.1    Nonhousehold Sources Worksheet to Search Census Records
          for Selected Person: 1976 Census of Travis County,Texas72
VI.4.2    Nonhousehold Sources Census Record Search and Telephone
          Follow-up Verification Record: 1976 Census of Camden, New
          Jersey                                                 75
VI.4.3    Nonhousehold Sources Record:   20th Decennial Census-
          1980                                                   76
                                ix
                          List of Tables
Figure                                                         Page
III.1     Major administrative Record Systems Pertaining to
          Individuals                                            12
III.2     Major administrative Record Systems Pertaining to
          Businesses                                             15
IV.1      National Income and Product Account Components Based on
          Administrative Records                                 30
IV.2      Input-Output Account Industry Estimates Based on
          Administrative Records                                 30
IV.3      Balance of Payment Account Components Based on
          Administrative Records                                 31
IV.4      National Income and Product Account Components Based on
          Current Surveys Using Administrative Record Based
          Sampling Frames                                        31
VI.2.1    Distribution of Site Research Sample Households by Sample
          Frame and Questionnaire Type                           58
VI.2.2    Distribution of Site Research Adult Respondents by Sample
          Frame and Questionnaire Type                           59
VI.2.3    A Sampling of AFDC Matching Results in the Site Research
          Survey                                                 60
VI.2.4    SSI Match Results for the 1978 Panel                   60
VI.3.1    Forming a Dual-System Estimate for One of the 61
          Divisions                                              69
VI.4.1    Camden Match Results                                   73
VI.4.2    Cross Tabulation of Age Reported on Drivers Licenses and
          Census Questionnaire (Camden, New Jersey)              74
VII.1     Comparison of Employment Estimates: CWHS, Census, UI, and
          CBP                                                    84
                                 x
                           Abbreviations
AFDC      Aid to Families with Dependent Children
BEA       Bureau of Economic Analysis
BEOG      Basic Education Opportunity Grant
BLS       Bureau of Labor Statistics
BMF       Business Master File (of IRS)
CAB       Civil Aeronautics Board
CBP       County Business Patterns
CofC      Comptroller of the Currency
CES       Current Employment Statistics
CETA      Comprehensive Employment and Training Act
CPS       Current Population Survey
CWBH      Current Wage and Benefit History
CWHS      Continuous Work History Sample
ED        Enumeration District
EEOC      Equal Employment Opportunity Commission
EI(N)     Employer Identification (Number)
ERP       Establishment Reporting Plan
FAA       Federal Aviation Administration
FCC       Federal Communications Commission
FDIC      Federal Deposit Insurance Corporation
FICA      Federal Insurance Contributions Act
FOIA      Freedom of Information Act
FPC       Federal Power Commission
FRB       Federal Reserve Board
FTC       Federal Trade Commission
GAO       General Accounting Office
GBF       Geographic Base File
HCFA      Health Care Financing Administration
HHS       Department of Health and Human Services
HEW       (Department of) Health, Education, and Welfare
ICC       Interstate Commerce Commission
IMF       Individual Master File (of IRS)
I-O       Input-Output
IRS       Internal Revenue Service
ISDP      Income Survey Development Program
LASS      Linked Administrative Statistical Sample
LTS       Labor Turnover Statistics
NCEUS     National Commission on Employment and Unemployment
          Statistics
NCHS      National Center for Health Statistics
NCI       National Cancer Institute
NIPA      National Income and Product Accounts
OASDI     Old Age, Survivors, and Disability Insurance
OES       Occupation Employment Statistics
OFSPS     Office of Federal Statistical Policy and Standards
OMB       Office of Management and Budget
                                xi
OPM       Office Of Personnel Management
ORS       Office of Research and Statistics (of SSA)
OSHS      Occupation Safety and Health Statistics
PES       Post Census Enumeration Survey
PPSC      Privacy Protection Study Commission
REA       Rural Electrification Administration
RFP       Request for Proposal
SBA       Small Business Administration
SER       Summary Earnings Record
SESA      State Employment Security Agency
SIC       Standard Industrial Classification
SIPP      Survey of Income and Program Participation
SMD       Statistical Methods Division (of Census)
SMOBE     Survey of Minority Business Enterprises
SMSA      Standard Metropolitan Statistical Area
SOI       Statistics of Income
SSA       Social Security Administration
SSEL      Standard Statistical Establishment List
SSI       Supplemental Security Income
SSN       Social Security Number
SSR       Supplemental Security Record
SUAR      Statistical Uses of Administrative Records
TCMP      Taxpayer Compliance Measurement Program
UI        Unemployment Insurance
USDA      United States Department of Agriculture
                                xii
                                                          CHAPTER I
                   Findings and Recommendations
     Statistical use of administrative records grew rapidly during
the 1970's, in large part as a response to legislative requirements
for timely data to use in the distribution of Federal funds to
State and local governments.  The principal reason for increasing
reliance on administrative records for statistical data is the
availability of administrative records which can be used to obtain
small area data at minimal cost and without increasing respondent
burden.  And cost is likely to be an increasingly important factor
in the statistical use of administrative records in the 1980's.
Although statistical use of administrative records is growing, many
unanswered questions remain concerning the quality of statistics
derived from administrative records.  From a statistical point of
view, the standards of quality and consistency in administrative
data collection and processing programs are frequently inadequate.
Difficulties in accessing administrative records, moreover, often
inhibit the efficient joint use of particular administrative record
sets with other administrative and statistical records in meeting
statistical needs.  Improved statistics from administrative records
will require modification in data collection and processing
procedures, modification of laws and administrative procedures
relating to access to records, and increased resources for
evaluating and upgrading the quality of administrative records for
statistical use.  While the costs of improving administrative
records for statistical applications can be significant, they will
often be substantially less than alternatives requiring expanded
censuses and surveys.  And in many instances both administrative
and statistical programs could benefit from reduced respondent
burdens and data processing costs obtainable by applying more
efficient statistical tools in the collection and use of
administrative records.
     To solve problem impeding efficient statistical use of
administrative records, coordinated treatment of a variety of
interagency issues is needed to serve as a counterweight to the
decentralized operations of Federal information collection
programs.  In addressing these issues, the Subcommittee on
Statistical Uses of Administrative Records has divided its
recommendations into dim sections concerned with:
     A.   Identifying and formulating solutions for common problems
          related to statistical standards for administrative
          information programs.
     B.   Identifying and meeting various problems related to
          access to administrative record systems.
     C.   Identifying collection programs and research activities
          requiring government-wide coordination and support.
     Individual recommendations are in some cases accompanied by
examples of subcommittee findings which illustrate the need for the
recommendation.
                     A. Statistical Standards
     There is a need for greater standardization in the procedures
for collecting and presenting data based on administrative records
in order to provide a basis for reducing duplicate collection
efforts and improving the quality and consistency of the
information that is collected.
Recommendation 1.   Common identifiers should be used whenever
possible in collecting information Pertaining to the sow
individuals or organizations.
     The capability for linking information from a variety of
sources is central in making efficient statistical use of
administrative records.  This capability depends on both
appropriate access to administrative records (see Section B) and
consistency among administrative and statistical agencies in
procedures for identifying respondents or reporting units.  The
subcommittee noted, for example, that household surveys could be
used more effectively in conjunction with administrative records if
social security numbers and related identifying information were
collected in selected surveys.  This would permit linking detailed
socioeconomic information from surveys with longitudinal records
from administrative sources concerned, for example, with employment
or medical histories.  Such linkages are performed in various areas
of social research including specialized fields such as
epidemiology.  In business data collection programs, employer
identification numbers should be supplemented with a common set of
identifiers for the individual establishments of large businesses.
Selected administrative record data for multi-establishment
businesses could then be linked more readily to economic census and
survey data for purposes of improving geographical and industrial
analysis of economic activity
                                 1
Recommendation 2.   The quality of administrative records to be
used for statistical purposes should be evaluated systematically to
determine the appropriateness of the records for the proposed use.
     The quality of administrative record files, including such
factors as the type and quality of identification on the file and
the completeness, definitional suitability, and quality of
individual or organizational characteristics on the file. will
determine the appropriateness of the use of the files for
particular statistical applications.  For example, in matching
applications the completeness of the coverage of the administrative
record files and the accuracy of identifiers will determine whether
a high match rate will be achieved.  Similarly, in such
applications as the distribution of Federal funds to State and
local governments. completeness and accuracy of administrative
records, will determine the extent to which estimates derived from
these records may serve as complements as well as substitutes for
census and survey data.
Recommendation 3.   Consistent procedures should be used in
administrative and statistical data collection efforts for defining
reporting units, identifying and coding reporting unit
characteristics, and developing standards
for data tabulation.
     When common reporting units are not appropriate there should
still be efforts to ensure that the more detailed reporting unit
breakdowns of one program can be readily combined into more
aggregative units used in other programs.  The subcommittee noted,
for example, a lack of congruity in the definition of companies
filing corporate income tax returns and companies reporting for
statistical Purposes to the Census Bureau.  The subcommittee also
found a particularly serious problem of inconsistency between
"establishment" reporting plans associated with administrative
programs and the definitions of establishments of multiunit
companies used in the Census Bureau's Standard Statistical
Establishment List.  The Social Security payroll tax program, for
example, involves a voluntary establishment reporting plan with
company self-identification of reporting units on a basis differing
from SSEL definitions.  The need for consistent reporting
requirements that eliminate duplicate and other unnecessary
reporting is highlighted by the fact that the compliance of large
companies with the SSA establishment reporting plan and other
voluntary statistical programs has been deteriorating in recent
years.
     Problems of inadequate procedures for coding reporting unit
characteristics have been emphasized by the subcommittee in such
areas as geographic coding and the industrial coding of business
establishments.  Reliable and detailed geographic coding in
administrative record systems, in particular. has become
increasingly important as administrative records have received
wider application in preparing statistics for use in distributing
Federal funds to State and local governments.  For many purposes
geographic coding is required at the municipal level, but substate
coding in administrative record systems tends to be restricted to
county identifiers.  The lack of current economic information by
municipality has hindered effective planning and economic policy
making at the Federal as well as State and local level.  For
business reporting systems, the SSEL coding system can provide a
basis for obtaining consistency in both geographic and industrial
coding.
     The need for consistent standards for data tabulation have
recently been highlighted by efforts to assemble a data base for
analyzing small business policy issues.  These efforts have been
hampered by inconsistencies among various administrative and
statistical programs in the ways in which data are identified and
tabulated by size of business.
                             B. Access
     A central issue related to meeting the differing requirements
of data for administrative vs. statistical applications efficiently
involves the problem of obtaining an appropriate balance between
the need to access individual records and the right to privacy as
well as consideration of confidentiality of responding persons and
businesses.  Resolution of this issue requires that distinctions be
made both in terms of the uses to be made of records and the types
of reporting units and information involved.
Recommendation 4.   Natural persons should be distinguished from
organizations and other entities when developing standards and
practices of record confidentiality.
     The need for confidentiality is not the same for businesses
and other organizations as for natural persons.  Often,, the need
for access to selected information pertaining to businesses
requires interagency transfer of information about organizations.
The subcommittee has found, for example, instances in which Federal
a#coca purchase privately produced lists of businesses containing
generally available information, such as name and address of the
businesses, because access to more complete and reliable lists such
as the Census Bureau's SSEL has been excessively restricted.  The
subcommittee is not persuaded that these restrictions are
reasonable or necessary.
Recommendation 5.   Legislation and administrative procedures
should be modified to make comprehensive Federal lists of
businesses and organizations, such as the
                                 2
Census Bureau's Standard Statistical Establishment List and SSA's
employer listing, more readily available for statistical uses.
     Legislation has been drafted to make the SSEL available to
Federal agencies for statistical purposes.  Passage of the proposed
legislation could aid in reducing the duplication and costs, and
the attendant differences in definition and coverage resulting when
independently developed lists are maintained.  SSA's listing of
employers is compiled from the applications for employer numbers
required of employers of workers covered by Social Security, now
virtually the entire workforce.  Availability of this list as a
statistical sampling frame has been closed by application of the
Tax Reform Act of 1976.
Recommendation 6.   For natural persons. the principles of
"functional separation" developed by the Privacy Protection Study
Commission, the White House Privacy Initiative, and the President's
Statistical Reorganization Project should be applied in
distinguishing records to be used for administrative (and
enforcement) purposes from records to be used for statistical
purposes.
     Functional separation will establish two discrete categories
of information according to the statistical or administrative and
enforcement functions to which the information is assigned.  The
separate category of statistical information- can be freely used
and transferred with individual identifiers intact for statistical
purposes.  Between the two categories, information that can be
uniquely associated with subject individuals flows only one way,
into the statistical category.  The flow from the statistical
category into other uses must be in a form or under conditions that
prevent unique association.  When administrative records are the
initial information source, the resultant copies or extracts which
have been incorporated into statistical files may not be
subsequently used in individually identifiable form for
administrative or enforcement purposes.'
Recommendation 7.   Particular legal and administrative barriers to
access to administrative records for statistical use should be
identified and eliminated for records pertaining to both natural
persons and organizations.
     The subcommittee, for example. has found limitations on access
to IRS data imposed under Section 6103 of the Tax Reform Act of
1976 to be excessively restrictive to statistical uses of the data.
In this connection it can be noted that the Internal Revenue
Service has denied other Federal agencies access to Taxpayer
Compliance Measurement Program data files for 1976 and subsequent
years.  In addition, the Tax Reform Act has prevented the Social
Security Administration from supplying the Bureau of Economic
Analysis with post- 1975 Continuous Work History Sample Files
needed to continue a long-standing cooperative program to use and
improve this important statistical data base.
     C. Other Government-Wide Program Coordination and Support
     In order to maximize the usefulness of administrative record
systems, it will be necessary to identify on a government-wide
basis those data collection programs, as well as research
initiatives, which need interagency support.  Further the needs of
data users should be considered in designing statistical series
based on administrative records.
Recommendation 8.   Procedures for planning and setting budget
priorities should be developed to ensure that agency and program-
specific budget allocations are responsive to those interagency
data needs that are met most effectively through the specific
programs under review.
     Many administrative programs are not explicitly budgeted for
supplying those general-purpose statistical needs which could be
met efficiently through statistical use of administrative records.
The subcommittee has found, for example, that geographic and
industrial data quality in the Social Security Administration's
Continuous Work History Sample has been declining because the data
have few applications for internal SSA programs and therefore
receive low priority in the agency budgeting process.  Geographic
and industrial data from the CWHS, however, are very important for
outside data users.  And they will become even more important if
administrative records are called on to play a central role in
providing intercensal estimates.  In planning alternatives to a
mid-decade census there should be careful cost-benefit analysis of
different approaches involving various combinations of survey and
administrative record data sources.
Recommendation 9.   As recommended by the President's Statistical
Reorganization Project, efficient statistical tools should be
applied in information collection programs extending well beyond
the confines of the principal statistical agencies.
     Statistics can contribute techniques for improving design of
forms. both to improve quality of response on administrative forms,
and to improve the multi-purpose utility of the information
provided.  Development and extension of such statistical techniques
as scientific sampling. record matching, and synthetic estimation
can be used effectively to economize on the amount of information
that needs to be collected, thereby reducing paperwork burdens and
budgetary costs associated with administrative as well as
statistical data collection programs.
                                 3
Many administrative record data collection  programs have lagged
well behind the "state of the art" in the application of
statistical tools, and modernization of programs is badly needed.
Recommendation 10.  To obtain statistical data. increased use
should be made of matches between sample surveys and administrative
files.  Samples based on linkageS among administrative record
systems also should be encouraged for statistical purposes.
     The subcommittee has investigated the statistical uses of
linking of administrative record files with sample survey data. as
well as with samples from other administrative records.  The
subcommittee endorses the use of matching to obtain statistical
data based on the combination of administrative records and sample
surveys.  The analytic potential of obtaining expanded. more
detailed data bases through successful matching is sufficiently
great that complicated procedures are often worth the effort.
However, for each specific program proposing to use linkage s to
obtain statistical data. it is necessary to examine the costs and
benefits to the program to determine whether the match should be
performed.
     The case studies in Chapter VI illustrate potential uses of
administrative records for important statistical programs'. each
case study has specific goals, applications, and advantages.  Mc
combined use of administrative record files and sample survey data
for linkage programs may be effective for a variety of masons.
including that: (1) respondent burden may be reduced while
estimates of subpopulation characteristics are improved and data
accuracy is assessed (see SIPP case study), (2) data which are
difficult for a survey respondent to provide may be obtained from
administrative record files (see LASS case study). (3) improved
counts of population from the 1980 Census may be obtained in a
cost-effective manner (see Nonhousehold Sources Program case
study), and (4) estimates of coverage of population for States and
selected subgroups of the population based on the 1980 Census my be
obtained (see case study on IRS/SSA/HCFA matched with CPS and
Census).
Recommendation 11.  The provision o  f services to users should be
recognized as a statistical program function to optimize the
availability of statistical information in Federal.  State and
local government and in the private sector, and to give the Federal
system the benefit of feedback from users in planning statistical
programs based on administrative records.
     A major obstacle to encouraging statistical use of ad-
ministrative records is the lack of knowledge (both inside and
outside the Federal Government) about the information in these
records and their coverage and quality.  The American Statistics
Index provides a comprehensive list of published statistics from
administrative and survey sources, but information on the quality
and availability of unpublished data, particularly from
administrative records, is seriously deficient.  Centralized
information is needed to make existing data more readily accessible
to potential users and to help in identifying unnecessary
duplication in data collection programs.  Promising recent
initiatives in this area include a Small Business Administration
program to document all Federal reporting requirements placed on
businesses and a National Center for Health Statistics program to
establish a clearinghouse for data relating to environmental health
hazards.  In addition, the proposed Paperwork Reduction Act of 1980
(H.R. 6410) provides for establishing a Federal Information Locator
System, as recommended by the Commission of Federal Paperwork.
                                 4
                                                         CHAPTER II
                     Introduction and Summary
A. Introduction
     The Federal Statistical System is under pressure to respond
simultaneously to a growing demand for statistical data and a
growing demand for reductions in the "paper blizzard" generated by
Government requests for information from individuals and
businesses.  These demands will necessarily conflict unless the
efficiency of current programs can be improved.  Responsiveness to
both demands will require reduced duplication among Government
information collection programs combined with more intensive
utilization of existing administrative information sources in
meeting statistical data needs.  The latter requirement will
involve bringing together information collected in numerous
different Government administrative programs in ways that make
possible their combined use for statistical analysis.  As stated by
Edgar Dunn (1965, P. 5) in a review of the Ruggles' Committee
proposal for a national data center.
     The central problem of data use is one of associating
numerical records.  No number conveys any information by itself.
It acquires meaning and significance only when compared with other
numbers.  The greatest deficiency of the existing Federal
Statistical System is its failure to provide access to data in a
way that permits the association of the elements of data sets in
order to identify and measure the interrelationship among
interdependent activities.
     As Dunn further notes (1965, Summary, p. 2) problems of access
and record association are particularly serious in the case of
statistical use of administrative records because: "Many of the
most useful records are produced as a by-product of administrative
or regulatory procedures by agencies that do not recognize a
general-purpose statistical service function as an important part
of their mission."
     The association or merger of administrative records from a
variety of sources is important for statistical applications
because: (1) populations of statistical interest do not always
correspond closely to populations covered in individual
administrative record systems; and (2) individual administrative
record files often identify relatively few of those characteristics
and attributes of the members of a population that social
scientists and policy analysts consider to be important in meeting
their statistical needs.  Merging individual administrative record
sets with other administrative and statistical data sources can
help to alleviate the deficiencies of many individual administra-
tive sources; but record merging is often difficult--particularly
when the records are collected and maintained by separate agencies.
Provisions for protecting the confidentiality of records pertaining
to identifiable individuals or businesses often preclude
interagency transfer of such records for statistical applications.
And even when access to the records needed for merging can be
arranged, differences in the ways different agencies identify
individual reporting units, and/or inconsistencies in the ways
agencies collect, process, and maintain information about reporting
units, can preclude successful data matching and merging operations
(see Chapter VI).
     Although difficult problems remain to be solved, statistical
uses of administrative records have been increasing and will
continue to increase because of high data collection costs and
heavy respondent burdens associated with censuses and surveys.
Many important statistical needs cannot be adequately met by a
system involving censuses, carried out every 5 or 10 years,
combined with intercensal surveys which provide national data.  And
the extra costs of moving to more frequent censuses and/or larger
sample surveys which might provide small area data are high both in
terms of direct government expenditure and response burden.  The
projected high cost to the government was an important factor in
the recent decision to disallow further planning funds for the 1985
mid-decade census.
     The most striking illustrations of the need to make improved
statistical use of administrative records arise in cases involving
the use of socioeconomic data to distribute Federal funds to State
and local areas.  For example, in reviewing alternatives for
meeting the legislative mandate to produce current local-area
unemployment estimates for use in allocating funds under the
Comprehensive Employment and Training Act, the National Commission
on Employment and Unemployment Statistics ( 1 979, p. 253) has
estimated that it would cost about $2.3 billion annually to expand
the Current Population Survey to provide monthly unemployment
estimates for the over 4,000 geographic areas potentially eligible
for CETA funding.  As important as the high money costs involved in
obtain-
                                 5
ing frequent small-area data by survey techniques is the
substantial increase in response burdens associated with greatly
expanded data collection efforts.
     For example, another alternative considered by the NCEUS was
improving the handbook method (called 70-step method) based on
unemployment insurance records.
     Not only is them pressure for statisticians to increase their
use of administrative records in developing general-purpose
statistics, but statisticians also have a strong interest in
supporting efforts to reduce the duplication and improve the
efficiency of administrative as well as statistical information
collection efforts.  Direct reporting for statistical purposes
accounts for a very small proportion of the overall Federal
reporting burden; major reductions in overall paperwork burdens
must be achieved through improvements in nonstatistical arm.  At
the same time; however, statistical programs could be more
adversely affected than other programs because statistical programs
tend to be more often viewed as optional than administrative record
systems and, therefore. more dependent on the voluntary cooperation
of the public in obtaining responses to information requests.
As the following statement from the President's Statistical
Reorganization Project's "Issues and Options" paper (1978, p. 7-1)
indicates, there is a growing recognition of the importance of
applying statistical tools to more general problems of information
collection in order to reduce reporting burdens:
     The tools used by statistical agencies (sampling, quality
     control, intensive analysis of existing data, etc.) are near
     the roots of reporting requirements, and the use of
     appropriate tools reduces reporting burden.  It is in this
     sense that. from the point of view of response burden, the use
     of appropriate statistical techniques is of major importance
     and should extend well beyond any formal definition of the
     Federal Statistical System.
The statistical system, however, cannot hope to dominate Government
information collection activities; There must be a genuine effort
to cooperate with administrators in nonstatistical programs in
order to achieve mutual goals of efficient information collection.
Statisticians must attempt to understand the needs and constraints
facing program administrator and statistical budgets should bear a
fair share of the costs of collecting and processing administrative
records in ways that permit efficient use for statistical purposes.
Much must be learned and many difficult problems confronted if
progress is to be made in the statistical use of administrative
records and in improving the overall efficiency of Government
information collection and use, With the hope of contributing to
progress in this area, this report attempts to: (1) identify major
administrative data files with significant potential for general-
purpose statistical applications; (2) indicate various kinds of
statistical uses of administrative records which are being made or
considered; (3) identify major technical and institutional or legal
problems which are impeding effective statistical use of
administrative records; and (4) suggest possible approaches to
improving information collection and statistical use of
administrative records.
     The Subcommittee on Statistical Uses of Administrative Records
has not attempted to provide comprehensive documentation of
administrative record systems and their uses.  The report instead
reflects largely the areas of interest and expertise of
Subcommittee members.  Important areas such as energy and
environmental statistics are not covered at all, and very little
attention is given to records generated by the complex array of
Government regulatory agencies.  There is, however, relatively
intensive coverage of administrative data from programs of the
internal Revenue Service and Social Security Administration, and
from related administrative programs that collect important social
and economic information from individuals and businesses.
                            B. Summary
     Chapter III of the report presents the results of a survey
conducted by the Subcommittee to obtain documentation of major
administrative record data files maintained by selected Federal
agencies.  Chapter IV presents a description of statistical
applications of administrative records in selected agencies.  The
following three chapters (V-VII) illustrate, largely by means of
case studies, specific approaches to statistical use of
administrative records and problems encountered in such approaches.
Chapter VIII reviews legal considerations, particularly those
related to restricted access to records, that influence the
statistical use of administrative records.
1.   Chapter III-Major Administrative Files
     This chapter summarizes the characteristics of major
computerized administrative record files that are maintained or
mandated by the Federal Government and contain statistically useful
information pertaining to (I) individuals or (2) businesses.  The
information contained in the administrative files for individuals
is compared to the information on individuals collected in
decennial censuses; and the information contained in the
administrative files for businesses is compared to the information
contained on the Census Bureau's Standard Statistical Establishment
List (which is itself assembled from a combination of
administrative and survey data sources).  The chap-
                                 6
ter also contains a description of the Social Security
Administration's Continuous Work History Sample which is a set of
statistical files of individual worker records assembled using
several SSA business and individual administrative record files.
     Compared with the decennial census, most administrative record
files for individuals contain relatively little information on
population characteristics and/or cover only a limited segment of
the population.  In addition, the, census usually provides more
reliable and detailed geographic information than administrative
files; and at best, administrative records can provide only tough
approximations to such census reporting units as the family and
household.  On the other hand, many administrative files provide
data at much more frequent intervals than the decennial census, and
the presence of social security numbers on most administrative
files opens the possibility of linking files over time
(longitudinally) or merging information from more than one
administrative file in order to increase the cove rage of
individuals and/or the number of characteristics identified for
particular individuals.  The absence of SSN's in census records
generally makes it difficult to integrate information from censuses
with information from administrative records.
     Administrative record coverage of businesses is complete than
is true for individuals.  In fact, administrative lists of
businesses provide the basis for conducting statistical censuses
and surveys.  For the most part, however, administrative records do
not maintain separate information for the different establishments
of a single legal business entity, even though the business may
operate in several different geographic areas and/or industrial
categories.  The Census Bureau does collect information for
individual establishments; and the SSEL, therefore, contains a
larger list of reporting units than most administrative files.
While most administrative business files do not contain the
establishment detail necessary for developing reliable geographic
and industrial data, the SSA and Unemployment Insurance payroll tax
programs do involve reports breaking out county level "establish-
ment" detail.  Unfortunately, however, the reporting units in these
programs are not consistent with the establishment concept used in
the SSEL, and there is currently no satisfactory basis for
coordinating the reporting of similar information (or resolving
data discrepancies) among the three systems.
     CWHS data files provide information on the demographic
characteristics (sex, age, and race) of. workers along with
longitudinal information on their employment and earnings patterns.
The CWHS program illustrates the potential statistical advantages
of administrative records for longitudinal analysis and for linking
together information about individuals and businesses.
2.   Chapter IV-Major Statistical Uses of Administrative Records
     This chapter illustrates statistical uses of administrative
records with reference to the programs of selected Federal
agencies, particularly programs of the Social Security
Administration, the Internal Revenue Service, the Bureau of
Economic Analysis, the Census Bureau, and the Small Business
Administration.  The SSA and IRS programs involve the development
of general-purpose statistics by statistical divisions of agencies
that collect large amounts of information from individuals and
businesses in the course of their administrative responsibilities.
The programs illustrate the large quantity and variety of adminis-
trative data collected as well as the limitations of incomplete
population coverage and lack of information on important population
characteristics that plague statistical use of administrative
records.
     The BEA programs illustrate the use of a wide variety of
administrative data (obtained from many agencies) for estimating
data series within the context of a systematic economic accounting
framework.  Administrative data are used in conjunction with census
and survey data (also generally obtained from other agencies); and
there are substantial variations among the administrative data
series in the extent to which they involve concepts and measurement
procedures that "fit" well with the concepts involved in the design
of the accounting framework and with concepts underlying the census
and survey data used.
     Census Bureau programs illustrate a wide variety of
applications of administrative records for both individuals and
businesses.  For example, records obtained from administrative
agencies are used in developing intercensal population and related
estimates, as a substitute for censuses in the collection of
economic data from many small businesses, in the development and
maintenance of sampling frames for surveys, and in the evaluation
of the completeness and, reliability of information collected in
censuses and surveys.  Again there are substantial variations in
the extent to which administrative record concepts match desired
statistical concepts.  A few census programs. primarily in the area
of economic statistics. art discussed in more detail than other
programs covered in Chapter IV.  These more detailed examples
illustrate the substantial cost savings as well as limitations
associated with the statistical use of administrative records.
     The SBA involvement in the statistical use of administrative
records stems largely from a recently initiated project to develop
a small business data base in conjunction with the 1980 White House
Conference on Small Business.  In part because of concerns over
reporting burdens, small businesses have been exempted from or
                                 7
covered on a very small sample basis, in most economic censuses and
surveys.  Therefore. a small business data base must rely heavily
on administrative records.  SBA efforts to develop such a data base
illustrate many of the problems that are often encountered in
gaining access to administrative records and adapting them for
statistical analysis.
3.   Chapter V-Developments in Data from Business Establishment
     Reporting
This chapter contains case studies of three important and related
statistical programs that are currently evolving based in large
part on developments in administrative record systems-(1) the
Census Bureau's SSEL program; (2) SSA's program for adapting its
CWHS data program to a new system of annual employer reports of
worker wages on forms W-2 and W-3; and (3) the Bureau of Labor
Statistics' program for developing work force statistics in
connection with the UI payroll tax program.  These programs produce
both complementary and overlapping statistical products in the area
of work force statistics; and they illustrate not only the
importance and potential of administrative records for developing
work force data, but they also illustrate some important problems
in the area of establishment reporting by multiestablishment
businesses and in the area of coordinating similar data collection
efforts in different agencies.  The Census Bureau program employs
the most satisfactory concept of establishment from a statistical
point of view, but the Census work force data assembled in
connection with the SSEL cannot match the frequency and timeliness
of BLS data based on the UI system, nor can the SSEL-based data
provide the information on demographic characteristics of workers
available from the SSA system.  And the different establishment
reporting plans of the three data systems combined with
difficulties of interagency transfers of records (for example, the
current restrictions on access to the SSEL) have severely limited
the scope for coordinating data collection and development efforts
in the three programs.
4.   Chapter VI--Potential Uses of Administrative Records for Data
     Linkages: Selected Case Studies
     This chapter involves four case studies that illustrate the
potential and the problems associated with record linkages as a
means of improving and extending the use of. administrative records
in developing primary data and in evaluating census and survey
data--(1) the "Linked Administrative Statistical Sample Project"
(2) the "Use of Administrative Records in the Survey of Income and
Program Participation," (3) the "Use of IRS/SSA/HCFA Administrative
Files for 1980 Census Coverage Evaluation," and (4) "Record Linkage
in the Nonhousehold Sources Program." In contrast to Chapter V,
where the difficulties of coordinating and linking business
establishment records among programs was highlighted, Chapter VI is
concerned with linkages involving records for individuals.
The LASS project involves efforts to link records from a variety of
administrative record sources in order to develop a general-purpose
statistical sample file that will be suited for mortality research.
The sampling procedures will conform closely to those involved in
the CWHS in order to facilitate longitudinal data analysis, but
CWHS records will be supplemented with records from IRS and the
National Center for Health Statistics.  The project illustrates the
substantial potential for combining complementary data through
interagency linkage of administrative record files.  But the
project also illustrates significant technical problems and
problems of access restriction that need to be resolved in linking
data files prepared in different agencies.
     The SIPP case study illustrates the importance of
administrative records in efforts to alleviate substantial survey
biases in coverage and income reporting for low-income groups
(participating in various income maintenance programs) and
administrative record importance as a source of income data to
evaluate the reliability with which selected types of income are
reported in surveys.
     The third and fourth case studies are both associated with
efforts to evaluate and improve the 1980 Census of Population and
Housing.  The IRS/SSA/HCFA files will be used primarily in efforts
to evaluate the extent of Census undercoverage, while the
Nonhousehold Sources Program will be concerned with improving
population coverage in selected areas of anticipated high
undercount.  The latter program involves, in addition lo the use of
Federal agency records, the use of such State and local
administrative records as drivers' license records.  Both projects
demonstrate the potential of administrative records to identify
individuals who are missed in censuses and surveys.  The projects
also illustrate; however, the difficulties and high costs of
linking administrative records to census records (which contain no
social security number) and the difficulty of determining the
extent to which particular groups are not covered in either census
or administrative record sources.
5.   Chapter VII-Technical Problems in the Statistical Use of
     Administrative Records
     This chapter illustrates technical problems encountered in
making statistical use of administrative records that arise or are
exacerbated because of limited statistical control in
administrative record systems over such factors as population
coverage,, definitions and comparability of information concepts
among programs, and reporting and
                                 8
processing procedures.  The CWHS data program is used as the
principal source of illustrations, in part because the CWHS program
involves the use of files containing information about businesses
as well as individuals, and perhaps more importantly because it
illustrates well the problems that can arise when important
statistical aspects of the reporting and processing of records we
largely outside the control of statisticians responsible for making
statistical use of the records.  In particular them is evidence of
significant and increasing numbers of geograPhic coding errors in
the CWHS that have resulted from low priority attached by SSA
administrators to the statistical problem of obtaining reliable
geographic reports and ensuring accurate coding and processing of
geographic information in employer payroll reports to SSA.
6.   Chapter VIII: Legal Issues in the Statistical Use of
     Administrative Records.
     This chapter illustrates legal and related institutional
barriers which inhibit the interagency access to records that is
needed for improving the efficiency and effectiveness of
statistical use of administrative records.  Emphasis is placed on
problems which arise because of a failure of existing
confidentiality laws to make an adequate functional distinction
between statistical and administrative processes which use records
about individuals.
     The basis for interagency transfer of administrative records
is often found in a logic that imposes regular Procedures or
conditions for expanding the scope 'of administrative actions or
decisions which can be based on the. particular content of records
about an individual.  Such a logic is generally irrelevant with
respect to legitimate statistical processes which, in contrast to
administrative uses, merely produce relationships and summaries of
data, and do not involve any direct Government action against (or
in favor of) the individual as a consequence of information in
records pertaining to that individual.
     Clearly not all statistical performance is functionally
divorced from administrative processes: program integrity and
quality assurance are functions which may explicitly---and quite
properly-rely on applied statistical techniques to identify
individual cases for administrative action.  Such functions are
within the reasonable expectations of program participants, and do
not rely, moreover, on collection of information from volunteers,
with assurances of confidential treatment.  In contrast, there are
particular statistical activities or collections of data whose
existence and rationale for compiling and making interagency
transfer Of data is limited by the degree to which statisticians
can fulfill a legal or ethical duty to protect the confidentiality
of individual information.
     Statistical uses in this latter category need to be separated
out as discrete functional uses, and be governed by different rules
and standards from those which govern administrative and compliance
uses.  Proposals for functional separation" of statistical from
administrative uses argue for separating these statistical records
about identifiable individuals from the decision/action stream, and
permitting the statistical results to be available to adminis-
trators only in summary or other unidentifiable form.  Functional
separation would allow summaries, of course, to be used
administratively in ways which my result indirectly in consequences
affecting all members of the group in uniform ways. However,
functional separation would not permit the direct use of individual
records as the basis for individual actions.  Alternative
legislative proposals for implementing the concept of functional
separation are reviewed in the chapter.
                                 9
                                                        CHAPTER III
                  Major Administrative Data Files
     This chapter describes the general properties of most of the
major Federal administrative record files containing statistically
useful information pertaining to individuals or businesses.  The
discussion is based largely on a survey of selected Federal
agencies conducted by the SUAR Subcommittee.  An attempt is made to
lay the groundwork and indeed begin the discussion, continued in
Chapter IV. of the statistical uses of administrative record
systems.
     Organizationally, the chapter is divided into four sections
and two appendices.  The first section indicates the scope of the
administrative record files covered and describes the survey
instrument used to obtain file documentation.  In the second
section there is a brief summary of the survey results.  In the
third section there is a brief description of the Social Security
Administration's Continuous Work History Sample files.  The CWHS
files illustrate the process of extracting and merging information
from basic administrative files to obtain files useful for
statistical analysis.  In the final section there is a discussion
of selected factors associated with the historical evolution of the
statistical use of administrative files covered in the chapter.
The survey questionnaire is reproduced in the first appendix, and a
more detailed description of the CWHS program and data files is
contained in the second appendix.
              A. Scope of Study and Survey Conducted
1. Scope of Study
     In compiling a list of "administrative" record files that
would be of greatest statistical interest, three criteria were
employed:
     1.   Does the file have extensive coverage of a Population
          (either individuals or businesses)?
     2.   Is the population covered by the administrative record
          set of statistical interest?
     3.   Is the file maintained by computer?
The systems chosen for examination under these criteria are shown
in Figure III.1. Information relating to individuals was sought
from ten Federal agencies; some twenty-four administrative record
files were involved in all.
Figure III.1   Major Administrative Record Files Surveyed by the
               Subcommittee on the Statistical Uses of
               Administrative Records
______________________________________________________________
Agency                        Administrative Record File
______________________________________________________________
                 Part I-Information on individuals
Bureau of the Census          1970 Census of Population
                              1980 Census of Population
Office of Personnel Man-      Central Personnel Data File
agement                       Civil Service Annuity Roll
Department of Defense         Active Military Personnel Data File
                              (Army, Navy, Air Force and Marines)
                              Military Retirement Compensation File
                              (Army. Navy Air Force, and Marines)
Department of Trans-          National Driver Register
portation
Internal Revenue Service      Individual Master Filer
Department of Education       Basic Education Opportunity Grant
Railroad Retirement           Research Master Beneficiary File
Board                         Service and Compensation (SCORE)
                              Railroad Retirement, Survivor  and
                              Pensioner Benefit Payment File
Social Security Adminis-      Summary Earnings Record
nation                        Master Beneficiary Record
                              Numerical Identification File (SS-3)
U.S. Coast Guard              Personnel Management Information
                              System
                              Retired Officers Support System
                              Retired Pay and Personnel System
Veterans Administration       Compensation and Pension Master
                              Record Insurance (In-Force) Master
                              Record File
                              Education Master Record File
                              Vocational Rehabilitation and
                              Education Statistical File
                              Insurance Awards Master Record File
                              Education Master File
______________________________________________________________
                 Part II-Information on Businesses
Bureau Of the Census          Standard Statistical Establishment
                              List
Bureau of Labor Statis-       Unemployment Insurance Address File
tics
Department of Agricul-        Producer Name and Address Master
ture File                     Economics, Statistics, and
                              Cooperative Service List Sampling
                              Frame
Department of Health          Master Facility Inventory
and Human Services
Internal Revenue Service      Business Master File
                              Exempt Organization Master File
Social Security Adminis-      Master Employer Name Directory
tration                       Multi-Unit Code File
                              Single-Unit Code File
                                11
     For businesses, the scope of the inquiry was restricted to
nine major Federal systems in six agencies.
     It should be noted that although the Subcommittee does, not
Classify the decennial censuses of population as administrative
data files. since their main purpose is statistical, they are
nonetheless. included to provide a basis for comparison with the
other files on individuals.  The Census Bureau's Standard
Statistical Establishment List was also treated as "in scope" for
comparison purposes. this time with business administrative record
files.
2.   Survey Conducted
     In late 1978. the Subcommittee conducted a survey of
the administrative files listed in Figure II.1. This survey was
entitled "Statistical Use Survey of Records Pertaining to
Individuals.  Individual Firms, and Employers Maintained and/or
Mandated by the Federal Government.
     A questionnaire was mailed to each agency maintaining one of
the selected files.  The principal purpose of the questionnaire was
to document the data elements on each file that might be of
statistical interest. it was not the intent of the survey to be
comprehensive, but simply to provide a starting point for
structuring inquiries about the files.
This survey collected data on both individual and business files by
providing optional sections to completed depending on the type of
file being considered.
     The survey consisted of only fifteen questions, but a number
of the questions contained several parts.  Respondents were asked
to report the availability of documentation concerning the file,
the information carried on the file, and the history of the file
development and maintenance.  For the most part, each agency made a
serious effort to provide detailed responses to the questions.
                         B. Survey Results
     This section briefly summarizes the survey results.  First.
the files pertaining to individuals are considered. then those
pertaining to businesses.  Detailed tabulations from the survey are
included in Tables II.1.1 and III.2.
1.   Files Pertaining Mainly to Individuals
     Not unexpectedly, there are extensive differences
among the administrative record files on individuals.  some of
those which deserve special mention are the differences in coverage
(or "universes") among the files, the degree of coded geographic
information; the demographic item included and the reporting units
used:
     a. Universe
     In terms of coverage of individuals in the U.S. population.
the decennial Census files are the most complete, followed by
Social Security's Summary Earnings Files and the IRS Individual
Master Fide.  No other files have the same breadth of coverage as
these.  However, several other files do provide comprehensive
coverage of important segments of the population.  For example, the
Health insurance Master File for the "65 + " population, the
[GRAPHIC] \SW612.GIF
                                12
[GRAPHIC] \SW613.GIF
                                13
[GRAPHIC] \SW614.GIF
                                14
[GRAPHIC] \SW615.GIF
                                15
[GRAPHIC] \SW616.GIF
                                16
[GRAPHIC] \SW617.GIF
Central Personnel Data File-for Federal government workers; and the
Military Personnel Data Files-for present and former Armed Forces
members.
     b. Geographic information
     Administrative files tend to have limited coded geographic
information.  Some contain a State code, but this was usually
derived from the mailing address.  The only exceptions appear to be
SSA's Master Beneficiary Record file, and the related HCFA Health
Insurance Master File, which contain a county code obtained by
clerically coding the mailing address.  By way of contrast, the
Census geographic data are collected on a residence basis and we
available to the block level.
     This lack of detailed "residence geography" is a major problem
in using administrative records to prepare small area statistics.
By using the mailing address, subcounty geography may be assigned
with a Geographic Base File developed for use in the 1970 or 1980
census.  However, this presents a number of problems.  First, the
mailing addresses are not always the usual place of residence.
Second, GBF's do not exist for areas located outside the built up
portion of SMSA's.  Third, people living outside the city limits
tend to report themselves as living in the city if they have a city
post office address.  Fourth, post office delivery or zip code
areas do not conform with political boundaries.  Also, the cost of
assigning geography with a GBF system is high.
     Another approach is to add a residence geographic code to the
administrative file.  This was done for the 1972 and 1975
Individual Master Files so that IRS data could be used in preparing
population and per capita total money income estimates for use in
distributing General Revenue Sharing funds.  The cost of this
straightforward approach makes it unlikely that it will be widely
implemented on other files.
     c.   Demographic information
     By   comparison with the Census data, all administrative
files contain very limited demographic information.  The Numerical
Identification (SS-5) file does contain sex, date of birth, and
race which have been transferred to the Summary Earnings Record and
the Master Beneficiary Record.  The personnel files also have some
race information.  However, other than this, there is very little
demographic data present.
     d. Reporting unit
     The Census data are the only data organized into households
and families.  Tax returns, and Social Security claims, however,
can for some purposes be treated as approximations to family units.
For the most part, however, the units are just individuals with no
potential for structuring them into households.
     One final point.  The survey showed that all the
administrative files for individuals are organized by social
                                17
security number.  This is distinct from the decennial census files
which do not-have the SSN recorded- BY and large, the SSN is the
major administrative identifier. Obviously, then, it is this
variable which would have to be employed for linkages among the
files-whether for statistical or operational purposes.
2.   Files pertaining Mainly to Businesses
     The employer identification number is a major identifier on
most of the administrative record files- including even the Census'
Standard Statistical Establishment List. Some other similarities
and differences in the files are:
     a. Universe
     The file with the largest coverage is the Master Employer Name
Directory with about 27 million records' However, this file is not
current and contains inactive businesses.  The SSEL is the most
comprehensive current list of businesses with the exception of the
very small businesses.  For these businesses, the IRS Business Mas-
ter File is more complete.  The Department Of Agriculture's
Producer name and Address Master File, and their Economics,
Statistics, and Cooperative Service List Sampling Frame have
extensive coverage of the farming sector.
     b. Geographic information
     As with the individual record systems, them is no subcounty
geography data,present on any of the business files with the
exception of the SSEL.  For businesses, location may have different
meanings.  Most of the geography reported on these files is in
terms of company headquarters and may not refer to the individual
establishment.  Consequently, a reporting of a major geographically
dispersed company at its headquarter's location can introduce a
significant error into the data.
     c. Economic data
     Number of employees, total payroll, and gross sales seem to be
the most common economic items present on the files.
     d. Reporting Unit
     The reporting unit of these files is mainly the Employer
Identification Number with the exception of the SSEL.  This creates
a problem in any statistical use of these files because some EIN's
represent only part of a company but an EIN may cover many
establishments.
              C. Continuous Work History Sample Files
     The survey results in the previous section indicate
clearly that individual administrative record files usually do not
contain the comprehensive population coverage and detailed
identification of population characteristics desired for most
statistical analysis.  The results also indicate, however, that it
is often technically possible to overcome some of the limitations
of single administrative files by linking several files and merging
the information contained in these files.  With files pertaining to
individuals the SSN provides the principal basis for linkage and
with business files the EIN is usually the basis for linkage.  Both
the problems and the potential benefits of file linkage we
increased significantly when interagency linkages are considered
(see, for example, the discussion of the Linked Administrative
Statistical Sample in Chapter VI); but highly valuable statistical
files can be developed through intra-agency linkages of
administrative files in such large agencies as IRS and SSA.  The
Continuous Work History Sample program of SSA illustrates well the
problems and potential of such intra-agency file linkages.
     The CWHS program involves the construction of several
statistical sample files from information contained in the SSA
administrative files documented in Tables III.1 and III.2., The 1
percent 1937-to-date CWHS file, for example, involves primarily the
extraction and merger of information from the Summary Earnings
Record and Master Beneficiary Record files documented in Table III.
1. Annual and longitudinal employee-employer CWHS files are
constructed largely by merging detailed earnings items which are
input to the Summary Earnings Record File with industrial and
geographic information obtained from the SSA employer files
documented in Table III.2.
     CWHS files do not contain occupational information for
workers, nor do they contain the detailed socioeconomic
characteristics available in census sample files.  CWHS files do,
however, contain information on worker sex, age, and race; and they
can provide much greater longitudinal detail relating to the
earnings history of workers than is available from any survey
source.  The CWHS program, moreover, has a considerable advantage
over household surveys in obtaining employer information because of
the possibility of direct links between employer and employee
administrative files.  The advantage of direct links between
employer and employee information; however, is offset somewhat by
quality problems associated with the geographic and industrial
coding in SSA employer files (sec Chapter VII).
     Because the CWHS program illustrates well both the potential
and the problems associated with the statistical use of
administrative records. examples of CWHS applications and
deficiencies are presented throughout the report.  Some of the more
detailed references to the CWHS program are included in: (1) the
discussion in
                                18
     Chapter V of the new joint IRS-SSA system of annual employer
reporting (on Form W-2) of individual worker wages; (2) the
discussion in Chapter VI of the development of the new Linked
Administrative Statistical Sample program; and (3) the discussion
in Chapter VII of technical problems encountered in the statistical
use of administrative records.  To permit the reader to better
follow the references to the CWHS made throughout the report, a
detailed description of the CWHS program and CWHS files is
presented in the second appendix to this chapter.
   D. The Evolution of Statistical Use of Administrative Records
     Chapter IV contains a detailed discussion of statistical uses
of administrative records from the perspective of selected Federal
agencies that make extensive use of administrative records in their
statistical and research programs.  Chapters V and VI then follow
with detailed case studies of selected projects and programs
involving intensive statistical use of administrative records.  To
provide additional background for the chapters on uses, this
section reviews some of the circumstances surrounding t he
evolution of statistical uses of administrative record files
covered in Tables III.1 and III.2.
     The use of administrative records as a source of statistical
information is not a new idea, but the last decade's extensive
computerization of these files has fostered an increasing interest
in the topic.  In fact, there seems to have been a progression in
the employment of administrative records for statistical purposes.
Initially, with the establishment of an administrative records
system, an agency prepared summaries of the data for guiding their
operations and for policy decisions.  This may be done with the
full data set or a sample.  Its purpose is primarily
administrative, not statistical.  Perhaps IRS is the best example.
What started out as a mainly administrative effort has evolved into
the current Statistics of Income program (see Chapter IV).  While
administrative considerations are still important, the Statistics
of Income sample is used extensively by researchers to study issues
of general statistical and economic interest.
     Administrative records systems were used very early in
evaluation projects such as the evaluation of the 1950 Census
income results using IRS and SSA data (NBER, 1958).  After each
decennial population census since then, there have been attempts to
understand and quantify any error in the results by matching a
small sample of census records to various administrative record
sets such as IRS data (Schneider and Knott. 1973), Medicare data
(U.S. Bureau of the Census 1973c), birth records (U.S. Bureau of
the Census, 1963 and 1973a), death records (Kitagawa and Hauser,
1973), and employment records (U.S. Bureau of the Census, 1965).
These evaluation efforts may be characterized by the relatively
small number of cases involved.  This limit on size is the result
of the objective of the project as well as cost considerations.
Most evaluation projects involving these Federal files are aimed at
National results only and do not attempt to measure differences at
the State or even regional level. (This is changing, however, for
the 1980 Census Evaluation, the matching will attempt to produce
estimates at the State level-see Chapter VI.)
     With the extensive computerization of administrative files in
the 1960's, the possibilities for expanded statistical uses became
obvious.  For example, IRS completed the computerization of the
Individual Master File with the 1967 file.  Also, over this same
period, there was a great reduction in the cost of computer data
processing and an increase in understanding how to process and
control large data files, thus making the use of these administra-
tive files feasible for statistical purposes.
     These developments and potential uses of administrative
records were understood and debated (Hansen, 1974).  While that
debate cannot be reviewed here, the outcome has been that no
centralization of administrative records has taken place in the
Federal government, but statistical uses of administrative records
have continued.  Some transfer of administrative records between
agencies has been permitted, but each transfer has been justified
and approved on a case-by-case basis (Kilss and Scheuren, 1979).
Some people feel that this case-by-case approach has retarded the
use of administrative records in developing useful statistical
data, but this has never been fully documented.
     In one sense, survey- and census-based data may be blamed for
the slow development of administrative records-based data.  Up
until recently (and perhaps still), survey- and census-based data
have had a real edge on administrative records in several areas.
For example, if small area data are needed, the Census of
Population and Housing provides small area data defined completely
and in the "correct" geography (i.e., by residence).  Adminis-
trative records-based data may be able to approximate the needed
data, but not at the same level of accuracy.  It is a question of
trading-off accuracy for currency.  If the need is for national.
regional, or even State data, surveys may be a more efficient way
to obtain needed data than the development of an administrative
records-based system.
     However, with the need for small area data on a regular basis,
the currency and small area advantages of administrative records
may now outweigh the disadvantages of definitional problems and
less accuracy.  For example, with the passage of the State and
Local Fiscal Assistance Act of 1972, the Bureau of the Census was
asked to
                                19
provide population and per capita total money income data for
38,500 governmental units.  The Bureau accomplished this by using
an extract from the 1969 and 1972 entire IRS Individual Master
File.  This required IRS to collect and clerically code the
residence address of all taxpayers on the 1972 IMF.  The cost of
the first set of estimates. including the IRS coding, was in excess
of $5 million.  This was the first administrative records-based
project of this magnitude and demonstrated the expense and benefit
of administrative records.  It should also be noted that this
successful application of administrative records used
administrative records to measure change since the 1970 census (Fay
and Herriot, 1979).  In this way. the definitional problems were
minimized.
     With the expanded interest in administrative records, them is
now taking place the needed experimentation and research to
understand the particular idiosyncracies of these files.  This
will, hopefully, come to fruition in the 1980's with useful data in
several areas.  For example, migration rates by race can be
computed by linking race from the SSA Summary Earnings File to the
IRS data.  This has been done on a sample basis and State estimates
prepared (Word 1978).  It is expected that this work will continue.
     By using tax returns (or W-2's) to establish a current
residence, and the Form 941 to link an employer to an employee, and
the Master Employer Name Directory (mainly SS-4) to define an
employer's location, current journey-to-work estimates are
possible.  The Bureau of the Census and the Bureau of Economic
Analysis have done some work in this area, so far, however, without
great success.  The problems of multi-establishment employers, low
quality geography coding of employers, etc.. are major obstacles
when trying to estimate the change in a particular journey-to-work
flow. (Chapter VII contains a more detailed discussion of the
problems encountered in the BEA journey-to-work study.)
Currently, the Census Bureau uses IRS adjusted gross income and
wages and salary data to update the 1970 census per capita income
estimates.  By using the age, race, and sex data from the Social
Security Administration, the IRS information could be adjusted for
differential reporting by age, race, and sex.  Updating income size
distribution estimates with IRS data has long been considered
desirable.  The inability to group IRS returns directly into
families or households makes such updating difficult, but synthetic
estimation procedures involving IRS data are being used in the
development of family personal income size distribution estimates
at BEA (see Chapter IV).
     The need for targeted surveys and more sampling efficiency for
small populations will continue to make administrative records
important as a sampling frame.  In the business files, the use of
the business lists as sampling francs may be their single most
important function, either to complete or to stratify a universe
for sampling.
     In summary. the statistical use of administrative records will
continue to grow, but not easily.  The use of administrative
records data in preparing statistics must be preceded by a period
of analysis and experimentation in order to understand the
particular problems inherent in each administrative record system.
                        E. Appendix III.1
                       The Survey Questionnaire
Statistical Use Survey of Records Pertaining to
Individuals, Individual Firms, or Employers Maintain
and/or Mandated by the Federal Government
Survey for: Subcommittee on Statistical Uses of Administrative Records
            Federal Committee on Statistis Methodology
            Office of Federal Statistical Policy and Standards
Please complete the following questions as applicable. Since this survey
covers individuals, householdsm and business organizations (firms and
employers), not all of the questions may pertain to the data file you are
answering the questions about.  If you have any questions concerning the
survey or concerning a particular question; or need additional copies of the
survey form, please contact Ms. Maria Gonzales on (202) 673-7953.
              (Please mark the appropriate category or categories
                      or supply the requested information)
1.  What is the name of the file?
    A) General name by which the file is usually called___________________________
    B) Technical or official name if different from
       the general name_______________________________________________________
2.  What type of documentation exists for the file?
    __ International Documentation
       __ Not available to anyone outside the agency.
       __ Available on request.
20
                           20
  _ Outside Documentation
    _ None currently prepared.
    _ Available on request.
    _ Not now available, but could be prepared upon request.
3.  What type of documentation is available outside the agency?
  _ Record Layout
  _ File description--technical description
  _ General file description without specific field description
  _ No documentation available outside agency
4.  What type of information is present on the file? The purpose of this
    question is to obtain a list of the kind of information present on the
    file which might have statistical uses.  You may respond to the
    appropriate questions below or provide a separate listing of the infor-
    mation on the file.  Is the reporting or filing unit an individual,
    household, business, or some other unit?
    _ Individual (Answer 4A)
    _ Household, Family, or Other Group of Individuals (Answer 4B)
    _ Business or Employer (Answer 4C)
    _ Other reporting unit (Answer 4D)
4A. What kind of information on individuals is present on the file?
                                        Please Circle Yes
                                       or No as Appropriate
1) Person's name                           Yes      No
2) Mailing address                         Yes      No
3) Residence address                       Yes      No
4) Has the address been assigned           Yes      No
   a geographic code? If yes, what
   level of geography are present?
     State                                 Yes      No
     County                                Yes      No
     Place                                 Yes      No
     Other, please specify__________
5) Race--If yes, what are the cate-        Yes      No
   gories?
6) Spanish or oher ethnic origin de-
   signation--If yes, what are the
   categories? ____________________        Yes      No
7) Date of birth or age                    Yes      No
8) Sex                                     Yes      No
9) Marital Status--If yes, what are
   the categories?__________________       Yes      No
10) Income--If yes, what are the           Yes      No
    types of income present?________
11) Person's family or household in-
    come--If yes, please specify type.
12) Social Security or Railroad Retire-
    ment Number                            Yes      No
13) Is the person's employer identified?   Yes      No
    If yes, is the employer's Empoly-
    er Identification Number present
14) Is the person's occupation identi-     Yes      No
    fied?
15) Is the person's occupation identi-     Yes      No
    fied?
16) Level of education or technical        Yes      No
    skill
17) Place of birth or foreign country      Yes      No
    of birth
18) Information on person's health or      Yes      No
    disability--If yes, please specify
    __________________________________
19) Other relevant statistical informa-    Yes      No
    tion --If yes, please specify_____
4B. What kind of information on a household, family, or other group
    of individuals is present on the file?
                                        Please Circle Yes
                                       or No as Appropriate
1) Person's name                           Yes      No
2) Mailing address                         Yes      No
3) Residence address                       Yes      No
4) Has the address been assigned           Yes      No
   a geographic code? If yes, what
   level of geography are present?
     State                                 Yes      No
     County                                Yes      No
     Place                                 Yes      No
     Other, please specify__________
5) Household or family size                Yes      No
6) Each household or family member         Yes      No
   identified
7) Household or family income              Yes      No
The following questions apply to the household or familly head or
primary applicant.
8) Date of birth or age                    Yes      No
9) Sex                                     Yes      No
10) Race--If yes, what are the cate        Yes      No
     gories? ______________________
11) Spanish or other ethnic origin des-    Yes      No
    ignation--If yes, what are the
    categories? ___________________
12) Social Security or Railroad Retire-    Yes      No
    ment Number
4C.  What kind of information on business organizations or employers
is present on this file?
                                                Employer         Other please
                 Company or      Establish-     Identification  specify in the
                 Enterprise         ment        Number (EIN)   Remark section
                 ___________________________________________________________________
The file is
organized by
(please check                                                      
the correct):
1) Name             Yes     No     Yes     No    Yes     No        Yes     No
2) Address          Yes     No     Yes     No    Yes     No        Yes     No
3) Location code    Yes     No     Yes     No    Yes     No        Yes     No
for establishment
or other report-
ing unit
                                21
4C.  What kind of information on business organizations or employers
     is present on this file? (Continued)
                                                            Employer         Other please
                            Company or      Establish-     Identification  specify in the
                             Enterprise         ment        Number (EIN)   Remark section
                 ___________________________________________________________________
4) Number of employees--       Yes     No     Yes     No    Yes     No        Yes     No
 If yes, as of what
  date?_________________
5) Total payroll               Yes     No     Yes     No    Yes     No        Yes     No
    Annually                   Yes     No     Yes     No    Yes     No        Yes     No
    Quarterly                  Yes     No     Yes     No    Yes     No        Yes     No
6) Primary industry-- if yes   Yes     No     Yes     No    Yes     No        Yes     No
   what industry coding
   system is used?  for
   example, 4 digit SIC,
   2 digit SIC, etc.
   ______________________
   ______________________
   ______________________
7) Secondary industry          Yes     No     Yes     No    Yes     No        Yes     No
8) Gross sales or receipts     Yes     No     Yes     No    Yes     No        Yes     No
9) Product description         Yes     No     Yes     No    Yes     No        Yes     No
10) Amount and description of  Yes     No     Yes     No    Yes     No        Yes     No
    capital base, total invest-
    ment in plant and equip-
    ment
11) What other items of statistical interest are available? Please list
    in Remarks section below.
4D. What kind of information is available for the "other reporting unit?"
    Please specigy the kind of information present on the file for the "other
    reporting unit" in the space provided below.
5.  What are the applications or forms which the data are derived? If
    possible, include the OMB (or other) form number.
6.  Briefly describe the process by which this information is obtained
    from the individual or business(firm, employer) and procesed
    to the data file being described.
7.  What is the purpose of the file?  If the purpose is to meet specific
    legislative requirements, please include a citation for applicable
    Federal law agency regulation, or agency requirement.
8. a) Is the file a computerized version of a "paper system?"
               Yes         No
   b) What year was the file first created?________________________
   c) Has the file been expanded or has the data on the file
      changed significanlty over its history?    Yes        No
        If yes, please explain how.
9. How many individuals or businesses are represented on the file?
   (An approximate number only.) __________________________________
10. What are the restrictions on the use of file?
    a) Legal Restrictions--
    b) Administrative Restrictions--
    c) Other Restrictions--
11. If either the SSN or EIN are present on the data file, what is their
    purpose?
12. Is the file currently being used for statistical purposes?
        Yes     No
    For example: Is the file used as a sampling frame for any surveys?
    Are tabulations prepared from the file that are used for statistical
    purposes?
    Please briefly describe any statistical uses of the data file.
13. How often are data collected and updated for this file?
        Collected                            Updated
        _ One time only                  _ As needed
        _ Annually                       _ Annually
        - Quarterly                      _ Quarterly
        _ Other, please specify          _ Other, please specify
14.  Please provide the name, address, and telephone number of a person
     who could answer questions concerning the data file (this persons
     need not be the same person who answers this survey).
        Name:  ___________________________________
        Address:  ________________________________
                  ________________________________
        City and State:___________________________
                       ___________________________
        Zip Code: ________________________________
        Telephone Number:  _______________________
15. Name and telephone number of person who completed this survey
    if different from above.
        Name:  ___________________________________
        Telephone Number:  _______________________
                                22
                         F. Appendix III.2
                       The CWHS Data System
     The Continuous Work History Sample is a system of general
multipurpose statistical data files designed primarily for
socioeconomic research.  The system consists of samples of records
of individuals with employment covered by social security.
Earnings, employment and benefit data for the individual along with
personal characteristics and employer characteristics are
maintained at varying degrees among five basic data files and two
special files that are produced in the CWHS system.
     This appendix describes: (1) the data sources for the CWHS
system; (2) the procedures used to construct the administrative
data files underlying the system; (3) the procedures used to create
statistical files from the records in the administrative files; (4)
the sample design used for the system; and (5) the principal data
elements in each of the five basic CWHS files.  The discussion
refers to data and procedures predating the start of annual wage
reporting in 1979 (for calendar year 1978).  A discussion of the
new annual reporting system is presented in Chapter V. And Chapter
VII contains considerable discussion of the limitations of CWHS
data.
1. Data Sources
     Data for the CWHS are obtained from records derived from
reporting and informational forms and applications used in
administering the retirement, survivors and disability programs of
the Social Security Administration.  The date of birth. sex and
race of the person is obtained from the Application for a Social
Security Number (Form SS-5).  Geographic and industry information
is obtained from the employer's Application for an Identification
Number (Form SS-4) and other related forms that are used
periodically to update this information (Form OAA-100, OAA-103 and
SSA-5019).  Initially, employers are assigned geographical and
industry classifications based on the location and nature of
business information sup. plied on the Form SS-4.  Information that
is not satisfactorily reported on the SS-4 is obtained through the
supplemental forms OAA-100 and OAA-103.
     Employers who operate more than one place of business and have
a total of 50 employees with at least six in a separate location
are asked to use the Establishment Reporting Plan.  Under this plan
the employer gives SSA- a list showing the location. industrial
activity and approximate number of employees of each establishment.
On subsequent wage reports the employer groups his employees by
establishment, identifying each group with a  preassigned
establishment number.  The arrangement allows SSA to properly
classify the employees according to geography and industry.
     Data on earnings and employment are derived from various
reporting forms submitted by employers and self-employed persons.
Prior to 1978, with the advent of annual wage reporting, taxable
wages of employees were reported quarterly by regular employers on
Form 941, household employers on Form 942, and State and local
government employers on Form OAR-S3.  Farm employers report
annually on Form 943 and self-employed persons use Schedule SE of
Form 1040 to report annually. (Refer to Chapter V for a discussion
of the new annual reporting system).
     Claims and benefits information is obtained from applications
and forms that are completed in the process of filing for and
determining entitlement to benefits.
2.   Processing Procedures--Administrative Records
     The demographic information (date of birth, sex and race)
furnished by the applicant on the Form SS-5 is extracted after the
social security number has been issued.  This information is
maintained on magnetic tape in a master file called the Summary
Earnings Record (see Table III.1).  This is the record in which the
lifetime earnings and quarters of coverage of the individual is
recorded for use in determining entitlement to benefits and
calculating benefit amounts at the time a claim for benefits is
made.
     The information supplied by the employer on the Form SS-4,
relating to the location and nature of his business, is manually
coded with geographic and industry codes.  This information is key
punched and maintained on magnetic tape in a master file of
employers called the Employer Identification file (see Table
III.2). Additionally, the information supplied on Form SSA-5019 by
multi-unit employers using the Establishment Reporting Plan per-
taining to the location and nature of business of each separate
reporting unit, is also manually coded with geographic and industry
codes and maintained in the EI file.
     The earnings data that are reported by employers are received
and processed at SSA in a variety of ways.  Hand filled paper forms
that meet certain criteria are optically scanned to produce a
machine-readable record, while others are keypunched.  Some
employers, usually having a large number of employees, report
directly on magnetic tape.  The reports of self-employed persons
are received directly from the Internal Revenue Service on magnetic
tape.  After all of the earnings data is in machine-readable form
with appropriate identifying information, the tapes enter a
computer balancing operation in which each page of each report is
checked to see that the wage items balance to the page totals
provided by the employer.  Out
                                23
of balance items are investigated and corrective action taken.
Balanced items are passed on to an operation where individual items
are sorted in social security number sequence and then matched to
the Summary Earnings Record on number and the first six letters of
the surname.  Earnings amounts are added to the summary records
where complete matches occur.  Unmatched records are rejected for
further investigation and processing.
     Prior to annual reporting, this processing occurred at regular
intervals four times during the year.  It generally takes about 9
months after the end of reference period to receive, process and
update the summary earnings records with virtually all of the items
for that period.
     Claims for social security benefits are filed in local social
security district offices.  Requests for earnings records and
benefit computations are made by the district offices to SSA
headquarters.  After the earnings record is located, benefit
computations are made and documentation of the claim is prepared
and forwarded to the requesting office where the claim is developed
and forwarded to program service centers for benefit authorization.
Upon authorization of benefits, the program service center sends a
notification of award to headquarters where a new beneficiary
record is established in the Master Beneficiary Record file (see
Table III.1).  Changes to records in the beneficiary file are made
through reports by the district office or program center.  The
Master Beneficiary Record file is used in the preparation of
monthly social security benefit check records which are forwarded
to the Treasury Department for payment.
3. Processing Procedures-Statistical Records
      Once a year after the Summary Earnings Record has been
updated with virtually all of the prior year's earnings, a 1
percent sample (based on specified digits of the social security
number) is extracted.  This file becomes the foundation for
producing the 1 percent 1937-to-date CWHS.  It is used along with
the prior year's CWHS, a 1 percent sample extracted from the Master
Beneficiary Record file, and miscellaneous correction files to
generate the required data elements for the current year's 1
percent CWHS.
     At the same time that earnings data for the current processing
period are posted to the Summary Earnings Record, the 1 percent
sample of earnings items records are written off separately on
magnetic tape.  The items are accumulated until all four quarters
of the year have been processed.  They are then summarized into one
record for each employee-employer-establishment combination with
quarterly earnings amounts maintained separately.  The resulting
records are matched to the Employer Identification file and
geographic and industry codes are inserted.  They are then
resummarized to an employee-employer level.  Cases having
employment with more than one establishment of the same employer
are assigned to the unit having the most activity in terms of
quarters of employment.  A match is the n made to a special extract
from the 1 percent sample 1937-to-date CWHS containing date of
birth, sex and race codes.  These personal characteristics are
inserted into the record to form the final 1 percent Sample Annual
Employee-Employer file.
     Another file of the earnings items that are posted to the
Summary Earnings Record, previously referred to, is written off
separately for another type of processing.  This is a 0.1 percent
sample and is a subset of the 1 percent sample.  These records are
accumulated over the same time period as the 1 percent sample
records and are processed along with the prior year's 0.1 percent
basic file and a special 0.1 percent write off of certain data
items from the current year's 1 percent CWHS file to create the
current year's 0.1 percent 1937-to-date CWHS.
     Information for self-employed persons. coming from the
Schedule SE of the Form 1040, is submitted to SSA from IRS directly
on magnetic tape.  After initial processing of these records in
order to properly credit and post earnings to the Summary Earnings
Record, the 1 percent sample records in this file are written off
for statistical processing.  In subsequent computer operations IRS
industry codes that are in the original record are converted to SSA
industry codes and addresses are converted to geographic codes
through a special coding file that utilizes Zip code and place
names.  Correspondence is generated for cases with missing and/or
incomplete information asking for the required data.  The final
resulting file from these operations is the 1 percent Sample Annual
Self Employed file.
     In addition to the regular statistical processing described
above, in recent years special processing has been done to generate
two additional files; the First Quarter Employee-Employer-
Establishment files for the 1 percent sample and a special 10
percent Sample First Quarter Employee-Employer-Establishment file.
Processing for these files is similar to processing for the Annual
Employee-Employer files except that it is done after all first
quarter receipts have been received and posted to the summary
earnings record.  Record contents are virtually the same as the
annual except that only first quarter data are included.  The 1
percent first quarter files have been prepared for the years 1970-
76, while the 10 percent first quarter files have been produced for
the years 1971, 1973, and 1975.
4. Sample Design
     The population from which the CWHS is selected consists of the
one billion possible nine-digit social security
                                24
numbers.  These numbers have the following digital arrangement:
 Area in which
    number
   assigned          Group number       Serial number
(three digits)       (two digits)       (four digits)
     XXX                XX                   XXXX
In the issuance of social security numbers, each State is assigned
one or more area numbers with the exception of a special block of
numbers assigned prior to August 1963 to persons covered under the
Railroad Retirement Act.  Each State number, in combination with a
given group number defines a stratum.  The population assigned
social security numbers is thus stratified geographically (by place
of application for social security number) and chronologically (by
the process of assigning these numbers).  Each number is an element
of a given stratum, and the population represented by the possible
one billion elements constitutes the sampling frame.
     The CWHS is a longitudinal sample of persons with covered
employment.  The sample consists of all persons who have social
security numbers with specified digits in certain of the serial-
number positions and who have covered employment during any defined
reference period.  The digital selection pattern remains constant.
The employment and earnings histories for persons in the sample are
available from 1957 forward, with limited additional earnings data
going back to 1937.
     The 1 percent CWHS may be described as a stratified cluster
probability sample of all possible social security numbers.  A
stratum consists of all social security numbers with the same area-
group number.  In a stratum for which all numbers have been issued,
the 1 percent sample consists of 100 of the 9,999 social security
numbers issued. (Numbers ending in 0000 are not assigned.)
     The clustering within a stratum arises from the particular
digital selection procedure used, in combination with past methods
of assigning social security numbers.  Because of the clustering,
sampling errors of estimates from the 1 percent CWHS are slightly
larger than those that would result from a stratified random sample
of the same size.
     The present design of the 1 percent sample evolved from
earlier sample designs--an initial 20 percent sample and a later 4
percent sample.  All past designs have used the same stratification
modes as are used in the present design.
     The 10 percent CWHS is a stratified systematic sample.  The
strata are the same as those used for the 1 percent sample, and the
digital selection procedure within strata is such that them is no
clustering effect.  Therefore, sampling errors of estimates from
the 10 percent CWHS are presumed to be about the same as or
slightly smaller than those that would result from a simple random
sample of the same size.
5.   Data Files
     A brief description of the files produced in the CWHS
system is shown below, including a listing of the major data
elements.  These files had been made available on a cost
reimbursable basis with precautions taken to preserve the
confidentiality of information relating to specific individuals or
reporting units.  These precautions included limiting the data
elements to those needed by the researcher for the purposes stated
and transformation of identifying numbers to unique case numbers
which still permit linking of common records among various files.
Additionally, a conditions-of-release agreement was signed by the
requestor.  At present, however,  SSA is not releasing CWHS files
to the public pending legal clarification of restrictions on
release imposed by the Tax Reform Act of 1976.
a.   One percent sample annual Employee-Employer (Ee-Er) File
     A 1 percent sample of social security numbers for which wage
and salary employment was reported in the reference year.  There is
one record for each employee-employer combination.  Basic data
elements: (1) personal characteristics--year of birth, sex, race;
(2) wages-annual taxable, quarterly taxable, and total estimated
wages; (3) employer-State and county, industry. coverage group
(farm, household, Federal civilian, etc.); (4) insurance status;
(5) benefit status.
b.   One percent sample annual Self-Employed (SE) file
     A 1 percent sample of social security numbers for which self-
employment earnings subject to social security coverage were
reported in the reference year.  Basic data elements: (1) personal
characteristics-year of birth. sex, race; (2) self-employment--
taxable income, net comings, State and county, industry; (3)
taxable earnings (including wages, if any); (4) type of work-farm
or nonfarm self-employment (and wage indication. if any); (5)
insurance status; (6) benefit status.
c.   One percent sample Longitudinal Employee-Employer Data (LEED)
     file
     Assembled from the 1 percent sample annual Ee-Er records which
art prepared yearly.  In the annual files. one record is created
for each employee-employer combination during the year.  In the
longitudinal file, the original records from the various annual
files have been skeletonized, resequenced, and merged so that all
records associated with an employee over the time span of the file
appear
                                25
together.  Basic data elements are the same as in the 1 percent
sample Ee-Er.
d.   One percent 1937 to date CWHS file,
     A 1 percent sample of social security numbers issued
through cut-off date of file reflecting entire work experience in
covered employment.  Basic data elements: (1) personal
characteristics- year of birth, sex, race; (2) employment-number
and pattern of years employed, first and last years employed,
pattern of quarters employed (last 2 years), number of quarters of
coverage 1937 to date, pattern of quarters of coverage 1957 to
date; (3) type of work-farm or nonfarm, wage or self-employment;
(4) taxable earnings each year 1951 to date; (5) self-employment--
taxable income each year 1951 to year prior to current year, net
earnings, for year prior to current year; (6) insurance status; (7)
benefit status.
e.   One-tenth of 1 percent 1937 to date CWHS file
     A 0.1 percent sample of social security numbers issued through
cutoff date of file reflecting entire work experience in covered
employment.  Basic data elements are generally the same as for the
1 percent CWHS except for more detailed earnings information, e.g.,
taxable wages each year 1937 to date, taxable farm wages each yew
1955 to date, quarterly wages each quarter of each year 1951 to
date, net earnings from self-employment each year 1956 to date.
In addition to the files described above, two others have been
created at the request of the Bureau of the Census and the Bureau
of Economic Analysis-the 1 percent sample and 10 percent sample
First Quarter Employee-Employer-Establishment file.  Microdata has
been made available from the 1 percent sample first quarter file;
however, only summary files and tabulations from the 10 percent
sample are available.
                                26
                                                         CHAPTER IV
         Major Statistical Uses of Administrative Records
     Most of this chapter is devoted to review of statistical uses
of administrative records in five selected Federal agencies.  These
agencies include: (1) the Internal Revenue Service and (2) the
Social Security Administration, which represent two of the largest
primary collectors of administrative data pertaining to individuals
and businesses. (3) the Bureau of Economic Analysis, which uses
administrative record data extensively in making estimates for the
national economic accounts and related statistical series; (4) the
Bureau of the Census, which uses a wide variety of administrative
records in developing sampling frames and evaluating survey data as
well as directly in estimating statistical-series; and (5) the
Small Business Administration, which is in the process of using
data from a variety of administrative sources in the development of
a general-purpose small business data base for use in research and
policy analysis.  Although them is no review of administrative
record use of the Bureau of Labor Statistics in this chapter,
Chapter V contains a major case study involving BLS use of
administrative records from the Unemployment insurance payroll tax
system.
     The discussion of uses in this chapter is not intended to be
comprehensive.  Brief overviews of uses by agency are supplemented
by a few more detailed discussions of uses in specific programs.
The more detailed discussions involve primarily Census Bureau
programs in the area of economic statistics.  A number of Census
Bureau uses of administrative records in population statistics
programs are covered in some detail in other chapters (especially
Chapter VI).The overviews of IRS and SSA programs are brief, but
examples of uses of IRS and SSA administrative records appear
repeatedly in other chapters.  The narrative discussion of BEA uses
is brief, in part because many of the uses of administrative
records in economic accounts can be conveniently summarized in
tabular form.  The SBA discussion involves a new program still
under development and is intended primarily to illustrate problems
facing the development effort.
     Chapter III has already provided some selected examples
illustrating the historical development of statistical use of
administrative records.  As with the examples cited in Chapter III,
most of the examples considered in this and subsequent chapters
involve direct or indirect use of primary administrative files such
as those documented in Chapter III. The distinction between
administrative and statistical data files, however, has not always
been made clear.  Therefore, to provide some additional perspective
on the process of making statistical uses of administrative
records, the first section of this chapter discusses some of the
general considerations involved in defining administrative record
files and in creating and using statistical files derived from
administrative records.  Following the first section, the remaining
five sections of the chapter discuss uses of administrative-based
statistical data on an agency-by-agency basis.  An appendix
contains selected tabular materials relating to the agency
discussions.
             A.  Defining Administrative Record Files
                   and Using Them statistically
     In statistical uses of records pertaining to persons or
businesses, the interest is generally in studying the
characteristics and attributes of groups of individual entities as
opposed to identifying specific entities and taking actions based
on their individual characteristics as in administrative uses.
Indeed, in censuses and surveys involving direct collection of
information for statistical use, it is usually felt to be important
to provide assurances to participating respondents that information
they supply will not be used as a direct basis for administrative
actions against (or for) them specifically.  Therefore, in this
report statistical (as opposed to administrative) record files will
generally be considered to be files which are not made available
for taking administrative action with respect to individual legal
entities (persons or businesses); i.e., files which are not used to
determine an individual reporting entity's legal obligations or
benefit entitlements.
     Given the distinction between statistical and administrative
files just suggested, it should be acceptable to create statistical
files from administrative files, but not vice versa.  This concept
of "functional separation" of records is being considered in
proposed legislation (see Chapter VIII), and is applied in SSA's
Notice of Proposed Rulemaking to revise its Regulation No. 1, but
is not yet well established in either the regulations or the
procedural policies followed in many Federal agencies.  The result
is
                                27
considerable variation and confusion in the extent to which
administrative records can be made available for statistical uses.
Problems related to limitations and confusion surrounding access to
administrative records for statistical use will be discussed in
connection with examples covered throughout the report; and the
legal aspects of the access issue will be reviewed in detail in
Chapter VIII.  The remainder of this section provides a brief, but
somewhat more general overview of considerations associated with
using administrative records for statistical purposes.
     The primary distinction between administrative records and
statistical records is the ultimate use to which they are intended
to be put.  This usually means a parallel distinction in the degree
to which the statistician is in control of the design and
collection of the records.  Survey records and their collection
procedures are designed, documented and controlled to yield the
desired statistical characteristics.  When administrative records
are used statistically, the statistician must locate existing
records and determine their conceptual suitability for the intended
use.  And the statistician must also devise methods for overcoming
technical problems frequently encountered in making new uses of
existing records.
     As noted in Chapter III, most statistical uses of admin-
istrative records have developed on an ad hoc basis.  With the
exception of uses by the collecting agency to generate statistics
needed for program administration, there are few examples of
administrative record systems that have been designed with
statistical uses in mind.  In most instances the statistician,
faced with the problem of generating statistics for a particular
policy analysis, fund distribution, or program evaluation purpose,
has approached an administrative record system from the standpoint
of what is available for the current application. in some instances
these ad hoc uses have become regularized and institutionalized,
but only rarely have statisticians specified changes in the design
or procedures of an administrative record system necessary to yield
more reliable statistics.  This is true even when the statistical
analysis provides essential feedback for the operation of the
administrative system.
     Statistical uses of the various administrative record sets
have generally been uncoordinated.  A body of uses and users have
developed independently for each record set.  For this reason, and
because the records are collected by different agencies with
differing legislation governing their collection and use, there is
very little standardization of the accessibility, documentation,
format, and quality of information available from the various
record systems.
     Statistical uses of administrative records, moreover, are
often met with some resistance from the operating personnel of the
collecting agency.  This is partially due to diffusion of
responsibilities.  Organizations which have responsibilities for
assembling statistics are usually no the same as those which have
responsibilities for maintaining administrative records and
consequently producing and using agencies have differing
priorities.  Even the statistical units of administrative agencies
are primarily responsible for meeting the statistical needs of that
program and only secondarily for meeting the statistical needs of
other Federal agencies, State and local governments, and other
public and private concerns.  Statistical uses are often viewed by
administrative personnel as an annoying addition to their already
overburdened work schedules.
     Other reasons for this resistance are related to confiden-
tiality restrictions and the massive nature of the record sets.
Many of the record sets are collected with either formal or
informal assurances of confidentiality to the participating
entities.  Administrative personnel are therefore either unable or
reluctant to make the records available for statistical use.  Many
of the record sets are so large, amounting to many millions of
records, that even a seemingly minor change in the information to
be collected or the collection and processing procedures could have
far reaching cost and timing repercussions.
                    B. Internal Revenue Service
     The Internal Revenue Service, in its role as tax collector,
acquires millions of records from nearly all units of the economy:
individuals, proprietorships, corporations, and nonprofit
institutions.  These records are collected for tax-administration
rather than statistical purposes.  They are, however, used to
generate a wide variety of statistics.  The Statistics Division of
the IRS has responsibility for assembling statistics from tax
records.  These statistics are used for program planning and many
are also published for general use.
     The program planning uses range from analyses of simple
operating statistics, such as the number of returns processed and
taxes paid, to analyses of alternative tax policies, including the
assessment of revenues that would be raised under alternative
policies and the impact of those policies on the economy.
     The publications for general use include the Statistics of
Income reports (annual) based on individual, corporate and other
business tax returns; occasional reports based on information
obtained from fiduciary, estate, foreign and other tax returns and
schedules; and first-time reports (in preparation) on finances of
tax-exempt organizations and pensions plans.  Supplemental reports
are prepared biannually which classify information from individual
returns by SMSA and by county.  These reports are used to provide
basic information for tax studies by Congress and
                                28
its committees, for administrative use by the Secretary of the
Treasury and the Commissioner of Internal Revenue, and by other
Federal agencies, as for example, in BEA's construction of national
and regional economic accounts.  They are also used for general
economic research in the areas of income and wealth.
Many of the IRS statistical series are produced from samples of tax
returns.  The sample files, devoid of identifying information, are
made available to bona fide researchers on a cost reimbursable
basis.  The appendix includes a description of the major
administrative record files maintained by IRS. as well as a list of
Statistics of Income publications.
     The extensive statistical use of IRS records is indicated not
only by the diversity of IRS publications and internal programs,
but also by the prominent role of IRS records and tabulations in
the uses to be discussed later in this chapter for the Bureau of
Economic Analysis, the Census Bureau, and the Small Business
Administration.  In addition, IRS data play prominent roles in many
of the case studies examined in Chapters V and VI.
                 C. Social Security Administration
     The Continuous Work History Sample statistical program of SSA
has already been discussed in Chapter III.  But the CWHS program
emphasizes work-related data from its payroll tax program much more
than data connected directly with SSA disbursement of benefits
under its various programs.  In addition to regular Old Age,
Survivors, and Disability Insurance benefit programs, SSA also
administers the Supplemental Security Income program for the needy,
aged, blind, and disabled and the Aid to Families with Dependent
Children program which provides financial assistance to certain
qualified needy children; and until a reorganization within the
Department of Health, Education, and Welfare in March 1977, admi-
nistered the health insurance program under Medicare.  The Medicare
program is now administered by the Health Care Financing
Administration, but SSA continues to provide selected data
processing services for HCFA.  And SSA is also continuing to
administer the distribution of certain black lung benefits to coal
miners and their families.  In this case SSA responsibility covers
some new claims as well as those claims that were filed before the
basic black lung program was transferred to the Department of
Labor.
     In administering these varied and complex programs, a great
many records are maintained from which statistics are regularly
generated.  These statistics relate to general and specific aspects
of the various SSA programs, dealing with number of claims, number
and amount of benefit payments, post entitlement actions,
administrative costs, etc.
     Throughout the development of the social security system,
research has been important to policy formulation and program
administration.  The Office of Research and Statistics is the chief
research resource of SSA and has the responsibility for all program
statistics and for analyses required by the Administration and by
Congress.  In carrying out its mission, ORS disseminates a large
volume of statistics in the monthly Social Security Bulletin and
its Annual Statistical Supplement as well as in other reports,
papers, and statistical releases.  The appendix (section G.2) gives
an illustration of the great variety of statistics that are
produced by ORS.  The tables listed there were taken from the table
of contents of the 1976 Annual Statistical Supplement to the Social
Security Bulletin.
                  D. Bureau of Economic Analysis
     BEA relies heavily on administrative records in the
preparation of national economic accounts and related measures.
BEA's estimates of current economic activity are based, with few
exceptions, on analysis of primary data obtained from other
agencies.  This use of available materials is economical because it
does not require extensive primary data collection activities.  It
has the further advantage of not adding to the reporting burdens of
businesses and individuals.  The process does, however, place a
burden on analysts in terms of adapting data designed for other
uses, remaining alert to changes. in source data-, and researching
potential new data sources.  In this dual role as an intensive user
and producer of government statistics, BEA accumulates more
experience than most other agencies with the systematic use of a
wide variety of administrative records.  The lack of consistent
definitions and procedures, uncoordinated formats and presentation
techniques, and inadequate timing are familiar to the BEA analyst
who must be aware of and make adjustments for deficiencies in
primary data.
     The list of administrative record tabulations which are used
directly to estimate components of the national income and product
accounts, the national input-output tables, and the international
accounts is extensive and includes various types of tax records,
regulatory records, financial records of the Federal Reserve System
and Federal Deposit Insurance Corporation, custom reports, and
budget documents.  Tables IV.1, IV.2, and IV.3 list the components
of the NIPA, input-output accounts and international accounts which
are based on administrative records.  The tables also indicate the
source of the records used.  In addition, Table IV.4 lists
components of the NIPA that are based on data from current surveys
for
                                29
which the sampling frames have been developed from administrative
record sources. (The development of such sampling frames is
discussed in the Census Bureau section).
     BEA's estimates of State and local area personal income
involve the use of many of the same administrative record sets
indicated for components of national personal income in Table IV.
1. In fact, since most current statistical surveys have sample
sizes that we too small to provide reliable State and local data,
administrative records play a relatively more important role in
State and local than in national personal income estimates.  Tax
records and budget documents are the most important sources.  The
Unemployment Insurance payroll tax program (see the case study in
Chapter V) is the principal source of wage and salary data, IRS tax
returns are the principal basis for estimating most components of
property income and nonfarm proprietors' income; and government
disbursement and related records are the basis for estimating the
bulk of transfer payments to individuals.
     For most of its work, BEA uses tabulations of records
maintained by other agencies rather than using microdata files
directly.  In a program to develop estimates of family personal
income size distribution, however,  BEA is working cooperatively
with SSA in the use of statistical matching techniques to merge
information from administrative-based microdata files with Current
Population Survey records.  The administrative data include SSA's
summary earnings and benefit records and IRS records from the
Individual Master File, Statistics of Income File, and Taxpayer
Compliance Measurement Program File.  An additional administrative-
based microdata file used extensively by BEA, particularly in
regional analysis, is SSA's employee-employer Continuous Work
History Sample (see Chapter III).  In each of these microdata files
used at BEA. individual identifiers have been removed or scrambled"
to protect confidentiality.  Even so, BEA access to several key
flies including the CWHS and TCMP files has been at least
temporarily halted by the Tax Reform Act of 1976.
         Table IV.1 National Income and Product Account
            Components Based on Administrative Records
NIPA Component                                  Administrative Record
Personal consumption expenditures:
   Tobacco and alcohol ...........         Tax records of Bureau
                                             of Alcohol,
                                             Tobacco and Firearms
   Medical and legal services ....         Business income tax returns
   Brokerage charges .............         Regulatory reports of the
                                             Securities and Exchange
                                             Commission
          Table IV.1 National Income and Product Account
       Component Based on Administrative Records -- Continued
NIPA Component                                  Administrative Record
Bank service charges ............         Regulaory reports
                                             of the Comptroller
                                             of the Currency,
                                             Board of Governors
                                             of the Federal Reserve
                                             System and the Federal
                                             Deposit Insurance Corporation
Consumer share of new                    State government motor
 motor vehicles                              vehicle-registration
                                             forms
Air transportation ..............        Regulatory report of the
                                             Civil Aeronoautics Board
Other intercity transportation           Regularoty reports of the
                                             Interstate Commerce Commission
Change in business inventories:
  Book value of inventories of           Business income tax
   nonfarm industires other                  returns
   than manufacturing and trade
Net exports:
  Merchandise trade ..............       Customs reports
Federal Government purchases             Budget documents
 of goods and service
Wages and salaries:
  Nonfarm ........................       Empoloyer payroll tax returns
  Federal government .............       Budget documents
  Employer contributions                 Employer payroll tax returns
    to social insurance
Other labor income:
  Pension plan contributions ......      Business income tax returns
Nonfarm proprietor's income .......                 "      "
Corporate profits .................                 "      "
    Corporate profit taxes ........                 "      "
    Dividends .....................                 "      "
Capital consumption allowances                      "      "
Business transfer payments ........                 "      "
Net interest ......................     Business income tax returns
                                          and regulatory reports
                                          of the FRB, FDIC,
                                          CofC, and Federal Savings
                                          and Loan Insurance
                                          Corporation
Indirect business taxes and             Various tax records
  subsidies
Transfer payments .................     Various budget documents
         Table IV.2 Input-Output Account Industry Estimates Board
                  on Administrative Records
     1-0 Industry                               Administrative Record
Agriculture, forestry, fisheries
   Receipts for use of national forest
     and forest services ...........      Reports of the US Federal Service
     Aerial application services          Reports of the FAA
Mining:
   Rental and royalty receipts            IRS, Statistics of......
                             30
         Table IV.2 Input-Output Account Industry Estimates Board
                  on Administrative Records--Continued
     1-0 Industry                               Administrative Record
Constrution:
  Installed cost of
    construction ..................         Regulatory reports of the
                                              ICC, FPC, FCC
Manufacturing:
  Addition of excise tax ..........         Administrative reports of the
                                              Treasury
  Addition of rents and royalties           IRS, Statistics of Income
  Small firm coverage in
    economic census ...............         Administrative records (Census)
  Addition of competive
    imports .......................         Customs data (Census)
Transportation:
  Operating revenues and
    expenses of:
    Regulated components of
    railroads, trucking, water
    and petroleum pipelines .......         Regulatory reports of ICC
 Regulated air ....................         CAB
 Unregulated components ...........         CAB, USDA, FAA, Corps of
                                               Engineers
Utilities:
  Operating revenues and
    expenses of regulated
    companies .....................          Regulatory reports of FCC, FPC
                                                ETA, REA
  Water and Sanitary Services                IRS, Statistics of Income
Wholesale and retail trade:
  Gross margins on sales ...........         IRS, Statistics of Income
  Sales and excise taxes
    and duties:
  Federal ..........................         Treasury reports
  State and local ..................         State and local administrative
                                                reports (Census)
Finance, insurance, and real estate:
    Banking and finance ............         FRB, FDIC, IRS Statistics of
                                                Income Administrative reports
                                                of Federally chartered banks
                                                and lending agencies
   Insurance agents and brokers              IRS, Statistics of Income
   Rents paid by business ..........         IRS, Statistics of Income
   Royalty receipts by business
     and persons ...................         IRS, individual income tax returns
   Rent and royalty receipts and
     payments by governments                  Budget documents
   Commissions for management
     and transfer of property .......         IRS, Statistics of Income
Other services:
   Activities outside the scope of
    economic censuses:
      Accounting, auditing, and
        other professional
         services ...................         IRS, Statistics of Income
     Medical services ...............         IRS, Statistics of Income
     Education service
       expenses .....................         Office of Education
Government enterprises:
   Federal enterprises ..............         U.S. Budget, Treasury Depart-
                                                 ment and agency reports
   State and local enterprises ......         State and local budget documents
                                                 (Census)
      Table IV.3 Balance of Payment Account Components
            Based on Administrative Records
Balance of Payments Component                    Administrative Records
Merchandise exports and imports                Customs-Census reports
Transportation ......................          Customs-Census reports
U.S. Government miscellaneous
  services ..........................          U.S. Post Office Department;
                                                 Department of Justice
Travel ..............................          Immigration and Naturalization
                                                 Service; Department
                                                 of Transportation;
                                                 Civil Aeronautics
                                                 Board; State Department;
                                                 Bank of Mexico; Statistics
                                                 Canada; Federal Reserve
                                                 Board
Official reserve assets ..............         U.S. Treasury
Claims and liabilities reported
 by U.S. Banks .......................         U.S. Treasury; Federal
                                                 Reserved System
Claims and liabilities on unaffiliated
  foreignes reported by U.S. non-
  banking concerns ...................         U.S. Treasury; Federal
                                                  Reserve System
U.S. Securities and foreign
  securities .........................         U.S. Treasury; Federal
                                                  Reserve System
                Table IV.4 National Income and Product Account
                  Components Based on Current Surveys
                Using Administrative-Record Based Sampling Frames
NIPA Components                                    Administrative Records
Personal consumption expenditures:
  Goods, less motor vehicles                   Monthly Retail Trade
                                                  Survey (Census)
  Personal and professional
   services ..........................         Monthly Selected Services
                                                  Survey (Census)
  Producer's durable equipment                 Annual Survey of Manufactures
                                                  (Census): Monthly
                                                  Manufacturers Shipments
                                                  Survey (Census)
                                                  Quarterly Plant and
                                                  Equipment Expenditures
                                                  Survey (BEA)
Structures ..........................          Construction put-in-place
                                                  (Census)
Change in business inventories,
 manufacturing and trade ............           Monthly surveys (Census)
Wages and salaries ..................           Monthly Establishment
                                                  Survey (BLS)
Corporate profits ...................           Quarterly Financial Report
                                                  (FTC)
                                31
                         E. Census Bureau
     The Bureau of the Census is the largest primary data
collection agency in the Nation.  It conducts the decennial
censuses of population and housing, economic censuses, agricultural
censuses, censuses of governments, special censuses and numerous
sample surveys.  In addition to these vast data collection
activities, the Bureau is also a major user of administrative
records.  It uses them directly to tabulate time-series information
and indirectly in a variety of ways including: design and
evaluation of censuses and surveys; identification of sampling
universe; estimates for non-surveyed portions of the universe; and
imputations for missing cells.
     The distinctions between administrative and statistical
records become particularly blurred with the Census Bureau
applications because so many of the records which we generally
consider as statistical are derived from censuses or surveys which
utilize administrative records in many important ways.  Even the
decennial censuses have in the past, utilized administrative
records in design and evaluation phases.
     Chapter III has already noted a major Census Bureau
administrative records program for developing intercensal
population and per capita income estimates for use in distributing
General Revenue Sharing funds to State and local areas.  Chapter
III also mentioned the importance of administrative records in
evaluation programs for the decennial censuses.  And Chapter VI
contains three detailed case studies illustrating administrative
record use in evaluation and improvement projects for the 1980
Census and in development plans for the proposed Survey of Income
and Program Participation household survey.  The examples of
administrative record uses cited in the remainder of this section
will be drawn primarily from areas of Census Bureau responsibility
for developing business and economic statistics.
1. Economic Censuses
     Under Title 13 of the United States Code, the Bureau of the
Census is required to conduct a group of economic censuses at five-
year intervals in the years ending in "2" and "7", the latest one
covering 1977.  This group includes the Census of Manufactures
(initiated in the year 1810), Mineral Industries (1840), Retail and
Wholesale Trade and Construction Industries (1929), Selected
Service industries(1933), Public Warehouses(1935), Transportation
(1963), and beginning in 1977 the remaining Service Industries
(Medical, Educational and Non-Profit Areas).
     In order to minimize the cost of the censuses and relieve the
business community of reporting burden, the Census Bureau makes
extensive use, under strict confidentiality restrictions, of
selected information derived from tax records.  These records form
an integral part of the preparatory and collection phases of the
economic censuses.  The universe of business firms is based on
selected information extracted from tax records for a tax year
period encompassing the census year.  This information. received on
computer tape includes (1) firm name and address; (2)
identification number, (3) legal form of organization. (4) business
activity code; (5) number of employees; and (6) payroll by quarter.
     For the 1977 economic censuses, the above basic information
was integrated with the Standard Statistical Establishment List
(see Chapter V) and other sources.  This process provided an almost
complete list of approximately 12,000,000 business firms engaged in
economic activity in the United States (including social and
professional services) classified by kind of business and
approximate size with employers and nonemployers separately
identified.  For this universe. the following subgroups were
identified:
     1.   Those 5,200,000 businesses that could be excused from
          filing any questionnaire because their kind of business
          as determined from tax records was not in scope of the
          economic censuses;
     2.   Those 3,800,000 in-scope small businesses that could be
          excused from filing any questionnaire since limited data
          (receipts, payroll) extracted from tax records could be
          used to develop equivalent census-type data;
     3.   Those 3,000,000 larger businesses that were engaged in
          activities in-scope of the Censuses.  Direct reporting
          was required for these firms in order to obtain all the
          information needed for the census results.
Therefore data for approximately 56% of the total business
establishments covered in the economic censuses are extracted from
administrative records.  Data for companies that were not canvassed
are obtained from the following additional items of information
extracted from tax records:
     1.   Employment
     2.   Payroll
     3.   Sales or receipts
     4.   Physical location (not available if left blank on tax
          forms)
     5.   Business status at end-of-year
     6.   Number of months in business
The cost of obtaining these extracts of tax records was less than
$2 million out of the total economic census budget.  The equivalent
cost to the Government of obtaining census reports from the excused
group of about 8.500,000 businesses would have been at least 10
times
                                32
that amount given the availability of a complete mailing list of
the excused businesses.
     The quality of statistics produced by this meshing of tax
records with reports to the Census Bureau would likely result in
more complete coverage than that obtained by full field enumeration
or combinations of field and mail enumeration techniques.  For
example, the field-enumerated 1948 Census of Business undercounted
the number of standard retail establishments by at least 150,000.
The undercount of nonstore business (e.g., mail order. house-to-
house, vending machine, and service businesses) was also
substantial but could not be determined using standard post
enumeration surveys.  In fact, the latter group in many cases can
only be identified from tax records.  In addition to identifying
the universe, data from IRS tax records are also used for companies
which fail to report and for editing the reported data provided by
the respondent. (See Chapter VII for a discussion of quality
problems with administrative-based statistics.)
2.   Census of Agriculture
     The Census of Agriculture, started in 1840 and taken at
5-year intervals beginning in the 1920's, is the only source of
statistics on agriculture that are comparable, county by county, on
a nation-wide basis for farms classified by size, tenure, type of
organization, market value of farm Products sold, and type of farm
enterprise.  The census data are widely used by Federal, State and
local governments in a variety of ways in the administration of
various farm programs, as benchmarks for the current crop NW
livestock estimates issued by the Department of Agriculture, and in
the preparation of overall measures of the economy such as the
input-output ut tables for the national economic accounts.
Prior to the 1969 census, data collection was by personal
interview.  Information copies were distributed by mail to all
households on rural routes and to post office boxes in rural
communities in the effort to locate all farm operators and have
them complete the report prior to its pickup by the enumerator.
Correlated with the burgeoning increase in the size of farms, there
has been continuing rise in the number of farmers who do not live
on the farm they operate--that is, a growing number of operators
for whom door-to-door enumeration is not a practical possibility.
Furthermore, the availability of capable people willing to accept
short-term employment as census enumerators has steadily declined,
making it more and more difficult to recruit an acceptable field
staff in all areas.  Fortunately, the availability of farm-related
mailing lists from administrative records had increased corres-
pondingly and this factor was instrumental in redesign of the dam
collection procedures.
     In planning for the 1969 Census of Agriculture, it became
evident that the method of data collection should be changed from
personal interview to a mail enumeration procedure based on
administrative records.  The size measure contained in the
administrative tax records was the controlling factor that enabled
the Bureau to send abbreviated report forms to small farmers and
thereby reduce the reporting burden for nearly one-half of the
nation's farm operators.  This resulted in an obvious reduction in
costs for collecting and processing the census data.  Subsequent
censuses, including the 1978 Census of Agriculture, which is
underway, have benefitted from the experiences and results obtained
from the 1969 undertaking where under-enumeration of small farms
was a severe problem.
3.   Survey of Minority-Owned Businesses (SMOBE)
     In 1969, SMOBE was conducted as a special project
and funded by various government agencies to determine the extent
of business ownership by minorities.  Beginning in 1972, SMOBE
became a part of the economic censuses that are required by law
every five years.  SMOBE is issued in a four part series covering
businesses owned by Blacks, persons of Spanish Origin, Asian
Americans, American Indians and Other Minorities.
Data published cover number of firms, gross receipts, and number of
paid employees.  Tax records are used extensively in developing the
statistics.  For example, minority ownership is measured for the
segments of the business population using HRS corporation,
partnership and sole proprietorship tax forms and Social Security
Administration race codes to identify businesses for "Whites",
"Negroes" and "Other Minorities." A mail survey is required to
determine businesses owned by persons of Spanish Origin and the
specific minority groups included in the "Other" minority category.
However, the mail survey is minimal compared to the effort and
costs that would be involved if tax records were not available.
(See Chapter VII for a further note on limitations of SSA race
codes.)
4.   Current Economic Indicators
     In addition to the quinquennial economic censuses and
the 5-year census of agriculture, the Census Bureau conducts a
broad series of weekly, monthly, quarterly, and annual sample
surveys in the industrial, distributive trades and service areas.
Some of these surveys have been in existence for several decades
and have been converted from a design based primarily on use of
area samples.i.e., an enumerator canvass of businesses located in a
sample of land area segments--to a mail canvass of sample of
businesses selected from the comprehensive tax file of firms
classified by size and industry.
     The samples used to collect information concerning the
distributive and service mules are primarily drawn from a
                                33
list of employer firms obtained from administrative tax records and
updated through reconciliation to the economic census results.  The
volatility of changes in the business universe, however, requires
that the sampling be updated often, if possible every quarter, to
include new business establishments and to delete those no longer
in operation.  This updating process is based on information
received from IRS on additions to and deletions from its list of
active businesses.  The total list of businesses obtained from IRS
source&serves as a control to assure that the data compiled in fact
fully cover the sectors surveyed.
     In the current industrial statistics program, similar updating
procedures from administrative records we followed but on a less
frequent basis. This includes the annual survey of manufactures,
the monthly survey of manufacturers shipments, inventories, and
orders and the more than 100 other current industrial reports
relate to specific commodity areas such as fats and oils, paper and
paperboard, and steel. The availability of updated complete tax
files has made it possible for the Bureau to undertake on very
short notice special surveys designed to meet policy-makers' needs.
Recently, for example, the Bureau undertook, at the request of the
Federal Reserve, a survey of industrial capacity to improve the
statistics relating to current business conditions.  Surveys
involving energy-related industries have also recently been
instituted.  In general, the availability of lists of businesses
classified by industrial category provides the Bureau with great
flexibility in meeting new or changed objectives.
5.   The Standard Statistical Establishment List Program
     The SSEL program is discussed in detail in Chapter V; but it
should be noted here that the SSEL provides an important mechanism
for coordinating most of the economic censuses and surveys
discussed above.  In addition, County Business Pattern publications
of employment and payroll data for State and local areas are now
based directly on the SSEL.
                 F. Small Business Administration
     Federal economic and business statistics have generally not
been well designed for the analysis of small business.  Many
agencies do not prepare tabulations by size of business and them
have been no standard guidelines for preparing size class data so
that data available by size frequently cannot be readily compared
or integrated across agency sources.  Size class data, moreover,
are often not available for comparable reporting units or on the
basis of comparable size indicators.  IRS corporate tax return
data, for example, are available for tax paying units which differ
from the establishment concept used in the preparation of most
Census Bureau business data.  Moreover, Census size class
statistics usually do not distinguish between establishments that
are separate business entities and establishments that are a part
of larger multi-unit companies, and most Census size class data use
employment as the indicator of establishment size, whereas IRS
business income tax returns collect no employment data and are
traditionally tabulated by size using such alternative indicators
as level of assets or business reports
     To address the problem of inadequate data relating to small
business, an interagency committee has recently been formed with a
mandate from the President to establish a small business data base.
SBA has been charged with the principal responsibility for
assembling the data base.  And because of the high paperwork costs
to small businesses of detailed Federal business reporting
requirements, emphasis in developing the new data base will be
placed on utilizing existing primary data sources and particularly
on more efficient statistical use of administrative records.  The
initial focus of the interagency committee has been placed on
developing proposed standards for tabulation of data by business
size and on developing approaches to resolving such problems as the
difficulty of obtaining size data based on comparable reporting
units and comparable indicators of business size.
     Some promising approaches to improving small business data are
being tried.  IRS, for example, is currently linking payroll tax
reports to corporate income tax returns in order to add employment
and payroll measures to its corporate tax data base.  And plans are
underway to use various tax records to develop a longitudinal data
base for a sample of business units.  Nevertheless, the problems
associated with improving the utilization of existing record
collection mechanisms are formidable.  One critical problem is the
lack of adequate access to a systematic business list, such as the
SSEL, which can be used to identify the various kinds of business
reporting units and link together business reports in ways that
desired variables can be tabulated on the basis of common size
classifications and reporting unit concepts.  Indeed, the SSEL
would appear to be a central factor in efforts to solve a variety
of data problems extending well beyond the need for small business
data per se, and even involving a variety of problems relating to
developing data files pertaining to individual workers.  Because of
its wide-ranging importance, the SSEL program is described in some
detail in the next chapter.  Issues of access to the SSEL are
covered in Chapter VIII.
                                34
              G. Appendix IV.1. Data from IRS and SSA
     This appendix contains descriptions of (1) IRS administrative
record data files; (2) special data files produced for the Bureau
of the Census from IRS administrative files; (3) IRS sample data
files developed from administrative records for statistical use;
and (4) IRS Statistics of Income publications.  In addition the
appendix contains a list of data tables available in the Annual
Statistical Supplement to the Social Security Bulletin.
1.   Data from IRS
Administrative Record Data Files
     Business Master File (BMF)--Contains selected data from the
return of partnerships, corporations, fiduciaries, charitable
trusts, and business related data of exempt organizations.  In
addition, it includes data from a=, gift, and various excise tax
returns, and employment tax return data is on this file for all
entities.
     Individual Master File (IMF)--Contains selected data from the
tax return records of all individual income tax return filers
including sole proprietorship data reported on Form 1040 Schedules
C and F.
     Exempt Organization Master File (EOMF)--Contains selected data
from the return of exemptions which have been granted tax
exemptions as organizations organized and operated exclusively for
religious, charitable, educational, governmental, or similar
purposes.  This file is an information file whose primary function
is to provide data to monitor the numerous types of exempt
organizations.  The organization is established on the EOMF when it
applies for and is granted a tax exemption.
     Employee Plans Master File (EPMF) - is maintained for use by
the Internal Revenue Service, Department of Labor, and Pension
Benefit Guaranty Corporation.  The file contains selected data on
plan characteristics obtained from applications for plan approval
or determination letters and data from the annual return records.
Unlike the ODMF which only established an entity on the file when
an exemption is granted, an entity is established on the EPMF upon
receipt of an application for approval or determination letter, or
when an annual return is filed.
     Individual Retirement Arrangement File (IRAF)--Contains
selected data on individual retirement arrangements.  Special Data
Files Produced for the Bureau of the Census from Master Files.
     The Business Master File Entity Change File--this file changes
and supplements the annual BMF.  Changes are to entity name and
address and filing requirements.  New entities are added and
indicators are set to mark inactive records.
Employer's Quarterly Federal Tax Return File--this file contains
quarterly payroll, taxable tips and FICA wages paid for all
companies with a 941 (domestic payroll), 941 PR (Puerto Rico
payroll) and 941 SS (Virgin Islands, Guam, etc.) filing
requirement.
     Corporation and Partnership Return File--file contains large
corporation (1120) and small corporation (1120S), and partnership
(form 1065) annual receipts data.
     Sole Proprietor Name and Address File - file contains names
and addresses for sole proprietors who report profit or loss from
business or profession (schedule C) and/or report farm income and
expenses (schedule F).
     1040 Schedule C and 1040 Schedule F Data File - this file
contains receipts data and physical address for sole proprietor
businesses.
     Exempt Organization Business Income Information Return Files
(990C, 990T, 990PF)--file contains business receipts for selected
organizations exempt from filing an income tax return.
     Employer's Annual Tax Return for Agricultural Employees File--
file contains annual FICA payroll for all employers with a 943.
     Alphabetic BMF Microfilm File (Name Directory)--this file is
the Business Master File, in alphabetic sequence, on microfilm.
Sample Data Files for Statistical Use
     Corporation Source Book--is based on a sample of corporation
returns.  It provides corporate income and balance sheet tables, by
asset size for approximately 175 industry groups.  These are
available to the public for a charge on hard copy, on microfilm,
and magnetic tape.  These tables form the basis for the annually
published reports, Statistics of Income, Corporation Income Tax
Returns.
     Statistics of Income Tape--derived from samples of United
States individual, corporation, fiduciary, estate, partnership,
exempt organization and pension plan returns are retained on
magnetic tape.  On a cost reimbursable basis, bona fide researchers
may obtain copies of these tapes devoid of identifying and
geographic information.
     Individual, Proprietorship, Partnership, and Corporation Tax
Model File--files which are based on the Statistics of Income
samples, and are available annually, contain, in general, the data
present in our annual individual Corporation and Business Income
Tax Returns reports.  On a reimbursable basis, the Service will
general statistical tabulations or simulate the administrative and
revenue impact of law changes.  The identity of taxpayers is kept
confidential in these files.  For individuals, proprietorships, and
partnerships, the most de-
                                35
Annual                      Statistics
Periodic                     of Income
and Supplemental           Publications
Reports
Department of the Treasury
Internal Revenue Service
Publication 711 (Rev. 7-80)
Publications are for sale by the Superintendent of Documents,
U.S. Government Printing Office, Washington, D.C., 20402
Other Reports Periodic and as Supplements
Estate Tax Returns, 1976
Publication 764
Gross estate by type of property
Lifetime transfers by asset type
Funeral and administrative expenses
Other deductions
Taxable estate; Estate tax, Tax credits
Data classified by- Taxable and nontaxable status; Size of gross
                    estate; Estate valuation method; Size of net
                    worth; Age, sex and marital status of decedent;
                    Tax rates; States
Personal Wealth Estimated from Estate Tax Returns, 1972
Publication 482
Provide estimates of the asset holdings if the living population
with gross wealth of more than $80,000:
     Composition of assets
     Distribution of assets by age, sex, and marital status.
     Number of millionaires by three measures of wealth
     Distributions by value of corporate stock, and by value of
     real estate.
     Historical statistics, selected years.
Fiduciary Income Tax Returns, 1974
Publication 808
Sources of Income, Taxable income
Exemption and Deductions
Income tax and tax credits
Additional tax for tax preferences; Allocation of accumulation
distributions
Data classified by-
     Trusts and Estates; Tax rates and type of tax; Size of total
     Income
Historical statistics, selected years
Sales of Capital Assets Reported on Individual Income Tax Returns,
1973
Publication 458 (scheduled September 1980)
Number of transactions
Gross Sales price
Cost or other basis plus expense of sale
Gross gain or loss
Details on sales of residences
Details on sales of business and farm property
Data classified by- Type of asset; Short-term vs. long-term; Length
                    of period held; Taxpayers age 65 and over;
                    States; Size of adjusted gross income; Size of
                    net capital gain or loss.
Individual Retirement Arrangements, 1976
Publication 1107 (scheduled August 1980)
Number of arrangements
Contributions
Compensation
Distributions
Penalty taxes
Data Classified by- Type of arrangement; Source of Compensation;
                    Size of adjusted gross income.
Private Foundations Exempt From Income Tax, 1974
Publication 1073 (scheduled September 1980)
Receipts, Including contributions, gifts, and grants
Deductions
Net Income
Net Investment Income and Tax
Assets and Liabilities]
Minimum Investment return
Distribution amount
Qualifying distributions]
Undistributed Income
Excise taxes paid by foundations
Unrelated business Income and tax
Data classified by- Exempt activity; Accounting period, State
     Size of-  Total receipts, Net income; Total assets
Small Area Data From Individual Income Tax Returns, 1974
Publication 1008
Number of returns and exemptions
Adjusted gross income
Salaries and wages
Dividends in adjusted gross income
Interest received
Total tax
Data classified by- Metropolitan areas; Counties; States; Size of
                    adjusted gross income
(Report for 1978 scheduled December 1981)
International Income and Taxes, Domestic International Sales
Corporation Returns, 1972-1974
Publication 1071
Receipts, including qualified export receipts
Deductions, including export promotion expenses
Net income
Amounts deemed or actually distributed
Assets and liabilities-  Trade receivables; Producer loans; Capital
                         accounts by type
Gross receipts of the DISC
Current and prior year gross receipts of the DISC and related U.S.
persons
Data classified by- Country of destination; Product; Industry;
                    Accounting period
Size of-  Total gross receipts; Total assets of both DISC and
          corporate parent
(Report for 1975 scheduled December 1980)
International Income and Taxes, Foreign Tax Credit Claimed in
Corporation Income Tax Returns, 1968-1972
Publication 479
Foreign tax credit-
     Foreign income and taxes
     U.S. net income and tax
     Data classified by-
          1968 and 1972; Foreign country; U.S. industry
     Credit limitation method
     Size of-
          Total assets; Foreign tax credit; U.S. net income
     1969 and 1970; Total assets; U.S. Industry
Western Hemisphere Trade Corporations, 1968 and 1972
(Report for 1974 scheduled September 1980)
Data similar to those for 1968 and 1972 for corporations with
assets of $250 million or more
International Income and Taxes, U.S. Corporations and their
Controlled Foreign Corporations, 1968 and 1972
Publication 1026
Net income and tax of U.S. parent corporations
Earnings, tax and transactions by type of foreign corporation with
U.S. parent corporation and other related persons
Data classified by- Foreign country, Year of incorporation, Size of
                    total assets, industry, and accounting period
                    of both U.S. parent and foreign corporation
(Report for 1974 scheduled February 1981)
Data similar to those for 1968 and 1972 for corporations with total
assets of $250 million or more
International Income and Taxes, Foreign Income and Taxes Reported
on Individual Income Tax Returns, 1975
Publication 1100
(scheduled October 1980)
Exemption if income earned abroad-
     Income earned abroad for personal services
     Tax-exempt amount
     U.S. taxable income and tax
     Data classified by- Foreign Country; Type of residence status
                         abroad; Size of adjusted gross income
Foreign tax credit-
     Foreign income and taxes
     U.S. taxable income and tax
     Data classified by Foreign Country-
     Credit ??? method; Size of adjusted gross income
                                36
Annual Statistics of Income Complete Reports
Individual Income Tax Returns Publication 79
Presents Information
annually or periodically on-
Sources of Income, including-
     Salaries and wages
     Dividends; Interest
     Rents and royalties
     Business or profession
     Farm
     Capital gains; Ordinary gains
     Pensions and annuities
Adjusted gross Income
Adjustments to Income
Exemptions
Computation of Itemized deductions, including-
     Contributions; Medical
     State and local taxes paid
     Home mortgage and total Interest paid
Zero bracket amount (standard deduction)
Taxable income
Income tax
Maximum tax
Tax credits, Including-
     Child care credit
     Earned Income credit
     Foreign tax credit
     Investment credit
     Jobs credit
     Residential energy and business energy Investment credits
     Retirement Income credit
Minimum tax and tax preference items
Tax withhold or due at filing time
Payments of estimated tax
Tax overpayment credits and refunds
High Income returns
Data classified by-
     Size of adjusted gross income
     States
     Tax rates and type of tax computation
     Taxpayer marital status
     Taxable and nontaxable status
     Tax payers age 65 or over
(Report for 1978 scheduled November 1980)
Corporation Income Tax Returns     Publication 16
Presents Information annually or periodically on-
Receipts, including-
     Business receipts; Capital gains
     Rents and royalties
     Domestic and foreign dividends
     Taxable and nontaxable Interest
Deductions, including-
     Cost of sales and operations
     Advertising; Rents; Repairs
     Interest and taxes
     Employee benefit plans
     Depreciation, depletion, and amortization
Depreciation under ADR procedures
Net Income and taxable Income
Statutory special deductions
Income tax
Foreign tax credit
Investment credit
Work Incentive credit
U.S. possessions tax credit
Minimum tax and tax preference items
Tax payments and overpayments
Distributions to stockholders
Book vs. tax net income
Consolidated returns
Small Business Corporations
Domestic International Sales
     Corporation returns
Members of controlled corporate groups
Foreign corporations with U.S. business operations
Foreign owned U.S. corporations
Number of pension plans
Assets and liabilities-
     Notes and accounts receivable
     Investments in Government obligations
     Depreciable and depletable assets
     Accounts payable
     Mortgages, notes, bonds payable
     Net worth
Data classified by-
     Industry; Accounting period
     Returns with net Income
     Size of-  Total assets; Income taxed at normal and surtax
               rates; Business receipts; Income tax
(Report for 1978 scheduled February 1981)
Business Income Tax Returns   Publication 438
Sole Proprietorships and Partnerships
Presents Information annually or periodically on-
Number of-
     Sole proprietorships
     Partnerships; Partners
Receipts, Including-
     Business receipts
     Partnerships-
          Dividends; Interest
          Rents, Royalties
Deductions Including-
     Cost of sales and operations
     Interest and taxes
     Rents; Repairs
     Depreciation, depletion, and amortization
Net Income
Profitable businesses
Inventories
Payroll
Partnership payments to partners
Partnership payments to retirement plans
Depreciation under ADR procedures
Cost of depreciable property
Partnership capital gains
Sale proprietors' adjusted gross Income and source of nonbusiness
Income
Partnership assets and Liabilities
Limited Partnerships
Jobs credit computation
Investment credit computation
Business energy investment
     credit computation
Partnership tax preference Items
Date classified by-
     Industry; State
     Number of partners
     Number of retirement plans
     Partnership year of organization
     Partnership accounting period
     Sex of sole proprietor
     Size of-  Receipts; Partnership assets; Sole proprietorship
               net income; Sole proprietors adjusted gross income
(Report for 1977 scheduled November 1980)
Preliminary Reports Precede complete report - contain several basic
tables
Individual Income Tax Returns, 1979
Publication 198 (scheduled February 1981)
Corporation Income Tax Returns, 1977
Publication 159 (scheduled November 1980)
Business Income Tax Return, 1979
Publication 453 (scheduled November 1980)
Reports currently available         Use order form provided on back
                                37
tailed data we could produce would be by Internal Revenue Service
District.  In most cases, districts are geographically coterminous
with States; however, there are four districts in New York State,
and two each in Pennsylvania, Ohio, Illinois, Texas, and
California.  We do not publish geographic data for corporations
since the place where the return was filed may be different from
the location of the principal business activity.
Statistics of Income Publications
     Statistics of Income publications include annual reports based
on individual corporate, and business returns; occasional reports
based on other tax returns and schedules; and Supplemental reports
classifying information from individual returns by geographic areas
(SMSA and county) prepared biennially.  Among the occasional
reports are:
     Fiduciary Income Tar Returns--this report presents estimates
of total income and its composition, deductions, taxable estate,
and tax for personal trusts with income $600 or more for which a
fiduciary flied an income tax return, Form 104 1. Important classi-
fications include type of trust, size of total income, and tax
rate.
     Estate Tar Returns--this report presents estimates of gross
estate by type of property, deductions, taxable estate, and tax for
decedents with gross estate in excess of $60,000 for whom an
executor filed an estate tax return, Form 706.  Important
classifications include size of estate, tax rate, and State.
     Personal Wealth Estimated from Estate Tax Returns--this report
presents estimates of the number and wealth of that portion of the
population with assets of more that $60,000 based on the
application of mortality weighting factors to estate tax return
data.  Important classifications include age, sex.marital status,
as well as various measures of gross and net wealth.
     Sales of Capital Assets reported on Individual Income Tax
Returns--this report presents estimates of the transactions by type
of property, gross sales price. basis of property and expense of
sale, and net gain or loss reported on individual income tax re-
turns with sales of capital assets.  Important classifications
include size of income including and excluding capital gain or
loss. and size of net gain or loss.
     Returns of Private Foundations Exempt from Income Tax--this
report presents estimates of the receipts, expenditures, net
income, assets and liabilities of organizations classified as
private foundations (and exempt from income tax) which file Forms
990-PF.  Additional data are provided on excise taxes relating to
excess investment income, investments jeopardizing exempt purpose,
and prohibited expenditures.
     Farmers' Cooperative Income Tax Returns--this report presents
estimates of the receipts, deductions, net income, tax, assets, and
liabilities for both exempt and nonexempt farmers' marketing and
purchasing cooperatives filing on Form 990-C and 1120,
respectively.  Important classifications include type of service,
type of commodity marketed, and State.
     Returns of Employees' Pension Plans and Pension Trusts--this
report presents estimates of the receipts, disbursements, assets
and liabilities of individuals or organizations who maintain
employees' pension plans or pension trusts and who file an annual
statement on Form 4848, 4849, and 990-P.  Additional data include
type of entity, type of plan, method of funding, and number of
employees covered and not covered.
     Returns of Organizations Exempt from Income Tax--this report
presents-estimates of the receipts, expenditures, assets and
liabilities of organizations (other than private foundations)
exempt from income tax under Section 501 (c) of the Internal
Revenue Code and which file Form 990.  Important classifications
include the subsection of the Code under which exempt and the
principal business activity.
     The description of available Statistics of Income reports on
pages 36 and 37 is copied from recent SSI publications.
2.. Data From SSA
     The following pages list data tables published by SSA in its
Annual Statistical Supplement to the Social Security Bulletin. The
list is copied from the Supplement which presents data for 1976.
SSA's sample data files maintained in connection with the
Continuous Work History Sample program are described in Appendix
III.2.
                                38
                          List of Tables'
Table                                                          Page
No.                                                             No.
                              General
Social Security and the economy
1.   Gross national product and social welfare expenditures under
     public programs, fiscal years 1928-29 to 1974-76            44
2.   Social welfare expenditures from public funds in relation to
     total government expenditures and Federal grants to State and
     local governments, fiscal years 1928-29 to 1974-76
3.   Public programs: Social welfare expenditures, fiscal years
     1928-29 to 1974-76                                          45
4.   Aggregate and per capita national health expenditures, by
     source of foods and percent of gross national product, fiscal
     years 1929-76                                               46
5.   Amount and percentage distribution of personal health care
     expenditures for the aged, by type of expenditure and source
     of foods, fiscal year 1976                                  46
6.   Personal income and social security payments, 1929-76       47
7.   Labor force and estimated workers covered under social
     insurance programs, 1939-76                                 48
8.   Total earnings, wages and salaries and earnings in employment
     covered by selected social insurance programs, 1946-76      49
Poverty data
9.   Weighted average poverty thresholds for non-farm families, by
     size, 1959-77                                               50
10.  Trends in poverty: Number and percent of persons poor, by age,
     1969-76                                                     51
11.  Trends in poverty among families: Families in poverty, by sex,
     age, and work experience of head, 1959-76                   52
12.  Poverty status and current living arrangements of persons aged
     65 and over                                                 52
13.  Poverty status and work experience of family heads and
     unrelated individuals, by age and sex                       53
14.  Poverty states of aged households receiving social security
     benefits                                                    54
15.  History of Federal minimum wage rates under the Fair Labor
     Standards Act, 1938-79                                      55
Interprogram social security data
16.  Social insurance and veterans' programs: Cash benefits and
     beneficiaries, by risk and program, 1940-76                 56
17.  Veterans' programs: Veterans receiving compensation or
     pension, by type of payment, and age, 1940-76               58
18.  Selected social insurance and veterans' programs: Benefits, by
     State                                                       59
19.  OASDHI and selected public assistance programs: Average
     monthly payments in current and 1975 prices, 1950-76        60
20.  Rejected social insurance programs: Source of funds from
     contributions and government transfers, 1965-76             61
21.  Selected social insurance trust funds: Financial operations,
     1937-76                                                     62
22.  Unemployment trust food: Status, 1940-76                    63
23.  OASDHI and SSI: Population aged 65 and over receiving OASDHI
     cash benefits, SSI payments, or both, 1940-76, and took by
     State,1976                                                  64
24.  Federal grants: Total to State said local governments, by
     purpose, fiscal yews 1929-30 to 1974-76                     65
25.  Federal grants: Total to State and local governments, amount
     and percent, by purpose and by State (ranked), fiscal year
     1976                                                        67
26.  Unemployment insurance: Summary data on State programs,1940-
     76, and by State, 1976                                      68
27.  Temporary disability insurance: Selected data on State and
     railroad programs                                           69
28.  Workmen's compensation: Coverage,benefits, and costs,
     1940-76                                                     70
Food stamp program
29.  Number of persons participating, value of bonus coupons,and
     average bonus per person, 1962-76                           71
       Old-Age, Survivors, Disability, and Health Insurance
Trust funds
30.  Old-age and survivors insurance trust fund: Status,
     1937-76                                                     72
31.  Disability insurance trust fund: Status,1957-76             73
32.  Combined OASI and DI trust funds: Status, 1957-76           74
33.  Hospital insurance trust fund: Status, 1966-76              75
34.  Supplementary medical insurance trust funds: Status,
     1966-76                                                     75
Workers
35.  Workers,earnings, social security numbers issued, and
     employers reporting taxable wages under OASDHI, 1937-76     76
36.  Workers and earnings of wage and sooty and self-employed
     workers, 1951-76                                            77
37,  Farm workers under OASDHI, 1951-75                          78
38.  With taxable earnings, by type of worker and sex, 1937-76   79
39.  With taxable earnings (all and 4-quarter): Percent with
     annual earnings below taxable limit, by sex, 1937-76        80
40.  With taxable earnings: Number, by age and sex, 1937-76      81
41.  With taxable earnings: Median earnings, by age and sex,
     1937-76                                                     82
42.  With taxable wages (all and 4-quarter): Number, by wage
     interval, 1937-76                                           83
43.  With taxable wages (male, all and 4-quarter): Number,
     by wage interval, 1937-76                                   84
44.  With taxable wages (female,all and 4-quarter): Number,
     by wage interval, 1937-76                                   85
45.  With taxable earnings (self-employed): Number,
     by age and sex, 1951-76                                     86
46.  With earnings credits (self-employed): Number,
     by earnings-credits interval and sex, 1951-76               87
47.  With taxable earnings: Number,earnings,and contributions,
     by type of employment and State                             89
48.  Insured: By insured status, 1940-77                         90
49.  Insured: By insured status, sex,and age, 1972-77            91
50.  Insured (aged 65 and over): Number eligible for and
     percent receiving benefits, by sex and age, 1941-77         92
51.  Insured (aged 62 and ever): Number eligible for and percent
     receiving benefits, by sex and broad age group, 1956-77     93
Summary benefit date
52.  Total benefits paid, by type of program,1937-76             94
53.  Number and average,monthly benefits in current
     payment status, by selected family groups,1940-76           95
54.  Benefits in current-payment status, number and amount,
     by type of beneficiary, 1940-76                             96
Benefits awarded
55. Individuals: By type of beneficiary, 1940-76                 97
 Social Security Bulletin, Annual Statistical Supplement, 1976  1
                                39
Table                                                          Page
No.                                                             No.
56.  Conversions: Number and average monthly amount, by reason
     for conversion,type of benefit awarded, and previous
     type of benefit                                             98
57.  Retired workers: By states of award and sex, 1950-76        99
58.  Retired workers with and without reduction for early
     retirement: Number ant! average amount, by status of
     award and sex, 1956-76                                      99
59.  Retired workers with and without reduction for early
     retirement:  Number and percent, by monthly amount and sex 101
60.  Retired workers with and without reduction for early
     retirement:  Number and percent, by primary insurance
     amount and sex                                             102
61.  Retired workers,disabled workers,and widows: Average
     amount and, for retired workers, primary insurance amount,
     1940-76                                                    103
62.  Disabled workers: By monthly amount and sex                104
63.  Wives and husbands: By type of beneficiary, 1950-76        105
64.  Children: By type of child beneficiary, 1940-76            106
65.  Mothers: By type of mother beneficiary, 1950-76            107
66.  Widows and widowers: By type of entitlement, 1950-76       107
67.  Lump sum and survivor,  Workers represented and average
     payment, by type of award, 1940-76                         108
Benefits awarded and/or In current-payment status
68.  Individuals: By type of beneficiary, race, age, and sex    109
69.  Individuals: Number and average amount, by type of
     beneficiary, alt, sex, and race                            120
70.  Women beneficiaries: Number and average amount, by type of
     beneficiary and race                                       122
71.  Individuals with reduction for early retirement: Number and
     average amount, by type of beneficiary,
     race, age, and sex                                         123
72.  Wives with reduction for early retirement: Number
     and percent, by type, 1956-76                              126
Benefits In current-payment states
73.  Individuals: Number and average age,
     by type of beneficiary                                     127
74.  Individuals: Number and average amount,
     by type of beneficiary and race                            127
75.  Aged beneficiaries,  By age, sex and race                  127
76.  Aged beneficiaries: By type of beneficiary,
     age, and sex                                               128
77.  Retired workers with delayed retirement, credit: Number,
     average amount, and average primary insurance amount,
     by age and sex                                             128
78.  Retired workers without reduction for early retirement and
     without delayed retirement credit: Number and
     average monthly amount, by sex and age                     129
79.  Retired workers: Number and percent, by year of
     entitlement and sex, 1940-76                               130
80.  Disabled workers: Number and percent, by year of
     entitlement and sex, 1960-76                               131
81.  Widows: Number and percent by year of entitlement,
     1940-76                                                    131
82.  Retired workers and dependents: Average amount,
     by type of beneficiary and sex, 1940-76                    132
83.  Retired workers: Number, average age, and percent,
     by sex and age, 1940-76                                    132
84.  Retired workers and dependents: Number and percent,
     by type of beneficiary and primary insurance amount        134
85.  Retired workers with and without reduction for early
     retirement:  Number and percent, by monthly amount and sex 134
86.  Retired workers with and without reduction for early
     retirement:  Number and percent, by primary insurance
     amount and sex                                             135
87.  Retired workers with benefits in nonpayment status;
     Number and percent, by monthly amount and sex              136
88.  Dual entitlement: Persons with retired-worker and secondary
     benefit, with and without reduction for early retirement,
     by primary insurance amount and sex                        137
89.  Dual entitlement: Persons with retired-worker and secondary
     benefit, by type of secondary benefit and sex, 1952-76     137
90.  Retired workers with and without reduction for early
     retirement:  Number and average amount, by sex, 1956-76    138
91.  Retired workers: Percent, by monthly amount, age, and sex  139
92.  Disabled workers and dependents; Number and percent,
     by type of beneficiary and primary insurance amount        140
93.  Disabled workers: Number and percent, by monthly amount
     and sex                                                    141
94.  Disabled workers and dependents: Average benefit,
     by type of beneficiary, 1937-76                            142
95.  Disabled workers: Number and monthly amount, by sex,
     1957-76                                                    142
96.  Disabled workers: Number, average age, and percent,
     by age and sex, 1957-76                                    143
97.  Wives and husbands: Number and monthly amount,
     by type of beneficiary, 1950-76                            144
98.  Children: Number and monthly amount, by type of child
     beneficiary, 1940-76                                       145
99.  Children: Number, by type of child beneficiary and sex of
     worker, 1950-76                                            146
100. Survivors of deceased workers: Average amount,
     by type of beneficiary, 1940-76                            147
101. Survivors of deceased workers: Number and percent,
     by type of beneficiary and primary insurance amount        147
102. Mothers: Number and monthly amount, by type of mother
     beneficiary, 1950-76                                       148
103. Widows and widowers: Number and monthly amount,
     by basis of entitlement, 1950-76                           149
104. Retired-worker, survivor, and disabled-worker families:
     Number, average primary insurance amount, and average
     benefit, by family group                                   149
105. Retired-worker, survivor and disabled-worker, families:
     Number, average primary insurance amount, and average amount
     payable, by family group with special minimum benefit      151
106. Disabled-child families: Number, average primary insurance
     amount, and average amount payable, by family group        151
107. Student-child families: Number, average primary insurance
     amount, and average amount payable, by family group        152
108. Retired-worker and disabled-worker families: Percent, by
     monthly amount                                             153
109. Survivor families: Percent, by monthly amount              154
Benefits withheld and terminated
110. Withheld from individuals: Number, by reason and by type and
     age of beneficiary                                         155
111. Withheld from wives and husbands and from children: Number, by
     reason and type of beneficiary                             155
112. Workers' compensation offset for disabled worker families:
     Number and average amount before and after onset,
     by type of family                                          156
113. Terminated for individuals: Number, by type of
     beneficiary, 1940-76                                       156
114. Terminated for individuals: Number, by reason and
     type of beneficiary                                        157
115. Terminated for wives and husbands and for children:
     Number, by reason and type of beneficiary                  157
2  Social Security Bulletin Annual Statistical Supplement, 1976
                                40
Table                                                          Page
No.                                                             No.
Benefits paid
116. Total paid from OASI trust fund: Amount and percent,
     by type of beneficiary, 1940-76                            158
117. Total paid from DI trust fund: Amount and percent,
     by type of beneficiary, 1957-76                            159
State monthly benefit date
118. Cash benefits paid: Total, by program                      160
119. Benefits in current-payment status: Number,
     by type of beneficiary                                     161
120. Benefits in current-payment status: Amount,
     by type of beneficiary                                     162
121. Benefits in current-payment status: Number,
     by age, race, and sex                                      163
122. Retired-worker benefits in current-payment status:
     Number and percent receiving, by monthly amount,
     ranked by State average                                    164
123. Disabled-worker benefits in current-payment status:
     Number and percent receiving, by monthly amount,
     ranked by State average                                    165
124. Child benefits in current-payment status:
     Number, by type of child beneficiary and basis of
      entitlement                                               166
125. Retired-worker benefits in current-payment status:
     Number and average amount, 1940-76                         167
126. Widow and widower benefits in current-payment status:
     Number and percent receiving, by monthly amount, ranked by
     State average                                              168
Beneficiaries residing abroad
127. Benefits in current-payment status: Number and total
     monthly amount, by country and type of beneficiary         169
Worker disability awards
128. Number and Percent, by selected causes of disability,
     1957-74, and by sex, 1974                                  171
129. Diagnostic group: Number and percent, by age and
     race, 1974                                                 172
130. Occupational division: Number and percent, by sex and
     race, 1974                                                 173
131. Age on birthday in year of onset of disability:
     Number and percent, by sex, 1974                           174
MEDICARE benefits
132. Hospital and supplementary medical insurance: Number of
     enrollees aged 65 and over, by age, sex, race, and census
     region, 1966-76                                            175
133. Hospital and supplementary medical insurance: Number of
     disabled enrollees under age 65, by age, sex, race, and census
     region, 1973-76                                            176
134. Hospital insurance: Number of enrollees,
     by State, 1966-76                                          177
135. Hospital insurance: Number of bills approved for Payment and
     amount reimbursed, by type of benefit and type
     of beneficiary, 1966-76                                    178
136. Hospital insurance: Number of inpatient short-stay hospital
     care bills, covered days of care, and charges, by type of
     beneficiary, 1966-76                                       178
137. Hospital insurance: Average covered charge per covered day of
     care in short-stay hospitals and skilled-nursing facilities,
     by State, 1971-76                                          179
138. Supplementary medical insurance: Number of reimbursed bills,
     charges and amount reimbursed,
     by type of service, 1966-76                                180
139. Supplementary medical insurance: Number of bills received by
     carriers and assignment rates, 1969-76                     181
140. Supplementary medical insurance: Reasonable charge
     determination for claims assigned and unassigned, 1971-76  181
141. Hospital and supplementary medical insurance:
     Benefit payment amounts, by State, 1972-76                 182
142. Hospital insurance: Number of inpatient hospital and
     skilled-nursing facility admissions and rates per 1.000
     enrollees, by type of beneficiary, 1966-76                 183
143. Hospital insurance: Number of inpatient hospital and
     skilled-nursing facility admissions and rates per 1,000
     enrollees, by State and type of beneficiary                184
144. Hospital and supplementary medical insurance: Number of
     facilities and beds for participating hospitals, skilled-
     nursing facilities, home health agencies, and independent
     laboratories, 1967-76                                      184
145. Hospital insurance: Number of participating hospitals
     and beds per 1.000 enrollees, by State                     185
146. Hospital and supplementary medical insurance: Number of
     participating skilled-nursing facilities, home health
     agencies, independent laboratories, and end-stage renal
     disease facilities, by State                               186
                   Supplemental Security Income
147. Number receiving federally administered payments,
     and total amount, by reason for eligibility and State      187
148. Number receiving State-administered supplementation
     and total amount, by reason for eligibility and State      188
149. Number receiving federally administered payments and
     average amount, by reason for eligibility and type of
     payment,  December 1976                                    188
150. Number of all persons receiving federally administered
     payments and average amount, by State,  December 1976      189
151. Number of aged receiving federally administered payments
     and average amount, by State, December 1976                190
152. Number of blind receiving federally administered payments
     and average amount, by State,  December 1976               191
153. Number of disabled receiving federally administered payments
     and average amount, by State,  December 1976               192
154. Number and percent receiving federally administered
     payments, by reason for eligibility and living
     arrangements,  December 1976                               193
155. Number of adult units and children receiving federally
     administered payments and average amount, by type of
     payment and reason for eligibility,  December 1976         193
156. Total payments,  Federal SSI payments, and State
     supplementation, by State                                  194
157. Number of blind and disabled children receiving federally
     administered payments, by State                            194
158. Persons receiving federally administered payments and
     number and percent in concurrent receipt of income,
     by reason for eligibility, source of income, and
     average amount, December 1976                              195
159. Percent of persons in concurrent receipt of federally
     administered SSI payments and social security benefits in
     December 1976 and average amount of social security
     benefits, by reason for eligibility and State              196
160. Number and percent of all, persons receiving federally
     administered payments, by reason for eligibility, sex,
     and race,  December 1976                                   197
161. Number and percent of all adults receiving federally
     administered payments, by reason for eligibility and age,
     December 1976                                              197
162. Number and percent of blind and disabled children receiving
     federally administered payments, by age, December 1976     197
3  Social Security Bulletin, Annual Statistical Supplement, 1976
                                41
Table                                                          Page
No.                                                             No.
163. Number and percent of persons receiving federally administered
     payments with representative payees, by team for eligibility,
     December 1976                                              197
164. Number and persons of individuals receiving Federal SSI
     payments, by reason for eligibility and monthly amounts
     December 1976                                              197
165. Number and percent of couples receiving Federal SSI
     payments, by reason for eligibility and monthly amount,
     December 1976                                              197
                        Black Lung Benefits
166. Currently payable to miners, widows, NW dependents:
     Number and amount, 1970-76                                 198
167. Currently payable to miners, widows, and dependents:
     Number and monthly amount, by State                        199
                         Public Assistance
168. AFDC and emergency assistance: Average monthly number of
     recipients, total amount of cash payments, and average
     monthly payment, 1936-76                                   200
169. OAA, AS, and APT,  Average monthly number of recipients, total
     amount of cash payments, and average monthly payment,
     1936-76                                                    201
170. General assistance: Average monthly number of recipients, low
     amount of cash payments, and overall monthly payment,
     1936-76                                                    202
171. Public assistance: Vendor payments for medical care, by
     program, 1951-76                                           203
172. AFDC and emergency assistance: Average monthly number of
     families and recipients of cash payments and total
     amount of payments, by State                               204
4  Social Security Bulletin, Annual Statistical Supplement, 1976
                                42
                                                          CHAPTER V
                Developments in Data from Business
                      Establishment Reporting
     Non-standardized concepts, definitions, and procedures used in
developing administrative record sets create serious difficulties
for statistical uses.  The potential for major new uses of
administrative records may in fact be quite limited because of
these problems and other Problems such as incomplete establishment
reporting, poor timing, and confidentiality restrictions.  there
are, however, some new developments which present opportunities for
improving the coordination and statistical use of key
administrative record sets.
     This chapter examines three evolving programs which illustrate
the potential and problems associated with efforts to improve the
statistical utilization of business reports obtained in connection
with tax-related administrative data collection.  The programs are
the Census Bureau's development of the Standard Statistical Estab-
lishment List, the Social Security Administration's effort to
adjust its data programs to new administrative procedures calling
for annual (forms W-2 and W-3) rather than quarterly (form 941)
employer reports of individual worker wages, and the Bureau of
Labor Statistics' cooperative program with State Employment
Security Agencies to make statistical use of records collected in
connection with Unemployment Insurance payroll taxes.
     The SSEL program represents an explicit attempt to identify
the most useful definition of business establishment units for
statistical analysis purposes, and to build "bridges," when
necessary, between these statistical units and legal entities for
which tax and other administrative reports are available.  The SSEL
not only is intended to facilitate more efficient direct use of
administrative records for statistical purposes, but it also has
been planned as a vehicle for coordinating statistical data
collection efforts so that data collected from business in
different programs can more easily be compared and integrated.  In
this connection the SSA and UI payroll tax programs represent
particularly important administrative data collection programs,
because both payroll tax programs have statistical components which
involve requests for multiestablishment businesses to provide
supplemental "establishment" information with their tax reports in
order to permit tabulation of employment and payroll data by
industry and geographic areas.  A number of important advantages
could be derived from better coordination of the SSA and UI
establishment reporting plans with each other and the SSEL; but
there are also a number of legal, institutional, and technical
obstacles to improved coordination.  The discussion in this chapter
and much of the remainder of the report (especially chapters VII
and VIII) illustrates these potential advantages and the barriers
to improvement in addition to describing applications of the data
collected through current business establishment reporting
procedures.
     While the emphasis in this chapter is on information collected
from businesses, both the SSA and UI payroll tax programs involve
the collection of data (from businesses) pertaining to individual
workers.  In fact, the focus of SSA statistical use of payroll tax
data has been the Continuous Work History Sample program which is
organized explicitly around individual worker records.  The UI
program has been directed primarily toward utilizing aggregate
establishment reports of employment and payroll, but a program to
develop a Continuous Wage Benefit History sample is underway using
individual worker records collected in connection with the UI prog-
ram.  Just as a general coordination of the SSA and UI
establishment reporting plans with the SSEL program would provide
important statistical advantages. so would coordination and linkage
of the CWHS and CWBH individual record systems.  This chapter does
not deal with such individual record linkage efforts, but Chapter
VI provides several case studies illustrating the advantages and
problems associated with efforts to link data from various
individual record systems.
            A. Standard Statistical Establishment List
     There has been a long history of endorsement of the general
principle that a centrally compiled list of firms and their
establishments should be available for multiagency use in the
conduct of statistical samples.  Presently, each government
statistical agency is responsible for compiling and maintaining the
business register needed
                                43
for their particular statistical applications.  The use of
independently developed lists. with attendant differences in
definition and coverage. seriously affects the comparability of the
economic data provided by the various agencies. and also results in
considerable duplication of effort and costs and increases in
respondent reporting burden.  Concerns such a-, these constitute a
substantial part of the criticism of government statistical
programs.
     The Office of Federal Statistical Policy and Standards of the
Department of Commerce (formerly Division of Statistical Policy of
the Office of Management and Budget) has been a consistent advocate
of a central list concept.  Towards this end. in 1968. the Bureau
of the Census was designated by OMB as the focal agency for the
development. establishment and operation of such a directory (known
as the Standard Statistical Establishment List--SSEL) on behalf of
Federal statistical agencies.  Funding for the project started in
fiscal year 1972 with an operational Directory available covering
data year 1974.
     Construction of the SSEL was known to be technically feasible
since the methodology had been followed previously in assembling
the economic censuses mailing list and in utilizing administrative
data.  Since the linkage among the principal source agencies. i.e..
Census, IRS., and SSA is the common usage of the Employer
Identification Number by all three agencies. and using the estab-
lishment as the basic "building block" of the SSEL, it is possible
to link together and identify the affiliation of parent companies.
subsidiary firms, and their establishments throughout all phases of
economic activity.
1. File Construction
     The SSEL now consists of a central multi-purpose computerized
name and address file of all known multiestablishment and single
establishment employer firms in the United States.  The systems
design for computer processing is predicated on variable word-
length record which permits additional information to be added as
desired.
2.   Multiestablishment Firms
     Information for multiestablishment firms was initially derived
from Census Bureau records.  From the 1972 Economic Censuses, the
necessary basic information had been assembled for the
organizational units of all firms included in the economic
censuses.  All establishments of these firms were linked to the
enterprise level and were identified by their individual SIC codes,
physical locations. employment size codes, etc.; and all known
domestic establishments of these multiunit firms were identified
regardless of activity.  This practice represented a departure from
that of previous censuses where records were maintained only for
establishments engaged in activities defined as within the scope of
the economic censuses.  Multiestablishment companies not covered by
the economic censuses were identified in a two-stage survey.  In
November 1972, as part of the Economic Census processing. all legal
entities with 50 or more employees were canvassed to determine
their enterprise structure.  Each legal entity was requested to
list all companies it owned or controlled and the name and EI
number of its controlling company, if any.  Information was also
requested on employment, kind of industrial activity, and number of
business locations operated under that EI number.  Detailed
listings of establishments were not requested in this survey since
the major emphasis was to consolidate those legal entities into
their correct enterprise structure.  This operation was coordinated
with the regular Economic Census processing to produce an
integrated file.  A similar survey was conducted in January 1974
covering calendar year 1973 to canvass smaller entities with 20-49
employees.  In addition, 175,000 small out-of-scope companies (less
than 20 employees) were canvassed in 1974 if classified in an
activity changed by the 1972 SIC revision.
3.   Single Establishment Firms
     Approximately 80% of the universe of business establishments
with one or more employees are single establishment firms
represented by one EI number.  For these establishments. the
enterprise, legal entity and establishment are identical.  For this
reason, information for single establishment firms was derived from
the administrative records of other government agencies since it
would be difficult to justify the government and respondent cost
involved in duplicating this information by direct survey contact.
     The Business Master File of IRS served as the basic universe
file from which the single unit company listing was derived.  This
source provided company name. address, EI number and legal form of
organization for all firms with one or more paid employees.
     March 12 employment and the Standard Industrial Classification
Code were obtained from the records of the Social Security
Administration.  The four quarters of payroll were obtained from
IRS records.
     In constructing the multiestablishment company file. the
Census Bureau recorded the EI number of the entity owning the
establishment in conjunction with the SSEL File Number.  Matching
these EI numbers of multiunit firms against the Business Master
File (El file) and unduplicating, the residual list resulted in the
establishment of the single unit file.  Using these inputs, the
SSEL became operational covering data year 1974 and is now used as
                                44
the mailing list source and sampling frame for all Census Bureau
economic programs.
4.   File Maintenance
     The use of administrative records has played an integral part
in creating, maintaining, and updating the SSEL file.  During
noncensus years, the single establishment file (approximately 4
million records) is updated solely from administrative records.
New births are received monthly from, IRS and SSA with information
on name and address, EI number, SIC code and legal form of
organization code.  Employment and payroll data are received
quarterly.  Geographic codes are assigned by Census from the
address information received from IRS and SSA.
     For multiestablishment firms, a company organization survey
was undertaken to insure that the organizational structure of each
company is updated at least once each year.  This survey includes
companies in scope of the Economic Censuses as well as out-of-scope
companies covered in a special survey.  Preprinted forms are sent
to each company. listing all establishments known to be operated by
it including name and physical location of each establishment.  The
company is requested to update these listings and report March 12
employment, first quarter payroll and annual payroll by
establishment location.  The reported payroll is then compared to
the IRS administrative payroll at the EI and company level, and
discrepancies resolved.  In addition, administrative record
employment and payroll is used to impute nonmail or delinquent
companies.  Several working papers describing the SSEL system have
been written (U.S. Bureau of the Census. 1979).  Copies can be
obtained from the Census Bureau.  Because of the cost of annual
maintenance, a complete file of zero employee cases is available
only from each quinquennial Economic Census.
5.   Confidentiality
     Current legislative restrictions, including title 13 of the
Census Act, do not permit the release of the SSEL to other agencies
for statistical use.  Legislation has been proposed, however, which
would permit the release of this file to certain other Federal
agencies (see Chapter VIII).
                      B. W-2 and W-3 Records
     Starting in 1979 with data for tax year 1978, a significant
change took place in the method of reporting to the Social Security
Administration the wages paid to employees by their employers.  A
single annual wage reporting system began under which forms W-2 are
used as the report of individual employee wages for both social
security and income tax purposes.  This eliminates the quarterly
reporting of a detailed listing of wages paid to each employee
covered under social security.  Employers still have to file
quarterly reports containing wage and tax liability information
with the Internal Revenue Service.  State and local government
employment is excluded from the annual reporting system.
      Under the annual reporting system, forms W-2 along with
transmittal forms W-3 (see Figure V.1) are received at one of four
SSA Data Operations Centers where the material is examined for
completeness and correspondence initiated with employers having
incomplete shipments.  After microfilming, the documents are
prepared for optical scanning or key-to-tape operations.  The data
on the output tapes are then transmitted to SSA's Central Office
via telecommunications equipment.  Here the data are merged with
data from employers who submit their reports directly on magnetic
tape and all the data are subjected to a series of computer
balancing and validation operations.  All validated earnings items,
those taxable under the Federal Insurance Contributions Act as well
as other earnings, are forwarded to IRS for processing for income
tax purposes.  Copies of the validated FICA items are retained by
SSA to update the Summary Earnings Record for individual employees.
     The new W-2, W-3. reporting system has a number of positive
and negative implications for SSA's Continuous Work History Sample
statistical programs. (See Chapter III for a description of the
current CWHS system.) The most important positive features of the
new annual reporting system are that for the first time SSA will
have information on total wages paid to an individual, thus
eliminating the need to estimate wages above the maximum that is
taxable for social security purposes; and that initially the system
will include information on employees not covered by social
security as well as covered employees.  Privacy and Tax Reform Act
questions, however, remain to. be resolved relating to the extent
to which data for uncovered employees can be used for statistical
purposes in the CWHS program.
     On the negative side, there will no longer be data on
individual earnings amounts by quarter.  Also, there are
preliminary indications that the items for statistical processing
will not be available until sometime later than under the quarterly
reporting system.  There are also indications that the SSA's
Establishment Reporting Plan could be adversely affected because of
the nature of the reporting requirements for forms W-2 and W-3.
     Another aspect of the new annual reporting system that has
great statistical potential is the employee's address on the form
W-2.  These addresses could be coded to obtain residence geographic
information.  Unfortunately, present procedure does not call for
SSA to capture this informa-
                                45
[GRAPHIC] \SW646.GIF
tion in any machine readable form.  However, the possibility of
retaining this information in the future is presently being
pursued.
     The units which employers use for establishing summary (W-3)
reports presently differ widely among employers under SSA's
voluntary establishment reporting plan (see chapter VII).  If
employers were to use the establishment definitions and codes
developed by the Census Bureau for its Standard Statistical
Establishment List, the resulting file of W-3 forms would be
immensely more useful for statistical purposes that is if the W-3
forms were collected with Census Bureau establishment codes and
confidentiality problems restricting SSA-Census interchange of
records were resolved, the SSEL could be used to code
establishments by industry and geographic location (State, county,
and possibly subcounty units), The resultant file could be used to
provide tabulations of annual wage and salary income and employment
by industry for detailed geographic units.  Such tabulations could
be used to improve a number of statistical programs, including
BEA's State and local area personal income accounts and the Census
Bureau's County Business Patterns program.  In addition, the
improved geographic coding for the individual records (W-2's)
associated with the W-3's would improve the CWHS program and if
used in conjunction with W-2 (or other;
                                46
residence information, would permit the development of valuable
intercensal commuting estimates for local areas.  Currently,
however, not only is vital SSA access to the SSEL limited by
legislation, but there would appear to be substantial employer
resistance to proposals that they report to SSA on the basis of
SSEL establishment concepts (which frequently involve more detailed
establishment reports than called for in SSA's voluntary establish-
ment reporting plan).
                 C. Unemployment Insurance System
     A case study of the statistical usefulness of administrative
records for establishments can be gleaned from the unemployment
insurance system.  This system was established as part of the
Social Security Act of 1935 to serve as a countercyclical income
maintenance program for offsetting losses in wage and salary income
of the experienced work force.  Initially, UI covered only
employers in the private nonfarm economy with eight or more
employees.  Over the years, the system has been continuously ex-
panded.  In March 1978, over 90 percent of employed workers were
covered by the State and Federal UI system.
     In the UI system, a variety of administrative data is
maintained.  Three important data sets which serve as the primary
source of statistical uses are discussed in this Chapter (see
Figure V.2).
     First of all, there is a master list of more than 4 million
subject employers which contains the names and addresses of covered
firms and both actuarial and statistical information.  Secondly,
information from the quarterly tax reports filed by employers is
maintained.  Finally, in all but 12 States, firms report the total
wages paid to each employee during the quarter to determine an
individual's eligibility and benefit amount when filing a UI claim.
1. Master List of Employers
     State agencies collect and process certain statistical
information to help provide standardization for reports and
tabulations.  Employers are assigned county and industry codes.
Industrial activity is reviewed on a three-year cycle, and attempts
are made at identifying multiestablishment employers and setting in
place a mechanism for Supplemental reports of employment and wages
by county and industry.  The UI list is used by State agencies to
draw samples in the Federal-State programs sponsored by BLS and
operated by the States.  A number of States also use the list to
publish industrial directories.  The lists are provided to the
Bureau of Labor Statistics to use for sampling purposes under a
pledge of confidentiality.  BLS uses the lists to develop its UI
Name and Address File which serves as a sampling frame for its
directly collected surveys.
     The UI Name and Address File has a number of drawbacks.  Since
it is derived from an administrative source, many of the
refinements needed for sampling purposes are not present.  For
example, the major identifying field in the file is a UI account
number which is assigned independently by the various States.
There is no unique way to identify firms or companies within a
corporate structure across States.  Also, identification of
multiestablishment employers varies from State to State.
2.   Employers' Quarterly Tax Report
     Taxes are collected quarterly from subject employers by
mailing each employer a tax form on which he reports the total
wages paid to employees during the quarter, the amount of these
wages that is subject to taxes, the taxes due, and the number of
employees on the payrolls for the period that includes the twelfth
of each month.  The tax forms are due at the State agency 30 days
after the end of the reference quarter.  Multiestablishment
employers are also mailed a statistical supplement with their tax
report requesting a breakdown of the monthly employment and wage
figures by reporting unit.  Five months after the end of the
quarter, State summaries in machine readable form are sent to BLS,
Washington.  Two summaries are required of each State: (1)
Statewide by four-digit industry, and (2) counties by two-digit
industry.  States that can provide four-digit industry by county,
need only send one summary.  These summaries are called ES-202
reports.
     Many programs of the BLS and BEA rely on the ES-202 report's
employment and wage data.  Within BLS, the Current Employment
Statistics, Labor Turnover Statistics, the Occupational Employment
Statistics.  Industry Projections, and Occupation Safety and Health
Statistics programs are benchmarked to industrial employment data
emanating from the ES-202 report.  The BEA national income and
personal income estimates rely heavily on the UI administrative
data.  In addition, personal income is used in formulas to allocate
billions in Federal funds to State and local governments.  At the
local level the average wages of workers covered by UI are used to
adjust the average annual wage payments allowed Comprehensive
Employment and Training Act Public Service Employees.  The State
agencies also make substantial use of employment and wage data to
assess the economic vitality of local labor markets in their labor
market information programs.  Practically every employment related
statistic that is generated in the BLS-BEA-State employment agency
enclave has the UI administrative as its base.  The ES-202 report
has its limitations and problems. There is no set mechanism of
quality control to assure that
                                47
[GRAPHIC] \SW648.GIF
                                48
all subject employers are reporting.  There is no program of
quality assurance for ascertaining the accuracy of data reported by
employers on their tax reports.  Statistical reports which are a
by-product of an administrative program often receive a low
priority.  The statistical functions in producing the ES-202 report
compete for basic UI program resources with tax collections.
benefit payment. and research activities.  Hence, many States
cannot fully implement industry coding and multiestablishment
"breakout" activities.
3. Individual Wage Records
     In most States, the collection of the quarterly tax reports
also involves an itemization of individual workers' wage payments
identified by social security number.  This data base provides a
rich source of information on an individual's earnings history.
The Current Wage and Benefit History program of the U.S. Department
of Labor is attempting to tap this data base to link earnings
experience with workers' eligibility and receipt of UI benefits.
Since each individual's earnings are linked to the employer,
studies on wage dispersions by industry and county (on a place of
work basis) are feasible.  These files are also being used to map
mobility patterns and labor turnover actions as part of Labor's
Employment Service Potential program.
4.   Improving Data Quality
     The UI administrative data have room for improvement because
of the large and cumbersome task of identifying multiestablishment
employers.  Their major strength is the quarterly collection and
timeliness versus other sources of establishment records-namely,
the Census Bureau's County Business Patterns program.  Census does
considerable work annually in identifying and maintaining
multiestablishment breakdowns of firms in its Company Organization
Survey.  Access to these data could help identify and refine
multiestablishment reporting problems in the UI record system.
     At the same time, one of the weaknesses of the Census'
establishment records is the industry codes of single-establishment
firms.  Those single unit firms not covered in the 1972 or 1977
Economic Censuses retain industry codes assigned from information
submitted when the application for an EI number was made.  A
matching of industry codes in the two data system could improve the
coding of single establishment firms on the Standard Statistical
Establishment List and help identify potential problem areas
between the two systems; i.e., such a match could determine how
much of the difference between BLS and Census series is due to
coding, how much is due to reporting differences, and how much is
the result of differences in treatment of central administrative
Offices.
                                49
                                                         CHAPTER VI
             Potential Uses of Administrative Records
             for Data Linkages: Selected Case Studies
                          A. Introduction
     In this chapter case studies of ongoing or completed research
using administrative records for data linkage studies are compiled.
These studies are in various stages of development; some have been
completed, others are in the planning stages, and still others have
been partially implemented.  Nevertheless, each included study
serves to illustrate important aspects of the research potential
and problems associated with uses of administrative records.
     The individual case studies exemplify the potential uses of
administrative records for linkages, illustrating some of the
benefits derived and the difficulties involved.  The wide range of
general issues addressed include confidentiality concerns,
operational feasibility, and data quality.  The specific topics
discussed are the data sources and identifiers used for matching,
the criteria used to determine acceptable matches, and methods used
to improve the quality of identifiers.  Project goals, and the
general methodologies used to carry out the match will also be
discussed for these selected cases.
     Administrative records have been used in the past in a number
of interagency data linkages for statistical purposes.  For
example, matching studies involving record checks have been
conducted to evaluate the last three decennial censuses.  Although
the case studies presented in this chapter differ in scope, methods
and objectives, they serve to illustrate some of the ways
administrative records can be used for statistical purposes:
     1.   The Linked Administrative Statistical Sample (LASS)
          project is an effort to produce an improved data base for
          mortality research by integrating samples from the record
          systems of three agencies:    IRS, NCHS, and SSA.
     2.   The Use of Administrative Records in the Survey of Income
          and Program Participation (SIPP) illustrates the use of
          administrative records in multiple frame Surveys, where
          issues of sampling efficiency are central, and in
          response error studies where the validity of survey re-
          ported data are compared to program data.  Future use of
          administrative records in the SIPP will emphasize data
          base enhancement through the integration of difficult to
          collect data obtained from administrative records with
          survey collected data.
     3.   Use of IRS/SSA/HCFA Administrative Files for 1980 Census
          Coverage Evaluation describes a multiple systems
          estimation procedure which will be used to obtain
          estimates of Census coverage for States and selected
          subgroups of the population.
     4.   Record Linkage in the Nonhousehold Sources Program is a
          study to improve the coverage of the 1980 Census in which
          administrative data sets (drivers license records and
          Immigration and Naturalization legal alien records) are
          used to augment the information in another data set (1980
          Census enumeration records).
     References to published and unpublished material related to
the study are included at the end of each case study.  The
supplementary information may provide interested readers with more
detail on the studies themselves and on the difficulties in
successfully implementing the linking of data files.
     In all administrative matching studies, conceptual differences
and operational difficulties, including access to administrative
records, may impede or even invalidate the attempt.  However, the
analytic potential of obtaining an expanded, more detailed data
base through successful matching is so great that complicated and
careful procedures are often worth the effort.  The increasing
numbers of attempts to improve statistics through matching testi-
fies to this conclusion.
              B.  Case Study 1: Linked Administrative
                 Statistical Sample (LASS) Project
     The Linked Administrative Statistical Sample or LASS project
is an, effort to upgrade the Social Security Administration's
Continuous Work History Sample.  The primary focus of the study is
to examine the issues surrounding the development of integrated
samples from the record
                                51
system of three agencies: the Internal Revenue Service, the
National Center for Health Statistics, and the Social Security
Administration.  The principle objective of the project is to
create an improved data base for mortality research.
     The material presented here discusses a few of the major
concerns which are being addressed in order to determine the
feasibility of producing such a sample.  Organizationally this case
study is divided into two main parts.  The first of these sets the
background of the study, its research objectives and the specific
data sources to be included.  The second describes the initial
planning activities being engaged in and some of the progress which
has been made thus far in each area.  There are also some
concluding comments on the issues to be faced if the project is to
enter an operational phase.
1.   Background and Initial Project Goals
     For over 40 years [1] both government and nongovernment
researchers have made extensive use of statistical information
about American workers derived from the Continuous Work History
Sample (CWHS).  The primary Social Security use made of the CWHS
has been in tabulating the characteristics of covered workers to
keep track of how this group has changed over time with changes in
the Social Security Act and in the demographic mix of the
population [e.g., 2].  The Bureau of Economic Analysis has made
considerable use of the CWHS as a source of regional workforce
characteristics and especially changes in the workforce, both
geographical and industrial [3].  Uses by nongovernment researchers
have also been extensive, covering the gamut from labor market
supply questions to the measurement of lifecycle earnings [e.g., 4-
5].  Recently in a pioneering effort by Goldsmith and Hirschberg
[6] attention has been focused on the CWHS' potential to address
industrial and environmental health issues.
     While the usefulness of the CWHS data has been demonstrated
repeatedly, it is limited in scope, content, and quality by program
requirements.  These weaknesses would, of course, have to be
corrected in order for the files to reach their full potential as a
general purpose data base for statistical research.  The support of
present and potential users who recognize the importance of these
data will be necessary to bring about the changes which will
improve its usefulness [7].
     Professionals concerned with epidemiological problems,
occupational safety, and general environmental issues are among
those interested in an improved, augmented CWHS.  In fact, the real
start of the Linked Administrative Statistical Sample project was a
meeting at the National Center for Health Statistics (NCHS) in
October of 1978 involving representatives of several agencies.
including Social Security, to explore areas of mutual concern that
relate to epidemiology studies.
     When the U.S. Congress [8] amended the Public Health Service
Act (Public Law 95-623), NCHS's mission for conducting and
coordinating research activities aimed at improving all aspects of
health services in the United States was greatly broadened.  Part
of this legislation calls for the development of a plan by the
National Center for Health Statistics for the collection and coor-
dination of statistical and epidemiological data on the effects of
the environment on health.  Therefore, NCHS desired to work with
other agencies to find feasible, cost-effective approaches to
developing an implementation plan for carrying out its new mandate.
     One effective and relatively inexpensive way to achieve this
goal is to integrate data already collected by Federal agencies in
pursuit of their individual missions.  Social Security and the
Internal Revenue Service (IRS) are two of the major agencies which
have current data that are not generally available for
epidemiological studies.  The proposed LASS project is an attempt
to exploit these data systems for studying the occupational and
industrial etiology of disease.
a.   LASS data elements
     The Linked Administrative Statistical Sample is to retain the
same simplicity of design as the CWHS, and takes that sample as its
starting point.  In particular, it is planned that ultimately a
common statistical sample will be created which is based on the
ending digits of the social security numbers used to select the one
percent Continuous Work History Sample.  The following data
elements are proposed for inclusion in the final linked sample:
     1.   Mortality information from the National Center for Health
          Statistics' processing of death certificates. (At a
          minimum, on a prospective basis the fact of death would
          be confirmed by matching the National Death Index to the
          CWHS.  Also, the basic demographic items from NCHS's sta-
          tistical record including cause of death would be added.
          Retrospectively, similar information might be obtained as
          far back as the late 1960's for every identified CWHS
          decedent.  Finally, for both the retrospective and
          prospective efforts, the decedent's usual occupation and
          industry during his or her lifetime, items not now coded
          by NCHS, would be obtained from the certificates
          themselves.)
     2.   Individual income tax items obtained initially from the
          Statistics of Income (SOI) program.  Eventually, the
          information will be derived directly as a by-product of
          IRS Master File
                                52
          processing. (Detailed income, deduction and tax data
          could be obtained from the Transaction Files now used to
          update the Master File.  Also available from that source
          would be any neeD residence information.  Last, but not
          least, the occupation entry on the return would have to
          be transcribed to the statistical records.)
     3.   Longitudinal earnings and benefit histories developed at
          Social Security as part of the Continuous Work History
          Sample. (The CWHS, as it now exists, can provide basic
          demographic information for the sampled individuals,
          details on every covered job by industry and place of
          work since 1956; total covered earnings since 1936 (by
          year since 1950); and, for beneficiaries, the nature of
          their claims and the amounts they and their dependents
          receive in benefits.)
b.   LASS research goals
     There are a number of general Ion run goals of the LASS
effort.  Three major ones are listed below:
     1.   To develop a basic source of socioeconomic and job-
          related mortality and morbidity data.  The resulting
          statistical sample proposed hem could be used to
          construct mortality rates by age. race, sex, industry,
          occupation, and place of work or residence.  This could
          lead eventually to a much greater understanding of the
          etiological factors associated with cancer and other
          causes of death.  By following individuals over time by
          occupation, industry and residence, for example, it may
          be possible to separate out the effects of these factors
          on health from the effect of health on these factors.
     2.   To construct longitudinal personal and administrative
          unit income profiles of the population at the National,
          State, and Substate regional levels.  These income
          distributions could be studied both before and after the
          imposition of Federal income and payroll taxes.
     3.   To study regional labor market conditions using the data
          on industry, occupation, wages, and self-employment
          earnings along with basic demographic characteristics
          such as age, race, and sex.  Mobility studies and other
          such work now done with the CWHS [3] would be greatly
          enhanced by the augmented dataset available under this
          proposal.  Particularly important in this regard is the
          occupation and residence data that might be obtained from
          tax returns. (For workers who don't file tax returns,
          residence information will be available from the new
          annual wage reporting system based on the W-2.)
     The short-run goals of the project are centered around
feasibility questions such as assessing data quality and estimating
operating costs.  An examination of a few of these goals is
provided in the next section along with a summary of the work done
so far to achieve them.
2.   Pilot Activities and Feasibility issues
     In planning for the operational phase of the LASS
project a number of activities have been undertaken.  Included
among these are-
     1.   attempting to resolve the confidentiality concerns of the
          participating agencies,
     2.   examining coverage and content differences between SSA
          and NCHS death information,
     3.   determining the problems which arise when adding cause of
          death and other data from death certificates to the CWHS,
     4.   assessing the codability and validity of the occupation
          entry on the individual income tax return,
     5.   developing procedures for upgrading the CWHS data on
          industry and place of work, and
     6.   studying the completeness of the W-2 residence
          information.
     Full details on the progress to date may be found in the LASS
Working Notes Series [9] or in the publication Statistical Uses of
Administrative Records with Emphasis on Mortality and Disability
Research [10].  In what follows, only a brief overview has been
given.
a.   Resolving privacy concerns
     Many privacy concerns must be addressed before the LASS
project becomes operational. in addition to disclosure laws with
government-wide application such as the Privacy and Freedom of
Information Acts, each of the participating agencies has legal
constraints--statutes and regulations-which control access to its
microdata.  At minimum, these need to be coordinated in terms of
some unifying principles of interagency data sharing.  In addition,
some of there may need to be amended.  For example. the Tax Reform
Act of 1976 has changed the character of information from earnings
reports for persons who are in covered employment under the Social
Security Act by defining this as tax return information subject to
confidentiality restrictions in the Internal Revenue Code [11].
The Act allows IRS to disclose identifiable tax return data to SSA
only if those data are required for the operation of SSA programs
or for IRS tax enforcement purposes.  These conditions will almost
certainly be too restrictive for some of the activities planned for
the CWHS. if the interpretation IRS has given the Tax Reform Act
prevails
                                53
[12], corrective legislation may be needed to overcome these
problems.
     Privacy requirements also raise policy issues.  Should
projects involving the linkage of records from various agencies be
undertaken at all if there is any identifiable  future possibility
that the resulting data will be used in form for administrative or
enforcement Purposes?
     SSA protects linked statistical files from non-statistical use
by regulation, but this may not have the force and permanence
afforded by the "shield" laws protecting Census Bureau and NCHS
data. and possibly also IRS data.  On the other hand, these
statutory confidentiality shields also circumscribe the development
and use of linked data in identifiable form outside each respective
agency, even for statistical purposes.  In the short-term pilot
phases of this work, the confidential data contributed by NCHS
could be protected by making SSA staff "Special agents" or
temporary employees of NCHS.
     Such a procedure has worked well in past linkage studies
(e.g., the 1973 CPS-IRS-SSA Exact Match Study [13] ); nonetheless,
a firmer basis is needed before this project reaches its
operational phase. i.e., by FY 1982 if not sooner.
     Discussions among the participating agencies to address the
many privacy issues are still at a fairly early stage.  Legislative
initiatives we proceeding, in order to protect SSA data and to
resolve problems of making tax return information available for
statistical linkage.  Various Presidential proposals aimed at
providing government-wide legislation for protection of statistical
and research data offer a major step towards resolving the access
issues raised by this project.
     Given the potential for disclosure that this rich data base
would have, the creation of public use files from an upgraded CWHS
presents difficulties which, at present seem insurmountable.  To
service potential users, we have been considering the possibility
of setting up a Research Center that would provide tabulations and
other statistical summaries.  Computerized methods such as random
rounding routines [ 14], would be built into such a center's
procedures so that the possibility of any inadvertent disclosures
could be prevented. (it is anticipated that such a center could be
largely user supported.)
b.   Examining SSA-NCHS death reporting differences
     There are two key questions that must be answered if the SSA
death reporting system is to be used to study industrial mortality
differentials:
     1.   How complete is the reporting of deaths to SSA?
     2.   Are there differences in the information shown on death
          certificates and SSA records?
     The reporting of deaths to Social Security is not required for
persons who are not OASDI beneficiaries; however, financial
incentives, like the lump sum death benefit. make such reports
common practice.  In order to determine the characteristics of
persons whose deaths are not "captured" by SSA, a cooperative
project--the 1975 NCI-NCHS-SSA Mortality Study--was initiated with
the National Center for Health Statistics and the National Cancer
Institute (NCI); this study took as its starting point a sample of
23,000 deaths reported to NCHS for 1975.  To date SSA has obtained
the death certificates of these decedents and has nearly finished
matching there to agency records.  A paper presenting preliminary
results were given at the August 1979 meetings of the American
Statistical Association [15].  Present plans call for the coverage
(or completeness) check to be followed by a comparison of the
agreement between conceptually identical items like age, race, sex,
and place of birth.
c.   Adding data from death certificates to the CWHS
     To add cause of death to the CWHS it is necessary to supply
each State with lists of the decedents identified using SSA
information on name, social security number, race, sex, date of
birth and date of death.  Each State vital records office will then
have to search its (microfilm) files and send copies of the death
certificates to Social Security.
     Several unanswered questions exist about this fairly simple
process.  Among these are
     1.   Will all the States be able to cooperate?
     2.   Will SSA's information be sufficient for the States to
          attempt a search?
     3.   What will be the quality of the searching?
     4.   What will be the total cost in money, time and staff?
     A pilot test is now underway which should help address these
questions.  Information on every decedent in the CWHS who was
identified as dying in 1975 has been sent to the States for death
certificate searching.  The CWHS decedents were combined, before
being sent, with a subsample of NCHS cases already returned as part
of the 1975 NCI-NCHS-SSA Mortality Study.  Merging the two sets of
decedents so they are simultaneously searched will make it possible
to measure the quality of the work done in each State. (the NCHS
cases were previously located by the States using death certificate
numbers; now they will be located using SSA identifying information
which does not include the certificate number).
d.   Usability of IRS occupation information
     For a number of years there has been a continuing (and
growing) interest among professionals concerned with
epidemiological problems, occupational safety and general
environmental issues, etc., in augmenting the
                                54
Continuous Work History Sample with an occupational variable.  One
approach for obtaining occupational data for earners in the CWHS is
to use the information from returns.  This creates difficult
individual income tax problems given the uncertainty of the
inclusion Of the OccuPation item on the tax return from Year to
Year as well as the lack of taxpayer instructions for reporting
occupation.
     One of the activities undertaken in preparation for the LASS
effort was to compile the many studies [16] which have been done of
the reporting of occupation on tax returns in order to make the
call that this very important Content item be transcribed routinely
as part of the Statistics of Income (SOI) program.  The evidence
from these studies suggests that at the major group level IRS
occupation data may be roughly comparable in quality to that in the
decennial censuses [17].
     As part of their Statistics of Income Tax Year 1979 program,
IRS has agreed to pick up occupation information.  This effort will
be supported by SSA with the ultimate objective of determining the
feasibility and cost Of coding occupations for the entire set of
tax returns in the 1 percent CWHS.
     At present a collaborative pilot study of the SOI procedures
is now underway involving a systematic sample of 6,700 returns.
Some results from this pilot will be available in 1980.  Plans for
validating the occupation entries obtained in the Statistics of
Income program are also being developed.
e. Upgrading CWHS industry and Place of work data
     One of the most important parts of the LASS effort is to
upgrade the quality of the CWHS coding of industry and place of
work.  To this end, there must be a further strengthening of the
existing cooperative efforts between the Bureau of Economic
Analysis (BEA) and.  SSA in thoroughly examining the data quality
problems which exist in the CWHS [7].  Equally important is the
need to revitalize and expand the longstanding cooperative
arrangements between the Census Bureau and SSA.
     With respect to the BEA-SSA relationship, at present, plans
call for the development of a detailed set of procedures to
"perfect" the CWHS files for the period 1957/1977.  A comprehensive
approach to the handling of misreported (and/or missing) data is
anticipated from this joint BEA-SSA effort.  The data editing and
imputation tasks are expected to be quite formidable indeed.
Because of their one-time nature, the use of an outside contractor
seems advisable (assuming the Tax Reform Act is changed to allow
it).  If all goes well the RFP could be written by FY 1982 with the
work potentially taking place during 1982-84.  Joint BEA-SSA plans
are also being developed to handle the new (post 1977) data quality
problems that are being encountered in the changeover to annual
wage reporting.
     It is also expected that the Census Bureau will participate in
the CWHS upgrading.  This effort, however, will have a different
focus from the plans developing with the Bureau of Economic
Analysis.  Traditionally, the Social Security Administration has
provided industry and Place of work data for new employers to the
Census Bureau in connection with the Bureau's Standard Statistical
Establishment List (SSEL) program [18].  After each Economic Census
the Bureau has returned to the Social Security Administration
updated industry data for use in the CWHS.  For single
establishment employers the incorporation of this data in the CWHS
is fairly routine.  For multi-unit employers real difficulties
arise because of differences in the identification of
establishments between Census and SSA plus, of course, failures by
SSA to obtain establishment-level information from some employers
at all.
     Two major changes in this arrangement are being proposed: (1)
that the Bureau provide to SSA from the SSEL annual updates on
place of work codes for single-unit employers (again if the
confidentiality issues can be worked out); and (2) that for multi-
unit employers an experimental study be undertaken to see if the
SSEL information on employer place of work can be combined with the
employee's residential address (from the individual income tax
return or the W-2) in order to create synthetic establishment
identification codes for CWHS cases where the voluntary SSA
establishment reporting plan is not working properly.
     The synthetic establishment assignment process, as it is
envisioned to date would use a Census Bureau address coding scheme
to determine the distance between the employee's home and all the
establishments of his employer.  The establishment closest to the
employee's residence could be chosen as the establishment that was
"most likely" to be the employee's place of work.  Complications
caused by address changes over time would have to be overcome; but
the scheme, in our opinion, offers real promise and should be
tested.  It is important to point out that discussions with the
Census Bureau on these recommendations are at a very early stage.
Realistically the likelihood is low that much progress will be made
on this effort during 1980 or even 1981.  However, some parts of
the task can be carried out during the period, e.g., coding the
addresses of the employees.  Building the full-scale system
envisioned here would probably have to take place starting in 1982
or later.
f.   Evaluating W-2 residence data
     One of the advantages of the switch to annual reporting
is that it provides access to new information not available
                                55
in the old quarterly system.  The residence data from the Form W-2
is perhaps the most important new item, however, for cost reasons
(and because of the complications inherent in the conversion). the
W-2 residence data is not being processed for administrative
purposes.  A pilot effort is now underway, though, to determine the
usability of this data for statistical purposes.  In the pilot, an
attempt is being made to go back to microfilm copies of the
original source documents from the employers.  Microprints will be
made and then examined for legibility and completeness.  If the
address data proves adequate, the W-2 could be a valuable adjunct
to the IRS tax returns as a source of residence information for the
CWHS.  Consideration also will be given to using the W-2 addresses
in a mail survey to learn about the occupation of income tax
nonfilers.
3.   Operational Implementation Issues
     In order to mount the proposed Linked Administrative
Statistical Sample project, a high degree of cooperation is
essential both within Social Security's Office of Research and
Statistics and among the other agencies involved.  Most of the
technical problems which must be faced have already been touched on
in this note.  Perhaps the hardest problems to be faced, as in any
large endeavor, are organizational or managerial in nature.
Although meetings with both the potential producer and user
agencies have been held frequently since October 1978, the LASS
project is still in its initial planning phase.  It will be some
time before all the options have been laid out and the costs
estimated.  Establishing priorities will be a difficult process
since each participating organization has its own missions.
research goals and administrative procedures.  There is also a
concern about the ability of each of the participating agencies to
obtain the new staff and budget that will be required.
Because of the formidable technical and resource problems that must
be overcome, it is envisioned that a 5 to 10 year developmental
period will be needed before the, Continuous Work History Sample
can be used to its fullest potential as a vehicle for monitoring
industrial and occupational health questions.  In the interim, the
intermediate products will be shared widely with interested members
of the research community.  To this end there was a special session
at the 1979 Annual Meetings of the American Statistical Association
on the LASS project [10].  Another such session is scheduled for
the 1980 meetings.
For more information on the LASS program, contact:
     Faye Aziz
     Office of Research and Statistics
     Social Security Administration
     1875 Connecticut Avenue.  N.W., Room 320H
     Washington.  D.C. 20009
     Beth A. Kilss
     Statistical Division PR:S
     Internal Revenue Service
     1201 E Street, N.W.. Room 403
     Washington, D.C. 20224
4. References
     [1]  Buckler, W. and Smith.  C., "The Continuous Work History
          Sample: Description and Contents," Policy Analysis with
          Social Security Research Files, U.S. Social Security
          Administration. 1978.
     [2]  U.S. Social Security Administration.  Annual Statistical
          Supplement series to the Social Security Bulletin.
     [3]  U.S. Bureau of Economic Analysis.  Regional Work Force
          Characteristics and Migration Data (A Handbook on the
          Social Security Continuous Work History Sample and Its
          Application), 1976.
     [4]  Schiller, B., "Relative Earnings Mobility in the United
          States," Policy Analysis with Social Security Research
          Files, U.S. Social Security Administration, 1978.
     [5]  Jacobson, L., "Worker Displacement in the Steel
          Industry," Policy Analysis with Social Security Research
          Files, U.S. Social Security Administration. 1978.
     [6]  Goldsmith, J. and Hirschberg.  D.. "Mortality and
          Industrial Employment (1)" J. Occupational Medicine Vol.
          18. pp. 161-164, 1976. (Them were also two other papers
          by Goldsmith in this journal and an important letter
          commenting on the results by Pierre De Couffle.)
     [7]  Cartwright.  D., "Major Geographic Limitations for CWHS
          Files and Prospects for Improvement." Review of Public
          Data Use. March 1979.
     [8]  Public Law 95-623, 95th Congress. 92 STAT. pp. 3443-3458.
     [9]  U.S. Social Security Administration.  LASS Working Notes
          Series, Nos. 1-7. 1979.
     [10] U.S. Social Security Administration.  Statistical Uses of
          Administrative Records with Emphasis on Mortal and
          Disability Research (Selected papers
                                56
          given at the 1979 Annual Meeting of the American
          Statistical Association in Washington, D.C.), October
          1979.
     [11] Alexander, L. and Jabine, T., "Access to Social Security
          Microdata Files for Research and Statistical Purposes."
          Social Security Bulletin, August 1978 .
     [12] Alexander, L., with Scheuren, F. and Yohalem, M., "The
          1976 Tax Reform Act arid the Statistical Program of the
          Office of Research and Statistics," working paper
          prepared for the Subcommittee on Oversight of the House
          Ways and Means Committee.
     [13] Kilss, B. and Scheuren, F., "The 1973 CPS-IRS-SSA Exact
          Match Study," Social Security Bulletin, October 1978.
     [14] Fellegi, I. P. and Phillips, J. L., "Statistical Con-
          fidentiality: Some Theory and Applications to Data
          Dissemination," Annals of Economic and Social
          Measurement, National Bureau of Economic Research, April
          1974.  For a more recent and complete discussion see
          Statistical Working Paper No. 2: Report on Statistical
          Disclosure and Disclosure Avoidance Techniques, Office of
          Federal Statistical Policy and Standards, 1978.
     [15] Alvey, W. and Aziz, F.. "Mortality Reporting in SSA
          Linked Data: Preliminary Results," Social Security
          Bulletin, November 1979.
     [16] U.S. Social Security Administration, LASS Working Notes
          No. 2, January 30, 1979.
     [17] Koteen, G. and Grayson, P., "Quality of Occupation
          Information on Tax Returns," 1979 American Statistical
          Association Proceedings.
     [18] U.S. Bureau of the Census, Standard Statistical
          Establishment List program, Technical paper No. 44,
          January 1979.
            C.  Case Study 2: The Use of Administrative
                  Records in the Survey of Income
                     and Program Participation
     The Office of the Assistant Secretary for Planning and
Evaluation within the Department of Health and Human Services
(HHS), in cooperation with the Bureau of the Census, initiated a
joint statistical project called the Survey Of Income and Program
Participation (SIPP).  A fundamental objective of the SIPP is to
provide data to support policy analysis of a wide range of Federal
transfer and service programs.  The survey data will be used to
analyze the Impact of Federal programs, to estimate program
participation and eligibility fates, future Program costs and
coverage, and to assess the effects of alternative policy decisions
on the various programs.  Timely estimates of participation will be
provided for many existing programs, as well as estimates of the
joint receipt of benefits across several programs.  The survey will
also support separate analyses of characteristics of Persons and
families who an eligible but not participating in specific
programs.  When possible, survey data will be supplemented by
administrative record data.
     in addition to collecting program and eligibility data, the
survey is expected to produce, on a timely basis, a comprehensive
assessment of the economic circumstances of the population.  The
assessment is intended to cover objective factors (e.g. income,
wealth, employment and family status) and selected subjective
measures (e.g., attitudes and expectations about programs and per-
sonal well-being).  The assessment will provide repeated
observations on the same individual to permit the measurement and
analysis of change over time.  To supplement the analytical program
undertaken primarily by the Department of Health and Human Services
and the Bureau of the Census, a series of public use data The will
be distributed at cost to researchers outside the government.
These tapes should provide a rich and, in many ways, unique data
base for studies of the working of government programs, the
economy, and society at large.  The field activities have been
undertaken to examine and resolve content, operational, and
technical issues prior to beginning the ongoing SIPP in 1982:
     1.   Site Research.--a small experimental study of 2,800
          households in five locations designed to provide a formal
          test of alternative survey design features, specifically
          recall period and questionnaire format.
     2.   1978 Panel.--a national survey of 2,400 households
          designed to evaluate the implementation of a number of
          field and processing activities.
     3.   1979 Panel.--a national survey of 11,000 households
          designed to study the effects of, (1) alternative
          questionnaires on income recipiency, (2) self vs. proxy
          response, and (3) length of recall on property income
          data.
     A characteristic of the sample design common to each field
activity was the use of several sample frame for the
                                57
selection of survey respondents.  The frames which were used
included a general area frame and special list frames consisting of
administrative records from several HHS programs.  Probability
samples were drawn independently from each frame.  Subsequent to
each field activity. sample survey records were matched to their
corresponding administrative records.  Although there has been
continuity of the learning process concerning matching and the use
of administrative records within the developmental stages of the
SIPP. the objectives of each matching operation have varied
somewhat as the program has developed.
1.   Objectives and Description
a.   Site research
     In the Site Research Survey, administrative records
were used as sampling frames primarily to facilitate evaluation of
the experiments on alternative survey design factors.  Two program
recipient files were used as list frames in addition to a general
area frame in each of the five locations.  The first file was the
June  1977 Aid to Families With Dependent Children (AFDC) master
file maintained by the Texas State Department of Public Welfare in
Austin, Texas.  This file is an administrative system which
maintains data on benefit amounts, payment history, demographic
characteristics, and other information needed to administer the
program.  The second program recipient file used for selecting
persons in the Site Research Survey was the Supplemental Security
Record (SSR) maintained by the Social Security Administration (SSA)
in Baltimore.  This record is the national master administrative
file for data on Supplemental Security income (SSI) benefits
amounts, payment history, and demographic data.  Table 1 provides
an indication of how the sample households were distributed among
the sample frames and questionnaire types in the Site Research.
Table 2 exhibits the number of completed adult interviews for each
sample frame.
     A match of survey records to administrative cases drawn from
each file was initiated to determine the accuracy and quality of
the income data collected in the Site Research.  By comparing
survey data to the record (control) data, the match allowed a
validation and response error analysis with subsequent evaluation
of the effects of the experimental treatments on income reporting.
Because data on income types other than AFDC or SSI was not current
and of questionable quality. the analysis comparing the survey data
with administrative data was possible only for the AFDC or SSI
income.
     The Statistical Methods Division (SMD) of the Bureau of the
Census was responsible for defining sampling specifications for the
three samples and for drawing the area probability sample.  The two
samples from program data were selected by the respective agencies
according to specifications developed by SMD.
     Because of the small sample sizes and limited geographic
scope, no effort was made to develop multiple frame estimates for
the Site Research Survey.  Thus, the file matching task was
relatively uncomplicated; only the cases drawn from each record
system needed to be
[GRAPHIC] \SW658.GIF
                                58
[GRAPHIC] \SW659.GIF
matched with their respective survey records.  The general
population sample was not part of the matching operation.
     The variables used to identify a match depended on the
availability of information in the administrative record system.
Since each sampled address was assigned a unique control number.
the matching of administrative records to their respective survey
records involved essentially a two-stage process.  First. the
control numbers of the sample addresses were matched to the survey
records.  Then, within each household on the matched household
file, a person match was attempted using the Social Security Number
(SSN) of the individual on the administrative record as the primary
match variable.  Difficulties with matching on SSN at the person
level were resolved by using age and sex as discriminating
variables.
     Although the process was used for both administrative record
systems, an essential difference existed between the SSI match and
the AFDC match.  In the case of the SSI match, a manual match.
using the procedure defined above, preceded an automated match.
Since the., cases which could not be matched manually were
discarded, the automated match appeared to be perfect.  An exact
count of discarded SSI cases is not readily available, but the
Census Bureau has indicated with some assurance that there were
relatively few.
     An automated procedure which mirrored the manual procedure
described above was used for matching the AFDC sample survey data
with administrative record data.  Descriptive statistics for the
entire Site Research file are not available; however, a sampling of
the results of the match procedure is given in Table 3. It is the
authors' understanding that these results are representative of the
results of the entire AFDC matching operation.
     One rather disturbing finding of the Site Research matching
procedure was that in a large number of cases (up to 30 percent),
the individual selected from the administrative record system was
not included in the household roster at the address shown in the
survey record.  This resulted from the procedures used to identify
the sample unit.  Interviewers were instructed to locate the sample
address (which was not always found) and interview the residents in
the household.  They were not told to search for the specific
individual on-the administrative record system, because of the fear
that such a procedure would bias the survey data.
b.   1978 panel
     In the second phase of the SIPP developmental field work, the
1978 Panel, a nationwide area probability sample of 1,950
households and a list sample of 411 households drawn from SSI files
was interviewed at quarterly interviews over a period of 15 months.
The purpose of again including this frame was to continue the
investigation of SSI reporting with new survey techniques.  Some of
these techniques affected the general quality of all income data
(e.g. new interviewer training procedures); other techniques were
specifically developed to improve SSI reporting (e.g.
distinguishing the color of the government-issued checks).
Although no experiments were involved, the matching of survey data
to administrative records has proved most informative in the
evaluation of these new techniques.  Thus, the context of the
matching
                                59
        Table VI.2.3.  A Sampling of AFDC Matching Results
                    in the Site Research Survey
November ISDP-3
     130  records to be matched
      15  records matched Non-interview Households
      73  records matched on HHLD ID. and SSN
       9  records matched on HHLD ID.. sex and age
      33  records matched on HHLD ID.. but could not match at
          person's level
October ISDP-4
     127  records to be matched
      12  records matched Non-Interview Households
      74  records matched on HHLD ID. and SSN
      12  records matched on HHLD ID.. sex and age
      29  records matched on HHLD ID.. but could nix match at
          Person's level
January ISDP-10
III  records to be matched
       3  records matched Non-interview Households
      71  records matched on HHLD ID. and SSN
       8  records matched on HHLD ID.. sex and age
      29  records matched on HHLD ID.. but could not match at
          person's level
January ISDP-15
     129  records to be matched
      15  records matched Non-interview Households
      69  records matched on HHLD ID. and SSN
      13  records matched on HHLD ID.. sex mid age
      32  records matched on HHLD ID.. but could not match at
          person's level
     Note:  Data concerning the ISDP-20 and ISDP-25 Questionnaires
are not readily available at this time: however, according to the
Demographic Surveys Division of the Bureau of the Census. the
results of these matching operations are similar to the ISDP-10 and
ISDP-15 match results.
activity, once again, has been limited to response error and
validation studies for one specific income type.  Preliminary
efforts at multiple frame estimation in the 1978 Panel were
considered in the early planning stages.  However, because of the
small size and low precision of the sample, this was not pursued.
     The goals of the 1978 Panel matching operation did not
substantially differ from the goals of the Site Research.  Some
refinements in locating list frame sample respondents and in the
matching procedures resulted in a higher match rate in the 1978
Panel than had occurred in the Site Research Survey.  To insure
that the list frame person was a member of the interviewed
households interviewers were instructed to go to the address listed
and ask for the person (by name) selected from the administrative
record.  The interviewers did not know how these people had been
selected; they only knew that the survey respondents were members
of a "person" sample rather than a "address" sample.
     If the person did not live at the address. procedures were
developed to assist the interviewing staff in locating the sample
persons and interviewing there at their current address.  These
procedures were not always successful and some sample loss occurred
when list frame persons could not be located.
     Table 4 provides the results of the automated match of SSI
data to the survey respondent for the 1978 Panel.  The matching
procedures for the 1978 Panel respondents were similar to those of
the Site Research Survey.  Unique household control numbers,
assigned to each sample address at the time of sample selection,
were used to match at the household level.  Within the household,
the sample program person was matched to his/her administrative
record using the SSN as the primary match variable.  Non-matches
resulting from this procedure were clarified by comparing the age
and sex variables.  As can be seen from Table 4. data on the number
of person level matches using only the Social Security Number are
not available.
        Table VI.2.4.--SSI Match Results for the 1978 Panel
April 1978 ISDP-303
     486  records to be matched
       1  record did not match at HHLD level
      58  records matched at HHLD level only
     427  records matched at person's level
July 1978 ISDP-403
     491  records to be matched
      23  records did not match at HHLD level
      51  records matched at HHLD level only
     417  records matched at person's level
October 1978 ISDP-503
     496  records to be matched
      29  records did not match at HHLD level
      49  records matched at HHLD level only
     418  records matched a person's level
January 1979 ISDP--603
     496  records to be matched
      30  records did not match at HHLD level
      76  records matched at HHLD level only
     390  records matched at person's level
April 1979 ISDP-703
     497  records to be matched
      38  records did not match at HHLD level
      72  records matched at HHLD level only
     387  records matched at Person's level
     Note:  An increase irk the number of household level non-
matches occurred in interviews two through five because households
which were not interviewed were included in the total number of
records to be matched. Since a questionnaire was not completed for
these households, a non-match was assured.
                                60
c.   1979 panel
     The use of two administrative record systems was incorporated
into the survey design for the third phase of the SIPP
developmental field work, the 1979 Panel.  Supplementary samples of
1,000 program participants each were drawn from the December 1978
Supplemental Security Record file maintained by SSA in Baltimore
and the 1978-1979 Basic Educational Opportunity Grants (BEOGs)
applicant file.  In the former, the respondents selected were blind
and disabled SSI recipients; in the latter case, the applicant file
was restricted to those determined eligible for a grant in the
1978-79 academic year.  The 1979 Panel is still being fielded, and
thus, no results are yet available regarding the use of the
different sampling frames.  Plans, however, include the use of
these administrative record systems both to evaluate income
reporting and to obtain multiple frame estimates, thus improving
the reliability of data regarding households in their programs.
The latter goal will considerably complicate the matching process,
since for this purpose the identification of the overlap domain
among the three sampling frames (i.e., area sample, SSI, and BEOGs)
is critical.  The former goal is comparable to the work performed
previously in the Site Research Survey and the 1978 Panel.  That
is, by matching administrative records to the survey records,
detailed program data can be compared with interview data.  Thus,
further analyses and evaluation of the quality of SSI and BEOGs
reporting are planned.  Since this work is quite similar to the
work completed on the 1978 Panel, the match of the sample
individuals selected from administrative records with their survey
records will follow essentially the same scheme as was used in the
1978 Panel.  The improved field procedures for locating the list
frame persons have been repeated, as well as the computer match
process.  In addition, the area sample will be matched to each of
the administrative records universes, so that a study of reporting
of the respective income types for the area sample can be
conducted.
     The creation of multiple frame weights requires that sample
respondents be placed in the correct domain of membership.  Since
the 1979 Panel is a developmental effort, two approaches to this
problem will be compared:
     1.   asking the sample respondents questions to determine
          their domain membership; and
     2.   matching the individual survey records with the universes
          of the administrative records systems used for sampling.
     In the first case, responses to survey questions about
participation in the SSI and BEOGs programs are used as indicators
of membership in the overlap domain.  This approach may be
particularly unsatisfactory because self-identification of
membership in the exact universes in an interview tends to be very
difficult.  The BEOGs cases were drawn from certified eligibles,
not all of whom are necessarily recipients; and, the SSI cases were
drawn from blind and disabled, but not aged recipients.  It is
difficult to formulate appropriate questions to permit proper
identification, and even more difficult for the respondent to give
an accurate response.  Questions have been developed and will be
asked in the Fifth Wave of the panel to permit determination of
respondents' ability to self-identify membership in the programs.
In the second case, a match of survey records for all interviewed
individuals to both administrative universes is proposed.  However,
deficiencies in the quality of the matching variables, particularly
the SSN and date of birth, will result in an undetermined number of
false non-matches (i.e., a person interviewed in the sample survey
who had non-zero probability of selection from one of the
administrative lists, but whom the record match did not identify as
having such a probability).  In order to reduce (but not eliminate)
false non-matches resulting from inaccurate or incomplete survey
data, a set of procedures to validate and correct survey-reported
SSN's or to supply missing SSN's will be implemented in conjunction
with the Office of Research and Statistics (ORS)/SSA.  Of course,
these procedures will not assist the SIPP in determining false non-
matches resulting from inaccurate data on the administrative record
system.
     The 1979 Panel matching procedures for the multiple frame
domain determination have, at this time, yet to be defined.  It is,
however, obvious that the SSN will be the primary matching variable
with name, date of birth, race, age, and sex serving as
confirmatory variables.  The results of this exercise should
provide valuable insight into the procedures required for a timely
and operationally successful multiple frame sample survey.
2.   Major  Difficulties
     The major problems arising from the use of administrative
records for sampling have been consistent throughout the SIPP
developmental work, affecting, to different degrees, all the
sampling frames which have been used and/or considered in the
program.  The problems stem from difficulties in:
     1.   Identifying individual sampling unit with a known
          probability of selection;
     2.   Locating units in the sample in the field;
     3.   Gaining access to the administrative files;
     4.   Determining matches and non-matches;
     5    Gaining timely access to updated administrative data for
          addition to the sample survey records; and
                                61
     6.   Finding administrative sources that are national in scope
          or similar from State to State.
     The basis of the first problem lies in the fact that a one-to-
one correspondence does not generally exist between survey units
and the units on the administrative record files.  A survey unit,
in the SIPP developmental work, is a household.  However, in many
administrative record systems, the SSI system for example, a
household can be identified by more than one individual's record
(e.g. when more than one person in a household is a ,program
participant). the is also possible that a single administrative
record can lead to more than one household, such as when the record
relates to a nuclear family which lives in more than one household.
In the case of the SSI system, records were maintained in such a
way that duplicate records for spouses could be deleted; records
for other recipients in the same household-were not unduplicated.
In other programs, however, an unduplication process was not
available or readily derivable, and sampling was deferred until
such time as methods could be devised.
     The second area of difficulty--identifying units selected in
the sample in the field--was briefly mentioned in the section on
the 1978 Panel.  The problem primarily resulted from; 1) inadequate
or inaccurate home addresses of individuals on the administrative
records or 2) recent moves by program participants.  In the latter
case, a time lag of 2 to 4 months from sample selection to
interview date contributed to the problem.  These difficulties were
handled by procedural changes in the 1978 and 1979 Panels,
instructing interviewers to use the address only to locate the
individual, and to interview the entire household where that
individual was currently living.  Individuals who moved and whose
new address could not be determined remained a problem and could
not be interviewed.  In order to avoid violating the privacy of the
sampled individuals and to avoid biasing the data, interviewers
were only told that the "person samples". had been drawn from
various government programs rather than from particular programs.
     The problem of obtaining access to the administrative files
which were ultimately used was not as difficult as had been
anticipated.  Most of the difficulty in this amp can be
characterized as a substantial expenditure of staff time from the
initial contact through the sample selection, and the production of
a substantial amount of paperwork to obtain access to the
administrative file.  However, since the SIPP developmental work is
a joint statistical project with the Census Bureau, confidentiality
of the data being assured under Title 13, U. S. Code, access to the
flies was granted.
     Several brief, tentative efforts at using other program files
maintained at the State or local level have been attempted.  In
these cases problems of access appeared more severe.  The timing,
amount of paperwork, and likelihood of being granted access
dictated against vigorous pursuit of such frames during the early
SIPP developmental program; further work in that area will be
pursued later in the program.
     The fourth problem of accurately identifying matches and non-
matches between the survey records and administrative records was
already discussed.  The problem has not been resolved; however, the
experience gained from the Site Research and 1978 Panel has
suggested that the quality of the survey data, particularly
reporting of SSN, can be improved by emphasizing its importance in
interviewer training.  This, of course, cannot improve the quality
of the SSN's on the administrative files.  In the 1979 Panel, an
attempt will be made to validate the SSN's provided by the
respondents.  Cases with invalid numbers will then be identified to
the interviewers, in order that they may attempt to obtain a
correct number during a later wave of the 1979 panel.
     The type of matching operation conducted in the Site Research
and 1978 Panel is considerably less sophisticated than that
envisioned for the 1979 Panel.  More will be learned during the
next year concerning the SIPP's ability to match lists of survey
respondents to administrative lists of program participants.  The
issues of survey reported and validated SSN's, inaccurate and
incomplete data on the administrative file, and the use of multiple
frame sampling in an ongoing survey will be affected by the ability
to identify correct matches.
      The fifth area mentioned--gaining timely access to updated
administrative data to Supplement the sample survey records-my
present a problem in the ongoing SIPP.  In the SIPP program,
emphasis has been placed on providing relatively fast turnaround of
the SIPP data for purposes of program evaluation and current
assessments of the socio-economic well-being of the nation.  If the
sample design is dependent on access to administrative records for
proper weighting, this access will have to be carefully timed to
coincide with the end of data collection, or alternative means of
providing preliminary data should be developed.
     The last problem of finding administrative record sources that
are national in scope or similar from State to State also will
affect the ongoing SIPP.  For many programs which have variable
record systems at the State or local level, sampling may be
operationally too difficult, despite the importance of the program.
This will reduce the effectiveness of the survey in providing data
on program participation for such programs.
3.   Uses of the Administrative Files
     Matched survey and administrative records of the Site
Research Survey and the 1978 Panel data have not yet
                                62
been made available to the public since confidentiality issues
still need to be resolved.  In the case of the Site Research files,
individual identifiers, such as SSN, address, name, and Census
control number, have been removed and the income amounts above a
fixed cutoff have been "topcoded" or reduced on the file to the
cutoff point.  Geographic codes identifying the city and the ad-
ministrative record data remain on the file.  These files are
currently being edited and will be made available as public use
tapes.  Confidentiality issues will determine the final record
layouts from this data collection activity.
     Individual identifiers-that is, name and SSN--on the 1978
Panel quarterly tapes have been removed.  However, at this time,
administrative data, detailed geographic codes, and income amounts
which have not been topcoded (i.e., coded to a fixed open-ended
category, usually $50,000 or more, if the amount exceeds the base
of the open ended category) remain on the file.  At this writing,
all five waves of the 1978 Panel (unedited) have been received by
HHS, from the Bureau of the Census.  Current plans include making
these tapes available as public use tapes once the confidentiality
issues become resolved.  Only two waves of the 1979 Panel have been
received at this time.  However, confidentiality issues concerning
the administrative data should be resolved in a manner similar to
the 1978 Panel data.  The SIPP Staff intends to make these data
available as public use tapes, retaining some minimal amount of
information from the administrative records.
     To date, the most important use of the matched files has been
in the evaluation of reports of income recipiency.  A major goal of
the developmental work of the SIPP has been the improvement of
reporting of income and related data through sampling procedures,
questionnaires, and estimation techniques.  The matching of survey
reports to administrative records has allowed some objective eval-
uation of the efficacy of these efforts.
     In the near future, the primary purpose of the use of
administrative records for sampling will be to improve the
reliability of estimates of recipiency of relatively rare income
types and of estimates of the characteristics of such recipients.
Their income types will include both cash and non-cash transfers
from federal programs.  Through oversampling from program records
and multiple frame estimation, the number of sample observations of
program participants will be greatly increased, leading to improved
reliability of program participation rates and characteristics of
program participants.  In addition, efforts will be made to add
Social Security earnings records to the individual survey records,
thereby enhancing the richness of the economic data base.
     In the long run, administrative records may provide a means of
adjusting SIPP estimates of recipiency and level of income using
administrative control totals.  Not as much thought has been given
to this use as a means of developing better estimates of program
participation.  However, alternative means of improving survey data
with administrative data are available and will be explored in the
SIPP.  For example, if the administrative data are known to be
accurate, and if practical, reliable matching procedures can be
developed, then individual data items on interview records might be
adjusted.  Alternatively, administrative data could be used as
control totals for adjusting aggregate estimates of recipiency of
particular income types or participation in particular programs.
4.   Quality of Results
     At this time, the only analysis of the quality of the
procedures and resulting information has been in the evaluation of
the effectiveness of using different field procedures to locate
sample cases.  Within the next year an evaluation of the quality of
the matching process will be conducted using the data from the 1979
Panel.
     For more information on the use of administrative records in
the development of the Survey of Income and Program Participation,
contact:
     Daniel Kasprzyk
     Income Survey Development Program
     SSA/ORS/ISDP
     Room 322B, Universal North Bidding
     1875 Connecticut Avenue NW.
     Washington, DC 20009
5.   Bibliography
L.   Hausman,  Characteristics of selected income-tested programs
     (May, 1977; 289 pp.)
H.   Huang and D. Kasprzyk,  An examination of the relative
     benefits of selected sample designs for the SIPP. ISDP Working
     Paper #5 (November, 1978: 29 pp.)
R.   Kaluzny. 'Site Test analysis: characteristics of the data base
     (May, 1979; 53 pp.)
R.   Kaluzny and John Scott Butler,  The effect of instrument
     design on the reporting of AFDC and SSI income: a multinominal
     approach (March, 1980; 35 pp.)
B.   Klein,  Validating AFDC recipiency from the site research
     survey using a known sample of recipients (Forthcoming in the
     documentation of the Site Research Test)
C.   Lininger, Ed.  Survey of Income and Program Participation
     (SIPP) confidential report (August. 1979. 77 pp.)
B.   Mahoney.  M. Ycas, D. Kasprzyk, and H. Huang, Trade-offs in
     the collection of income, wealth, and program statistics
     (June, 1978; 29 pp.)
                                63
J.   Steinberg,  Multiple frame sampling approach-general framework
     of alternative approaches (December, 1976: 18 pp.)
J.   Steinberg,  Multiple frame sampling approach--pro-   posed
     design of a pilot test (February. 1977; 18 pp.)
S.   Stephenson,  Ed.  Survey research issues workshop: proceedings
     (August. 1978; 274 pp.)
D.   Vaughan,  Errors in reporting Supplemental Security Income
     recipiency in a pilot household survey (August, 1978. 6 pp.)
M.   Ycas,  An introduction to the Income Survey Development
     Program (August, 1979; 31 pp.)
               D.  Case Study 3: Use of IRS/SSA/HCFA
               Administrative Files for 1980 Census
                        Coverage Evaluation
1.   Introduction
     One of the major objectives of the 1980 Census Coverage
Evaluation Program is to develop estimates of the coverage of
population and housing in the census at the state and substate
level.  The Current Population Survey (CPS). which is conducted on
a monthly basis, will provide these estimates.  Persons listed in
the CPS are matched on a one-to-one basis with the census listing
of names in order to estimate census coverage error.
     Special enumeration surveys were conducted as part of the 1950
and 1960 census evaluation programs.  However, the results of these
studies were considered not to be successful for providing accurate
estimates of the undercount for certain subgroups of the
population.  One can conclude from these results that certain types
of persons enumerated in the census are much easier to enumerate in
the CPS than persons missed in the census.  This bias is often
referred to as "correlation bias;- a major objective of the 1980
CPS-census match will be to reduce this bias.
     There are two means by which the Census Bureau hopes to reduce
"correlation bias"'
     1.   By maintaining, as much as possible, independence of the
          CPS and the census.
     2.   By utilizing "independent" administrative files for
          purposes of improving the estimates of coverage error.
     It is the latter process that will be primarily addressed in
this case study.  To the extent that a satisfactory match between
the administrative files and the census and CPS can be achieved
without impairing independence of the sample data, we should be
able to obtain more accurate estimates of coverage error than were
obtained in 1950 and 1960.
     Two administrative flies are being considered: the IRS tax
return file for persons aged 17 to 64 years of age and the Medicare
file for persons 65 or over.  Two research projects are being
conducted to determine the feasibility of using the IRS and
Medicare files and will be described in this case study.  They
involve matching the February 1978 CPS records to corresponding IRS
and Medicare records.
     It should be noted that to a great extent this program is
still being developed.  Thus, the projects described in this report
could be subject to revision.
2.   Objectives of the Program to Estimate the Census Undercount
     The primary objective of the 1980 Census Coverage Evaluation
Program is to develop estimates of the coverage of population and
housing in the census.  The estimates can be made using two
different methods: Demographic analysis and survey estimates.
     A.   Demographic Analysis--The demographic method (demographic
analysis) of census evaluation that will be used involves
developing expected values for the population at the census date by
the adjustment and combination of demographic data from sources
essentially independent of the census being evaluated and comparing
these expected values with the census counts.  The particular
method that is used for demographic subgroups depends on the nature
of the available data.  For ages under 45 in 1980, estimates will
be developed on the basis of birth, death and immigration
statistics.  For ages over 65 aggregated medicare data will
provided the basis for estimates of coverage.  For the remaining
age groups an analysis of all censuses since 1880. along with death
and immigration statistics, provides the basis for developing
coverage estimates in 1980 (1).
     Demographic analysis will provide national estimates of net
census errors for age, sex and race groups.  These estimates are
measures of net error for age, sex, and race groups, combining
coverage errors and errors of content.  The demographic method is
considered by Census staff to be more effective than a post-census
sample survey for developing satisfactory estimates of net census
errors at the national level for the total U.S. population.
However, problems do exist with demographic analysis. the major one
is the estimation of the number of undocumented aliens.  At the
present time. nd definitive methodology is available for including
this segment of the population in the demographic estimates.
     Demographic analysis will also provide are estimates of net
census errors for broad age categories, by sex, and for white and
black racial groups.  However, it is questionable whether they will
be better estimates due those produced from the CPS and to what
extent they will be utilized.
                                64
     B.   Current Population Survey (CPS)-The data does not
currently exist for using demographic analysis techniques to
provide reliable estimates of coverage error for subnational
geographic area such as cities, SMSA's and revenue sharing areas;
in addition, the data now available for demographic analysis cannot
provide estimates of coverage error for some important
socioeconomic categories.  The Census Bureau will utilize the
April, 1980 and the August, 1980 Current Population Surveys to fill
this void.  Persons listed in the CPS are matched on a one-to-one
basis with the census listing of names.  Census resources exist for
providing reliable estimates of net coverage error at the state
level for the total population.  Furthermore, the CPS will enable
methodology to be developed (e.g., regression-synthetic estimation
techniques) that might provide reasonably accurate estimates of
coverage error for certain demographic, socioeconomic categories at
the state level and for the total population at certain substate
area levels (large cities, SMSA's, some revenue sharing areas,
etc.).
     The emphasis in conducting the 1950 and 1960 postenumeration
surveys was on obtaining data of good quality. Highly qualified
staff were hired, given extensive training, and a considerable
amount of time was devoted to seeing that procedures were properly
conducted.  The effect was to reduce errors due to poor enumerators
and carelessly implemented procedures; however, the correlation
biases arising from the tendencies of certain segments of the
population not to be enumerated were largely unaffected (in fact,
they may have been increased).
     The emphasis in the 1980 program will be on independence from
the census, in addition to quality.  The 1980 program will utilize
"independent" administrative files for purposes of improving the
estimates of coverage error.  To the extent that a satisfactory
match between the administrative files and the census and CPS can
be achieved without impairing independence of the sample data. we
should be able to obtain more accurate estimates of coverage error
than were obtained in 1950 and 1960.  The feasibility of using
these administrative files is being investigated in a study
currently underway.  Data were collected from the persons in the
February 1978 Current Population Survey (CPS) in order to
facilitate a match with administrative files.  Dual system
estimates of the true total population will be made as of February
1978 and compared with estimates based on births, deaths, and
previous censuses.  If the two estimates of total population are
reasonably close and the processing problems of administrative file
matching are surmountable, administrative files will be used to
adjust the CPS estimates of coverage error in the 1980 census.
     The procedure for doing the August, 1980 CPS follows:
     A listing is made of all Persons currently residing in the
sample housing units together with all persons who died in there
households subsequent to the census.  A determination is made where
each listed person was living at the time of the census.  These
addresses are then searched in census records to see if the sample
persons were enumerated (Procedure B).
     Since this procedure is only concerned with obtaining a roster
of persons at the current address, we would expect this procedure
to yield a more complete listing and a better estimate of
undercoverage than was feasible under the procedure used in the
1950 & 1960 evaluation surveys (A listing is made of all persons
who resided at the sample housing unit at the time of the Census.
The census records for the sample addresses are then searched to
see if the persons were enumerated.)
     In addition to estimating a gross omission rate from the CPS,
we also plan to estimate erroneous enumerations in the census;
therefore, the purpose of the Undercount estimation program will be
to estimate a net coverage error, gross omissions minus erroneous
enumerations.
     A person is "correctly enumerated" if he was enumerated in the
census at the address reported by the CPS as the census date
residence.  A person is "missed" if he was not enumerated at the
census date residence that was reported in the CPS.  An enumeration
is considered to be "erroneous" if the CPS reports that the person
was not living at the location where the census recorded him.  For
example, the CPS could report that no such person exists, or that
the person was born after the census. died before the census or was
living elsewhere on day.  Also a person was erroneously enumerated
if he/she was enumerated more than once.
     A separate sample of approximately 100,000 households will be
selected from census enumerations to determine if they were
erroneously enumerated.
3.   Matching Techniques
     One of the most difficult operations to design and
implement is the development of matching techniques that involves:
     1.   matching of CPS housing unit and person records to census
          enumerated housing units and persons.
     2.   matching of CPS and census enumerated housing unit and
          person records to "administrative" file records.
     These matching operations are different in that the
                                65
former involves a searching operation in a file arranged by
address, whereas the latter involves searching files arranged on
some other basis (in the case of the IRS and Medicare: files the
search is on the basis of a social security number).  Therefore,
our research effort has taken different paths in determining
optimum procedures for these two operations.
a.   Matching of survey housing unit and person records to census
     records
     The matching operations conducted for the Oakland, Richmond
and Colorado postenumeration surveys* were clerical in nature with
explicitly written matching rules.  The Oakland PES was our first
attempt to create a set of matching rules; since they were changed
a number of times during the experiment, a definitive set of rules
does not exist for Oakland.  Based on our Oakland experience a set
of explicit rules for persons was devised for Richmond and
Colorado.  The basic matching operation consisted of the following:
     1.   Coding the PES addresses to tract, ED, block, serial
          number, and form type.  This information is needed to
          locate initially the address in the census address
          register which then guides us to the corresponding census
          questionnaire.  Maps with corresponding map-spotted units
          were used when searching for geocoding census addresses.
          Also the block header record that identifies the ED and
          block for a given street name and house number proved to
          be very useful when searching for census addresses.
          Telephone and city directories were used to a lesser
          extent in the searching operation.
     2.   Matching PES listed housing units against the census
          address register in order to obtain an estimate of census
          housing unit coverage.
     3.   Transcribing information from the PES interview forms to
          a special form to be used to control and facilitate the
          person matching.
     4.   Matching persons on the PES interview form to persons on
          the census questionnaires.  Name, relationship, sex, age,
          date of birth, and race were used its matching variables
          for Richmond and Colorado.
     5.   For the Oakland PES, all Procedure B nonmatches, and
          possible match cases were followed up to see if
          additional information could be obtained to determine
          match status for the "possible" match cases or to obtain
          additional address information for the nonmatch cases.
     6.   Lastly, a final matching operation to census
          questionnaires was conducted to determine final match
          status.
     The following are general observations based upon our
experience with the matching operations:
     1.   Follow-up (or reconciliation) will involve only cases for
          which additional CPS information is needed to determine
          match status.  If the additional information cannot be
          obtained, the will be included as part of a noninterview
          adjustment and a search for a corresponding census record
          will not occur.
     2.   Matching in movers has been a difficult task.
     Indications are that we were unable to locate a significant
number of reported census day addresses (addresses other than the
PES address); also, many addresses that were located were done so
only with a great deal of difficulty.  This experience was
especially noted in predominantly rural areas.
b.   Matching of CPS and census enumerated housing unit and person
     records to "administrative" file records
     Certain groups of persons are particularly likely to be missed
by both the CPS and the census; examples are: black males, males in
urban "ghetto" areas, low income adult males and migrants.  Two
administrative files are being used to provide alternative
estimates to the CPS Census match coverage estimates for these
groups.  These files are the Internal Revenue Service (IRS) tax
return file for persons of ages 18 to 64 and the Medicare file for
persons of ages over 64.
     The methodology to be used in forming a "triplesystem" census
coverage estimate will consist of matching CPS records and a sample
from census enumerations to the IRS and Medicare files.  A brief
description of dual-system estimation is explained later in this
presentation.  Matching will be done on the basis of a reported
social security number (SSN).  The Social Security Administration's
alphadata and Summary Earnings Record File will be used to obtain
SSN's for certain census and CPS records, and to validate reported
CPS numbers.  This is discussed more fully in Section IV.
4.   Administrative Matching
     A possible improvement to using the CPS to estimate net
Undercoverage in the census by a match to census records (dual-
system estimation) is to additionally match to administrative
records to form triple-system estimates.  The two sources planned
for use in 1980 are the tax returns filed in 1980 for 1979 fiscal
year and the Medicare file of all Medicare records for the year
1980.  There are several problems with using these files, the major
one
     *Special postcensal surveys ( PES's) were conducted for the
Oakland, Richmond, and Colorado census pretests for the purpose of
estimating the census undercount.
                                66
being the size of the files; the IRS tax file alone contains about
85 million records, stored on 131 data tapes in SSN order.
     Names and addresses are given to the Bureau exactly as they
are listed on the tax return, meaning the address could be the
address of the tax filer's bank, lawyer, or whoever prepares his
tax return, or a family member.  The Medicare file problems are
similar, but on a smaller scale.  Thus information may be reduced
for confirming or negating matches.
     To match to either of these files, it is necessary to have a
SSN for the record to be matched.  Note that this is true for
records matched to the IRS or Medicare files, but not necessary if
matching is done from either file to the census.  The distinction
will be clearer in a moment.  The reason for needing the SSN is
twofold:
     1.   Since the files are in order by SSN, it is most cost
          effective to search the files using that indicator.
          Matching to these files using names or other variables
          would be prohibitively expensive.
     2.   The SSN is nearly a unique identifier.  While one person
          may have several SSN's, possessing more than one SSN is a
          relatively rare event, and on the IRS files each SSN
          should belong to only one individual.  However,
          identification using a person's name and matching in
          either direction can have problems when the individual
          possesses a common name (e.g., Robert Smith).
     Unfortunately, for these purposes, SSN is not collected in the
census, even on a sample basis.  However, we plan to collect this
information as part of the census erroneous enumeration survey,
which is a sample taken from the census.
     Matching can go in the other direction, too.  A sample of
cases with name and address can be drawn from the IRS and Medicare
files and matched back to the census, in much the same way the CPS
is matched to the census.  However, problems with matching in this
direction arise due to the need for a timely state sample; special
arrangements would have to be made with IRS to draw a state sample
while they are receiving return forms.  This is necessary because
the final IRS tax return file with names and addresses is not
available to the Census Bureau until approximately a year after the
receipt of the forms.  Also we have had some indications that
special problems in matching could occur due to the nature of the
Address that is filed with IRS, e.g., children who have moved away
from home very often still file their parents, address as their
residence.
     It is also anticipated that a follow-up operation would be
necessary because of the portion of the sample from IRS which would
list an address used for tax return purposes which was not the
residence as of census day.  This could introduce a substantial
bias into the dual-system or triple-system estimates by causing a
low matching rate at the person's residence.
     A supplement was administered as part of the February, 1978
CPS, collecting information necessary to matching the sample into
the IRS tax return file for fiscal 1977.  Dual-system estimates
were developed from this matching project and are presently being
compared to demographic estimates for 1978.  This project should
give us an indication both of the problems to be encountered in
matching in this direction and will also tell us, by comparing the
dual-system estimate to demographic estimates, whether the
assumption of independence of sources in the dual-system estimate
holds.
5.   Research Conducted for Proposed Match Study
a.   February 1978 CPS/IRS match study
     A special supplement was administered as part of the February
1978 CPS, collecting information necessary to validate and obtain
SSN's at Social Security Administration.  SSN's were then matched
into the IRS tax return file for 1977.  Dual-system estimates of
the total population will be developed and compared with
demographic estimates.  This project should give us an indication
of the problems encountered in matching, as well as whether the use
of the IRS file for estimation purposes will lower the "correlation
bias."
     There are two separate operations that are involved in this
match study.  An operation at the Social Security Administration
that involves validating and obtaining reported SSN's and a
matching operation at the Census Bureau involving a SSN match of
CPS records to the IRS files.
     1.  Social Security Validation Study--Social Security numbers
were reported for about 80 percent of the eligible persons in the
February 1978 CPS.  Of the remainder, some SSN's were unknown to
the respondent and could not be obtained by means of follow-up ;
some respondents refused to report SSN; some persons reported that
they did not have a SSN; and some SSN fields on the questionnaire
were left blank without explanation.
     Initially the CPS records with a reported SSN were validated
at SSA by matching against the Summary Earnings Record file
(SER).The validation was accomplished by comparing the first six
letters of the surname, month and year of birth, sex, and race (the
only comparable data in both the CPS and SSA files).  The CPS
records on which all comparable characteristics agreed with the SSA
data, records with varying degrees of disagreement, and those
records with reported SSN's that did not exist in the
                                67
SSA system were compiled for the Census Bureau.  A further
validation of records with varying levels of disagreement and CPS
records that could not be located in the SSA numeric file was made
by manually matching a sample of these records with a SSA
alphabetic file.  In order to test this procedure a test sample of
1000 CPS records with a validated SSN was also run simultaneously
through the process with the valid SSN removed.  Clerks were used
to find these CPS enumerated persons, by name and date of birth, by
searching in a microfilm file of all applications for SSN's.  The
CPS records included the following information that could be used
in the searching operation.
     -    Person's full name and its corresponding soundex code
     -    Up to two previous or alternative names (maiden name,
          former married name, name before adoption, etc.)
     -    Date of birth: (month--day-century-year)
     -    Sex and race
     -    Mother's maiden name
     -    Father's name
     -    Place of birth (city or county, State or foreign country)
     This information was included on a match form that included
room for corresponding Social Security Administration data.  An
evaluation will be done to determine the extent of the use of the
above information in determining match status.
     The microfilm file-of SSN applications included the following
information for each person:
     -    Soundex, code of the last name--(The soundex code is a
          device for grouping together spelling variants of the
          same name, and names that are spelled differently but
          sound alike and could easily be confused by an
          interviewer.)
     -    Last, first, middle name
     -    Date of birth: (month-year)
     -    Sex and race
     -    Social security number
     -    Mother's maiden name
     -    Father's name
     -    Place of birth
     Records in the microfile file were arranged:
     -    By soundex code of the last name
     -    Within soundex group by first name and middle name (or
          middle initial)
     -    Within name group, by date of birth Confidentiality of
          all census forms was maintained by having the matching
          done by Census Bureau employees and having the study
          directed at the Social Security Administration by
          professional personnel who are census agents.
     2.  Match of CPS SSN's to IRS Tax Return File After the work
done at Social Security Administration to validate and obtain
SSN's. the CPS records were returned to the Census Bureau
accompanied by a SSN.  At the present time it appears that we will
not be able to obtain valid SSN's for approximately 10 percent of
appropriate CPS records (adults who could report on the IRS file).
Since incorrect SSN's could still remain on this file, an
additional validation study of SSN matching will be done; this will
involve using name and address information that is available on
both the CPS and IRS files, to determine the proportion of cases
incorrectly matched by SSN.  This is the first and only use of
address information in the matching.
     In order to obtain dual-system estimates, a tabulation of age,
race and sex totals in the IRS file has to be prepared.  This is
being done on a 20 percent basis.
b.   IRS-Census match study (involving Richmond, Va. and Southwest
     Colorado dress rehearsal censuses)
     Approximately 1,000 tax returns were sampled from the IRS file
for Richmond, Va. and approximately 1,300 sample cases from
southwest Colorado.  These were then matched to census records for
these two areas.  The purpose of the test was to determine if a
match in that direction was feasible.  Since the match is on the
basis of name and address (no SSN is available for census records),
we were especially concerned that IRS tax file addresses could
result in a large nonmatch rate, resulting in a need for extensive
field follow-up work.  These results are now being evaluated.
Preliminary indications are that this approach may be feasible and,
in fact, more extensive tests. possibly on a national basis, could
be warranted.
6.   Estimation
     The primary purpose of the estimation procedure is to
provide estimates of the net Undercount for states (including the
District of Columbia), and selected substate areas.  A primary goal
of the coverage evaluation program is to provide a methodology for
determining corrected population counts at the state and substate
area level.  Since we cannot afford a survey to accomplish this
objective at the local area level, we are developing a program that
could be utilized in developing synthetic estimates at this level.
Broadly speaking, this will involve two CPS samples that
collectively will provide reliable estimates of the corrected
population of specified minorities at the national level.  The
first estimates that could be formed after the census is concluded,
would be dual-system estimates of the total corrected population
for each state and for certain large SMSA's and cities.  To obtain
these estimates, the CPS will be matched back to the census, with
the match
                                68
Table VI.3.1   Forming a Dual-System Estimate for one of the 61
               Divisions
                                Census
Census Population Survey      In        Out       Total
In                            M'        --        N'P
Out                           --        --        --
Total                         N'C      --        N'T
              N'P * N'C
where NT  = ____________     is the dual-system estimate of the
total
                         corrected population for one of the 61
                  M'     divisions.
     N'P is the estimate from the CPS of the total population;
     M'   is the estimate from the CPS of the number of persons
          enumerated in both the CPS and the census. adjustment is
          made for CPS nonresponse cases and for CPS insufficient
          information for matching cases:
     N'C is the total population count obtained in the census.
          minus the estimate of erroneous enumerations and of the
          total number of imputations made.
status ascertained for each person in the household.  The CPS
sample is being drawn as a state sample with supplementation of the
largest SMSA's and cities.  Each person or household in the sample
will ultimately be classified as correctly enumerated, omitted, or
erroneously enumerated.  The sample estimates of the proportion of
matches and of erroneous enumerations will be used in the dual
system estimate to obtain the total corrected population in each of
the states and designated SMSA's and cities.
     The dual-system estimate is basically that used in capture-
recapture methodology to provide population counts of migratory
animals, birds, and fish.  Of necessity, one or two modifications
have been introduced to allow for the vagaries of survey data.  The
estimate is formed as shown in Table VI.3. 1.
     The only assumption required in this model is that the two
sources be independent.  If independence holds, then N'T is the
maximum likelihood estimate; N'T is the final estimate of the
total corrected population.  It already allows for processing
errors. census refusals and other cases which could not be matched
since the cases are represented in N'P but not in M'.  To estimate
the completeness of the census count or to estimate the census
Undercoverage, we must add the imputations and erroneous
enumerations back to N'C.  That is
      NC
PC = _____ =   estimated completeness of census enumeration
      N'T
     where NC  =   N'C + E'C + IC  =     actual census count
                                             including erroneous
                                             enumeration (E'C) and
                                             imputations (IC)
           N'C           M'
also wC = _____  = _____  =  proportion matched estimated
                              completeness of the actual field
          N'T      N'P      enumeration, excluding erroneous
                              enumerations and before any
                              imputations.
     Imputations and erroneous enumerations have to be excluded in
estimating NT because none of the imputations or erroneous
enumerations will be matched and thus will not be included in M'.
     Also using the above notation
     OC  =  N'T - N'C is the number of persons not counted in
                         the census.
     OC  =  N'T - NC  =  O'C - E'C - IC     is the difference
                                                  between the total
                                                  corrected
                                                  population and
                                                  the census count.
                          O'C
     qC  =  I  -  pC  = _____    is the net Undercoverage rate.
                          N'T
                           O'C
     and rc  =  I -  wc  =_____  is the gross undercoverage rate.
                           N'T
     These procedures can be found in Marks, Seltzer and Krotki who
also develop a three system estimator (2).
     Following the work of Deming and Chandrasekaran (3), the dual-
system estimate is formed for demographic subgroups within the
region for which the estimate is being formed.  These estimates are
made for the smallest mutually exclusive demographic categories
(e.g., young black males), and added across categories to obtain
the estimate for the region.  This is done to reduce both the
variance and the bias of the estimate.
     These estimates would be revised as more information about the
undercount becomes available from administrative record matching.
Matching will be done using administrative records, and separate
estimates of the undercount can be formed from a Census/IRS match
and from a Census/Medicare match.  These would be compared to the
Census/CPS estimate and an adjusted estimate prepared.  Demographic
estimates for the U.S. as a whole will also be available.  The
state estimates obtained from matching can be adjusted to these
national totals.  As mentioned previously, there art timing
problems in obtaining estimates from matching to administrative
records, which lead to these estimates being produced later than
the CPS estimates; hence the need for revisions.
     A more complex estimator can be formed which involves a good
deal more work.  The concept of the dual-
                                69
system estimate can be expanded to comprise an n-system estimate,
where now three sources are used in the matching process: the
census, CPS. and a combination of Medicare records and the IRS tax
return file.  Matching problems faced in the dual-system estimate
increase threefold because of the number of relations possible.
Offsetting the increased matching problems, however, gains are made
in both reduced variance and reduced bias when employing three
systems.  This is illustrated in work by Woltman and Smith (4) and
Wittes (5).
7.   Anticipated Cost and Timing of Administrative Record Match
     Study
     Results of the two IRS studies should be available by August
1980.  The costs of the studies are approximately:
     A.   IRS-Richmond/Colorado Match Study-Processing of 3,000.
          records . . . . . . . . . . . . . . . . . . . . . $13,000
     B.   CPS-IRS Match:
          1.   CPS Supplement involving 97,000 persons. Data
               collection-preparation . . . . . . . . . . . $95,000
          2.   'Computer matching to SSA numeric file 78,000
               person-records . . . . . . . . . . . . . . . $10,000
          3.   SSA Soundex Lookup involving 12,000
               person records . . . . . . . . . . . . . . . .$6,500
          4.   Keying of SSA Lookup Records involving 5,700 person
               records. . . . . . . . . . . . . . . . . . . . .$500
          5.   Computer matching to the IRS file (involve's two
               passes of the IRS file, matching a total of 82,000
               person records from CPS) . . . . . . . . . $125,000*
          6    Tabulations. . . . . . . . . . . . . . . . . $15,000
          7.   Other (salaries. etc.) . . . . . . . . . . . $50,000
     *This is a partial cost because the project was shared with
other independent studies.
     For more information on the 1980 Census Coverage Evaluation,
contact:
     David Bateman
     Statistical Methods Division
     Bureau of the Census
     Washington, DC 20233
8.   References
     (1)  U.S. Bureau of Census.  Census of Population and
          Housing:  1970.  Evaluation and Research Program. PHC(E)-
          4.  Estimates of Coverage of Population by Sex.  Race.
          and Age: Demographic AnalYsis, 1973.
     (2)  Marks, Eli S., William Selmer, and Karol J. Krotki.
          Population Growth Estimation.  A Handbook of Vital
          Statistics Measurement. New York: The Population Council,
          1974.
     (3)  Chandrasekaran, C., and W. E. Deming.  On a Method of
          Estimating Birth and Death Rates and the Extent of
          Registration. Journal of American Statistical Association
          No. 245 (March): 101-15, 1949.
     (4)  Woltman, Henry and William Smith.  An internal Census
          Bureau Memorandum, Preliminary Finding on Dual vs.
          Triple System Estimation. June 4, 1979.
     (5)  Wittes, Janet T. Applications of a Multinomial Capture-
          Recapture Model to Epidemiological Data.  Journal of the
          American Statistical Association, Vol. 69, p. 93-97,
          March 1974.
              E.  Case Study 4: Record Linkage in the
                   Nonhousehold Sources Program
1.   Introduction
     The Nonhousehold Sources Program is a large-scale record check
developed at the Bureau of the Census.  The record check process is
to match names and address records developed independently from the
census to names and addresses collected in the census in order to
identify persons who may have been missed in the census
enumeration.  The program will be carried out as an intrinsic part
of 1980 Decennial Census procedures in selected areas of the
country.  The basic purpose of the Nonhousehold Sources Program is
to reduce within-household undercoverage and, in particular, to
concentrate efforts on minority populations which are most likely
to be undercounted.
     The major steps in the Nonhousehold Source Program are:
     (1)  identification of the target geographic universe; (2)
procurement of appropriate records, collected independently of the
census, which specify names. addresses. and minimal demographic
characteristics of in-scope persons, (3)  precensal processing of
the record lists to screen on geography and other characteristics
of interest, and to prepare materials for matching; (4) a clerical
match of the nonhousehold source records to census listings after
completion of the first phase of census enumeration; and (5),
follow-up of nonmatches to determine enumeration status whenever
possible.  In this last step. if it is determined that a given
person had not been enumerated. he/she is added to the census
questionnaire for the appropriate housing unit.  As a further
coverage improvement. the roster of persons reported for that
housing unit is verified to add any other persons in the household
who we c missed in the initial phase of enumeration.
     The Nonhousehold Sources Program is only one of several
coverage improvement operations planned for the
                                70
Decennial Census.  During the developmental phase for the 1980
census, several other coverage improvement programs have been
initiated, expanded, and/or improved.  The target population of
many of these other coverage improvements overlaps with that of the
Nonhousehold Sources Record Check; that is, a person missed in the
early phase of enumeration may be added to the count from any of a
number of coverage checks.  Therefore, it has been determined that
the Nonhousehold Sources Program will have the greatest payoff, in
terms of coverage improvement, by checking a great number of cases
under relatively liberal criteria than by checking for fewer cases
under strict or conservative rules.
     The Nonhousehold Sources Program has been extensively
pretested in planning for the 1980 census.  Procedures evolved
beginning with the Travis County, Texas census test in April 1976;
going to the Camden, New Jersey pretest in September 1976;
continuing with the Oakland, California pretest in April 1977; and
finally in the Dress Rehearsals (Richmond, Virginia, April 1978;
and Lower Manhattan, New York, September 1978).1  As of this
writing, only the Travis and Camden results have been analyzed in
detail.  In Travis, 7.5 percent of the persons from the record
lists could have been added to the census, but a mechanism to
actually change the counts was not yet developed.  The equivalent
number for Camden was 6.3 percent of the lists.  In Camden, the
missed persons were actually added to the census counts.  The
"yield" of the Nonhousehold Sources Program was even higher, as a
number of persons were added as a result of the roster check at
follow-up households.  In Travis, an additional 3.3 percent, on the
base of the number of persons record checked, was added through the
roster check giving a total of "yield" of 10.8 percent of the list.
In &;Zen, an additional 2.5 percent of the list was added, giving a
total "yield" of 8.8 percent.  Results for the other tests will be
forthcoming.
     Based on data available to date, the results of the program
with respect to coverage improvement were sufficiently encouraging
so as to lead to the inclusion of the program in the 1980 census.
In 1980, plans are to record check 7,000,000 names and addresses of
persons in urban areas of minority concentration.  The independent
record sources will be lists of holders of drivers licenses,
supplied by the various States; and lists of persons from selected
countries of origin and registering as resident aliens in January
1979, supplied by the Immigration and Naturalization Service.
Drivers license lists are a desirable nonhousehold source bemuse:
(1) they are public records and therefore are fairly readily
obtained from the States2; (2) they are universally computerized,
and thus facilitate mass processing; and (3) name and address
information is relatively recent--only licenses with reported
addresses less than two years old are used.  In addition to drivers
licenses, a smaller number of cases will come from the registered
alien lists, which have the same advantages as license lists.  The
INS lists were first used in the Oakland, California census
pretest; although definitive results are not yet available from
Oakland, preliminary counts indicate the yield from the INS list
was similar to that from drivers licenses.  The INS lists contain
not only the same name, address, and demographic data as the
license lists, but also supply "country of origin" so that
appropriate race/ethnicity screening can be done.
2.   Results from the Travis County, Texas and Camden, New Jersey
     Pretests
     The reminder of this report will concentrate on the matching
phase of the Nonhousehold Sources record check, as studied using
the results of the Travis County.  Texas and Camden, New Jersey
census pretests.
     For the Travis County pretest, a total of 3,002 names and
addresses went through the entire record check procedure.  Of
these, 2,342 cases were from drivers licenses.  For cost purposes,
the Travis driven license cases were confined to males, aged 17-35,
in two Zip code areas of Austin City identified as having high
minority populations.  The additional 660 names and addresses were
supplied by local community organizations.  These encompassed both
sexes, a larger age range, and more geography.  The names and
addresses were transcribed to the control section of an office
worksheet, to be used later in geocoding, matching and recording
follow-up results.
     In the Travis local census office, the addresses were assigned
census geographic codes (geocodes). this was done successfully for
2,910 of the original 3,002 records.  Once a geocode was assigned,
the worksheets were matched to the master address listings for the
a geography.  A serial number identifying the address was located
and the census questionnaire for that serial number was obtained.
The name from the record source was matched to the household roster
on the questionnaire to determine if the person had already
definitely been enumerated, or if further follow-up efforts were
necessary.
     The address and name matching rules used in both Travis and
Camden can be found in the Appendix (section
___________________________
     1Actually. the first attempt at a large-scale Nonhousehold
Sources Record Check was in the context of a special census
conducted in Pima County.  Arizona. in 1975.  However, because the
procedures used varied considerably from those in the pretests and
the census, the results of that check will not be discussed here.
     2In developing the 1980 program, some States have cited
Privacy Act restrictions in denying records to the Bureau.
However, in further discussions, this limitation is found not to
apply since the records are treated in accordance with Title 13
when in the Census Bureau's possession.
                                71
[GRAPHIC] \SW672.GIF
7).  For Travis, in the match between the geocoded addresses and
census records, 2,719 or 93.4% of the initial 2,910 geocoded
addresses were successfully matched; 86 or 3% were called possible
matches; and 105 or 3.6% were nonmatches.  The possible matches
were eligible to go through the name match and further follow-up
efforts; nothing further could feasibly be done with no matches.
     In the match between the 2.805 names on address matched or
possibly matched records and the census questionnaire rosters,
1,378, or 49%, were classified as name matches. 159, or 6% were
possible matches; and 1,268, or 45%, were nonmatches.  The possible
name matches and nonmatches were sent for telephone and, if neces-
sary, personal visit follow-up to obtain further information.  As a
result of these follow-ups, 207 of the 1,427 unmatched persons were
determined definitely to have been missed; 154 were out-of-scope
(deceased, moved from test area, etc.); and, for 1,046 persons,
enumeration status could not be determined.
     For the Camden Nonhousehold Sources Program, as in all later
efforts including the census, the geocoding of addresses from the
drivers license lists was done by computer (the 275 in-scope names
on local lists were hand geocoded).  In order to answer questions
regarding yield rates for different demographic groups, the
nonhousehold sources sample was allocated such that all adult
persons, male and female, were represented, although the emphasis
was still on younger males.  After geocoding the license lists
supplied by New Jersey, a total of 19,840 records remained, from
which a stratified sample of cases was selected.  A small
unstratified sample of addresses not geocoded by computer was also
included.  In all, a total of 6,099 cases were processed through
the Nonhousehold Sources Program in Camden.  The names, addresses,
and geocodes were printed on record search forms and sent to the
local office to be matched to the census roles.
     As in Travis, the clerical match was performed in two steps:
first on address, and where that was successful, on name.  For
address matching, note that the categories of address matching had
been expanded.  This change came about because of two problem
situations noted in Travis.  When the record address was matched to
a unit which was "vacant" or "deleted" on the census list, there
was no place on the Travis form to indicate the situation.  In such
cases there is no reason to go to the census questionnaire to
attempt a name match.  Therefore, it was decided that such cases
would be noted and set aside, the assumption is made that other
census operations would add the household members to the address,
if appropriate.  The second problem arose when the basic address on
the nonhousehold source record matched to a basic address for a
multiunit structure in the census, but the independent source gave
no apartment designation.  When this happened, it was not possible
to readily identify the serial number of the appropriate census
questionnaire for the name match.  In Travis, this was handled by
searching all the questionnaires for the basic address; in Camden,
the category "multi-unit structure" was added to allow the matchers
to indicate when this was done.
     The result of the Camden nonhousehold sources
                                72
address match showed 5,763, or 94.5 percent, of the 6,099 cases
were matches; 360 of these matched to a vacant or deleted unit.
There were 18 (0.3% of 6,099) possible matches and 224 (3.7% of
6,099) addresses that matched to multi-units with no apartment
designation.  Only 94 cases were non-matches.  Those 5,645 cases
which were not classified as address nonmatches of matched to
vacant or deleted units went on to the name match.
     The Camden name match categories were also expanded with the
addition of the "Unable to Locate Questionnaire" classification.
The nonmatches were postcensally classified to separate the cases
where the name could not be matched because the household was a
refusal in the enumeration, from cases where the person was just
not matched to an existing roster.  The results of the Camden name
match were as follows:
[GRAPHIC] \SW673.GIF
     It can be seen that 2,574, or 45.6 percent of the names
matched initially  a result almost identical to Travis.  In Camden,
however, it was possible to examine the match rates by the three
demographic groups shown above as represented in drivers licenses.
It can be seen that females matched at a higher rate than males,
and that males 25-44 matched at a much lower rate than the other
males in the sample.
     Postcensally, a more thorough review of the matching operation
was carried out.  Of the 227 "Possible Matches' " 154, or 67.8
percent of the person were eventually verified enumerated, 61, or
26.9 percent were undetermined, and 12 persons (5.3 percent) were
added to the census.  This distribution supplies much of the
argument for the eventual elimination of the "Possible Match"
category.  Another postcensal study looked at the consistency of
drivers license records with census data, and the accuracy the
initial name matching operation.  The name matching criteria used
in the initial office matching operation did not require an
examination of the answers to the census age or sex questions to
establish a "Match."  To evaluate the consistency between the age
and sex of nonhousehold sources cases that were classified as a
harm "Match" and the corresponding age reported to the Census
Bureau, the census questionnaires for 2,338 cases classified as a
name "Match" in Camden were reexamined.  It was first determined
if, in fact, the cases were a name "Match" according to the Camden
matching criteria.  Of the 2,338 cases studied, 22 (0.9 percent)
had erroneously been classified as matches in Camden.  There were
42 cases which had erroneously been called "Nonmatches," for a
gross error or total of 64 (2.7 percent of the total number of
cases studied plus erroneous nonmatches).  This result indicated
that, even though the matching clerks had minimal training and
supervision, the matching rules were applied relatively well.
     A comparison was the made for "sex" as reported from drivers
licenses and the Census for the 2,316 names correctly matched in
Camden.  Of these, sex differed on the two sources in 15 cases.
This error could have come from misreporting or misallocation on
either source.
     Age was then compared on the two sources for the 2,301 cases
found to be name matches of the same sex.  The following table
displays the result of this match:
                                73
[GRAPHIC] \SW674.GIF
                                74
[GRAPHIC] \SW675.GIF
     The above table presents a cross tabulation of age reported on
the drivers licenses and that on the Census questionnaire.  Along
with row and column distributions, diagonal totals are presented
and summarized.  The amount of agreement noted is evidence of the
quality of age reporting on drivers licenses, as well as the
accuracy of the matching operation.  Within the age ranges tabu-
lated, 93 percent of the cases fall on the diagonal and an
additional 3 percent fall within one cell.  For the off-diagonal
cases, we suspect a large number of there arise because a parent's
name was matched to a child's, or vice versa.  For program
purposes, these imperfections are acceptable.  It would not be
worth additional time, training, and follow-up effort to resolve
such discrepancies.  The result of not reconciling these
differences is that a minute amount of coverage improvement may not
be realized, but the cost of reconciliation would be prohibitive.
3.   Plans for the 1980 Census Nonhousehold Sources Program
     In the Oakland pretest and the Dress Rehearsals, further
modifications were introduced into the nonhousehold sources
matching records.  The procedures and forms for the 1980
Nonhousehold Sources Program have evolved on the basis of these
pretest and dress rehearsal experiences.  The section for recording
match results has been expanded to cover all relevant situations,
and procedures for how to handle each case appear appropriately
(see Section B):
     The basic matching instructions have been modified somewhat
from the pretests.  The "Possible Match" category has been dropped.
This was done because relatively few cases were categorized this
way in the pretests; more importantly, however, "Possible Matches"
would be treated, at each point, just like matches.  Also, to
handle the problem of no apartment designation appearing on the
record source when the census shows a multi-unit structure, a
distinction is made which depends on the size of the structure.
For basic addresses with ten or more units, nothing further is
done.  For those with fewer than ten units, an attempt is made to
identify the correct unit by matching the surname to the census.
This is done to keep the operation workable and to keep the
matching clerks
                                75
[GRAPHIC] \SW676.GIF
honest.  However, if the person is not found, no follow-up is
possible without a specific unit to call or visit.
4.   Summary and Future Considerations
     In summary, it is felt that the match of administrative
("Nonhousehold") records and the census is sufficiently accurate to
meet the aim of cost-effective coverage improvement.  Perfection in
procedures and accuracy in the independent record source have been
shown unnecessary in generating a highly acceptable yield from the
processes involved.  Perhaps the most disturbing aspect of the
results of the program is the large number of "undetermined status"
cases which have consistently arisen in pretests.  Given that the
matching procedure itself is accurate to the degree it was in
Camden, the fact that enumeration status is never determined for at
least one-fifth of the records checked must be a function of
procedures other than the clerical match.  It is probably a
function of incorrect addresses reported to the Department of Motor
Vehicles; the inability to conduct a follow-up interview in the
census; the mobility of the population; an unwillingness of the
target person to be interviewed; and other factors.  The degree to
which each of these factors contribute to the "undetermined's" is a
subject for further research.
     One last word regarding the choice of administrative lists to
use in the Nonhousehold Sources Program might be appropriate.  The
previously discussed requirements--currency, availability,
computerization, presence of minimal data--are met by drivers
license and INS records.  The experience of using locally-supplied
lists in
                                76
Travis and Camden showed there to be costly to use on a large scale
basis and, more importantly, less effective in terms of percentage
yield than drivers licenses.  It has often been suggested that some
form of Public Assistance lists be used, as these might be fruitful
to enumerate the types of persons likely to be missed.  In fact,
for the final Dress Rehearsal in Lower Manhattan, a welfare file
(comprised of recipients of AFDC, General Public Assistance, and
Medicare) supplied by the city was used.  The results of its use,
when available, will indicate whether this list may give a higher
yield rate.  However, such lists may be very difficult to procure,
particularly when they are controlled at local rather than State
levels.  They are also protected to a great extent by privacy laws
and provisions; for instance, the Department of Agriculture has
denied access to Food Stamp Roles.  However, in spite of the
decision to use drivers license and alien lists in the 1980 census,
the issue of which administrative record sources to use is not
closed.  It is expected that efforts to improve the Nonhousehold
Sources Program will continue into and beyond the 1980 census, and
the investigation of other list sources will undoubtedly be a part
of them.
5.   Sources of Further Information
     Further information regarding the Nonhousehold Sources Program
is available as methodological research documentation at the Bureau
of the Census.  After the 1970 census, a small scale evaluation
study of the use of drivers licenses as an administrative record
source was performed in Washington, D.C. The results of this study
are described in the 1970 Census Preliminary Results Memoranda
Series. [1]
     Original interest in this program, and preliminary recom-
mendations for implementation, may be found in the memorandum
series of the Task Force on Coverage Improvement Procedures, active
after the 1970 census. [2]  Credit for the original tabulation and
analysis of results for the Travis and Camden Nonhousehold Sources
Program is given to John Thompson, Statistical Methods Division.
Further discussion of the Travis and Camden programs can be found
in three memoranda by that author. [3], [4], [5].
     A comprehensive summary of these results can be found in a
paper entitled, "The Nonhousehold Sources Coverage Improvement
Program, " presented by Thompson at the American Statistical
Association Annual Meetings, August 1978. [6]
     The extensive computer programming efforts for the
Nonhousehold Sources Program have been carried out under the
direction of Roger Lepage, Decennial Census Division.  Information
on processing the drivers license and INS lists, including the
geocoding match, may be obtained from Lepage.
     An overview of 1980 census coverage improvement efforts,
including the Nonhousehold Sources Program, may be found in a
paper, "Plans for Coverage Improvement in the 1980 Census," by
Peter Bounpane and Clifton Jordan, presented at the American
Statistical Association Annual Meetings.  August 1978. [7]
     For more information on the Nonhousehold Sources Program,
contact:
     Susan Miskura
     Statistical Methods Division
     Bureau of the Census
     Washington, DC 20233
6.   References
     Copies of all documents cited below may be obtained from the
Research Documentation Repository, Statistical Research Division,
U.S. Bureau of the Census.
[1]  Novoa, Ralph (1971), "Preliminary Evaluation Results
     Memorandum of the 1970 Census.  No. 21.  Subject: Listing
     Census Coverage through Drivers Licenses (E22-No. 3)," October
     21, 1971.
[2]  Marks, Eli S., Jones, Charles D., Cullimore.  Stanley O., and
     Foster, Barbara (1974), "Memorandum for the Task Force on
     Coverage Improvement Procedures, Subject: Proposal for Use of
     Nonhousehold Sources for Coverage Improvement," October 18,
     1974.
[3]  Thompson, John H. (1977), " 1967 Census of Travis County
     Results Memorandum No. 34.  Subject: Travis County
     Nonhousehold Sources Program. December 8, 1977.
[4]  ______ (1977), "1967 Census of Camden.  New Jersey Results
     Memorandum No. 15, Subject: Primary Results of the Camden
     Nonhousehold Sources Coverage Improvement Program," October
     28, 1977.
[5]  ______ (1978 ), 1976 Census of Camden.  New Jersey Results
     Memorandum No. 24.  Subject: Additional Results of the Camden
     Nonhousehold Sources Coverage Improvement Program." October
     25, 1978.
[6]  _______ (1978), "The Nonhousehold Sources Coverage Improvement
     Program," 1978 Proceedings of the Social Statistics Section,
     American Statistical Association, 1978, 435-440.
[7]  Bounpane, Peter A., and Jordan, Clifton (1978).  "Plans for
     Coverage Improvement in the 1980 Census, " 1978 Proceedings of
     the Social Statistics Section.  American Statistical
     Association. 1978. 12-20.
                                77
7.   Appendix.  Matching Instructions
1.   Address Match Terms
     A.   An address is considered matched under the following
          conditions:
          1.   The identical street name, house number, apartment
               number (if any), State and Zip code appear in the
               register, or the house numbers are the same and the
               street names have only minor spelling variations.
               For example.  "Freeman St." vs.  "Freemen St."
          2.   The identical Post Office lockbox number, State and
               Zip code appear in the register.
     B.   An address is considered possibly matched under the
          following conditions:
          1.   The house numbers and street names appear to be the
               same, but the street types are different.  For
               example, the word "Street" in one source and the
               word "Avenue" in the other source.  This includes
               variations,between "Road," "Court," "Circle," etc.,
               as well as a street type in one source but not in
               the other.
          2.   The house numbers and street names appear to be the
               same but the compass point is present on one source
               and absent in the other.  For example.  "301 Main
               St." vs.  "301 N. Main St"' This DOES NOT include
               contradictory compass points such as "E.  Oak" vs.
               "W.  Oak".
          3.   The house number and street name were matched or
               possibly matched but the identical apartment number,
               letter, or location description is not found.
          4 .  The house numbers appear to be the same but digits
               may have been transposed in the register.  For
               example, you are searching for the number "382" and
               do not find that number in the register, but find
               instead
                    "380 Elm Ct."
                    "328 Elm Ct."
                    "384 Elm Ct."
                    "386 Elm Ct."
               Note that the sequence of listings provides evidence
               of transposition.
     C.   An address that is neither matched nor possibly matched
          is considered a nonmatch.
II.  Name Match Terms
     A.   A name is considered matched when both a given and
          surname are shown in each source and one of the following
          conditions exist:
          1.   The names shown in both sources are identical.
          2.   The names are pronounced the same but are spelled
               differently.  For example, "William A. Ralph" vs.
               "William A. Ralf".
          3 .  An abbreviated name is provided on one source and is
               noncontradictory to the name provided from the other
               source.  For example, "Jim E. Johnson" vs.  "James
               E. Johnson".
     B.   A name is considered possibly matched when one of the
          following conditions exists:
          1.   Only surname is given in one source and that surname
               is identical to the surname in the other source.
               Slight differences may exist as long as they may be
               attributable to errors in spelling or handwriting.
          2.   Surname and one or more initials, but no given names
               appear in one source and that surname is identical
               to the surname in the other source and the
               initial(s) are noncontradictory.  Slight differences
               may exist as long as they may be attributable to
               errors in spelling or handwriting.
     C.   A name is said to be a nonmatch if it is not one of the
          above.
                      F. Concluding Comments
     The case studies presented illustrate the actual and potential
benefits and difficulties involved in ca trying out studies using
matching of administrative records to obtain statistical data.  We
will highlight some of the main issues raised by these studies.
     1.  The case study on the Linked Administrative Statistical
Sample (LASS) project is intended to illustrate some of the main
concerns being addressed in order to determine the feasibility of
developing integrated sa triples from several administrative record
systems.  In LASS, the main use of sampling from administrative
records will be to create an improved database for industrial and
occupational mortality research.  There are at least three major
issues which will have to be resolved before this objective is
accomplished:
     1.   access restrictions and disclosure issues,
                                78
     2.   potential incompatibilities among the systems being
          linked, and ,
     3.   problems of data quality.
     The suitability of an upgraded CWHS for studying industrial
and occupational mortality depends, in part, on the results of
efforts to:
     1.   Add cause of death and other death certificate
          information to the CWHS. (It is not known yet if SSA
          information for decedents on name, social security
          number, race, sex, date of birth and date of death is
          sufficient for the States to attempt a search for the
          death certificate.)
     2.   Create detailed occupation codes from the occupation
          entry on individual tax returns and SSA industry
          information. (The usability of the occupational entry is
          being assessed given the lack of taxpayer instructions
          for reporting occupation.)
     3.   Upgrade the CWHS data on industry and place of work.
          (Data quality problems exist partly because of the
          voluntary nature of the SSA establishment reporting
          system.  Other data quality problems are being
          encountered in the changeover to annual wage reporting.)
     Access questions, though, are among the most important issues
that have to be addressed before the Continuous Work History Sample
can be used to its fullest potential for mortality research.There
are at present many restrictions imposed on data access by laws
such as the Privacy and Freedom of Information Acts as well as the
statutes and regulations of each of the participating agencies.
Interagency data sharing is very limited, as a result.  If Social
Security is to proceed with the numerous activities planned for
upgrading the Continuous Work History Sample, many confidentiality
restrictions will have to be overcome.  Legislative initiatives to
resolve problems of making information available for statistical
linkage (i.e., tax return data) and Presidential proposals aimed at
providing government-wide legislation for protection of statistical
and research data offer possible solutions.
     2.  The Use of Administrative Records in the Survey of Income
and Program Participation describes the difficulties encountered in
using administrative records as sampling frames in three
experimental field activities prior to the ongoing SIPP.  Three
major problems have arisen in the SIPP development work.  First,
locating sample cases in the field has been more difficult than
initially anticipated.  The source, of this difficulty stems from
several causes: (1) inadequate or inaccurate addresses, (2) recent
moves by program participants, and (3) a minimum delay of several
months from sample selection to interview date.  Field procedures
have been adopted to help minimize the problem.  These procedures
seem to have improved the interviewers ability to locate the sample
person; however, a further analysis of the procedures' impacts
would be useful.
     Unlike the first problem which became apparent in the survey
field operations, the two remaining problems were first observed
while investigating potential administrative record systems for the
SIPP.  Thus, the second problem concerns finding administrative
record systems which are national in scope and relevant for the
study of current policy issues.  Few systems of interest for
sampling maintain records at the national level.  Systems available
only at the State or local level would substantially increase the
sampling and data access problems of the ongoing survey.
     Finally, the third problem concerns the identification of
sampling units with a known probability of selection.  This arises
when the survey unit does not coincide with the administrative data
units.  Some modification of the sampling frame is necessary to
ensure a well-defined probability of selection.
     In the developmental program, the main use of sampling from
administrative records in the SIPP has been for validation studies
and response error analyses conducted by comparing survey data with
administrative data.  In the future, however, the main uses of
administrative records systems will be for improving estimates for
particular segments of the population through multiple frame
weighting and/or for augmenting the survey data base with data
which is difficult to collect, such as work history or earnings
history data.  Ultimately, data from administrative records may, be
used to adjust individual survey data or to develop control totals
for adjusting aggregate estimates of recipiency of particular
income types and participation programs.
     3.  The case study on Use of IRS/SSA/HCFA Administrative Files
for 1980 Census Coverage Evaluation serves to illustrate the
difficulties of matching when different units are being linked and
the identifiers differ.  The CPS identifies households by address
and includes SSN for household members; the 1980 Census also
identifies households by address, but does not include the indi-
vidual's SSN; the IRS/SSA/HCFA administrative record files list
persons with the SSN as an identifier.  Matching of the CPS records
and the Census records is based on the geographic location of the
household units; after a potential matched household was identified
based on the address, the characteristics used for matching
individuals were name. relationship, sex, age, date of birth and
race.  For the match of CPS records with IRS/SSA/HCFA admi-
nistrative records the main identifier used for matching
individuals was the SSN; if a match could not be established on the
basis of the SSN, then other identifiers were used, such as date of
birth and last, first and middle name.  The research study
conducted using the February 1978 CPS match to IRS records showed
that a valid SSN was
                                79
maintained for about 10 percent of the individuals' CPS records.
     The main use of the 1980 coverage estimates is to estimate the
undercount of the official Census estimates that are published in
January 1, 1981.  The estimates of 1980 coverage of population in
the 1980 Census at the State, Substate level. and for selected
subgroups would not be available at the same time as the Decennial
Population counts were released: when they become available they
could be of crucial importance in the distribution of billions of
dollar of Federal money based on population data (e.g. in programs
such as General Revenue Sharing and various grant-in-aid programs).
Another important use of the estimates could be in the program to
develop intercensal population estimates.
     4.  Record Linkage in the Nonhousehold Sources Program was
developed through a series of pretests for the 1980 Census of
population.  This program is a good illustration of the need to
modify matching procedures over time after problem areas have been
identified.  These modifications will enhance the efficiency of
this program in the 1980 Census.
     To choose only administrative record lists with highest
quality was an important decision in this program; the quality of
the list influences to a great degree the proportion of matches
which can be achieved.  Locally supplied lists are of uncertain
quality, difficult to use because they are not computerized. and
overall were not as efficient as Drivers' License lists available
from the States.  In the final 1980 Census operation only cases.
with geocodable addresses went through the clerical name matching
operation.  About 8,000,000 names will be matched in this operation
in areas selected for their high concentration of minority
population.  Because of the magnitude of this project. the list of
households to undergo the clerical name matching was created by
computer based address matching.  Inaccuracy in name matching
(i.e.. false nonmatches) leads to further field follow-up. which
increases cost and time.  However, because the match is to augment
the Census rather than to make statistical estimates. degree of
matching uncertainty is acceptable.
     The Nonhousehold Sources Program is one of several coverage
improvement program for the 1980 Census; therefore, it need not
yield "perfect" results.  It is only required to be a cost-
effective operation to improve the coverage of the Census.
                                80
                                                        CHAPTER VII
               Technical Problems in the Statistical
                   Use of Administrative Records
     Previous chapters have discussed the importance of
administrative records for such uses as the generation of current
statistics for small geographic areas.  There are a number of
technical problems which have been encountered with past and
current uses that must be resolved if the statistical potential of
administrative records is to be realized.
     The technical problems are similar to those encountered in the
use of census and survey records.  With each administrative record
set to be used, the statistician must ask: Who is reported? (is the
appropriate population of persons. organizations, etc., fully
covered in the record set?); What is reported? (Is it appropriate
for the intended statistical use?); How is it reported and
processed? (Is the information accurate?); When is it reported and
processed? (is the information timely?).  The unique aspect of
administrative record uses is that the questions are asked after
the record collection has already taken place.
     Administrative record sets are often not designed for
statistical purposes.  They may not cover the entire population of
interest, they may contain administratively convenient concepts and
definitions that are not appropriate to the statistical use, there
may be lack of adequate control over the accuracy of key
information, and it may be difficult to access the records in a
timely fashion.  Some of these problems are inherent in the nature
of data processing in general (such as keypunching errors), but
most can. with greater attention and planning, be resolved. in fad,
a resolution of technical problems preventing effective use of
administrative records for statistical purposes can, in many
instances, improve administrative efficiency as well as produce
better statistics.  This is particularly true when technical
problems impeding statistical use of records arise because of
duplicative and inconsistent reporting requirements associated with
different administrative programs dealing with overlapping
populations.  The political barriers to improved coordination among
administrative programs, and between statistical and administrative
programs, may not be easy to remove; but, in a number of areas,
improved coordination offers the potential for higher quality data,
more efficient administration, and reduced reporting burdens for
individuals and organizations.
     This chapter will illustrate common technical problems that
arise in making statistical use of administrative records by using
the Social Security Administration's Continuous Work History Sample
and related administrative record files as the principal examples.
The CWHS has the advantage of making extensive use of
administrative files containing both individual and organizational
records; and it also has been used in a number of recent tests
designed to assess the quality of administrative records for
statistical applications. (See Chapter III for a detailed
description of CWHS data programs.) The remainder of the chapter is
organized into four main sections dealing with problems relating
to: (1) incomplete coverage of administrative record sets; (2) lack
of comparability among record sets; (3) reporting and processing
errors: and (4) questions of data timing.  A summary of problems
and potential for improvement concludes the chapter.
                            A. Coverage
     Information for nearly all employed persons is contained in
one or more of the administrative record systems currently in
existence, either because they have accrued income subject to
taxation or are eligible for benefits under one or more Federal
programs.  There is not. however, a single record system containing
information for the entire U.S. population.  The statistician often
must deal, therefore. with differences between the population of
interest for the statistical purpose and the population covered by
the record system.  Such "coverage gaps" sometimes are difficult
and costly to correct (although not nearly as costly as sample
surveys to collect comparable information at detailed geographic
levels.).
     Most administrative record files contain information for
specific groups such as persons receiving public assistance
payments under a particular program.  There are, however, at least
three major record systems which cover large segments of the
population: Internal Revenue Service records from income tax
returns; Social Security
                                81
Administration records; and records collected by State agencies for
unemployment insurance purposes.  Each of these record systems has
complex coverage limitations defined in terms of groups
specifically excluded from mandatory participation in the
administrative program.
     Annual employee-employer CWHS files have been assembled
principally from three major SSA administrative files--(1) a file
of personal characteristics, which contains information on sex,
race, and date of birth taken from applications for social security
numbers; (2) an employer file which contains industry and
geographic codes for employer reporting units, taken from applica-
tions for employer identification numbers and related supplemental
informational forms; and (3) a wage item record file which contains
worker wage information taken from regular employer reports of
individual wages subject to the social security payroll tax  The
personal characteristics file covers all individuals with social
security numbers, the employer file covers all employers with em-
ployees subject to the social security payroll tax, and the wage
item record file covers all individual wage and salary jobs subject
to the payroll tax.
     The personal characteristics file covers nearly all adult
Americans, but information on earnings and location and industry of
employment is available for individuals only for periods in which
they work in social security-covered jobs.  Nonworkers and those
working only in noncovered jobs do not appear in the annual
employee-employer CWHS file.  The largest groups of noncovered jobs
include Federal civil service and railroad jobs-which are excluded
because of coverage by alternative pension plans-and jobs in those
State and local government and nonprofit organizations that have
not chosen to take the optional coverage available to such
organizations.  Most self-employed workers are covered by social
security and a file of self-employment records can be merged with
the employee-employer file.
     The significance of the coverage limitations of the CWHS
depends on the desired applications of the data.  Since the
employee-employer CWHS provides information only for covered
workers. contains only covered wage income. and provides no
measures of family status or family income, it would be a seriously
deficient data base for analyzing the overall economic welfare of
particular demographic groups.
     The CWHS files have been used to develop statistics on
regional workforce characteristics and inter-regional migration.
One of the results of the coverage exclusions is that persons who
move between covered and uncovered employment appear as contracts
to or dropouts from the workforce, thus overstating both of these
categories and understating true migration.  Another effect is that
because of workforce composition, coverage will tend to vary from
region to region.  Workforce estimates for an area like Washington,
D.C., with a larger concentration of noncovered Federal workers.
are therefore deficient.  Similarly, workforce and migration
estimates for areas with high concentrations of noncovered State or
local government employees are adversely affected.
     One approach to resolving "coverage gaps" is to merge micro
records from different administrative record systems.  There have,
for example, been test efforts to merge Federal civil service
employment records and Railroad Retirement Board records with the
CWHS.  These efforts were complicated, however, by noncomparabili-
ties between the files in records relating to such important
information as wages and salaries (the CWHS shows covered wages
received, while civil service records indicate only grade level and
rate of pay).  Greater coordination of recordkeeping procedures
among administrative agencies would facilitate data mergers.  The
Civil Service Commission, for example, is considering statistical
applications in its design of a new information system and
consequently may include payroll as well as personnel information
in the records.
     Matches of different administrative record sets for sta-
tistical purposes would help to overcome coverage problems and
greatly improve the usefulness of data sets.  As has been indicated
in Chapter VI. however, both technical problems and problems of
access can prom serious barriers to successful statistical projects
to link records from programs under different administrative
jurisdictions.  The linkage barriers are particularly serious in
such cases as State-administered welfare programs. where there may
be significant State-to-State variations in recordkeeping practices
and access restrictions.
     The most promising route to more complete employment coverage
in the CWHS is related to the recent shift to a joint SSA-IRS
program to collect a single annual report on individual wages in
place of the current annual IRS report (form W-2) and four
quarterly SSA reports.  This coordinated record collection program
ism Chapter V for a detailed discussion) is designed primarily to
reduce the reporting and paperwork burden for employers and to
improve administrative efficiency. but it potentially makes
available for the CWHS a virtually complete set of annual reports
for all wage and salary jobs.
     In general, greater coordination among administrative agencies
in record collection and processing should not only reduce
paperwork burdens, but should also make administrative records more
suitable and cam" to use for statistical purposes.  The Commission
on Federal Paperwork (1977), which initially recommended
coordinated annual wage reporting to IRS and SSA. has, for example,
also recommended greater interagency coordination of record
collection for welfare benefit programs.  If im-
                                82
plemented, their program (Single Application for Verification of
Eligibility or SAVE) could potentially make it much easier, from a
technical point of view, to merge welfare records with other
records such as those in SSA and IRS files, in order to obtain a
reasonably comprehensive statistical picture of individual and
family income from administrative records.  In order to insure that
administrative recordkeeping changes reduce rather than increase
noncomparabilities among record sets and do not add to other
technical problems impeding statistical use of administrative
records, however, there must be effective coordination between
statistical and administrative agencies as well as coordination
among administrative agencies.  The next section discusses data
comparability and quality problems arising from imperfectly
coordinated programs for establishment reporting of employment and
payrolls in administrative and statistical systems.
                         B. Comparability
     The statistician is often faced with the problem of adapting
administrative record concepts and definitions to statistical
needs.  Not only do administrative concepts frequently differ from
statistical concepts, but they can also differ among administrative
record systems.  One consequence of these differences is that
measurement of the same phenomena (employment by industry, for
example) will yield different results from the different adminis-
trative record systems.  Reconciliation of the differences (and
consequently an assessment of accuracy) is extremely difficult and
complex.  Another factor is that concepts are often interpreted and
implemented differently by the various reporting entities in the
same record system.
     One of the primary uses of administrative records, as noted
earlier, is the production of statistics for subnational areas.  An
important conceptual problem in the use of such statistics is that
some record systems measure economic activity at the individual's
place of work whereas other systems measure activity at the
individual's place of residence.  Social security and unemployment
insurance data, since they are based on employer reports, reflect
place of work.  Decennial census and IRS (1040) data generally
reflect place of residence.
     To illustrate the effects of data comparability problems that
arise in using administrative records to develop employment
estimates, Table VII.1 compares first quarter 1970 CWHS employment
estimates from the 1970 census.  Also compared for the Nation and
New York State are first quarter CWHS employment estimates for 1971
and 1975 with employment estimates based on unemployment insurance
records and employment estimates from the Census Bureau's County
Business Patterns program.
     The CWHS and decennial census estimates have a number of
noncomparabilities.  The census is by place of residence, whereas
the CWHS is by place of work.  The census estimates are based on
questions regarding the person's employment during the week prior
to the census while the CWHS counts persons with covered employment
at any time during the first quarter.  The census estimates include
self-employed persons while the CWHS estimates include only covered
wage and salary workers.
     The 1971 CBP data are derived primarily from social security
records, and thus should cover essentially the same workers as the
CWHS.  There are, however, some important differences between the
two series.  The CWHS employment estimates have been tabulated from
a 1 percent sample of records for individual workers and represents
an estimate of workers in covered employment at any time during the
first quarter of 1971, classified on the basis of the location and
industry of their major job during the period.  The CBP estimates,
by contrast, represent a count of jobs filled during a single (mid-
March) pay period derived from employer reports of aggregate em-
ployment submitted along with quarterly payments of social security
payroll taxes.  The CBP estimates moreover are based on regular SIC
industry coding conventions and omit the government SIC category
(because of incomplete coverage), while the CWHS estimates include
government workers coded to nongovernment SIC categories whenever
the government reporting unit is engaged in activities for which
there are corresponding private SIC categories (e.g., school
district employees would be coded into the educational services
industry).
     An additional important difference between CWHS and CBP
employment estimates arises because of Census Bureau supplementary
efforts to obtain more complete and reliable industrial and
geographic detail that can be derived from basic reports to SSA.
Industrial and geographic breakdowns of employment by
multiestablishment employers are supplied to SSA on a voluntary
basis, and Census has long had its own program to collect
geographic and industrial employment data from large
multiestablishment employers that have not voluntarily supplied the
information to SSA.  The supplemental information supplied to
Census is incorporated into CBP's estimates, but it does not
contain individual worker records and cannot be incorporated
directly into the CWHS.  Therefore, the CWHS contains some
geographic and industry distortion because of incomplete
establishment reporting.
     CBP data for the years 1974 and later are based on SSA data
for single establishment employers only.  Data for
multiestablishment employers are obtained from the Census Bureau's
Annual Organizational Survey which is used both for data collection
purposes and to update the Stan-
                                83
[GRAPHIC] \SW684.GIF
                                84
dard Statistical Establishment List (see Chapter V for a detailed
discussion of the SSEL program).
     The UI data in Table VII.1, like the CBP data, represent a
count of total jobs held at reporting establishments during a
single mid-March pay period.  The establishment concept used in the
UI system is generally consistent with that used by SSA (industry-
county combinations), but it is an independent reporting system
with different establishment numbering and some differences from
State-to-State in reporting requirements and processing procedures.
The worker coverage provisions of the UI system, moreover, also
differ somewhat from State-to-State and likewise differ somewhat
from social security coverage.  In 1971 and earlier years, in
particular, a number of States exempted many small employers (e.g.,
four or fewer employees) from UI coverage. (See Chapter V for a
detailed discussion of the UI program.)
     The UI estimates of employment are the lowest of the three
series for most industrial categories for 1971.  The lower UI
estimates probably reflect primarily less comprehensive UI than
social security coverage, particularly in service industries where
small employers are common.  The CWHS estimates are the largest for
most industries, in part because the CWHS covers persons working
who didn't work during the March pay period covered by the UI and
CBP data, but did work during some other part of the first quarter.
The presence of government workers in " nongovernment" industries
also raises CWHS estimates relative to UI and CBP estimates in some
industries, particularly services.  The CWHS, as tabulated in Table
VII.1, however, counts each worker only once, whereas the UI and
CBP data count a worker once for each job he may hold during the
reference pay period.
     There is no fully satisfactory way to quantify the various
conceptual factors that contribute to differences among the
employment series in Table VII.1.  Nor can conceptual differences
always be readily distinguished from differences that may arise
from errors in reporting, coding, and processing the primary
records entering into the three systems.  While administrative
record sets have been used to produce statistical series,
confidentiality restrictions have limited attempts to use
combinations of different administrative record sets.  Matches
between micro records from different systems would help con-
siderably to quantify and resolve noncomparabilities between
series.  A match between micro records from the UI and SSA systems,
for example, could help identify inconsistencies in reporting unit
definitions as well as inconsistent geographic and industrial
coding.  A match between SSA and IRS records could provide a link
between place of work and place of residence, which would not only
alleviate the place-of-measurement problem, but would provide a
basis for construction of current  data on commuting patterns.
                C. Reporting and Processing Errors
     Administrative agencies make great efforts to ensure the
accuracy of information needed to administer their programs (net
income reported to IRS or taxable wages reported to SSA, for
example).  Other information, important to statistical uses of the
records, but only marginally applicable to administrative purposes
(such as geographic and industrial information reported to SSA) are
often imperfectly reported, checked, and processed.
     An illustration of this problem can again be drawn from the
CWHS.  Not all information collected by SSA from individuals and
employers is of equal importance for program administration.
Therefore, given limited resources available for ensuring accurate
reporting and processing of information, it is logical to
concentrate the greatest resources on the most important items.  As
a result, information which may be highly important for statistical
applications, but of marginal importance for program
administration, tends to receive low priority in competition for
the resources needed to ensure timely and accurate reporting and
processing of information.  Information on the industry and
geographic location of employer establishments, in particular, has,
sufficiently little administrative importance that SSA has im-
plemented only a voluntary establishment reporting plan for
multiestablishment employers.  And voluntary establishment
reporting combined with limited resources for monitoring the
reporting and processing of establishment records has resulted in
CWHS geographic and industrial data that are frequently of
questionable accuracy.  Resources have not permitted a thorough
evaluation. of reporting and processing accuracy, but some recent
studies have suggested substantial data inaccuracies, particularly
in the geographic indicators in the CWHS that have been used to
develop work force migration and commuting estimates.  Users of the
employee-employer CWHS have noticed for some time that geographic
coding errors in the data files occasionally result in large,
spurious movements of workers between geographic locations.  More
recently, as worker home address information has been added to the
CWHS for selected years, a significant number of cases of workers
with inconsistent work and residence location codes (locations
beyond reasonable commuting range) has also been evident.  SSA has
investigated some of the more serious apparent problems and has
documented a number of types of error.  Resources have not been
available to correct individual errors on a signifi-
                                85
cant scale, or even to estimate the relative incidence of various
kinds of errors.  There have been some recent studies, however,
that provide some indication of the overall impact of geographic
errors on selected types of data.  The types of errors and overall
indicators of the extent of errors are reviewed below.
1.   Reporting Problems
     Because the SSA establishment reporting plan for multiunit
companies is voluntary, some of the problems of incomplete and
inaccurate geographic data in the CWHS result from conscious
decisions of employers not to participate in the ERP.  In general,
however, large multiunit employers make some effort to divide
employees into distinct reporting units; and when their failure to
separate worker reports geographically would result in clear data
distortions, SSA tries to provide special designations for the
workers.  The largest case of nongeographic reporting involves
members of the armed forces who are placed in a special military
category in the CWHS.  In the case of private employer
noncompliance with the ERP, SSA generally codes the workers
involved into a special "Statewide" category.
     While most multiunit employers do break their employees into
more than one reporting unit, there is increasing evidence that
many employers do not follow ERP guidelines completely in their
reports.  Again, the best evidence concerning incomplete compliance
with the ERP involves large government employers.  A few State
governments, for example, provide no reporting unit breakdowns of
State workers-generally reporting there as if they were all located
at the State capitol.  Most State governments do divide State
workers into several reporting units, but evidence suggests that in
most cases the reporting units tend to be divided along agency
rather than geographic lines-with geographic locations being.
assigned to agency-headquarters or to some other centralized
payroll accounting location.  The agency reporting unit pattern
appears also to hold for those few Federal civilian workers (e.g.,
temporary employees) subject to social security taxes. Currently,
incomplete or incorrect Federal Government compliance with the ERP
may not cause significant geographic distortion in the CWHS; but,
with the advent of annual reporting and possible full CWHS coverage
of Federal workers, the distortions could become major if ERP
reporting guidelines are not adopted by Federal agencies.
     Incomplete private compliance with the ERP is probably less
pervasive than is the case for the Federal Government and State
governments.  Nonetheless, a wide variety of problems appears in
the "establishment" reports of multiunit private employers.  A
common practice, for example, appears to be some form of regional
reporting that does not conform to county units as requested in the
ERP.  In addition, some companies appear to report part of their
workers (such as production workers) by county and other workers
(such as managerial staff) from a central location.  For the most
part, it would appear that these and other forms of chronic
incomplete or incorrect compliance with the ERP result when
employer payroll accounting procedures are not organized along
lines that permit a convenient breakdown of individual employees by
county of work, and employers supply instead those geographic or
other organizational breakdowns that are most readily available in
their payroll records.
     In addition to chronic misreporting under the ERP, there is
evidence of a variety of other temporary and continuing errors in
geographic reporting.  Employers, for example, occasionally provide
establishment reports in which groups of workers have been
interchanged or other. wise intermixed incorrectly among reporting
units.  Employers may change their reporting practices without ril-
ing appropriate updated geographic information on reporting units
to SSA.  Multiunit or. single unit employers can supply incorrect
initial information concerning geographic location (e.g., supply a
mailing address that differs from the actual county of work).
Often, however, careful tracing of erroneous CWHS records is
necessary to deter. mine whether information has been reported
incorrectly to SSA or has been processed incorrectly at SSA.
2.   Processing Problems
     Some of the errors in the CWHS can be attributed to clerical
errors in coding and processing employer reports to SSA.  The
tracing of individual errors in the CWHS suggests several ways in
which coding and keypunching errors can affect the geographic
information in CWHS files.
     1.   The reported county of work nay be incorrectly coded.
          Investigation of individual CWHS errors has revealed
          evidence of this.  In some cases. workers residing in a
          particular county were coded as working in a county of
          the same name in another State.  In other instances.
          workers were shown as working in another county of the
          same code in another State.  There were also incidents of
          transposition such as workers residing in county code 410
          shown as working in county 140.  There also appeared to
          be some confusion between city and county names. such as
          reporting units in Ada County, Idaho (Boise City) being
          coded to Boise County.  Idaho.
     2.   The county of work may be coded correctly, but keypunched
          incorrectly.  An example of this was discovered while
          investigating large commuting
                                86
          flows which appeared between Now York and Alaska.
          Reporting units in New York were shown in Alaska because
          of similarities in State codes.  Albany, New York, for
          example, is code 21000, which, if mispunched one position
          to the right, becomes 02100, the code for Haines Divi-
          sion, Alaska.
     3.   The employer or reporting unit number may be mispunched.
          If this occurs and the mispunch is to a nonexistent EI or
          unit number, the worker will be unclassified by State,
          county, and industry.  The number of unclassified workers
          in the preliminary first quarter files has risen dramati-
          cally in recent years, from 3 percent in 1971 to 7% in
          1975.  If the mispunch is to a legitimate EI or unit
          number, the workers will be coded to the wrong
          establishment and erroneous geographic and industrial
          information will likely result.
     The timing of updates is another processing problem which can
be important.  The speed with which the employer file is updated
with information on new employers and changes to established
reporting practices has a bearing on both the number of
unclassified and the number of incorrectly classified workers.  If
an employer notifies SSA of a change in reporting procedure at the
same time they file a report on the revised basis, it is possible
that the wage items from the report will be processed and matched
to the, employer file before the employer file has been updated
with the new information.
3.   Extent of Errors
     While there has been no systematic study designed to quantify
the importance of the various kinds of reporting and processing
errors in geographic coding in the CWHS, several studies designed
to develop migration and commuting data from CWHS files have
indicated that the overall incidence of errors is substantial and
may seriously impair the CWHS for use in such applications.  A
recent study comparing place-of-work codes with, place-of-residence
codes in the CWHS, for example, was particularly indicative of the
magnitude of place-of-work coding problems for large employers in
the CWHS.  This study was conducted as part of a larger SSA-BEA
effort, sponsored by the Department of Housing and Urban Develop-
ment, to prepare mid-decade commuting estimates.  A 10 percent
sample of workers from social security records was matched to an
IRS mailing address file in order to obtain information on the
workers' State and county of residence.  This work was done at the
request of the Secretary of HUD prior to the enactment of the Tax
Reform Act of 1976, and with the concurrence of the Secretaries of
Commerce and Treasury. (See Chapter VIII for a discussion of the
implications of the Tax Reform Act for interagency data linkages.)
     The worker records were summarized by employer, State, county,
and industry so that each summary record approximated an SSA
reporting unit.  Units with an estimated 60 or more covered workers
suspected of having inaccurate or incomplete county-of-work coding
were flagged on the basis of the following criteria:
     1.   50 percent or more of the establishment's workers lived
          outside the county of work.
     2.   10 percent or more of the establishment's commuters
          (county of residence and county of work differ) lived in
          a different BEA economic area.*
     Only 3.8 percent of the reporting units were flagged.  Those
units flagged, however, accounted for nearly 36% of the workers
with known commuting status and 60 percent of those identified as
commuters.  Even when the criteria was tightened to include only
those units with 100 percent commuting ratios nd those with
commuting ratios greater than 50 percent and more than 30 percent
of the commuters from outside the economic area*, the file
contained 11 percent of the workers and 29 percent of the
commuters.  Units with 100 percent commuting ratios accounted for
nearly 8 percent of the commuters.  Many of these units had worker
residences clustered an unreasonable distance away, indicating
county-of-work errors.  Approximately 13 percent of the commuters
in the file were commuting between counties in noncontiguous
States.  Commuting rates for most counties were more than double
the comparable rates from the 1970 census; and 1975 comparisons for
selected areas covered in the Annual Housing Survey suggests the
high 1975 CWHS based rates result primarily from geographic coding
errors rather than increasing commuting rates, generally.
     Geographic coding problems in the CWHS not only lead to
erroneously large estimates of commuting, but they also bias upward
estimates of work force migration based on the CWHS.  Annual
estimates of average interState worker migration rates derived from
the 1 percent CWHS for the period 1964-74 range from a low of 6.3
percent in 1964-65 to a high of 10.1 percent in 1973-74.  Data from
the Current Population Survey suggest that the rates should be much
lower, perhaps in the range of 3-4 percent.  The estimated sharp
rise in CWHS migration rates after 1970, in particular, contrast
markedly with CPS data and suggest that declining SSA resources de-
voted to monitoring establishment reporting and geographic coding
may be leading to a serious deterioration in the quality of CWHS
migration data.  In fact, without a substantial effort to edit and
correct CWHS files, the
     *BEA economic areas are county groupings centered on major
urban areas and defined in such a way that inter-area commuting is
usually minimal.
                                87
potential value of the CWHS as an inexpensive source of useful
commuting and migration data is likely to remain largely
unrealized.
4.   Related Problems with Other Data
     Both the Census Bureau and the UI employment and payroll
reporting systems require mandatory establishment reporting by
multiunit employers.  These programs may also devote more resources
to monitoring geographic information than SSA.  Hence, it is likely
that UI and CBP geographic data are more reliable than CWHS data.
Many States, however, are reluctant to push multiunit employers for
accurate county reports of employment and payroll in the UI program
(although accurate State breakdowns are important for
administrative purposes).  Census. moreover, normally permits
"estimates" of data items when accounting records do not lend
themselves readily to the kinds of reports desired for statistical
purposes.  Hence, UI and CBP data may also be affected by the
unstandardized establishment payroll accounting systems that appear
to lead to incomplete and incorrect establishment reporting in the
SSA reporting plan.
     Errors in reports for particular establishments are difficult
to monitor. but the Census Bureau has conducted some tests of the
accuracy of geographic coding in its business establishment files.
A recent evaluation study of the geographic coding in the 1972
Census of Retail Trade showed that the error rate at the place
(city) level was about 11 percent for establishments whose reports
were based on administrative records. (it should be noted that
these errors affected primarily data on the number of
establishments located within the city.  Since the establishments
involved tended to be small, the impact of the coding errors on
other data such as sales was less serious.) Many of the problems
noted in this study were similar to the problems found in the CWHS
commuting study (e.g., reporting from headquarters location), but
relatively fewer problems seemed to result from combining
information for several establishments and proportionately more
problems were associated with the difficulty of using address
information, supplied initially in administrative programs, in
conducting censuses.
     Often mailing address rather than actual location is the only
geographic information available from administrative records and
use of mailing addresses to compile geographic statistics can
result in serious biases in the data, particularly for cities and
other places in highly urbanized areas.  And with increasing use of
administrative mailing lists and mailed reports, the problems of
obtaining reliable small area geographic data for organizations or
individuals are becoming more serious.  Unfortunately, moreover,
Federal administrators often have little reason to be concerned
about either establishment reporting or the precise geographic
location of the organizations or individuals reporting to them; and
coordination between Federal administrators and State and local
administrators (who do need reliable geographic information), and
between administrators and statisticians has been inadequate to
prevent a trend of deteriorating geographic coding in many data
files at the same time that increasing use of geographic data is
being made for such purposes as Federal fund allocation.
5.   Errors in Other Information
     Geographic reporting and coding errors are perhaps the most
noticeable problem associated with using SSA and other
administrative records of businesses to obtain data on employment,
payroll. and other regional economic indicators.  There is,
however, also evidence of serious problems with other
administrative records that are not central to program
administration.  As already noted, establishment reporting problems
in the CWHS create industrial as well as geographic coding errors.
As would be expected, industry coding problems tend to increase as
the desired level of industrial detail becomes finer.  In a
reconciliation study of establishments in the 1972 Economic
Censuses and in the area sample portion of the Current Business
Surveys, it was found that there were many differences in the SIC
coding.  For Retail Trade, the study revealed an 11 percent
disagreement rate at the 2-digit SIC level and an 18 percent
disagreement rate at the 4-digit SIC level. (Jeans, 1977, Table 6).
Since most of the establishments in this study are nonemployers or
small employers, the SIC code used in the Census would usually be
derived from administrative records, while the SIC code in the area
sample is derived from a business description obtained by an
enumerator.  These disagreement rates point to problems in the SIC
coding derived from administrative records.  The impact of the SIC
coding errors on aggregated data such as sales would be less
serious than the disagreement percentages cited above would
indicate, since the establishments involved were relatively small.
In the CWHS, the quality of information on individual
characteristics such as sex, age, and race generally appears to be
of higher quality than business characteristics such as the
location and industry of jobs.  Nevertheless, there are problems
with the demographic characteristics in the CWHS, particularly the
race indicators.  Applicants for social security numbers self
identify their race as White, Negro, or Other, and evidence
suggests that members of various minority groups which have been
considered white for statistical purposes (such as most Mexican
Americans) often have a tendency to erroneous-
                                88
ly designate themselves in the "Other" race category.  Moreover,
the tendency toward such erroneous designation may change over time
in response to such factors as the strength of cultural or legal
motivations to be identified as a member of a minority group.
              D. Problems With the Timing of the Data
     A problem which concerns statisticians with all data gathering
activities is that of data timeliness.  Generally, the more current
the information is, the more useful it is to decisionmakers.  An
additional dimension is added to this problem when administrative
records are involved-the statistician's lack of control over the
timing of the data.  Since the data are collected and processed by
an administrative agency, processing for administrative purposes
has a much higher priority and is done on a more timely basis than
is processing for statistical purposes.
     There are three major elements to the timeliness problem:
     1.   The promptness and frequency with which the data are
          reported to the administrative agency.  In this regard
          administrative records are often superior to surveys and
          censuses.  The data are reported under an ongoing program
          and are required by law.  Reporting entities will
          generally provide the required information more promptly
          than they will respond to a periodic or one-time survey
          questionnaire.
     2.   The time required for the administrative agency to
          process the information and make it available for
          statistical uses.  This is where the above noted conflict
          in priority between administrative and statistical uses
          often causes long delays in the availability of the
          records for statistical purposes.  The CWHS files, for
          example, are generally not available until 21/2 years
          after the end of the subject year.  Many important
          statistical 'applications, such as the generation of data
          for fund distribution, require much more current data.
     3.   The time and difficulty of producing the desired
          statistics from the records.  Administrative record files
          are often large and complex.  Even when the data are made
          available promptly and only a sample is tabulated for
          statistical purposes, it can require a considerable
          amount of time and resources to produce the statistics.
          The 1% CWHS employer-employee file, for example, is
          approximately 1.5 million records per year.
                           E. Conclusion
     While the CWHS illustrates the multitude of technical problems
involved in using administrative records, it can also be used to
illustrate ways in which administrative records can be improved for
statistical uses, as well as the potential for such records to
provide a powerful source of local area information for policy,
planning, and research purposes.
     Many of the problems described in this chapter could be
resolved through improved coordination between program
administrators and statistical users.  In the case of the CWHS,
such coordination could result in improved timing and accuracy
through higher priorities and greater resources assigned. to the
assembly of statistical files.  Coordination between data producers
and users could result in additional editing techniques to ensure
the accuracy of data.  Improved coordination could also increase
the informational content of files available for statistical use,
such as the addition of information on worker residences from W-2
forms (see Chapter V).
     Improved coordination among different data collection programs
could help to resolve many geographic and industrial coding
problems.  For example, comparability between Census and SSA
geographic and industrial codes is limited because the Census and
SSA definitions of establishment differ and because coding for
multiestablishment companies is carried out independently in the
two systems.  SSA requests that employers report on the basis of
county-industry combinations--permitting, for example, a combined
report for all the separate stores a retail chain operates in the
same county.  Moreover, SSA requests that employers assign their
own four-digit reporting unit cod es to separate reporting units.
For many programs, however, Census requires data for small
(subcounty) geographic areas, so the SSEL has defined establish-
ments in terms of operations at a single location and has assigned
its own numeric codes for individual establishments.  As a result,
it is very difficult to check SSA establishment reports against
Census materials for multiunit companies; and the effectiveness of
joint Census-SSA efforts to maintain the SSA establishment
reporting plan is thereby limited.
     If, however, SSA requested that employers report on the same
establishment basis and used the same codes as they do for the
Census Annual Company Organization Survey, the SSEL could be used
(provided SSA were granted access to the SSEL--see Chapter VIII) to
maintain the quality of geographic and industrial coding on the
CWHS.  If, in addition, the UI system used the same units and
codes, the SSEL could be used to ensure uniform geographic coding
among UI, CBP, and CWHS files.
                                89
Moreover, if establishment reporting were to become a mandatory
requirement of the new joint IRS-SSA payroll ,reporting program,
and if the W-3 form (establishment summary) were modified to
request geographic and industrial activity information, it might be
possible to eliminate some statistical forms presently required of
employers and achieve both a reduced reporting burden for employers
and improved statistical information.
     A third type of coordination which can help to alleviate some
of the technical problems is coordinated use of administrative
records.  If, as previously suggested, micro records from different
administrative record sets were merged, resulting statistical files
would be far more useful than any of the individual record sets
from which they were built.  Such mergers could help to eliminate
coverage gaps and resolve noncomparabilities between record sets.
Improvements to administrative record data systems could have far
reaching results.  If the SSEL could be used to assign geographic
codes in the CWHS, for example, it might be possible to code the
records to subcounty levels.  The W-3 records and associated
summary statistics could then be used to produce current
information for urban areas of the Nation.  Since workers' sex,
race, and age are available from applications for social security
numbers, an expanded and improved CWHS would be capable of
producing both economic and demographic information on employment,
earnings, migration, and commutation for urban areas.  This
information would be useful to State ,and local governments, urban
planners, researchers, and officials interested in targeting
government programs to areas and populations most in need of
assistance.  The investments necessary to correct the technical
problems associated with the use of administrative records would be
small relative to the costs of developing alternative sources for
comparable information.
                                90
                                                       CHAPTER VIII
                Legal Issues in the Statistical Use
                     of Administrative Records
      This chapter explores the complex issues with legal
implications that arise when statisticians and researchers employ
administrative records to carry out their purposes.  The inquiry
attempts to present a sense of the structure of law- built on
statutes, regulations and formal policy which affect the activities
of statisticians in both positive and negative ways, and which in
turn are affected and changed by those activities.  there is an
effort to relate the projects described in other chapters to this
legal structure.  And there is in addition an attempt to suggest
the kinds of change in law which can improve the effectiveness of
the statistical user of administrative records, while at the same
time preserving and strengthening the administrative system in its
ability to carry out its other functions.
     With these aims, the first part introduces the concept of
"functional separation," which is the cornerstone of current
proposals for responsible expansion of the use of administrative
records for statistics and research.
     The first part examines the interests and needs of stat-
isticians Which lead there to use information contained in
administrative records.  Section I points out reasons for the
statistical use of administrative records.  The concept of
functional separation is developed in section 2 as an analytical
tool for data usage.  Statistical use and administrative use are
defined, differentiated, and illustrated in section 3. along with
terms that are relevant to legal issues.  This leads to a
formulation in section 4 of functional separation in general
legislative terms as a way to realize the conceptual goal.
      The second part of the chapter uses a characterization of the
existing legal and administrative systems as a frame of reference,
and suggests a set of organizing principles in which legal and
statistical imperatives converge.  Section 5 deals with the
difficulties associated with the actual application of functional
separation concepts to government agency records.  Section 6
discusses the application of several confidentiality statutes to
particular situations.
     Finally in section 7, a brief summary of the chapter provides
some suggestions for the future.
              A.  The Legal and Administrative System
1.   Factors precipitating the shift toward greater statistical use
     of administrative records
     Both the increased availability of administrative records, and
the growing limitations on information obtainable directly from
individuals on a voluntary basis, have precipitated a shift toward
greater statistical use of program and other administrative
records.
     Advanced data processing techniques and sophisticated
methodologies have had both cause and effect implications for
collection of data.  As tools for statistical analysis of a broad
range of public issues, they can extract, distill, and illuminate
information from massive volumes of data.
     At the same time, data processing capability acts as a
catalyst in the development of social programs which develop
complex and fine-tuned adjustments in mm of defined categories of
participants, differential eligibility requirements, and other such
variables.  The interaction of technique and information leads to
more highly refined standards of detail and quality of supporting
data, and to rich program resources for decision making.  The very
existence of such a data base challenges the statistician to probe
its availability and its adaptability for statistical uses.  On the
other hand, the fact that the content of administrative records is
selected and shaped to the needs of, the particular, often very
narrow, administrative use, creates built-in problems of definition
and comparability for the statistician.  This in turn generates
pressure from the statistician to influence the design of
administrative data collection instruments.
      The statistician's interest in using administrative records
is precipitated by other factors as well, reflecting the growing
resistance of respondents, both persons and firms, to cooperate
with voluntary data collections While the relative strength and
significance of the underlying causes of this growing reluctance
are imperfectly measured and understood, some explanations seem
relevant.
                                91
     One is simply burden.  Personal interviews by public opinion
and survey research organizations, including the census and survey
activities of government agencies, have proliferated in number,
frequency and detail.  This burden is generally imposed, moreover'
without any obvious compensating personal benefit to participants.
     Another factor is public distrust of information gatherers,
both governmental and private, and decline in confidence in the
ability of survey organizations to preserve the confidentiality of
information entrusted to diem. (1)  This distrust is probably not
lessened, moreover, by recent Federal requirements that respondents
be told more fully the legal risks and consequences to there of
providing information about themselves, including the extent of
data sharing and the limitations on ability to assure con-
fidentiality. (2)
     Resistance of the public is reinforced when the growing volume
of information collected through voluntary surveys is superimposed
on the massive and regularly expanding volume of administrative
collections, reaching more and larger segments of the general
population, and making demands for detailed information from each.
Where there is a quid pro quo, such as welfare payments or social
security benefits, or a cost for not responding, such as tax duties
or penalties, respondents provide administrative information in the
required detail rather than forego the personal gain or suffer the
cost.  They may not be willing, however, to repeat or supplement
the information to other collectors for other purposes.
     These factors combine to raise concern about the acceptable
level of response burden. counting both voluntary and involuntary
collection, which can reasonably be imposed on the reporting
public.  Whether public resistance to burden is looked at as a
decrease in the quality of data collected, or as an increase in the
cost of maintaining a given level of quality of data, the perceived
decline in cooperation is a development which has to be factored
into agencies' data collection plans and budgets.  Where
administrative and statistical requirements for information
compete, moreover, the program requirements generally take
precedence.
     As an alternative to the mounting of new surveys, the
extraction of data from information collected by Federal agencies
or their local counterparts in administering their social and
economic programs has obvious appeal.  Compiling administrative
data in a microdata file can synthesize the response to a personal
interview.  Even where a "survey" of persons or firms is simulated
by linking data about there from records scattered among several
different programs or agencies, the cost may still be relatively
small compared with the cost of conducting an actual survey.  In
some instances, cost is a secondary factor, where personal contact
would be difficult or even impossible because of inability to
interview the necessary sample population, for example, deceased
persons.
     Another development that had consequences for the efforts of
the statistician to compile and adapt administrative data was the
emergence of the various privacy initiatives of the 1970's.  Those
initiatives grew out of the feeling of helplessness expressed by
many persons about the dissemination of information about
themselves, recorded in computerized records, then shared and used
without their knowledge in ways that harmed or offended them.  Thus
the starting principle of the Privacy Act was the requirement that
no disclosure be made without the consent of the person whose
information was being disclosed.  The practical imperatives of
government were accommodated, however, in broad exceptions from the
requirement of individual consent.  Two other principles
compensated for the erosion of consent.  The first of these is the
principle of notice to inform individuals what uses their
information is put to.  The second is the principle of
accountability to the individual for the uses made without personal
consent.  In combination these principles permitted normal use and
exchange of information collected by government, subject to self-
help methods of individual challenge to check abuse.  At the same
time, the development of a third principle was necessary. to
accommodate the special needs for information which the
statistician and researcher uses, while at the same time giving the
individual full benefits of the primary principles of notice and
accountability.  That principle is the concept of "functional
separation."
2.   Concept of "Functional Separation"
     "Functional separation" is a term which was chosen to
conceptualize a treatment of records appropriate to the basic uses
(or functions) for which those records are prepared and kept.
Administrative uses and statistical uses have a polarity which
needs to be recognized and built into the rules and procedures
which control them.  The uses of administrative records are
individual in their very essence, as they are collected to do
things to or for individual persons on the basis of those
individuals' rights and responsibilities.  Statistical records we
exactly the opposite.  Individuals are examined. and their
information collected and combined, as the individuals we perceived
to belong in chosen study groups, and to be statistically
interchangeable with others in those groups The method is to
summarize.  The individual is important in defining and
characterizing the group, but the information about a particular
individual is important not because d will be used to accomplish an
individual result, but because the one individual is a proxy for
many individuals.  This difference in basic relationship of
individual to ultimate use requires that the rules of treatment of
statistical in-
                                92
formation be the obverse of the rules for treatment of
administrative records.  This set of concerns is the genesis of the
concept of "functional separation."
     The issue of statistical use of administrative records has
been scrutinized both from the confidentiality side by such
agencies and commissions as HEW and the Privacy Protection Study
Commission (PPSC) (3), and from the burden side by others such as
the Office of Management and Budget (OMB) (4), the General
Accounting Office (GAO), and the Commission on Federal Paperwork
(5), The President's Statistical Reorganization Project also has
more recently looked at both confidentiality and burden.  From
these inquiries has emerged a consensual view that the public will
benefit from better access by statisticians to administrative
sources of information.  A caveat is added, that better access must
be combined with better protection of statistical compilations of
administrative data to prevent unauthorized use for non-statistical
purposes.
     In the quest for better statistical access combined with
better data protection, increasing attention has been focused on
the important concept of "functional separation" as it originated
in the work of the Privacy Protection Study Commission, and was
recommended for statutory treatment by both the PPSC and the
President's Statistical Reorganization Project.  These projects
both proposed mechanisms which took account of qualitative
differences between program-administrative functions and
statistical research functions, and established differential
standards for managing the information needed by each.
     These standards relate to access, use and disclosure of data.
Functional separation means that a separate and distinct approach
is necessary for the development of principles, legal rules and
practices applicable to data for statistical use.  While the
principles and standards applicable to statistical use have to take
into account the principles and standards which apply to
administrative use of information, and in some respects are
constrained by administrative rules, the rules for statistical data
need not be similar or parallel to those for administrative use.
     Applying the principle of functional separation, to make the
rules appropriate for the function that the information serves,
data cannot be mixed indiscriminately in statistical and
administrative uses.  Information designated for statistical use
would not be available to administrators for their use except in
anonymous or aggregate form, regardless of whether the data were
obtained directly through surveys or indirectly from administrative
files.  With that constraint, records compiled in administering
particular programs can be used by statisticians without risk of
breaching the rights and expectations of program participants about
the intended uses of information they give.  This aspect of
functional separation has provoked considerable debate with
compliance and enforcement officials, and is at the cutting edge of
legislative proposals to provide legal protection to statistical
files.
3.   A language framework for legal issues.
     The statutory background for functional separation is
expressed in terms of privacy, confidentiality, disclosure, access,
and other terms with special technical implications.  In addition,
in proposing different legal treatment of records based on
different operational functions, the concept itself has added some
terms, with particular meanings.  This section is offered as a
bridge between the legal framework which controls the flow of
information to the statistician, and the workplace within which the
information is stored, used and transferred.
     Generally administrative records mean records which contain
information used in making decisions or determinations, or for
taking actions affecting individual subjects of the records.
Commonly the term refers to records about natural persons, although
other entities may be treated by law as legal "persons," about whom
decisions and actions are taken.  Corporations, for instance, are
fictitious "persons" whose actual being is created by law, and
about which records are kept and decisions made.  Partnerships and
sole proprietorships likewise may have legal rights and duties
which are separate and distinct from the legal rights and duties of
the natural persons whom they represent, individually or
collectively, or who conduct the business of the entity.  To
indicate a further level of abstraction, the estate of a deceased
person - amounting merely to a bundle of residual claims and
obligations - is a "taxpayer" under the Internal Revenue Code, and
its records are subject to disclosure rules just as if the taxpayer
were a living natural person.  In other contexts, legal rules on
disclosure might vary depending on whether the particular
information refers to an individual in his capacity as a private
person, for instance, or as a business proprietor.  The juncture of
Freedom of Information Act (6) and Privacy Act disclosure rules
with respect to a particular set of data may raise just such an
issue.
     This chapter deals with only one segment of the large volume
of administrative records kept by public and private record
keepers.  The focus is on records kept by government agencies,
mainly Federal, compiled principally in managing their social and
economic programs.  While agency personnel, law enforcement,
regulatory. and other records are also administrative records in
the broad sense, they have not been treated in scope of this
discussion.  Although there was no initial intention to exclude
such classes of records, they demanded little attention.
                                93
     It appeared in the course of examining statistical uses that
agencies in which such records predominated were by and large
neither providers nor users of general purpose statistical files
built on an administrative record base.  In the case of law
enforcement records, in particular, both the administrative and the
research records are subject to special legal restrictions limiting
use and disclosure, and are not easily integrated into a pool of
general purpose statistical data.  there are some areas of study,
to be sum, such as follow-up analysis of work history of ex--
offenders. that suggest the potential for careful merging of
information from one data base to another.  However, this potential
is not likely to be realized in the form of general transfer
between law enforcement and other types of data bases.
     Finally, there are some arguments for excluding decennial
census records from consideration as administrative records. since
their purpose is almost exclusively statistics.  They are, however,
used for redistricting, calculating revenue sharing, providing
genealogical data, and similar administrative types of use.  They
have been included in this study because of the special reciprocal
relationship of Census with agencies using administrative records
in statistical operations.  Census plays a focal role in acting as
a broker between agencies in receiving, processing and merging
administrative data that sometimes cannot be transferred directly
between agencies.  Resulting merged files can be purged of
identifiers and made available to the source agencies for their
statistical uses, and in many cases, to the general public for
statistical use.  Moreover,  Census is drawing with increasing
regularity on administrative files to help improve its intercessal
estimate and to correct its undercount.
     Statistical purposes describe purposes for which information
about individual members of a defined study population is
aggregated and presented without reference to individual
identities.  Statistical records may be kept, used. and published
in microdata form-i.e., a collection of data items pertaining to
one particular individual--to maximize flexibility for examining
and analyzing the composition, characteristics, behavior, etc., of
the group under consideration.  Personal identifiers may be kept on
microdata records for purposes of record validation and linkage,
and the files may be transferred to statistical users with
identifiers.  The fact that identifiers are used in the statistical
process and are a necessary incident to the statistical file is
often overlooked.  Indeed, even the Privacy Act, which was meant to
be a statute which would deal definitively with the issue, provided
only for transfer of statistical records without individual
identifiability.  Of course, the individual identities of the
persons making up the statistical group are not associated with the
statistical files once processing is completed, nor are they
material to the ultimate statistical results of the process.
     Access, use, and disclosure.  There are some subtle
distinctions in the ideas of access, use and disclosure of records.
"Access" to (or availability of) records suggests the right or the
ability to see, hear, examine, or otherwise be cognizant of the
information contained in the records. (in the Privacy Act sense,
"access" has a further special meaning limited to the right of a
natural person to examine record information about himself or
herself.) "Use" generally refers to the purposes which can be
served, or the operations which can be performed with the records
by the person who has access.  A basic distinction between
statistical and non-statistical use is of principal concern.  In
this connection, the application of statistical methodology is not
equated with statistical use.  An identifiable person may be
singled out for any number of administrative actions-such as
promotion, tax audit, and so on--on the basis of a statistical
operation, such as ranking by specified characteristics.  This
would be an administrative use of statistical techniques, and not a
statistical use.  Quality assurance programs often involve hybrid
uses of this sort, and are considered to make administrative rather
than statistical use of the data.
     An issue of use can be illustrated by experience of the Social
Security Administration.  An item designating the applicant's race
is collected by SSA on its form application for a Social Security
Number, exclusively for statistical use.  The race item is not used
in assigning the Social Security Number, nor is it used in making
program determinations about the individual, which would be
administrative uses.  The information is used to draw samples and
subsamples, and to describe racial composition of specified samples
or groups of persons based on other characteristics.  Inclusion of
this statistical item in microdata records which are used for
preparing tabulations showing the racial composition of a
particular work force would be a statistical use.  Such tabulations
are occasionally requested by litigants in Equal Employment
Opportunity Commission (EEOC) and similar actions raising issues of
discrimination in hiring or promotion.  Use of the tabulations
themselves as evidence in such litigation would not alter their
basic statistical character.  But an attempt to use those same
microdata records to identify and characterize the race of
particular members of that work force and to inform there that they
were parties to a class action suit in which the tabulations were
presented in evidence would not be a statistical use of the
information.  It would be an administrative use not in keeping with
the statistical character of the data.
     Finally, "disclosure" involves providing access or
availability to another user, usually by transfer of records,
                                94
although it is evident that disclosure can take place also by word
of mouth.  The significant overlapping--and to some extent
circularity-of these concepts of access, use, and disclosure
sometimes blurs the practical distinctions among them.  Though they
may seem artificial, however, the distinctions are not trivial in
their relationship to the legal issues of administrative record
use.
     Confidentiality and privacy. These are terms which have been
associated with a variety of meanings, both subjective and
technical.  In this chapter the terms have no arcane meanings, but
make a rather simple and important distinction.  Confidentiality
refers to limitations which protect records from unauthorized
access, use or disclosure. (For this purpose, "unauthorized"
disclosure means without the consent of the person whose informa-
tion is divulged, or without some other legal authority to
disclose.) Privacy refers to the protected right of the individual
not to be disturbed, or not to have intrusive invasions of his
person or property.  In this context, invasions include any type of
personal contact made on the basis of record information.  Finally,
using the convention adopted in the Privacy and Freedom of
Information Acts, the privacy concept is limited to natural
persons.
     Functional separation. To summarize what is stated elsewhere,
functional separation establishes two basic divisions among
records, according to whether they serve administrative functions
intended to have consequences for the individual subject of the
record, or whether they serve statistical functions of studying
groups.  Functional separation principles allow information about
individuals to flow from administrative sources to statistical
uses, but not to return to administrators in a form associated with
the identities of the individuals once the information has been
incorporated in statistical records.
     The concept of functional separation expresses an underlying
principle of fairness in data use.  That principle holds that
actions and determinations about persons should be made on the
basis of information which is used with their knowledge and
consent.  As long as statisticians and researchers do not use data
in any individual way to affect the subjects of the information,
their personal knowledge and consent may not be relevant.  However,
the collection and retention of individual information, and its use
in generating new information by the researcher, require insulation
from the decision process.
4.   Options: Legislative approaches to functional separation
      There are two principal approaches by which functional
separation can lead to protected status for data committed to
statistical uses.  Both approaches can be found in some recent
legislative proposals.
     The first approach is to protect designated statistical
organizational activities.  The method is to name certain units as
being qualified users of statistical data, to require safeguards
for all statistical data within the controlled environment which
they manage, and to impose limitations on access and disclosure.
This is the design for the "Protected statistical center" which is
described in the proposed Confidentiality of Federal Statistical
Records Act. (7) The model for this approach is the Census statute
(Title 13 of the United States Code) which limits examination and
use of statistical records to employees of the Census Bureau.  The
difference which would be introduced by this proposed extension of
the Census concept is that use would not be limited exclusively to
employees of the organization which does the actual collection of
the data.  Instead, under this proposal, data could be transferred
among approved centers with relative ease.  Since no data could be
disclosed except among protected centers in a way which would
permit such data to be associated with identifiable persons or
business reporters, the agency which collected the information
could even be ordered to transfer its data to other centers which
demonstrated their need.
      The second approach is to protect specified records or files,
regardless of where they are physically located.  The method is to
designate particular data elements or collections of data elements
as "statistical" (or research) records, and to place special
conditions on the purposes for which the files can be used.  In
addition, this approach would restrict disclosures, both as to the
form of records disclosed, and as to the type of authorized
recipients.  This approach is developed in the proposed Research
Records Act. (8)
     The Research Records Act would apply to research records as
defined in the proposed statute.  With respect to information about
natural persons, this definition is somewhat broader than the
statistical records included in it, except as records are excluded
by coverage in such statutes as the proposed Statistical Records
Act,  Census Act, etc.  In another respect, the research proposal
is narrower in scope than the proposed Statistical Records Act,
since it would not apply to information about firms or other
entities which are not natural persons.  The proposed Research
Records Act incorporates most of the recommendations of the Privacy
Protection Study Commission to provide separate and distinct
treatment and disclosure rules--functional separation--for the
statistical and research records which it would cover.
     The approach is also used in the proposed Statistical Records
Act referred to above, with respect to files which would be
designated by a Chief Statistician as "protected statistical
centers".  These latter conditions would be
                                95
somewhat less stringent than the conditions attaching to files in
protected centers, and would include the use of protected files as
sampling frames for disclosure of names and addresses of entities
to contact in order to obtain additional information through
surveys or interviews for statistical and research purposes.  Both
approaches have been considered in developing the "Standard
Statistical Establishment List," and the legislative proposals to
widen its availability.  At present, the SSEL is a comprehensive
national list of business entities, described by type of
organization, size and activity codes, and associated with detailed
financial and commercial information.  The file is maintained by
the Census Bureau, is used in identifiable form only by Census
personnel to prepare tabulations which are made available to others
in a form not permitting identification of particular firms or
establishments.  Some proposals for broadening access have
recommended the first approach, described above, which would be to
name the statistical units qualified to use the SSEL information
both for preparing tabulations and for drawing samples of
enterprises for surveys and questionnaires, and to exclude other
statistical users.  Other proposals have taken the second approach,
making files available to responsible statistical users, strictly
limited to statistical and research applications.  In addition, a
third type of proposal has offered a "two-tier" compromise.  This
would create one level of establishment data to users in the
general research community for approved statistical and research
applications.  A second level of information would be available
only to Federal statistical agencies for their statistical use, and
would contain data which is restricted from public availability
because of its sensitive nature or because of its particular
legally protected' sources.  Proposals to broaden access to the
SSEL are complicated by the fact that the file contains information
which is Census information subject to Title 13 restrictions on
disclosure as well as information which is tax return information
subject to Internal Revenue Code restrictions.  Because both laws
restrict disclosure merely of the identity of a reporting unit, as
well as disclosure of any information associated with that
identity, the availability of information from the file is quite
restricted.
               B.  Dynamics of Functional Separation
1.   Dimensions and characteristics of the legal framework
     Traditionally, a certain amount of statistical activity is
associated directly with program operation, at least to the extent
of tabulating classes and frequencies for measuring such variables
as receipts, expenditures, program participation, and so on.
Preparation of such statistical aggregates, in some cases, has been
so closely linked to the programs whose records they reflect as to
be regarded as an administrative function.
     Expanding from that traditional base, statistical activities
have commonly become functionally separate and independent of the
operational aspects of the programs and program populations they
examined.  Satellite components operating within the governmental
agencies which administer programs have continued as a routine
matter to use the agency's administrative files as the source of
statistical inquiry.  The propriety of such use has seldom been
questioned, at least within the Federal establishment.  Most agency
staff, indeed, would not ordinarily consider the availability of
program records to in-house statisticians as disclosure at all,
although in a legal sense it may be.  However, the laws have
usually been silent about the conditions of such internal use.
In the obverse situation, questions have arisen as to the proper
extent of access which administrators can or should have to
information produced by statisticians from those same
administrative records which they sample and use statistically.
Currently, for example,  HHS's Office of Inspector General has
broad statutory powers to demand information about individuals in
compliance efforts.  If exercised, such power could infringe on the
policy of statistical units in HHS-contained in the Social Security
Administration, the Health Cam Financing Administration, and the
Public Health Services. including the National Center for Health
Statistics--to release information to administrators only in
aggregate or anonymous form.  The agency's auditors and its Office
of General Counsel may similarly claim broad access powers. and
recognize few limitations on the uses which they may make of
information, regardless of its statistical or nonstatistical
source.
a.   Disclosure within the agency, a broader view.
     Authority for use of an agency's records by the agency's own
employees for various agency purposes is implicitly assumed on a
need-to-know basis, as observed above.  Frequently there is no
express authorization for such intra-agency disclosures, although
the converse, restrictions on use or transfer, even within the
agency, may be imposed by law.  The Privacy Act, in contrast,
provides explicitly for disclosures to the agency's own employer.
While the principles of functional separation between statistical
and other files are often carried out in administrative practice
with respect to intra-agency use, they are less often subject to
statutory treatment than are transfers for inter-agency use.
     A somewhat different dimension of record availability may
occur in a Department such as HHS, a conglomera-
                                96
tion of quasi-autonomous agencies administering a variety of
separate programs which serve: partially overlapping client
populations.
     The legal definitions of Federal "agencies" are such that the
term may mean either a Department or an operating component of that
Department or both, depending on the particular statutory
provisions being applied.  Disclosure of identifiable individual
data extracted from records compiled in administering one program
to statisticians associated with another program administered by a
different component within a Department has subtle but real legal
implications.
     Some disclosure anomalies can result from complex statutory
matrices.  For instance, the Tax Reform Act of 1976 (9) contains a
provision allowing release of tax information to HEW (now HHS) from
the Treasury Form W-2 for the sole purpose of processing the
information for IRS (a component of the Treasury "agency") accord-
ing to an interagency agreement. (10)  The Treasury-HHS agreement
provides that the Social Security Administration, an operating
component of HAS, will do the processing for IRS.  The Tax Reform
Act contains another provision by which SSA can use tax information
to administer its own programs. (11)  The interface of these
provisions results in a paper transfer by HHS to SSA of data which
HHS never actually obtains, and which HHS employees as such cannot
use in identifiable form.  SSA receives and processes the
information for IRS purposes, and uses it as needed for social
security purposes.  But SSA must receive written approval from IRS
to use the information HHS has released to it, before it can
produce statistical tabulations, even though they involve no
individual disclosures, when they are prepared for HHS purposes
which are not related to the administration of the Social Security
Act.
     Furthermore, release of identifiable return information
outside SSA to other HHS employees is not permitted.  Indeed, even
the Continuous Work History Sample (CWHS) microdata files from
which identifiers have been purged is not released to researchers
in HHS' Office of the Assistant Secretary for Planning and
Evaluation (ASPE), despite the important research function ASPE
performs for HHS, any more than they are released to the Bureau of
Economic Analysis (BEA) or to any member of the general public.
This is because of the residual difficulty described elsewhere of
stripping all possible association with identifiable business
entities, even though no substantive information about those
entities is divulged.
b.   Disclosure to agency contractors.
     The relationship between the program and the statistician who
actually performs the work may become attenuated, and the issues
then become more complex.  For instance, an agency may wish to use
information in its program records to study particular aspects of a
client population.  It may find that it lacks sufficient or
suitable staff resources to commit to the necessary tasks of prepa-
ration and analysis.  In such a case, the agency may enter into a
contractual arrangement to have the work performed to its
specifications by outside organizations.  While the work product
may be the same as that which would result if the agency relied on
its own staff resources, the legal issues and relationships are
different when the work is performed by outsiders.  The agency must
then deal with legal questions related to the disclosure of
confidential information to others.  These questions may involve a
variety of statutory considerations.  Conditions are different for
data controlled by the Privacy Act, for example, than for data
controlled by the Census statute or the Internal Revenue Code.
That is, the Census statute permits no one but Census employees to
examine census returns.
     The Census Bureau, as a result, does not employ contractors to
perform surveys or analysis for it.  On the other hand, the Privacy
Act allows disclosure of covered records for a "routine use", and
many agencies have determined that disclosure of information needed
by contractors to perform their contractual duties would qualify as
a routine use of personal information protected by the Privacy Act.
In contrast, the Internal Revenue Code (as amended by the Tax
Reform Act of 1976) has a provision requiring a particular type of
agreement with a contractor to perform data processing functions
with tax return information for purposes of tax administration.
This provision applies to information about business and other tax-
paying entities, as well as information about individual taxpayers.
(12)
     This provision enables IRS to use contractors to perform
various functions involved in the administration of the tax laws
including statistical activities of both IRS and the Treasury
Department's Office of Tax Analysis.  The sections which make
return information available to other Federal agencies-for example,
the Social Security Administration, the Department of Labor, and
the Census Bureau--however do not make any provision for redis-
closure, nor do they provide for disclosure to contractors of those
agencies.  Thus those agencies cannot release return information to
their contractors even in situations in which they normally employ
contractors to assist there in administering their duties.  The use
of contractors is thus dependent on other considerations than the
needs of the agency performing the work.
     A number of files discussed elsewhere in this report have been
unavailable for other agencies' projects because of this
restriction.  The CWHS file, which was used in the past by
contractors of state agencies in unemploy-
                                97
ment insurance studies, cannot currently be used in those projects.
Studies of subsidized housing performed for HUD by private
organizations under contract, and pension studies performed for the
Department of Labor by its contractors cannot have access to SSA
earnings information classed as return information, even though
SSA's Office of Research and Statistics has an interest in the
findings, and would be willing to provide the needed information
with proper safeguards.  In an important pension project, SSA and
the Department of Labor have been handicapped by their inability to
use their contractors to process and merge return information. in
this case earnings information obtained by SSA in its retirement
and survivors program.  Although both SSA and DOL had access to the
necessary return information, the agencies' contractors could not
be given access, and the scope of work to be performed by the
contractors was substantially restricted, with jeopardy to the
quality of the final product, because of the necessity to treat
return information differently from other agency information in
carrying out the steps of augmenting the files with earnings
information.
     Indeed, the restrictions prevent ORS from placing files
containing return information in its own computer tape library
which is maintained for it, with remote terminal access by an
organization under contract to SSA.
     Thus, in determining what information can be released to an
agency's contractors, and in providing for the disposition of files
upon completion of work which agencies contract for, careful
consideration has to be given to the statutes which impinge on the
relationship and affect the conditions and scope of work.  Even
when release to contractors is legally permissible, the agency will
need to make adequate provision for safeguarding identifiable
information contained in working files, and take appropriate steps
for purging identifiers before the files are released for secondary
analysis by others.
     The nature of the provisions for purging of identifiers,
destruction of records, and so on will be influenced by the
statutory authority under which the contractual work is done.
whether or not the contractor is "maintaining a system of records"
as defined by the Privacy Act.
c.   Disclosures among Federal agencies.
     Agencies serve populations whose members are also covered in
whole or in part by programs or activities administered by other
agencies.  In such cases two or more agencies may benefit from
creating an enriched file which merges information about a sample
of individuals extracted from the separate records of each agency.
For instance, a group of social security beneficiaries might also
be recipients of benefits administered by the Veterans
Administration, and both agencies may have an interest in studying
the combination of benefits.
     Statistical matching techniques (13) may be used, of course,
without any individual identification or disclosure.  Thus, records
of individuals can be selected from each agency's files on the
basis of a set of specified attributes, (e.g. age, sex, race,
marital status, etc.), and can be compiled without personal
identifiers.  The separate files without identifiers can be merged
solely on the basis of similar attributes, thus synthesizing
individual records without any effort to ascertain whether records
of the same individuals were in fact merged.
     It is more common, however, to create a merged file on the
basis of identifiers known to both agencies.  When information
about a sample of individuals known to the agency is used to create
such a merged file, the procedure ordinarily involves disclosure in
the legal sense from one agency to another of both identifiers and
administrative record information.  Depending on the form of the
resulting file and the content of the source records, the process
may involve a range of disclosure possibilities.  Thus, there may
be a one-way flow of identifiable data from a source agency to the
agency performing the match, with a return flow of files containing
merged records purged of identifiers.  There may be a two-way flow
of identifiable records between the participating agencies.  Or
there may be a one-way flow from each of the participating agencies
to a third agency (for example to the Census Bureau) which would
perform the operations of merging and "sanitizing" the files, and
then return the resulting records only in anonymous form to both
source agencies.  This is the process used to perform match pro-
jects which combine SSA and IRS data.
     Legal implications depend on the legal character of the source
information, the cooperative agreements between and among the
agencies, and the nature of the resultant files in term of the
potential for matching back against the program or statistical
files of the participating agencies.
     A technique used by ORS for releasing microdata files has been
the restricted use agreement, as described in Chapter III.  Files
from which obvious identifiers have been removed, but which
continue to have non-negligible risk of individual identification,
may be released under user agreements to maintain their statistical
anonymity, in entering into these agreements, users must stipulate
that they cannot, and must agree that they will not make any effort
whatsoever, to identify individuals in the file.  These agreements
have carried Social Security Act and Privacy Act sanctions for
unauthorized disclosure.  The CWHS files are not currently eligible
for this kind of treatment, however, in view of IRS' restrictive
position on release of microdata containing return information.
                                98
IRS has been engaged for several years in Freedom of Information
Act litigation, seeking to refuse release of its Taxpayer
Compliance Measurement Program (TCMP) files, which are files
generated in microdata form from samples of income tax records to
use for statistically analyzing tax audit formulas and audit
selection criteria.  Until the issues in that case are finally
resolved, the future of the CWHS user agreement is indefinite.
     The CWHS illustrates a number of use and disclosure issues.
As noted in Chapter III and discussed elsewhere, the CWHS merges
SSA files containing both benefit data compiled in its program
operations and earnings data compiled in its wage reporting
operations carried out in common with IRS, and defined in as tax
return information controlled by the Internal Revenue Code.  The
CWHS does not contain occupation information, however, because that
it not reported on the Form W-2 (formerly on the form 941) filed
with IRS and processed by SSA.  SSA access to return information
does not include income tax information, in which the occupation
data is contained.  The CWHS consequently does not at present
contain occupation.  The CWHS files do contain geography and
industry coded from the Form SS-4 Application for an Employer
Identification Number, which is regarded by IRS as a tax return.
Because of the high degree of visibility of some employers on the
basis of Standard Industrial Classification (SIC) codes associated
with county code of their location, the CWHS data may be
identifiable to employers, and consequently cannot currently be
released to users who do not have access authorized by the Internal
Revenue Code.  A particularly troublesome complication has arisen
with respect to BEA.  BEA has had an ongoing association with ORS
to perfect and use the CWHS files.  In addition, BEA has provided
user service by preparing tabulations on a reimbursable basis from
CWHS files, including the 10 percent file which has never been
publicly available in microdata form.  Under the 1976 Tax Reform
Act, however, BEA was given access only to corporate return
information.  Since the CWHS contains noncorporate employer codes
for geography and industry, it cannot be provided even to BEA for
analysis.  This arrangement was beneficial not only to BEA and to
outside users, but also to ORS, because it conserved the limited
SSA resources available for servicing reimbursable requests and
gave ORS the benefit of BEA's editing and improvement of the file,
which invariably develops from familiarity with a file.
     Another difficulty associated with the Form SS-4, Application
for an Employer Identification Number (EIN) is the business birth
and employer listings which were available in former years to other
Federal statistical users.  The Department of Agriculture can no
longer make use of the SS-4 file to select a sample listing for its
farm surveys.
     The Department of Agriculture currently would benefit from use
of the SS-4 file as a sampling frame for energy surveys, and is
unable to obtain such access.
     The potential value of the SSEL for statistical use is
described elsewhere in this report, and cannot be overstated.
Broader access, at least at the Federal level, is regarded as a
necessity by most contributors to this report, and many consider
that public availability to statistical users would be desirable.
The legal impediments to broader access are numerous and complex.
SSEL is currently compiled under Census Title 13 authority, with
information supplied by SSA and IRS subject to the same disclosure
restrictions as the information furnished by Census.  Proposals
have been under consideration since 1972 for legislative changes to
broaden access.  One suggestion is that name and address
information, together with industry and size codes, might be made
more widely available than at present, but that other information
in the file would retain Title 13 restrictions on release.  One of
the issues raised by this proposal is that the name. address,
industry, and size code information has tax return character, at
least at the time of original acquisition by Census, and its
availability outside census would require changes in the Internal
Revenue Code restrictions on return information.  As a sampling
frame, the SSEL has various advantages, but from the access
standpoint. the difficulties are similar to those discussed in
connection with SSA's Form SS-4 application for a Social Security
Number.
     It may be observed that the complexities increase if the
cooperating agencies include both a Federal and a State counterpart
agency, with legal consequences under both Federal and State law.
For instance, difficulties are attached to the use by BLS of
information provided by states from their UI reports, which contain
EIN's and other information from the Form SS-4.  The problems and
their solution are not well defined at the present time. but it
appears certain that Federal-state access conditions with respect
to return 'information will be reviewed by IRS.
     One of the significant conclusions reached by the Privacy
Protection Study Commission, in this connection was that States
should be encouraged to insulate statistical and research records
from non-statistical uses.  For this, PPSC urged enactment of State
statutes following PPSC policy guidelines, parallel to its
recommendations for Federal records. applying the principles of
"functional separation."
d.   Use by non-statisticians of statistical files compiled from
     administrative source records.
     Statistical analysis selects a small population segment to
serve as proxy for a larger target population, focusing on salient
characteristics, behavior, relationships, etc.
                                99
The statistical files and their analysis may, by their design or
purpose, provide important information to program administrator's,
oversight agencies, legislators, auditors, and courts.  When these
users are satisfied with statistical results based on anonymous or
aggregated data, the purposes of the statistician and the non-
statistician are compatible, and the statistician can
conscientiously make the files available even though the ultimate
uses are foreign to his own intended purposes.
     Often, however, the administrator, auditor, or regulatory or
enforcement officer wishes not only to use statistical results to
identify population segments in which he is interested, but wishes
also to locate and take action affecting individuals in the group
thus identified. (The epidemiological researcher may have a similar
design, though for what may be regarded as more benign purposes.)
Here the objectives of the statistician are thwarted.  Such uses
raise doubts about the objectivity of the statistician, the premise
of confidentiality on which he bases individual data collection,
and the essential fairness of permitting the statistician to have
free access to otherwise confidential information provided by
persons for purposes associated with their participation in
particular programs.
     Moreover, the statistician's sample is usually selected on
attributes not associated with the action purposes of the non-
statistical user, and the sample data may selectively preserve data
about individuals in the sample population which are no longer
retained in the underlying program files.  The marriage of
information from the separate files may also generate new
information which was not itself contained in either of the source
files.  For example a level of income reported by the records in
one file which is legally inconsistent with eligibility for
benefits whose payment is reported by records in the other file.
There are .numerous other possibilities.  For example, records in a
drivers' license register could be linked with records in a file
containing benefit information about blind disabled persons.  Of
course, a "hit" (a match indicating that a particular individual
has records in both files) does not automatically mean that a law
has been violated.  One of the listings may be erroneous; or a
blind individual may have retained a drivers' license for
identification to cash checks.  However, discovery of such matches
may suggest abuses of some sort.  Similar discoveries may attach to
information about earnings which would be inconsistent with, and
require disallowance of certain pensions or unemployment benefits,
if the pertinent records were matched on an individual basis.
     The Internal Revenue Code, for instance, contains a
requirement (section 7214(a) (8)) that Treasury employees report
information about taxpayer noncompliance.  If this duty applies to
information acquired in the course of performing statistical
studies, it clearly cannot be reconciled with the functional
separation principle of insulating individually identifiable
statistical files from administrative actions.
     Such possibilities raise ethical issues which are beyond the
scope of this paper.  The statistician takes the general position,
however, that the administrator or enforcer ought to have access to
aggregate information only, and not to individual data which has
been matched for statistical purposes.
2.   A closer look at some Federal statutes affecting statistical
     use of administrative records and protection of statistical
     records from nonstatistical use
     In general, Federal statutes which have provided confidential
treatment of record information have, by providing essentially
equivalent treatment to administrative and statistical records, had
a dampening effect on productive statistical efforts.  For the most
part, the laws have discouraged harmless interagency disclosures of
identifiable data for statistical purposes at least to the same
degree that they have impeded administrative disclosures, and prob-
ably more than they have impeded enforcement transfers.  They have
neither assisted the statistician in gaining access to program
records, nor protected the record subjects from administrative
actions based on statistical records.
     An exception is the Census statute, Title 13 of the U.S. Code,
which gives the Census Bureau broad authority to obtain
information, including data contained in agencies' administrative
records, at the same time it protects the Census records from being
disclosed either voluntarily or by compulsion in a form which makes
individual identification possible.  The Census statute makes no
distinction between information about natural persons and informa-
tion about business entities, with the result that Census does not
ordinarily publish micro-data records about businesses which are
compiled under its Title 13 authority, even though the information
content itself may be publicly available from other sources.  A
literal reading of Title 13 prevents disclosure of any information
collected under its authority and as a consequence, strict suppres-
sion procedures are applied to assure that no given item of
information can be attributed directly or by inference to an
identifiable respondent.
     The Federal Reports Act [l3a] is a record management statute
which applies to solicitation of information by Federal agencies
from ten or more respondents.  Because of its restrictive
provisions on interagency transfer, it is not an effective
mechanism for authorizing transfers of identifiable data for
statistical purposes.  Under its provisions, statistical data can
generally be transferred only in anonymous format, unless the
requesting agency either has consent of each record subject, or has
the power,
                                100
supported by criminal sanctions, to compel the public to provide it
with the pertinent information: Such power is exceptional,
particularly with reference to information for statistical use.
     Some recent statutes which have been enacted to protect
privacy and confidentiality of information collected by the Federal
government have dealt with statistical information in ways that
still frustrate legitimate statistical needs.  Unless the
individual consents to the disclosure, the Privacy Act of 1974
prohibits any disclosure of identifiable information except to
specified classes of recipients.  Statistical information can be
disclosed only in a form which does not permit individual
identification.  Under this provision by itself, no administrative
file linkage in identifiable form would be possible for statistical
purposes except within the agency which collected all the
information in the files to be matched.  The Privacy Act basis on
which agencies have disclosed data for statistical use is the
provision that allows disclosure for a "routine use" which is
"compatible" with the purpose for which the agency originally
collected the information.  Under this provision, some
administrative file linkage is performed by agencies which have
joint statistical interest in the merged records, and which
demonstrate compatibility of agency purposes in order to warrant
the necessary disclosure.  The Social Security Administration and
Treasury, for instance, have created some match files which merge
demographic, earnings, and income tax information for a sample of
individuals whose records are contained in both agencies'
administrative files.  Once these files are created, they are
purged of explicit identifiers, and are used in anonymous form for
analysis by both agencies and by Congressional oversight committee
staff.  With additional suppression of information (such as
geography or extremely high income level) which might lead
inferentially to identification of some individuals, a public use
microdata version can be produced. [14]
     As noted elsewhere in this report, the Tax Reform Act of 1976
has placed stringent restrictions on the disclosure and use of
information collected by IRS from and about taxpayers, both
individual and business or institutional.  Information about
earnings and withholding subject to the Social Security Act,
including self-employment earnings, is defined by the Tax Reform
Act to be within its scope.  As such, it is governed by the
confidentiality provisions of the Internal Revenue Code, which
provides expressly but not generously for statistical applications,
and which does not allow discretion for disclosure to statistical
agencies not named in the statute.  As described elsewhere in this
report, these provisions have caused serious obstacles to many
useful applications of files such as the CWHS and the SSEL.
Other statutes of record confidentiality and statutory treatment of
statistical data tend to be piecemeal in coverage and rather
arbitrary in scope:
     Data collections sponsored by the Department of Justice Bureau
of Statistics, formerly the Law Enforcement Assistance
Administration (LEAA) for research, including statistical
compilations, are protected from compulsory disclosure, and are
permitted to be disclosed voluntarily to other researchers in
accordance with LEAA approved transfer agreements.  These
statistical records may be compiled from administrative files such
as arrest and conviction records, and obtain protection as a func-
tion of LEAA funding. [15]
     Certain statistical files compiled under HHS drug treatment
research authority may likewise acquire immunity from compulsory
disclosure, either on the basis of their funding, or on the basis
of their designation by the HHS Secretary.[16]
     With opposite effect, some legislative and policy initiatives
create pressures for greater statistical use, but also for
administrative use of statistical findings.  The National Center
for Health Statistics (NCHS) for instance not only has a mandate to
continue its statistical activities, but is also directed to be the
central force for expanding epidemiological studies in
environmental and occupational exposures to harmful substances.
This mandate also includes a duty to locate and inform persons who
have been exposed, and to assist there in obtaining treatment.
Related to this, the National Institutes of Occupational Safety and
Health (NIOSH) was provided an exception from IRS confidentiality
rules in order to locate individuals found to have been exposed to
known hazards. (26 U.S.C. 6103).
     The Freedom of Information Act (FOIA) makes a somewhat jagged
cut across these various disclosure provisions.  Information about
natural persons which is covered by the Privacy Act, for instance,
must be disclosed under FOIA unless its disclosure would be a
"clearly unwarranted invasion of personal privacy," or unless it is
protected by another statute, such as Census Title 13.  FOIA
requires that information about business firms must be disclosed
unless its disclosure would breach trade secrets or reveal
confidential financial information, or unless the disclosure is
prohibited by another statute, such as the Internal Revenue Code.
Other statutes interact similarly with FOIA in various patterns of
inconsistency, insofar as the substantive content of the files is
concerned.
     In addition to statutes, government agency regulations or
guidelines may complicate statistical applications based on
administrative records.  The Office of Management and Budget
recently published guidelines under its Privacy Act authority,
applicable to Federal agencies, record matching activities for
purposes of fraud detection. [17]  These guidelines also apply
(somewhat less string-
                                101
ently) to matches for purposes other than antifraud enforcement.
Although the guidelines do not prohibit file linkage, they do
require reporting to Congress and OMB in advance of any matching
activities.  there is an exception for matching of files within an
agency, for statistical purposes, but it is by no means clear
whether agencies must give prior notice of planned interagency
matches derived from administrative files for statistical analysis.
Similarly unclear is the status of user files which are provided to
agencies to identify sets of individuals for whom record
information is to be extracted and matched to augment user
information in a file which the agency is asked to create in order
to prepare specified statistical tabulations.
     More recently, OMB and EEOC have jointly published a notice of
proposed guidelines for the collection of race/ ethnic data on
application forms.  In their present language, those guidelines
permit the collection of such information, subject to their
required availability for equal rights compliance.  Social
Security's Application for a Social Security Number (Form SS-5),
collects race/ ethnic data on a voluntary basis for statistical
use, but prohibits its disclosure in identifiable form for non-
statistical use, thus permitting release only in summary form for
compliance purposes.  While these principles of collection and
disclosure need not be in conflict, considerable care and
sensitivity will be needed to assure faithful treatment of
confidential information provided, for statistical purposes, as
well as effective pursuit of affirmative action goals.
             C. Summary and Directions for the future
     The discussion makes clear that the legal issues associated
with expanding statistical use of administrative records are
complex, often changing, and sometimes inconsistent in their
results.  Some insights are possible when the legal issues are
examined as questions of access, use and disclosure of records.
From that starting point the emerging principles can be related to
privacy and confidentiality as key concepts underlying those
principles, and as embodied in legislative efforts to achieve
functional separation.
     The current Administration's Privacy Initiative and the
President's Statistical Reorganization Project have made
recommendations leading to legislative proposals for functional
separation which would create quite different mechanisms for the
protection of statistical records than for protection of research
records, as the terms are defined in the respective proposals.
Nevertheless, the line of demarcation between statistical and
research records and uses is not an obvious one, and the two bills
would interact to produce a complicated matrix of criteria.
These legislative proposals are complex and need careful thought
for the full implications to collectors and users of statistical
information in specific applications.  In general, however, their
dual thrust is first to establish conditions permitting freer
availability of information among agencies for statistical use,
including agency access to Census records, and then to protect
files from being used for individual actions and decisions, once
the information in them has been compiled and designated by
statisticians for statistical use.  These broad goals are of great
importance to the work of statisticians both inside and outside the
government agencies which maintain administrative files.
                     D.  Notes and References
(1)  See, for example: Louis Harris and Associates, Inc., and Dr.
     Alan F. Westin, "The Dimensions of Privacy: A National Opinion
     Research Survey of Attitudes Toward Privacy, conducted for the
     Sentry Insurance Co., 1979.
(2)  Privacy Act of 1974; 5 U.S.C. 552a(e).
(3)  Personal Privacy in an Information Society.  Report of the
     Privacy Protection Study Commission, July 1977.  Chapter 15,
     "The Relationship Between Citizen and Government: The Citizen
     as Participant in Research and Statistical Studies."
(4)  Office of Management and Budget (OMB) Circular A-46.
(5)  Report of the Commission on Federal Paperwork, 1978.
(6)  Freedom of Information Act, 5 U.S.C. 552.
(7)  The Administration's proposed bill "Confidentiality of Federal
     Statistical Records," is based on the recommendations of the
     President's Statistical Reorganization Project, and was
     circulated by  OMB for agency review in mid-1979.
(8)  Privacy of Research Records Act of 1979, and administration
     bill, based on recommendations of the Privacy Protection Study
     Commission and the President's Privacy initiative.
(9)  The Tax Reform Act of 1976, P.L. 94-455, 94th Congress.
     October 4. 1976.
(10) 26 U.S.C. 6103(1) (5)
(11) 26 U.S.C. 6103(l) (1)
(12) 26 U.S.C. 6103(n)
(13) Radner, D., "Report on Exact and Statistical
                                102
     Matching Techniques," to be published in 1980 as an Office of
     Statistical Policy and Standards (OFSPS) Working Paper.
(13a)     Federal Reports Act, 44 U.S.C. 3.501-3511
(14) DelBene, L., 1972 Augmented Individual Income Tax Model Exact
     Match File, Report No. 9, Studies from Interagency Data
     Linkages, 1979.
(15) 28 CFR Part 22, implementing section 524(a) of the Omnibus
     Crime Control and Safe Streets Act of 1968, as amended, 42
     U.S.C. 3371.
(16) 42 U.S.C.. 242m, 4582; 21 U.S.C. 1175
(17) Office of Management and Budget (OMB) "Guidelines for the
     Conduct of Matching Pro.grams," Federal Register, March 30,
     1979.
                                103
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*U.S. GOVERNMENT PRINTING OFFICE: 1981-327~698/7103
                                106
 Reports Available In the Statistical Policy working Paper Series
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