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1990 CPH-E-3

1990 Census of

Population and Housing

Evaluation and Research Reports

Programs to

Improve Coverage

in the 1990 Census

U.S. Department of Commerce

Economics and Statistics Administration

BUREAU OF THE CENSUS
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The Decennial Planning Division, Susan M. Miskura, Chief, coordinated

and directed all census operations. Patricia A. Berman, Assistant Division

Chief for Content and Data Products, directed the development and

implementation of the 1990 Census Tabulation and Publication Program.

Other assistant division chiefs were Robert R. Bair, Rachel F. Brown,

James L. Dinwiddie, Allan A. Stephenson, and Edwin B. Wagner, Jr.

The following branch chiefs made significant contributions: Cheryl R.

Landman, Adolfo L. Paez, A. Edward Pike, and William A. Starr. Other

important contributors were Linda S. Brudvig, Cindy S. Easton, Avis L.

Foote, Carolyn R. Hay, Douglas M. Lee, Gloria J. Porter, and A. Nishea

Quash.

The Decennial Operations Division, Arnold A. Jackson, Chief, was

responsible for processing and tabulating census data. Assistant division

chiefs were: Donald R. Dalzell, Kenneth A. Riccini, Billy E. Stark, and

James E. Steed. Processing offices were managed by Alfred Cruz, Jr.,

Earle B. Knapp, Jr., Judith N. Petty, Mark M. Taylor, Russell L.

Valentine, Jr., Carol A. Van Horn, and C. Kemble Worley. The following

branch chiefs made significant contributions: Jonathan G. Ankers,

Sharron S. Baucom, Catharine W. Burt, Vickie L. Cotton, Robert J.

Hemmig, George H. McLaughlin, Carol M. Miller, Lorraine D. Neece,

Peggy S. Payne, William L. Peil, Cotty A. Smith, Dennis W. Stoudt, and

Richard R. Warren. Other important contributors were Eleanor I. Banks,

Miriam R. Barton, Danny L. Burkhead, J. Kenneth Butler, Jr., Albert A.

Csellar, Donald H. Danbury, Judith A. Dawson, Donald R. Dwyer,

Beverly B. Fransen, Katherine H. Gilbert, Lynn A. Hollabaugh, Ellen B.

Katzoff, Randy M. Klear, Norman W. Larsen, Peter J. Long, Sue Love,

Patricia O. Madson, Mark J. Matsko, John R. Murphy, Dan E. Philipp,

Eugene M. Rashlich, Willie T. Robertson, Barbara A. Rosen, Sharon A.

Schoch, Imelda B. Severdia, Diane J. Simmons, Emmett F. Spiers,

Johanne M. Stovall, M. Lisa Sylla, and Jess D. Thompson.

The Housing and Household Economic Statistics Division, Daniel H.

Weinberg, Chief, developed the questionnaire content, designed the data

tabulations, and reviewed the data for the economic and housing characteristics. Gordon W. Green, Jr., Assistant Division Chief for Economic

Characteristics, and Leonard J. Norry, Assistant Division Chief for Housing Characteristics, directed the development of this work. The following

branch chiefs made significant contributions: William A. Downs, Peter J.

Fronczek, Patricia A. Johnson, Enrique J. Lamas, Charles T. Nelson,

and Thomas S. Scopp. Other important contributors were Eleanor

F. Baugher, Jeanne C. Benetti, Robert L. Bennefield, Robert W.

Bonnette, William S. Chapin, Higinio Feliciano, Timothy S. Grall,

Cynthia J. Harpine, Selwyn Jones, Mary C. Kirk, Richard G. Kreinsen,

Gordon H. Lester, Mark S. Littman, Wilfred T. Masumura, John M.

McNeil, Diane C. Murphy, George F. Patterson, Thomas J. Palumbo,

Kirby G. Posey, John Priebe, Anne D. Smoler, and Carmina F. Young.

The Population Division, Paula J. Schneider, Chief, developed the

questionnaire content, designed the data tabulations, and reviewed the

data for the demographic and social characteristics of the population.

Philip N. Fulton, Assistant Division Chief for Census Programs, directed

the development of this work. Other assistant division chiefs were

Nampeo R. McKenney and Arthur J. Norton. The following branch and

staff chiefs made significant contributions: Jorge H. del Pinal, Campbell J.

Gibson, Roderick J. Harrison, Donald J. Hernandez, Jane H. Ingold,

Martin T. O’Connell, Marie Pees, J. Gregory Robinson, Phillip A.

Salopek, Paul M. Siegel, Robert C. Speaker, Gregory K. Spencer, and

Cynthia M. Taeuber. Other important contributors were Celia G. Boertlein,

Rosalind R. Bruno, Janice A. Costanzo, Rosemarie C. Cowan, Arthur

R. Cresce, Larry G. Curran, Carmen DeNavas, Robert O. Grymes,

Kristin A. Hansen, Mary C. Hawkins, Rodger V. Johnson, Michael J.

Levin, Edna L. Paisano, Sherry B. Pollock, Stanley J. Rolark, A. Dianne

Schmidley, Denise I. Smith, and Nancy L. Sweet.

The Data User Services Division, Gerard C. Iannelli, then Chief,

directed the development of data product dissemination and information to

increase awareness, understanding, and use of census data. Marie G.

Argana, Assistant Chief for Data User Services, directed preparation of

electronic data products and their dissemination. Alfonso E. Mirabal,

Assistant Chief for Group Information and Advisory Services, directed

activities related to the National Services Program, State Data Centers, and

preparation of training materials. The following branch chiefs made significant contributions: Deborah D. Barrett, Frederick G. Bohme, Larry W.

Carbaugh, James P. Curry, Samuel H. Johnson, John C. Kavaliunas,

and Forrest B. Williams. Other important contributors were Molly

Abramowitz, Celestin J. Aguigui, Barbara J. Aldrich, Delores A.

Baldwin, Albert R. Barros, Geneva A. Burns, Carmen D. Campbell,

James R. Clark, Virginia L. Collins, George H. Dailey, Jr., Barbara L.

Hatchl, Theresa C. Johnson, Paul T. Manka, John D. McCall, Jo Ann

Norris, David M. Pemberton, Sarabeth Rodriguez, Charles J. Wade,

Joyce J. Ware, and Gary M. Young.

The Geography Division, Robert W. Marx, Chief, directed and coordinated the census mapping and geographic activities. Jack R. George,

Assistant Division Chief for Geoprocessing, directed the planning and

development of the TIGER System and related software. Robert A.

LaMacchia, Assistant Division Chief for Planning, directed the planning

and implementation of processes for defining 1990 census geographic

areas. Silla G. Tomasi, Assistant Division Chief for Operations, managed

the planning and implementation of 1990 census mapping applications

using the TIGER System. The following branch chiefs made significant

contributions: Frederick R. Broome, Charles E. Dingman, Linda M.

Franz, David E. Galdi, Dan N. Harding, Donald I. Hirschfeld, David B.

Meixler, Peter Rosenson, Joel Sobel, Brian Swanhart, and Richard

Trois. Other important contributors were Gerard Boudriault,

Desmond J. Carron, Anthony W. Costanzo, Paul W. Daisey,

Beverly A. Davis, Carl S. Hantman, Christine J. Kinnear, Terence D.

McDowell, Linda M. Pike, Rose J. A. Quarato, Lourdes Ramirez,

Gavin H. Shaw, Daniel L. Sweeney, Timothy F. Trainor, Phyllis S.

Willette, and Walter E. Yergen.

The Statistical Support Division, John H. Thompson, Chief, directed

the application of mathematical statistical techniques in the design and

conduct of the census. John S. Linebarger, Assistant Division Chief for

Quality Assurance, directed the development and implementation of

operational and software quality assurance. Henry F. Woltman, Assistant Division Chief for Census Design, directed the development and

implementation of sample design, disclosure avoidance, weighting, and

variance estimation. Howard Hogan and David V. Bateman were

contributing assistant division chiefs. The following branch chiefs made

significant contributions: Florence H. Abramson, Deborah H. Griffin,

Richard A. Griffin, Lawrence I. Iskow, and Michael L. Mersch. Other

important contributors were Linda A. Flores-Baez, Larry M. Bates,

Somonica L. Green, James E. Hartman, Steven D. Jarvis, Alfredo

Navarro, Eric L. Schindler, Carolyn T. Swan, and Glenn D. White.

The 1990 Census Redistricting Data Office, Marshall L. Turner, Jr.,

Chief, assisted by Cathy L. Talbert, directed the development and

implementation of the 1990 Census Redistricting Data Program.

The Administrative and Publications Services Division, Walter C.

Odom, Chief, provided direction for the census administrative services,

publications, printing, and graphics functions. Michael G. Garland was a

contributing assistant division chief. The following branch and staff chiefs

made significant contributions: Bernard E. Baymler, Albert W. Cosner,

Gary J. Lauffer, Gerald A. Mann, Clement B. Nettles, Russell Price,

and Barbara J. Stanard. Other important contributors were Barbara M.

Abbott, Robert J. Brown, David M. Coontz, and John T. Overby.

The Data Preparation Division, Joseph S. Harris, Chief, provided

management of a multi-operational facility including kit preparation,

procurement, warehousing and supply, and census processing activities.

Plummer Alston, Jr., and Patricia M. Clark were assistant division

chiefs.

The Field Division, Stanley D. Matchett, Chief, directed the census

data collection and associated field operations. Richard L. Bitzer,

Richard F. Blass, Karl K. Kindel, and John W. Marshall were assistant

division chiefs. Regional office directors were William F. Adams, John E.

Bell, LaVerne Collins, Dwight P. Dean, Arthur G. Dukakis, Sheila H.

Grimm, William F. Hill, James F. Holmes, Stanley D. Moore, Marvin L.

Postma, John E. Reeder, and Leo C. Schilling.

The Personnel Division, David P. Warner, Chief, provided management direction and guidance to the staffing, planning pay systems, and

employee relations programs for the census. Colleen A. Woodard was

the assistant chief.

The Technical Services Division, C. Thomas DiNenna, Chief, designed,

developed, deployed, and produced automated technology for census

data processing.

ACKNOWLEDGMENTS
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1990 CPH-E-3

1990 Census of

Population and Housing

Evaluation and Research Reports

Programs to

Improve Coverage

in the 1990 Census

Issued October 1993

U.S. Department of Commerce

Ronald H. Brown, Secretary

Economics and Statistics Administration

Paul A. London, Acting Under Secretary

for Economic Affairs

BUREAU OF THE CENSUS

Harry A. Scarr, Acting Director
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BUREAU OF THE CENSUS

Harry A. Scarr, Acting Director

Charles D. Jones, Associate Director for

Decennial Census

William P. Butz, Associate Director for

Demographic Programs

Bryant Benton, Associate Director for

Field Operations

Clifford J. Parker, Acting Associate Director

for Administration

Peter A. Bounpane, Assistant Director for

Decennial Census

Economics and Statistics

Administration

Paul A. London, Acting Under Secretary

for Economic Affairs

Special Acknowledgments

This report was prepared by Florence H. Abramson, Joy Aso, Diane F. Barrett, Kevin Cecco, Jeffrey

S. Corteville, Deborah H. Griffin, John S. Linebarger, Joan M. Pausche, George A. Sledge, Michael C.

Tenebaum, James B. Treat, and Susan C. Wajer, under the general supervision of John S. Linebarger,

Assistant Division Chief for Quality Assurance and Evaluation and Henry F. Woltman, Assistant Division

Chief for Census Design, and under the direction of John H. Thompson, Chief, Decennial Statistical Studies

Division. Important contributions were made by O. Annetta Clark, Nancy J. Corbin, Karen A. Cowles,

Bonnie J. DeMarr, Robert G. Edson, Tracy A. Ewings, Douglas K. Hallam, Vicki L. Harrison, Tracey

McNally, Christopher Moriarity, Emilda Rivers, Chad E. Russell, Carnelle E. Sligh, Gloria Spriggs,

Martha L. Sutt, Amy L. Tillman, Eric R. Williams, Pamela A. Windsor and Kent T. Wurdeman, former and

current staff of the Decennial Statistical Studies Division; Jonathan G. Ankers, Laura L. Blackwell, Sandra

K. Bruner, Patricia A. Berman, Rachel C. Brown, Donald R. Dalzell, Judith A. Dawson, Charles R.

Eargle, Alvin L. Etzler, Avis L. Foote, Charles J. Kahn, Karen A. Kane, Ellen B. Katzoff, Edward L.

Kobilarcik, Peter J. Long, Susan P. Love, George H. McLaughlin, Laureen H. Moyer, Dan E. Phillip, A.

Edward Pike, Gloria J. Porter, Willie T. Robertson, Nancy E. Rogers, Richard A. Schwartz, Judith J.

Stryker, Edwin B. Wagner, David C. Whitford and Donna A. Williams, former and current staff of the

Decennial Management Division; Adele G. Alvey, Darren F. Althouse, Karen A. Bagwell, Shirley A.

Breeland, Janet R. Cummings, Timothy J. Devine, Charles F. Fowler, Rhonda G. Geddings, Christine

L. Hough, Claire L. Hovland, Jay K. Keller, Sandra L. Lucas, Lynn I. Minneman, Alicia A. Morris, Sharon

M. Neugebauer, William J. Phalen, Glenda M. Pickett, Glenn C. Schneider, Eli S. Serrano, and Karen E.

Wyatt, former and current staff of the Field Division; and Frederick L. McKee and the former and current

staff of the Evaluations Unit of the Statistical Methods and Quality Control Branch of the Data Preparation

Division. (Note that in 1992 the Statistical Support Divsion was renamed the Decennial Statistical Studies

Division and the Decennial Planning Division merged with the Decennial Operations Division to form the

Decennial Management Division.)

For sale by the Superintendent of Documents, U.S. Government Printing Office,

Washington, DC 20402.
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CHAPTER 1. INTRODUCTION AND BACKGROUND------------------------------------------ 3

INTRODUCTION -------------------------------------------------------------------------------- 3

TERMINOLOGY--------------------------------------------------------------------------------- 3

ORGANIZATION OF THE REPORT ----------------------------------------------------------- 4

SUMMARY COVERAGE IMPROVEMENT AND COST DATA ------------------------------- 4

OPERATIONAL DESCRIPTIONS -------------------------------------------------------------- 6

CHAPTER 2. ADDRESS LIST DEVELOPMENT ------------------------------------------------ 13

VENDOR FILE----------------------------------------------------------------------------------- 13

PRELIST MAILOUT/ MAILBACK --------------------------------------------------------------- 14

PRELIST UPDATE/ LEAVE --------------------------------------------------------------------- 16

ADVANCE POST OFFICE CHECK I ----------------------------------------------------------- 17

ADVANCE POST OFFICE CHECK II, III------------------------------------------------------- 21

ADVANCE POST OFFICE CHECK RECONCILIATION--------------------------------------- 23

PRECANVASS ---------------------------------------------------------------------------------- 26

PRECANVASS RECONCILIATION/ YELLOW CARD CODING------------------------------- 29

PRECENSUS LOCAL REVIEW----------------------------------------------------------------- 33

CASING CHECK -------------------------------------------------------------------------------- 34

CHAPTER 3. QUESTIONNAIRE DELIVERY AND ENUMERATION -------------------------- 39

RURAL UPDATE/ LEAVE----------------------------------------------------------------------- 39

URBAN UPDATE/ LEAVE ---------------------------------------------------------------------- 42

URBAN UPDATE/ ENUMERATE--------------------------------------------------------------- 44

POSTMASTER RETURN DELIVERY ---------------------------------------------------------- 46

SHELTER AND STREET NIGHT ENUMERATION ------------------------------------------- 51

CHAPTER 4. POST-CENSUS DAY COVERAGE IMPROVEMENT---------------------------- 57

TELEPHONE ASSISTANCE ADDS ------------------------------------------------------------ 57

CENSUS CLOSEOUT ADDRESS CHECK----------------------------------------------------- 60

VACANT/ DELETE/ MOVERS CHECK --------------------------------------------------------- 62

PUERTO RICO MULTIUNIT COVERAGE IMPROVEMENT OPERATION------------------- 66

RECANVASS OPERATION--------------------------------------------------------------------- 68

POSTCENSUS LOCAL REVIEW--------------------------------------------------------------- 71

POP ONE REENUMERATION ----------------------------------------------------------------- 74

PRIMARY SELECTION ALGORITHM REVIEW ----------------------------------------------- 77

CHAPTER 5. SEARCH/ MATCH COVERAGE IMPROVEMENT ------------------------------- 83

CHARACTERISTICS OF SEARCH/ MATCH ADDITIONS ------------------------------------ 83

PAROLEE/ PROBATIONER COVERAGE IMPROVEMENT PROGRAM AND

FOLLOWUP ------------------------------------------------------------------------------------ 87

USUAL HOME ELSEWHERE ------------------------------------------------------------------ 94

WERE YOU COUNTED? ----------------------------------------------------------------------- 97

APPENDIXES

A. GLOSSARY ---------------------------------------------------------------------------------- 99

B. FACSIMILES OF DECENNIAL FORMS AND QUESTIONNAIRES ----------------------- 103

C. MAPS ----------------------------------------------------------------------------------------- 143

CONTENTS

Page

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CHAPTER 1.

Introduction and Background

INTRODUCTION

This report presents the results of evaluations of various

components of the 1990 Census Coverage Improvement

Program. The coverage improvement program consisted

of a series of operations, some of which were redundant by

design in order to make the coverage of persons and

housing units as complete as possible. A review of the

basic methodologies used to conduct the census provides

a context for discussing the coverage improvement program and the results of that program.

Prior to the 1970 census, enumeration by mail was

considered an experimental procedure. However, in each

of the past three censuses (1970, 1980, and 1990), the

mailout/ mailback approach has been the basic method of

enumeration. Under the mailout/ mailback methodology, a

questionnaire was mailed out and the respondent returned

it by mail to either a local district office or to a centralized

processing office. Nonresponding households were visited

by an enumerator who completed a questionnaire for the

housing unit and/ or household. In the 1990 census, the

update/ leave methodology was used in areas where U.S.

Postal Service (USPS) delivery problems were anticipated.

Under the update/ leave methodology, census enumerators delivered the questionnaires and respondents were

asked to return them by mail. Together, the mailout/ mailback

and update/ leave methodologies are considered mailback

techniques. In each census, the mailback approach was

supplemented by list/ enumerate (previously referred to as

conventional census) procedures.

The mailback procedure was used to enumerate the

majorityof householdssince1970.In1970,themailout/ mailback

approach was used to enumerate about 60.0 percent of

the population. About 95.5 percent of the population in

1980 was enumerated under the mailout/ mailback procedure. The percent of the population included in the mailback universe remained at about 95.5 percent in 1990.

TERMINOLOGY

To provide common ground for understanding the 1990

Census Coverage Improvement Program,this section describes

the primary approaches used to develop a mailing list and

to enumerate persons and housing units. A glossary of

census terms also is provided in appendix A at the end of

the report.

Types of Enumeration Area

There were four basic types of enumeration areas in the

1990 census:

TAR Mailout/ Mailback—In densely populated urban areas

and areas surrounding these central cities, the initial

mailing list was purchased from a commercial vendor. The

list was updated through two USPS checks (the Advance

Post Office Check and the Casing Check) and through a

dependent canvass by census enumerators (precanvass).

Census questionnaires were mailed out from a central

location and respondents were requested to return them to

either a local district office or a centralized processing

office. Housing units for which questionnaires were not

returned by mail were visited by census enumerators.

Prelist Mailout/ Mailback—In suburban areas, Census

Bureau enumerators listed addresses to create the initial

mailing list. Prelisted addresses in the mailout/ mailback

area were updated by the same two USPS checks as the

TAR addresses. In place of a precanvass, census enumerators conducted a field reconciliation of the Advance Post

Office Check results. Mailout and followup activities were

similar to TAR areas.

Prelist Update/ Leave—The Census Bureau anticipated

problems with USPS questionnaire delivery in small towns

and rural areas (mostly in the South and Midwest) which

contained a high proportion of noncity style addresses

(such as rural route and box numbers and general delivery). Address lists were developed by prelisting but were

updated at the same time that census enumerators delivered questionnaires. Mail return and followup were similar

to the TAR and Prelist mailout/ mailback areas.

List/ Enumerate—In very rural areas, census enumerators compiled the basic address list and completed the

enumeration in one operation.

Data Collection and Data Processing Offices

There were three types of offices set up to conduct the

1990 census. Regional census centers and district offices

were mostly involved in collecting the data. Processing

offices were established to provide most of the processing

functions for the census.

Regional Census Centers—There were 13 regional census centers temporarily set up to manage the district office

activities and to provide administrative, procedural, and

geographic support to the district offices. Each of the 13

regional census centers was under the direction of one of

the permanent Census Bureau regional offices. With the

exception of the San Francisco Regional Census Center,

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which was under the Los Angeles Regional Office, each of

the remaining 12 regional census centers were located in

the regional office city. These 12 regional census centers

were located in the cities of Atlanta, GA; Boston, MA;

Charlotte, NC; Chicago, IL; Dallas, TX; Denver, CO; Detroit,

MI; Kansas City, KS; Los Angeles, CA; New York, NY;

Philadelphia, PA; and Seattle, WA. Appendix C provides a

map showing the regional census center boundaries.

District Offices—There were five types of district offices

for the 1990 census. There were 103 Type 1 district offices

which covered large central cities, mostly in TAR areas.

There were 197 Type 2 district offices which generally

covered smaller city, suburban, and some rural areas.

Type 2 district offices covered mailout/ mailback areas;

about 60 percent of the housing units were in TAR areas

and the rest were in prelist mailout/ mailback areas. Seventynine Type 2A district offices covered city, suburban, and

rural areas in the South and Midwest. About 20 percent of

the housing units in Type 2A district offices were in TAR

areas, about 25 percent were in prelist mailout/ mailback

areas, and about 55 percent were in prelist update/ leave

areas. Type 3 district offices covered most of rural areas of

the West and the most rural parts of the northernmost

States. There were 70 Type 3 district offices. About 20

percent of the housing units in Type 3 district offices were

in TAR areas,about 30 percent were in prelist mailout/ mailback

areas, and the remaining 50 percent were in list/ enumerate

areas. There were also nine Type 3 district offices in

Puerto Rico; all nine were entirely list/ enumerate. An

additional 38 Type 4 district offices were set up as ‘‘outreach offices’’ but had no formal enumeration responsibilities.

Processing Offices—There were seven processing offices

used to process the data and control the 1990 census. The

processing offices were located in the cities of Albany, NY;

Baltimore, MD; Jacksonville, FL; Kansas City, MO; Jeffersonville, IN; Austin, TX; and San Diego, CA.

ORGANIZATION OF THE REPORT

The coverage improvement methods used in the 1990

census fall into several broad categories:

• Address list development

• Questionnaire delivery and enumeration techniques

• Post-Census Day coverage improvement operations

• Search/ Match coverage improvement operations

The basic methods used to develop and update the

address list, to enumerate persons and housing units, and

to improve the coverage of persons and housing units are

described in the Operational Descriptions section of this

chapter. The remaining chapters in this report discuss

results of only the methods that were evaluated as part of

the 1990 Census Research, Evaluation and Experimental

Program.

Many of the methods used in the 1990 census were

developed and tested after the 1980 Census; however,

others were modifications and improvements to procedures used in 1970 and 1980. For each method evaluated

and discussed in this document, table 1.1 indicates if it had

been used in 1970 or 1980.

Results of the evaluations of the programs to improve

coverage in the 1990 census are organized in this report

on the basis of the four basic categories, with each

category forming a chapter. Within each chapter, each

section discusses the evaluation of one of the coverage

improvement operations identified in table 1.1.

SUMMARY COVERAGE IMPROVEMENT AND

COST DATA

Table 1.2 summarizes the costs and effectiveness of

the major programs to improve coverage in the 1990

census. Only those programs for which evaluation data are

available are included. The programs described in this

report that were designed to compile the initial address list

(that is, vendor file and prelist) rather than to improve the

list are excluded from the table.

Table 1.1. Coverage Improvement Methods

Method 1970 1980

ADDRESS LIST DEVELOPMENT

Vendor File ................................ X X

Prelist Mailout/ Mailback Areas. . . . . . . . . . . . . . . X

Prelist Update/ Leave Areas . . . . . . . . . . . . . . . . .

Advance Post Office Check I . . . . . . . . . . . . . . . . X X

Advance Post Office Check II/ III . . . . . . . . . . . .

Advance Post Office Check Reconciliation . . .

Precanvass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X X

Yellow Card Coding . . . . . . . . . . . . . . . . . . . . . . . . X

Precensus Local Review . . . . . . . . . . . . . . . . . . .

Casing Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X X

QUESTIONNAIRE DELIVERY AND ENUMERATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Rural Update/ Leave . . . . . . . . . . . . . . . . . . . . . . .

Urban Update/ Leave . . . . . . . . . . . . . . . . . . . . . . .

Urban Update/ Enumerate . . . . . . . . . . . . . . . . . .

Postmaster Return Delivery . . . . . . . . . . . . . . . . .

Shelter and Street Night . . . . . . . . . . . . . . . . . . . .

POST-CENSUS DAY COVERAGE IMPROVEMENT

Telephone Assistance Adds ................

Census Closeout Address Check. . . . . . . . . . . . .

Vacant/ Delete/ Movers Check . . . . . . . . . . . . . . . X X

Puerto Rico Multiunit Coverage Improvement operation. . . . . . . . . . . . . . . . . . . . . . . . . . . .

Recanvass operation . . . . . . . . . . . . . . . . . . . . . . .

Postcensus Local Review . . . . . . . . . . . . . . . . . . . X

POP One Reenumeration ...................

Primary Selection Algorithm Review . . . . . . . . .

SEARCH/ MATCH COVERAGE IMPROVEMENT

Parolee/ Probationer Coverage Improvement Program and Followup . . . . . . . . . . . . . . .

Usual Home Elsewhere . . . . . . . . . . . . . . . . . . . . . X

Were You Counted? Campaign . . . . . . . . . . . . . . X X

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Table 1.2. Cost and Effectiveness of 1990 Census Coverage Improvement Programs

Coverage improvement method

Housing units added Persons added Cost (1990 dollars)

Number

(thousands) Percent

Number

(thousands) Percent

Total

(thousands)

Per added

housing unit

Per added

person

Programs to Improve the Address List Prior

to Enumeration

Advance Post Office Check I . . . . . . . . . . . . . . . . 1,353.3 1.3 - - 57,845 2.17 -

Advance Post Office Check II/ III and

Advance Post Office Check Reconciliation9 . . 1

1,171.7 1.1 - - 19,762 4.54 -

Precanvass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

5,963.0 5.8 - - 15,886 1.66 -

Yellow Card Coding . . . . . . . . . . . . . . . . . . . . . . . . 394.3 0.4 - - 785 1.99 -

Precensus Local Review . . . . . . . . . . . . . . . . . . . . 367.3 0.4 - - 1,904 85.18 -

Casing Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 931.1 0.9 - - 12,558 2.08 -

Questionnaire Delivery and Enumeration

Rural Update/ Leave . . . . . . . . . . . . . . . . . . . . . . . . 399.4 0.4 - - 6

18,556 1.77 -

Urban Update/ Leave . . . . . . . . . . . . . . . . . . . . . . . NA NA - - 6

63 NA -

Urban Update/ Enumerate . . . . . . . . . . . . . . . . . . . NA NA NA NA 6

27 NA NA

Shelter and Street Night Enumeration . . . . . . . . - - 3

240.1 0.1 3,886 - 16.18

Post-Census Day Coverage Improvement

Telephone Assistance Adds .................. 2

158.0 0.2 4407.0 0.2 7NA NA NA

Census Closeout Address Check. . . . . . . . . . . . . . - - 2

0.9 0.0 1 - 1.56

Vacant/ Delete/ Movers Check . . . . . . . . . . . . . . . . 344.8 0.3 1,505.4 0.6 67,589 69.05 44.90

Recanvass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138.6 0.1 4

178.2 0.1 14,684 8105.97 82.40

Postcensus Local Review . . . . . . . . . . . . . . . . . . . . 80.9 0.1 4

124.9 0.1 9,604 34.39 76.89

POP One Reenumeration . . . . . . . . . . . . . . . . . . . . - - 56.3 0.0 7NA - NA

Primary Selection Algorithm Review . . . . . . . . . . . - - 350.4 0.1 2,880 - 8.22

Search/ Match Coverage Improvement . . . . . . .

Parolee/ Probationer Coverage Improvement

Program and Followup . . . . . . . . . . . . . . . . . . . . . 25.0 0.0 447.8 0.2 5,800 232.00 12.95

Other Search/ Match Forms10. . . . . . . . . . . . . . . . 24.9 0.0 608.6 0.2 6,319 253.76 10.38

NA Not available.

General notes:

• Total 1990 housing unit and population counts, used as base of percentages, were 102,264,000 and 248,710,000, respectively. Many coverage

improvement programs were conducted only in some areas of the country. Therefore, coverage improvement rates discussed in other sections of this

report may not be comparable to the ones presented here.

• Total cost for most programs includes only USPS or Field costs, unless the operation was conducted only in a processing office (for example,

search/ match activities). Certain incremental costs, such as those for planning, preparation, processing, and data capture, are not included. Also, the

table excludes the cost of enumerating housing units added prior to Census Day.

• Costs per added housing unit are calculated based on the share of the total cost attributable to adds (as opposed to geographic transfers,

corrections, etc.).

Footnotes:

1

Housing unit adds include all addresses added during the operation, including those addresses that were deleted in later operations. (Housing unit

adds for all other operations include only those addresses with a final census status of occupied or vacant.)During precanvass processing, some

geographic transfers were not recognized as such and are included in the count of precanvass adds. 2

Estimated number of adds are based on a sample.

3

This is a count of the persons enumerated at emergency shelters, shelters for abused women, shelters for runaway and neglected children, and street

locations. S-Night was not designed to (and was never intended to be) a complete count of the homeless population. 4

Estimated number of person adds are based on an average household size of 2.63 persons per household in occupied housing units.

5

Total cost includes the cost of geocoding the APOC I added addresses, in addition to USPS costs.

6

The major portion of the total cost for these operations covers delivery of census questionnaires to all housing units in the areas in which the

operations were conducted. 7

Total cost for these operations cannot be separated from other costs.

8

The number of units transferred during this operation was not available and could not be factored into the per added housing unit cost.

9

Housing units identified by the USPS as adds during APOC II and III were not added until verified during APOC Reconciliation. Therefore, the number

of housing unit adds and cost per housing unit add cannot be separated by APOC and APOC Reconciliation. 10Other search/ match forms include individual census reports, military census reports, shipboard census reports, usual home elsewhere forms, and

were you counted? forms.

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As shown in the table, programs to improve the address

list prior to enumeration were considerably more cost

effective compared to the per unit cost of adding housing

units in the Post-Census Day programs. Some of the

reasons for this have to do with the way in which the cost

data were derived. For example, most of the costs are

obtained from the field charges to the operation. The

programs conducted during the data collection phase of

the census include the cost of enumerating the housing

units and the persons residing in the units in addition to the

cost of adding the units. Also, as the census was conducted and the files of addresses and persons became

more complete, there were fewer available housing units

and persons to add. This resulted in higher per unit costs

to locate and add missed housing units and persons.

Post-Census Day programs designed to add persons

(including the Shelter and Street Night Enumeration and

search/ match activities), as opposed to the early coverage

improvement programs aimed at improving the address

list, added approximately 1.6 percent of the total 1990

population at an average cost of about $30.00 per added

person.

Refer to each section of this publication for the references identifying the results memoranda which document

each evaluation. Complete cost data and limitations of the

cost data should be presented in these memoranda.

OPERATIONAL DESCRIPTIONS

This section provides a brief description of each of the

major coverage improvement operations used in the 1990

census.

Advance Post Office Check

Objective—A USPS coverage improvement operation to

verify the accuracy and completeness of the purchased

address lists and the lists compiled during prelist.

Where It Occurred—Advance Post Office Check I was

completed in TAR areas and Advance Post Office Checks

II and III were completed in Prelist mailout/ mailback areas.

Procedure—The addresses of all known living quarters

were computer printed on cards. These cards were sorted

by ZIP Code and letter carrier route and shipped to the

respective Post Office. Letter carriers sorted or ‘‘cased’’

the cards into slots for each address on their route, made

corrections, and identified duplicates and undeliverables.

For those addresses for which a card was not provided,

the letter carrier filled out a blue ‘‘add’’ card.

Advance Post Office Check Reconciliation

Objective—To determine the status of addresses identified by the USPS during Advance Post Office Checks II

and III as adds or duplicates and to obtain a better mailing

address for undeliverables. In addition, enumerators assigned

geographic codes to valid adds.

Where It Occurred—Prelist mailout/ mailback areas.

Procedure—The district offices received the blue ‘‘add’’

cards that contained the addresses of living quarters that

the USPS delivers to but for which an Advance Post Office

Check card had not been sent to the USPS. The district

offices also received listings from the processing office

that showed the undeliverables and duplicates. The adds,

undeliverables, and duplicates were field checked by enumerators. For each blue card address, the enumerator

verified that the address represented a missing housing

unit and updated the census map to indicate the physical

location of the unit. Enumerators resolved duplicates and

tried to obtain more accurate mailing addresses for undeliverables.

Casing Check

Objective—A USPS operation to verify the completeness

of the census address list prior to delivering questionnaires

to each housing unit in the mailout/ mailback census area.

Where It Occurred—TAR and Prelist mailout/ mailback

areas.

Procedure—Between late February and mid-March 1990,

each letter carrier cased the census address check cards

for his or her route in order to identify deliverable and

undeliverable (including duplicate) addresses as well as to

identify addresses missing an address card. For each

address missing a census address check card, the carrier

completed a blue ‘‘add’’ card. All blue cards were checked

in the district office or in the field to determine if the

addresses were missing from the census address file. The

district offices labeled and mailed questionnaire mailing

packages to the missing addresses if they were processed

early; addresses not sent a mailing package were enumerated during Nonresponse Followup.

Census Closeout Address Check

Objective—To utilize the knowledge of local USPS letter

carriers to obtain limited information about unenumerated

cases in the final stages of followup operations.

Where It Occurred—All district offices were given the

option to use it.

Procedure—Those district offices which participated in

this program prepared an address check card for each

unenumerated address and submitted the cards to local

postal officials. The letter carriers were instructed to

provide very limited information about the addresses (type

of structure, occupancy status on Census Day, and number of Census Day occupants) based on their knowledge

of the living quarters. If the carriers provided usable

information for an address, the district office could use that

information to classify the address in lieu of information

obtained by a field visit.

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Field Followup

Objective—To obtain missing questionnaires or more

complete information from returned questionnaires and to

verify the status of vacant and deleted units.

Where It Occurred—All areas.

Procedures—The following types of cases required field

followup:

• Failed Edit—Mail return questionnaires that contained

missing, illegible, or incomplete entries that could not be

resolved during telephone followup were assigned for a

personal visit.

• Vacant/ Delete/ Movers Check—Addresses that were

classified as either vacant or delete during List/ Enumerate

or Nonresponse Followup were visited by enumerators

to verify the Census Day status of the housing unit and

to complete questionnaires for deleted housing units

converted to vacant or occupied and vacant housing

units converted to occupied. Questionnaires were also

completed for post Census Day movers who were not

previously enumerated.

• Residual Nonresponse—Enumerators visited addresses

to complete a questionnaire for those households for

which a census questionnaire was not checked in.

The failed Edit and Residual Nonresponse followups

were done only in TAR and Prelist mailback areas. The

Vacant/ Delete/ Movers Check was done in all areas.

Group Quarters Enumeration

Objective—To enumerate those persons living in institutional and noninstitutional group quarters found at special

places, such as hospitals and dormitories.

Where It Occurred—All areas.

Procedure—This operation began the day after Census

Day and continued for 2 weeks. Enumerators visited each

special place with group quarters and listed the names of

the persons staying there. They left Individual Census

Reports for each person to complete. They returned at a

later date to pick up the forms and, if necessary, conduct

interviews to obtain any missing information.

List/ Enumerate

Objective—To list housing units, update maps, and enumerate persons in sparsely populated rural areas.

Where It Occurred—List/ Enumerate areas.

Procedure—About a week before Census Day, the USPS

letter carriers delivered Advance Census Reports to all

known residential addresses in these areas. A member of

the household was asked to complete the questionnaire

and hold it for pick-up by an enumerator. Beginning the day

before Census Day, enumerators canvassed the address

register area, listed the address of each housing unit and

updated the census map to indicate the physical location

of each unit. The enumerator entered a map spot number

on the map and on the corresponding line on the address

register page. He or she also picked up or completed a

questionnaire for every housing unit in the address register

area. The lines on the address register pages were preprinted to indicate whether a household was to receive a

long form or a short form. For long form households, the

enumerator collected the short form, transferred the information to the long form, and conducted an interview to

obtain the rest of the information.

Nonresponse Followup

Objective—To obtain a completed questionnaire for housing units and households for which a questionnaire was not

returned by mail and to add housing units not already

listed.

Where It Occurred—TAR and Prelist mailout/ mailback

and update/ leave areas.

Procedure—Enumerators visited housing units for which

a census questionnaire had not been checked-in and

conducted an interview using an ‘‘Enumerator-Friendly’’

version of the census questionnaire. During the course of

enumerating nonresponding households, enumerators were

also instructed to add units not listed in the address

register and to complete a census questionnaire for each

added unit.

Parolee/ Probationer Coverage Improvement

Program and the Followup Program.

Objective—To improve coverage for a segment of the

population that may have been subject to a substantial

undercount which could affect the overall differential undercount.

Where It Occurred—All areas.

Procedure—Each State was given the opportunity to

participate in this program. If a State chose to participate,

it was asked to provide names of Department of Corrections contact persons. Parolee/ Probationer Information

Records were distributed and collected through the contact persons and mailed back to the census processing

offices. All Parolee/ Probationer Information Records were

processed through the Search/ Match operation. Because

of the low response rate from the original program, a

followup program was initiated using field staff to obtain

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administrative records of persons on parole or probation.

Parolee/ Probationer Information Records were completed

for parolees and probationers that had a verified Census

Day address. The Parolee/ Probationer Information Records

from the followup program were also processed through

the Search/ Match operation.

POP One Reenumeration

Objective—To reenumerate selected housing units which

were reported during Nonresponse Followup to contain

one-person households.

Where It Occurred—In 24 district offices with high proportions of one-person households enumerated in the

closeout phase of Nonresponse Followup. Also, a national

sample of 1,000 one-person households enumerated during Nonresponse Followup was selected and included in

the POP One Reenumeration.

Procedure—Trained enumerators who usually work on

the Census Bureau’s ongoing programs reinterviewed the

sample households during October 1990. In the 24 district

offices, the counts were changed to that found in the

reinterview. In the 1,000 case sample, the census counts

were not changed.

Postcensus Local Review

Objective—To provide local officials of all functioning

governments an opportunity to review housing unit counts

and group quarters population counts for their political

jurisdiction.

Where It Occurred—All areas.

Procedure—Listings of census counts of housing units

and group quarters population at the block level were

given to the local officials along with a set of maps showing

the current census blocks and statistical areas. The maps

also showed political boundaries as of January 1, 1990.

Local officials had 15 working days to review these counts

to identify and document discrepancies. These listings,

along with proper documentation, were returned to the

district office. The counts were compared, block by block,

to the Census Bureau counts and the differences calculated. For those discrepancies that could not be resolved

in the office, blocks were selected to be recanvassed

based on specific criteria.

Postmaster Return Delivery

Objective—To hand deliver questionnaires returned by

the USPS as undeliverable in order to increase mail

response and thus reduce the nonresponse followup workload.

Where It Occurred—TAR and Prelist mailout/ mailback

areas.

Procedure—Enumerators attempted to deliver census

questionnaires which had been returned by the USPS

letter carriers as undeliverable. This operation occurred in

April 1990, prior to Nonresponse Followup.

Precanvass

Objective—To update and correct the list of mailing

addresses purchased from a vendor by systematically

canvassing within an assigned address register area.

Where It Occurred—TAR areas.

Procedure—Eumerators systematically canvassed assigned

address register areas and conducted brief interviews to

verify residential address information and unit designations, to determine the existence of additional living quarters, and to ensure that each address was coded to the

correct address register area and census block. This

information was then used to update the pre-printed address

registers. Additions, deletions, and corrections were made

as needed. Census maps were also updated to reflect any

changes or errors in street features.

Precanvass Reconciliation and Yellow Card

Coding

Objective—To resolve geographic discrepancies between

the precanvass enumerators’ coding and the geographic

codes assigned by computer and to assign geographic

codes to uncoded addresses.

Where It Occurred—TAR areas.

Procedure—There were two types of cases assigned for

yellow card coding:

• Enumerators received yellow cards containing addresses

added during precanvass whose computer assigned

geographic location differed from that observed by a

precanvass enumerator. Each address was field checked

and assigned an address register area and block number to resolve the discrepancy. These cases are referred

to as Precanvass Reconciliation cases.

• Addresses from the original vendor lists and addresses

added during Advance Post Office Check I that could not

be computer geocoded were also printed on yellow

cards and sent to the district offices. Enumerators

attempted to locate each address and assign it to an

address register area and block.

Precensus Local Review

Objective—To provide local officials of functioning governments the opportunity to check preliminary housing unit

and special place counts for their political jurisdiction.

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Where It Occurred—TAR and Prelist mailout/ mailback

areas.

Procedure—Listings of housing unit and special place

counts were delivered to local officials for each block

within their jurisdiction. Local officials had approximately

45 working days to review these counts to identify and

document discrepancies. These listings, along with proper

documentation, were returned to the district office. The

counts were compared, block by block, to the Census

Bureau counts and the differences calculated. For those

discrepancies that could not be resolved in the office,

blocks were selected to be recanvassed based on specific

criteria.

Prelist

Objective—To create the initial census address list, primarily in suburban and rural areas, by identifying and listing

the addresses for all places where people live or could live

within a specified area and updating census maps to

indicate the physical location of each unit.

Where It Occurred—Prelist mailout/ mailback andupdate/ leave

areas.

Procedure—Enumerators canvassed an address register

area, one block at a time in a clockwise direction, conducting brief interviews and listing the residential addresses

and related information in blank address registers. The

enumerator also marked the housing unit locations on

census maps and entered map spot numbers on the map

and on the corresponding line on the address register

page.

Primary Selection Algorithm Review

Objective—To review data records for questionnaires

which were not selected to represent a given census

household when two or more first form questionnaires

were received for the same census identification number.

Where It Occurred—TAR and Prelist mailout/ mailback

and update/ leave areas.

Procedure—The identified cases were processed by sending microfilm copies of the selected and not selected

questionnaires through a modified Search/ Match operation to determine if the not selected persons were counted

in the census. They were added to the census if they were

not already counted. Also, in some cases, if the selected

persons were found to be counted more than once, the

duplicate enumerations were removed.

Puerto Rico Multiunit Coverage Improvement

Operation

Objective—To improve coverage of address listings in

large multiunit structures in Puerto Rico.

Where It Occurred—In municipios in the four district

offices comprising and surrounding San Juan.

Procedure—Census address listing books were clerically

matched to a mailing list of residential customers supplied

by the Puerto Rico electric company. If the number of units

at the basic street address on the electric company list was

greater than the number of units at the basic street

address on the census list, a unit-by-unit comparison was

done and any unit on the electric company list that was not

on the census list was field checked. If the unit existed on

April 1, 1990, the address was added and the enumerator

completed a census questionnaire for the housing unit.

Questionnaire Coverage Edits

Objective—To compare respondent supplied, office coded,

and computer interpreted data to determine cases which

needed to go to followup.

Where It Occurred—All areas.

Procedure—Coverage edits identified four types of coverage edit failures:

• Count Failures—The count edit was performed on all

types of questionnaires except short form enumerator

returns which did not fail the vacant-usual home elsewhere edit (see below). A questionnaire failed edit due to

a count discrepancy when the ‘‘For Census Use’’ Item A

value was not equal to the number of persons for which

two or more 100-percent questions (basic characteristics) had been answered.

• Question H1a/ H1b failures—This edit was limited to

mail return questionnaires. A questionnaire failed if the

H1a/ H1b question was answered ‘‘Yes’’ or the H1a/ H1b

question was answered ‘‘No’’ or was ‘‘Blank’’ and had a

write-in entry.

• Continuation form failures—The continuation form edit

was limited to mail return questionnaires. A mail return

questionnaire failed edit for coverage if the number of

persons for which two or more 100-percent questions

had been answered was seven and Item A was seven

with no continuation form.

• Vacant-Usual Home Elsewhere Failures—The vacantusual home elsewhere edit was performed on all types

of questionnaires (mail and enumerator). The questionnaire failed if the circle in question 1b was coded and/ or

an address was listed in question 1b which was different

from the mailing label, indicating that everyone at the

address usually lives somewhere else.

Recanvass

Objective—To improve coverage in areas where research

indicated some evidence of deficient housing unit counts.

Where It Occurred—All areas.

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Procedure—During Stage 1, enumerators visited targeted

areas to identify and list missing addresses. During Stage

2, enumerators determined if each housing unit added

during Stage 1 existed on Census Day. If it had, he or she

completed a census questionnaire. If not, the enumerator

deleted the listing from the address register.

Rural Update/ Leave

Objective—To update the prelisted address lists and to

deliver questionnaires in areas where the Census Bureau

anticipated problems with USPS delivery of the census

questionnaires.

Where It Occurred—Prelist update/ leave areas.

Procedure—Field staff verified and updated the initial

address lists, updated the maps, and hand-delivered preaddressed census questionnaires. For each housing unit

that was missing from the address lists, enumerators hand

addressed and delivered the appropriate type of questionnaire and updated the census map to indicate the physical

location of the unit.

Search/ Match

Objective—To improve coverage by adding and/ or enumerating persons at their correct address.

Where It Occurred—All areas.

Procedure—The Search/ Match operation was divided

into five parts: sorting the search forms, geocoding the

search forms, address control file browse, a USPS deliverability check, and searching for and matching addresses

and persons to those on the filmed census questionnaire.

Not all search forms went through all five processes. The

Search/ Match operation processed the following forms:

D-190 Search Records that included Whole Household

Usual Home Elsewhere cases and Mover-Usual Home

Elsewhere cases, Individual Census Reports, Military Census Reports, Shipboard Census Reports, Parolee/ Probationer

Information Records, and Were You Counted? forms.

Shelter and Street Night Enumeration

Objective—To improve coverage in the census by enumerating persons in shelters and at pre-identified street

locations.

Where It Occurred—All areas.

Procedure—Census enumerators counted persons and

collected data at pre-identified locations on March 20,

1990, and March 21, 1990, in two phases; the shelter

phase and the street phase. The shelter phase took place

on March 20, 1990, from 6:00 p.m. until midnight. It

covered enumeration of persons found in shelters, such as

emergency shelters, shelters for abused women, shelters

for runaway and neglected children, low cost motels

(costing $12.00 or less), subsidized units at motels, and

YMCA’s and YWCA’s pre-identified by local areas as

places where homeless persons stay. The street phase

covered enumeration of persons found at selected preidentified street locations, abandoned buildings, commerce

places such as bus depots and train stations, and other

places where homeless persons may spend the night,

such as all-night restaurants, parks, and vacant lots.

Enumerators collected data at street locations and commerce places on March 21, 1990, from 2:00 a.m. until 4:00

a.m. Persons leaving from abandoned buildings were

enumerated from 4:00 a.m. until 8:00 a.m. on March 21,

1990.

Special Place Prelist

Objective—To classify special places by type, verify the

geographic codes, and list the individual living quarters

associated with the special place.

Where It Occurred—All areas.

Procedure—Enumerators visited each pre-identified special place and met with the person in charge. The address

was verified and corrected, if necessary, and geographic

codes verified or assigned. The type of special place was

identified and the living quarters listed and classified as

housing units or institutional or noninstitutional group quarters.

Telephone Assistance Adds

Objective—To help respondents complete questionnaires

and, where appropriate, to send questionnaires to persons

who called to say they had not received one or to ensure

enumeration of these persons during followup operations.

Where It Occurred—All areas.

Procedure—Telephone Questionnaire Assistance was provided to help respondents complete their questionnaires.

During Telephone Questionnaire Assistance, there was a

larger than expected number of calls from persons reporting they had not received a census questionnaire. Procedures were implemented to ensure that addresses missing

from the census files were added and that the persons

living at these addresses were enumerated.

Transient Night Enumeration

Objective—To enumerate those persons staying in transient quarters, such as hotels, motels, tourist homes,

campgrounds, and marinas.

Where It Occurred—All areas.

Procedure—The day after Census Day, enumerators visited certain transient quarters and left an Individual Census

Report at each transient unit. Each respondent was asked

to fill out an Individual Census Report and mail it back to

the district office.

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Urban Update/ Enumerate

Objective—To improve coverage in pre-identified blocks

consisting almost entirely of boarded-up units.

Where It Occurred—TAR areas.

Procedure—Enumerators completed questionnaires for

occupied and vacant housing units. They annotated questionnaires for deleted or nonexistent units with a deletion

reason. For each unit not in the address register, the

enumerator added the address and completed a questionnaire for the household. Units in the Urban Update/ Enumerate

blocks were suppressed from all subsequent followup

operations.

Urban Update/ Leave

Objective—To improve coverage in pre-identified blocks

consisting almost entirely of public housing developments.

Where It Occurred—TAR areas.

Procedure—Enumerators hand delivered census questionnaires and updated lists using procedures similar to the

rural Update/ Leave procedures.

Vendor File

Objective—To purchase a commercial mailing list which

would serve as the initial address list in urban areas.

Where It Occurred—TAR areas.

Procedure—Addresses from commercial vendors were

used to develop an address list in areas where all of the

following conditions existed: a commercial address list

existed, city type mail delivery was provided by the USPS,

and the Census Bureau had the ability to assign geographic codes by computer.

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CHAPTER 2.

Address List Development

VENDOR FILE

Introduction and Background

In densely populated urban areas as well as areas

surrounding these central cities (TAR areas), the Census

Bureau purchased the initial inventory of addresses from a

commercial vendor. The commercial address list for each

urban area was selected through a competitive procurement process. To become a part of the census list, each

address had to be assigned to specific census geography.

This assignment, or ‘‘geocoding,’’ included computer, clerical, and field coding, as necessary. Addresses from the

commercial vendor were used in areas of the United

States where all of the following conditions were present:

• a commercial address list existed.

• city type mail delivery (that is, delivery to house number

and street name addresses) was provided by the USPS.

• the Census Bureau had the ability to assign geographic

codes by computer.

For these areas, the commercial address list served as

the basis of the census address file.

Methodology

The geocoding of the vendor addresses and the

APOC I operation occurred concurrently. The data for this

evaluation were supplied with the data which were requested

for the APOC I evaluation. The data were supplied prior to

Census Day 1990.

Limitations

Special procedures were used to obtain and geocode

vendor addresses for Hawaii. The results for Hawaii are

not included in this section. Therefore, this section represents the remaining 49 States and the District of Columbia.

Results

Only a portion of the vendor addresses could be used

as the initial list in TAR areas (see figure 2.1). A total of

69.3 million addresses were purchased in 1988 by the

Census Bureau. These addresses represented the entire

commercial vendor’s inventory. The Census Bureau identified ZIP Codes that contained areas which receive city

type mail delivery from the USPS. About 55.2 million

addresses were identified in these ZIP Codes. The remaining addresses were in ZIP Codes without USPS city type

mail delivery and therefore were not included in the TAR

address list. Within the selected ZIP Codes, about 94.6

percent of the addresses (52.2 million) were identified by

the Census Bureau as being complete city type addresses.

The remaining addresses (three million) were dropped

from the census address file because they were non-city

delivery, duplicate, or incomplete addresses. All addresses

in the selected ZIP Codes were sent to APOC I since the

geocoding of the vendor addresses and the APOC I

operation occurred concurrently.

After the geocoding, the census address file included

approximately 51.6 million addresses. The Census Bureau

was unable to geocode 0.6 million city type addresses.

These rates reflect only computer and clerical geocoding.

The rates do not include the field geocoding procedures

which were implemented prior to questionnaire delivery

(see section on Precanvass Reconciliation/ Yellow Card

Coding in this chapter). Computer and clerical geocoding

resulted in a national geocoding rate of 98.9 percent. The

State level geocoding rates ranged from 97.0 to 99.8

percent with a median value of 99.0 percent (see figure

2.2).

Conclusions

The initial address list development activities in TAR

areas included compilation and geocoding of addresses

from the commercial vendors. These activities resulted in

approximately 51.6 million addresses in TAR areas. The

final count of TAR addresses was approximately 56.9

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million address. The vendor lists contributed approximately

90.7 percent of the addresses. Therefore, the vendor lists

were responsible for the majority of the addresses in TAR

areas. In addition, the Census Bureau had a high geocoding rate at the State and national levels for the vendor list

addresses.

References

[1] Griffin, Deborah H., Joan M. Pausche, Emilda B. Rivers,

Amy L. Tillman, and James B. Treat. 1990 Decennial

Census Preliminary Research and Evaluation Memorandum No. 33, ‘‘Preliminary Results—Improving the Coverage of Addresses in the 1990 Census.’’ U.S. Department

of Commerce, Bureau of the Census. December 3, 1990.

PRELIST MAILOUT/ MAILBACK OPERATION

Introduction and Background

The Prelist Mailout/ Mailback operation was the first

address compilation operation conducted to obtain address

data for small cities and suburban and some rural areas for

the 1990 census. The objectives of the Prelist were:

• To obtain a complete and accurate mailing address for

each occupied or vacant living quarters and special

place within the prelist areas.

• To record the physical location description and householder name for living quarters that do not have house

number and street name mailing addresses.

• To annotate census maps to show the location of all

living quarters and to identify features and feature name

changes and/ or updates.

• To assign each living quarters to its correct 1990 census

geography.

The Census Bureau conducted the Prelist in Mailout/

Mailback areas in smaller cities and suburban and rural

areas with a population density of approximately 50 persons per square mile or greater. This was accomplished in

four waves, distributed regionally throughout the country,

during the period of February through August 1988.

Methodology

The evaluation results of the Prelist were obtained by

examining a sample of Prelist Address Registers and by

reviewing numerous observation reports, debriefing questionnaires, and the prelist data file summaries.

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Results

There were a total of 2,271,462 blocks canvassed

during the Prelist operation in mailout/ mailback areas. Of

these blocks, 1,692,835 (75 percent) contained living

quarters, and 578,627 (25 percent) contained no living

quarters. In the blocks containing living quarters, the prelist

enumerators listed a total of 27,895,927 living quarters.

The prelisted addresses may be classified into the following address types: 1) Seventy-six percent of the total units

listed were city delivery addresses with house number and

street name; 2) Thirteen percent of the units listed were

rural route and/ or box number addresses, with an available road name and householder name; 3) Five percent of

the addresses were Post Office (PO) boxes with road

name and householder name. 4) Less than 1 percent of

the addresses were General Delivery, Star Route, or

Highway Contract Routes; 5) Five percent of the units

listed had incomplete or partial addresses, most of these

listings had just road name and/ or physical location descriptions; and 6) Less than 1 percent of the total units listed

were for Special Places and clusters. [1].

Of the 27.9 million prelisted addresses, 5.5 percent

(1,544,446) had some combination of characteristics that

made them nonmailable. For example, rural route without

box number or invalid ZIP Codes. These cases were

assigned for reconciliation during later field operations, at

which time the Census Bureau attempted to obtain a

mailable address.

There were a total of 7,850 clusters (0.1 percent of total

listings) listed as a result of buildings or roads that were

found to be inaccessible, which made it impossible to

determine the exact number of living quarters at the

addresses. These clusters were assigned for listing during

the delivery phase of the operation.

A total of 83,890 Special Places (0.3 percent of total

listings) were identified during the Prelist operation. The

prelisters were provided with a pre-printed listing of known

Special Places within their assignment areas. The prelisters then dependently verified and updated the pre-printed

listing. The occupants at these special places were included

in the Group Quarters Enumeration for the 1990 census.

For Rural Route, PO Box, General Delivery, Star Route,

and Highway Contract Routes, the householder name was

obtainable approximately 90 percent of the time. However,

for ‘‘Other’’ address types, householder name could only

be obtained 22 percent of the time.

Approximately 11 percent (3,158,585) of the prelist units

were vacant. Of the vacant units, approximately 25 percent

were for ‘‘Other’’ address types.

Even though ‘‘Other’’ address types were undefined,

approximately 91 percent of the time a physical location

description was obtained which would allow an enumerator

to conduct followup operations.

The Prelist Mailout/ Mailback operation was conducted

nationwide. The total addresses prelisted were divided

among 13 regional census centers. See figure 2.3.

Following are the national and regional census center

breakdowns of the major prelist address types: 1) Nationally, urban addresses accounted for approximately 76

percent of all units. The regional census centers ranged

from less than 1 percent in New York to 13 percent in

Detroit and Atlanta; 2) Nationally, rural route addresses

2accounted for approximately 13 percent of all units and

ranged from less than 1 percent in Los Angeles and New

York to 21 percent in Chicago; 3) Nationally, PO Box

addresses accounted for approximately 6 percent of all

units. The Boston and Chicago Regional Census Centers

contained the highest percents of PO Box addresses (13

and 15 percent, respectively); and 4) Nationally, ‘‘Other’’

addresses accounted for approximately 5 percent of all

units and ranged from 1 percent in Los Angeles to 25

percent in Boston.

Conclusions

While the Prelist Mailout/ Mailback operation successfully obtained addresses for over 27 million living quarters,

there are several aspects that need to be re-examined for

the 2000 census.

For future censuses, if a Prelist operation is to be used,

the Census Bureau needs to ensure that it can be accomplished within a realistic time frame. For example, the

Census Bureau may need to extend the field collection

phases beyond 6 weeks and build in flexibility for unexpected situations and system failures so that time allotments stay reasonable and realistic.

The Census Bureau needs to establish better channels

of communication with the U.S. Postal Service. There were

occurrences where the local Post Office refused to cooperate with Census Bureau personnel; this contributed to

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prelist addresses being classified as undeliverables. Methodology should be explored to encourage the postal

system to convert rural style addresses to city type and to

systematically provide address information for persons

who maintain Post Office boxes.

In several aspects, the Prelist Mailout/ Mailback was an

operational improvement over Test Census and Dress

Rehearsal Prelist operations. The crew leaders, advance

listers, and enumerators received extensive training on

map reading and more practice exercises, although observation reports suggest that more detailed training is required

for map orientation and interpretation and the use of

multiple map sheets and the reading of map scales.

Reference

[1] Sledge, George. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 38, ‘‘Evaluation of the 1988 Prelist Operation.’’ U.S. Department of

Commerce, Bureau of the Census. August 12, 1992.

PRELIST UPDATE/ LEAVE OPERATION

Introduction and Background

The Prelist Update/ Leave operation was performed to

create an address list in areas where Census Bureau

delivery of census questionnaires would be done. This

delivery methodology, called update/ leave, was used in

rural areas in the south and midwest where the Census

Bureau had reason to believe that there would be problems associated with developing an accurate mailing list

and the USPS would have problems delivering the census

questionnaires.

The objectives of the Prelist Update/ Leave operation

were:

• To obtain a complete and accurate address, or adequate location description if an address was not available, for each occupied or vacant living quarters and

special place within the Prelist areas.

• To annotate census maps to show the location of all

living quarters and to identify map features and feature

name changes and/ or updates. This aspect was important so that census enumerators could locate the units

during the Delivery operation.

• To assign each living quarter to its correct 1990 census

geography.

The Prelist in update/ leave areas was conducted from

June through September 1989, primarily in the rural south

and in the midwest. The Census Bureau conducted the

Prelist in areas with anticipated postal delivery problems

and a population density of approximately 50 persons per

square mile or less. The goal of the Prelist was to list a

projected 11 million addresses, that would be enumerated

using the Update/ Leave method. The Update/ Leave methodology called for Census Bureau personnel, using address

registers and the annotated census maps, to deliver the

questionnaires and update the listing of addresses. The

households were responsible for mailing back the questionnaires.

Methodology

The evaluation results of the Prelist Update/ Leave were

obtained by examining a sample of Prelist Address Registers and by reviewing numerous observation reports, debriefing, questionnaires, and the prelist data file summaries.

Results

There were a total of 1,364,835 blocks canvassed

during the Prelist operation. Of these blocks, 927,995, (68

percent) contained living quarters, and 436,840 (32 percent) contained no living quarters. In the blocks containing

living quarters, the prelist enumerators listed a total of

10,157,368 living quarters. The prelisted housing units

were classified into the following address types: 1) Approximately 31 percent of the total units listed were ‘‘urban,’’

defined as house number and street name addresses; 2)

Approximately 31 percent of the units listed were ‘‘rural

route,’’ with an available road name. About 89.5 percent of

these had an available householder name; 3) Approximately 9 percent of the addresses were ‘‘PO Box’’ type,

with road name and householder name available; 4) About

0.7 percent of the addresses were General Delivery, and

approximately 0.4 percent were Star Route; 5) About 1

percent of the addresses were Highway Contract Routes;

6) Approximately 26 percent of the units listed had Other

Types of addresses. Most of these listings had only road

name and/ or physical location descriptions; 7) Less than 1

percent of the total listings were for Clusters (0.1 percent)

and Special Places (0.3 percent). [1]

Twenty-four percent of the prelist addresses (2,418,528)

were missing some combination of characteristics; for

example, rural route without box number, or no street

name, householder name and/ or physical location description. Thirty-one percent (742,426) of these addresses were

vacant.

There were a total of 5,704 clusters (0.1 percent of total

listings) listed as a result of roads or buildings that were

found to be inaccessible, which made it impossible to

determine the exact number of living quarters at the

addresses. These clusters were assigned for listing during

the delivery phase of the operation.

A total of 29,034 Special Places (0.3 percent of total

listings) were identified during the Prelist operation. The

prelisters were provided with a pre-printed listing of known

Special Places within their assignment areas. The prelisters then dependently verified and updated the preprinted

listing. The occupants at these Special Places were included

in the Group Quarters operation for the 1990 census.

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For Rural Route, PO Box, General Delivery, Star Route,

and Highway Contract Routes, the householder name was

obtainable approximately 91 percent of the time. However,

for ‘‘Other’’ address types, householder name could only

be obtained 15 percent of the time.

Approximately 10 percent (1,001,696) of the prelist units

were vacant. Of the vacant units, approximately 72 percent

were for ‘‘Other’’ address types. Even though ‘‘Other’’

address types were undefined, approximately 98 percent

of the time a physical location description was obtained

which would allow an enumerator to deliver a mailing

piece.

The Prelist operation was conducted nationwide. The

total prelisted addresses were divided among 7 of the 13

regional census centers. See figure 2.4.

Following are the national and regional census center

breakdowns of the major prelist address types: 1) Nationally, urban addresses accounted for approximately 31

percent of all units. The regional census centers ranged

from 22 percent urban addresses in Detroit to 33 percent

in Charlotte; 2) Nationally, rural route addresses accounted

for approximately 32 percent of all units, with a range from

29 percent in Dallas to 35 percent in Denver; 3) Nationally,

PO Box addresses accounted for approximately 9 percent

of all units; the Detroit and Denver Regional Census

Centers contained the highest percentage of PO Box

addresses (17 and 20 percent, respectively); and 4) Nationally, ‘‘Other’’ address types accounted for approximately

26 percent of all units; the distribution was consistent

across all regional census centers.

Conclusions

The Prelist operation successfully obtained addresses

for over 10 million living quarters in update/ leave areas.

Among other operational issues, the Census Bureau

needs to redefine and re-examine Address Register Area

configurations based on housing unit counts, distances,

terrains, and climates. The Address Register Area configurations, in general, were too large. One solution might be

a flexible decentralized address file that can redefine

problem Address Register Areas in the field on an as

needed basis.

References

[1] Sledge, George. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 72, ‘‘Evaluation of the 1989 Prelist Operation.’’ U.S. Department of

Commerce, Bureau of the Census. September 24, 1991.

[2] Sledge, George. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 38, ‘‘Evaluation of the 1988 Prelist Operation.’’ U.S. Department of

Commerce, Bureau of the Census. August 12, 1992.

ADVANCE POST OFFICE CHECK I

Introduction and Background

This section documents results from the analysis of the

1990 APOC I operation that was conducted for the 1990

census. APOC I was an address list compilation coverage

improvement program conducted by the USPS in late

summer and early fall, 1988. The purpose of APOC I was

to verify and update the address lists the Census Bureau

purchased from vendors (vendor lists) for TAR areas. The

vendor list addresses were printed on cards, and during

APOC I, USPS letter carriers cased (sorted) these cards

into slots for each address on their route. The USPS

carriers were instructed to make any necessary corrections to the addresses and to identify duplicate and

undeliverable addresses. If a card was not provided for an

address on their route, the carrier completed a blue card

for the missing address (see appendix B for a copy of the

blue card). The Census Bureau had to assign these

address adds to the correct geography (a process known

as geocoding). Any address that could not be geocoded

was not added to the census address files. The geocoded

APOC I address adds represent the coverage gain from

this operation.

The APOC I Suppression Study was designed for the

evaluation of the APOC I operation. The purpose of the

APOC I Suppression Study was to estimate the ability of

the USPS to add residential addresses which were missing. The evaluation involved suppressing a sample of

addresses from the APOC I operation and determining

whether the USPS returned these addresses as adds.

Methodology

Status of Vendor List Addresses Sent to APOC I—The

national level status (deliverable as addressed, deliverable

with correction and undeliverable) of vendor addresses

sent to the APOC I operation were tallied from the Census

Bureau’s Address Control File in September, 1988.

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APOC I Housing Units Adds—The APOC I housing unit

adds and the demographic characteristics of the persons

enumerated at the APOC I housing unit adds were identified from various census computer files. The APOC I

housing unit adds were assigned a code that reflected that

the source of the address was the APOC I operation. All

APOC I housing unit adds in two district offices in Hawaii

were excluded from this analysis. Because of difficulties in

obtaining a vendor list that was on a computer file in the

areas in these two district offices prior to the census, the

Census Bureau purchased an address list from a vendor

that was supplied on printed address cards. Special procedures to process these vendor addresses included

giving all of these vendor addresses the code that reflected

an APOC I add (although the source of all of the Hawaii

addresses was not APOC I). Therefore, the true APOC I

housing unit adds cannot be distinguished from the purchased vendor list addresses in these two Hawaii district

offices.

APOC I Suppression Study—A two-stage systematic

sample design was used to select a sample of addresses

from the original address list for TAR areas. All Post Office

box addresses were excluded from the sample selection.

The first stage sampling rate was .0079. The second stage

sampling rate was .0052, and the overall sampling rate

was .000041 [3].

Following APOC I, the suppressed addresses were

clerically matched to the address add cards (geocoded

and ungeocoded) for the sample ZIP Codes to determine

which of the suppressed addresses were added during

APOC I. After matching, the next step was to determine

which of the suppressed addresses that did not match to

an address add card were legitimate residential addresses

and therefore should have been added during APOC I. The

final census status listed on the address control file was

the means by which this was determined. If the status for

a given address was ‘‘accept,’’ it was assumed the address

was a legitimate residence at the time APOC I was

conducted. A suppressed address was not considered to

be a legitimate residential address if the status was ‘‘field

delete’’ or if the address was not found on the address

control file.

Limitations

APOC I Housing Unit Adds—The number of APOC I

housing unit adds reflect the final address control file

housing units added in APOC I. That is, some housing units

that may have been added to the address control file

during APOC I may have subsequently been removed from

the file due to the various address list and coverage

improvement programs and are not reflected in the final

census counts.

Preliminary results from APOC I based on September

1988 address control file data reported that nationally

there were 1,622,625 APOC I housing unit adds, excluding

Hawaii [1]. Final results from the address control file show

approximately 136,000 fewer APOC I housing unit adds.

This difference is mostly due to the time period of about 5

years since the initial APOC I data were tabulated, and as

was previously mentioned, due to various precensus and

postcensus activities.

Lastly, as previously mentioned, APOC I housing unit

adds could not be determined for two district offices in

Hawaii.

APOC I Suppression Study—The estimates obtained

from this study were from a small sample of census

addresses. As such, they are subject to sampling error and

variability.

Results

Status of Vendor List Addresses Sent to APOC I—

Table 2.1 documents the status of the vendor addresses

sent to the APOC I operation. The addresses were classified by the USPS as deliverable as addressed, deliverable

with correction, or undeliverable (undeliverable also included

any address that had a house number correction, and

duplicate addresses). The USPS could correct street name,

unit designation and ZIP Code. Note that the correction

categories (street name, unit designation, etc.) are hierarchical as shown, so that each corrected address was

included in only one category. Thus, ‘‘street name’’ correction represents addresses that had a corrected street

name and possibly other corrected address fields. Unit

designation corrections refer to addresses that had no

change to street name, but had a corrected unit designation, and possibly other corrected fields.

Approximately 95.9 percent of the 55 million vendor

addresses sent to the APOC I operation were classified by

the USPS as ‘‘deliverable as addressed.’’ About 2.1 percent of the vendor addresses received an ‘‘undeliverable’’

flag on the address control file as a result of the APOC I

operation. The remaining 2 percent of the vendor addresses

were classified by the USPS as ‘‘deliverable with correction.’’ The majority of these corrections were to the street

name field. The street name corrections were tallied but

not data captured to ensure that precanvass address

registers and precanvass maps agreed on street name.

Table 2.1. Status of Vendor Addresses Sent to the

APOC I Operation: National Level

APOC I status Total addresses

sent to APOC I Percent

Total vendor addresses . . . . . . . . . . . . 55,135,935 100.0

Deliverable as addressed . . . . . . . . . . . 52,875,283 95.9

Total undeliverable . . . . . . . . . . . . . . . . 1,165,502 2.1

Undeliverable ................... 850,162 1.5

House number correction . . . . . . . . . 93,037 0.2

Duplicate . . . . . . . . . . . . . . . . . . . . . . . 222,303 0.4

Total deliverable with correction . . . . 1,095,150 2.0

Street name. . . . . . . . . . . . . . . . . . . . . 582,616 1.1

Unit designation . . . . . . . . . . . . . . . . . 358,361 0.6

ZIP Code . . . . . . . . . . . . . . . . . . . . . . . 154,173 0.3

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APOC I Housing Unit Adds—There were 1,486,645 APOC

I housing unit adds. Using address control file summary

TAR housing unit counts dated November 9, 1989, these

APOC I housing unit adds represent approximately 2.6

percent of all TAR housing units on the address control file

as of that date.

About 91 percent of the APOC I housing unit adds, or

1,353,251 housing units adds, had a final status of occupied or vacant unit, and the balance of 9 percent, or

133,394 housing units, were deleted units.

Figure 2.5 displays the distribution of the type of structure of the APOC I housing unit adds. Note that this

distribution is only applicable to housing unit adds that

were occupied or vacant units (1,353,251 APOC I adds).

A majority of the housing unit adds were within multiunit

structures (61.1 percent). According to 1990 census data,

only about 27.4 percent of all housing units nationally were

multiunits. Therefore, units in multiunit structures were

overrepresented in the APOC I housing unit add population

by almost 34 percent. Note that there is a higher rate of

multiunit structures in TAR areas, which could explain

some of this overrepresentation. However, this does indicate that APOC I did a good job increasing coverage of

units in multiunit structures in TAR areas.

Single family units, including one family detached and

one family attached units, comprised about 34.5 percent of

the APOC I housing unit adds. Mobile homes and ‘‘other’’

type units together represented about 4.4 percent of the

APOC I housing unit adds.

The majority of the APOC I housing unit adds had a

tenure of rented (55.9 percent). This seems to correspond

with the majority of these units also being in multiunit

structures. The next largest proportion of housing unit

adds had a tenure of owned (32.9 percent), and for the

balance (11.2 percent), the tenure was unknown. Tenure is

only applicable to the housing unit adds that were occupied units.

The next table presents coverage gain within a basic

street address. Within basic street address coverage occurs

when multiple units are added within the same basic street

address. For example, 50 apartments can be added to the

address 101 Main Street; the basic street address is 101

Main Street. The within basic street address coverage is

presented by regional census center for all APOC I housing unit adds.

Table 2.2 displays the within basic street address coverage where more than 50 units were added during APOC

I at the same basic street address. This table shows, by

regional census center, the total number of APOC I adds,

the number of these APOC I adds where more than 50

units were added at the same basic street address, and the

percentage of the regional census center’s APOC I adds

that were at a basic street address where more than 50

units were added. The order of the regional census centers

is by frequency of the number of housing unit adds where

more than 50 units were added at the same basic street

address.

The Los Angeles Regional Census Center had the most

APOC I adds where more than 50 units were added at the

same basic street address (39,054 APOC I housing unit

adds). This within basic street address coverage gain

represented 18.5 percent of the Los Angeles Regional

Center’s APOC adds. Note that the Los Angeles Regional

Center also had the most APOC adds—210,534 housing

units. But notice that it is not always true that the regional

census centers with higher numbers of APOC I housing

unit adds also had the best within basic street address

coverage gain percentage. For example, the Denver Regional

Census Center had the best within basic street address

coverage gain percentage where more than 50 units were

added at the same basic street address with 28.7 percent,

yet had only 75,182 total APOC I housing unit adds (which

is the third lowest number of APOC I adds by regional

census center). This trend is also true for the Seattle and

the San Francisco Regional Census Centers; that is, a

smaller number of total APOC I housing unit adds, yet a

higher percentage of within basic street address coverage

Table 2.2 APOC Adds Within Basic Street Address

Coverage Where More Than 50 Units Were

Added at the Same Basic Street Address—

Regional Census Center Level

Regional census

center (RCC)

APOC adds:

greater than 50

at same basic

street address

(BSA)

Total APOC

adds

Percent of RCC’s

APOC adds that

were greater

than 50 at

same BSA

Los Angeles . . . 39,054 210,534 18.5

Atlanta . . . . . . . . 23,609 122,796 19.2

Boston . . . . . . . . 23,433 184,628 12.7

Dallas. . . . . . . . . 21,721 105,979 20.5

Denver . . . . . . . . 21,545 75,182 28.7

New York . . . . . 19,370 109,975 17.6

Philadelphia . . . 17,464 147,551 11.8

Seattle . . . . . . . . 17,393 76,867 22.6

Kansas City. . . . 16,793 86,530 19.4

Chicago . . . . . . . 15,759 134,988 11.7

San Francisco . 15,475 73,386 21.1

Charlotte . . . . . . 15,392 88,042 17.5

Detroit . . . . . . . . 7,502 70,187 10.7

National . . . 254,510 1,486,645 17.1

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gain where more than 50 units were added at the same

basic street address. This may indicate poor vendor list

coverage of large multiunits in some areas of the western

United States (Los Angeles, Seattle, San Francisco and

Denver).

Some possible explanations for these areas having the

large numbers of adds where more than 50 units were

added at the same basic street address are:

1. High growth.

2. Unusual addressing schemes.

3. New ZIP Codes initiated between 1988-1990.

4. A large volume of multiunits in some of these areas.

5. A changing of unit designations within basic street

addresss (for example, from Apartments 1, 2 and 3 to

Apartments 1-A, 1-B and 1-C).

6. Lack of unit designations on addresses supplied by

vendors.

These are all possible reasons why some areas would

have high numbers of large multiunit APOC I adds within

the same building. It is likely many of these circumstances

worked together, which caused initial poor coverage of

large multiunits, but which APOC I helped to correct.

This next section discusses the demographics of the

persons enumerated in the housing units added during the

APOC I operation. The demographic characteristics discussed are sex, age, race, and Hispanic origin. These

demographics of the persons enumerated at APOC I

housing unit adds are compared to national 1990 census

demographic distributions. The purpose of this comparison

is to see whether persons enumerated as a result of a

coverage program such as APOC I are demographically

similar to the general population, or if the persons enumerated at APOC I housing unit adds are more similar to

hard-to-enumerate subgroups.

There were 2,864,387 persons enumerated at the APOC

I housing unit adds. The sex distribution of the persons

enumerated at APOC I housing unit adds was almost

identical to the 1990 national sex distribution from the

1990 census (apporximately 49 percent male and 51

percent female).

A review of the age distribution of persons enumerated

in APOC I housing unit adds compared to the national age

distribution from 1990 census data showed that persons in

the age groups 20-29 years old and 30-44 years old were

overrepresented in the APOC I population. This overrepresentation of age groups 20-29 and 30-44 years old may

be a reflection of the large number of multiunits that

comprised the APOC I adds. That is, there are mostly

renters in multiunits, and it is likely that many renters are in

the age ranges of 20-29 and 30-44 years old.

An examination of the race distribution of persons

enumerated in APOC I housing unit adds compared to

1990 census data showed that ‘‘other’’ race persons

represented about 14.2 percent of all APOC I persons,

whereas ‘‘other’’ race persons were only 3.8 percent of the

national 1990 population. This represents a difference of

over 10 percentage points. The same trend, although not

quite as large a difference, was shown in the distribution of

Black persons. Persons enumerated in APOC I housing

unit adds were 17.7 percent Black, whereas Black persons

represented about 12.3 percent of the national 1990

population; this is a little more than 5 percentage points

difference. Lastly, due to the overrepresentation of Black

and ‘‘other’’ race persons, White persons were underrepresented in the APOC I population compared to the

national 1990 population (68.1 percent versus 83.9 percent, respectively).

Approximately 15.7 percent of the persons enumerated

in APOC I housing unit adds were Hispanic, compared to

only 9 percent of the general population in 1990. Thus, the

same trend is evident here as for the race distribution; that

is, minority persons were overrepresented in the APOC I

population.

APOC I Suppression Study—Table 2.3 summarizes the

estimated add rates for missing addresses for the USPS.

Included are the results for single unit addresses and

multiunit addresses.

The estimated overall add rate of the APOC I was 62.6

percent with a standard error of 2.7 percent. The USPS

added approximately 66.0 percent of the missing single

unit addresses compared to 54.6 percent of the missing

addresses within multiunit structures. Contrary to the findings from the analysis of the APOC I housing unit adds

previously discussed, this suggests that the USPS was

more likely to add missing single unit addresses than

missing multiunit addresses.

Figure 2.6 shows the estimated add rate for ZIPCodes/ Post

Offices grouped according to the number of TAR addresses

located in the ZIP Code. It appears that the add rate is not

related to Post Office size. There was speculation that

larger Post Offices may have had greater difficulty administering the APOC I operation, but the results indicate that

this was not true.

Conclusions

Status of Vendor Addresses Sent to APOC I—The 1990

APOC I operation was an effective method of verifying the

coverage and accuracy of the purchased vendor list addresses.

In addition, the USPS was able to locate a large proportion

of the vendor list addresses, both deliverable as addressed

and deliverable with corrections.

Table 2.3. Estimated Add Rates from the APOC I

Suppression Study

Description Add rate (percent) Standard error (percent)

Single units . . . . . . . . . . 66.0 2.7

Multiunits. . . . . . . . . . . . 54.6 5.0

Overall . . . . . . . . . . . . . . 62.6 2.7

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APOC I Housing Unit Adds—APOC I increased the

number of TAR housing units on the address control file by

approximately 2.6 percent. The majority of these housing

unit adds were in multiunit structures. Thus, this operation

was successful in increasing coverage in multiunits. Since

multiunits continue to be difficult structures to enumerate,

and enumeration errors and delivery errors are more likely

to occur in multiunits (as was shown by the evaluation of

the Primary Selection Algorithm Review in chapter 4), the

Census Bureau should continue to update its address list

utilizing the USPS during the APOC operation.

It also was shown that within basic street address

coverage was improved by the APOC I operation. This is

another reason to recommend the continuation of the

APOC operation. Finally, a review of the demographic

characteristics of the persons enumerated in APOC I

housing unit adds showed that minorities were enumerated

at a greater rate than their representation in the total 1990

population.

APOC I Suppression Study—The estimated USPS add

rate for missing addresses was significantly lower than

expected given the additional procedures, training, and

quality assurance. The addresses that were not added

could have had significant coverage implications for the

decennial census. Because the APOC I operation did not

add these addresses, the Census Bureau had to rely on

subsequent coverage improvement programs, such as

precanvass and casing, to do so.

The quality assurance plan for the APOC I operation

should include a procedure similar to this evaluation (that

is, address suppression) in order to monitor the adding of

missing addresses on an individual carrier basis.

References

[1] Tillman, Amy. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 24, ‘‘Preliminary 1990 Advanced Post Office Check I and Vendor File

Geocoding Results.’’ U.S. Department of Commerce, Bureau

of the Census. May 8, 1990.

[2] Wajer, Susan C. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 244, ‘‘ Coverage Gain from the 1990 Advanced Post Office Check I

Operation.’’ U.S. Department of Commerce, Bureau of the

Census. August 6, 1993.

[3] Wurdeman, Kent. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 161, ‘‘Evaluation of the 1990 Advanced Post Office Check for Tape

Address Register Areas (APOC I).’’ U.S. Department of

Commerce, Bureau of the Census. July 22, 1992.

ADVANCE POST OFFICE CHECK II AND III

Introduction and Background

In 1989, the USPS conducted the APOC as part of the

1990 census coverage improvement program. The APOC

was the initial coverage check on the list of addresses

purchased from vendors or listed in the Prelist Mailout/ Mailback

areas. The APOC in urban areas, or TAR areas, was

known as APOC I. The APOC II and III operations were

conducted in Prelist Mailout/ Mailback areas.

The purpose of the APOC II/ III was to have the USPS

review the addresses listed in the Prelist Mailout/ Mailback

areas in preparation for the 1990 census. The postal

carriers were instructed to check the deliverability of each

prelist address, identify any duplicate addresses, and

complete a form D-702, Report of Missing Address (blue

card), for any residential address on their route that was

missing from the prelist file (See appendix B for an

illustration of the form). Although special places such as

hospitals and marinas were excluded from APOC II/ III,

blue cards completed for special places were keyed into

the special place file. In addition, the USPS was instructed

to make necessary corrections to improve the deliverability

of the addresses obtained during prelist.

Incomplete addresses that were classified as undeliverable during prelist processing (called known undeliverables) were withheld from the APOC II/ III. Also, addresses

in ZIP Codes that covered both TAR and prelist areas (split

ZIP Codes) were excluded from APOC II/ III but the city

delivery portions of these split ZIP Codes were included in

the APOC I.

Following the APOC II/ III, APOC Reconciliation was

conducted in the field to reconcile blue cards, undeliverables, and duplicates and to resolve clusters; that is, living

quarters not accessible at the time of Prelist due to

situations such as washed out roads or locked gates.

Originally, the Census Bureau planned to include the

entire prelist area in one national APOC. However, since

the Prelist operation for some parts of the country was

completed later than for other areas, it was decided to

conduct APOC in two phases, hence, APOC II and APOC

III. Selected Prelist Mailout/ Mailback areas were included

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in the first phase, APOC II, in February 1989 and field

reconciled in June 1989. Addresses for the remaining

Prelist Mailout/ Mailback areas were reviewed during APOC

III in April 1989 and reconciled in August 1989.

In this section, the results for APOC II and APOC III are

combined and referred to as the APOC, since no substantial differences exist between the two phases.

Methodology

Since the blue cards were not counted prior to delivery

to the district offices, a sample was taken to obtain

estimated totals and estimated proportions of blue cards

for the APOC Reconciliation status categories. A multiplestart systematic sample of 30 district offices was taken

from 151 district offices containing APOC II workloads. For

the APOC III workloads, a multiple-start systematic sample

of 30 district offices was taken from 181 district offices.

Limitations

The final status of APOC adds from blue cards is not

available for this publication.

Data for blue cards are presented as estimates from a

sample of the cards and are subject to sampling error and

variability.

Results

Table 2.4. shows the results of the APOC prior to field

reconciliation.

Deliverables—As shown in table 2.4., 20,058,057 (72.1

percent) of the prelist addresses were sent to the USPS for

casing. Of these, 15,805,103 (78.8 percent) were classified

as ‘‘deliverable as addressed.’’

The USPS carriers made acceptable corrections to

825,696 (4.1 percent) of the addresses sent to the APOC.

Carriers corrected address items such as unit designations; however, they were instructed to classify addresses

with incorrect house numbers as undeliverable.

During APOC, carriers corrected street name more

frequently than other address items. Street name corrections refer to street name suffix changes, such as ‘‘Ave.’’

to ‘‘St.,’’ as well as rural route or box number corrections.

Householder name was the second most frequently corrected address item.

The Census Bureau determined that some corrections

made by the carriers during the APOC required field

verification. These ‘‘unacceptable corrections’’ were identified during processing. The unacceptable corrections

were not changed in the file but were flagged for APOC

Reconciliation as undeliverable. About 4.3 percent of the

addresses sent to APOC had these types of corrections.

Unacceptable corrections included changes made to house

numbers and all PO Box corrections. For city delivery

addresses, corrections to basic street names were considered unacceptable.

Undeliverables and Duplicates—Of the addresses reviewed by the USPS during APOC, 2,025,338 (10.1 percent) were classified as undeliverable. The carriers were

instructed to place an address in the undeliverable stack if

it was not recognizable for mail delivery, the house number

was incorrect or the structure was not on their route. In

addition, approximately 2.7 percent of the addresses reviewed

were considered duplicates of another address already

cased. The undeliverable and duplicate addresses were

assigned for APOC Reconciliation; these categories were

indistinguishable to the enumerators.

Addresses Withheld from APOC—Of the addresses

coming out of prelist, 7,749,815 (27.9 percent) were withheld from the APOC. Approximately 80.1 percent of these

were withheld because they were in split ZIP Codes.

Although these addresses were withheld from APOC II/ III,

the city delivery portions were included in APOC I. These

addresses were not included in the APOC Reconciliation

workload.

The decision to withhold the split ZIP Codes from the

prelist APOC was based on anticipated operational problems and additional costs associated with including split

ZIP Codes in both the TAR and prelist APOCs. The Census

Bureau concluded that performing both APOCs on the split

ZIP Codes potentially would have resulted in substantial

control problems, especially in growth areas with shifting

delivery route boundaries, and duplicate adds. The inclusion of split ZIP Codes in APOC II/ III, as well as APOC I,

would have resulted in an estimated additional cost of $1.4

million.

Approximately 19.9 percent (1.5 million) of the addresses

withheld from APOC did not contain enough address

information to be cased by the carriers. Based on prelist

data, 83.5 percent of the known undeliverables did not

contain a householder name where it was necessary for

rural route delivery, 15.3 percent did not have box numbers, and the remaining 1.2 percent did not have street

names or rural route numbers.

Table 2.4. APOC Results

Address type Volume Percent

Prelist addresses . . . . . . . . . . . . . . . 27,807,872 100.0

Sent to APOC . . . . . . . . . . . . . . . . 20,058,057 100.0

(72.1 percent of total)

Deliverable as addressed . . . . 15,805,103 78.8

Deliverable with corrections . . 825,696 4.1

Unacceptable corrections. . . . 863,996 4.3

Undeliverables . . . . . . . . . . . . . 2,025,338 10.1

Duplicates . . . . . . . . . . . . . . . . . 537,924 2.7

Not sent to APOC . . . . . . . . . . . . 7,749,815 100.0

(27.9 percent of total)

Units in split ZIP Codes. . . . . . 6,210,978 80.1

Known undeliverables . . . . . . . 1,538,837 19.9

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In addition, 5,634 clusters were withheld from APOC.

These are not included in the total Prelist volume in table

2.4.

Blue Cards—The USPS carriers completed an estimated

1,457,351 blue cards (standard error= 2667) during APOC

IIand an estimated 1,412,169 blue cards (standard error= 4630)

for APOC III based on sample data. These cards were sent

to the appropriate district offices and assigned for APOC

Reconciliation. An estimated 21 percent of the total blue

cards (standard error= 2.64) were classified as adds during APOC Reconciliation. Note that this does not include

individual added units on blue cards for multiunit addresses.

Conclusions

Of the prelist addresses sent to APOC, about 83

percent were deliverable with or without corrections. The

APOC carriers corrected 8.4 percent of the addresses sent

to APOC (including unacceptable corrections), classified

10.1 percent as undeliverable and considered 2.7 percent

as duplicates. Overall, the APOC operation made a valuable contribution to updating the list of addresses obtained

during the Mailout/ Mailback Prelist.

References

[1] Pausche, Joan M. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 37, ‘‘Evaluation of the APOC II/ III and APOC Reconciliation in the

1990 Decennial Census.’’ U.S. Department of Commerce,

Bureau of the Census. February 14,1991.

[2] Pausche, Joan M. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 88, ‘‘Prelist

Address File Creation for the 1990 Census.’’ U.S. Department of Commerce, Bureau of the Census. October 30,

1991.

ADVANCE POST OFFICE CHECK

RECONCILIATION

Introduction and Background

The APOC and associated field reconciliation operation

(APOC Reconciliation) were integral parts of the coverage

improvement program. The APOC was conducted in two

phases, APOC II and APOC III, in the Prelist Mailout/ Mailback

areas, whereas APOC I was conducted in the TAR areas.

APOCReconciliationwasconductedonlyinPrelistMailout/ Mailback

areas following APOC II/ III.

During APOC II/ III, the USPS reviewed the addresses

listed in the Prelist Mailout/ Mailback areas. The postal

carriers checked the deliverability of each Prelist address,

identified any duplicate or undeliverable addresses, and

completed a Form D-702, Report of Missing Address (blue

card), for any residential address on their route that was

missing from the census files (See appendix B for an

illustration of the form). In addition, the carriers were

instructed to make any necessary corrections to improve

the deliverability of the Prelist addresses. Incomplete

addresses identified during the Prelist processing (known

undeliverables) were withheld from the APOC II/ III.

Following the APOC II/ III, a field check known as APOC

Reconciliation was conducted to verify that each blue card

was for a residential address not already accounted for in

the census files. In addition, the APOC Reconciliation

enumerators attempted to get a better mailing address for

undeliverables, verify the existence of each address classified as a duplicate by the USPS, and resolve clusters;

that is, living quarters not accessible at the time of Prelist

due to situations such as washed out roads or locked

gates.

Similar to APOC, the APOC Reconciliation occurred in

two phases, APOC Reconciliation II and III. Originally, the

Census Bureau planned to include the entire Prelist Mailout/

Mailback area in one national APOC. However, since the

Prelist operation for some parts of the country was completed later than for other areas, it was decided to conduct

APOC in two phases, APOC II and APOC III. Selected

Prelist Mailout/ Mailback areas were included in APOC II in

February 1989 and field reconciled in June 1989. Addresses

for the remaining areas were reviewed during APOC III in

April 1989 and reconciled in August 1989.

APOC Reconciliation results are presented as combined tallies for the two phases since no substantial

differences exist between them.

Methodology

The APOC Reconciliation add rate is defined as the

number of addresses added during APOC Reconciliation

divided by the number of addresses in the Census Bureau’s

files following the Prelist in Mailout/ Mailback areas.

Since exact counts of blue cards prepared during the

APOC were unavailable, a sample was taken to obtain

estimated totals and estimated proportions of blue cards

for the APOC Reconciliation status categories. A multiplestart systematic sample of 30 district offices was taken

from 151 district offices containing APOC II workloads. For

the APOC III workloads, a multiple-start systematic sample

of 30 district offices was taken from 181 district offices.

Limitations

The final status of APOC Reconciliation adds is not

available for this publication. Add rates for other coverage

improvement operations which are based on adds with a

final status of occupied or vacant will differ from those

presented in this section.

Data for blue cards are presented as estimates from a

sample of cards and are subject to sampling error and

variability.

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The number of blue cards may underestimate the

number of blue card adds because 1 blue card could

contain up to 12 separate unit designations.

Results

Table 2.5. shows the APOC Reconciliation results of

addresses reviewed during the APOC. Note that addresses

in split ZIP Codes are excluded since they were not

included in the APOC or APOC Reconciliation workload.

Although deliverable addresses with and without APOC

corrections were not assigned for APOC Reconciliation,

enumerators were instructed to correct or delete them if

address changes were discovered in the field. Table 2.5.

contains data for housing units only; clusters and special

places are not included. Note that APOC undeliverables

include addresses which were considered unacceptable

corrections during processing following APOC; they were

flagged as undeliverables for APOC Reconciliation.

Unchanged Reconciliation Cases—APOC undeliverables,

duplicates, and known undeliverables were indistinguishable on the APOC Reconciliation field assignment listing.

The enumerators were instructed to obtain the correct

mailing address for each address assigned for APOC

Reconciliation, provided the structure was a valid living

quarters. The enumerators also deleted any listings verified as duplicates of valid addresses.

As shown in table 2.5, approximately 38 percent of the

APOC undeliverables and known undeliverables assigned

for APOC Reconciliation and 56 percent of the APOC

duplicates were not changed during the field check. Based

on observation reports, many of these addresses were not

resolved because the enumerator could not locate the

occupant or a knowledgeable person to verify the addresses

(revisting the address was not approved for this operation).

Also note that 89 percent (16,048,517/ 18,043,135) of

the addresses that were not changed during APOC Reconciliation had been classified as deliverable during APOC.

Corrections—The APOCReconciliation enumerators obtained

a corrected address for about 56 percent of the APOC

undeliverables and 55 percent of the known undeliverables.

The enumerators obtained corrections from two sources,

respondents and blue cards that matched address listings.

Respondents were often able to provide additional address

information so that the USPS would recognize the address

during questionnaire delivery.

During the APOC, carriers completed blue cards for

many of the valid but incomplete addresses withheld from

the APOC. If the enumerators were able to match the blue

cards to known undeliverable listings in the address register, they used the more complete address information

from blue cards to correct the listings.

Street name or rural route/ box number corrections were

the most prevalent address item corrections for addresses

in the APOC Reconciliation workload. Based on addresses

assigned for APOC Reconciliation and corrected in the

field, 52.1 percent had street name corrections. House

numbers were the second most frequently corrected items.

Note that house numbers were not acceptable corrections

during the APOC, but were valid corrections during APOC

Reconciliation.

Addresses Coded for Deletion—Approximately 6 percent of the APOC undeliverables and 7 percent of the

known undeliverables were coded for deletion during

APOC Reconciliation. The enumerators were instructed to

mark for deletion undeliverable addresses that they were

unable to locate, as well as those that had been demolished, condemned or converted to nonresidential use.

These addresses were flagged as deletes during processing but remained on the Census Bureau’s files.

The enumerators were also instructed to delete listings

verified as true duplicates. Of 537,924 addresses classified

as duplicates during the APOC, 64,683 (12 percent) were

verified as true duplicates in the field and removed from

the Census Bureau’s files.

Clusters—APOC Reconciliation enumerators were assigned

5,634 Prelist clusters for which they were instructed to

obtain address information for each of the clustered housing units. They resolved cluster listings by deleting the

listings for the cluster and listing the housing unit addresses

on the blank add pages in the front of the address register.

Of the total number of cluster listings, the enumerators

resolved 1,854 (32.9 percent). The remaining clusters

were unresolved and reassigned during later operations.

Adds—Figure 2.7 shows APOC Reconciliation adds and

coverage improvement by regional census center. These

totals do not account for addresses such as clusters and

true duplicates which were removed from the Census

Bureau’s files during processing.

During APOC Reconciliation, the field staff added nearly

1.2 million addresses (4.2 percent) which were not already

in the census files. These added units include addresses

missed during Prelist with no corresponding blue cards, as

well as blue card addresses verified as adds by the

enumerators.

Table 2.5. APOC Reconciliation Results

Reconciliation status

APOC status

Total

(percent)

Deliverable

(percent)

Undeliverable

(percent)

Duplicate

(percent)

Known

undeliverables

(percent)

No change . 18,043,135

(83.5)

16,048,517

(96.5)

1,106,420

(38.3)

300,547

(55.9)

587,651

(38.2)

Correction . 3,153,225

(14.6)

524,479

(3.2)

1,609,980

(55.7)

172,694

(32.1)

846,072

(55.0)

Delete . . . . . 400,534

(1.9)

57,803

(0.3)

172,934

(6.0)

64,683

(12.0)

105,114

(6.8)

Total . . . . 21,596,894

(100.0)

16,630,799

(100.0)

2,889,334

(100.0)

537,924

(100.0)

1,538,837

(100.0)

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As shown in figure 2.7, the highest APOC Reconciliation

add rates are in the western portion of the country,

specifically the Los Angeles, San Francisco, and Seattle

Regional Census Centers.

The 1990 coverage gain from APOC Reconciliation for

the combination of Los Angeles, San Francisco, Seattle,

and Denver Regional Census Centers is 6.2 percent, which

exceeds the national APOC Reconciliation add rate of 4.2

percent and may indicate a continuation of western growth.

From 1988 through 1989, approximately 30 percent of

all building permits for new privately owned housing units

were issued in the West. For this period, the authorization

of new housing units in the West resulted in the highest

regional percent increase (4.8 percent) based on 1980

housing unit stock. Although no definite conclusions can

be drawn as to the reason why the add rate was high in the

West, the growth data suggests that it may have been in

large part due to new construction between Prelist and

APOC Reconciliation.

Blue Card Reconciliation Status—Table 2.6 shows the

estimated relative outcome of the blue cards assigned for

APOC Reconciliation. An estimated 1,457,351 blue cards

(standard error= 2667) were assigned for APOC Reconciliation after APOC II and an estimated 1,412,169 (standard

error= 4630) were assigned after APOC III.

Approximately 21 percent of the APOC blue cards were

classified as housing unit adds (an estimated 4.2 percent

coverage gain). In addition to blue cards, APOC Reconciliation adds also resulted from address register repairs,

resolved clusters, and missed units; that is, units missed

during Prelist and not found until APOC Reconciliation.

Approximately 35 percent of the blue cards matched to

undeliverables (or duplicates) already listed in the file. The

majority of these blue cards were completed for valid

known undeliverable addresses that were withheld from

APOC and subsequently classified as missing during the

APOC casing.

Approximately 16 percent of the blue cards matched

deliverable listings. Many of these blue cards were erroneously completed for addresses involved in ZIP Code

changes.

If the blue card address was thought to be located in a

different address register area from its assigned address

register area, the APOC Reconciliation enumerators were

instructed to note the correct address register area and

return it to their supervisor for reassignment. In some

cases, the blue card could not be reassigned because the

field materials for the correct address register area had

been completed and returned to the district office. An

estimated seven percent of the blue cards could not be

reassigned.

Approximately 3 percent of the blue cards could not be

found by enumerators after reassignment. A clerical review

of a sample of the ungeocoded blue cards revealed that 20

percent matched to addresses already on the file in

Update/ Leave areas, indicating that APOC carriers had

erroneously completed blue cards for housing units outside the Prelist Mailout/ Mailback area. For ZIP Codes

containing both Prelist and Update/ Leave areas, the carriers may not have effectively followed the Prelist boundaries shown on the APOC maps when completing blue

cards.

Approximately 8 percent of the blue cards were unclassified. The majority of these cards were completed pockets

of Prelist Mailout/ Mailback addresses surrounded by Update/

Leave areas. Since the cards were presumably located in

surrounding Update/ Leave areas, they were not assigned

for APOC Reconciliation.

Conclusions

Following Prelist, the APOC Reconciliation, in conjunction with the APOC, provided additional coverage gains,

especially in the Los Angeles, San Francisco, Seattle, and

Denver Regional Census Centerss. The APOC and APOC

Reconciliation yielded an overall coverage gain of 4.2

percent, although the results of subsequent operations

determine the final coverage gain.

Table 2.6. Blue Card Reconciliation Status

Blue card status after

APOC Reconciliation Estimated percent Standard error

Housing unit adds. . . . . . . . . 20.76 2.64

Special place adds . . . . . . . 0.14 0.03

Matched deliverable. . . . . . . 16.35 1.76

Matched undeliverable . . . . 35.26 4.98

Non-living quarters . . . . . . . 9.72 0.81

No reassignment . . . . . . . . . 6.62 2.12

Out of range . . . . . . . . . . . . . 3.39 1.60

Unclassified . . . . . . . . . . . . . 7.76 5.06

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The APOC Reconciliation enumerators corrected 55.7

percent of the undeliverable addresses assigned for the

field check. The enumerators classified 12 percent of the

APOC duplicates as true duplicates during APOC Reconciliation. Overall, the APOC Reconciliation was a valuable

part of the precensus coverage improvement effort.

References

[1] ‘‘Current Construction Report C40-88-13.’’ U.S. Department of Commerce, Bureau of the Census.

[2] ‘‘Current Construction Report C40-89-13.’’ U.S. Department of Commerce, Bureau of the Census.

[3] Pausche, Joan M. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 37, ‘‘Evaluation of the APOC II/ III and APOC Reconciliation in the

1990 Decennial Census.’’ U.S. Department of Commerce,

Bureau of the Census. February 14, 1991.

[4] Pausche, Joan M. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 88, ‘‘Prelist

Address File Creation for the 1990 Census.’’ U.S. Department of Commerce, Bureau of the Census. October 30,

1991.

PRECANVASS

Introduction and Background

Following the APOC operation in TAR areas, lists of all

addresses were printed in census address registers organized by census geography. About 25,000 census workers

travelled all streets in these areas to update the address

lists with missing addresses, make corrections to existing

addresses, correct census geography, and identify duplicate, nonexistent and commercial addresses. Census workers used maps with census geography during canvassing.

Missing addresses were assigned to census geography

during this canvassing operation, known as ‘‘Precanvass.’’

Precanvass address registers were keyed during which

units were added, deleted, and corrected. Appropriate

updates were made to the Address Control File.

During precanvass processing, the normal procedure

was to flag a deleted address as a delete. A computer

match was conducted to determine which of the precanvass deletes represented geographic corrections. If an

address was deleted from one census block and added to

another, it was recognized as a geographic transfer. The

delete was purged from the Address Control File. All other

deletes remained on the Address Control File.

Upon the completion of precanvass processing, it was

recognized that the transfer, add, and delete rates were

significantly higher than expected for some parts of the

country. Approximately 173,000 basic street addresses

(representing over 310,000 housing units) in 33 district

offices in Pennsylvania, New York, and New Jersey were

removed from the Address Control File. A special operation (the Precanvass Delete Review) was conducted to

review the precanvass deletes. If the address was confirmed to represent a geographic transfer, it remained

deleted. If this could not be confirmed, the address was

‘‘re-added’’ to the Address Control File.

This section summarizes the impact that the Precanvass operation had on improving housing unit coverage by

adding, deleting, and correcting addresses. Data are provided on the results of an independent evaluation of

precanvass updating. The results of the Precanvass Delete

Review operation are also summarized.

Methodology

Source codes allowed us to identify all updates from

precanvass as originating from this operation. Files were

created from the Address Control File at various geographic levels summarizing counts of adds, deletes, and

corrections. Detailed data were requested on the 33

district offices included in the Precanvass Delete Review.

Characteristics of the ‘‘re-added’’ units were included in

this request.

As part of the evaluation of the Precanvass operation, a

systematic, random sample of housing units was suppressed from the precanvass address registers. A sample

of 840 out of 34,840 address registers was selected that

covered 387 census district offices. Generally, four housing units were suppressed in each of the sampled address

registers.

After the Precanvass operation was completed and all

data were keyed, the sample was matched to the address

registers to determine whether the suppressed units were

added or missed, and to estimate the rate at which units

were missed. A suppressed unit was not missed when it

was found to have been added to the listing during

precanvass. The address was not considered missed if it

was deleted by a later operation. Miss rates were calculated as the ratio of the weighted number of missed

housing units to the weighted estimate of census housing

units with a final status of vacant or occupied. An adjustment was made to account for false nonmatches. Separate estimates were produced for single unit and multiunit

structures. Data were also analyzed at the address register

level to determine if misses tended to be clustered.

Limitations

Summary tallies from the Address Control File do not

have any major limitations. The source codes were assigned

by computer based on precanvass keying and therefore

are not subject to the limitations of the source codes that

were assigned clerically.

The Precanvass Delete Review was conducted only in

33 district offices. The results are therefore only generalizable to that universe. The review operation involved

clerical matching and was therefore subject to error.

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For the suppression study, selection of the sample units

in multiunit structures was not completely random. The

‘‘last’’ unit (that is, the unit with the highest unit designation) was suppressed in all multiunits. As a result, housing

units in multiunit structures had a lower probability of

selection than those in single unit structures. The clerical

matching of suppressed units to precanvass address

registers was imperfect and false matches as well as false

nonmatches could contribute to some level of error in the

miss rates.

Results

Precanvass—Table 2.7 summarizes the effect of Precanvass on the count of addresses and basic street addresses

(abbreviated as BSA in table 2.7). Add, delete, and transfer

rates are based on the before precanvass address and

basic street address counts. Almost 6 million addresses

were added at 3.3 million basic street addresses as a

result of the Precanvass operation. This represents an

11.5 percent increase in the national list of TAR addresses.

About 2.2 million addresses (4.3 percent of the before

precanvass addresses) were flagged as precanvass deletes.

An additional 1.4 million addresses (0.9 million basic street

addresses) were identified as geographic transfers. This

reflects a transfer rate of 2.6 percent.

The add rates varied by State (see figure 2.8). The

highest add rates occurred in Pennsylvania (26.6 percent),

Mississippi (24.0 percent), and South Carolina (18.5 percent). High add rates could indicate areas where the

vendor and the USPS updates could not be geocoded.

High rates also resulted in areas undergoing address

conversions. This was true in Mississippi. Relatively low

add rates were found in the District of Columbia (5.5

percent), Wisconsin (6.4 percent), and Louisiana (6.6

Table 2.7. Precanvass Results

Number Percent

Before precanvass

BSA’s. . . . . . . . . . . . . . . . . . . . . . 35,321,500 100.0

Addresses. . . . . . . . . . . . . . . . . . 51,649,482 100.0

Adds

BSA’s. . . . . . . . . . . . . . . . . . . . . . 3,347,341 9.5

Addresses. . . . . . . . . . . . . . . . . . 5,962,985 11.5

Deletes

BSA’s. . . . . . . . . . . . . . . . . . . . . . - -

Addresses. . . . . . . . . . . . . . . . . . 2,222,195 4.3

Transfers

BSA’s. . . . . . . . . . . . . . . . . . . . . . 890,925 2.5

Addresses. . . . . . . . . . . . . . . . . . 1,367,029 2.6

- Not available.

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percent). Low growth areas, areas with relatively high

geocoding success, and areas with updated vendor files

were expected to fall into this category.

Figure 2.9 demonstrates that most census district offices

had geographic transfer rates of less than 2 percent;

however, over 8 percent of the district offices had rates

over 5 percent. The highest transfer rates were found in

several offices in Pennsylvania. The extremely high rates

in these areas led to the Precanvass Delete Review

operation.

Precanvass Delete Review—References [1] and [3] detail

the results of this operation. Approximately 33.7 percent of

the precanvass basic street address deletes that were

reviewed during this operation were confirmed to be

duplicates. These addresses represent geographic transfers that were not recognized during precanvass processing. The review confirmed that deleting over 87,000 potential duplicate housing units in over 58,000 basic street

addresses was the correct decision. No evaluation was

undertaken to see if any of these addresses were deleted

in error.

The remaining 66.3 percent were re-added to the ACF.

Of these, about 46 percent were eventually deleted in the

census. Most were deleted with the explanation of ‘‘no

such address.’’ Thus, 54 percent of the re-added precanvass deletes were enumerated as occupied or vacant

units. These 121,064 housing units and 261,335 persons

may have been missed if the Precanvass Delete Review

did not take place.

District offices in the New York Regional Census Center

had a much higher ‘‘re-add’’ rate than district offices in the

Philadelphia Regional Census Center (87.0 percent versus

63.5 percent). This indicates that geographic transfer

errors were likely not the cause of the high delete rates in

the New York area. A review of the re-added addresses in

New York shows that about 68 percent were eventually

deleted. It appears that nonresidential addresses, flagged

as deletes during precanvass, were the cause of the higher

than expected delete rates in New York.

Substantial levels of duplication were identified in several of the Philadelphia district offices. It appears that

geographic transfer errors were causing the high delete

rates that were noted in this area. The re-add rate was only

63.5 percent, whereas the rate in the New York district

offices was 87.0 percent. In these areas, the decision to

delete and re-add, after review, is likely to have had a

major impact on housing unit data quality. A review of the

re-added units indicates that only 35 percent had a final

status of delete, the remaining 65 percent were enumerated as occupied or vacant units.

Suppression Study—This study estimates the overall

‘‘miss rate’’ to be approximately 30.0 percent, with a 1.6

percent standard error. Housing units in multiunit structures had a significantly higher miss rate than single

units—45.2 percent versus 24.3 percent. The estimated

standard errors of these estimates are 4.1 percent (multiunits) and 1.5 percent (single units). Even as high as these

rates appear to be, they are still better than initially

anticipated. Table 2.8 summarizes the distribution of the

number of misses among the 839 sample address registers. Of the address register areas sampled, 63.6 percent

contained at most one miss. This suggests that the majority of enumerators did fairly well. In 4.0 percent of the

address register areas, all suppressed units were missed.

Further information on this study can be found in [2].

Conclusions

The Precanvass operation was effective in adding missed

housing units and identifying geographic coding errors.

Higher than expected add, delete, and transfer rates were

recognized. In some areas it is clear that the list of

addresses used in precanvass was very deficient and that

this operation was critical to producing an accurate housing unit inventory.

The Precanvass Delete Review operation demonstrated

that duplication was a likely byproduct of the procedure

that was followed to identify geographic coding errors. This

procedure required that adds and deletes match exactly in

order for them to be recognized as transfers. The precanvass computer match processing could not handle variations in addresses and intended transfers remained on the

census files. In the future, it is recommended that corrections be allowed to the geographic codes in lieu of adding

an address into one geography and deleting it from

another.

Table 2.8. Distribution of Missed Units

Number of misses Number of registers Percent of registers

0 . . . . . . . . . . . . . . . . . . . . . . 290 34.6

1 . . . . . . . . . . . . . . . . . . . . . . 244 29.0

2 . . . . . . . . . . . . . . . . . . . . . . 172 20.5

3 . . . . . . . . . . . . . . . . . . . . . . 99 11.8

4 . . . . . . . . . . . . . . . . . . . . . . 34 4.0

Total. . . . . . . . . . . . . . . . 839 100.0

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The precanvass suppression study indicates that a fairly

high proportion of missed units were not recognized during

precanvass. Given that the units that were suppressed

originated from the vendor list or APOC, they did not

represent ‘hidden’’ units that should be that hard to detect.

If 30 percent of these types of housing units were missed,

it is likely that a higher rate of hard to locate units also were

missed. Although the overall miss rate was high, it is noted

that 34.6 percent of the sampled areas missed no suppressed units.

More emphasis needs to be given to the coverage

improvement aspect of precanvass—in training and through

monitoring and feedback of the precanvasser’s performance. It was proposed that a formal quality assurance

plan in the 1990 precanvass include some type of suppressed unit check to provide feedback to the precanvassers on their work. Budget considerations led to a dramatic

change in the proposed quality assurance plan. It is likely

that these changes resulted in a poorer precanvass that

did not detect as high a rate of missed units as it might

have. In addition, the considerable amount of required

corrections to census geography made it harder for the

precanvassers to focus on identifying missed units.

References

[1] Corteville, Jeffrey S. 1990 Decennial Census Preliminary Research and Evaluation Memorandum No. 200,

‘‘Evaluation of the Addresses Added as a Result of the

Precanvass Delete Review.’’ U.S. Department of Commerce, Bureau of the Census. November 9, 1992.

[2] Russell, Chad E. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 148, ‘‘Results

of the Precanvass Suppression Study.’’ U.S. Department

of Commerce, Bureau of the Census. May 7, 1992.

[3] Tillman, Amy L. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 25, ‘‘Preliminary Results of Precanvass Delete Review.’’ U.S. Department of Commerce, Bureau of the Census. June 21, 1990.

PRECANVASS RECONCILIATION AND YELLOW

CARD CODING

Introduction and Background

The Yellow Card Coding operation was a precensus

activity that was conducted in TAR areas in January 1990

through February 1990. The purpose of the Yellow Card

Coding operation was to assign the correct geocode to

addresses that were either (1) ungeocoded after prior

precensus activities (the yellow card universe) or (2) had

conflicting geocodes after the Precanvass operation (the

precanvass reconciliation universe). Thus, the Yellow Card

Coding operation processed addresses from two sources:

yellow cards and precanvass reconciliation.

All cases in the Yellow Card Coding operation (ungeocoded and multiple-geocoded addresses) were printed on

yellow cards and sent to the servicing district offices. The

ungeocoded yellow card cases were clerically geocoded

using block header records (listings of addresses within

census blocks) and address control file listings. If the

address range for an ungeocoded address was found in a

block header record, district office clerks checked to see if

the exact address was in an address control file listing. If

the ungeocoded yellow card address was in an address

control file listing, it was a duplicate of an existing address

and therefore required no further processing. If the exact

address was not in an address control file listing, the

geocode from the block header record was used. If the

address range was not in a block header record, and the

address was a house number, street name type that was in

a TAR area, it was sent to the field for geocoding. If an

ungeocoded yellow card address was found to be in a

prelist area or a rural type address, then it was not

processed further.

In contrast, all of the precanvass reconciliation cases

that were processed on yellow cards had primary and

secondary geocodes, and in some cases more than two

geocodes. These were all sent directly to the field for

reconciliation of the geocodes.

This section documents several aspects of the Yellow

Card Coding operation. An analysis of the added addresses

(from the yellow card portion of the operation) and the

reconciled addresses (from the precanvass reconciliation

portion) is presented. In addition, selected demographic

characteristics of the persons enumerated at the added or

reconciled addresses are discussed.

Methodology

Computer data files of all census identification numbers

(ID’s) and accompanying housing unit and person data that

were identified as either yellow card adds or reconciled

precanvass addresses were supplied for this evaluation.

In presenting the housing unit characteristics of the

added or reconciled addresses, benchmark data from the

1989 American Housing Survey were also used. The data

employed from the 1989 American Housing Survey were

estimates of central city/ metropolitan area housing unit

counts and their characteristics. The reason for using

these data for comparison purposes is twofold: 1) The

universe of central city/ metropolitan area is very similar to

the TAR universe from the Yellow Card Coding operation;

and 2) Given that these two populations are similar, it was

of interest to see if their respective distributions for a given

housing unit characteristic were similar.

Limitations

The yellow card adds were identified by source code (a

code on the census computer files that identified the

source of the address). Research has suggested the

existence of errors in the assignment of source codes.

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However, the extent of the error is not known. Also note

that some data from the census files did not go through the

edit and allocation processes, therefore results that include

a ‘‘no response’’ category are presented.

Results

The Yellow Card Coding Operation Combined Results−

Workload—The workload for the Yellow Card Coding

operation was 2,052,333 addresses. There were 1,316,690

addresses (64.2 percent of the workload) that were ungeocoded yellow cards and 735,642 addresses (35.8 percent

of the workload) that were precanvass reconciliation cases

[2].

A total of 1,206,376 addresses were identified as either

yellow card adds or reconciled addresses. This represents

58.8 percent of the workload. The majority of these were

addresses that had multiple geocodes and were reconciled during the Yellow Card Coding operation—714,566

addresses, or 59.2 percent. The balance—491,810 addresses,

or 40.8 percent, were yellow card cases. Thus, approximately 37.4 percent of the yellow card workload resulted in

adds to the address control file, whereas about 97.1

percent of the precanvass reconciliation workload resulted

in reconciled addresses on the address control file.

The lower percentage of adds from the yellow card

universe may signify that many of the yellow card cases

were duplicate addresses. Also, the yellow card universe

likely included more potentially incomplete or nonexistent

addresses, whereas precanvass reconciliation addresses

were units added in precanvass so they should have been

legitimate addresses that were easier to locate and geocode.

There were 57,612,468 TAR addresses on the address

control file after the Precanvass operation [2]. Thus, the

yellow card adds (491,810 addresses) represent about a

0.9 percent increase of TAR addresses on the address

control file.

Approximately 75.0 percent of the housing units (904,418

housing units) from the yellow card adds and the precanvass reconciliation addresses combined were occupied

units. Vacant units represented 9.8 percent (117,999 housing units) of all added or reconciled housing units. Deleted

units represented 15.2 percent (183,959 housing units) of

all added or reconciled addresses. The fairly large percentage of deleted units shows that many units that were

added or reconciled during the Yellow Card Coding operation were identified as non-existent or duplicate housing

units during a later census operation.

The Yellow Card Coding Operation Combined Results−

Persons Enumerated at Added or Reconciled Housing

Units—There were 2,156,452 persons enumerated at the

added or reconciled housing units from the Yellow Card

Coding operation. Selected demographics (age, race, and

Hispanic origin) of these persons were compared to the

demographics of gross national estimates of persons

identified by the Post Enumeration Survey as missed by

the census. The reason for the comparison to missed

persons is to investigate if persons that were added by this

coverage improvement program were demographically

similar to persons that were likely to be missed in the

census. This comparison showed that the selected demographic distributions of the persons enumerated in added

or reconciled housing units were dissimilar to the same

demographic distributions of persons likely to be missed in

the census and more representative of the total population

enumerated in the 1990 census.

The Yellow Card Universe−Housing Unit Characteristics—

Figure 2.10 shows the breakdown by size of basic street

address of the housing units added to the address control

file from the yellow card universe compared to the basic

street address size of 1989 housing unit estimates of

central city and metropolitan areas at the U.S. level. Note

that the 1989 housing unit estimates are from the 1989

American Housing Survey. The plurality of the housing unit

adds had a basic street address size of single unit. There

were 239,102 single unit addresses added to the address

control file, which represents 48.6 percent of all of the

yellow card universe adds. The percentage of single units

for the yellow card adds is almost 20 percent less than this

same basic street address size category for the 1989

housing unit estimates of central city/ metropolitan areas.

Structures containing 50 or more units represented the

next largest basic street address size category of the

yellow card universe adds. There were 96,946 addresses

added to the address control file that had a basic street

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address size of 50 or more units, which represents 19.7

percent of all of the yellow card universe adds. The

percentage of units with a basic street address size of 50

or more units is much larger for the yellow card adds than

for the 1989 housing unit estimates for central city/ metropolitan

areas. Thus, the Yellow Card Coding operation improved

coverage for large multiunits.

Because of the large number of housing unit adds that

had a basic street address size of 50 or more units, it was

of interest to determine if there was any clustering of units

within a building, or if perhaps an entire apartment building

was added. Census geography was used (at the block

level) to determine clustering. Of the 96,946 yellow card

adds with a basic street address size of 50 or more units,

87,573 housing units (90.3 percent) were in blocks that

had 50 or more units added during the Yellow Card Coding

operation. Although this does not show whether or not

entire apartment buildings were added, it is hypothesized

that where entire apartment buildings were not added,

clusters of units within buildings were.

Approximately 48.4 percent of the occupied housing

units (166,127 housing units) added to the address control

file from the yellow card universe had a tenure status of

rented. About 46.9 percent of the yellow card adds, or

161,102 housing units, had a tenure status of owned. Note

that approximately 4.6 percent of the housing units added

from the yellow card universe had no response for the

tenure item. It is assumed that the distribution of tenure for

nonrespondents is similar to that of the respondents.

The Yellow Card Universe—Geographic Distribution of

the Added Housing Units—Table 2.9 shows the top 10

States with the highest number of housing unit adds for

that State as a percentage of total housing unit adds from

the yellow card universe. California had the highest number of housing unit adds—72,497 addresses—which is

approximately 14.7 percent of all addresses from the

yellow card universe. Florida had the second highest

number of housing unit adds from the yellow card universe—

63,789 addresses—which is about 13.0 percent of all

added addresses from the yellow card universe. According

to 1990 census figures, California is the State with the

largest number of housing units in the U.S.; Florida ranks

fourth. Only 3 of these States with the highest number

of housing unit adds— Georgia, Massachusetts, and Arizona—

were not included in the top 10 States with the largest

number of housing units nationwide.

The Precanvass Reconciliation Universe—Housing Unit

Characteristics—The precanvass reconciliation workload

portion of the Yellow Card Coding operation was 735,643

TAR addresses [2]. From this workload, 714,566 housing

units had geographic codes reconciled on the address

control file. Thus, 97.1 percent of the precanvass reconciliation workload was processed and reconciled. The

remaining 2.9 percent of the workload may have been

addresses that were found to be outside of TAR areas and

thus not processed further, or perhaps during a later

census operation it was determined that some of these

addresses should be removed from the address control

file.

Figure 2.11 shows the distribution by basic street address

size of the precanvass reconciliation addresses compared

to central city and metropolitan area housing unit estimates from the 1989 American Housing Survey. The

distribution by basic street address size of the precanvass

reconciliation addresses was similar to the yellow card

universe adds. For 2 categories, single units and units with

a basic street address size of 50 or more units, the

distribution of basic street address size for the precanvass

reconciliation addresses is dissimilar to the distribution of

the 1989 central city/ metropolitan area housing unit estimates.

Table 2.9. Top 10 States With Highest Number of Yellow Card Adds

State Housing unit adds

Percent of housing

unit adds

CA. . . . . . . . . . . . . . . . . . 72,497 14.7

FL . . . . . . . . . . . . . . . . . . 63,789 13.0

NY . . . . . . . . . . . . . . . . . 45,807 9.3

NJ. . . . . . . . . . . . . . . . . . 30,282 6.2

IL. . . . . . . . . . . . . . . . . . . 27,805 5.7

GA . . . . . . . . . . . . . . . . . 18,717 3.8

PA. . . . . . . . . . . . . . . . . . 18,168 3.7

MA . . . . . . . . . . . . . . . . . 16,218 3.3

TX. . . . . . . . . . . . . . . . . . 15,877 3.2

AZ. . . . . . . . . . . . . . . . . . 14,356 2.9

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About 45.5 percent of the housing units (324,862 housing units) had a basic street address size of single unit.

This is in contrast to the estimate of about 67.5 percent of

the 1989 central city/ metropolitan area housing units

having a basic street address size of single unit. The

second largest size of basic street address was multiunit

structures with 50 or more units; there were 187,017

addresses of this size that were reconciled on the address

control file from the precanvass reconciliation universe,

which represents approximately 26.2 percent of the precanvass reconciliation housing units. As was true with the

yellow card adds, the basic street address size of 50 or

more units was much larger for the precanvass reconciliation addresses than for the 1989 estimates of U.S. level

central city and metropolitan area housing units.

Again, because of the large number of reconciled units

with a basic street address size with 50 or more, an

analysis at the block level was conducted to determine

geographic clustering of units of this basic street address

size. Of the 187,017 addresses from the precanvass

reconciliation universe that had a basic street address size

of 50 or more units, 98.0 percent (183,189 units) were in

blocks that had 50 or more units reconciled within the

same block. Therefore, there seems to be clustering of

units with this basic street address size, whether the entire

unit was geographically reconciled, or whether units within

large apartment buildings were reconciled.

The largest tenure category for the precanvass reconciliation occupied housing units was rented. There were

269,358 housing units (48.0 percent) that had a reported

tenure status of rented. It seems that the Yellow Card

Coding operation did a good job of improving coverage of

renters—a group that is missed at a higher rate than

owners. Owners comprised about 47.6 percent, or 267,560

occupied housing units. Note that this question had an

item nonresponse of approximately 4.4 percent for the

precanvass reconciliation addresses. Again, it is assumed

that the distribution of tenure for the nonrespondents is

similar to that of the respondents.

The Precanvass Reconciliation Universe—Geographic

Distribution of Reconciled Housing Units—The top 10

States with the highest number of reconciled housing units

from the precanvass reconciliation universe is shown in

table 2.10, along with the percentage of reconciled housing units to total number of reconciled housing units.

The top 10 States with the highest number of reconciled

housing units has 9 States in common with the top 10 from

the yellow card adds. Thus, there were consistent geographic coding problems in these States. Some plausible

explanations may be that perhaps purchased address lists

were outdated and they did not cover some new construction; or perhaps geographic coding work done by some

contractors tended to have errors.

New York had the highest number of reconciled housing

units with 99,767. This number represents about 14.0

percent of all reconciled housing units from the precanvass reconciliation universe. Pennsylvania had 88,451

reconciled housing units, the second highest number by

State representing about 12.4 percent of the total number

of precanvass reconciliation housing units. There were

extensive geographic coding problems in the Philadelphia

area which likely explains why Pennsylvania appears so

high on this list.

Conclusions

While most of the workload (97.1 percent) in the precanvass reconciliation universe was reconciled, only 37.4

percent of the yellow card universe resulted in adds to the

address control file. From these yellow card adds, 80.2

percent were valid census housing units (occupied and

vacant). In contrast, 87.9 percent of the reconciled housing

units were valid census housing units (occupied and

vacant). Thus, in terms of quality of housing units with

respect to true versus deleted housing units, the precanvass reconciliation portion of this coding operation reconciled more valid addresses.

The plurality of the added or reconciled units from the

Yellow Card Coding operation had a basic street address

size of single unit, and the second most common basic

street address size was structures with 50 or more units. It

was shown that the added and reconciled units with a

basic street address size of 50 or more units were clustered at the block level. For both added and reconciled

units, over 90.0 percent of these housing units with a basic

street address size of 50 or more units were in blocks that

had 50 or more units added within the same block. It is

evident that large multiunit structures pose a problem

during the census, and it seems that the Yellow Card

Coding operation improved the coverage of these structures at a rate higher than their representation (of large

multiunits) in the TAR area housing unit population. Perhaps some special procedures should be tested during the

census test cycle to help better identify and geocode these

units, and thus continue to improve coverage.

This evaluation showed that the demographic characteristics of the persons enumerated at both yellow card

added housing units and reconciled housing units reflect

those of the general population and do not seem to be

similar to the demographics of PES-identified missed persons.

Table 2.10 Top 10 States With Highest Number of

Reconciled Housing Units

State Reconciled

housing units

Percent of reconciled

housing units

NY . . . . . . . . . . . . . . . . . 99,767 14.0

PA. . . . . . . . . . . . . . . . . . 88,451 12.4

CA. . . . . . . . . . . . . . . . . . 86,042 12.0

FL . . . . . . . . . . . . . . . . . . 58,306 8.2

NJ. . . . . . . . . . . . . . . . . . 44,478 6.2

IL. . . . . . . . . . . . . . . . . . . 42,649 6.0

TX. . . . . . . . . . . . . . . . . . 34,922 4.9

MA . . . . . . . . . . . . . . . . . 27,521 3.9

HI . . . . . . . . . . . . . . . . . . 4,782 2.1

GA . . . . . . . . . . . . . . . . . 14,200 2.0

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References

[1] Beverage, Susan C. 1990 Decennial Census Preliminary Research and Evaluation Memorandum No. 147,

‘‘Results from the 1990 Yellow Card Coding Operation.’’

U.S. Department of Commerce, Bureau of the Census.

June 12, 1992.

[2] Tillman, Amy. DSSD 1990 Research, Evaluation, and

Experimental Memorandum No. G-3, ‘‘Preliminary Results

From the Precanvass Operation in the 1990 Decennial

Census.’’ U.S. Department of Commerce, Bureau of the

Census. January 18, 1990.

[3] Wajer, Susan C. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 242, ‘‘Additional Results from the Yellow Card Coding Operation.’’

U.S. Department of Commerce, Bureau of the Census. July

28, 1993.

PRECENSUS LOCAL REVIEW

Introduction and Background

The Precensus Local Review operation was conducted

in January and February 1990 in all mailout/ mailback

enumeration areas of the nation. The operation provided

eligible local government officials the opportunity to review

Census Bureau counts of housing units and special places

as well as boundary maps to identify any major discrepancies in the counts or maps. These counts were provided to

the eligible governmental units prior to the operation and

each governmental unit was invited to participate in Precensus Local Review.

The regional census centers were responsible for sending a list of housing unit counts to local officials for each

block within their jurisdiction. Local officials had approximately 45 days to review these counts, using local estimates derived from documents such as tax records, utility

hookups, or building permits, to identify discrepancies.

During Precensus Local Review, each governmental

unit could appoint a representative to review the counts

and work with Census Bureau staff to resolve any discrepancies. If the housing unit counts in these governmental

units differed from the Census Bureau housing unit counts,

the local census liaison informed the Census Bureau of

these differences. Census Bureau representatives and

local officials worked together to resolve the differences.

Some discrepancies were resolved through discussions

over the telephone or by consulting other sources. If the

discrepancies could not be resolved in the office, then

additional field review occurred. For some discrepancies,

the Census Bureau recanvassed the block. During the

recanvass, an enumerator revisited the block and, using

the census address registers, made additions, deletions, or

geographic transfers to the listing of housing units in that

block.

This section documents such data as governmental unit

eligibility and participation in Precensus Local Review, the

extent of blocks challenged and recanvassed, the coverage improvement, and the final census occupancy status

of added units.

Methodology

The data within this evaluation were supplied from two

areas within the Census Bureau. A large portion of the data

were obtained from the Address Control File which provided data on the extent of coverage from the operation.

Data from the regional census centers was also used in

the evaluation. These data summarized the participation of

governmental units in Precensus Local Review as well as

provided results of recanvassing activities during the operation.

The add rate is defined as the ratio of valid added

housing units (units added as a consequence of Precensus

Local Review which remained either occupied or vacant

when final census counts were issued) to the total number

of housing units in mailout/ mailback enumeration areas

prior to Precensus Local Review.

Limitations

No attempt is made to determine the extent of overlap

between Precensus Local Review and other coverage

improvement operations.

Results

Eligibility and Participation of Governmental Units in

the Precensus Local Review Operation—Eligibility in

the Precensus Local Review operation was limited to

governmental units which were located in mailout/ mailback

enumeration areas. Governmental units in Update/ Leave

enumeration areas were not eligible for Precensus Local

Review because the address listings were not available in

time. Governmental units in List/ Enumerate areas were

not eligible because there was no pre-Census Day address

listing. Participation in Precensus Local Review could

occur in any one of the following three ways. A governmental unit could respond by:

1. Agreeing with the census housing unit counts

2. Disagreeing with census housing unit counts, but not

providing the Census Bureau with the proper documentation to identify these discrepancies

3. Disagreeing with the census housing unit counts and

providing the Census Bureau with the proper documentation for identifying the discrepancies.

There were a total of 39,198 governmental units nationwide at the time of the Precensus Local Review operation.

Of these 39,198 governmental units, 21,048 (53.7 percent)

were eligible to participate in the operation since the

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governmental units were located in mailout/ mailback enumeration areas. The national Precensus Local Review

response rate (that is, the ratio of participating governmental units to eligible governmental units) was 16.3 percent,

or 3,440 governmental units participated in Precensus

Local Review out of the 21,048 eligible governmental

units.

Governmental Units which Challenged Census Housing Unit Counts During the Precensus Local Review

Operation—Of the 3,440 governmental units which participated, 2,883 (83.8 percent) challenged (with appropriate documentation) the Census Bureau’s housing unit

counts (that is, disagreed with the census housing unit

count and had proper documentation to identify discrepancies). The remaining 557 (16.2 percent) governmental

units either responded by agreeing with the census counts

or responded by disagreeing with the census counts but

did not have the proper documentation to identify the

discrepancies.

Challenged and Recanvassed Blocks From the Precensus Local Review Operation—Nationally, there were

slightly more than 4 million blocks in the mailout/ mailback

enumeration areas. Of the 4 million blocks, governmental

unit officials challenged approximately 121,000 (3.0 percent) blocks.

Of the approximately 121,000 challenged blocks, Census enumerators recanvassed 52 percent of the blocks

during Precensus Local Review. The remaining 48 percent

of the challenged blocks were not recanvassed because

the blocks did not meet the recanvassing guidelines for

Precensus Local Review. Recanvassing guidelines for

Precensus Local Review stated all blocks within the district

office with a housing unit count difference (the governmental unit housing unit count minus the census housing unit

count for any challenged blocks) greater than five would

be recanvassed during the operation. The district offices

also had the option, if time permitted, to recanvass the

remaining positive discrepancies and as many negative

discrepancies as time permitted.

Added Housing Units From the Precensus Local Review

Operation—The Precensus Local Review operation added

367,313 housing units to the national housing inventory.

This translates into a 0.43 percent add rate when comparing the number of valid added housing units from the

operationto thetotalnumberof housingunitsinmailout/ mailback

areas before Precensus Local Review.

Figure 2.12 provides a distribution by State of the

respective add rates. The shaded areas are defined by the

add rates from Precensus Local Review.

Final Occupancy Status of Precensus Local Review

Added Housing Units—Figure 2.13 highlights the final

occupancy status rates of Precensus Local Review added

housing units at the national and census region levels.

Overall, 69.3 percent of the added housing units from

Precensus Local Review remained occupied, 14.6 percent

of the added housing units were vacant housing units, and

the remaining 16.1 percent of the housing units eventually

were deleted by later census operations and/ or activities.

The figure reveals that the occupancy status rates from the

four regions were consistent with the national rates. Results

indicate that the Northeast Census Region had the highest

occupied rate at 73.2 percent. The South Census Region

had the highest vacant rate at 17.8 percent. Both the

Midwest and the South region had a delete rate at 18.1

percent.

Conclusions

Precensus Local Review proved to be a successful

coverage improvement operation for those governmental

units which elected to participate in the program. This local

review program gave local governments an opportunity to

update address listings prior to Census Day by comparing

Census housing unit counts to their estimated housing unit

counts. Overall, the Precensus Local Review operation

added 367,313 valid housing units to the Address Control

File from all mailout/ mailback enumeration areas nationwide.

With the exception of the lower than expected number

of governmental units which participated in the operation,

overall results reveal that the Precensus Local Review did

an excellent job of improving coverage to the 1990 census

final housing unit counts. The results of the operation show

that Local Review should continue to play a vital role in

improving coverage in future decennial censuses.

References

[1] Cecco, Kevin. ‘‘1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 116, ‘‘1990

Precensus Local Review Evaluation.’’ U.S. Department of

Commerce, Bureau of the Census. February 3, 1992.

[2] Cecco, Kevin and Florence H. Abramson. ‘‘1990 Decennial Census Preliminary Research and Evaluation Memorandum No. 184, ‘‘Evaluating the Local Review Operation

from the 1990 Decennial Census.’’ U.S. Department of

Commerce, Bureau of the Census. November 13, 1992.

CASING CHECK

Introduction and Background

The 1990 Casing Check operation was a precensus

activity to update the census address file before delivering

the census questionnaires. The casing check was done in

all TAR and prelist mailout/ mailback areas between February 26, 1990, and March 16, 1990. Casing refers to the

sorting process the USPS mail carriers use to put mail in

the proper sequence for delivery.

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The Census Bureau gave the USPS a form D-701,

Census Address Card (buff card), for each address in the

census mailout file (see appendix B for a copy of form

D-701). The USPS mail carriers cased the buff cards in

order to identify the deliverable, duplicate, and undeliverable addresses, as well as to identify residential addresses

missing from the census mailout file.

The USPS carriers completed a form D-722, Post Office

Report of Missing Addresses (blue card), for each residential address missing a buff card (see appendix B for a copy

of form D-722). All blue card addresses were checked in

the district offices or in the field to determine if the

addresses were missing from the census address file and

were valid residential units. The district offices labeled and

mailed census questionnaires to the missing addresses if

they were processed early; addresses not sent a census

questionnaire were enumerated during the Nonresponse

Followup operation.

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Methodology

The intent of the Casing Check evaluation was to

measure the results in terms of coverage yield. Information

from the USPS and tabulations from the Census Bureau’s

data files provided the data for most of the analysis. The

remainder of the data came from clerical tabulations of a

sample of the duplicate and undeliverable buff cards and

the blue cards. There were five separate samples:

1. Buff Cards-Duplicates: Buff cards annotated only

with ‘‘D,’’ ‘‘Dup,’’ or ‘‘Duplicate.’’

2. Buff Cards-Undeliverables with annotations: Undeliverable buff cards annotated with something other

than ‘‘D,’’ ‘‘Dup,’’ ‘‘Duplicate.’’

3. Blue Cards-Matches: Blue Cards with the ‘‘Match’’

box marked.

4. Blue Cards-Ungeocoded: Blue Cards without a district office code, address register number, and

block number.

5. Blue Cards-Adds: Blue cards with the ‘‘Add’’ box

marked.

Samples 1 through 4 were selected using a systematic

sampling scheme; that is, separate ‘‘start with’’ and ‘‘take

every’’ numbers were generated for each sample. For the

fifth sample, Blue Cards-Adds, five add cards were purposively selected from each district office. If there were

five or fewer blue card adds from the district office, all the

cards were included in the sample. Table 2.11 presents the

samples and their respective sample sizes.

Limitations

Results pertaining to casing adds are based on data

that were extracted from the Census Bureau files using the

source code variable. Other research has suggested the

existence of errors in the assignment of the source codes.

However, the extent of the error is not known.

The number of blue cards may underestimate the

number of casing adds because 1 blue card could contain

up to 12 separate unit designations.

The count of duplicates may also be underestimated

because of clerical errors annotating the duplicate buff

cards.

Since detailed information on casing undeliverables was

not available, the Postmaster Return indicator from the

Census Bureau’s files was used as a proxy for the casing

data on undeliverables. As a result, the count of undeliverables may be overstated because of errors in using the

indicator. In addition, the count of casing deliverables was

derived by subtracting the count of undeliverables and

duplicates from the count of buff cards sent to casing.

Because some of the duplicates, undeliverables, and

blue cards were lost, the Census Bureau and the USPS

counts of these cards were different. Also, because of the

missing cards, all of the district offices may not be represented in the sample.

At the time this publication went to print, only preliminary

results from the Casing Check evaluation were available.

Final results may differ from those presented in this

section.

Results

USPS—The Census Bureau sent approximately 83,855,000

buff cards to the USPS for casing. As shown in table 2.12,

the USPS considered about 93.9 percent as deliverable;

1.3 percent as duplicates of another buff card; and 4.8

percent as undeliverable as addressed.

Characteristics of the Added Units—It is interesting that

approximately 23.3 percent (931,097/ 3,988,818) of the

blue cards completed by the USPS were added to the

census files. This may indicate that many of the blue card

addresses already existed in the files; could not be assigned

a district office number, an address register area number,

and a block number; or were not valid residential units.

Of the approximately 931,097 housing units added to

the census address file from casing, about 47.0 percent

were in TAR areas and 53.0 percent were in prelist

Table 2.11. Sample of Buff and Blue Cards

Type of address card Estimated cards Sample size

Buff cards:

Duplicates . . . . . . . . . . . . 535,830 2,022

Undeliverables . . . . . . . . 74,214 1,767

Blue cards:

Matches. . . . . . . . . . . . . . 766,800 2,556

Ungeocoded . . . . . . . . . 259,400 2,594

Adds. . . . . . . . . . . . . . . . . 1,679,380 2,115

Table 2.12. Results of Casing as Reported by the

USPS

USPS status of buff cards Number Percent

Total. . . . . . . . . . . . . . . 83,855,000 100.0

Deliverable . . . . . . . . . . . . . 78,751,180 93.9

Duplicate . . . . . . . . . . . . . . . 1,070,781 1.3

Undeliverable . . . . . . . . . . . 4,033,039 4.8

Table 2.13. Housing Units Added by Casing

Tenure

Type of enumeration area

Total

TAR

Prelist

mailout/ mailback

Number Percent Number Percent

Total. . . . . . . . . 931,097 437,329 100.0 493,768 100.0

Owner . . . . . . . . . . . 531,962 193,787 44.3 338,175 68.5

Renter . . . . . . . . . . . 269,660 176,868 40.4 92,792 18.8

Vacant . . . . . . . . . . . 129,475 66,674 15.3 62,801 12.7

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mailout/ mailback areas. More owner-occupied units were

added in prelist mailout/ mailback areas than in TAR areas,

but the opposite was true for renter-occupied units. Of the

units added in TAR areas, 44.3 percent were owneroccupied and 40.4 percent were renter-occupied; compared to 68.5 percent and 18.8 percent, respectively, in

prelist mailout/ mailback areas. The percentage of vacant

units was similar in both types of enumeration areas.

As shown in table 2.14, 99.3 percent of the TAR adds

had a city delivery address. The majority of the adds had a

basic street address size of one unit, 55.8 percent and 64.0

percent for city delivery and noncity delivery, respectively.

The size of the basic street address was not collected in

the 1990 census as a data item and does not reflect the

final census count of units in structure. It is presented only

as an indicator of single units and of multiunit structures.

Table 2.15 shows information for added units in prelist

mailout/ mailback areas. Of the 493,768 added units, 85.6

percent were for city delivery addresses and 14.4 percent

were for noncity delivery addresses. Information on basic

street address for prelist mailout/ mailback areas was not

available from the census files.

Reasons for Undeliverable Buff Cards—Table 2.16

presents an estimated count of the undeliverables and the

reasons addresses were undeliverable. The reasons were

provided by the USPS carriers; if the carrier provided more

than one reason, the first reason given was considered the

primary reason. Of the estimated 74,214 undeliverables

over half (60.2 percent) had ‘‘No Such Address’’ as the

reason the USPS could not case the buff cards. The next

highest category was ‘‘Other Reasons’’ with 34.2 percent;

this included reasons such as ‘‘Unclaimed,’’ ‘‘Undeliverable,’’ ‘‘Addressee Unknown,’’ and ‘‘No Mail Receptacle.’’

The data are based on a sample of the undeliverable

buff cards discussed earlier under Methodology.

Conclusions

Of the 3,988,818 blue cards completed by the USPS,

23.3 percent were added to the census address file which

resulted in a coverage gain of about 0.7 percent in TAR

and 1.7 percent in prelist mailout/ mailback areas.

The preliminary results indicate that the casing check is

an important tool for updating the census address file. It

helped improve the coverage of city delivery addresses in

TAR and prelist mailout/ mailback areas.

References

[1] Aso, Joy. DSSD 1990 Research, Evaluation, and

Experimental Memorandum No. L-1, Revision 2, ‘‘1990

Evaluation Overview—Census Address Check.’’ U.S. Department of Commerce, Bureau of the Census. March 3, 1992.

[2] U.S. Department of Commerce, Bureau of the Census.

IA-0-002, ‘‘Statement of Work—Interagency Agreement

Between the U.S. Department of Commerce, Bureau of the

Census, and the U.S. Postal Service.’’ September 1989.

Table 2.14. Type of Address and BSA for TAR Adds

Type of address and

size of basic street address Number Percent

Total. . . . . . . . . . . . . . . . . . . 437,329 100.0

City delivery . . . . . . . . . . . . 434,455 100.0

(99.3 percent of total)

1 unit . . . . . . . . . . . . . . . . 242,522 55.8

2 to 9 units . . . . . . . . . . . 82,649 19.0

10 to 19 units. . . . . . . . . 25,208 5.8

20 units or more . . . . . . 84,076 19.4

Noncity delivery . . . . . . . . . 2,874 100.0

(0.7 percent of total)

1 unit . . . . . . . . . . . . . . . . 1,838 64.0

2 to 9 units . . . . . . . . . . . 135 4.7

10 to 19 units. . . . . . . . . 116 4.0

20 units or more . . . . . . 785 27.3

Table 2.15. Type of Address for Prelist Mailout/ Mailback Adds

Type of address Number Percent

Total . . . . . . . . . . . . . . . . . . . . 493,768 100.0

City delivery . . . . . . . . . . . . . . . . . . 422,744 85.6

Noncity delivery . . . . . . . . . . . . . . 71,024 14.4

Table 2.16. Casing Undeliverables

Reasons for undeliverables Number Percent

Standard

error

Total . . . . . . . . . . . . . . . . . . . . . . . 74,214 100.0 -

Undeliverables:

No such address . . . . . . . . . . . . . . 44,688 60.2 1.16

Insufficient address . . . . . . . . . . . 1,680 2.3 0.04

Moved . . . . . . . . . . . . . . . . . . . . . . . 168 0.2 0.10

Not deliverable as addressed . . . 2,268 3.1 0.41

Other reasons. . . . . . . . . . . . . . . . . 25,410 34.2 1.13

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CHAPTER 3.

Questionnaire Delivery and Enumeration

RURAL UPDATE/ LEAVE

Introduction and Background

For the 1990 decennial census, about 38 million residential addresses were obtained by Census Bureau enumerators prior to questionnaire delivery during a canvassing operation called Prelist. During Prelist, enumerators

canvassed their assigned areas, listed each address in a

register, and spotted each living quarters on a map. If the

enumerators could not obtain a complete mailing address

for a living quarters, they recorded a location description

for the unit.

The Prelist was conducted in two phases. The first

phase, known as Prelist Mailout/ Mailback, was conducted

from June 1988 through January 1989, primarily in small

cities and suburban areas containing city type addresses

(house number and street name). Addresses listed during

the first phase were enumerated using the mailout/ mailback

methodology since, in general, city type addresses are

recognized by the USPS as mailing addresses.

The second phase, known as Prelist Update/ Leave,

was conducted from June through September, 1989 in

small towns and rural areas in the South and Midwest

where the Census Bureau anticipated problems with the

postal delivery of census questionnaires. The majority of

the addresses in the Prelist Update/ Leave area were rural

type addresses. These addresses were later enumerated

using the Update/ Leave methodology. The Update/ Leave

methodology called for enumerators to deliver the questionnaires; respondents were responsible for mailing them

back.

For the Update/ Leave operation, the enumerators were

supplied with prelabeled census questionnaires for each

housing unit listed in the address register. The enumerators canvassed census blocks in a clockwise direction,

verified address information by brief interviews, and delivered the appropriate questionnaire (short or long form). For

each housing unit that they verified as being missing from

the address register, the enumerators hand addressed the

appropriate blank census questionnaire. For each housing

unit at which no one was home, the enumerators were

instructed to leave the questionnaire in a safe and secure

place.

In addition, the Update/ Leave enumerators updated the

address list by adding, deleting, or transferring listings in

the address register, as necessary. In conjunction with

updating address information, the enumerators were instructed

to update and make corrections to the original Prelist

maps.

Methodology

The Update/ Leave add rate is defined as the number of

addresses added during Update/ Leave with a final status

of occupied or vacant divided by the number of addresses

in the Census Bureau’s files following the Prelist operation

(including deletes).

Limitations

Results pertaining to Update/ Leave adds are based on

data that were extracted from Census Bureau files using

the source code variable. Other research has suggested

the existence of errors in the assignment of Update/ Leave

add source codes. However, the extent of the error is not

known.

The number of Prelist addresses in table 3.1 is slightly

less than that which is presented in the section on the

Prelist/ Update Leave operation in chapter 2, due to processing office file maintenance and the exclusion of special places and clusters; that is, living quarters not accessible at the time of Prelist.

Results

Table 3.1 shows Update/ Leave coverage improvement

results for the regional census centers including all units

added by enumerators during Update/ Leave and adds

remaining on the files after final deletes (true adds).

Table 3.1 shows that 399,404 Update/ Leave addresses

were added to and remained on the census file, which

resulted in a national increase of 4.0 percent to the Prelist

Table 3.1. Update/ Leave Coverage Improvement

Results

Regional

Census

Center

Prelist

addresses

Update/

Leave

adds

Adds Deleted

after

Update/ Leave

True adds

(not deleted)

Volume

Percent

of

adds Volume

Percent

of

Prelist

Detroit . . . . . 446,163 19,979 1,518 7.6 18,461 4.1

Chicago . . . . 70,010 2,588 159 6.1 2,429 3.5

Kansas City 2,458,611 68,743 4,285 6.2 64,458 2.6

Charlotte . . 3,250,572 176,971 18,608 10.5 158,363 4.9

Atlanta . . . . 1,617,280 85,065 7,834 9.2 77,231 4.8

Dallas . . . . . 2,124,026 84,660 7,412 8.8 77,248 3.6

Denver . . . . . 53,458 1,254 40 3.2 1,214 2.3

Total. . . . 10,020,120 439,260 39,856 9.1 399,404 4.0

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file. Note that the Charlotte and Atlanta Regional Census

Centers had the two highest add rates among Update/ Leave

regional census centers. In addition, the Charlotte and

Atlanta Regional Census Centers had the two highest

rates of building permits issued for new, privately-owned

housing units in 1989 among the seven regional census

centers shown in table 3.1. These data suggest that the

Charlotte and Atlanta Regional Census Centers contained

areas of high growth.

The Update/ Leave enumerators added 439,260 addresses

to the original 10,020,120 Prelist addresses (4.4 percent

before final deletes). Of these adds, 39,856 (9.1 percent)

were later deleted from the census as erroneous adds. A

unit was considered a final delete if it was deleted during

the Field Followup operation.

Approximately 50 percent of the erroneous adds were

classified as no-such-unit and 32 percent were classified

as duplicates. Of the remaining erroneous adds, 7 percent

were demolished, condemned, or unclassified, 6 percent

were ‘‘open to the elements,’’ 3 percent were businesses,

and 2 percent were whole special places already on the

Census Bureau’s files.

Comparison of Rural Addresses in the Update/ Leave

and Prelist Mailout/ Mailback Areas—Figure 3.1 illustrates the differences between the Update/ Leave and

Prelist Mailout/ Mailback areas in proportion of rural addresses

for each regional census center. Rural type addresses

include Rural Route and box number addresses, Highway

Contract Route, Star Route, Post Office Box, and General

Delivery addresses.

Figure 3.1 shows that the Prelist Update/ Leave areas

were predominately rural type addresses whereas the

Prelist Mailout/ Mailback areas were primarily composed of

city type addresses, that is, house number and street name

addresses. The proportion of rural addresses shown in

figure 3.1 are based on the volume of Prelist addresses. At

the national level, 69.1 percent of the Prelist Update/ Leave

addresses were rural, compared to 24.2 percent rural

addresses for the Prelist Mailout/ Mailback area.

Comparison of Vacancy Rates in the Update/ Leave

and Prelist Mailout/ Mailback Areas—Figure 3.2 illustrates the differences between the Update/ Leave and

Prelist Mailout/ Mailback areas in vacancy rates for each

regional census center. The vacancy rate is the proportion

of valid census units verified during Field Followup as

vacant on Census Day.

For the seven regional census centers shown in figure

3.2 that contain both Update/ Leave and Prelist Mailout/

Mailback units, the vacancy rates for the Update/ Leave

area are higher than the vacancy rates for the Prelist

Mailout/ Mailback area, with the exception of the Atlanta

Regional Census Center (the Update/ Leave vacancy rate

is one percentage point lower than the Prelist Mailout/ Mailback

vacancy rate in the Atlanta Regional Census Center). The

Update/ Leave area had an overall vacancy rate of 13.5

percent, compared with the 9.4 percent rate for the Prelist

Mailout/ Mailback area. The New York Regional Census

Center had the highest Prelist Mailout/ Mailback vacancy

rate, which suggests that some of the 235,095 Prelist

Mailout/ Mailback units in this regional census center should

probably have been included in Update/ Leave.

Vacant addresses may be less recognizable to postal

carriers than addresses for occupied units since the USPS

generally does not deliver mail to vacant housing units.

Based on this assumption, it appears that the Census

Bureau generally did a good job of determining which

areas should be enumerated using the Update/ Leave

methodology in lieu of a Mailout/ Mailback census.

Comparisonof Incomplete Addressesinthe Update/ Leave

and Prelist Mailout/ Mailback Areas—Figure 3.3 illustrates the differences between the Update/ Leave and

Prelist Mailout/ Mailback areas in proportion of Prelist

addresses that the Census Bureau classified as incomplete for each regional census center.

During Prelist, the enumerators were sometimes unable

to obtain complete addresses. In some cases, the enumerator could not obtain a complete address for a vacant unit

or could not find a knowledgeable respondent to provide a

complete address for a unit where the occupant was

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unavailable. These addresses were flagged as incomplete

during processing using a computer algorithm. This algorithm identified Prelist addresses missing certain items that

the Census Bureau determined as necessary for USPS

questionnaire delivery, such as house number or rural

route number.

The incomplete addresses for the Prelist Mailout/ Mailback

area were not sent to the APOC since they were presumably undeliverable. The APOC is the first postal check on

the Prelist Mailout/ Mailback address list. The algorithm

was applied to Prelist addresses in the Update/ Leave area

even though the USPS was not involved in questionnaire

delivery.

Note that the data in figure 3.3 do not include addresses

with only location descriptions since this type of address

was considered acceptable during the 1989 Prelist operation.

Figure 3.3 shows that in the regional census centers

with both Prelist Update/ Leave and Prelist Mailout/ Mailback,

the Update/ Leave areas had a consistently higher percentage of incomplete addresses than the Prelist Mailout/ Mailback

areas. Overall, 21.2 percent of the Prelist addresses for the

Update/ Leave area were incomplete, compared to 3 percent for the Prelist Mailout/ Mailback area.

Conclusions

The Update/ Leave enumerators did a good job of

updating the 1989 Prelist address list and delivering over

10 million questionnaires. The enumerators added 399,404

valid addresses, which resulted in a 4.0 percent increase.

The high proportion of incomplete Prelist addresses

(not including those with only location descriptions) for the

Update/ Leave area, along with the high proportions of

vacant units and rural addresses, suggest that the Census

Bureau accurately identified areas of potential USPS questionnaire delivery problems in delineating the Update/ Leave

area.

References

[1] ‘‘Current Construction Report C40-89-13.’’ U.S. Department of Commerce, Bureau of the Census.

[2] Pausche, Joan M. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 71, ‘‘Evaluation of the Update/ Leave Methodology in the 1990 Decennial Census.’’ U.S. Department of Commerce, Bureau of

the Census. November 1, 1991.

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[3] Pausche, Joan M. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 88, ‘‘Prelist

Address File Creation for the 1990 Census.’’ U.S. Department of Commerce, Bureau of the Census. November 1,

1991.

URBAN UPDATE/ LEAVE

Introduction and Background

The Urban Special Enumeration has two separate components, Urban Update/ Leave and Urban Update/ Enumerate.

The Urban Update/ Leave procedures were developed to

address potential questionnaire delivery problems in many

of the nation’s urban areas. This enumeration methodology was implemented within preidentified census blocks

consisting almost entirely of inner city public housing

developments containing 500 or more units. Census blocks

meeting the criteria, especially those containing units with

small or broken mail receptacles, were identified by the

regional census centers with input from inner city housing

authorities.

An integral part of Urban Update/ Leave was the outreach program, which provided direct and detailed information to the targeted population. The effort included

hiring residents of housing developments to distribute

census literature and brochures and to place census

posters in high visibility areas of the developments. The

outreach staff attended tenant association meetings, local

church group meetings, and other local gatherings to raise

tenant awareness and answer questions about how the

census data would benefit them and their community.

The Urban Update/ Leave operation began on March 8,

1990 and was completed by Census Day, April 1, 1990.

Blocks that were part of the Urban Update/ Leave were

printed on address registers and enumerators were given

precanvass maps. At each address, the enumerator conducted a brief interview to verify the address. Based on this

information, the enumerator made corrections to the address

register and annotated questionnaires for all deleted units.

Respondents were given a prelabeled questionnaire to

complete and return to the processing office. For addresses

not in the register, the enumerator addressed a blank label

questionnaire. Questionnaires followed the same processing route as TAR questionnaires, except those from Los

Angeles District Office 3230, which were processed in the

district office.

When the Urban Update/ Leave workload file was produced for the questionnaire mailing package vendor, some

Urban Update/ Leave areas were erroneously included in

the TAR listings. This problem was twofold in that the

district offices were unable to retrieve their missing Urban

Update/ Leave questionnaires from the Post Offices and

the district offices did not have a sufficient supply of blank

questionnaires for the units that were missing questionnaires. Based on Census Bureau Headquarters staff observations, many units never received questionnaires during

the Urban Update/ Leave operation since the district offices

were unable to obtain additional blank questionnaires in

time for delivery by the enumerators.

Urban Update/ Leave was conducted in 346 census

blocks in Chicago, Detroit, Los Angeles, Baltimore, Cleveland, and Philadelphia, all of which were TAR areas.

Methodology

To assess the effectiveness of the Urban Update/ Leave

compared to the TAR mailout/ mailback, whole blocks

were chosen from TAR areas as a control group to which

the Urban Update/ Leave methodology could be compared.

Initially, New York City and the District of Columbia were

included in the Urban Update/ Leave workload but their

regional census center directors chose to exclude the

cities from special urban enumeration procedures. Blocks

in these cities that were originally selected for the special

urban operation were chosen as the Urban Update/ Leave

control group.

The mail return rate is defined as the ratio of the number

of households that returned a questionnaire by mail to the

number of occupied units that should have received a

questionnaire by mail or through delivery by an enumerator.

Limitations

Although the Urban Update/ Leave procedure was intended

for inner city public housing developments containing 500

or more units, only 77.2 percent of the units in the Urban

Update/ Leave area were in multiunit structures, 20.7 percent were single units, and 2.1 percent were classified as

other types of units, such as a basement apartment. Thus

definitive conclusions cannot be made about the effectiveness of Urban Update/ Leave in public housing developments based on the data presented in this memorandum.

Conclusions about the Urban Update/ Leave methodology versus the TAR mailout/ mailback enumeration methodology based on comparisons of results from Urban

Update/ Leave areas and the control group should be

made with caution, considering the limitations inherent in

the method used to select the control group.

No data for added units are presented in this evaluation

due to problems identifying Urban Update/ Leave adds.

Since special urban enumeration adds are not distinguished from other address listings on the Address Control

File, an attempt was made to extract add data directly from

the Urban Update/ Leave address registers. However, only

17 address registers from 346 blocks in the Urban Update/

Leave workload could be located at the time of the

evaluation.

Results

Urban Update/ Leave Area Profile—Figure 3.4 shows

the types of structures in the Urban Update/ Leave blocks

within each city. These data are tabulated responses to

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question H2 on the census questionnaire, ‘‘Which best

describes this building?’’ Figure 3.4 shows only housing

units with a final status of occupied or vacant.

In figure 3.4, single units include one-family detached

units, one-family attached units, trailers, and mobile homes,

as described on the questionnaire. Multiunit structures are

buildings with two or more apartments.

In the Urban Update/ Leave area, Chicago, Cleveland

and Los Angeles have the highest proportions of multiunit

structures (93.7 percent, 90.9 percent and 87.3 percent,

respectively). Baltimore has the lowest percentage of

multiunit structures (53.3 percent) in the Urban Update/ Leave

area.

In the Urban Update/ Leave area, 20.7 percent are

singleunitscomparedto 2.1percent fortheUrbanUpdate/ Leave

control group, although the Urban Update/ Leave procedure was originally intended for inner city blocks consisting

almost entirely of multiunit housing developments.

Figure 3.5 presents the vacancy rates for the Urban

Update/ Leave and Urban Update/ Enumerate areas within

each city. Figure 3.5 contains only housing units with a final

status of occupied or vacant.

As shown in figure 3.5, the vacancy rates vary considerably among cities containing Urban Update/ Leave blocks.

Detroit had the highest vacancy rate (53.7 percent) compared to Los Angeles, which had the lowest vacancy rate

(3.3 percent).

The proportion of vacant units in the Urban Update/ Leave

area (27.1 percent) was substantially higher than the

proportion for the Urban Update/ Leave control group (2.4

percent). The method by which the Urban Update/ Leave

control group was chosen may explain these large differences in vacancy rate and characteristics of vacant units.

Figure 3.6 shows race distribution in Urban Update/ Leave

areas within each city.

Figure 3.6 shows that Urban Update/ Leave blocks in

Los Angeles have the most even distribution of Black (31.6

percent), White (30.1 percent), and non-Black minority

persons (38.3 percent) compared to Urban Update/ Leave

areas within other cities. The Urban Update/ Leave blocks

in Chicago contain the highest proportion of Black persons

at 98.8 percent.

The majority of the persons in the Urban Update/ Leave

area were Black (88.4 percent), while only a small percentage of the persons were White or classified as non-Black

minority (6.9 and 4.7 percent, respectively). The Urban

Update/ Leave control group contained a higher proportion

of White and non-Black minority persons (16.6 and 23.8

percent, respectively) than the Urban Update/ Leave area.

In addition, approximately 5.6 percent of persons in the

Urban Update/ Leave areas were of Hispanic origin compared to 36.8 percent for the control group.

Mail Return Rates—Figure 3.7 shows mail return rates for

Urban Update/ Leave areas in six cities, as well as the

control group which is comprised of blocks in the District of

Columbia and New York.

Figure 3.7 shows the highest mail return rate for Urban

Update/ Leave areas in Detroit (65.4 percent) and the

lowest for Philadelphia (40.8 percent). Overall, the mail

return rate for the Urban Update/ Leave areas was 51.5

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percent compared to 61.7 percent for the Urban Update/

Leave control group. Results were similar for the shortform and long-form mail return rates. In Urban Update/ Leave

areas, the short form rate was 52.4 percent and the long

form rate was 47.5 percent. For the control group, the

short form and long form mail return rates were consistently higher at 62.5 and 56.9 percent, respectively.

Lower mail return rates in the Urban Update/ Leave area

may be due in part to the lack of available blank questionnaires for housing units that did not have vendor-labeled

questionnaires because of a questionnaire distribution

problem. This problem affected the entire Urban Update/ Leave

area and was not clustered in certain district offices. In

addition, differences between the Urban Update/ Leave

area and the control group for race distribution and the

proportions of single versus multiunit stuctures may have

affected mail return rates.

Conclusions

The mail return rate for the Urban Update/ Leave areas

was 51.5 percent compared to 61.7 percent for the Urban

Update/ Leave control group. Lower mail return rates in the

Urban Update/ Leave area may be due in part to the lack of

available blank questionnaires for housing units that did

not have vendor-labeled questionnaires because of a

questionnaire distribution problem. In addition, conclusions

about the Urban Update/ Leave mail return rate versus the

mail return rate for the TAR mailout/ mailback control

group should be made with caution considering the inadequacies in identifying the target population and the limitations inherent in the method used to select the control

group.

Based on Census Bureau Headquarters staff observation reports, conducting Urban Update/ Leave in densely

populated, urban housing projects ensured the delivery of

the appropriate questionnaire to the correct unit, which

greatly reduced apartment mixup problems. However, many

of the areas where the Urban Update/ Leave enumerators

were working were dangerous; enumerators worked in

groups of two or more. Staff members reported that the

enumerators were resourceful and conscientious during

Urban Update/ Leave, despite difficult working conditions.

The outreach program in the Urban Update/ Leave area

appeared to be successful, based on Headquarters staff

reports. The program increased awareness of and enthusiasm toward the 1990 census. Outreach program staff

reportedly received positive responses from the communities in which the Urban Update/ Leave outreach was

conducted.

References

[1] 1990 Decennial Census Information Memorandum No.

95, ‘‘Operation Requirements Overview—1990 Urban Enumeration.’’ U.S. Department of Commerce, Bureau of the

Census. November 8, 1988.

[2] Aponte, Maribel. DSSD/ FLD 1990 Decennial Census

Observation Report No. 76, ‘‘Observation Report—1990

Urban Update/ Leave.’’ U.S. Department of Commerce,

Bureau of the Census. April 17, 1990.

[3] Bates, Lawrence. DSSD/ FLD 1990 Decennial Census

Observation Report No. 70, ‘‘Observation of Urban Update/

Leave in Chicago.’’ U.S. Department of Commerce, Bureau

of the Census. April 4, 1990.

[4] Dingbaum, Tamara. DSSD 1990 Research, Evaluation,

and Experimental Memorandum No. N-1, revised, ‘‘1990

Evaluation Overview—Urban Enumeration Procedures.’’

U.S. Department of Commerce, Bureau of the Census.

January 4, 1990.

[5] Roberts, Michele A. DSSD/ FLD 1990 Decennial Census Observation Report No. 68, ‘‘Observation Report—

1990 Urban Update/ Leave.’’ U.S. Department of Commerce, Bureau of the Census. April 11, 1990.

URBAN UPDATE/ ENUMERATE

Introduction and Background

The Urban Special Enumeration has two separate components, Urban Update/ Leave and Urban Update/ Enumerate.

The Urban Update/ Enumerate procedures were developed to field verify the occupancy status of concentrated

areas of boarded up units, thus eliminating the Field

Followup vacant/ delete check, and to enumerate persons

living in these structures who might otherwise be missed

by the 1990 census. This procedure was used in selected

cities to enumerate pre-identified whole census blocks of

boarded up, presumably vacant units.

The Urban Update/ Enumerate operation was conducted

around Census Day. During Urban Update/ Enumerate,

enumerators canvassed their areas, returned completed

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questionnaires for any occupied and vacant units, and

completed a deletion record for each deleted unit. Questionnaires followed the same processing route as TAR

mailout/ mailback questionnaires, except that Urban

Update/ Enumerate questionnaires were excluded from

Telephone Followup, Nonresponse Followup, and the Field

Followup vacant/ delete check.

Urban Update/ Enumerate was conducted in 96 blocks

in Detroit and New York City.

Methodology

Since the Urban Update/ Enumerate operation was conducted in whole blocks, the results presented in this

publication are based on questionnaire level data for each

housing unit within an Urban Update/ Enumerate block.

Limitations

Although the Urban Update/ Enumerate procedure was

intended for blocks consisting entirely of boarded up units,

the regional census centers experienced problems in

accurately identifying blocks that met this criterion. In New

York, census staff selected blocks by driving around areas

that presumably consisted of boarded up housing developments. Although the blocks contained what appeared to

be vacant, boarded up units, only 8.3 percent of the vacant

units were boarded up. In Detroit, census staff selected

blocks in which units were to have been vacated in

preparation for the building of an airport in one address

register area and an automobile manufacturing plant in

another address register area. Due to a delay in these

construction projects, many of the units were still occupied

on Census Day; only 21.3 percent of the vacant units in

these blocks were boarded up.

Since only 10.4 percent of the vacant units in the Urban

Update/ Enumerate were boarded up and 86.5 percent of

all units were occupied on Census Day, definitive conclusions cannot be made regarding the effectiveness of this

special enumeration procedure in areas consisting almost

entirely of boarded up housing units.

No data for added units is presented in this evaluation

due to problems identifying Urban Update/ Enumerate

adds. Since special urban enumeration adds are not

distinguished from other address listings on the Address

Control File, an attempt was made to extract add data

directly from the Urban Update/ Enumerate address registers. However, address registers for only one of the 96

blocks in the Urban Update/ Enumerate workload could be

located at the time of the evaluation.

Results

The Urban Update/ Enumerate area in New York City

was almost completely composed of multiunit structures

(99.7 percent), whereas only 37.3 percent of the Detroit

housing units were multiunit structures. These data are

tabulated responses to question H2 on the census questionnaire, ‘‘Which best describes this building?’’ The results

describe only housing units with a final status of occupied

or vacant. Multiunit structures are buildings with two or

more apartments, whereas single units include one-family

detached units, one-family attached units, trailers and

mobile homes, as described on the questionnaire.

Overall, 10.9 percent of the structures in the Urban

Update/ Enumerate area were single units.

The vacancy rate for Urban Update/ Enumerate blocks

in Detroit (12.9 percent) is comparable to the vacancy rate

for New York Urban Update/ Enumerate blocks (13.6 percent). The data are based on housing units with a final

status of occupied or vacant.

The overall proportion of vacant units that were boarded

up in the Urban Update/ Enumerate area was 10.4 percent,

although this proportion should have exceeded 95 percent

by definition.

Figure 3.8 shows race distribution in Urban Update/

Enumerateareaswithineachcity.FortheUrbanUpdate/ Enumerate

areas, the proportion of Black persons in Detroit (88.6

percent) is comparable to New York (91.4 percent), although

the proportion of White persons in the Detroit blocks (9.8

percent) is higher than New York (2.2 percent) and the

proportion of non-Black minority persons in the Detroit

blocks (1.6 percent) is lower than New York (6.4 percent).

Overall, the Urban Update/ Enumerate area contained

90.8 percent Black persons and approximately 7.6 percent

of the persons in the Urban Update/ Enumerate area were

of Hispanic origin.

Conclusions

The Urban Update/ Enumerate was not conducted in

blocks consisting entirely of boarded up units, as was the

original intent.

Since only 10.4 percent of the vacant units in the Urban

Update/ Enumerate were boarded up and 86.5 percent of

all units were occupied on Census Day, definitive conclusions cannot be made regarding the effectiveness of this

special enumeration procedure in areas consisting almost

entirely of boarded up housing units.

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References

[1] 1990 Decennial Census Information Memorandum No.

95, ‘‘Operation Requirements Overview—1990 Urban Enumeration.’’ U.S. Department of Commerce, Bureau of the

Census. November 8, 1988.

[2] Dingbaum, Tamara. DSSD 1990 Research, Evaluation,

and Experimental Memorandum No. N-1, revised, ‘‘1990

Evaluation Overview—Urban Enumeration Procedures.’’

U.S. Department of Commerce, Bureau of the Census.

January 4, 1990.

POSTMASTER RETURN QUESTIONNAIRE

DELIVERY

Introduction and Background

The Postmaster Return Questionnaire Delivery operation was an effort by the district offices to deliver census

questionnaires. These questionnaires were for addresses

identified as undeliverable by the USPS. This project

occurred in April 1990 before the Nonresponse Followup

operation. Low mail response rates and a high postmaster

return questionnaire rate prompted the decision by the

Census Bureau to conduct the Postmaster Return Questionnaire Delivery operation.

The operation required the district office staff to attempt

to deliver questionnaires that the USPS did not deliver. For

the purpose of discussion, ‘‘undelivered questionnaires’’

are postmaster return questionnaires that both the USPS

and the district offices were unable to deliver. The district

offices attempted to collect all of the postmaster return

questionnaires. The district offices were instructed to

identify postmaster return questionnaires by geographic

area and to get the address listing pages (forms D-108A,

see appendix B) for these areas. The postmaster return

questionnaires were geographically grouped by ZIP Code

or address register area. The district offices furnished the

enumerators with postmaster return questionnaires and

the corresponding address listing pages. The enumerators

were to use the address listing pages to check off each

address to which they delivered a postmaster return

questionnaire and to mark on the address listing pages if

the housing unit was vacant, demolished, or nonexistent

[6]. The district offices implemented these procedures to

try to increase the mail response rate and thus reduce the

nonresponse followup workload. The evaluation of the

Postmaster Return Questionnaire Delivery operation is

divided into the following three separate components:

• Stage I District office survey

• Stage II Undelivered questionnaires

• Stage III Address listing pages—forms D-108A

Methodology

District Office Survey—Immediately after the Postmaster

Return Questionnaire Delivery operation, the Census Bureau

requested each district office to complete and return a

Postmaster Return Questionnaire Delivery project survey

(see appendix B). The goals of the survey were to determine how the district offices conducted the operation, to

obtain estimates of the workloads, and to determine what

materials were available for further analysis. The survey

requested information on:

• the number of postmaster return questionnaires received,

assigned, and delivered and

• anecdotal information from the district office staff about

the reasons for undeliverability.

The results of the survey were examined to fulfill the

goals of the Stage I evaluation and to plan for Stage II and

Stage III.

Undelivered Questionnaires—After the district offices

completed the operation, the undelivered questionnaires

were examined. The undelivered questionnaires were stored

in cartons on 659 pallets. For the postmaster return

questionnaire evaluation, a 10- percent systematic sample

of the 659 pallets of undelivered questionnaires was

selected. From the 66 sample pallets, 8 cartons of undelivered questionnaires from each pallet were selected with

equal probability. This resulted in a total of 528 cartons of

undelivered questionnaires for the Stage II evaluation

sample processing. The USPS undeliverability reason was

annotated on the questionnaire by the postal carrier. The

undelivered questionnaires were sorted by the USPS undeliverability reason.

Once the undelivered questionnaires were sorted and

batched by USPS undeliverability reason, the batches

were sent for data entry. The Census Bureau estimated

totals, percents, and sampling variances for each of the

USPS undeliverability reasons by final census status and

type of enumeration area. The sampling variances were

calculated using jackknife replication. Tests for significance were calculated for α = 10 percent.

Address Listing Pages—The only available records for

assessing the delivered universe were the address listing

pages. A review of a sample of the address listing pages

and data from the district office survey led the Census

Bureau to conclude that they were a reliable source.

Therefore, the address listing pages were examined. A

total of 187 cartons of address listing pages were reviewed.

All the address listing pages having any annotations were

pulled for further review. The census ID numbers corresponding to delivered and undelivered postmaster return

questionnaires were coded and later keyed. The codes

summarized the district office reasons for undeliverability

and not the USPS reasons for undeliverability.

The mail response rate was calculated in order to

estimate the increase in mail response due to the delivery

of the postmaster return questionnaires. For the mail

response rate, the denominator is the count of postmaster

return ID numbers annotated on an address listing page as

having been delivered as a result of this operation. The

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numerator includes all of these ID numbers that were

checked in on or before May 3, 1990. Additional data

allowed the Census Bureau to profile, for the delivered ID

numbers, the type of enumeration area and the final

census status.

Limitations

District Office Survey—Conclusions about the reasons

for USPS undeliverability for delivered postmaster return

questionnaires must be based on the district office staff’s

recollection and any records maintained.

Two of the district offices contained no mailout areas.

Therefore, it was impossible for these two district offices to

have any postmaster return questionnaires. Thus, the two

district offices were removed from the analysis. After

removing the two district offices, 410 of the 447 district

offices responded to the survey. The figures are not

weighted to account for the 37 nonresponding district

offices. The nonresponding district offices could distort the

data distributions slightly; however, the totals represent

lower bounds for each category.

The postmaster return questionnaire rates may understate the scope of the USPS delivery problems. The rates

are calculated as a ratio of the postmaster return questionnaires to the total housing counts. The postmaster return

questionnaires were only in mailout areas; TAR areas and

prelist mailout/ mailback areas. The denominator of the

ratio included housing counts for all areas, including nonmailout areas.

Undelivered Questionnaires—The Census Bureau can

determine the USPS undeliverability reason for only the

postmaster return questionnaires that were not delivered

by the district offices.

After the sample was selected, the questionnaires were

sorted by USPS deliverability reason. At that time the

information that connected the questionnaire to the sampling unit (pallet and box) was lost. This is not a problem at

the first stage, as each pallet had an equal probability of

selection. This is a problem at the second stage, due to

variation in the number of boxes per pallet. Therefore,

there was not a way of computing the second stage

sample weights in strict accordance with the sample

design. Several possible solutions to the problem were

investigated. Based on the investigation, the average of

the second stage weights was used.

There were some questionnaires in the boxes for nonmailout areas which were included in the sample. These

questionnaires were removed from the analysis since they

were not part of the mailout universe.

The count estimates are rounded to the nearest thousand; however, the unrounded values were used when

calculating the percentages.

The ID number was not obtained for approximately 900

questionnaires. These questionnaires were probably for

addresses added as a result of an operation such as

casing or telephone questionnaire assistance. We did not

include these questionnaires in the evaluation. This represented about 0.7 percent of the sampled universe.

Address Listing Pages—The analysis only reflects data

from the address listing pages shipped to the processing

office. In addition, the district offices might not have

correctly annotated the address listing pages. Based on

the Stage I analysis (the district office survey) we estimated that between 1.5 million and 2.4 million postmaster

return questionnaires were delivered. The data from Stage

III represent less than 80,000 cases. Some of the address

listing pages may have been shipped erroneously to the

regional census centers or destroyed. Therefore, the analysis of the address listing pages is able to identify only a

minority of the delivered postmaster return questionnaires.

The evaluation universe does not equally represent all

parts of the country. Annotated address listing pages were

received from all regional census centers with the exception of New York. A closer look at this universe indicates

that only 34 district offices provided address listing pages

that identified one or more delivered postmaster return

questionnaires. The data should be fairly representative of

those 34 district offices. Even though the evaluation universe does not equally represent all parts of the country,

the distribution of delivered postmaster return questionnaires with a final census status of occupied or vacant by

district office type is close to the national figures. Caution

should be used when constructing profiles of the universe

of delivered and undelivered postmaster return questionnaires from these data. The true universe of delivered and

undelivered postmaster return questionnaires may be quite

different.

Results

District Office Survey—The objectives of the district

office survey are classified into two areas: first, to quantify

the scope of the postmaster return questionnaire workload; and second, to facilitate planning the Stage II and

Stage III evaluations.

Postmaster Return Questionnaire Workload—Approximately

45.9 percent, 41.2 percent, and 42.9 percent of the

responding district offices provided the actual number of

postmaster return questionnaires received, assigned, and

delivered, respectively. The remaining district offices checked

a range value. Based on these results we estimate that

between 5.4 and 7.6 million postmaster return questionnaires were received, between 3.1 and 4.3 million postmaster return questionnaires were assigned for delivery,

and between 1.5 and 2.4 million postmaster return questionnaires were delivered.

Table 3.2 shows the district office level distribution of

the received, assigned, and delivered postmaster return

questionnaires. Of the 410 responding district offices, 95

(23.2 percent) received between 20,001 and 50,000 postmaster return questionnaires. Only four district offices (1.0

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percent) received more than 50,000 postmaster return

questionnaires. The majority of the responding district

offices received 15,000 or less postmaster return questionnaires.

Of the 410 responding district offices, five (1.2 percent)

assigned none of the received postmaster return questionnaires for delivery. The majority of the responding district

offices assigned 15,000 or less postmaster return questionnaires; however, 42 district offices (over 10 percent)

assigned over 20,000 postmaster return questionnaires.

Of the 410 responding district offices, 122 (30.0 percent) delivered less than 1,000 postmaster return questionnaires. For 263 (64.4 percent) responding district offices,

the district office staff delivered 5,000 or less postmaster

return questionnaires. More than 15,000 postmaster return

questionnaires were delivered in 27 (6.6 percent) of the

responding district offices.

Stage II Evaluation Planning Data—Approximately 79.5

percent of district offices stated that most or all of the

postmaster return questionnaires had the USPS undeliverability reason annotated on the envelope. Based on this

finding, the Census Bureau decided to select a sample of

undelivered questionnaires and summarize the USPS undeliverability reasons.

Approximately 73.9 percent of the district offices reported

that the USPS undeliverability reason of ‘‘Vacant Unit’’

occurred often. In addition, 17.1 percent of the district

offices reported the USPS undeliverability reason of ‘‘Vacant

Unit’’ occurred sometimes. Similarly, a large proportion of

the district offices reported that the USPS undeliverability

reasons of ‘‘Duplicate Unit,’’ ‘‘Demolished or Nonexistent,’’ and ‘‘Post Office Box’’ occurred often and occurred

sometimes. Based on these findings, the Stage II undeliverability categories were developed.

Stage III Evaluation Planning Data—Less than 10 percent

of the responding district offices reported that accurate

records exist of the received postmaster return questionnaire ID numbers and/ or the delivered postmaster return

questionnaire ID numbers. In addition, over 50 percent of

the responding district offices stated that no records of the

address register areas involved in the delivery existed.

However, a large proportion of the district offices stated

that the address listing pages (forms D-108A) were used

during delivery and that enumerators annotated the address

listing pages. This information supported the use of the

address listing pages as a way of identifying the delivered

questionnaire universe.

Undelivered Questionnaires

USPS Undeliverability Reasons— In TAR and prelist mailout/

mailback areas, the Census Bureau mailed out approximately 88.2 million questionnaires [3]. These addresses

were added to the census address file from one of the

following precensus operations: vendor list, prelist, APOC,

precanvass, APOC reconciliation, re-added precanvass

deletes, yellow cards, special place prelist, precensus

local review, and casing. From table 3.3 there were

approximately 5,272,000 (standard error = 460,000) undelivered questionnaires. This represents approximately 6.0

percent of the mailout universe.

Table 3.3 shows that 33.6 percent of the undelivered

questionnaires were annotated as ‘‘vacant’’ by the USPS.

The procedures for delivery did not specifically instruct the

USPS to deliver a census questionnaire to a vacant unit [7]

and [8]. In general the USPS does not deliver to vacant

units. By removing the questionnaires that the USPS

annotated as ‘‘vacant,’’ we are examining the questionnaires which are undeliverable by the Census Bureau

definition. Therefore, we estimate 3,501,000 (standard

error = 334,000) questionnaires were truly undeliverable,

representing about 4.0 percent of the mailout universe.

Table 3.3 shows that 36.6 percent of the undelivered

questionnaires (excluding vacants) were annotated as ‘‘no

such address’’ by the USPS. Note that 12.8 percent of the

undelivered questionnaires were annotated as ‘‘no such

apartment’’ by the USPS. The ‘‘no such apartment’’ category is a subgroup of ‘‘no such address.’’ Collapsing the

two categories would result in 49.4 percent (standard

error = 2.6 percent) of the undelivered questionnaires

(excluding vacants) being annotated as ‘‘no such address/

apartment’’ by the USPS.

Table 3.2. Distribution of Postmaster Return Questionnaires

Number of postmaster

return questionnaires

Number of district offices

Received Assigned Delivered

None . . . . . . . . . . . . . . . . . . . . . . . . . . 0 5 0

Less than 1,000 . . . . . . . . . . . . . . . . 4 36 122

1,000 to 5,000 . . . . . . . . . . . . . . . . . 43 100 141

5,000 to 10,000 . . . . . . . . . . . . . . . . 105 106 71

10,001 to 15,000 . . . . . . . . . . . . . . . 94 64 33

15,001 to 20,000 . . . . . . . . . . . . . . . 63 48 18

20,001 to 50,000 . . . . . . . . . . . . . . . 95 41 9

More than 50,000 . . . . . . . . . . . . . . . 4 1 0

Don’t know. . . . . . . . . . . . . . . . . . . . . 1 5 5

Nonresponse . . . . . . . . . . . . . . . . . . . 1 4 11

Total . . . . . . . . . . . . . . . . . . . . . . 410 410 410

Table 3.3. Reasons for Undeliverability

USPS undeliverability

reason

Including vacants Excluding vacants

Percent

Standard

errror Percent

Standard

error

1. Vacant . . . . . . . . . . . . . 33.6 (2.5)

2. Duplicate . . . . . . . . . . 9.1 (1.1) 13.7 (1.6)

3. Demolished/ new

construction . . . . . . . . 3.2 (0.4) 4.8 (0.6)

4. Nonresidential . . . . . . 1.9 (0.3) 2.8 (0.4)

5. No such address . . . 24.3 (1.7) 36.6 (2.2)

6. No such apartment . 8.5 (0.8) 12.8 (1.2)

7. Post office box . . . . . 0.4 (0.1) 0.6 (0.1)

8. No mail receptacle . . 4.9 (0.8) 7.4 (1.2)

9. Other . . . . . . . . . . . . . . 5.2 (0.5) 7.8 (0.8)

10. No reason written . . 9.0 (1.9) 13.5 (2.8)

Total . . . . . . . . . . . 100.0 100.0

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Type of Enumeration Area of the Undelivered Questionnaires—

Table 3.4 compares the percentages of undelivered questionnaires by USPS undeliverability reason within TAR and

prelist mailout/ mailback areas. Several of the USPS undeliverability reasons are collapsed in table 3.4. The proportion of undelivered questionnaires annotated as ‘‘vacant’’

was slightlyhigher in TAR areas versus prelist mailout/ mailback

areas. The proportion of undelivered questionnaires annotated as ‘‘no such address/ apartment’’ was slightly higher

in prelist mailout/ mailback areas versus TAR areas. Two

possible reasons for the larger percentage of ‘‘no such

address/ apartment’’ in prelist mailout/ mailback areas could

be that the addresses are in areas that do not receive city

mail delivery and/ or the Prelist operation did not obtain the

mailing address.

The only percentages which are not significantly different across type of enumeration area are for the ‘‘duplicate’’ and ‘‘other or no reason written’’ categories. For all

the remaining categories the percentages for TAR and

prelist mailout/ mailback areas are significantly different.

Final Census Status of the Undelivered Questionnaires—

Table 3.5 contains the estimates of the percent of undelivered questionnaires by their final census status. Slightly

more than one-half of the undelivered questionnaires were

enumerated as occupied or vacant; 23.7 percent and 32.7

percent, respectively. Therefore, if the USPS classifies an

address as undeliverable it does not automatically follow

that the address does not exist.

Table 3.6 contains the percentages of occupied, vacant,

and delete by the USPS undeliverability reasons. Several

of the USPS undeliverability reasons are collapsed in this

table. Approximately 60.6 percent of the undelivered questionnaires annotated as ‘‘vacant’’ by the USPS were

enumerated as vacant during the census. This suggests

that the USPS is able to identify vacant units at a high rate.

It is interesting to note that the Census Bureau wanted the

USPS to deliver questionnaires to vacant units. However,

the procedures for delivery stated that each carrier should

have a census questionnaire for each living quarters

(occupied or vacant) on his/ her route. The procedures did

not specifically tell the USPS to deliver to vacant units [7]

and [8]. An estimated 19.8 percent of the undeliverable

questionnaires annotated as ‘‘vacant’’ by the USPS were

enumerated as occupied. This could indicate erroneous

enumerations. The addresses could have been vacant at

the time of delivery and sometime between delivery and

enumeration the addresses may have become occupied. If

so, these units should have been enumerated as vacant.

This also could indicate USPS misclassification error.

An estimated 39.6 percent and 11.3 percent of the

undelivered questionnaires annotated as ‘‘duplicate’’ had

a final status of occupied and vacant, respectively. The

percentages could indicate some duplication of both people and housing units. This could also indicate that the

USPS erroneously classified these cases as duplicates.

About one-half of the undelivered questionnaires annotated as ‘‘duplicate’’ were deleted.

For questionnaires identified by the USPS as ‘‘demolished, new construction, nonresidential’’ or ‘‘no such address,

apartment,’’ 81.1 percent and 64.1 percent, respectively,

were deleted on the census address list. This suggests

that the USPS is able to identify nonexistent units at a high

rate.

Of the undelivered questionnaires annotated as ‘‘Post

Office box or no mail receptacle’’ by the USPS, about

three-quarters had a final status of occupied or vacant.

This would suggest that the addresses identified with

these codes are units that exist; however, they do not

receive mail delivery at the unit.

Address Listing Pages

Undelivered Questionnaires—Approximately 57.0 percent

of the 181,316 (103,380) postmaster return questionnaires

identified on the address listing pages by the district offices

Table 3.4. Type of Enumeration Area Summary were undeliverable. The reasons were annotated on the

USPS undeliverability

reason

Percent

TAR

Prelist

mailout/

mailback

Difference

(standard

error)

Vacant . . . . . . . . . . . . . . . . . . . . 36.7 28.9 7.7 (3.9)

Duplicate . . . . . . . . . . . . . . . . . 9.1 9.0 0.2 (1.7)

Demolished, new construction or nonresidential . . . . . 6.6 2.8 3.8 (0.9)

No such address or no such

apartment . . . . . . . . . . . . . . 30.5 36.2 –5.7 (2.7)

Post office box or no mail

receptacle . . . . . . . . . . . . . . 3.2 8.5 –5.3 (1.3)

Other or no reason written . . 13.9 14.6 –0.7 (3.2)

Total . . . . . . . . . . . . . . . . . 100.0 100.0

Table 3.5. Final Census Status Summary

Final census status Percent

Standard

error

Occupied . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.7 1.1

Vacant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.7 1.7

Delete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43.6 1.8

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100.0

Table 3.6. Final Census Status Summary

USPS undeliverability reason

Percent (standard error)

Occupied Vacant Delete

Vacant . . . . . . . . . . . . . . . . . . . . 19.8 (0.9) 60.6 (1.6) 19.5 (1.2)

Duplicate . . . . . . . . . . . . . . . . . . 39.6 (2.2) 11.3 (2.2) 49.1 (2.6)

Demolished, new construction or nonresidential . . . . . . 6.2 (0.6) 12.8 (1.3) 81.1 (1.8)

No such address or no such

apartment . . . . . . . . . . . . . . . 23.3 (1.6) 12.6 (1.2) 64.1 (2.3)

Post office box or no mail

receptacle . . . . . . . . . . . . . . . 29.2 (3.3) 45.6 (4.4) 25.2 (3.0)

Other or no reason written . . 28.0 (3.2) 29.2 (2.9) 42.8 (3.0)

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address listing pages, coded, and keyed for this evaluation. Table 3.7 summarizes the percent of undelivered

questionnaires by district office undeliverability reason.

Approximately 30.7 percent of the undeliverables were

classified as ‘‘vacant’’ and 21.3 percent were classified as

having ‘‘no such street number.’’

Delivered Questionnaires—Approximately 43.0 percent of

the 181,316 (77,936) postmaster return questionnaires

identified on the address listing pages were delivered by

the district offices. Examining the delivery rates within type

of enumeration area indicates that only 36.1 percent of the

postmaster return questionnaires in the TAR areas were

delivered while 51.9 percent of the postmaster return

questionnaires in prelist mailout/ mailback areas were delivered.

Table 3.8 contains the delivered postmaster return

questionnaires by final census status. The majority of the

housing units associated with delivered postmaster return

questionnaires (51.5 percent) had a final status of occupied, although 35.2 percent were vacant and 13.3 percent

were deletes.

An estimated 26.9 percent of the delivered postmaster

return questionnaires resulted in a mail return on or before

May 3, 1990. Although questionnaires were not supposed

to be delivered to vacant, duplicate, or demolished units,

the data suggest that this did occur. We estimate that the

rate of response from occupied units was approximately

52.2 percent.

Conclusions

District Office Survey—Between 5.4 and 7.6 million

postmaster return questionnaires were received by the

district offices. This represents between 5.7 percent and

8.0 percent of the total housing count in these district

offices. More than 6 percent of the district offices received

postmaster return questionnaires that represented between

15.0 percent to 31.5 percent of the housing units in the

district office. For these district offices the received postmaster return questionnaires represented a large percentage of their housing units. These district offices were not

geographically clustered; however, 76 percent of them

were type 2 district offices.

Since the responding district offices were able to deliver

over 50 percent of the assigned postmaster return questionnaires, this raises the question of why the Census

Bureau could deliver questionnaires that the USPS classified as undeliverable.

Undelivered Questionnaires— The USPS undeliverability reasons can be used with a high level of accuracy to

determine the final status. If the Census Bureau used the

USPS undeliverability reasons to classify housing units as

vacants or deletes before the Nonresponse Followup

operation, this would result in a savings of both time and

money.

The USPS annotated an estimated 1,771,000 undelivered questionnaires as ‘‘vacant.’’ This represented approximately 33.6 percent of the undelivered universe. The high

rate indicates that the USPS did not deliver to vacants.

Address Listing Pages—The last minute decision to

implement the Postmaster Return Questionnaires Delivery

operation greatly impacted the Census Bureau’s ability to

collect critical evaluation information. The Census Bureau

used all available data but had no supplemental or alternative data sources. The data summarized have major

limitations but the data give some insights into the operation.

It is not surprising that this operation was most successful in prelist mailout/ mailback areas, for noncity type

addresses, and for addresses added to the census address

list by Census Bureau field operations. It is likely that

additional reference materials, such as map spots and

familiarity with the area, helped the enumerators locate the

addresses. The Census Bureau maps and address listing

pages indicating the locations of prelisted addresses were

not available to the USPS.

References

[1] Griffin, Deborah H. and Eric Williams. 1990 Decennial

Census Preliminary Research and Evaluation Memorandum No. 243, ‘‘Additional Evaluation Results for the Postmaster Return Questionnaire Delivery Project.’’ U.S. Department of Commerce, Bureau of the Census. July 28, 1993.

[2] Moriarity, Christopher and Karen Cowles. 1990 Decennial Census Preliminary Research and Evaluation Memorandum No. 227, ‘‘Additional Results from the Postmaster

Return Questionnaire Delivery District Office Survey.’’ U.S.

Department of Commerce, Bureau of the Census. April 29,

1993.

Table 3.7. Reasons for Undeliverability

District office undeliverability reason Percent

Vacant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.7

No such street number. . . . . . . . . . . . . . . . . . 21.3

Demolished/ nonexistent . . . . . . . . . . . . . . . . 9.8

Duplicate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6

Geocoding problem . . . . . . . . . . . . . . . . . . . . 0.5

No mail receptacle . . . . . . . . . . . . . . . . . . . . . 0.4

Post office box . . . . . . . . . . . . . . . . . . . . . . . . 0.0

Other undeliverables. . . . . . . . . . . . . . . . . . . . 33.7

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100.0

Table 3.8. Final Census Status Summary

Final census status Percent

Occupied. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.5

Vacant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.2

Delete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3

Total. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100.0

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[3] Paez, Al. Unpublished Internal Census Bureau Memorandum, ‘‘1990 Census Mailback Questionnaire Check-in

Rates.’’ March 14, 1991.

[4] Treat, James B. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 141, ‘‘Results

from the Postmaster Return Questionnaire Delivery District

Office Survey.’’ U.S. Department of Commerce, Bureau of

the Census. April 3, 1992.

[5] Treat, James B. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 177, ‘‘Results

from the Postmaster Return Questionnaire Delivery Project:

Stage II Evaluation.’’ U.S. Department of Commerce, Bureau

of the Census. August 26, 1992.

[6] U.S. Department of Commerce, Bureau of the Census.

1990 Decennial Census Regional Census Center MemorandumNo.90-D-186,‘‘PostmasterReturnsRetrievalProcedureImplement Immediately.’’ April 3, 1990.

[7] U.S. Department of Commerce, Bureau of the Census.

IA-0-002, ‘‘Statement of Work-Interagency Agreement Between the U.S. Department of Commerce, Bureau of the

Census, and the U.S. Postal Service.’’ September 1989.

[8] U.S. Department of Commerce, Bureau of the Census.

Training and Development Series, TD-025-2A, ‘‘Training

Program for the 1990 Decennial Census Address Check.’’

January 1990.

THE SHELTER AND STREET NIGHT

ENUMERATION

Introduction and Background

The Shelter and Street Night enumeration, which was

also known as S-Night, was a one-night operation developed for the 1990 census to include persons not covered

by regular Census Bureau procedures for households or

persons in the standard enumeration of group quarters.

S-Night was conducted nationwide to improve coverage of

the census by counting persons in selected locations

where homeless persons tend to be found at night.

Census enumerators counted persons and collected

data at preidentified locations on March 20, 1990 and the

early morning hours of March 21, 1990 in two phases; the

shelter phase and the street phase. The shelter phase

covered enumeration of persons found in shelters, such as

emergency shelters, shelters for abused women, shelters

for runaway and neglected children, low cost motels

(costing $12.00 or less), subsidized units at motels, and

YMCA’s and YWCA’s preidentified by local areas as

places where homeless persons stay. The shelter phase

took place on March 20, 1990, from 6:00 p.m. until

midnight. The street phase covered enumeration of persons found at selected preidentified street locations, abandoned buildings, commerce places such as bus depots

and train stations, and other places where homeless

persons may spend the night, such as all-night restaurants,

parks, and vacant lots. Enumerators collected data at

street locations and commerce places on March 21, 1990

from 2:00 a.m. until 4:00 a.m. Persons leaving from abandoned buildings were enumerated from 4:00 a.m. until 8:00

a.m. on March 21, 1990.

Methodology

This report provides the number of persons and the

number of ‘‘populated’’ sites enumerated stateside during

S-Night. The data do not include counts from Puerto Rico.

Counts are also provided by State and for 10 selected

cities. Records for group quarters (including S-Night locations) for which no persons were found, were deleted from

the census files. The number of sites with ‘‘zero population’’ cannot be determined. Therefore, the data in this

report include the number of ‘‘populated’’ sites for which at

least one person was enumerated. Tabulations of the data

were derived from the 1990 census files.

The ‘‘coverage gain percent’’ is the number of persons

enumerated on S-Night compared to the total number of

persons counted in the 1990 census.

Census Bureau data products provide counts of persons at selected locations where homeless persons are

found. Thus, in this report, the data are shown by the

following four types of S-Night locations for each of the

various geographical levels.

• Emergency Shelters—include subsidized units at hotels

and motels, low cost motels, YMCA’s and YWCA’s

identified as places where homeless persons stay.

• Shelters for Abused Women—include group homes and

safe houses for women of domestic violence.

• Shelters for Runaway and Neglected Children—include

group homes or youth centers identified as places for

runaway and neglected children.

• Street Locations—include various street locations such

as parks, street corners, river banks etc., abandoned

buildings and commerce places, such as all-night restaurants, train stations, bus depots, subways, etc.

Other locations where homeless persons may be found,

such as campgrounds, detoxication centers, carnivals, and

prisons, were counted during the standard enumeration of

special places and group quarters. Homeless children in

foster care and persons temporarily living doubled up with

other families could not be separately identified and were

enumerated using standard procedures for households.

This paper documents the number of persons counted at

preidentified locations specified for S-Night only.

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Limitations of the Data

The S-Night operation was not designed to (and was

never intended to be) a complete count of the homeless

population at any given geographical level. The data

include the number of persons counted on a single night at

specified preidentified locations where homeless persons

tend to be found. S-Night represents one of the Census

Bureau’s efforts to include homeless persons in the 1990

census. There are important factors to understand before

using the S-Night data. Some of these factors are discussed below:

• The data may not include persons who were well hidden,

moving about, or in shelters or street locations other

than those identified as a part of S-Night.

• Some street locations identified for S-Night may have

been excluded because of the potential danger to

enumerators and homeless persons. Persons living in

cars, dumpsters, and on rooftops may have been missed.

• Other factors, such as weather conditions, availability of

shelters, and presence of the press and police, may

have also influenced the number of persons visible on

the street.

• The overall variability in how the street phase was

conducted across the country may have affected the

quality of the street counts. Analysis of the S-Night

counts of persons visible at street locations disclosed

that some of the enumerators did not always follow the

procedures designed for the operation. Enumerators

were to count all persons at preidentified locations,

except those in uniform, on staff with a usual home

elsewhere or engaged in obvious moneymaking activities. Reports indicate some enumerators may have been

selective in whom they approached to interview, whom

to count by observation, or whom to count at all. It is

impossible to measure the impact of procedural irregularities on coverage. Enumerators were not to ask

persons if they were homeless or if they had a usual

home elsewhere. Persons with a regular place to stay

may have been at shelters or visible at street locations

during S-Night and may have been included in the

counts.

Results

National Level

Table 3.9 provides the overall number of persons enumerated at ‘‘populated’’ sites by enumeration phase and

type of S-Night location. It also includes the number of

persons counted per 10,000 persons of the United States

population, as well as the percent of coverage improvement to the census. The total count of persons from the

S-Night operation improved coverage of persons in the

1990 census by 0.1 percent.

Shelter Phase—There were more persons and sites counted

during the shelter phase of S-Night than the street phase.

There were 168,309 persons counted at 6,664 emergency

shelters, 11,768 persons counted at 1,009 shelters for

abused women, and 10,329 persons counted at 788

shelters for runaway and neglected children. Emergency

shelters accounted for 88.4 percent of the total persons

enumerated during the shelter phase and 70.1 percent of

the total persons enumerated during S-Night.

There was a total of 248,709,873 [3] persons counted in

the United States during the 1990 census. For every

10,000 persons counted in the U.S., about 7 persons were

counted on S-Night at emergency shelters. The persons

counted at emergency shelters represent a .068 percent

coverage gain to the census. There was less than one

person counted per 10,000 population for the other two

shelter types. The persons counted at shelters for abused

women and for runaway and neglected children, together,

improved the coverage of persons in the 1990 census by

.009 percent.

Street Phase—There were 49,734 persons counted at

6,669 street locations during the street phase. These

numbers represent 20.7 percent of the total persons

enumerated and 44.1 percent of the total number of

‘‘populated’’ sites visited during S-Night.

Nationally, there were 2 persons per 10,000 population

enumerated at street locations. The coverage gain at

street locations was .02 percent.

Table 3.9. S-Night—Number of Sites and Persons Counted by Phase and Type of Location

Phase

Type of location

Sites Persons

Number

Percent of

phase

Percent of

S-Night Number

Percent of

phase

Percent of

S-Night

Per

10 thousand

population

Coverage

gain percent

Shelter . . . . . Emergency shelters 6,664 78.8 44.0 168,309 88.4 70.1 6.8 .068

Shelters for abused women 1,009 11.9 6.7 11,768 6.2 4.9 .48 .005

Shelters for runaway and

neglected children 788 9.3 5.2 10,329 5.4 4.3 .42 .004

Street . . . . . . Street locations 6,669 100.0 44.1 49,734 100.0 20.7 2.0 .020

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The 50 States and the District of Columbia

See figure 3.9 for an illustration of the number of

persons enumerated by State for two of the S-Night

locations (emergency shelters and street locations).

The number of enumerations at emergency shelters

varied across States. Eight States (New York, California,

Pennsylvania, New Jersey, Texas, Illinois, Florida, and

Massachusetts) each had over 5,000 persons counted at

emergency shelters. These eight States accounted for 61

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percent of the nation’s population counted at emergency

shelters. The 2 States with the highest number of persons

counted, New York (31,436 persons) and California (29,830

persons), accounted for about 36 percent of all enumerations at emergency shelters. Wyoming had the least number of persons (129) counted at emergency shelters.

There were less than 1,000 persons counted at street

locations for the majority of the States and the District of

Columbia. There were 9 States (California, New York, New

Jersey, Arizona, Florida, Illinois, Texas, Pennsylvania and

Hawaii) that enumerated more than 1,000 persons each

during the street phase. These States accounted for 83

percent of all enumerations at street locations. Fifty-eight

percent of the total population visible and counted at street

locations were in the 2 States with the highest counts (New

York—10,732 persons and California—18,081 persons).

The least number of persons visible and counted on the

street among the States and the District of Columbia was

in Maine (seven).

Persons Per State Population—In emergency shelters,

there were 73 persons per 10,000 population counted in

the District of Columbia. Among the 50 States, New York

State had the highest rate, about 17 persons per 10,000

population were enumerated in emergency shelters.

At both shelters for abused women and shelters for

runaway and neglected children, the rate was less than 1

person per 10,000 population for all States except Alaska

and the District of Columbia. In Alaska, the rate was about

3 persons per 10,000 population at shelters for abused

women. In the District of Columbia, 4 persons were

counted per 10,000 population at shelters for runaway and

neglected children.

Hawaii had the largest rate of persons counted on the

street per 10,000 population among the 50 States and the

District of Columbia. There were about 10 persons enumerated per 10,000 population in Hawaii. California had

the next to the largest rate of persons counted on the

street (6 persons per 10,000 population).

Refer to figure 3.10 which illustrates the percentage of

the States (includes the District of Columbia) within a range

of the number of persons per 10,000 State population.

According to the figure, the majority (about 63 percent and

94 percent) of the States had a rate of less than 6 persons

per 10,000 population counted at emergency shelters and

at street locations, respectively.

Selected Cities

S-Night tended to favor areas where most of the

homeless population is likely to be sheltered; for example,

cities versus rural areas. Local jurisdictions with high

concentrations of homeless persons generally had more

information about their likely locations.

New York City and Chicago had the largest number of

persons counted at emergency shelters among the largest

cities. About 23,000 persons were enumerated at emergency shelters in New York City and about 4,800 persons

were counted in Chicago. New York City and Los Angeles

had the highest number of persons counted at street

locations. Over 10,000 persons were enumerated at street

locations in New York City and over 3,000 persons were

enumerated in Los Angeles.

New York City had the largest number of persons at all

four S-Night locations among the largest cities. There were

236 persons counted at abused women shelters and 674

persons enumerated at shelters for neglected children in

New York City.

Similar to the national trend, there were more persons

counted at emergency shelters than at street locations for

each of the largest cities. Ten of the largest cities (New

York, Los Angeles, Chicago, Philadelphia, San Diego, San

Francisco, Washington, DC, Boston, Seattle, and Atlanta)

each had more than 2,000 persons counted at emergency

shelters, with a total of 53,122 persons. Six of the 10 cities

had more than 1,000 persons counted at street locations

during S-Night. A total of 20,654 persons were enumerated

at street locations in these 10 cities. These 10 cities

accounted for 31.6 percent of the total population counted

at emergency shelters and 41.5 percent of the total

population enumerated at street locations.

Refer to table 3.10, which shows the number of sites

and persons counted on S-Night, as well as the city rank by

population size, the city population and the rate of persons

per 10,000 population by type of location for 10 selected

cities.

Persons Per 10,000 City Population—Although New

York City had the largest number of persons counted

among the cities at each type of location, it did not have

the largest rate of persons counted per 10,000 population

at either shelters or street locations. Washington, DC had

the largest rate of persons per 10,000 population counted

at emergency shelters and San Francisco had the largest

rate of persons per 10,000 population counted at street

locations. In Washington, DC, the rate was about 73

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persons per 10,000 population at emergency shelters.

About 22 persons per 10,000 population were counted at

street locations in San Francisco.

Figure 3.11 compares the number of persons counted

per 10,000 city population at emergency shelters to the

number of persons counted per 10,000 city population at

street locations for 10 selected cities. The figure also

shows the cities in order by population size.

Conclusions

California and New York were the States with the

highest number of persons counted on S-Night. New York

City and Los Angeles had the largest counts of persons at

all four types of locations among the largest cities.

Review of the shelter data indicates that the overall

count of persons in emergency shelters is generally reasonable. As part of census coverage improvement operations, local officials had the opportunity to provide the

Census Bureau lists of emergency shelters and night time

street locations. The local officials were also asked to

review the count of persons in shelters before census

district offices closed. Relatively few problems were reported.

The count of persons at emergency shelters gives some

idea of relative differences in the shelter population among

areas of the country.

Caution should be exercised when using the counts of

persons at street locations. Deviations from the procedures designed for conducting the S-Night street phase

may have presented irregularities in coverage of the

various sites at street locations. The impact of procedural

deviations on coverage is impossible to measure. Users of

the evaluation data are encouraged to consider the limitations as discussed earlier.

Although the results from the S-Night enumeration do

not represent (and were never intended to be) a complete

count of the homeless population, the S-Night operation

was an effective coverage improvement method to include

persons at locations where homeless persons are found in

the 1990 census. The S-Night operation successfully added

persons to the overall census count who otherwise would

not have been included by using regular Census Bureau

procedures. Persons enumerated at emergency shelters

improved coverage by .068 percent. Coverage gains at

shelters for abused women and shelters for runaway and

neglected children were .005 percent and .004 percent,

Table 3.10. S-Night Enumeration—Number of Sites and Persons Counted by Type of Location for 10 Selected

Cities

City

size Population City

Emergency

shelters

(a)

Shelters for

abused women

(b)

Shelters for

runaway and

neglected

children

(c)

Street locations

(d) Persons per 10,000 city population

Sites

Persons SitesPersons SitesPersons SitesPersons (a) (b) (c) (d)

1 7,322,564 New York 259 22,709 10 236 29 674 849 10,447 31.01 0.32 0.92 14.27

2 3,485,398 Los Angeles 99 4,459 6 102 7 138 416 3,109 12.79 0.29 0.40 8.92

3 2,783,726 Chicago 106 4,806 4 124 18 374 250 1,584 17.26 0.45 1.34 5.69

5 1,585,577 Philadelphia 83 3,366 1 48 5 50 213 1,069 21.23 0.30 0.32 6.74

6 1,110,549 San Diego 50 2,750 4 26 2 96 197 2,101 24.76 0.23 0.86 18.92

14 723,959 San Francisco 100 3,986 3 96 3 17 178 1,566 55.06 1.33 0.23 21.63

19 606,900 Washington, DC 141 4,419 6 49 12 263 38 131 72.81 0.81 4.33 2.16

20 574,283 Boston 39 2,134 3 89 6 111 69 218 37.16 1.55 1.93 3.80

21 516,259 Seattle 58 2,161 6 113 1 9 82 369 41.86 2.19 0.17 7.15

36 394,017 Atlanta 45 2,332 1 5 2 99 9 60 59.19 0.13 2.51 1.52

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respectively. Persons counted at street locations represent .020 percent gain. Overall, the S-Night enumeration

improved coverage of persons in the 1990 census by 0.1

percent.

References

[1] 1990 Census of Population and Housing. CPH-L-87,

‘‘Fact Sheet for the 1990 Decennial Census Counts of

Persons in Selected Locations Where Homeless Persons

are Found,’’ U.S. Department of Commerce, Bureau of the

Census. July 1992.

[2] 1990 Census of Population and Housing. CPH-1-1,

‘‘Summary Population and Housing Characteristics of the

United States.’’ U.S. Department of Commerce, Bureau of

the Census. March 1992.

[3] 1990 Census of Population and Housing. Summary

Tape Files 1, 2, 3, and 4 Technical Documentations. ‘‘User

Note on Shelter and Street (S-Night).’’ U.S. Department of

Commerce, Bureau of the Census. March 1993.

[4] Barrett, Diane F. 1990 Preliminary Research and Evaluation Memorandum No. 231, ‘‘The 1990 Census Shelter

and Street Night Enumeration (S-Night)—Number of Sites

and Persons Enumerated.’’ U.S. Department of Commerce, Bureau of the Census. May 11, 1993.

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CHAPTER 4.

Post-Census Day Coverage Improvement

TELEPHONE ASSISTANCE ADDS

Introduction and Background

For the 1990 census, the Census Bureau implemented

a Questionnaire Assistance program to encourage response

to the census by providing assistance to persons who had

questions about how to complete their census questionnaire. The Census Bureau provided telephone and walk-in

assistance in various languages and at various locations,

including telephone assistance at the district offices and

processing offices. Six of the seven processing offices

provided telephone assistance for the type 1 district offices

and the type 2 and 3 district offices provided on-site

telephone assistance. Clerks in the district offices and

processing offices were specially trained to help the respondent complete the questionnaire on the telephone or to

provide other types of assistance. A toll-free ‘‘800’’ number was provided on the census questionnaire for the use

of persons desiring questionnaire assistance.

Soon after the delivery of census questionnaires in

March 1990, the district offices and processing offices

began to receive a large volume of telephone calls from

persons requesting assistance in completing the form.

While most of these calls were related to specific assistance with completing the questionnaire or requests for a

foreign language questionnaire, it soon became apparent

that a much larger than expected number of calls were

from persons reporting that they had not received a

census questionnaire.

For the first 2 weeks of April, the telephone assistance

clerks were instructed to fill a report form (Form D-399,

Record of Telephone Contact, see appendix B) for persons calling to request a census questionnaire and then do

a records search to determine if the caller’s address was

already accounted for in the census files. If the clerk found

the address in the Address Control File, he/ she sent a

letter to the caller informing them that a census enumerator would visit to collect the census information. If the

caller’s address was not found in the Address Control File,

a census questionnaire was sent to the address in Tape

Address Register areas or the case was assigned for field

verification in other areas. The Address Control File maintenance procedure was then used to add the missing

address to the census files.

As the number of callers requesting a census questionnaire increased and the redelivery of postmaster return

questionnaires (see Postmaster Return Delivery in chapter

3) was completed, the Census Bureau implemented a

revised procedure for handling calls from persons requesting a census questionnaire in an attempt to reduce the

personal visit followup costs. Starting in mid-April, the

telephone assistance clerks sent a census questionnaire

(long or short form, as determined by a clerical check-off

sheet) to each caller who requested one, with instructions

for the household to fill the questionnaire and hold it for

pickup by a nonresponse followup enumerator. The clerks

continued to search for the callers’ addresses in the

Address Control File and add those which were missing

from the census files. At the end of nonresponse followup,

a special check was done to ensure that an enumerator

had visited each D-399 case to pick up the questionnaire.

Since the telephone assistance program resulted in the

addition of missing addresses to the Address Control File,

it can be viewed as a source of coverage improvement for

the census, although the program was not intended to be

a coverage improvement program when it was designed.

This section provides some results of an evaluation of the

coverage improvement derived from the telephone assistance program. It provides an estimate of the number of

adds that resulted, the characteristics of the added units,

an estimate of how many D-399 addresses were already in

the census files, and some characteristics of the household respondents who called to request a census questionnaire.

Methodology

After the end of the census field work, the forms D-399

which were filled by the telephone assistance clerks were

tabulated. Clerks first sorted the forms and identified those

for which the call was to request a census questionnaire.

About 2.5 million forms D-399 were received and, of these,

about 1 million were requests for a census questionnaire.

The 1 million forms D-399 were then sorted by date to

identify those filled before April 12 and those filled after

April 12. The April 12 date was important because it was

the day on which the clerks implemented the revised

procedures for handling calls from persons requesting a

census questionnaire. Furthermore, it was assumed that

the early callers (pre-April 12) were more likely to be

accounted for in the Address Control File, since they called

before the district offices had a chance to hand deliver the

postmaster returned questionnaires or they may have

called before the postal service or the update/ leave

enumerators had a chance to deliver their questionnaire.

A sample of about 0.5 percent of the D-399’s filled

before April 12 and a 1.0 percent sample of the D-399’s

filled after April 12 were randomly selected. This resulted in

a sample of about 3,000 early forms and 3,000 late forms.

The clerks then obtained census identification numbers for

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the sample cases. This resulted in finding valid census

identification numbers for 2,260 early sample cases and

2,253 late sample cases. The remaining cases were

dropped from the analysis because they were not found in

the Address Control File under the address shown on the

D-399 even after repeated attempts by the matching staff.

These cases may be in the Address Control File under an

alternative address or may be in list/ enumerate areas

which were not in the control file as of the time of the

clerical search; it also is possible that some percentage of

them may have been missed in the census.

The census identification numbers for the sample cases

were then used to obtain detailed information for the cases

from the census files, such as the source of the unit, the

number of units at the basic street address, and so forth.

Table 4.1 shows the universe and sample sizes for the

evaluation. The ‘‘early’’ cases are those with call dates

before April 12, the ‘‘late’’ cases are those with call dates

after April 12.

Limitations

The 2.5 million forms D-399 processed for this evaluation may not be the total of such forms filled during the

census. Due to various problems with controlling the

disposition of these forms, it is highly likely that some

forms were misplaced. To the extent that the D-399’s for

persons requesting a census questionnaire were not available for sampling, the sample used for this evaluation may

be somewhat biased. Another potential bias in the sample

is that about 25 percent of the sample cases had to be

dropped from further analysis because they could not be

found in the Address Control File.

The major limitation to the estimates produced by this

evaluation is that there was no definitive way to identify

which adds resulted from the telephone assistance program. It was necessary to approximate the telephone

assistance adds by identifying the sample cases with

post-delivery source codes. Other research has suggested

the existence of errors in the assignment of source codes.

However, the extent of the error is not known. The data in

this evaluation are in error to the extent that the source

code information is incorrect or misleading.

Results

Table 4.2 shows the Census Day status of the early and

late sample cases, based on the final Address Control File

source code data.

The table suggests that about 60 percent of the addresses

of callers in the sample who requested a census questionnaire before or after April 12 were already accounted for in

the census address files prior to questionnaire delivery.

Another 20 percent of the sample early callers were

time-of-delivery adds; that is, the postal carriers added

them during the casing check or the update/ leave enumerators added them during the Update/ Leave operation.

Only about 5 percent of the sample cases were in update/ leave

areas, so the update/ leave adds do not account for very

many of the time-of-delivery adds for this sample of cases;

the majority were casing adds made by the postal carriers.

Based on these data, at least 80 percent of the early

callers and 75 percent of the late callers would have

eventually received a census questionnaire or been visited

during Nonresponse Followup, regardless of whether they

called to request a census questionnaire.

The cases which comprise the potential coverage improvement from the telephone assistance program are those

which were added to the Address Control File after delivery; that is, the nonresponse followup adds, the other field

adds, and the processing office adds. Based on the data in

table 4.2, it appears that as an upper bound, about 19

percent of the early callers and about 25 percent of the

late callers were added to the census files as a result of the

telephone assistance program.

Estimated Coverage Improvement For Housing Units—

Based on the universe totals of about 1 million calls

requesting a census questionnaire, a ratio-estimation procedure produced a weighted national estimate that the

telephone assistance program may have added as many

as 158,000 addresses to the census file (standard error =

6,000 addresses). This is a rough estimate that must be

used with caution, given the limitations of this evaluation

sample. The potential coverage improvement from the

telephone assistance program is estimated at about 0.15

percent of the total housing units in the nation.

Estimated Coverage Improvement For Persons—Based

on the estimated 158,000 addresses added to the census

files as a result of persons calling to request a census

Table 4.1.Universe and Sample Sizes

Early Late Combined

Number

Percent NumberPercent NumberPercent

Calls . . . . . . . . . . 655,891 64.1 355,977 35.9 991,868 100.0

Sampling rate . . 0.5 1.0 0.6

Sample . . . . . . . 3,056 0.5 3,028 0.9 6,084 0.6

Valid ID’s . . . . . 2,260 0.4 2,253 0.6 4,513 0.5

Table 4.2.Census Day Status of Sample Cases

Early Late Combined

Number

Percent NumberPercent NumberPercent

Sample cases. . . . . 2,260 100.0 2,253 100.0 4,513 100.0

In ACF at time of

delivery . . . . . . . . 1,366 60.4 1,339 59.4 2,705 59.9

Casing or

update/ leave

add . . . . . . . . . . . . 450 19.9 349 15.5 799 17.7

Potential adds: . . . . 444 19.1 565 25.1 1,009 22.4

NRFU adds. . . . . 146 6.4 199 8.9 345 7.6

Other field adds . 96 4.2 132 5.8 228 5.1

Processing

office adds . . . . 202 8.9 234 10.4 436 9.7

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questionnaire, it is estimated that about 407,000 persons

may have been added by the telephone assistance program. This estimate is derived by multiplying 158,000

housing units by 2.63 persons per unit (which represents

the average household size for occupied housing units in

the Nation) and multiplied by 0.98 to take into account that

about 2 percent of the added units had a final status of

delete. This estimate represents about 0.16 percent of the

total census population count.

Characteristics Of Added Housing Units—The profile

for adds derived from this evaluation suggests that they

are predominately city delivery single unit structures, which

is consistent with the characteristics of the general housing inventory. The only notable deviation from the general

housing inventory is that there were substantially fewer

small multiunit apartment buildings containing 2-9 units

than in the general housing inventory. This is a surprising

finding, because the Census Bureau has traditionally had

problems compiling an accurate mailing list in small multiunit buildings where the units are often unnumbered and

unlettered. In general, the address and structure characteristics of the adds do not shed any light on why they were

missed in the census address compilation efforts.

Characteristics Of Added Persons—Similar to the characteristics of the added housing units, the characteristics

of the persons occupying the adds were quite similar to the

general population characteristics and shed no light on

why the households were missed in the census address

compilation efforts. Their general profile is that they are

predominately White, non-Hispanic households who reported

a male as Person 1 on the census questionnaire (indicating

a male head of household). One interesting finding is that

the percentage of Hispanic households is substantially

higher for the early adds (11.4 percent) and slightly higher

for the late adds (7.7 percent) than in the general population (6.5 percent). Most likely the Hispanic households

called to request a Spanish language census questionnaire rather than calling to say they had not received a

census questionnaire. Regardless of the reason for the

call, however, the data suggest that the differential undercount of Hispanic households may have been reduced by

the telephone assistance program.

Characteristics Of Non-Added Cases—The final aspect

of this evaluation involved reviewing the characteristics of

the 3,504 sample cases which were in the Address Control

File on Census Day; that is, the cases where a person

called to request a census questionnaire even though their

address was already in the census files. These are cases

where the postal service or update/ leave enumerator may

have encountered delivery problems.

The evaluation data suggest that the characteristics of

callers whose addresses were already in the Address

Control File are very similar to those of callers whose

address was apparently missing from the Address Control

File on Census Day. The same general profile holds for

both groups; that is, the housing units are predominately

city delivery single unit structures occupied by White,

non-Hispanic households, who reported a male as Person

1 on the census questionnaire. The higher level of Hispanic origin persons in the sample than in the general

population shows up in both the adds and the non-adds. In

addition, a slightly higher frequency of persons aged 65+

reported as Person 1 on the census questionnaire is

evident for both groups. It is interesting to note that the

percentage of persons reporting their race as ‘‘Other’’ is

substantially higher in the group whose addresses were in

the Address Control File than it is for the adds or for the

general population (11.3 percent for non-adds, 6.4 percent

for adds, and 5.6 percent for the general population.)

Conclusions

Within the limitations of this evaluation, the data suggest

that the telephone assistance adds improved national

housing counts and population counts by an estimated

0.15 percent, as an upper bound. It is clear that the Census

Bureau will have to provide some type of questionnaire

assistance for the 2000 census, so the coverage improvement aspects of the program may be viewed as a by-product,

whose only operational costs are searching for the address

in the census files and mailing the appropriate questionnaires.

The characteristics of the housing units and persons

presumably added by the telephone assistance program

are very similar to those for the general population. However, there seems to have been some impact on reducing

the differential undercount of Hispanic households, since

the sample cases had a higher frequency of persons who

reported their origin as Hispanic than in the general

population. These data lend support to various ethnographic studies that suggest the need for a combined

English and Spanish questionnaire in areas of high Hispanic concentrations.

The data in this evaluation suggest that about 60

percent of the addresses of persons who called to request

a census questionnaire were already accounted for in the

address control file as of Census Day. These persons

apparently encountered some type of questionnaire delivery problems, although at least some of them may have

received a census questionnaire and discarded it or did not

realize that someone else in the household had received

the questionnaire. The data from this evaluation shed no

light on why these households encountered delivery problems, since their characteristics do not differ from those of

the general population. It is possible that this evaluation

lends credence to the notion that mis-delivery or nondelivery was a fairly random event in the 1990 census.

For 2000 census planning, if a similar telephone assistance program is planned, the Census Bureau needs to

implement a way to search for the address in the census

files while the caller is on-line and perform the enumeration

over the telephone, as appropriate.

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The telephone assistance program in the 1990 census

was a multi-purpose program that will probably play an

increasingly important role in future censuses as the

Census Bureau searches for ways to improve the response

rate. While it was not intended as a coverage improvement

operation, the results of this evaluation suggest that the

telephone assistance program can be a valuable source of

identifying addresses which are missing from the census

files at an early stage in the census process.

Reference

[1] Tenebaum, Michael C. 1990 Census Preliminary Research

and Evaluation Memorandum No. 162, ‘‘Telephone Questionnaire Assistance Adds Evaluation.’’ U.S. Department of

Commerce, Bureau of the Census. May 12, 1992.

CENSUS CLOSEOUT ADDRESS CHECK

Introduction and Background

In response to recommendations from various members

of Congress to actively involve the USPS in the data

collection process, the Census Bureau designed and implemented a program called the Census Closeout Address

Check. During the final days of Nonresponse Followup and

subsequent followup operations, the district offices were

encouraged to utilize the mail carriers to obtain very limited

information about unenumerated cases. The Census Closeout Address Check was targeted at district offices which

were late in completing Nonresponse Followup, but all of

the district offices were given the option to implement the

program.

Those district offices which decided to implement the

Census Closeout Address Check prepared a special address

card for each unenumerated case and submitted the cards

to the local postal officials. The postal carriers were asked

to provide limited information about each address for

which they received a card. The information consisted of

type of structure, Census Day occupancy status, and

number of Census Day occupants. The carriers were

cautioned not to obtain the information from mail addressed

to the unit since this would violate confidentiality, but to

rely on their own knowledge and observation. If an address

card was returned to the district office with usable information, the carrier information could be used to classify the

case on the Address Control File in lieu of information

obtained by field visit.

The Census Closeout Address Check was planned and

implemented late in the census process so it did not

undergo testing during the planning cycle nor did it have

the rigorous administrative control that characterized most

other census operations. In addition, many district offices

did not implement the program due to timing constraints

and other administrative problems.

This section describes the results of an evaluation of

the Census Closeout Address Check and provides information on the number of cases, the number of cards

returned by the USPS with usable information, the clustering of the addresses and the characteristics of the addresses

for which the USPS was able to provide useful information.

Due to the limitations of the evaluation data, the results

cannot be generalized beyond the small number of district

offices for which completed address cards were available

for the clerical review.

Methodology

After the end of census operations, the completed

Forms D-550P, Census Closeout Address Check Cards

(see appendix B for an illustration of the D-550P), were

reviewed and tabulated.

As discussed in the Limitations section below, only

about 28 percent of the expected number of completed

cards were received in the clerical unit, and most of these

were from the New York Regional Census Center.

One of the issues that this evaluation was designed to

measure was whether the Census Closeout Address Check

cases were clustered by block or by basic street address

within the district offices. This information might be useful

in determining whether there were any systematic problems with unenumerated units in specific geographic areas.

In order to address this objective, the clerical staff examined 32,574 forms D-550P in the 10 district offices which

had the largest number of available cards. The results of

this clerical review were then used to determine if the

cases were clustered either by block or by basic street

address.

The other main objective of this evaluation was to obtain

some information about the characteristics of the addresses

for which the mail carriers provided useful information. In

order to obtain this information, the completed cards were

separated into two strata. One stratum included all the

cards for the New York Regional Census Center and the

other stratum included all the cards from the remaining

regional census centers. The clerical review staff pulled

every 25th card for the New York stratum and every 7th

card for the other stratum. This resulted in a sample of

1,026 cases for the New York stratum and 1,000 cases for

the other stratum. The clerical staff then reviewed the

sample cards to tabulate various summary data.

Limitations

This evaluation relied primarily on a clerical review of

the completed address cards. Based on management

reports, we expected to receive and tally information for

about 125,000 cards, since about 142,000 cards were

reportedly sent to the USPS by the district offices throughout the nation, and we projected about an 85-90 percent

return rate. After several months of checking-in the forms,

it became apparent that the majority of the forms sent to

the USPS were either never returned or not forwarded to

the clerical review center from the district offices. Only

about 35,000 completed cards were available for clerical

review and repeated attempts to locate more cards were

unsuccessful.

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More than 79 percent of the cards available for clerical

review came from 10 district offices within New York City.

The remaining cards were spread across seven district

offices in five regional census centers. The cards represent about 78 percent of the estimated number of cards

sent to the USPS by these 17 district offices.

Based on the small number of available cards and the

heavy concentration in one regional census center, the

data in this evaluation must be used with caution since the

results are not based on a representative sample of district

offices or completed cards. The evaluation is a measurement study which provides a snapshot of the results of the

program in a small number of district offices. The results

are not representative of the nation.

It was not possible to identify the specific followup

operation which resulted in the completed cards so the

results of the program cannot be compared across the

various followup phases.

As mentioned in the Introduction section, the Census

Closeout Address Check was implemented late in the

census process so it did not have the rigorous administrative control that characterized most other census coverage

improvement operations. In addition, many district offices

did not implement the program.

Results

A total of 142,356 address cards were reportedly sent to

the USPS by the district offices. Of these, 23,040 (16

percent) were sent during the closeout phase of Nonresponse Followup. The remainder were reportedly sent

during Field Followup (41 percent), Housing Coverage

Check (3 percent) and POP One Reenumeration (40

percent). The cards were used in every regional census

center except Kansas City. The vast majority of the cases

were in the New York Regional Census Center; that

regional census center accounted for 79 percent of the

cases sent during Nonresponse Followup and 65 percent

of the cases sent during later followups.

Clustering Effects—One of the issues this evaluation was

designed to measure was whether the cases were clustered by census block or by basic street address. To obtain

this information, the clerical review staff examined 32,574

completed address cards in the 10 district offices with the

largest number of available cards (7 of these district offices

were in New York City and the remaining 3 were in other

regional census centers.) These cards were in 5,364

blocks and 14,829 basic street addresses. The clerical

review found that there was at least 1 case in about 31

percent of the total blocks in the 10 district offices. In the

seven district offices that were examined in New York City,

the cases were evenly distributed among the census

blocks which suggests no clustering effects among blocks.

In the other district offices, the cases were located in about

23 percent of the blocks, which suggests a slight clustering

effect by census block.

For these 10 district offices with the largest number of

available cards, about 24 percent of the blocks with cases

had more than 6 cases. For the New York offices, this

percentage was 38 percent, which is consistent with the

high density of the typical block in New York City.

This evaluation also looked at clustering by basic street

address. The 32,574 reviewed cases were found in 14,829

basic street addresses. The clerical review found that, on

the average, about 4 percent of the basic street addresses

in the 10 district offices with the largest number of available cards had at least 1 case for at least 1 followup

operation and these addresses were evenly distributed

throughout the district office areas.

About two-thirds of the basic street addresses with

cases had only one case. This result suggests that the

cards were not clustered by basic street address. For the

New York district offices there was a slight clustering effect

since 42 percent of the cards were in basic street addresses

with two or more cases. This clustering effect is most likely

the result of the large number of multiunit buildings in the

New York area.

In summary, there was some evidence of slight clustering by block and by basic street address, but the clustering

does not seem to be of practical importance.

Address Characteristics—The clerical staff reviewed a

sample of 2,026 address cards to tabulate various summary data about the characteristics of the addresses for

which the USPS provided usable census data. The results

are shown in table 4.3.

Table 4.3. Summary Characteristics Based on

Weighted Estimates From the Sample

Cases

Number

Estimated

percent of

cases

Standard

error

Occupancy Status: . . . . . . . . . . . . . .

Occupied on April 1, 1990 . . . . . . 1,756 86.1 (1.1)

Vacant on April 1, 1990 . . . . . . . . 96 4.4 (0.6)

Non-residential . . . . . . . . . . . . . . . . 23 0.8 (0.2)

Not determined. . . . . . . . . . . . . . . . 153 8.7 (0.5)

Type of Structure:

Mobile home . . . . . . . . . . . . . . . . . . 2 0.1 (0.1)

Detached 1-family . . . . . . . . . . . . . 171 4.5 (0.5)

Attached 1-family . . . . . . . . . . . . . . 189 5.4 (0.6)

Multiunit . . . . . . . . . . . . . . . . . . . . . . 1,522 81.5 (1.1)

Not determined. . . . . . . . . . . . . . . . 142 8.5 (0.8)

Estimated population for occupied

units:

1-person. . . . . . . . . . . . . . . . . . . . . . 158 8.4 (0.9)

2-persons. . . . . . . . . . . . . . . . . . . . . 123 4.3 (0.6)

3-persons. . . . . . . . . . . . . . . . . . . . . 51 1.8 (0.4)

4-persons. . . . . . . . . . . . . . . . . . . . . 30 1.0 (0.3)

5-persons. . . . . . . . . . . . . . . . . . . . . 17 0.5 (0.2)

6 or more persons . . . . . . . . . . . . . 21 1.1 (0.3)

Not determined. . . . . . . . . . . . . . . . 1,356 82.9 (1.2)

Information used for closeout:*

Occupancy status . . . . . . . . . . . . . 261 5.7 (0.5)

Type of structure . . . . . . . . . . . . . . 6 0.2 (0.1)

Estimated population. . . . . . . . . . . 357 14.0 (0.9)

None. . . . . . . . . . . . . . . . . . . . . . . . . 1,651 85.6 (0.9)

* Percentage is greater than 100 since more than one data item may

have been used.

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The table shows that the majority of the sample cases

were reported by the mail carriers as occupied on Census

Day. For the occupied units, the carriers were able to

provide an estimate of the number of Census Day occupants about 17 percent of the time.

Over 80 percent of the sample cases were located in

multiunit buildings. This lends support to the notion that

multiunit structures are difficult to enumerate.

The mail carriers reported that about 906 persons lived

in the 400 units for which they provided an estimate of

Census Day population. This suggests an average household size of about 2.27 persons (standard error = 0.17),

which is lower than the 2.63 average household size for

occupied housing units for the Nation. The smaller household size is most likely the result of the mail carriers

reporting only the number of persons to whom mail is

addressed at the household. It is important to note that the

mail carriers did not record the Census Day population for

about 83 percent of the occupied sample cases.

The district offices were supposed to indicate on the

address cards which information, if any, they used to help

resolve (‘‘closeout’’) the case. Based on the sample of

cards reviewed for this evaluation, it appears that some

mail carrier information was used to resolve an estimated

14.4 percent of the total cases available for clerical review.

The estimated population was most useful, followed by the

occupancy status. The type of structure was not very

useful, presumably because it was already available from

enumerator observation at the address.

Conclusions

The Census Closeout Address Check was implemented

in an attempt to obtain information for unenumerated

cases during followup operations. To that end, it is noteworthy that the district offices reported that they used the

information provided by the mail carriers to closeout about

14.4 percent of the cases reviewed for this evaluation.

While it is not possible to know for sure why the mail carrier

information was not used more often (and perhaps it was,

but the district offices failed to report it), two hypotheses

seem reasonable: 1) the district offices did not stop field

work on the cases while the mail carriers were working on

them, so the enumerators may have obtained more complete information for the cases, 2) the mail carrier information may have been received too late to be used for

closeout. Whatever the reason, it is apparent for the

sample cases in this evaluation, the mail carrier information was not the primary source of closeout data, although

the information from the mail carriers was used for a

substantial number of cases.

The mail carriers reported that about 86 percent of the

cases they reviewed were occupied on Census Day. They

were unable to report Census Day occupancy status for

8.7 of the sample cases. For the cases where they were

able to report Census Day occupancy status (occupied or

vacant), they reported that about 95 percent of the units

were occupied on Census Day. Since we are unable to

identify which cases came from which specific census

followup operation, it is difficult to compare this occupancy

rate to other rates. However, internal administrative reports

suggest that the final occupancy rate for last resort cases

converted by field enumerators was about 72 percent at

the end of Nonresponse Followup. This difference in

reported occupancy rates suggests that the mail carriers

were probably more likely to fill out an address card for

units to which they actively deliver mail, thereby inflating

the Census Day occupancy rate. In addition, since the

majority of the sample cases were in multiunit buildings, it

is possible that the mail carriers reported occupancy status

for the structure rather than for the specific living quarters

identified on the address card.

The average household size reported by the mail carriers for occupied units was somewhat lower than the

national average. This is probably the result of the mail

carriers reporting the number of persons to whom mail is

delivered at the unit. It may also reflect the fact that about

40 percent of the cases may have originated from the POP

One Reenumeration, which was a special followup conducted in households reported as being occupied by one

person during Nonresponse Followup.

The results of this evaluation suggest that the Census

Closeout Address Check provided useful information to

help closeout the unenumerated cases. The mail carriers

seemed able to provide the very limited information requested

on the address card, although the type of structure information was probably not necessary. While the Census

Closeout Address Check did not result in any significant

savings of field enumeration cost since the enumerators

continued to revisit the addresses while the USPS conducted the work, it provided the opportunity for the Census

Bureau and the USPS to work together on the data

collection phase of the 1990 census.

Reference

[1] Tenebaum, Michael C. 1990 Census Preliminary Research

and Evaluation Memorandum No. 89, ‘‘Census Closeout

Address Check Evaluation.’’ U.S. Department of Commerce, Bureau of the Census. October 7, 1991.

VACANT/ DELETE/ MOVERS CHECK

Introduction and Background

Results from the 1980 census evaluations showed that

substantial coverage improvement was obtained through

the followup of housing units identified during Nonresponse Followup as vacant and nonexistent housing units.

The followup of vacant and nonexistent housing units also

provided a technique for identifying and counting postCensus Day movers. This operation, referred to here as

the Vacant/ Delete/ Movers Check, was an integral part of

the 1990 Census Field Followup operation.

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The results of the followup of vacant and deleted units

for the 1990 census were evaluated to show how much

coverage improvement was obtained from the Vacant/ Delete/

Movers Check and to make recommendations for the 2000

census.

Housing units for which a questionnaire was not returned

by mail were visited during Nonresponse Followup to

determine the status of the unit and to complete a census

questionnaire. Units classified as vacant or deleted (nonexistent) in Nonresponse Followup, or during List/ Enumerate

field work, were revisited during the Vacant/ Delete/ Movers

Check to determine if the unit was classified correctly. If

the Vacant/ Delete/ Movers Check status matched the

status given to the unit in Nonresponse Followup, no

further processing was done. If the Vacant/ Delete/ Movers

Check classification was found to be different than the

status assigned in Nonresponse Followup, the housing

unit and/ or occupants were enumerated and the change in

unit status was made to the Address Control File, although

vacant units reclassified to delete remained as vacant on

the Address Control File.

In the 1980 census and in the early 1990 Test Censuses, the procedure involved de facto enumeration of

movers; that is, the unit was enumerated as it was at the

time of vacant/ delete followup and the current residents

were enumerated if they had not been enumerated at their

Census Day address. The method implemented for the

1988 Dress Rehearsal and the 1990 census involved de

jure enumeration of movers. This method entailed classifying the unit as it should have been on Census Day and

enumerating households who moved to a unit after Census

Day at their Census Day address, if they were not already

counted there. During the field enumeration, the respondent’s word was accepted as to whether they were enumerated at their Census Day address.

The Nonresponse Followup status was accepted as a

proxy for Census Day status, unless during the Vacant/ Delete/

Movers Check it was determined that the unit was occupied on Census Day by the current household. If it was

occupied by an in-mover, a Mover-Usual Home Elsewhere

(UHE) questionnaire was completed for the in-movers

(who said they were not counted elsewhere) and a Search/

Match operation was performed to verify that the household was correctly enumerated at their reported Census

Day address. The status of a housing unit where in-movers

were discovered remained as it was in Nonresponse

Followup, unless a Mover-Usual Home Elsewhere questionnaire was received as a result of the Vacant/ Delete/ Movers

Check at another address.

Methodology

The results categories were based on vacant and delete

counts extracted from the Address Control File. Vacant

and deleted units which were converted to occupied were

matched to the associated census data files to obtain

person counts and demographic information. For postcensus movers, this evaluation used the results generated

by the Search/ Match processing of forms D-190, Search

Records, completed for Mover-UHE cases. Refer to chapter 5 for details of this operation.

Results

Nationally—A total of 10.2 million vacant and deleted

units were followed up. Of these, 2.9 million units were

deletes (nonexistent) and 7.3 million units were vacant.

Of the 2.9 million deleted units, 188,591 (6.4 percent)

were converted to occupied, 156,198 (5.3 percent) were

converted to vacant and 2.6 million (88.3 percent) remained

deletes. The Vacant/ Delete/ Movers Check resulted in a

total of 344,789 units (11.7 percent) being added back to

the census, representing a coverage gain of 0.34 percent.

Of the 7.3 million vacant units, 634,129 (8.7 percent)

were converted to occupied, 4,688 (0.1 percent) were

converted to delete, and 6.6 million (91.2 percent) remained

vacant. The vacant units converted to delete were not

reclassified as delete on the ACF.

In the vacant and deleted units converted to occupied,

a total of 1,505,415 persons were added to the census

representing a coverage gain of 0.6 percent.

Tape Address Register Areas—A total of 5,554,990

vacant and nonexistent units were followed up in the TAR

areas.This representedabout 54.3percent of theVacant/ Delete

universe. The followup of 1.7 million deleted units resulted

in the addition of 196,749 (0.35 percent increase) housing

units to the housing inventory in the TAR areas. Of the

1,682,180 TAR units classified as delete, 90,839 (5.4

percent) were converted to vacant and 105,910 (6.3

percent) were converted to occupied.

Of the 3.8 million TAR units classified as vacant,

402,377 (10.4 percent) were converted to occupied. A total

of 508,287 units originally classified as deleted or vacant

were converted to occupied. Of these units, 105,910 (6.3

percent) were originally classified as deleted and 402,377

Table 4.4. 1990 Census Vacant/ Delete/ Movers Check

National results

Number Percent

Percent

of U.S.

total1

Total vacant/ delete housing units. . . 10,231,111 100.0 10.00

Total deletes . . . . . . . . . . . . . . . . . . . . . 2,956,891 100.0 2.89

Remained deletes. . . . . . . . . . . . . . . 2,612,102 88.3 2.55

Deletes converted to occupied . . . 188,591 6.4 0.18

Deletes converted to vacant. . . . . . 156,198 5.3 0.15

Total vacants . . . . . . . . . . . . . . . . . . . . . 7,274,220 100.0 7.11

Remained vacant . . . . . . . . . . . . . . . 6,635,403 91.2 6.49

Vacants converted to occupied . . . 634,129 8.7 0.62

Vacants converted to delete. . . . . . 24,688 0.1 0.00

Total other converted units. . . . . . . . . 3

231,147 2.3 0.23

1

Percent of total national housing units (102,263,678).

2

Vacants converted to deletes remain as vacants on the ACF.

3

TheseconversionsareinadditiontothosemadeduringVacant/ Delete/ Movers

Check and were not made as the direct result of the vacant/ delete

followup but were included in the workload.

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units (10.4 percent) were originally classified as vacant. As

a result of converting vacant and deleted units to occupied,

875,169 persons were added to the census. This figure

represents about 0.63 percent of the persons counted in

TAR areas.

Non-TAR Areas—A total of 4,676,121 vacant and nonexistent units were followed up in non-TAR areas. These

areas include Prelist Mailout/ Mailback, Prelist Update/

Leave, Prelist Pocket, and List/ Enumerate areas. The

workload represents approximately 45.7 percent of the

Vacant/ Delete universe. The followup resulted in an addition of 148,040 (0.33 percent increase) housing units in

non-TAR areas. Of the 1,274,711 units classified as delete,

65,359 (5.1 percent) were converted to vacant and 82,681

(6.5 percent) were converted to occupied. Of the 3.4

million non-TAR units classified as vacant, 231,752 (6.8

percent) were converted to occupied.

A total of 314,433 units originally classified as deleted or

vacant were converted to occupied. Of these units, 82,681

units (26.3 percent) were misclassified as deleted and

231,752 units (73.7 percent) were misclassified as vacant.

As a result of converting vacant and deleted units to

occupied, 630,246 persons were added to the census.

This figure represents about 0.58 percent of the persons

counted in non-TAR areas.

Comparison to 1980—The overall field conversion rate at

which vacant units were converted to occupied was 8.7

percent. This rate is substantially lower than the conversion rate of 10.1 percent during the 1980 census. This rate

does not reflect conversions made during the Search/ Match

operation. One reason for a lower conversion rate in the

field for the 1990 census versus the 1980 census is that in

1980, all housing units containing in-movers who were not

previously enumerated were converted to occupied; but in

the 1990 census, the housing unit remained vacant and

the in-movers were enumerated at their Census Day

address.

The rate at which deleted units were converted to

vacant or occupied for the nation was 11.7 percent. This

rate was also lower than the 17.3 percent conversion rate

in the 1980 census. It is possible that during the 1990

Nonresponse Followup operation the enumerators did a

better job of identifying nonexistent units than in the 1980

census. This may be attributed to better training and

procedures. Evidence was also provided by observers that

when a Vacant/ Delete enumerator converted a deleted

unit, it was often one that was difficult to find and/ or one

that looked at first to be uninhabitable.

There was an overall average of 2.63 persons per

occupied housing unit for the Nation. The average household size for units converted to occupied during the

Vacant/ Delete/ Movers Check was 1.83 persons per occupied housing unit, which is 0.80 persons lower than the

average household size for occupied housing units for the

Nation. For the 1980 census, the average number of

persons per converted occupied unit added by the

Vacant/ Delete/ Movers Check was 0.50 persons lower

than the average household size for the nation. See table

4.5.

The following tables reflect various demographic characteristics of persons in housing units added by the

Vacant/ Delete/ Movers Check for the nation. The data

were obtained by matching the Address Control File for

housing units added by the operation to the census data

file to obtain the characteristics. Total figures do not agree

exactly because of rounding.

By adding higher percentages of Blacks (21.6 percent

versus 12.1 percent) and Hispanics (11.6 percent versus

9.0 percent) than are found in the overall U.S. counts, the

Vacant/ Delete/ Movers Check improved the coverage of

certain subpopulations that have historically been undercounted in the census. See table 4.6.

As seen in table 4.7, the Vacant/ Delete/ Movers

Check added a higher percentage of White, male, Hispanic

(3.2 percent versus 2.3 percent) persons and White,

female, Hispanic (3.0 percent versus 2.3 percent) persons

than found in the overall U.S. counts. For Black, Hispanic,

males and females, there was very little difference. However, when looking at Black, male, non-Hispanic (10.0

percent versus 5.5 percent) and Black, female, nonHispanic (11.3 percent versus 6.2 percent) persons, the

Vacant/ Delete/ Movers Check added a substantially higher

percentage than found in the overall U.S. counts.

For other race, the Vacant/ Delete/ Movers Check data

show a slightly higher percentage for both male, Hispanic

and non-Hispanic persons and for female, Hispanic and

non-Hispanic persons.

Post-Census Movers—This evaluation includes the results

of the Search/ Match processing of the Mover-UHE forms.

Table 4.5. Average Household Size Per Occupied Unit

Average persons per unit 1990 census 1980 census

Persons per occupied unit . . . . . . . . . . . . . 2.63 2.75

Added persons per converted

occupied unit . . . . . . . . . . . . . . . . . . . . . . . 1.83 2.25

Difference . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.80 0.50

Table 4.6. Race and Hispanic Origin of Added

Persons

Persons in

V/ D units

converted to

occupied Percent

Census

population [1] Percent

Total. . . . . . . . . 1,505,415 100.0 248,709,873 100.0

Race. . . . . . . . . . . . .

White . . . . . . . . . . 1,035,296 68.8 199,686,070 80.3

Black . . . . . . . . . . 324,702 21.6 29,986,060 12.1

Other . . . . . . . . . . 145,717 9.7 19,037,743 7.7

Total. . . . . . . . . 1,505,415 100.0 248,709,873 100.0

Origin . . . . . . . . . . . .

Hispanic. . . . . . . . 175,008 11.6 22,354,059 9.0

Non-Hispanic . . . 1,330,407 88.4 226,355,814 91.0

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The results are based on clerical tallies and the clerks’

ability to sort and search forms correctly. The result

categories used during the sort operation were dependent

on the results being recorded correctly on the Search/ Match

Status Label (see chapter 5). If the clerks did not fill the

Search/ Match Status Label correctly, the search form

would be included in the ‘‘Other’’ sort category. The true

number of search forms in specific categories may be

overstated or understated. For this reason, the numbers

presented in table 4.8 may differ from those presented in

chapter 5.

Search/ Match Results—The following Mover/ UHE results

were reported by the processing office Search/ Match

staff.

The overall Search/ Match workload for movers was

relativelysmall.It was about 3.0 percent of all the search/ match

work. Of these cases, 8.3 percent were unsearchable

(incomplete address or no names or characteristics) and

10.5 percent were ungeocodeable (the clerks could not

assign the search address to a geographic location).

Thus, about 18.9 percent of the Mover-UHE cases were

not processed beyond the Geocoding operation. This is

surprising, given that clerks were instructed to only fill out

a D-190 Search Record for Mover-UHE cases if the UHE

address was searchable.

Approximately 29.3 percent of the Mover-UHE cases

were matched to persons already listed on a census

questionnaire. Of the total mover addresses sent to Search/

Match, a very high proportion resulted in adds (about 36.0

percent). ‘‘Adds’’ include forms for which at least one

person was transcribed to the questionnaire at their reported

Census Day address. Of the matched and transcribed

cases, the add rate is about 55.1 percent. Although the

Mover-UHE cases represent only 3.0 percent of the total

search forms, they had one of the highest potential coverage improvement rates.

The ‘‘other’’ category includes cases that were not

completely processed during the Search/ Match operation

for various reasons. It also includes cases with clerical

coding errors that could not be resolved for this evaluation.

The 1990 Search/ Match operation was instrumental in

adding movers who otherwise would have been missed in

the Census. If the processing office methodology was

improved to reduce the number of uncoded and unsearchable cases, which made up 18.9 percent of the Mover-UHE

workload, an estimated 50 percent of these cases would

have resulted in additional mover adds. There is still the

problem of ensuring that Mover-UHE cases get sent to

Search/ Match. If the Vacant/ Delete enumerator did not

mark the questionnaire as a Mover-UHE case, no D-190

Search Record was completed. There were no quality

assurance checks to ensure that these procedures were

properly implemented.

Conclusions

The Vacant/ Delete/ Movers Check was an effective

census operation which identified and added missed housing units and persons.

The cost per converted unit and cost per added person

was much higher in the 1990 census Vacant/ Delete/ Movers

Check than for previous test censuses and in the 1980

census. Some of the increased per unit cost was due to

lower conversion rates in the field and to higher wages,

more mileage, and the difficulty associated with classifying

hard to enumerate units. The de jure method definitely had

Table 4.7. Race by Sex by Hispanic Origin of Added Persons

Race Sex Origin Added persons Percent Census population Percent

Total 1,505,415 100.0 248,709,873 100.0

White Male Hispanic 48,657 3.2 5,819,289 2.3

Male Non-Hispanic 486,348 32.3 91,656,591 36.9

Female Hispanic 44,490 3.0 5,738,485 2.3

Female Non-Hispanic 455,501 30.3 96,471,705 38.8

Black Male Hispanic 2,027 0.1 391,024 0.2

Male Non-Hispanic 151,022 10.0 13,779,127 5.5

Female Hispanic 2,122 0.1 378,743 0.2

Female Non-Hispanic 69,531 11.3 15,437,166 6.2

Other Male Hispanic 41,509 2.8 5,177,746 2.1

Male Non-Hispanic 35,159 2.3 4,415,641 1.8

Female Hispanic 36,203 2.4 4,848,772 1.9

Female Non-Hispanic 32,846 2.2 4,595,584 1.8

Table 4.8. Results for National Mover-UHE’s

Search/ Match

Number of

mover

households

[3] Percent

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94,711 100.00

Unsearchable. . . . . . . . . . . . . . . . . . . . . . . . . . 7,885 8.3

Ungeocoded . . . . . . . . . . . . . . . . . . . . . . . . . . 9,986 10.5

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17,871 18.9

Matched to questionnaire . . . . . . . . . . . . . . . 27,784 29.3

Transcribed (‘‘Add’’). . . . . . . . . . . . . . . . . . . . 34,082 36.0

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61,866 65.3

Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14,974 15.8

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some effect on the lower field conversion rates per site.

Under the de jure methodology, in-movers would be counted

at their Census Day address to the extent possible. Thus,

the Vacant/ Delete/ Movers Check address would remain

as vacant in the census files unless Search/ Match resulted

in assigning another Census Day household to that address.

This is in contrast to the 1980 census de facto rules

whereby in-movers were enumerated at the followup address.

In both the mailback and List/ Enumerate areas, the

proportion of Black and Hispanic persons among the

census adds from the Vacant/ Delete/ Movers Check were

higher than found in the general census population. It

appears that the followup of vacant and deleted units can

be beneficial in reducing the differential undercount.

Overall, the Vacant/ Delete/ Movers Check added a

substantial number of persons to the 1990 census; however, at relatively great expense. As in 1970 and 1980,

there was a coverage problem with units that were incorrectly classified as vacant or nonexistent during Nonresponse Followup. It is recommended that a review of

vacant and deleted units be part of the 2000 census.

References

[1] 1990 Census of Population and Housing. 1990 CPH1-1, ‘‘Summary Population and Housing Characteristics of

the United States.’’ U.S. Department of Commerce, Bureau

of the Census. March 1992.

[2] Sledge, George. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 38, ‘‘Evaluation of the 1990 Vacant/ Delete/ Movers Operation.’’ U.S.

Department of Commerce, Bureau of the Census. August

12, 1992.

[3] Tillman, Amy. DSSD 1990 Research, Evaluation, and

Experimental Memorandum No. T-19, ‘‘Preliminary Results:

1990 Search/ Match Workloads by Result From the Sample Selection Sort Operation Conducted in the Processing

Offices.’’ U.S. Department of Commerce, Bureau of the

Census. October 24, 1991.

PUERTO RICO MULTIUNIT COVERAGE

IMPROVEMENT OPERATION

Introduction and Background

The Census Bureau implemented a clerical matching

operation to improve the coverage of address listings for

multiunit structures completed by enumerators in the 1990

Census of Puerto Rico. A clerical matching operation

between the census address listing books and the mailing

list of residential customers supplied by the Autoridad de

Energia Electrica de Puerto Rico (Puerto Rico Electric

Company) was conducted in municipios in the four district

offices comprising and surrounding the San Juan municipio since the majority of large multiunit structures in

Puerto Rico were located within this area. For this operation, eligible multiunit structures were defined as any

structure with at least 50 apartment units located within the

boundaries of the San Juan I, San Juan II, Bayamon, or

Carolina District Offices.

The Multiunit Coverage Improvement operation was

completed in three steps. The first step involved clerks

using the basic street address or condominium name on

the Puerto Rico Electric Company match list to geocode

the multiunit structures to census geography. The clerks

used census maps, municipio locator maps, commercial

index maps, and other geographic materials to identify the

address register area containing the basic street address.

The next step was to complete a two-stage matching

operation. In the first stage, clerks compared census

address registers completed by the enumerators during

the List/ Enumerate operation with the mailing lists of the

residential customers supplied by the Puerto Rico Electric

Company. If the number of units for the structure listed in

the address register was greater than or equal to the

number of units found on the electric company list, no

further action was required. If the number of units for the

structure listed in the address register was less than the

number of units on the electric company list, clerks then

completed the second stage of the matching operation.

This was a unit by unit match between the two listings to

identify any units listed on the electric company listing, but

not listed in the address registers for the respective

structure.

Once the matching was completed, any unit identified

on the electric company list, but not listed in the address

register, was field checked. The enumerators visited the

structure to determine if the units missing from the address

register existed on April 1, 1990. If the unit existed, the

address was added to the address register and a census

questionnaire was completed.

Methodology

The data for this evaluation were supplied from the

census staff within the four district offices. Along with the

electric company address listings from the 262 eligible

multiunit structures were 2 forms summarizing the results

of the operation. The Summary of Office Geocoding and

Matching, D-1021 PR (see appendix B for an illustration of

the form) provided the final results of geocoding and

matching activities within each district office. The Summary of Field Review operations, D-1022 PR (see appendix B for an illustration of the form) tracked the final results

of field enumeration activities from the operation.

The add rate is defined as the ratio of added units (units

which existed on April 1, 1990) to the total units within the

262 eligible multiunit structures in the 4 district offices.

Results

Coverage Gain—The Puerto Rico Multiunit Coverage

Improvement operation added 143 units (add rate of 0.39

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percent) to the 36,388 units in the census address registers for the eligible multiunit structures in the 4 district

office areas of San Juan, Puerto Rico.

The 4 district offices had 262 eligible multiunit structures

involved in the operation. Among the 262 multiunit structures, a total of 34,289 units were listed in the electric

company address listings and 36,388 units were listed in

the address registers. A comparison of these 2 values

shows the census address registers had 2,099 more units

listed. This resulted in the address registers having 6.1

percent more listings than the electric company. Figure 4.1

shows that the number of census address listings were

higher than the electric company listings in all four district

offices.

Geocoding and Matching Operation—During the matching operation, clerks used the form D-1021 PR, to summarize geocoding and matching activities from the operation.

Figure 4.2 summarizes the first stage of the matching

operation for each district office. Absolute differences in

unit counts between the 2 listings ranged from no difference to a difference of 188 units. A summary of the

matching activities across the four district offices exhibited:

• 210 of the 262 structures (80.2 percent) had more units

listed in the address registers than on the electric

company listings. Of these 210 structures, 118 (56.1

percent) had a unit count difference of 5 units or less,

and 54 structures (25.7 percent) had a unit count

difference of 10 or more units.

• 38 of the 262 structures (14.5 percent) had the same

number of units on both listings.

• 14 of the 262 structures (5.3 percent) had more units on

the electric company listings than in the address registers.

The clerks completed the second stage of the matching

operation for the 14 structures for which there were more

units on the electric company listings than in the address

registers. They performed a one-way match comparing the

electric company listing to the address register listing on a

unit-by-unit basis. Final results indicated there were 162

units listed on the electric company listings that were not in

the census address register listings.

Field Enumeration—Enumerators revisited each of the

14 structures in order to resolve the 162 units on the

electric company listings which did not appear in the

address registers. With the completion of the field enumeration, enumerators identified 143 valid added units and

completed census questionnaires for these units. That is,

143 units existed on April 1, 1990 and should have been

included in the 1990 census. The remaining 19 nonmatched units did not exist on April 1, 1990.

Table 4.9 illustrates the results of field enumeration

activities. These data were obtained from the Form D-1022PR,

Summary of Field Review operations. The occupancy

status for the 143 units revealed that 88 of the 143 units

were occupied units, 41 units were vacant units, and 14

units were last resort households. Occupancy status for

the 14 last resort units could not be determined for the

evaluation.

Conclusions

The goal of this operation was to improve the coverage

of address listings completed by the enumerators for the

262 multiunit structures found in the 4 San Juan district

Table 4.9. Occupancy Status of Nonmatched Units

After Field Enumeration

Occupied

units

Vacant

units

Last resort

units

Units did

not exist

San Juan I . . . . . . . 69 29 14 18

San Juan II . . . . . . 11 2 0 1

Bayamon . . . . . . . . 1 0 0 0

Carolina . . . . . . . . . 7 10 0 0

Total . . . . . . . . 88 41 14 19

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offices. This was done by matching address listings from

the address registers to the mailing list of residential

customers supplied by the Puerto Rico Electric Company.

The goal of this evaluation was to determine how complete

the census enumerators listed addresses at the multiunit

structures and determine the effectiveness of using this

specific independent list to improve coverage.

The final outcome of this operation brought very minimal

coverage improvement to the 1990 Census of Puerto Rico.

With the completion of the matching and field operations,

a total of 143 units were added to the Address Control File.

Results from this operation illustrated that the census

address register listings were more comprehensive than

the electric company listings in providing a complete list of

possible addresses found in the 262 multiunit structures.

This was evident from the 2,099 additional addresses

found within the address registers when compared to the

electric company listings.

The results of the operation have provided sufficient

evidence that the enumerators were successful in listing

addresses in their address registers when compared to the

electric company address listings. Given these results, the

electric company listing could better serve the Census

Bureau as an administrative records source of coverage

improvement in multiunit structures in Puerto Rico, rather

than as the source of a mailing list for Puerto Rico.

Reference

[1] Cecco, Kevin. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 86, ‘‘Puerto

Rico Multiunit Coverage Improvement Operation Evaluation.’’ U.S. Department of Commerce, Bureau of the Census. October 10, 1991.

RECANVASS OPERATION

Introduction and Background

In order to improve coverage for the 1990 decennial

census, the Census Bureau implemented the Recanvass

operation. The objective of the Recanvass operation was

to improve coverage in areas where census research may

have indicated evidence of deficient housing unit counts.

The Recanvass operation was conducted in the summer of 1990 and was completed in two stages. During

stage 1, pairs of census enumerators visited targeted

areas to identify and list missing addresses. During stage

2, enumerators completed census questionnaires at each

housing unit added during stage 1, if appropriate. That is,

enumerators had to determine if the unit existed on

Census Day. If the unit added during stage 1 did not exist

on Census Day, then the enumerator deleted the unit from

the address register.

To meet the objective of the operation, nine specific

census research sources were used to identify areas.

These sources provided data indicating which geographic

areas (for example, block, address register area, tract,

and/ or county) may have had deficient housing units

counts. Once the areas were identified, census enumerators recanvassed the area to determine if any housing

units were omitted from census records. All types of

enumeration areas were eligible for the Recanvass operation.

This section provides information on the number of

blocks recanvassed, the number of housing units added

during the operation, the final occupancy status of these

added housing units, and the type of address and structure

of the added housing units. The final portion of this section

assesses how the distribution of challenged blocks in

Postcensus Local Review would have changed if the

Recanvass operation had not been implemented.

Methodology

A large portion of the data were obtained from the

Address Control File which provided data on the extent of

coverage from the operation. Data from the regional

census centers were also used in the evaluation. These

data summarized the results of recanvassing activities

during the operation.

The recanvass rate is defined as the ratio of blocks

recanvassed during the Recanvass operation to the total

number of blocks.

The add rate is defined as the ratio of valid added

housing units (units added as a consequence of the

Recanvass operation which remained either occupied or

vacant when final census counts were issued) to the total

number of housing units in all enumeration areas before

the Recanvass operation.

Limitations

Results pertaining to Recanvass added units are based

on data that were extracted from Census Bureau files

using the source code variable. Other research has suggested the existence of errors in the assignment of Recanvass add source codes. However, the extent of the error is

not known.

In areas where the Recanvass and Postcensus Local

Review operations were conducted concurrently, this evaluation cannot document the Recanvass coverage yield

separately from the Postcensus Local Review yield.

Results

Number of Blocks Which Were Recanvassed During

the Recanvass Operation—Table 4.10 provides the number of blocks recanvassed during the Recanvass operation

as well as the recanvass rate for the four census regions

(see appendix C for an illustration of the four census

regions) and nationally. Of the approximately 6.5 million

blocks nationwide, enumerators recanvassed over 522,000

(8.1 percent) blocks during the Recanvass operation. The

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table shows the largest number of blocks recanvassed

was in the South Census Region with 190,762 blocks and

the largest recanvass rate was in the Midwest Census

Region at 9.1 percent.

Added Housing Units From the Recanvass Operation—

The Recanvass operation added 138,568 housing units to

the national housing inventory. This translates into a 0.14

percent add rate when comparing the number of valid

added housing units from the operation to the total number

of housing units before the Recanvass operation.

Figure 4.3 provides a distribution by State of the respective add rates. The shaded areas are defined by the add

rates from the operation and range from less than 0.10

percent to greater than 0.25 percent (see figure 4.3

legend). The figure shows that the Recanvass operation

had the largest impact in the Southeastern States as well

as some States in the western portion of the nation.

Delaware and Florida had the highest add rates during the

Recanvass operation at 0.39 percent.

Final Occupancy Status of Recanvass Added Housing

Units—Figure 4.4 highlights the final occupancy status

rates of Recanvass added housing units by the four

census regions, as well as nationally.

Overall, 48.9 percent of the added units from the

Recanvass operation remained occupied, 29.4 percent of

the added housing units were vacant housing units, and

the remaining 21.6 percent of the added housing units

eventually were deleted by later census operations and/ or

activities. Results indicate that the Northeast Census

Region had the highest occupied rate among the four

regions at 55.1 percent. The West Census Region had the

highest vacant rate at 32.8 percent and the Midwest

Census Region had the highest delete rate at 27.3 percent.

Added Units by Type of Address and Type of Structure—

Table 4.11 provides the percentage of Recanvass added

units by type of address and census region. The address

types were split into three categories. The ‘‘City Delivery’’

Table 4.10. Blocks Recanvassed From the Recanvass

Operation by Census Region

Census region Total blocks

Blocks

recanvassed

Recanvass rate

(percent)

National . . . . . . 6,461,820 522,805 8.1

Northeast . . . . . . . . 975,161 76,562 7.9

Midwest . . . . . . . . . . 1,884,305 170,763 9.1

South . . . . . . . . . . . . 2,479,535 190,762 7.7

West. . . . . . . . . . . . . 1,122,819 84,718 7.5

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type includes added units with house number and street

name addresses. The ‘‘Rural Delivery’’ type includes those

added units which were of rural route, highway contract

route, or star route type. The ‘‘Other’’ category includes

added units which were of non-delivery type (that is, PO

Box or general delivery) as well as added units listed with

physical locations only (no address associated with the

added unit).

Results from table 4.11 clearly show that the majority of

Recanvass added units were city delivery type addresses.

All four census regions had a city delivery type address

percentage greater than 80 percent.

Table 4.12 provides the percentage of Recanvass added

units by type of structure and census region. The structure

types were split into four groups. The ‘‘Mobile Home’’ type

includes added units which were either individual mobile

homes or mobile homes in trailer parks. The ‘‘One Family’’

type includes all one-family houses which were either

attached or detached from one another. The ‘‘Apartment’’

type includes all structures with two or more units. The

‘‘Other’’ category includes the remaining added units which

were not considered to be a mobile home, one-family

structure, or a unit within an apartment structure.

Table 4.12 shows that slightly less than 60 percent of

the total Recanvass added units were one-family structures. Units within apartment structures accounted for

approximately 30 percent of the total Recanvass added

units. The highest percentage of added units which were

mobile homes were in the South Census Region and the

highest percentage of added units within apartment structures were in the Northeast Census Region. The West

Census Region had the highest percentage of added units

which were one-family structures.

Postcensus Local Review Simulation—The final aspect

of the Recanvass evaluation involved a simulation of the

Postcensus Local Review as if the Recanvass operation

had not occurred. This aspect of the evaluation assessed

how the distribution of challenged blocks in Postcensus

Local Review would have changed if the Recanvass

operation had not been implemented. The goal was to

determine the difference in the number of blocks and

percentage of housing units which would have been

eligible for Postcensus Local Review recanvass if the

Recanvass operation had not been implemented.

Two identical files containing all challenged blocks from

the Postcensus Local Review operation were created. The

first file containing all Postcensus Local Review challenged blocks was not altered. The second file was altered

when census staff removed all Recanvass added housing

units from the respective Postcensus Local Review challenged blocks. The only difference between the two files

was the first file contained Recanvass added housing units

and the second file had the Recanvass added housing

units removed.

Results from the first file illustrated that using the

Postcensus Local Review recanvassing guidelines, 139,312

blocks representing 6.43 percent of the total housing units

nationwide were eligible for Postcensus Local Review

recanvass. Using the same Postcensus Local Review

recanvassing guidelines and removing the Recanvass

added housing units from the second file (as if the Recanvass operation had not been implemented), results show

that 139,953 blocks, representing 6.44 percent of the total

housing units, would have been eligible for Postcensus

Local Review recanvass. The difference was 641 blocks or

an additional 0.01 percent of housing units being eligible

for Postcensus Local Review recanvass if the Recanvass

operation had not been implemented.

Challenged blocks from Postcensus Local Review were

matched to blocks with valid added units from the Recanvass operation. The purpose of completing this comparison was to determine the extent of valid added housing

Table 4.11. Percentage of Recanvass Added Units by

Type of Address

Census region City delivery

(percent)

Rural delivery

(percent)

Other

(percent)

National . . . . . . 86.9 5.1 8.0

Northeast . . . . . . . . 87.3 4.6 8.1

Midwest . . . . . . . . . . 90.2 5.2 4.6

South . . . . . . . . . . . . 80.8 8.1 11.2

West. . . . . . . . . . . . . 95.4 0.5 4.1

Table 4.12. Percentage of Recanvass Added Units by

Type of Structure

Census region

Mobile

home

(percent)

One-family

(percent)

Apartment

(percent)

Other

(percent)

National . . . . . . 10.4 58.6 29.2 1.9

Northeast . . . . . . . . 3.2 38.0 54.9 4.0

Midwest . . . . . . . . . . 9.1 62.4 26.5 2.1

South . . . . . . . . . . . . 14.6 60.1 24.1 1.2

West. . . . . . . . . . . . . 8.4 66.8 23.2 1.6

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units from the Recanvass operation which would not have

been added to census records if the Recanvass operation

had not been implemented.

Results from matching both files showed that 24.4

percent (33,831 out of 138,568 units) of the added units

from the Recanvass operation were found in challenged

blocks during Postcensus Local Review. The remaining

75.6 percent of the added units from the Recanvass

operation were found in blocks which were not challenged

during Postcensus Local Review. These results indicate

that three quarters of the valid added units during the

Recanvass operation would not have been added if the

Census Bureau elected only to complete Postcensus

Local Review and not the Recanvass operation.

Conclusions

A total of 522,805 blocks were recanvassed during the

Recanvass operation. From this recanvassing, the Census

Bureau added 176,808 units to the Address Control File, of

which 138,568 units remained either occupied or vacant

when final census counts were released.

Results from the Postcensus Local Review simulation

showed minimal change between the two distributions of

challenged blocks (with and without Recanvass added

units), indicating that the Recanvass operation was conducted in areas that were not challenged by local government officials. Results from comparing blocks with Recanvass added units to challenged blocks from Postcensus

Local Review illustrated that three quarters of the Recanvass added units would not have been added to final

census counts if the Census Bureau elected to only

complete Postcensus Local Review.

The Recanvass operation was conducted in the later

stages of the 1990 decennial census, so it played an

important role in the completion of the census. The

operation was a valuable coverage improvement operation, but equally important was its role in assuring the

Census Bureau that the 1990 decennial census was

completed correctly and accurately. Specifically, the completion of the Recanvass operation gave the Census

Bureau a measure of assurance prior to Postcensus Local

Review that no major areas of geography or segments of

housing units were missed.

References

[1] Cecco, Kevin. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 191, ‘‘Final

Evaluation of the 1990 Recanvass Operation.’’ U.S. Department of Commerce, Bureau of the Census. October 16,

1992.

[2] Cecco, Kevin and Florence H. Abramson. 1990 Decennial Census Preliminary Research and Evaluation Memorandum No. 184, ‘‘Evaluating the Local Review Operation

From the 1990 Decennial Census.’’ U.S. Department of

Comerce, Bureau of the Census. October 13, 1992.

POSTCENSUS LOCAL REVIEW

Introduction and Background

The Postcensus Local Review operation was conducted

in the fall of 1990 in all enumeration areas of the nation.

The operation provided local government officials the

opportunity to review Census Bureau counts of housing

units and group quarters population, as well as boundary

maps to identify any major discrepancies in the counts or

maps. Housing unit counts were provided to all 39,198

governmental units prior to the operation and each governmental unit was invited to participate in Postcensus

Local Review.

The regional census centers were responsible for sending a list of housing unit counts and group quarters

population counts to local officials for each block within

their jurisdiction. During the local review program, each

governmental unit could appoint a representative to review

the counts and work with Census Bureau staff to resolve

any discrepancies. Local officials had 15 working days to

review these counts, using local estimates derived from

documents such as tax records, utility hookups, or building

permits to identify discrepancies.

If the housing unit counts in these governmental units

differed from the Census Bureau housing unit counts, the

local census liaison informed the Census Bureau of these

differences. Census Bureau representatives and local officials worked together to resolve the differences. Some

discrepancies were resolved through discussions over the

telephone or by consulting other sources. If the discrepancies could not be resolved in the office, then additional

field review occurred. For some discrepancies, the Census

Bureau recanvassed the block. During the recanvass, an

enumerator revisited the block and, using the census

address registers, made additions, deletions, or geographic

transfers to the listing of housing units in that block.

This section documents such data as governmental unit

eligibility and participation in Postcensus Local Review, the

extent of blocks challenged and recanvassed, the add

rates, the final census occupancy status of added units,

and the added units by type of address and by type of

structure.

Methodology

A large portion of the data were obtained from the

Address Control File which provided data on the extent of

coverage from the operation. Data from the regional

census centers also were used in the evaluation. These

data summarized the participation of governmental units in

Postcensus Local Review as well as provided results of

recanvassing activities during the operation.

The add rate is defined as the ratio of valid added

housing units (units added as a consequence of Postcensus Local Review which remained either occupied or

vacant when final census counts were issued) to the total

number of housing units prior to Postcensus Local Review.

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Limitations

Results pertaining to Postcensus Local Review added

units are based on data that were extracted from Census

Bureau files using the source code variable. Other research

has suggested the existence of errors in the assignment of

Postcensus Local Review add source codes. However, the

extent of the error is not known.

In areas where Postcensus Local Review and the

Recanvass operations were conducted concurrently, this

evaluation cannot document the Postcensus Local Review

coverage yield separately from the Recanvass coverage

yield.

Results

Eligibility and Participation of Governmental Units in

the Postcensus Local Review Operation—Eligibility in

the Postcensus Local Review operation was open to all

functioning governmental units within all enumeration areas.

Participation in Postcensus Local Review could occur in

any one of the following three ways. A governmental unit

could respond by:

1. Agreeing with the census housing unit counts.

2. Disagreeing with census housing unit counts, but not

providing the Census Bureau with the proper documentation to identify these discrepancies.

3. Disagreeing with the census housing unit counts and

providing the Census Bureau with proper documentation for identifying the discrepancies.

Since all enumeration areas were eligible for Postcensus Local Review, all 39,198 functioning governmental

units were eligible to participate in the operation. The

national Postcensus Local Review response rate (the rate

of participating governmental units to eligible governmental units) was 25.1 percent, or 9,847 governmental units

participated in Postcensus Local Review out of the 39,198

eligible governmental units.

Governmental Units Which Challenged Census Housing Unit Counts During the Postcensus Local Review

Operation—Of the 9,847 governmental units which participated, 6,602 (67.0 percent) challenged (with appropriate documentation) the Census Bureau’s housing unit

counts. The remaining 3,245 (33.0 percent) were split into

governmental units which responded by agreeing with the

census counts and governmental units which responded

by disagreeing with the census counts but did not have the

proper documentation to identify the discrepancies.

Challenged and Recanvassed Blocks From the Postcensus Local Review Operation—Of the approximately

6.5 million blocks delineated by the Census Bureau, governmental unit officials challenged (that is, disagreed with

the census housing unit counts and provided documentation showing the differences for) 270,650 blocks (4.2

percent).

Of the 270,650 challenged blocks, Census Bureau

enumerators recanvassed 62 percent of the blocks during

Postcensus Local Review. The remaining 38 percent of the

challenged blocks were not recanvassed because the

blocks did not meet the recanvassing guidelines for Postcensus Local Review. Recanvassing guidelines for Postcensus Local Review stated that 2 percent of the total

housing units within the governmental unit would be recanvassed, starting with the blocks with the largest positive

housing unit count differences and continuing in descending order. In addition, all blocks with a housing unit count

difference (the governmental unit housing unit count minus

the census housing unit count for any challenged blocks)

greater than one or negative five would be recanvassed.

The district offices also had the option to recanvass the

remaining challenged blocks, if time permitted.

Added Housing Units From the Postcensus Local

Review Operation —The Postcensus Local Review operation added 80,929 housing units to the national housing

inventory. This translates into a 0.08 percent add rate

when comparing the number of valid added housing units

to the total number of housing units before the Postcensus

Local Review operation.

Figure 4.5 provides a distribution by State of the respective add rates. The shaded areas are defined by the

respective add rates from Postcensus Local Review which

range from less than 0.10 percent to greater than 0.25

percent (see figure 4.5 legend).

Final Occupancy Status of Postcensus Local Review

Added Housing Units—Figure 4.6 highlights the final

occupancy status rate of Postcensus Local Review added

housing units at the national and census region levels (see

appendix C for an illustration of the four census regions).

Overall, 58.7 percent of the added housing units from

the Postcensus Local Review operation remained occupied units, 29.6 percent of the added housing units were

vacant housing units, and the remaining 11.7 percent of

the added housing units eventually were deleted by later

census operations and/ or activities. The figure illustrates

that the West Census Region had the highest vacancy rate

at 38.0 percent. Results also indicate that the South

Census Region had the highest occupied rate at 61.7

percent and the Northeast region had the highest delete

rate at 17.2 percent.

Added Units by Type of Address and Type of Structure—

Another component of the Postcensus Local Review evaluation assessed the percentage of added units by type of

address and type of structure.

Table 4.13 provides the percentage of Postcensus

Local Review added units by type of address and census

region. The address types were split into three categories.

The ‘‘City Delivery’’ type includes added units with house

number and street name addresses. The ‘‘Rural Delivery’’

type includes those added units which were of rural route,

highway contract route, or star route type. The ‘‘Other’’

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category includes added units which were of non-delivery

type (that is, PO Box or general delivery) as well as added

units listed with physical locations only (no specific address

associated with the added unit).

Results from table 4.13 clearly show that the majority of

the Postcensus Local Review added units were city delivery type addresses. All four census regions had a city

delivery type address percentage greater than 75 percent.

Table 4.14 provides the percentage of Postcensus

Local Review added units by type of structure and census

region. The structure types were split into four categories.

The ‘‘Mobile Home’’ type includes added units which were

either individual mobile homes or mobile homes in trailer

parks. The ‘‘One Family’’ type includes all one-family

houses which were either attached or detached from one

another. The ‘‘Apartment’’ type includes all added units

within a structure with two or more units. The ‘‘Other’’

Table 4.13. Percentage of Postcensus Local Review

Added Units by Type of Address

Census region City delivery

(percent)

Rural delivery

(percent)

Other

(percent)

National . . . . . . 86.4 4.1 9.5

Northeast . . . . . . . . 91.2 3.1 5.7

Midwest . . . . . . . . . . 86.9 4.0 9.1

South . . . . . . . . . . . . 78.7 7.3 13.9

West. . . . . . . . . . . . . 92.7 0.5 6.9

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category includes the remaining added units which were

not considered to be a mobile home, one-family structure,

or a unit within an apartment structure.

Table 4.14 reveals that one-family structures and units

within apartment structures accounted for approximately

the same percentage of added units nationwide at about

44 percent. The highest percentage of added units in the

Northeast and Midwest Census Regions were units within

apartment structures. The highest percentage of added

units in the South and West Census Regions were onefamily structures.

Address Control File Transactions—Table 4.15 documents the Address Control File transactions from the

Postcensus Local Review operation. The table provides

the number of accepted deletes and accepted geographic

transfers at the census region as well as nationally. An

accepted delete is defined as a unit which was deleted as

a consequence of Postcensus Local Review and was

accepted by the Address Control File maintenance edits.

An accepted geographic transfer is defined as moving a

unit from one block to another block and the process of

moving this unit being accepted by the Address Control

File maintenance edits.

One of the most important contributions from Postcensus Local Review was the updating of the Address Control

File which deleted many unacceptable addresses and

corrected numerous geocoding errors. National Address

Control File transactions reveal that 101,887 units were

deleted as a consequence of Postcensus Local Review

and a total of 198,347 units were geographically transferred as a consequence of Postcensus Local Review.

Table 4.15 illustrates that the West Census Region had

the highest number of accepted deleted units with 32,945

deletes and the South Census Region had the highest

number of accepted geographic transfers with 74,166

geographic transfers.

Conclusions

Approximately 25 percent of the governmental units

participated in Postcensus Local Review and about 67

percent of participating governmental units challenged

census housing unit counts. The Census Bureau had

hoped for a larger participation rate, however, because of

problems with imposing or confusing local review documentation, many smaller governmental units elected not to

participate. In the future, the Census Bureau should develop

documentation that can be modified to suit either governmental units with technical capabilities or governmental

units which lack extensive resources for the operation.

After receiving all legitimate documentation, governmental

unit officials challenged 270,650 blocks and the Census

Bureau recanvassed 168,255 of these challenged blocks.

More important than the added unit coverage of the

operation, was the updating of the Address Control File

through the numerous geographic transfers and deletes

during the operation. From the recanvassing, the Census

Bureau added 91,611 units to the Address Control File, of

which 80,929 units remained either occupied or vacant

when final census counts were released. Results also

revealed that 101,887 units were deleted and 198,347

units were geographically transferred as a consequence of

the Postcensus Local Review.

The Postcensus Local Review operation was conducted

in the later stages of the 1990 decennial census and

played an important role in the completion of the census.

The operation’s primary success was correcting numerous

geocoding problems as well as deleting unacceptable

housing units from the Address Control File. However,

equally important to the address updates was the Census

Bureau’s role in providing the local government officials

the opportunity to review and improve the housing unit

counts within their jurisdictions.

References

[1] Cecco, Kevin. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 237, ‘‘1990

Postcensus Local Review Evaluation.’’ U.S. Department of

Commerce, Bureau of the Census. June 29, 1993.

[2] Cecco, Kevin and Florence H. Abramson. ‘‘1990 Decennial Census Preliminary Research and Evaluation Memorandum No. 184, ‘‘Evaluating the Local Review Operation

from the 1990 Decennial Census.’’ U.S. Department of

Commerce, Bureau of the Census. October 13, 1992.

POP ONE REENUMERATION

Introduction and Background

Allegations were made that, during the closeout phase

of Nonresponse Followup, enumerators in a few district

Table 4.14. Percentage of Postcensus Local Review

Added Units by Type of Structure

Census region

Mobile

home

(percent)

One-family

(percent)

Apartment

(percent)

Other

(percent)

National . . . . . . . . . . 9.2 43.4 44.5 2.9

Northeast . . . . . . . . 2.3 28.5 67.7 1.5

Midwest . . . . . . . . . . 6.5 42.3 48.7 2.6

South . . . . . . . . . . . . 16.4 53.5 29.3 0.9

West. . . . . . . . . . . . . 7.0 43.3 42.7 7.1

Table 4.15. Address Control File Transactions From

Postcensus Local Review by Census

Region

Census region Accepted deletes

Accepted geographic

transfer

National . . . . . . . . . 101,887 198,347

Northeast . . . . . . . . . . . 21,275 38,177

Midwest . . . . . . . . . . . . . 20,152 44,352

South . . . . . . . . . . . . . . . 27,515 74,166

West. . . . . . . . . . . . . . . . 32,945 41,652

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offices were recording households as one-person households without benefit of an interview. All offices so identified were selected for reenumeration, as well as all district

offices with enumeration characteristics similar to the

offices so identified. In all, 24 district offices were selected.

In these offices interviewers proceeded to reenumerate all

one-person households originally enumerated after June 6

under Nonresponse Followup, last resort procedures and,

if the number of persons in the household was different, a

new questionnaire was completed. For this reason, this

operation, called the POP One Reenumeration, is considered a coverage improvement activity.

The Census Bureau also conducted an evaluation of the

enumeration of one-person households during the Nonresponse Followup operation of the 1990 census. The

evaluation was designed to collect data about all 447

census district offices that conducted a Nonresponse

Followup operation. Field interviewers were sent to reinterview a sample of one-person households in the fall of

1990, 6 months after Census Day (April 1, 1990).

Results of this evaluation [1] show the effect of potential

errors in one-person household enumeration on the total

population count and the effectiveness of our quality

assurance efforts during the precloseout phase of Nonresponse Followup operations.

Methodology

In 24 district offices, all households enumerated after

June 6 and under last resort procedures as one-person

households were reinterviewed. Tabulations from this

reenumeration were summarized.

In order to see whether district offices with possible

fabrication problems with respect to one-person households had been isolated, a nationwide sample of 1,000

one-person households enumerated during the Nonresponse Followup operation was selected. This sample

included cases enumerated on or before the date Nonresponse Followup closeout procedures were authorized

and cases enumerated after that date. In this way the

sample contained cases subject to our quality assurance

process (those cases enumerated before closeout) and

cases completed after quality assurance activities were

suspended (those cases enumerated during closeout).

Before sampling, the 447 district offices were ranked by

the ratio of occupied housing units enumerated using last

resort procedures over the total number of occupied

housing units enumerated during Nonresponse Followup.

Using this ratio, the offices were divided into two strata:

• Stratum ‘‘X’’: Those offices having a high proportion

(ratio > .163) of last resort enumerations.

• Stratum ‘‘Y’’: Those offices having a low proportion

(ratio < .163) of last resort enumerations.

We then defined a third stratum:

• Stratum ‘‘Z’’: Those 24 offices referred to earlier for

which we had completed a reenumeration of all last

resort one-person household enumerations completed

after June 6, 1990.

For the ‘‘Z’’ stratum, a sample was created from only

those cases completed before closeout, regardless of the

date at which closeout procedures were begun. Interviewers had already reenumerated all of the households completed after June 6 under last resort procedures (which

means some before closeout and all during closeout cases

enumerated under last resort procedures). This evaluation

sample was limited to the before closeout cases because,

for some purposes of comparison with the ‘‘X’’ and ‘‘Y’’

strata cases, only reinterviews of before-closeout oneperson household enumerations were needed.

The following table presents the distribution of oneperson households enumerated during Nonresponse Followup as proportions of all addresses enumerated within

each stratum.

There were 24 district offices in the ‘‘Z’’ stratum, 88 in

the ‘‘X’’ stratum, and 335 in the ‘‘Y.’’

Enumerators from the Census Bureau’s ongoing programs reinterviewed the evaluation sample households

during the second and third weeks of October 1990. These

enumerators were used to maintain independence from

the census. Additionally, these staff have more experience

than temporary decennial census employees, which gave

a greater degree of confidence in the accuracy of their

work.

The reinterview data were compared to the data on the

census data captured questionnaires for each household.

Difference rates were calculated for each stratum and

comparisons were made.

Limitations

1. An independent reinterview with no validation of differences, conducted 4 to 6 months or more after an

original interview, may not provide an accurate assessment of the quality of enumeration conducted during

Nonresponse Followup. Experience in earlier research

[2] has shown that with further probing, the response

difference rates decrease appreciably. Some reasons

for this are:

a. The interviewer involved in the reinterview, by

definition, is different from the nonresponse enumerator. While one interviewer may be better

Table 4.16. Percent One-Person Households by

Strata

Stratum Percent

Z—Intense scrutiny. . . . . . . . . . . . . . . . . . 11.6

X—High proportion . . . . . . . . . . . . . . . . . . 7.8

Y—Low proportion 5.2

Total U.S. . . . . . . . . . . . . . . . . . . . . . . 5.8

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trained than another, the nonresponse enumerator being indigenous to the area may be better

able to collect accurate responses from householders living there.

b. The respondent in the nonresponse interview may

differ from the respondent in the reinterview.

c. Even if the respondent is the same, respondents

may have a problem, due to recall bias, recalling

accurately the situation in the household 4 to 6

months ago.

d. The respondent may be less willing to report fully

and accurately in the reinterview having already

given the information to the Census Bureau several months earlier.

e. The reinterviewer may collect data from the household living there in October rather than trying to

reconstruct the roster of Census Day residents.

Therefore, these types of data may be useful to

provide insights into possible effects, but should not

be used as conclusive measures of data quality during

nonresponse enumeration.

2. For some study data, it could not be determined

whether the reinterviewers were reporting vacancy

status as of Census Day or as of the reinterview day.

Since it could not be verified these cases in fact were

vacant on Census Day, two sets of estimates were

calculated based on both vacancy assumptions. The

ranges of estimates in the two tables reflect these

different sets of assumptions.

3. Some of the sample cases were dropped from the

evaluation because interviewers were not able to determine Census Day status. When the vacants were

treated as unknown, over 22 percent of the cases were

dropped. When the vacants were counted as response

differences, about 15 percent of the cases were dropped.

These missing data were corrected for when estimating the household difference rates and confidence

intervals in table 4.17, but not when calculating the

effect on the population count shown in table 4.18.

4. Sampling errors from these sample results and confidence intervals were determined to inform the reader

about the reliability of the estimates from this sample.

However, caution against making statements about

these results is recommended, especially in comparing

differences, when the estimates are within sampling

error.

Results

During the reenumeration in the 24 district offices

selected for intense scrutiny, 56,785 of the 128,873 oneperson households reinterviewed showed a response difference (44.1 percent). These district offices, as a group,

tended to exhibit response difference rates higher than the

estimated rates for the other 423 district offices. This

suggests that these were the proper offices on which to

focus our attention. Table 4.17 shows the response difference rates in the sampled enumerations for every stratum.

For the X and Y strata, which represent 423 or 95 percent

of all district offices, the response difference rates range

from 19.2 percent to 30.0 percent (90 percent confidence

interval of 12.2 to 37.1 percent).

In the 24 district office reenumerations, a net number of

56,341 additional persons were added to the count. While

most of the reinterviewed households remained as oneperson households (55.9 percent), some households contained more than one person and some were classified as

vacant. This equates to an effect of 0.71 percent on the

total population count for these offices.

Table 4.18 shows that the effect on population counts

within each stratum component was small. For the X and Y

strata, the net effect on population coverage of the difference in the census enumeration count and the reinterview

count of the number of persons in one-person households

enumerated during Nonresponse Followup ranged from

missing 0.25 percent to 0.59 percent (90-percent confidence interval of –0.05 percent to 0.85 percent) of the total

population.

Table 4.17. Estimates1 of Response Difference Rates

by Strata

Strata/ census phase

Response

difference

(percent)

90-percent

confidence

interval

‘‘Z’’Strata

Before closeout. . . . . . . . . . . . . . . . . . . . . . 16.7-24.2 11.8, 29.4

100-percent reenumeration of 24

offices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44.1 NA

‘‘X’’ Strata

Before closeout. . . . . . . . . . . . . . . . . . . . . . 19.3-26.1 14.7, 31.0

During closeout . . . . . . . . . . . . . . . . . . . . . . 22.9-30.0 16.3, 37.1

‘‘Y’’ Strata

Before closeout. . . . . . . . . . . . . . . . . . . . . . 19.9-23.0 15.7, 27.6

During closeout . . . . . . . . . . . . . . . . . . . . . 19.2-25.0 12.2, 32.7

1

The estimates are shown as a range due to the two assumptions

about Census Day vacancy status.

Table 4.18. Estimated Effect1on Population Count by

Strata

Strata/ census phase

Effect on

strata population

(percent)

90-percent

confidence interval

‘‘Z’’ strata

100-percent reenumeration of 24 offices . . . . . 0.71 NA

‘‘X’’ strata . . . . . . . . . . . . . . 0.25-0.59 –0.05, 0.85

‘‘Y’’ strata . . . . . . . . . . . . . . 0.25-0.38 0.07, 0.54

1

The estimates are shown as a range due to the two assumptions

about Census Day vacancy status.

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Conclusion

The response differences found in this study cannot be

characterized as either within or beyond expectation,

because there are no other data comparable to this

evaluation. However, because the effect on the overall

population coverage (shown in table 4.18) is quite small,

the response differences are not believed to be a major

problem.

While the evaluation study was designed to assess the

error level in the enumeration of one-person households,

biases and variances on the estimates inhibited drawing

definitive conclusions. Because of the difference in universes (in the 24 district offices, only last resort cases

checked in after June 6 were reenumerated, while in the

study, all one-person households were sampled), the study

estimates cannot be statistically compared with the reenumeration estimates. Also due to incomplete data, the

accuracy of the estimates were questioned.

However, it may be concluded from these tables that

the stratification and intense scrutiny of the 24 offices was

efficient. While direct comparisons cannot be made, all of

the rates for the sample reinterview strata are within

sampling error, and exclude the point estimate for the 24

district office reenumeration. The response difference rates

and effect rates are higher for the 24 district office strata.

It does appear the stratification largely isolated the

offices where the potential effect on the population count

was nontrivial as shown in table 4.18.

There is no evidence from these results that dropping

quality control checks during the one-week closeout period

yielded a quality loss. Table 4.18 shows the response

difference rates before and during closeout. These rates

are generally the same for the 95 percent of district offices

comprising strata X and Y. These numbers show there is

no cause to believe dropping the reinterview check led to

a loss in quality.

References

[1] Linebarger, John S. 1990 Decennial Census Preliminary Research and Evaluation Memorandum No. 41, ‘‘An

Evaluation of the Enumeration of Households Characterized During the Nonresponse Followup Operations of the

1990 Census as Containing Only One Person.’’ U.S.

Department of Commerce, Bureau of the Census. April 9,

1991.

[2] U.S. Department of Commerce, Bureau of the Census.

Technical Paper 19, ‘‘The Current Population Survey Reinterview Program, January 1961 through December 1966.’’

December, 1968.

PRIMARY SELECTION ALGORITHM REVIEW

Introduction and Background

During the 1990 decennial census, some census identification (ID) numbers had multiple first form questionnaires data captured. Multiple data captures of census

questionnaires occurred when more than one questionnaire was received for a given census ID, or when the

same questionnaire was recycled through data capture

processing. (For brevity and consistency, throughout this

report the ‘‘multiple data capture records’’ are referred to

as ‘‘multiple questionnaires.’’) The occurrence of multiple

questionnaires required a method for choosing which

questionnaires would be ‘‘selected’’ to represent a given

census ID. The primary selection algorithm was developed

to select the best ‘‘questionnaires of record’’ per census

ID. The remaining questionnaires were not selected and

therefore the person data, when different, were not counted

in the census.

The Primary Selection Algorithm Review was conducted

to review data captured questionnaires whose data records

were not selected by the primary selection algorithm to

represent a given census household. The Primary Selection Algorithm Review was conducted from late October

1990 through the end of December 1990 (the end of the

census processing cycle). This review was prompted due

to concern that the persons reported on the not selected

questionnaires may be missed in the census. This section

documents coverage gain from the Primary Selection

Algorithm Review operation.

The Primary Selection Algorithm Review was initiated

due to concern regarding the persons enumerated on the

‘‘not selected’’ questionnaires. There were two categories

of the not selected questionnaires defined:

1. Those questionnaires for which the number of data

defined persons on all not selected questionnaires

was equal to the number of data defined persons on

the selected questionnaire.1

2. Those questionnaires for which the number of data

defined persons on one or more of the not selected

questionnaires was not equal to the number of data

defined persons on the selected questionnaire.

After reviewing questionnaires in each category, it

was decided that the questionnaires in the second

category would be the universe for the Primary Selection Algorithm Review.

District office level tallies of the number of census

ID’s to be included in the Primary Selection Algorithm

Review were produced. The ID’s were assigned priorities based on specific criteria[1]. The highest priority

work was to be completed first; the lowest priority work

was to be completed only if time permitted.

The processing of these cases involved sending

microfilm copies of the selected and not selected

questionnaires through a modified Search/ Match matching operation to determine if the not selected persons

were counted in the census. First, a matching operation was performed between the selected and not

selected questionnaires to determine if the selected

1

To be ‘‘data defined,’’ a person column had to have machinereadable answers to at least two of the six 100-percent population

questions.

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and not selected persons were the same household.

This was the stage 1 portion of the operation. Not

selected persons found to be different households that

did not match to the selected questionnaire advanced

to an ‘‘extended’’ Search/ Match operation. This was

the stage 2 portion of the operation. The extended

Search/ Match operation involved identifying up to10

neighboring addresses to the address of the census ID

and searching for the not selected and selected persons at these addresses. These neighboring addresses

were referred to as the ‘‘extended search area.’’ If it

was determined that the not selected persons were

not counted in the census, they were added to the

census during the Primary Selection Algorithm Review

operation. Also, in some cases, if it was determined

that the selected persons were counted elsewhere in

the extended search area, the duplication of the

selected persons was removed from the census. [1].

Methodology

During the matching/ transcription portion of the Primary

Selection Algorithm Review operation, clerks were instructed

to annotate the census questionnaires with codes that

would facilitate processing and evaluation. These codes

define the universe of census ID’s for this analysis. Data

for all census ID’s were supplied from various census

computer files; demographics of the primary selection

algorithm person adds and characteristics of the primary

selection algorithm housing units are from these data.

Note that these data were unedited and therefore some

variables included a ‘‘not reported’’ category. These data

have been adjusted proportionately. By doing this, it is

assumed that the nonrespondents of a particular question

are distributed similarly to the respondents.

The term ‘‘add’’ rate is defined as the number of census

ID’s where persons were added from this operation divided

by the total number of review ID’s.2 Note that this is not a

true ‘‘add’’; that is, the housing unit was not added to the

census address control file as a result of this operation.

The ‘‘add’’ rate actually tells us how many census ID’s had

some sort of data capture activity resulting in a person add

from the Primary Selection Algorithm Review. The ‘‘add’’

rates are presented nationally, by regional census center,

and by State.

Limitations

Primary selection algorithm adds are identifiable only as

a result of the clerical coding. Thus, if clerks did not

correctly annotate the census questionnaire during

matching/ transcription, the number of primary selection

algorithm adds may be understated. In addition, if other

operations incorrectly annotated the census questionnaires, the coverage gain could be overstated.

List/ enumerate type of enumeration areas were excluded

from this operation because the rural type addresses

common in these areas were not on the census address

control file; thus the extended search could not be performed. The extended search area was limited to 10

neighboring addresses of the review ID. If the extended

search area would have been larger, more not selected

persons may have been located and/ or more duplicate

enumerations of selected persons may have been discovered. Either of these circumstances could have resulted in

better quality enumerations from this review.

The primary selection algorithm person adds do not

represent a net increase in the 1990 population. The

reason is that during the Primary Selection Algorithm

Review, duplicate enumerations of persons were deleted.

The added persons do not represent a net increase in

population, but rather the added persons minus the deleted

persons would have been the net population increase.

Hence, the person adds stated in this report represent an

‘‘upper bound’’ of coverage gain.

Results

Coverage Gain

U.S. Level—There were 401,174 review ID’s. The Primary

Selection Algorithm Review operation added at least one

person to 161,541 census ID’s. Thus, we can define the

‘‘add’’ rate for this operation to be about 40.3 percent.

Nationally, there were 350,448 Primary Selection Algorithm Review person adds. Demographics of Primary Selection Algorithm Review person adds are discussed further in

this section.

Regional Census Center Level—Table 4.19 shows ‘‘add’’

rates by regional census center. The Chicago Regional

Census Center had the highest ‘‘add’’ rate—58.2 percent.

The New York Regional Census Center was a close

second with a 57.2 percent ‘‘add’’ rate, and the New York

2

The ‘‘review ID’s’’ were the Primary Selection Algorithm Review

workload.

Table 4.19. Primary Selection Algorithm Review

‘‘Add’’ Rates by Regional Census Center

Regional Census Center

ID’s with

added

persons Review ID’s ‘‘Add’’ rate

Chicago . . . . . . . . . . . . . . . . 15,477 26,600 58.2

New York . . . . . . . . . . . . . . 36,007 62,978 57.2

Los Angeles . . . . . . . . . . . . 24,071 45,711 52.7

Atlanta . . . . . . . . . . . . . . . . . 21,667 42,432 51.1

Detroit . . . . . . . . . . . . . . . . . 10,039 21,722 46.2

San Francisco Bay area. . 8,775 20,223 43.4

Charlotte . . . . . . . . . . . . . . . 12,770 35,273 36.2

Boston . . . . . . . . . . . . . . . . . 6,534 24,908 26.2

Philadelphia . . . . . . . . . . . . 11,022 43,394 25.4

Dallas. . . . . . . . . . . . . . . . . . 7,179 31,194 23.0

Kansas City. . . . . . . . . . . . . 4,521 20,096 22.5

Seattle . . . . . . . . . . . . . . . . . 2,654 13,248 20.0

Denver . . . . . . . . . . . . . . . . . 825 13,395 6.2

Total. . . . . . . . . . . . . . . 161,541 401,174 40.3

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Regional Census Center had more than double the number of census ID’s where persons were added than did the

Chicago Regional Census Center. There were 15 district

offices in New York City that conducted a second mailout

of census questionnaires. The workload from this second

mailout likely contributed to the primary selection algorithm

workload for the New York Regional Census Center. This

is one result that can be applied to the 2000 census: if we

plan on multiple mailouts and/ or multiple modes of data

collection for 2000, we must also maintain the best possible questionnaire selection algorithm.

Some other points of interest from table 4.18 include:

• Seven regional census centers had ‘‘add’’ rates either

close to or greater than the U.S. overall rate of 40.3

percent.

• Many of the regional census centers with the lower

‘‘add’’ rates also had a lot of low priority work in their

jurisdiction. For the most part, low priority work had

lower ‘‘add’’ rates, thus they affected the regional census center level ‘‘add’’ rates.

• The Seattle and Denver Regional Census Centers had a

lot of list/ enumerate areas within their jurisdiction. As

previously mentioned, list/ enumerate areas were excluded

from the Primary Selection Algorithm Review. This helps

to explain why these regional census centers had relatively low ‘‘add’’ rates.

State Level—All 50 States and Washington, DC had at

least one census ID where persons were added. The State

level ‘‘add’’ rates ranged from a high of 63.7 percent in

Illinois to a low of 1.7 percent in Idaho. The State level

‘‘add’’ rates take into account size differences between

States. For instance, even though a State may have had a

small number of review ID’s, if most of these cases

resulted in a questionnaire that had a person add, this

would result in a high ‘‘add’’ rate for that State. An example

is the District of Columbia, which is among the top States

in terms of the highest ‘‘add’’ rates. Note that the States

with lower ‘‘add’’ rates may have had a large amount of low

priority work, which may not have been completely processed.

Table 4.20 shows the top States with the highest ‘‘add’’

rates. All other States had ‘‘add’’ rates less than 47

percent.

Most of these States had district offices with high ‘‘add’’

rates. Also, half of these top States also had high workloads. Some other plausible explanations for high ‘‘add’’

rates are:

• It is likely that complex households helped to contribute

to high ‘‘add’’ rates. Consider that one household member may have completed a mail return census questionnaire a few weeks late. The address was then probably

in the Nonresponse Followup workload. When visited by

an enumerator, a different household member may have

been the respondent, and this respondent may have

reported a slightly different roster, resulting in multiple

questionnaires with different rosters for the same household.

• The presence of movers near Census Day may have

helped contribute to high ‘‘add’’ rates as well. Persons

may have had trouble recalling when they moved. They

may have reported an old address as a Census Day

address, when in fact another household was at the old

address on Census Day. This would result in two different rosters for one address.

• Poorly conducted field activities in some areas may have

resulted in multiple questionnaires with different reported

household rosters for some ID’s. For example, unit

mix-ups, or two or more enumerators working the same

area.

These situations, as well as others, could have resulted

in different household rosters for a given address.

Characteristics of Primary Selection Algorithm Review

Housing Units—This section discusses characteristics of

the housing units in the Primary Selection Algorithm review

universe. Primary selection algorithm housing units are

defined as having had at least one selected questionnaire

where at least one person was added. The characteristics

discussed are average household size, type of enumeration area, type of address, and size of basic street address.

There were 161,541 housing units that had at least one

person add during the Primary Selection Algorithm Review.

The overall average household size after the person adds

were processed was 4.22 persons. This is higher than the

published average household size for occupied housing

units for the 1990 census, which was 2.63 persons. The

difference in average household size for the primary

selection algorithm households is likely due to doubling up

households if the not selected persons were not located in

the extended search area and no vacant unit was available

to add them to.

Table 4.21 displays the type of enumeration area distribution of the Primary Selection Algorithm Review housing

units compared to the 1990 U.S. level type of enumeration

Table 4.20. Primary Selection Algorithm Review

‘‘Add’’ Rates—Top States

State

ID’s with

added

persons Review ID’s ‘‘Add’’ rate

Illinois. . . . . . . . . . . . . . . 11,356 17,826 63.7

Indiana. . . . . . . . . . . . . . 3,041 4,801 63.3

Alabama . . . . . . . . . . . . 4,378 7,483 58.5

Ohio . . . . . . . . . . . . . . . . 4,947 8,569 57.7

New York . . . . . . . . . . . 37,515 68,894 54.4

California . . . . . . . . . . . . 32,846 65,934 49.8

Georgia . . . . . . . . . . . . . 5,882 11,850 49.6

Florida . . . . . . . . . . . . . . 11,407 23,099 49.4

District of Columbia. . . 1,571 3,210 48.9

South Carolina . . . . . . . 2,401 5,000 48.0

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area distribution. There was no list/ enumerate in the

Primary Selection Algorithm Review universe; this type of

enumeration area was excluded from the operation. Therefore, the percentages for the U.S. do not total 100 percent,

since list/ enumerate also is excluded from this table for

comparison purposes.

The majority of Primary Selection Algorithm Review

housing units had a type of enumeration area of ‘‘tape

address register’’ (TAR—75.8 percent). This is about 20

percentage points higher than for the 1990 U.S. level TAR

distribution. It seems that multiple data captures occurred

more often in TAR areas. This could be due to several

factors; for example, many multiunits in TAR areas, which

are more likely to have unit designation mix-ups during field

activities and therefore result in multiple questionnaires

data captured per census ID. Also, there is a greater

potential for duplicate enumerations and/ or duplication of

ID numbers in mailback areas due to Field Followup and

Nonresponse Followup, since mail return households may

have been visited by an enumerator if they mailed in their

census questionnaire a little late.

Primary Selection Algorithm Review housing units were

89.8 percent city type addresses. For the U.S., 84 percent

of addresses were city type [3]. The overrepresentation of

TAR addresses in the Primary Selection Algorithm Universe contributed to the higher rate of city type addresses

since most TAR area addresses are also city type addresses.

Rural type addresses comprised 5.8 percent of the Primary

Selection Algorithm Review housing units, and ‘‘other’’

type addresses made up the balance of the primary

selection algorithm housing units—4.4 percent (‘‘other’’

type addresses were defined here as P.O. boxes, general

delivery and location description addresses).

Figure 4.7 displays the distribution of basic street address

size for the primary selection algorithm housing units

compared to the same distribution for 1990 census housing units. Multiunits constituted 46.6 percent of all primary

selection algorithm housing units. This is almost double the

percentage of multiunits nationally. The balance of the

Primary Selection Algorithm Review housing units were

single units—53.4 percent.

These data suggest that it may be more likely for

multiple questionnaires with different household rosters to

be received from multiunit households than from single

units. This is probably due to USPS delivery errors or field

operations such as Nonresponse Followup. That is, during

field operations, enumerators may have visited a unit that

had already sent in their census questionnaire and completed a second questionnaire for the unit, when in fact

they should have visited a different unit. In addition,

research conducted prior to the beginning of the Primary

Selection Algorithm operation showed that some of the

multiple questionnaires were a result of incorrectly processed housing unit adds.

Some of the multiple questionnaires received per housing unit may be attributable to movers. That is, the Census

Bureau may have received questionnaires for both old

tenants and new tenants from the same housing unit.

Given the high occurrence of multiple data captures in

multiunits, it is very important that the best possible

selection algorithm be developed for the 2000 census. It

also is important to improve field operations so time and

money are not wasted and errors are not introduced by

enumerating a household twice.

Demographics of Primary Selection Algorithm Review

Person Adds—There were 350,448 persons added to the

census or moved in the census nationwide during the

Primary Selection Algorithm Review. This section discusses selected demographics of these person adds and

compares them to 1990 census data at the national level.

The demographics discussed are race and Hispanic origin.

As previously mentioned in the Methodology section, the

data for the Primary Selection Algorithm Review person

adds have been proportionately adjusted for item nonresponse.

Figure 4.8 shows the race distribution for Primary Selection Algorithm Review person adds and U.S. level 1990

census data. The item nonresponse rate for the race

question was about 12.9 percent for the Primary Selection

Algorithm Review persons adds. These nonrespondents

were proportionately allocated to the race groups.

Nationally, Primary Selection Algorithm Review person

adds were 53.9 percent White; national census figures

show that the 1990 census population was about 83.9

percent White. About 32.5 percent of the Primary Selection

Table 4.21. Type of Enumeration Area Distribution of

Primary Selection Algorithm (PSA) Review

Housing Units Compared to U.S. Level

Type of Enumeration Area Distribution

Type of enumeration

area

Percent of PSA

housing units

Percent of U.S.

housing units1

TAR . . . . . . . . . . . . . . . . 75.8 55.7

Prelist. . . . . . . . . . . . . . . 14.7 24.9

Prelist pocket . . . . . . . . 1.3 3.7

Update/ leave . . . . . . . . 8.2 10.1

1

Reference 1

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Algorithm Review persons adds were Black, compared to

about 12.3 percent of the 1990 census population. ‘‘Other’’

race persons comprised about 13.6 percent of all Primary

Selection Algorithm Review persons adds, whereas ‘‘other’’ race persons were about 3.8 percent of the 1990

census population.

Some of the possible reasons that minorities (Black and

‘‘other’’ race persons) were overrepresented in the Primary Selection Algorithm Review person adds population

are:

• There were many multiunit structures in the Primary

Selection Algorithm Review universe and a large portion

of the Primary Selection Algorithm Review universe was

in urban areas. Urban areas tend to have large percentages of minority persons.

• Urban, high minority areas, which may also be low

income areas, are traditionally more difficult to enumerate, contributing to the large number of ID’s for which

multiple questionnaires were received. That is, these

units may have been visited multiple times during Nonresponse Followup or Field Followup or there may have

been apartment mixups during these field operations.

• Finally, enumerators may have experienced language

barriers in high minority areas. Language barriers could

have resulted in incomplete questionnaires or apartment

mixups. Both of these could have contributed to multiple

questionnaires per ID.

Figure 4.9 displays the distribution of Hispanic origin at

a national level for the Primary Selection Algorithm Review

person adds compared to 1990 census data. The item

nonresponse rate for the Hispanic origin question for

Primary Selection Algorithm Review persons adds was

17.3 percent.

Primary Selection Algorithm Review person adds were

25.7 percent Hispanic, whereas persons in the 1990

census were only 9 percent Hispanic, nationally. Thus,

Hispanic persons were represented at a greater rate in the

Primary Selection Algorithm Review universe. This is likely

due, in part, to the large number of district offices in urban

areas that were assigned a high priority during the Primary

Selection Algorithm Review. Other explanations for the

high percentage of Hispanic persons in the Primary Selection Algorithm Review universe include those previously

discussed for the high percentage of minorities.

Conclusions

Both the Nonresponse Followup workloads and the

second mailout in New York City helped to contribute to

the number of not selected questionnaires per census ID.

Thus, these operations contributed to the workload for this

coverage improvement operation. It is obvious that the

Census Bureau must have an algorithm to select the

‘‘best’’ questionnaire per census ID; this is especially true

if we plan to conduct multiple modes of data collection for

the 2000 census.

The Primary Selection Algorithm Review was successful

at adding to the census persons who likely would have

been missed if we had not reviewed the not selected

questionnaires. In addition, it was shown that the person

adds included a high percentage of minorities (Black and

Hispanic persons). This seems to indicate that enumeration methods, and perhaps outreach and education, for

these racial and ethnic groups need to be improved.

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Finally, these results show that multiple questionnaires

with varying rosters per census ID occurred more often in

multiunit structures. Multiunit structures are another area

where enumeration methods need improvement. We recommend that research be conducted into obtaining administrative records to improve coverage of multiunit structures. If we have more complete coverage of multiunits,

including accurate unit designations, it should help to

improve enumeration of persons within these structures.

References

[1] Beverage, Susan C. DSSD 1990 Research, Evaluation,

and Experimental Memorandum No. LL-2, ‘‘Evaluation

Overview Primary Selection Algorithm Review.’’ U.S. Department of Commerce, Bureau of the Census. October 17,

1991.

[2] Wajer, Susan C. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 221, ‘‘Final

Results from the Primary Selection Algorithm Review.’’

U.S. Department of Commerce, Bureau of the Census.

March 16, 1993.

[3] Internal Census Bureau Memorandum. ‘‘Results of

Processing List/ Enumerate Addresses.’’ February 25, 1992.

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CHAPTER 5.

Search/ Match Coverage Improvement

CHARACTERISTICS OF SEARCH/ MATCH

ADDITIONS

Introduction and Background

The Search/ Match operation was conducted during the

1990 decennial census to help ensure that all persons

were enumerated at what is defined as their ‘‘usual residence.’’ All persons must be counted at their usual residence for apportionment purposes. A usual residence is

‘‘the place where the person lives and sleeps most of the

time.’’ Search/ Match was designed to improve both within

household and housing unit coverage.

There were six different search forms processed during

Search/ Match. Many persons listed on a search form were

not at their usual residence on Census Day; for example,

they may have been at a hotel on Census Day. In order to

ensure that they were counted at their usual home, the

Census Bureau searched the census questionnaire at their

reported usual residence to determine if they were counted

there. If they were not counted at their reported usual

residence, they were added to the census at that address.

The concept of the Search/ Match operation is to verify

that persons reported on any of the search forms (the

search forms are described later in this section) were

enumerated at their Census Day address. If any of the

persons were not found at their reported Census Day

address, they were added to the census at this address.

Although the concept seems simple, the Search/ Match

operation was long and complex. Search/ Match took

place from July 1990 through December 1990. The following is a brief description of the Search/ Match operation.

All search forms were sent to the census processing

offices. All search forms were sorted by form type (the

form types being the six different search forms), and from

there on were organized by form type during all later

stages of processing. A search/ match status label was

affixed to each form to record the results of the search/ match

processing steps. This processing information would tell

the disposition of each search form, and the data recorded

on the label would later be used for various analyses.

After the search/ match status label was affixed to the

search form, the form was reviewed to determine if it was

searchable. A searchable form had to contain both of the

following:

• Complete data: A name and at least two responses to

the 100 percent population questions (sex, age, race,

hispanic origin, marital status or relationship) for at least

one person.

• Searchable address: The search address reported on

the search form had to have either: house number,

street name, city, State, ZIP Code, or rural route (or

comparable route), box number, city, State, ZIP Code.

If the search form did not contain these necessary

items, it was not processed further. If the search form

contained the required items, it went to the next step of

processing called geocoding. The geocoding step was

usually performed simultaneously with the Address Control

File address match, or Address Control File browse.

The geocoding and the Address Control File browse

processing steps involved searching, or browsing the

Address Control File to see if:

• The search address could be geocoded.

• The exact search address or the basic street address (if

the search address was a multiunit) was on the Address

Control File.

If the address could not be geocoded, no further

processing was done on the case. If the address was

geocoded, it fell into one of two categories—geocoded but

not found on the Address Control File or found on the

Address Control File. If the exact address was on the

Address Control File, a copy of the census questionnaire

for that address was printed. From there, the search form

and the copy of the census questionnaire were sent to the

next step of Search/ Match—matching/ transcription.

If the exact address was geocoded but not found on the

Address Control File, the address was sent to the USPS to

check if the address was correct and deliverable. Once it

was verified by the USPS as deliverable, the process of

searching the Address Control File was again repeated, in

case there were changes made to either the address by

the USPS or to the Address Control File since it was last

checked. If the address was still not found on the Address

Control File, the address was added and the search form

was sent to the next step of search/ match processingmatching/ transcription.

If the address was found on the Address Control File, a

copy of the census questionnaire was printed, and the

search form and the census questionnaire copy were sent

to matching/ transcription. If the address was returned

from the USPS classified as undeliverable, no further

processing of the case was done.

The matching/ transcription portion of the Search/ Match

operation involved reviewing a copy of the census questionnaire for the search address to determine if the persons reported on the search forms had been enumerated

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on the actual census questionnaire for their reported

Census Day address. Any search persons not found to be

counted at their reported Census Day address were added

to the census at that address.

There were six different search forms processed during

the Search/ Match operation, each designed to enumerate

either persons staying temporarily at a special place, but

who had a usual home elsewhere (UHE), or to ensure

accurate coverage of certain subpopulations. The different

search forms are listed below.

1. Individual Census Report

2. Military Census Report

3. Shipboard Census Report

4. Parolee/ Probationer Information Record

5. Were You Counted? Form

6. D-190 Search Record

The Parolee/ Probationer Information Record is discussed in the Parolee/ Probationer Coverage Improvement

Program and Followup section of this chapter.

The following describes each of the other five search

form types that are discussed in this section and the

purpose of each in the 1990 Search/ Match operation.

Individual Census Reports—Enumeration of special places

such as hotels, and the Nonresponse Followup and Field

Followup operations, generated Individual Census Reports.

Individual Census Reports were completed for individuals

found at a special place, or for visitors or nonfamily

residents found at housing units during the Nonresponse

Followup and Field Followup operations who felt they may

not have been counted. An Individual Census Report listed

only one person. If the respondent indicated that they were

at the housing unit or special place temporarily and usually

lived somewhere else, the Individual Census Report was

processed during Search/ Match.

Military Census Reports—Group Quarters enumeration

generated Military Census Reports. Military Group Quarters are a large subset of all Group Quarters. Military

personnel completed a Military Census Report. The Military Census Report listed only one person. If the respondent listed an off-base UHE address and they indicated

that the address was not a barracks but a family-type

housing unit, the form was processed during Search/ Match.

Shipboard Census Reports—Group Quarters enumeration also generated Shipboard Census Reports. Shipboard

personnel, both military and maritime, completed a Shipboard Census Report. The Shipboard Census Report listed

only one person. If a respondent listed a UHE address, the

Shipboard Census Report was processed in Search/ Match.

Were You Counted?—The print and electronic media

generated Were You Counted? forms. Respondents who

believed their household, or persons within their household, were missed in the 1990 decennial census either

completed a Were You Counted? form that was displayed

in local print media or called the Census Bureau’s processing offices or district offices. In some cases, the processing

office or district office staff completed Were You Counted?

forms for the respondents who called to report that they

had not been counted. The Were You Counted? form

could list more than one person. All searchable Were You

Counted? forms were processed during the Search/ Match

operation.

The D-190 Search Record—A D-190 Search Record was

generated for either whole households that usually lived

elsewhere, or for recent mover whole households that

lived elsewhere on Census Day. All searchable D-190

Search Records were processed during the Search/ Match

operation.

If a respondent indicated on his/ her census questionnaire that the usual residence of the entire household was

somewhere other than the address where they received

their census questionnaire, the district office or processing

office staff completed a D-190 Search Record for the

household. After verification, the household was removed

from the census questionnaire where they reported that

they do not usually reside. The persons were listed on the

D-190 Search Record and the D-190 Search Record was

sent to Search/ Match to determine whether they were

counted at their usual residence, and if not, to add them

there.

The vacant/ delete/ movers check generated moverUHE cases. This operation revisited vacant and deleted

housing units. If an enumerator located a respondent who

indicated that the entire household moved into the unit

sometime after Census Day and did not complete a

questionnaire at the Census Day address, the enumerator

completed a census questionnaire for the household,

indicating that this household recently moved. District

office or processing office staff then completed a D-190

Search Record for the household. The mover-UHE box on

the D-190 Search Record distinguished whole household

usual home elsewhere cases from mover-UHE cases.

Methodology

The data used in these analyses are from census files

that identified housing units and persons that were added

to the census files by the Search/ Match operation. A

person add or a housing unit add from the Search/ Match

operation is defined as a person or a housing unit reported

on a census questionnaire that had certain codes completed on the census questionnaire. (Refer to [1] for the

definitions of these codes.)

This section compares the characteristics of the housing units and persons added to the census file as a result

of the five Search/ Match forms described earlier to all the

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housing units and persons in the census file. Additions

made to the census file as a result of the Parolee/ Probationer

Coverage Improvement Program are excluded from this

section, but can be found in the Parolee/ Probationer

Coverage Improvement Program and Followup section of

this chapter.

Limitations

The primary limitation of this analysis is that the census

file does not identify which search form resulted in the

persons added to the census during Search/ Match. This

limits the analysis by making it impossible to determine

which search form added more persons in which demographic group.

The data used in this analysis were unedited. This

accounts for high rates of ‘‘unknown’’ responses for some

of the person characteristics.

The Search/ Match operation was conducted using many

clerical operations such as the filling in of the search/ match

circle and the transcription of data to a census form. The

data obtained from these operations are subject to clerical

errors.

Results

Characteristics of Housing Units Added—The 24,875

housing unit addresses that were added to the census file

as a result of the Search/ Match operation (excluding

Parolee/ Probationer added housing units) can be classified by type of enumeration area and type of mailing

address. A comparison can be made between the Search/

Match operation and the 1990 census with regard to these

two housing unit classifications.

Type of Enumeration Area—Addresses in the 1990

decennial census address list were classified as to which

type of enumeration area they came from. The different

type of enumeration areas were TAR, Prelist Mailout/ Mailback,

Prelist Pocket, Prelist Update/ Leave, and List/ Enumerate.

As shown above in figure 5.1, the Search/ Match operation produced a higher percentage of prelist addresses

and a lower percentage of TAR addresses, indicating that

the search/ match housing units that were added to the

1990 census were more likely to have come from a prelist

or rural address area. The high add rate of prelist addresses

during the Search/ Match operation was probably due to

the fact that housing units in rural areas were the most

likely to have been missed in the 1990 census.

Type of Mailing Address—Addresses were also classified by type of mailing address. For this analysis the

categories have been collapsed into city style, rural, and

other. Rural addresses are rural route, highway contract, or

star route addresses while the other category contains

Post Office box, general delivery, and location description

addresses.

Shown are the percentages of each type of mailing

address category for both the Search/ Match operation

and the 1990 census.

Figure 5.2 shows that the Search/ Match operation

added a higher percentage of city style addresses than the

1990 census. Approximately 91 percent of the addresses

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added during Search/ Match were city style addresses

while only about 83 percent of all addresses in the 1990

census file were city style addresses.

Characteristics of Added Persons—The five search

forms described previously in this section accounted for

608,557 persons. Sample-based estimates described in

later sections of this chapter allow us to estimate that the

Were You Counted? form produced the most adds with an

estimated 260,000 person adds while the D-190 Search

Records produced an estimated 236,000 adds. Only about

70,000 person adds came from the two military forms, the

Military Census Report and the Shipboard Census Report.

The characteristics of the 608,557 persons added to the

census file as a result of the Search/ Match operation

(excluding Parolee/ Probationer person adds) can be compared to the characteristics of all persons on the census

file. This comparison shows if any specific race, age group,

or sex was more likely to be added to the census by the

Search/ Match operation.

Sex—Figure 5.3 shows the sex of the Search/ Match

added persons compared with the sex of all persons in the

census file.

A higher percentage of persons added during Search/ Match

were male. This is different from the census file which

contains a higher percentage of females. This suggests

that males were more likely to be missed initially by the

census.

Age—The age of the persons added during Search/ Match

is compared with the age of all persons in the census.

If the percentage of unknown ages shown in figure 5.4

is distributed proportionately among the age groups, most

age group percentages for Search/ Match adds are close

to the percentages from the 1990 decennial census. The

age group in which Search/ Match produced a higher

percentage was the 18-24 age group. This suggests that

Search/ Match contributed to adding missed persons from

this age group.

Race—The race of the persons added by the Search/ Match

operation is compared to the race of all persons in the

1990 decennial census in figure 5.5.

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The Search/ Match operation added a higher percentage of Black and Hispanic persons and a lower percentage

of White persons than were enumerated in the 1990

census. This indicates that Search/ Match contributed to

adding missed persons in these race categories.

Conclusions

The overall conclusion is that the Search/ Match operation was able to add persons to the census from demographic groups that are traditionally undercounted during

the census. These groups include Black males and persons in the 18-24 age group.

Reference

[1]Beverage, Susan C. DSSD 1990 Research, Evaluation,

and Experimental Memorandum No. T-2, ‘‘Data Needs

From the 1990 Search/ Match Operation and the Primary

Housing Unit Selection Study.’’ U.S. Department of Commerce, Bureau of the Census. April 16, 1991.

[2] Wajer, Susan C. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 214, ‘‘Results

from the 1990 Search/ Match Operation: Add Rates and

Erroneous Enumeration Rates by Search Form Type.’’

U.S. Department of Commerce, Bureau of the Census.

February 1, 1993.

PAROLEE/ PROBATIONER COVERAGE

IMPROVEMENT PROGRAM AND FOLLOWUP

Introduction and Background

The Parolee/ Probationer Coverage Improvement Program and the Parolee/ Probationer Coverage Improvement

Followup Program were conducted during the 1990 decennial census to help ensure complete enumeration of all

persons. These programs targeted parolees and probationers, a subset of the population that the Census Bureau

believes is subject to substantial undercount. In addition,

because of overrepresentation of Black males in the

parolee and probationer populations, the Census Bureau

also felt that targeting this population would help to

address the problem of differential undercount.

The following is a brief background description of the

Parolee/ Probationer Coverage Improvement Program and

the subsequent followup program. For a more detailed

background description of these programs, refer to [1].

Briefly, operations were conducted as follows:

• Parolee/ Probationer Information Records were sent to

State parole and probation departments to be distributed to parolees and probationers when they visited

(see appendix B for a copy of a Parolee/ Probationer

Information Record).

• After the parolees and probationers completed the

Parolee/ Probationer Information Records, the corrections offices returned them to the Census Bureau’s

processing offices.

• The Parolee/ Probationer Information Records were processed through the Search/ Match operation. If the

parolee or probationer was not counted at their reported

Census Day address, they were added to the census.

The Parolee/ Probationer Coverage Improvement Followup Program was developed due to a low response rate

from the Parolee/ Probationer Coverage Improvement Program. The Census Bureau anticipated a 50 percent response

rate from the initial program, but as of August 1990, the

response rate was estimated at only about 25 percent [1].

Due to the importance of this program in addressing

differential undercount, the low response rate could not be

ignored. Therefore, a followup program was developed.

The followup program was different from the initial

program in that the followup program employed knowledge

from States’ Departments of Corrections and their administrative lists of parolees and probationers to obtain name,

the parolee or probationer’s Census Day address, and a

minimum of two demographic characteristics. These data

were certified by State Department of Corrections officials

that they were the parolee or probationer’s April 1, 1990,

address. Note that parolees and probationers are required

to keep their parole and probation officers informed of their

residence. In many States, a verified address is required of

all parolees before they are released from jail [3].

Census Bureau field personnel collected the administrative lists from the States’ Departments of Corrections

offices and completed a Parolee/ Probationer Information

Record for each person on the list who had a verifiable

Census Day address. The processing of the Parolee/ Probationer Information Records from the followup program was

the same as for the initial program. Note that while the

initial program was a nationwide effort, the followup program targeted only selected counties that satisfied certain

criteria, mainly urban, high minority areas (for more details

on the criteria, refer to [1]). Also note that by conducting

the followup program mainly in high minority areas, the

Census Bureau hoped to obtain a larger representation of

minority persons, specifically Black males. This was in

accordance with the main objective of the program of

reducing differential undercount.

Methodology

Coverage Gain—The results from the combination of

these two programs for the number of persons and housing units added to the census, as well as person and

housing unit characteristics, are from final census files. A

person add or a housing unit add from either of these

programs is defined as a person (or a housing unit)

reported on a census questionnaire that had certain codes

completed on the census questionnaire (for the specific

definitions of these codes, refer to [5].

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After some preliminary analysis of these data, it was

discovered that there was a large portion of housing units

that resembled a housing unit add, but were lacking a

certain code. Many of these housing units previously had a

status of deleted unit, which was changed when a Parolee/

Probationer Information Record was processed for that

census identification number (ID). For this evaluation,

these are not considered housing unit ‘‘adds.’’ Results are

discussed separately for housing unit adds and for units

that were converted from a deleted or vacant status.

Erroneous Enumeration Rates—The Post Enumeration

Survey (PES) was a national survey that was conducted

after the census to measure errors in the census. Data

from the PES were used to estimate the erroneous enumeration rate of the persons added to the census from

these two programs.

The Parolee/ Probationer Information Records that identified persons to be added to the census were geographically sorted into PES and non-PES blocks. Data from

these search forms that were in PES sample blocks were

keyed. The PES files include a set of codes that represent

the conclusion from the PES of whether persons were

correctly or incorrectly enumerated in the census. These

sample data were merged with the PES files; the merged

data file was used to generate the estimated erroneous

enumeration rate. An erroneous enumeration is defined as

an enumeration that was considered incorrect because the

person should not have been counted at the specified

address on Census Day. For example, they may have been

born after, or died before, Census Day, or they may have

had a usual residence elsewhere. Erroneous enumerations

are also duplicate enumerations (that is, persons who were

counted more than once), fabricated enumerations, and

enumerations that were assigned to the wrong census

geography due to a geocoding error. The PES final enumeration status for these person adds (that is, erroneous

enumeration versus correct enumeration) was deemed to

be the ‘‘truth.’’

A 90 percent confidence interval on the erroneous

enumeration rate is presented. Rather than the traditional

confidence interval formula, the Bonferroni Method for

multiple confidence statements (Johnson and Wichern,

1988) was used. (Later in this chapter, other components

of the Search/ Match operation are presented; hence the

use of the multiple confidence statements.) With this

method, confidence statements about multiple intervals

can be made simultaneously with 90 percent confidence.

Note that this method makes the interval more conservative (that is, larger). For p simultaneous confidence statements, the Bonferroni method uses z $ α

2p

$ instead of z $

α

2

$ ,

yielding longer confidence intervals.

Limitations

There are several limitations to these results that must

be taken into consideration when using these data.

First, the data that were used were unedited; therefore

the results include a ‘‘not reported’’ category for the

demographic characteristics.

Second, correctly identifying person adds from these

programs was dependent upon the clerks in Search/ Match

transcribing the person data from a Parolee/ Probationer

Information Record to a census questionnaire and correctly completing certain codes on the census questionnaire.

Next, it is possible that the late initiation of the followup

program might have had an impact on the quality of the

data. While the processing deadline approached, the number of Parolee/ Probationer Information Records received

from the followup program approached one million. Some

of these forms may have been ‘‘rushed’’ through processing in order to meet deadlines.

The PES was not designed to measure enumeration

errors from the Search/ Match operation. Any person adds

from the Search/ Match operation that were in the PES

sample fell in sample by chance, not design. Therefore,

this is not the best possible measurement of erroneous

enumeration rates. However, this is the only available

measurement of erroneous enumeration rates.

Lastly, for the purposes of the calculation of estimated

erroneous enumerations, the PES final enumeration status

was deemed to be the correct determination. It must be

recognized that there were errors in the PES that led to

limitations in these results. PES followup activities occurred

in the fall of 1990. It is possible that the time between April

1, 1990 and PES followup resulted in recall and other

errors. In some instances response error could lead to an

incorrect categorization of an enumeration as erroneous.

These factors should be taken into consideration before

drawing conclusions from these results.

Results

U.S. Level Coverage Gain—

Person Adds—The total number of persons added to the

census from the Parolee/ Probationer Coverage Improvement Program and the subsequent followup program was

447,757. This represents an approximate 0.2 percent

increase in the total 1990 population. Based on estimates

from data from the Search/ Match sorting and keying

operations, it was estimated that approximately 73.7 percent of the person adds were generated from the followup

program, and the balance, about 26.3 percent, were from

the initial program [6].

Housing Unit Adds—There were housing units added to

the census from these two programs. The breakdown by

address type for the 10,937 housing unit adds was 88.8

percent were city type addresses (that is, house number

and street name), approximately 10.0 percent were rural

type addresses (including rural routes, highway contract

routes, and star routes) and about 1.2 percent were other

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type addresses, (which were defined here to be descriptions such as ‘‘green house behind the barn at the end of

dirt road,’’ as well as Post Office boxes).

Converted Housing Units—There were 18,810 housing

units that were converted from a deleted or a vacant status

as a result of these programs. About 74.9 percent of these,

or 14,079 housing units, had a status of delete at an earlier

point in the census processing cycle. These prior deletes

are the universe of converted housing units (converted

from a delete status to an occupied housing unit). The

remaining 4,731 housing units (about 25.1 percent) did not

have a prior status of delete; most of these were previously

vacant units. The following is an analysis of these housing

units. First the deletes (14,079 housing units) are discussed, followed by some discussion of the previously

vacant units (4,731 housing units).

The reasons why these housing units were deleted

gives some insight into different errors that may have

occurred during Search/ Match. Note that errors could

have also occurred during field operations where these

units may have been misclassified as deletes.

There were eight delete reasons allowed during the

various field operations. In examining the delete reasons, it

was discovered that over 75.0 percent of them utilized only

three of the eight delete reasons; these three reasons

were:

1. No such address (5,930 housing units, or 42.1 percent

of the deletes)

2. Duplicate address (2,979 housing units, 21.2 percent

of the deletes)

3. Business/ commercial address (1,700 housing units,

or 12.1 percent of the deletes)

The Parolee/ Probationer Coverage Improvement Programs added persons to housing units that at least one

field operation identified as nonexistent. We can only

speculate as to which was correct: the delete status or the

parolee or probationer that reported they lived at one of

these housing units. Perhaps enumerators could not locate

an address and assumed it did not exist. On the other

hand, perhaps bad addresses were obtained from the

parolees and probationers themselves, or from the administrative records.

Another concern for the Search/ Match operation is the

number of potential duplicate addresses where persons

were added (note these were duplicates as defined by field

operations). One reason for this may be slight variations in

the spelling of a street name or street designation variation

(that is, street versus road versus avenue). The large

number of duplicate addresses also suggests that improvement is needed in Address Control File maintenance, such

as address standardization.

A fairly large percentage of the prior deletes (12.1

percent) had a delete reason of ‘‘business or commercial.’’

This raises concern over the difficulty of locating housing

units within business establishments. Identifying these

types of housing units has always been a problem and will

continue to be for future censuses.

About 94.6 percent of the housing units that were

converted from a delete status had a final census household size of one person. Thus, the added parolee or

probationer was the only person counted at the housing

unit.

There were 4,731 housing units that were previously

vacant units out of the 18,810 converted housing units.

These housing units represent about 25.1 percent of this

universe. About 63.0 percent of these housing units were

in Field Followup. Of the housing units that were in Field

Followup, a majority were in Field Followup due to the

vacancy check and remained vacant after Field Followup.

Thus, we added parolees and probationers to previously

vacant units.

About 88.1 percent of these units had a final census

household size of one person. Again, this indicates that the

parolee or probationer that was added to the housing unit

during these operations was the only person enumerated

at the unit.

Average Household Size—This section discusses the averagehouseholdsizeof thehousingunitsintheParolee/ Probationer

Coverage Improvement Program and the followup program universe. This universe is divided into the following

subgroups:

1. Existing enumerated housing units where a parolee or

probationer was added

2. Housing unit adds from these programs

3. Converted housing units—prior deletes

4. Converted housing units—vacants.

The average household size for existing enumerated

housing units where a parolee or probationer was added

during these programs was 3.64 persons. This is larger

than the U.S. level average household size of approximately 2.63 persons. For all of the remaining groups (the

housing unit adds, the prior deletes, and the vacants), the

average household sizes are much smaller. This is expected

since it was usually the enumeration of the parolee or

probationer that generated either the housing unit add or

the conversion of the unit from a delete or a vacant status.

Note that procedures did allow for the enumeration of

more than one parolee or probationer to be added to a

single address (even though only one parolee or probationer could be reported per Parolee/ Probationer Information Record).

For the housing unit adds, the average household size

was about 1.07 persons; for the prior deletes the average

household size was approximately 1.05 persons; and for

the nondeletes the average household size was slightly

higher—about 1.24 persons.

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State Level Person and Housing Unit Data—The person

add rates by State as a percentage of the 1989 population

of parolees and probationers in each State are shown in

figure 5.6. (The 1989 parolee and probationer population

was obtained from [4 ].)Seven States had person add rates

greater than 20 percent— Mississippi, Connecticut, Rhode

Island, New Jersey, California, Massachusetts, and Virginia. Mississippi had a person add rate greater than 40

percent; this State had the highest person add rate as a

percentage of its 1989 eligible parolee and probationer

population—48.6 percent. In other words, 48.6 percent of

all of Mississippi’s eligible parolees and probationers were

added to the census from these programs. Three States,

Connecticut, Rhode Island, and New Jersey, had person

add rates between 30 percent and 40 percent. The last

three States, California, Massachusetts, and Virginia, had

person add rates between 20 percent and 30 percent.

Keep in mind that about 73.7 percent of the person adds

were generated by the followup program, which used

administrative lists. It is uncertain whether the high person

add rates experienced in many States (for example, Mississippi, Connecticut, Rhode Island, and New Jersey)

meant that these States had good quality administrative

records, or if the administrative records were so outdated

that the parolee or probationer was not found during

Search/ Match because they had moved from the search

address, and therefore were added to the census. However, we can probably conclude that many of these

persons would have remained missed in the census if they

hadn’t been added during these programs. Prior research

suggested that the subpopulation of parolees and probationers were not very likely to participate in the census. It

also suggested that these persons were likely to be

deliberately concealed from the census. There were various reasons cited for concealment, from the fear of

interference with social service benefits such as welfare, to

a need for concealment due to various past, present, or

future illegal economic activities [7]. Given that many of

these persons did not want to be counted, the Census

Bureau did improve coverage of this population.

Note that the participation of only selected States in the

followup program affected the person and housing unit add

rates. For example, 10 States did not have any targeted

areas in the followup program [1]. These States likely had

the lowest person and housing unit add rates.

Figure 5.7 shows the State level distribution of the

housing unit adds from this program as a percentage of the

total housing unit adds generated from this program. Note

that this is for the 10,937 true housing unit adds.

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As expected, States with large numbers of housing units

also had high numbers of housing unit adds. There were

three States that had a housing unit add rate greater than

10 percent—Texas, Florida, and California. Texas had

1,765 housing unit adds, representing about 16.1 percent

of all housing unit adds. Texas was the only State with a

percentage greater than 15 percent. Florida had about

11.1 percent of all housing units added from these two

programs and California had about 10.8 percent of all

housing unit adds.

Demographics of Added Persons—This section presents an examination of the demographic characteristics

of thepersonsaddedto thecensusfromtheParolee/ Probationer

Coverage Improvement Program and the Parolee/ Probationer

Coverage Improvement Followup program. The demographics presented are age, race, and cross tabulations of sex

and race. In some instances, the demographics of the

persons added to the census are compared to selected

demographics of the 1989 parolee/ probationer population

or to 1990 census population demographics.

Recall that there were 447,757 persons added to the

census from these two programs. The age breakdown of

persons added to the census from the Parolee/ Probationer

Coverage Improvement Program and the subsequent followup program showed no surprises, with the age groups

that are likely to include persons on parole or probation

being the most represented. Of those that reported age,

the age group 30-44 years old was the largest, with 29.7

percent of all person adds in this group. The second

largest age group of those that reported age was the 20-29

year olds, representing about 28.5 percent of all parolees

and probationers added to the census. The age item had a

very high unreported rate—32.9 percent. A contributing

factor to this high unreported rate may be the use of

administrative records in the followup program, which

generated a majority of the adds. From looking at these

data, it seems that age is unlikely to be a characteristic

reported on parolee and probationer administrative records.

Note that the age item had a very small percentage (0.4

percent) of person adds that had a reported age less than

10 years old. These are likely due to transcription errors.

Figure 5.8 shows the distribution of race for the parolee

and probationers added to the census compared to the

racial composition of the 1989 parolee and probationer

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population. Recall that one of the major objectives of these

programs was to help address differential undercount. As

the graph shows, the majority of the person adds were

White—about 44.9 percent, which is slightly lower than the

percentage of Whites in the 1989 parolee/ probationer

population. But the percentage of person adds that were

Black—about 33.8 percent—was over 10 percentage points

higher than Black representation in the 1989 parolee/

probationer population, which was about 23.2 percent.

This may be a direct result of the followup program design,

which targeted high minority areas.

There was also a slightly higher representation of

‘‘other’’ race for the parolee/ probationer person adds than

there was for ‘‘other’’ race in the 1989 parolee and

probationer population, 4.2 percent versus 2.1 percent,

respectively. Lastly, there is a ‘‘not reported’’ category for

race. About 17.1 percent of the person adds were in this

category, whereas a much larger percentage of the 1989

parolee/ probationer population did not report race—28.5

percent. Note that the differences in percentage of persons for each category of race for the person adds versus

the 1989 parolee/ probationer population could be attributable to the large ‘‘not reported’’ category for the 1989

parolee/ probationer population.

The next graph displays selected sex and race characteristics of the parolees and probationers added to the

census and compares them to 1990 census data. These

data help determine whether the Parolee/ Probationer Coverage Improvement Program and the subsequent followup

program addressed differential undercount. As figure 5.9

shows, a smaller percentage of White males were added

than their representation in the 1990 population. Approximately 36.2 percent of the persons added were White

males, whereas White males comprised about 41.1 percent of the 1990 population. On the other hand, 27.1

percent of the person adds were Black males, in contrast

to 5.8 percent of the 1990 population being Black males.

Given that the definition of differential undercount is missing persons (in this context, specifically Black males) at a

disproportionately higher rate than their representation in

the total population, these programs did indeed seem to

address differential undercount, since the programs added

Black males at a disproportionately higher rate than their

representation in the 1990 census population.

Erroneous Enumeration Rate—The estimated erroneous enumeration rate for all parolee/ probationer person

adds is approximately 57.24 percent. A 90 percent confidence interval for the true erroneous enumeration rate is

48.87 percent to 65.61 percent.

Although this erroneous enumeration rate seems high,

the converse implies that over 40 percent of all the

parolees/ probationers added to the census were confirmed to be correctly enumerated. Given that these persons are believed to be a traditionally hard to enumerate

population, it is likely that many of these persons would

have remained missed in the census if we had not added

them during this program. It also is reasonable to assume

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that some of the erroneously enumerated persons would

have remained uncounted if we had not added them to the

census, even though we may have ended up adding them

in the wrong block. It must also be reiterated that these

erroneous enumeration estimates obtained from the PES

are not the best measurement of erroneous enumeration

rates since the PES sample was not designed to measure

errors in search/ match enumerations.

One reason for this high erroneous enumeration rate

may be the source of the addresses that were reported on

Parolee/ Probationer Information Records from the followup program–State parole/ probation offices’ administrative records. No verification of the accuracy of the administrative lists was conducted by the Census Bureau prior to

its use.

Additional errors could have occurred if the person

completing the Parolee/ Probationer Information Record

did not accurately identify his or her ‘‘usual residence’’

according to census rules. This would occur if a parolee or

probationer gave us the address where he or she received

mail, versus where he or she lived. It also is possible that

the original household respondent was in error in leaving

this person off of the census form. Most errors seem to

result from a misunderstanding on the part of the parolee

or probationer and the household respondent about whether

or not that was their ‘‘usual residence.’’ The concept of

usual residence may be especially difficult to apply for

persons with tenuous or multiple attachments to households such as parolees or probationers.

Conclusions

These programs were successful in adding many persons from the targeted parolee and probationer population

to the census. These persons represent a subgroup of the

population that are not very likely to participate in the

census. Thus, targeting parolees and probationers helped

to increase coverage of this population, especially young

men ages 20-44. In addition, given the percentage of

young Black males added to the census from these

programs (about 27.1 percent of all parolee/ probationer

person adds), the Census Bureau seemed to be successful in addressing differential undercount.

The 447,757 persons added to the census from these

two programs represented about 15 percent of the 1989

national parolee/ probationer population. By State, the

person add rates ranged from a high of 48.6 percent of all

eligible parolees/ probationers per State, to a low of about

0.2 percent. Again, it can be seen that coverage of this

population was increased, even if it was only marginal in

some States. The disparity in the person add rates is likely

a reflection of which States had large parolee/ probationer

populations, and if a given State participated either partially or fully in one or both of the programs.

The Census Bureau used two different sources of

addresses in these programs—the parolees and probationers themselves, and the administrative lists. It is recommended that in the future, the Census Bureau should use

only one source. It is obvious that the use of administrative

lists produced more person adds. However, if it is decided

to utilize administrative lists of this sort in the future, they

must be researched and tested. In addition, any administrative lists that may be used in the future should be

verified as up-to-date, and the Census Bureau must request

them and use them close to Census Day. Given the high

estimated rate of erroneous enumerations, it is recommended that this program be carefully scrutinized before

any future implementation, in order to capitalize on adding

a large number of persons to the census from this subgroup, but to also improve on the quality of the enumerations.

It is clear from these results that we need to examine

the methodology for adding persons from this type of

operation to improve census coverage. Forms that collect

this type of data must clarify the importance of collecting

the address that corresponds to the individual’s ‘‘usual

residence’’ on Census Day. Clearly defining where persons

should be counted and developing the tools and procedures to collect sufficient information to ensure their

correct enumeration is critical to the success of programs

such as this in the future.

References

[1] Beverage, Susan C., Amy L. Tillman, and Fay F. Nash.

1990 Decennial Census Preliminary Research and Evaluation Memorandum No. 132, ‘‘Preliminary Results From

the Parolee/ Probationer Coverage Improvement Program

and the Parolee/ Probationer Coverage Improvement Followup Program.’’ U.S. Department of Commerce, Bureau

of the Census. February 4, 1992.

[2] Johnson, Richard A. and Dean W. Wichern. Applied

Multivariate Statistical Analysis. 2nd ed., 1988. Prentice

Hall, Englewood Cliffs, NJ 07632.

[3] Unpublished Internal Census Bureau Memorandum,

‘‘Certification of Parolees’ and Probationers’ Addresses by

Agents.’’ August 1, 1989.

[4] U.S. Department of Justice. Probation and Parole 1989.

Bureau of Justice Statistics Bulletin, Office of Justice

Programs, Bureau of Justice Statistics, Washington, DC

20531.

[5] Wajer, Susan C. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 169, ‘‘The

Final Results From the Parolee/ Probationer Coverage

Improvement Program and the Parolee/ Probationer Coverage Improvement Followup Program.’’ U.S. Department

of Commerce, Bureau of the Census. August 26, 1993.

[6] Wajer, Susan C. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 214, ‘‘Results

from the 1990 Search/ Match Operation: Add Rates and

Erroneous Enumeration Rates by Search Form Type.’’

U.S. Department of Commerce, Bureau of the Census.

February 11, 1993.

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[7] Wilson, Jerusa C. ‘‘Reducing the Undercount of Black

Male Persons and Young Black Children in the 1990

Decennial Census.’’ Washington, DC: Bureau of the Census, Statistical Standards and Methodology, Center for

Survey Methods Research. October, 1988.

USUAL HOME ELSEWHERE

Introduction and Background

This section of the Search/ Match Coverage Improvement Chapter discusses the rates at which persons were

added to the census from certain search forms, as well as

the estimated rates of erroneous enumerations from the

specified search forms. The specified search forms discussed in this section are ones in which respondents

reported a ‘‘usual home elsewhere.’’ These search forms

are: Individual Census Reports, Military Census Reports,

Shipboard Census Reports, and D-190 Search Records,

which included whole households that usually live elsewhere and movers that lived elsewhere on Census Day.

Copies of these search forms are shown in appendix B.

For detailed background information on the Search/ Match

operation, refer to the Characteristics of Search/ Match

Additions section of this chapter.

Methodology

Person Adds—After the Search/ Match operation, a sampling plan was developed for this evaluation. The sampling

plan involved a two-stage sort of all search forms and

systematic sampling. The two-stage sort was by form type

and processing outcome. Dependent upon the total number of forms in each category, a random start and a take

every interval were assigned. The sample sizes were

determined such that we would be able to calculate

reliable estimates of person adds by form type. Data from

the sampled search forms were keyed. The estimates of

the number of persons added to the census are from these

keyed data. The estimates are rounded to the nearest

hundred.

The estimates of the number of persons added to the

census from each form type are weighted estimates (based

on the take every interval described above). There is no

standard error due to sampling associated with the estimates of persons added from the form types that had only

one person listed on them (Individual Census Reports,

Military Census Reports, and Shipboard Census Reports).

This is because there is no variation in the data; that is, all

of these search forms could have only one person added

to the census. However, there is nonsampling error associated with these estimates which cannot be measured for

this analysis (for example, errors in the sorting and sampling operation described above). Note that the standard

errors that are measurable are not rounded (although the

estimates of the number of person adds are).

The add rates were defined as the number of search

forms that produced at least one person add divided by the

total number of search forms received for a given type of

search form.

Error Rates—The PES was a national survey that was

conducted after the census to measure errors in the

census. Data from the PES were used for this analysis.

The search forms that identified persons to be added to

the census from the Search/ Match operation were geographically sorted into PES and non-PES blocks. Data

from the search forms that were in PES sample blocks

were keyed. The PES files include a set of codes that

represent the conclusion from the PES of whether persons

were correctly or incorrectly enumerated in the census.

The sample data were merged with the PES files; the

merged data file was used to generate the estimated

erroneous enumeration rates. An erroneous enumeration

is defined as an enumeration that was considered incorrect because the person should not have been counted at

the specified address on Census Day. For example, they

may have been born after, or died before, Census Day, or

they may have had a usual residence elsewhere. Erroneous enumerations are also duplicate enumerations (that is,

persons who were counted more than once), fabricated

enumerations, and enumerations that were assigned to the

wrong census geography due to a geocoding error. The

PES final enumeration status for these person adds (that

is, erroneous enumeration versus correct enumeration)

was deemed to be the ‘‘truth.’’

Note that this analysis did not include estimates of the

erroneous enumeration rates for the persons added to the

census from Military Census Reports or Shipboard Census

Reports. This is due to the PES sample being used for our

analysis. The erroneous enumeration rate estimates are

from PES data and by design, the PES sample did not

include barracks on military bases or ships.

Ninety percent confidence intervals on the erroneous

enumeration rates are presented. Rather than the traditional confidence interval formula, the Bonferroni Method

for multiple confidence statements (Johnson and Wichern,

1988) was used. With this method, confidence statements

about all of the intervals can be made simultaneously with

90 percent confidence. Note that this method makes the

intervals more conservative (that is, larger). For p simultaneous confidence statements, the Bonferroni method uses

z $ α

2p

$ instead of z $

α

2

$ , yielding longer confidence intervals.

Limitations

There are several limitations to these results that should

be taken into consideration when using these data.

First, the Search/ Match operation, the sorting and

sampling of the search forms, and the keying of the

sampled forms were all clerical operations. Thus, the data

obtained from these operations are subject to clerical

errors.

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Second, estimates of the number of persons added to

the census by each form type are presented (exact counts

are unknown). The reason that exact counts of the number

of persons added to the census by form type are unknown

(even though counts of the total number of search forms

by category are known) is that these persons were potentially added to the census. The person data were transcribed from the search form onto a census questionnaire

and the census questionnaire was data captured. However, if there were transcription errors, the census questionnaire was not accepted and the person was not added

to the census. Also, only one search form was accepted

per household. Thus, if more than one search form was

processed for a household, only the persons on the last

processed form were added to the census. These types of

circumstances necessitate the use of the estimates, rather

than exact counts.

Third, the PES was not designed to measure Search/ Match

errors. Any Search/ Match person adds that were in the

PES sample fell in sample by chance, not design. Therefore, this is not the best possible measurement of erroneous enumeration rates. However, this is the only available

measurement of erroneous enumeration rates.

Lastly, for the purposes of this analysis, the PES final

enumeration status is deemed to be the correct determination. It must be recognized that there were errors in the

PES that led to limitations in these results. PES followup

activities occurred in the fall of 1990. It is possible that the

time between April 1, 1990 and PES followup resulted in

recall and other errors. In some instances response error

could lead to an incorrect categorization of an enumeration

as erroneous. These factors should be taken into consideration before drawing conclusions from these results.

Results

Individual Census Reports—There were about 203,000

Individual Census Reports received for processing during

Search/ Match. Approximately 36,100 persons were added

to the census from Individual Census Reports. About 17.8

percent of the Individual Census Reports received for

processing during Search/ Match resulted in a person add.

As previously discussed, the estimate of the number of

persons added to the census from Individual Census

Reports does not have a standard error due to sampling

since only one person could be reported on a form.

The estimated erroneous enumeration rate for persons

added to the census on Individual Census Reports is about

15.6 percent. This form type had the second smallest

representation in the sample, which contributed to a high

standard error (10 percent). As a result, the 90 percent

confidence interval includes zero; the 90 percent confidence interval is between zero and 42.5 percent. The

erroneous enumeration rate is different from zero since

enumeration errors were found and measured.

Military Census Reports—During Search/ Match, about

697,400 Military Census Reports were received. The Census Bureau added approximately 56,000 persons to the

census from Military Census Reports, which represents

about an 8.0 percent person add rate. Again there is no

associated sampling standard error for this estimate, only

unmeasurable nonsampling error as previously described.

As mentioned earlier in this section, there are no

erroneous enumeration rates for persons added to the

census from Military Census Reports.

Shipboard Census Reports—This form type had the

smallest number of processed forms during Search/ Match—

the Census Bureau received only about 79,600 Shipboard

Census Reports. From these, about 14,000 persons were

added to the census. Thus, about 17.6 percent of all the

Shipboard Census Reports received resulted in a person

add. As was the case for the Individual Census Reports

and the Military Census Reports, only one person could be

enumerated on a Shipboard Census Report; therefore

there is no standard error for this estimate.

The PES sample did not include any ships; therefore,

there is no estimate of the erroneous enumeration rate for

persons added to the census from Shipboard Census

Reports.

D-190 Search Records—Whole Household Usual Home

Elsewhere Cases and Mover Usual Home Elsewhere

Cases—About 375,300 D-190 search records that were

whole household usual home elsewhere cases were received

and processed during Search/ Match. From these, about

162,800 persons were added to the census. This estimate

has a standard error of 2,645 persons. The estimated

erroneous enumeration rate of the persons added to the

census that were whole household usual home elsewhere

cases is 40.5 percent. This estimate also had a high

standard error (11.1 percent), contributing to a very wide

90 percent confidence interval. A 90 percent confidence

interval for the true erroneous enumeration rate is between

10.6 percent and 70.4 percent.

Approximately 85,300 of the D-190 search records that

were whole household usual home elsewhere cases resulted

in a person add. Thus, about 22.7 percent of all whole

household usual home elsewhere cases resulted in at

least one person being added to the census during Search/

Match.

The Census Bureau received about 95,600 D-190 search

forms that were mover usual home elsewhere cases. From

these cases, we estimate that approximately 73,100 persons were added to the census. The standard error of this

estimate is 1,282 persons.

Approximately 34,900 D-190 search records that were

mover usual home elsewhere cases resulted in a person

add. Thus, about 36.5 percent of all mover usual home

elsewhere cases resulted in a person add.

The estimated erroneous enumeration rate for the persons identified as movers that had a usual home elsewhere is high—about 58.2 percent. A 90 percent confidence interval for the true erroneous enumeration rate is

between 37.3 percent and 79.1 percent. This high error

rate may suggest that the training and procedures used

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during the 1990 Vacant/ Delete/ Movers Check need review.

This high estimated erroneous enumeration rate for movers also suggests that there may have been recall bias

during PES followup operations. That is, persons may have

had difficulty recalling exactly when they moved. In addition, this mobile group of movers may also be difficult to

enumerate in the PES.

Figure 5.10 summarizes the estimated number of persons added to the census by each of the search form types

discussed.

Add Rates—This section presents some discussion about

the previously mentioned add rates. Recall that the add

rate is defined as the ratio of the number of forms that

produced a person add to the total number of forms

received. Figure 5.11 summarizes these add rates by form

type.

It was expected that both the Military Census Reports

(8.0 percent add rate) and the Shipboard Census Reports

(17.6 percent add rate) would have relatively low add rates.

Most persons reported on these forms tended to have

been enumerated at their usual residence. We hypothesize

that these persons were more aware of their responsibility

to be included on the census questionnaire for their usual

residence.

The relatively low add rate for the Individual Census

Reports (17.8 percent) seems to verify that most persons

who were temporarily away from their residence and

identified a usual home elsewhere were correctly enumerated at their usual residence.

The whole household usual home elsewhere add rate of

22.7 percent seems to imply that for a large majority of

these cases, the persons were counted at their usual

residence. That is, they received census questionnaires at

more than one residence, and were likely to have been

counted at the residence they live at most of the year.

However, this also suggests that a small percentage of

persons with multiple residences are at risk of not being

counted at their usual residence.

On the other hand, the higher add rate for movers (36.5

percent) was not expected. This is a difficult subgroup to

enumerate, especially if they move on or near Census Day.

There could be many reasons why movers were difficult to

enumerate. For example, moving is a hectic time; persons

may not have taken the time to complete a census

questionnaire at their old residence, yet when they got to

their new residence and were visited by an enumerator,

they were eventually added to the census. We should not

assume that movers will complete a census questionnaire

at their old residence before moving. Also consider that

many movers are mobile in general; that is, they may have

multiple residences within a one year period. These persons seem to represent a hard-to-enumerate group. These

data confirm that special procedures are needed to correctly enumerate movers.

Conclusions

The individual estimates of erroneous enumeration rates

by form type are not very reliable; they have relatively high

standard errors caused by small sample sizes. In addition,

almost all of the intervals for the individual erroneous

enumeration rates are overlapping, suggesting that the

rates may not be significantly different.

Although evidence exists that confirms errors were

introduced from the Search/ Match operation, data also

show that in most cases, and for most form types, persons

added to the census from these search forms were

correctly enumerated. This point must not be overlooked

when examining erroneous enumeration rates.

It is clear from these results that we need to examine

the methodology for adding persons from this type of

operation to improve census coverage. All search forms

that collect this type of data must clarify the importance of

collecting the address that corresponds to the individual’s

‘‘usual residence’’ on Census Day. Clearly defining where

persons should be counted and developing the tools and

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procedures to collect sufficient information to ensure their

correct enumeration is critical to the future success of

programs such as Search/ Match.

References

[1] Johnson, Richard A. and Dean W. Wichern. Applied

Multivariate Statistical Analysis. 2nd ed., 1988. Prentice

Hall, Englewood Cliffs, NJ 07632.

[2] Tillman, Amy. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 105, ‘‘Preliminary Results: 1990 Search/ Match Workloads by Result

from the Sample Selection Sort Operation Conducted in

the Processing Offices.’’ U.S. Department of Commerce,

Bureau of the Census. January 10, 1992.

[3] Wajer, Susan C. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 214, ‘‘Results

from the 1990 Search/ Match Operation: Add Rates and

Erroneous Enumeration Rates by Search Form Type.’’

U.S. Department of Commerce, Bureau of the Census.

February 11, 1993.

WERE YOU COUNTED?

Introduction and Background

This section of the Search/ Match Coverage Improvement Chapter documents some results of the Were You

Counted? Campaign. Included in this section are the

number of forms received, the rate at which persons were

added to the census from these forms, and the estimated

rate at which persons were erroneously added to the

census. For a detailed background description of the

Search/ Match operation (which was the operation that

processed the Were You Counted? forms), refer to the

Characteristics of Search/ Match Additions section of this

chapter.

The print and electronic media generated Were You

Counted? forms. Respondents who believed their household, or persons within their household, were missed in the

1990 decennial census either completed a Were You

Counted? form that was displayed in local print media or

called the Census Bureau’s processing offices or district

offices. In some cases, the processing office or district

office staff completed Were You Counted? forms for the

respondents who called to report that they had not been

counted. These forms could list more than one person at

the same address. All searchable Were You Counted?

forms were processed during the Search/ Match operation.

Refer to appendix B for a copy of a Were You Counted?

form.

Methodology and Limitations

Refer to the Usual Home Elsewhere section of this

chapter for the methodology used and the limitations of

these results.

Results

The Census Bureau received about 352,800 Were You

Counted? forms. From these forms, about 260,000 persons were added to the census. The standard error of this

estimate is 2,511 persons. The estimated erroneous enumeration rate for the persons added to the census from

Were You Counted? forms is 35.2 percent. A 90 percent

confidence interval for the erroneous enumeration rate is

between 20.4 percent and 50.1 percent.

Approximately 34.6 percent of all Were You Counted?

forms resulted in at least one person being added to the

census. This is a fairly high add rate compared to other

search forms that were processed during the 1990 Search/

Match operation (see the Usual Home Elsewhere section

of this chapter for comparisons to other search form

types). But it is also reasonable, given that most persons

reported on these forms believed they were not counted in

the census.

Conclusions

The Were You Counted? Campaign seemed to be

successful at adding persons to the census. Specifically,

this campaign was a mechanism for persons who believed

that they had been missed to be included in the census. In

addition, an estimated 65 percent of all persons added to

the census from this campaign were confirmed to be

correctly enumerated. Given these results, it is recommended that this campaign be continued for future censuses.

Reference

[1] Wajer, Susan C. 1990 Decennial Census Preliminary

Research and Evaluation Memorandum No. 214, ‘‘Results

from the 1990 Search/ Match Operation: Add Rates and

Erroneous Enumeration Rates by Search Form Type.’’

U.S. Department of Commerce, Bureau of the Census.

February 11, 1993.

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APPENDIX A.

Glossary

ACF—Address Control File

ACR—Advance Census Report

Address Control File—The 1990 decennial census automated address control list used as an inventory of all

housing units within specified geographic areas.

Address Register—A book used by census enumerators

that contains listings of each housing unit and special

place within the enumerator’s assigned area.

Address Register Area—A small geographic area used

as a basic unit for data collection by a census enumerator.

Advance Census Report—An unaddressed short-form

questionnaire delivered by the USPS in list/ enumerate

areas. The respondent completes the questionnaire and

holds it for pickup by a census enumerator.

Advance Post Office Check I—Defined in Chapter 1,

Advance Post Office Check.

Advance Post Office Check II/ III—Defined in Chapter 1,

Advance Post Office Check.

Advance Post Office Check Reconciliation—Defined in

Chapter 1, Advance Post Office Check Reconciliation.

APOC—Advance Post Office Check

Basic Street Address—The house number and street

name address representing a structure or group of structures (such as a single family house, an apartment building,

or an apartment complex).

Casing Check—Defined in Chapter 1, Casing Check.

Census Closeout Address Check—Defined in Chapter

1, Census Closeout Address Check.

Census Day—April 1, 1990

Census Region—One of four areas resulting from a

partition of the 50 States and the District of Columbia. The

four regions are labelled Northeast, Midwest, South, and

West.

Closeout Procedures—Procedures used during the last

phase of Nonresponse Followup as a final effort to obtain

an acceptable questionnaire for any remaining unenumerated cases.

Delete—An address was deleted if it did not qualify as a

housing unit, if it was a duplicate of another address, or if

the address did not exist.

District Office—A temporary office established during the

census for data collection purposes.

EFQ—Enumerator Friendly Questionnaire

Enumerator Friendly Questionnaire—A questionnaire

written in a form to be read by an enumerator to a

respondent.

Field Followup—Defined in Chapter 1, Field Followup.

Geocode—A process of assigning addresses to their

correct census geography.

GQ—Group Quarters

Group Quarters—A place where people live that is not a

typical household-type living arrangement. There are institutional and noninstitutional group quarters.

Group Quarters Enumeration—Defined in Chapter 1,

Group Quarters Enumeration.

Housing Coverage Check—Defined in Chapter 1,

Recanvass/ Housing Coverage Check.

Housing Unit—A house, apartment, mobile home, or

other dwelling that is occupied as a separate living quarters or, if vacant, is intended for occupancy as a separate

living quarters.

HU—Housing Unit

ICR—Individual Census Report

ID—Identification Number

Individual Census Report—A census questionnaire used

to obtain population information for persons in group

quarters and for individuals who are not enumerated as

part of a household.

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Last resort procedures—Procedures used during the

later phases of Nonresponse Followup. The completion of

‘‘last resort’’ questions represents the least amount of

information for the questionnaire to be acceptable.

L/ E—List/ Enumerate

List/ Enumerate—Defined in Chapter 1, List/ Enumerate.

Long Form—The census questionnaire containing 100

percent and sample questions.

Mail Response Rate—The total number of census questionnaires returned by mail divided by the number of

questionnaires mailed by the USPS or delivered by census

enumerators; this is sometimes referred to as a Check-In

Rate.

Mail Return Rate—The total number of questionnaires

returned by mail divided by the number of occupied

housing units included in the mailback universe.

Mailout/ Mailback—The type of enumeration method in

which the USPS delivers census questionnaires and the

respondents return them by mail.

Map Spot—A dot with a number to identify the physical

location of housing units on a map, mostly used in rural

areas.

MCR—Military Census Report

Military Census Report—A census questionnaire used to

obtain population information for military personnel residing at military installations.

MSA—Metropolitan Statistical Area

Metropolitan Statistical Area—An area qualifies for recognition as an MSA if it includes a city of at least 50,000

population or an urbanized area of at least 50,000 with a

total metropolitan area population of at least 100,000.

Nonresponse Followup—Defined in Chapter 1, Nonresponse Followup.

Occupied Unit—A housing unit was classified as occupied

if anyone lived there on Census Day or considered it their

usual place of residence on Census Day.

Parolee/ Probationer Coverage Improvement Followup

Program—Defined in Chapter 1, Parolee/ Probationer Coverage Improvement and the Followup Program.

Parolee/ ProbationerCoverageImprovementProgram—Defined

in Chapter 1, Parolee/ Probationer Coverage Improvement

and the Followup Program.

Parolee/ Probationer Information Record—A type of

questionnaire completed by or for parolees and probationers and then processed through the Search/ Match operation.

PES—Post Enumeration Survey

POP One Reenumeration—Defined in Chapter 1, POP

One Reenumeration.

Postcensus Local Review—Defined in Chapter 1, Postcensus Local Review.

Post Enumeration Survey—A coverage measurement

survey conducted as part of the 1990 census.

Postmaster Return—A mailed out questionnaire returned

by the USPS as undeliverable.

Postmaster Return Delivery—Defined in Chapter 1, Postmaster Return Delivery.

PPIR—Parolee/ Probationer Information Record

Precanvass—Defined in Chapter 1, Precanvass.

Precanvass Reconciliation—Defined in Chapter 1, Precanvass Reconciliation and Yellow Card Coding.

Precensus Local Review—Defined in Chapter 1, Precensus Local Review.

Prelist Mailout/ Mailback—Defined in Chapter 1, Prelist.

Prelist Pocket—A geographic area in which prelist mailout/

mailback procedures were used but which was located in

either an update/ leave or list/ enumerate county.

Prelist Update/ Leave—Defined in Chapter 1, Prelist.

Primary Selection Algorithm—A computer algorithm used

to select the best data capture record when two or more

questionnaires were data captured or a questionnaire was

recycled through processing for the same identification

number.

Primary Selection Algorithm Review—Defined in Chapter 1, Primary Selection Algorithm Review.

Processing Office—A temporary office established for

the census to process the data.

Puerto Rico Multiunit Coverage Improvement Operation—

Defined in Chapter 1, Puerto Rico Multiunit Coverage

Improvement operation.

Recanvass—Defined in Chapter 1, Recanvass/ Housing

Coverage Check.

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Regional Census Center—A temporary office established

during the decennial census to manage and support

district office activities.

Regional Office—A permanent office used to manage

and support the collection of data for ongoing programs.

Reinterview—A quality control procedure to verify that

enumerators collected accurate information.

REX—Research, Evaluation, and Experimental Program

SCR—Shipboard Census Report

Search/ Match—Defined in Chapter 1, Search/ Match.

Shelter and Street Night Enumeration—Defined in Chapter 1, Shelter and Street Night Enumeration.

Shipboard Census Report—A questionnaire used to

collect population information for persons on military and

maritime vessels.

Short Form—The census questionnaire containing only

100 percent questions.

Source Code—A designation on the Address Control File

used to indicate the origin of each address on the file.

Special Place—A place where people either live or stay

other than the usual house, apartment or mobile home;

requiring special census procedures because it contains

group quarters. A special place may also contain separate

housing units for staff or other persons.

Special Place Prelist—Defined in Chapter 1, Special

Place Prelist.

S-Night—Shelter and Street Night Enumeration

Tape Address Register area—An area where the initial

address list is a purchased vendor file.

TAR—Tape Address Register area

Telephone Assistance Adds—Defined in Chapter 1, Telephone Assistance Adds.

TIGER—Topologically Integrated Geographic Encoding

and Referencing System

T-Night—Transient Night

Topologically Integrated Geographic Encoding and

Referencing System—A computer database that contains all census required map features and attributes.

Transient Night Enumeration—Defined in Chapter 1,

Transient Night Enumeration.

UHE—Usual Home Elsewhere

Update/ Leave—Defined in Chapter 1, Rural Update/ Leave.

Urban Update/ Enumerate—Defined in Chapter 1, Urban

Update/ Enumerate.

UrbanUpdate/ Leave—DefinedinChapter1,UrbanUpdate/ Leave.

USPS—United States Postal Service

Usual Home Elsewhere—The questionnaire classification used for a housing unit that is temporarily occupied by

a person or household that usually resides at another

address.

Vacant Unit—A housing unit was classified as vacant on

Census Day if no one lived there on Census Day.

Vacant/ Delete/ Movers Check—Defined in Chapter 1,

Field Followup.

Vendor File—An automated address list purchased from

a commercial vendor.

Were You Counted? Campaign—A program aimed at

identifying and enumerating persons through self-identification.

WYC?—Were You Counted?

Yellow Card Coding—Defined in Chapter 1, Precanvass

Reconciliation and Yellow Card Coding.

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APPENDIX B.

Facsimiles of Decennial Forms and Questionnaires

Form number Page

D-1, Official 1990 U.S. Census Form................................................................................................. B–2

D-20A, 1990 Individual Census Report ............................................................................................. B–9

D-21, 1990 Military Census Report .................................................................................................... B–11

D-23, 1990 Shipboard Census Report............................................................................................... B–16

D-25, Were You Counted?.................................................................................................................. B–21

D-59B, Parolee-Probationer Information Record.............................................................................. B–22

D-108A, Address Listing Page............................................................................................................ B–23

D-190, Search Record ......................................................................................................................... B–24

D-399, DO/ PO Record of Contact/ Referral—Questionnaire Asistance ....................................... B–25

D-550P, Census Closeout Address Check........................................................................................ B–26

D-701, Census Address Card ............................................................................................................. B–27

D-702, Post Office Report of Missing Addresses ............................................................................ B–28

D-722, Post Office Report of Missing Addresses ............................................................................ B–29

D-1021 PR, Summary of Office Geocoding and Matching B–30

D-1022 PR, Summary of Field Review Operation............................................................................ B–31

D-2037, Search/ Match Status............................................................................................................ B–32

Hand Delivery of Postmaster Return Questionnaires (Debriefing Questions) .............................. B–33

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APPENDIX C.

Maps

Maps start on the following page.

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NOTE TO THE READER

This Census of Population and Housing Evaluation and Research Report is designed to

inform the public about the results for the major coverage improvement operations of the

1990 decennial census. If you would like additional information on any of the topics

presented in this publication or other information about the coverage improvement program,

please write to:

Mr. John H. Thompson

Chief, Decennial Statistical Studies Division

C/ O Coverage Improvement REX Publication

Bureau of the Census

Washington, DC 20233

We welcome your questions and will provide any requested information, as available.