Current

Population

Survey

Design and

Methodology

Technical Paper 63RV

U.S. Department of

Commerce

Economics and Statistics

Administration

U.S. CENSUS BUREAU

U.S. Department

of Labor

BUREAU OF

LABOR STATISTICS

TP63RV
Acknowledgments

This technical paper was written under the supervision of Donna L. Kostanich of the

U.S. Census Bureau and Cathryn S. Dippo of the Bureau of Labor Statistics. It has

been produced through the combined efforts of many individuals. Some wrote sections

of the paper, some reviewed sections of the paper, and others did both. Some are still

with their agencies and some have since left.

Contributing from the U.S. Census Bureau were Adelle Berlinger, Chet Bowie,

John Bushery, Lawrence S. Cahoon, Maryann Chapin, Jeffrey S. Corteville,

Deborah Fenstermaker, Robin Fisher, Richard Griffiths, Carol A. Gunlicks, Frederick W. Hollmann, David V. Hornick, Vicki J. Huggins, Melinda Kraus, J. Mackey

Lewis, Khandaker A. Mansur, Pamela D. McGovern, Thomas F. Moore, John

Paletta, Maria Reed, Brad L. Rigby, Jennifer M. Rothgeb, Robert Rothhaas, Blair

Russell, Wendy Scholetzky, Irwin Schreiner, Harland H. Shoemaker Jr., Katherine

J. Thompson, Ronald R. Tucker, Gregory D. Weyland, Patricia Wilson, Andrew

Zbikowski, and the many other people who have held positions in the Current

Population Survey Branch of the Demographic Statistical Methods Division, as well as

other divisions throughout the Census Bureau.

Contributing from the Bureau of Labor Statistics were John E. Bregger, Martha Duff,

Gloria P. Green, Harvey R. Hamel, Brian Harris-Kojetin, Diane E. Herz, Vernon C.

Irby, Bodhini R. Jayasuriya, Janice K. Lent, Robert J. McIntire, Sandi Mason,

Thomas Nardone, Anne E. Polivka, Ed Robison, Philip L. Rones, Richard Tiller,

Clyde Tucker, and Tamara Sue Zimmerman.

Sharon Berwager, Mary Ann Braham, and Cynthia L. Wellons-Hazer provided

assistance in preparing the many drafts of this paper. John S. Linebarger was the

Census Bureau’s Contracting Officer’s Technical Representative.

The review of the final manuscript prior to publication was coordinated by Antoinette

Lubich of the Census Bureau. Patricia Dean Brick served as an editorial consultant

and Joseph Waksberg of Westat, Inc. assisted in the document’s review for technical

accuracy. Kenton Kilgore and Larry Long of the Census Bureau provided final

comments. EEI Communications constructed the subject index and list of acronyms.

Kim D. Ottenstein, Cynthia G. Brooks, Frances B. Scott, Susan M. Kelly,

Neeland Queen, Patricia Edwards, Gloria T. Davis and Laurene V. Qualls of the

Administrative and Customer Services Division, Walter C. Odom, Chief, provided

publications and printing management, graphics design and composition, and editorial

review for print and electronic media. General direction and production management

were provided by Michael G. Garland, Assistant Division Chief, and Gary J. Lauffer,

Chief, Publications Services Branch.

William Hazard and Alan R. Plisch of the Census Bureau provided assistance

in placing electronic versions of this document on the Internet (see

http://www.census.gov/prod/2002pubs/tp63rv.pdf).

We are grateful for the assistance of these individuals, and all others who are not

specifically mentioned, for their help with the preparation and publication of this

document.
Current

Population

Survey

Design

and

Methodology

Technical Paper 63RV

 Issued March 2002

TP63RV

U.S. Department of Commerce

 Donald L. Evans,

 Secretary

Samuel W. Bodman,

Deputy Secretary

 Economics and Statistics Administration

Kathleen B. Cooper,

 Under Secretary for

Economic Affairs

U.S. CENSUS BUREAU

 William G. Barron, Jr.,

 Acting Director

U.S. Department of Labor

 Elaine L. Chao,

 Secretary

Bureau of Labor Statistics

 Lois Orr,

Acting Commissioner
U.S. CENSUS BUREAU

William G. Barron, Jr.,

Acting Director

William G. Barron, Jr.,

Deputy Director

Nancy M. Gordon, Associate Director for

Demographic Programs

Cynthia Z. F. Clark, Associate Director for

Methodology and Standards

ECONOMICS

AND STATISTICS

ADMINISTRATION

Economics

and Statistics

Administration

Kathleen B. Cooper,

Under Secretary

for Economic Affairs

BUREAU OF LABOR STATISTICS

Lois Orr, Acting Commissioner

Lois Orr, Deputy Commissioner

Cathryn S. Dippo, Associate Commissioner

for Survey Methods Research

John M. Galvin, Associate Commissioner

Office of Employment and Unemployment

Statistics

Philip L. Rones, Assistant Commissioner

Office of Current Employment Analysis
Foreword

The Current Population Survey (CPS) is one of the oldest, largest, and most well-recognized

surveys in the United States. It is immensely important, providing information on many of the things

that define us as individuals and as a society—our work, our earnings, our education. It is also

immensely complex. Staff of the Census Bureau and the Bureau of Labor Statistics have

attempted, in this publication, to provide data users with a thorough description of the design and

methodology used in the CPS. The preparation of this technical paper was a major undertaking,

spanning several years and involving dozens of statisticians, economists, and others from the two

agencies.

This paper is the first major update of CPS documentation in more than two decades, and, while

the basic approach to collecting labor force and other data through the CPS has remained intact

over the intervening years, much has changed. In particular, a redesigned CPS was introduced in

January 1994, centered around the survey’s first use of a computerized survey instrument by field

interviewers. The questionnaire itself was rewritten to better communicate CPS concepts to the

respondent, and to take advantage of computerization.

This document describes the design and methodology that existed for the CPS as of December

1995. Some of the appendices cover updates that have been made to the survey since then.

Users of CPS data should have access to up-to-date information about the survey’s methodology. The advent of the Internet allows us to provide updates to the material contained in this report

on a more timely basis. Please visit our CPS web site at http://www.bls.census.gov/cps, where

updated survey information will be made available. Also, we welcome comments from users about

the value of this document and ways that it could be improved.

Kenneth Prewitt

Director

U.S. Census Bureau

March 2000

Katharine G. Abraham

Commissioner

Bureau of Labor Statistics

March 2000
Summary of Changes...................................................................... xi

Chapter 1. Background

Background .................................................................................. 1–1

Chapter 2. History of the Current Population Survey

Introduction................................................................................... 2–1

Major Changes in the Survey: A Chronology .............................................. 2–1

References ................................................................................... 2–6

Chapter 3. Design of the Current Population Survey Sample

Introduction................................................................................... 3–1

Survey Requirements and Design .......................................................... 3–1

First Stage of the Sample Design .......................................................... 3–2

Second Stage of the Sample Design....................................................... 3–7

Third Stage of the Sample Design.......................................................... 3–14

Rotation of the Sample ...................................................................... 3–15

References ................................................................................... 3–17

Chapter 4. Preparation of the Sample

Introduction................................................................................... 4–1

Listing Activities .............................................................................. 4–4

Third Stage of the Sample Design (Subsampling)......................................... 4–6

Interviewer Assignments..................................................................... 4–6

Chapter 5. Questionnaire Concepts and Definitions for the Current

Population Survey

Introduction................................................................................... 5–1

Structure of the Survey Instrument ......................................................... 5–1

Concepts and Definitions.................................................................... 5–1

References ................................................................................... 5–7

Chapter 6. Design of the Current Population Survey Instrument

Introduction................................................................................... 6–1

Motivation for the Redesign of the Questionnaire Collecting of Labor Force Data ...... 6–1

Objectives of the Redesign.................................................................. 6–1

Highlights of the Questionnaire Revision................................................... 6–3

Continuous Testing and Improvements of the Current Population Survey and Its

Supplements .............................................................................. 6–8

References ................................................................................... 6–8

Chapter 7. Conducting the Interviews

Introduction................................................................................... 7–1

Noninterviews and Household Eligibility.................................................... 7–1

Initial Interview ............................................................................... 7–3

Subsequent Months’ Interviews ............................................................. 7–4

v

Contents
Chapter 8. Transmitting the Interview Results

Introduction................................................................................... 8–1

Transmission of Interview Data ............................................................. 8–1

Chapter 9. Data Preparation

Introduction................................................................................... 9–1

Daily Processing ............................................................................. 9–1

Industry and Occupation (I&O) Coding ..................................................... 9–1

Edits and Imputations........................................................................ 9–1

Chapter 10. Estimation Procedures for Labor Force Data

Introduction................................................................................... 10–1

Unbiased Estimation Procedure ............................................................ 10–1

Adjustment for Nonresponse ................................................................ 10–2

Ratio Estimation ............................................................................. 10–3

First-Stage Ratio Adjustment................................................................ 10–3

Second-Stage Ratio Adjustment ............................................................ 10–5

Composite Estimator ........................................................................ 10–9

Seasonal Adjustment ........................................................................ 10–10

Monthly State and Substate Estimates ..................................................... 10–10

Producing Other Labor Force Estimates ................................................... 10–12

References ................................................................................... 10–14

Chapter 11. Current Population Survey Supplemental Inquiries

Introduction................................................................................... 11–1

Criteria for Supplemental Inquiries.......................................................... 11–1

Recent Supplemental Inquiries.............................................................. 11–2

Annual Demographic Supplement (March Supplement)................................... 11–4

Summary ..................................................................................... 11–8

Chapter 12. Data Products From the Current Population Survey

Introduction................................................................................... 12–1

Bureau of Labor Statistics................................................................... 12–1

U.S. Census Bureau......................................................................... 12–2

Chapter 13. Overview of Data Quality Concepts

Introduction................................................................................... 13–1

Quality Measures in Statistical Science .................................................... 13–1

Quality Measures in Statistical Process Monitoring........................................ 13–2

Summary ..................................................................................... 13–3

References ................................................................................... 13–3

Chapter 14. Estimation of Variance

Introduction................................................................................... 14–1

Variance Estimates by the Replication Method ............................................ 14–1

Method for Estimating Variance for 1990 Design .......................................... 14–2

Variances for State and Local Area Estimates ............................................. 14–3

Generalizing Variances ...................................................................... 14–3

Variance Estimates to Determine Optimum Survey Design ............................... 14–5

Total Variances As Affected by Estimation ................................................. 14–6

Design Effects................................................................................ 14–7

References ................................................................................... 14–9

vi
Chapter 15. Sources and Controls on Nonsampling Error

Introduction................................................................................... 15–1

Sources of Coverage Error.................................................................. 15–2

Controlling Coverage Error.................................................................. 15–2

Sources of Error Due to Nonresponse ..................................................... 15–4

Controlling Error Due to Nonresponse ..................................................... 15–5

Sources of Response Error ................................................................. 15–6

Controlling Response Error ................................................................. 15–6

Sources of Miscellaneous Errors ........................................................... 15–8

Controlling Miscellaneous Errors............................................................ 15–8

References ................................................................................... 15–9

Chapter 16. Quality Indicators of Nonsampling Errors

Introduction................................................................................... 16–1

Coverage Errors ............................................................................. 16–1

Nonresponse ................................................................................. 16–3

Response Variance .......................................................................... 16–5

Mode of Interview............................................................................ 16–6

Time in Sample .............................................................................. 16–7

Proxy Reporting.............................................................................. 16–10

Summary ..................................................................................... 16–10

References ................................................................................... 16–10

Figures

3–1 CPS Rotation Chart: January 1996 - April 1998....................................... 3–16

4–1 Example of Systematic Sampling ...................................................... 4–3

5–1 Questions for Employed and Unemployed............................................. 5–6

7–1 Introductory Letter....................................................................... 7–2

7–2 Noninterviews: Types A, B, and C...................................................... 7–3

7–3 Noninterviews: Main Housing Unit Items Asked for Types A, B, and C.............. 7–4

7–4 Interviews: Main Housing Unit Items Asked in MIS 1 and Replacement

Households............................................................................. 7–5

7–5 Summary Table for Determining Who Is To Be Included As a Member of the

Household .............................................................................. 7–6

7–6 Interviews: Main Demographic Items Asked in MIS 1 and Replacement

Households............................................................................. 7–7

7–7 Demographic Edits in the CPS Instrument ............................................ 7–8

7–8 Interviews: Main Items (Housing Unit and Demographic) Asked in MIS 5 Cases... 7–9

7–9 Interviewing Results (December 1996) ................................................ 7–10

7–10 Telephone Interview Rates (December 1996) ......................................... 7–10

8–1 Overview of CPS Monthly Operations ................................................. 8–3

11–1 Diagram of the ADS Weighting Scheme ............................................... 11–6

16–1 Average Yearly Noninterview and Refusal Rates for the CPS 1964 - 1996 ......... 16–2

16–2 Monthly Noninterview and Refusal Rates for the CPS 1984 - 1996 ................. 16–4

F–1 2000 Decennial and Survey Boundaries............................................... F–2

J–1 SCHIP Rotation Chart: September 2000 to December 2002......................... J–5

Illustrations

1 Segment Folder, BC-1669(CPS) ....................................................... B–2

2 Unit/Permit Listing Sheet (Unit Segment).............................................. B–4

3 Unit/Permit Listing Sheet (Multiunit Structure)......................................... B–5

4 Unit/Permit Listing Sheet (Large Special Exception).................................. B–6

5 Incomplete Address Locator Actions, BC-1718(ADP) ................................. B–7

vii
Illustrations—Con.

6 Area Segment Listing Sheet............................................................ B–8

7 Area Segment Map...................................................................... B–10

8 Segment Locator Map .................................................................. B–11

9 Map Legend ............................................................................. B–12

10 Group Quarters Listing Sheet .......................................................... B–13

11 Unit/Permit Listing Sheet................................................................ B–14

12 Permit Address List...................................................................... B–15

13 Permit Sketch Map ...................................................................... B–16

Tables

3–1 Number and Estimated Population of Strata for 792-PSU Design by State ......... 3–6

3–2 Summary of Sampling Frames ......................................................... 3–10

3–3a Old Construction Within-PSU Sorts .................................................... 3–11

3–3b Permit Frame Within-PSU Sort ......................................................... 3–12

3–4 Sampling Example Within a BPC for Any of the Three Old Construction Frames .. 3–12

3–5 Index Numbers Selected During Sampling for Code Assignment Examples ........ 3–13

3–6 Example of Postsampling Survey Design Code Assignments Within a PSU........ 3–14

3–7 Example of Postsampling Operational Code Assignments Within a PSU ........... 3–14

3–8 Proportion of Sample in Common for 4-8-4 Rotation System ........................ 3–16

10–1 State First-Stage Factors for CPS 1990 Design (September - December 1995) ... 10–5

10–2 Initial Cells for Hispanic/Non-Hispanic by Age and Sex .............................. 10–6

10–3 Initial Cells for Black/White/Other by Age, Race, and Sex............................ 10–6

11–1 Current Population Survey Supplements September 1994 - December 2001 ...... 11–3

11–2 Summary of ADS Interview Month ..................................................... 11–5

11–3 Summary of SCHIP Adjustment Factor ................................................ 11–8

12–1 Bureau of Labor Statistics Data Products From the Current Population Survey .... 12–3

14–1 Parameters for Computation of Standard Errors for Estimates of Monthly Levels .. 14–5

14–2 Components of Variance for FSC Monthly Estimates................................. 14–6

14–3 Effects of Weighting Stages on Monthly Relative Variance Factors.................. 14–7

14–4 Effect of Compositing on Monthly Variance and Relative Variance Factors ......... 14–8

14–5 Design Effects for Total Monthly Variances ............................................ 14–8

16–1 CPS Coverage Ratios by Age, Sex, Race, and Ethnicity............................. 16–2

16–2 Components of Type A Nonresponse Rates, Annual Averages for 1993 - 1996 .... 16–3

16–3 Percentage of Households by Number of Completed Interviews During the 8

Months in the Sample ................................................................. 16–4

16–4 Labor Force Status by Interview/Noninterview Status in Previous and Current

Month ................................................................................... 16–5

16–5 Percentage of CPS Items With Missing Data ......................................... 16–5

16–6 Comparison of Responses to the Original Interview and the Reinterview ........... 16–6

16–7 Index of Inconsistency for Selected Labor Force Characteristics February - July

1994 .................................................................................... 16–6

16–8 Percentage of Households With Completed Interviews With Data Collected by

Telephone (CAPI Cases Only) ........................................................ 16–7

16–9 Effect of Centralized Telephone Interviewing on Selected Labor Force

Characteristics ......................................................................... 16–8

16–10 Month-In-Sample Bias Indexes (and Standard Errors) in the CPS for Selected

Labor Force Characteristics ........................................................... 16–9

16–11 Month-In-Sample Indexes in the CPS for Type A Noninterview Rates January -

December 1994 ........................................................................ 16–10

16–12 Percentage of CPS Labor Force Reports Provided by Proxy Reporters ............ 16–10

B–1 Listings for Multiunit Addresses ........................................................ B–3

E–1 Reliability of CPS Estimators Under the Current Design ............................. E–1

F–1 Average Monthly Workload by Regional Office: 2001................................. F–3

H–1 The Proportion of Sample Cut and Expected CVs Before and After the January

1996 Reduction ........................................................................ H–2

viii
Tables—Con.

H–2 January 1996 Reduction: Number and Estimated Population of Strata for

754-PSU Design for the Nine Reduced States ...................................... H–3

H–3 Reduction Groups Dropped From SR Areas .......................................... H–3

H–4 State First-Stage Factors for CPS January 1996 Reduction ......................... H–4

H–5 Components of Variance for FSC Monthly Estimates................................. H–6

H–6 Effects of Weighting Stages on Monthly Relative Variance Factors.................. H–7

H–7 Effect of Compositing on Monthly Variance and Relative Variance Factors ......... H–7

H–8 Design Effects for Total Monthly Variance ............................................. H–8

H–9 Components of Variance for Composited Month-to-Month Change Estimates ...... H–8

H–10 Effect of Compositing on Month-to-Month Change Variance Factors ................ H–9

J–1 Size of SCHIP General Sample Increase and Effect on Detectable Difference by

State .................................................................................... J–3

J–2 Phase-in Table (First 11 Months of Sample Expansion) .............................. J–4

J–3 Effect of SCHIP on National Labor Force Estimates: June 2001 .................... J–6

J–4 Effect of SCHIP on State Sampling Intervals and Coefficients of Variation.......... J–7

J–5 CPS Sample Housing Unit Counts With and Without SCHIP ........................ J–8

J–6 Standard Errors on Percentage of Uninsured, Low-Income Children Compared at

Stages of the SCHIP Expansion ...................................................... J–9

Appendix A. Maximizing Primary Sampling Unit (PSU) Overlap

Introduction................................................................................... A–1

Adding a County to a PSU .................................................................. A–3

Dropping a County From a PSU............................................................ A–3

Dropping and Adding PSU’s ................................................................ A–4

References ................................................................................... A–5

Appendix B. Sample Preparation Materials

Introduction................................................................................... B–1

Unit Frame Materials ........................................................................ B–1

Area Frame Materials........................................................................ B–3

Group Quarters Frame Materials ........................................................... B–9

Permit Frame Materials...................................................................... B–9

Appendix C. Maintaining the Desired Sample Size

Introduction................................................................................... C–1

Maintenance Reductions .................................................................... C–1

Appendix D. Derivation of Independent Population Controls

Introduction................................................................................... D–1

The Population Universe for CPS Controls ................................................ D–3

Calculation of Population Projections for the CPS Universe .............................. D–4

Adjustment of CPS Controls for Net Underenumeration in the 1990 Census............ D–20

The Post-Enumeration Survey and Dual-System Estimation ............................. D–20

The Monthly and Annual Revision Process for Independent Population Controls ....... D–22

Procedural Revisions ........................................................................ D–22

References ................................................................................... D–23

Summary List of Sources for CPS Population Controls ................................... D–23

ix
Appendix E. State Model-Based Labor Force Estimation

Introduction................................................................................... E–1

Time Series Component Modeling.......................................................... E–1

Estimation .................................................................................... E–4

Diagnostic Testing ........................................................................... E–4

Monthly Processing .......................................................................... E–4

End-of-the-Year Processing ................................................................. E–4

Results ....................................................................................... E–5

References ................................................................................... E–6

Appendix F. Organization and Training of the Data Collection Staff

Introduction................................................................................... F–1

Organization of Regional Offices/CATI Facilities........................................... F–1

Training Field Representatives.............................................................. F–1

Field Representative Training Procedures ................................................. F–1

Field Representative Performance Guidelines ............................................. F–3

Evaluating Field Representative Performance ............................................. F–4

Appendix G. Reinterview: Design and Methodology

Introduction................................................................................... G–1

Response Error Sample ..................................................................... G–1

Quality Control Sample...................................................................... G–1

Reinterview Procedures ..................................................................... G–2

Summary ..................................................................................... G–2

References ................................................................................... G–2

Appendix H. Sample Design Changes of the Current Population Survey:

January 1996

Introduction................................................................................... H–1

Changes to the CPS Sample Design....................................................... H–2

Revisions to CPS Estimation................................................................ H–4

Estimation of Variances ..................................................................... H–4

Appendix I. New Compositing Procedure

Introduction................................................................................... I–1

Composite Estimation in the CPS .......................................................... I–1

Computing Composite Weights ............................................................. I–2

A Note About Final Weights ................................................................. I–2

References ................................................................................... I–3

Appendix J. Changes to the Current Population Survey Sample in

July 2001

Introduction................................................................................... J–1

General Sample Increase in Selected States .............................................. J–1

Other Aspects of the SCHIP Expansion.................................................... J–8

Effect of SCHIP Expansion on Standard Errors of Estimates of Uninsured

Low-Income Children....................................................................... J–8

References ................................................................................... J–10

Acronyms.......................................................................... ACRONYMS–1

Index......................................................................................... INDEX–1

x
Summary of Changes

(Changes made to Current Population Survey Technical Paper 63 to Produce Technical Paper 63RV, March 2002)

Chapter 1. Background

• page 1-1, left column, fourth paragraph:1 added a footnote about the sample size increase

detailed in Appendix J.

Chapter 2. History of the Current Population Survey

• page 2-3, third paragraph of December 1971-March 1973: changed 1992 to 1972.2

• page 2-4, April 1984: changed 1995 to 1985.

• page 2-5: added a section January 1998 describing a two-step composite estimation

method.

• page 2-5: added a section July 2001 describing the SCHIP.

Chapter 3. Design of the Current Population Survey Sample

• page 3-1, chapter heading: added a reference to Appendix J.

• page 3-1, after second paragraph of INTRODUCTION: added text on the SCHIP.

• page 3-11, Table 3-3a: corrected sorts.

Chapter 9. Data Preparation

• page 9-1, first paragraph of INDUSTRY AND OCCUPATION (I&O) CODING:

added a footnote about the increase of cases for coding because of the SCHIP.

Chapter 10. Estimation Procedures for Labor Force Data

• page 10-1, last paragraph of INTRODUCTION: added text about a new compositing

procedure detailed in Appendix I.

• page 10-5, Table 10-1: changed 1999 in heading to 1990.

• page 10-6, Table 10-3: inserted missing age category 55-59.

• page 10-11, second paragraph of Estimates for States: added a footnote that all states

are now based on a model.

Chapter 11. Current Population Survey Supplemental Inquiries

• pages 11-1–11-3: made clarifications and revisions to improve readability.

• pages 11-3–11-8, Annual Demographic Supplement (March Supplement): major

revisions due to more current methodologies and the inclusion of the SCHIP.

Chapter 14. Estimation of Variance

• page 14-5, Table 14-1: added a footnote that refers to Appendix H.

Chapter 16. Quality Indicators of Nonsampling Errors

• page 16-1, chapter heading: added a reference to a BLS internet site.

Appendix E. State Model-Based Labor Force Estimation

• page E-1, Table E-1: added a footnote about the SCHIP.

Appendix F. Organization and Training of the Data Collection Staff

• pages F-1 – F3, all sections: updated several of the numbers/percentages with 2001 data.

• page F-3, Figure F-2: changed to Table F-1 and updated it.

• page F-3, fourth paragraph of FIELD REPRESENTATIVE PERFORMANCE GUIDELINES:

changed CARMIN to CARMN.

1

Page number, column, and paragraph number are those from TP63. They indicate where the change begins.

2

Boldface indicates a change or addition from TP63 to TP63RV.

xi
Appendix G. Reinterview: Design and Methodology

• pages G-1–G-3, all sections: major revisions/deletions due to more current methodologies.

Appendix H. Sample Design Changes of the Current Population Survey: January 1996

• page H-1, chapter heading: added a reference to Appendix J.

Appendix J. Changes to the Current Population Survey Sample in July 2001

• This is an entirely new appendix, focusing on the changes that are collectively known as

the SCHIP sample expansion.

xii
Chapter 1.

Background

The Current Population Survey (CPS), sponsored jointly

by the U.S. Census Bureau and the U.S. Bureau of Labor

Statistics (BLS), is the Nation’s primary source of labor

force statistics for the entire population. The CPS is the

source of numerous high-profile economic statistics including the Nation’s unemployment rate and provides data on a

wide range of issues relating to employment and earnings.

The CPS also collects extensive demographic data which

complement and enhance our understanding of labor market conditions in the Nation overall, among many different

population groups, the various states, and even substate

areas.

Although the labor market information is central to the

CPS, the survey provides a wealth of other social and

economic data that are widely used by social scientists in

both the public and private sectors. In addition, because of

its long history, the CPS has been a model for other

household surveys, both in the United States and in many

other countries.

Thus, the CPS is a source of information for both social

science research and the study of survey methodology.

This report aims to provide all users of the CPS with a

comprehensive guide to the survey. The report focuses on

labor force data because the timely and accurate collection

of those data remains the principal purpose of the survey.

The CPS is administered by the Census Bureau using a

scientifically selected sample of some 50,000 occupied

households.1 The fieldwork is conducted during the calendar week that includes the 19th of the month. The questions refer to activities during the prior week; that is, the

week that includes the 12th of the month.2 Households

from all 50 states and the District of Columbia are in the

survey for 4 consecutive months, out for 8, and then return

for another 4 months before leaving the sample permanently. This design ensures a high degree of continuity

from 1 month to the next (as well as over the year). The

4-8-4 sampling scheme has the added benefit of allowing

for the constant replenishment of the sample without

excessive response burden.

To be eligible to participate in the CPS, individuals must

be 15 years of age or over and not in the Armed Forces.

Persons in institutions, such as prisons, long-term care

hospitals, and nursing homes are, by definition, ineligible to

be interviewed in the CPS. In general, the BLS publishes

labor force data only for persons age 16 and over, since

those under 16 are substantially limited in their labor

market activities by compulsory schooling and child labor

laws. No upper age limit is used, and full-time students are

treated the same as nonstudents. One person generally

responds for all eligible members of the household. The

person who responds is called the ‘‘reference person’’ and

usually is the person who either owns or rents the housing

unit. If the reference person is not knowledgeable about the

employment status of the others in the household, attempts

are made to contact those individuals directly.

Within 2 weeks of the completion of these interviews, the

BLS releases the major results of the survey. Also included

in BLS’s analysis of labor market conditions are data from

a survey of nearly 400,000 employers (the Current Employment Statistics (CES) survey, conducted concurrently with

the CPS). These two surveys are complementary in many

ways. The CPS focuses on the labor force status (employed,

unemployed, not in labor force) of the working-age population and the demographic characteristics of workers and

nonworkers. The CES focuses on aggregate estimates of

employment, hours, and earnings for several hundred

industries that would be impossible to obtain with the same

precision through a household survey. The CPS reports on

individuals not covered in the CES, such as the self

employed, agricultural workers, and unpaid workers in a

family business. Information also is collected in the CPS

about persons who are not working.

In addition to the regular labor force questions, the CPS

often includes supplemental questions on subjects of interest to labor market analysts. These include annual work

activity and income, veteran status, school enrollment,

contingent employment, worker displacement, and job

tenure, among other topics. Because of the survey’s large

sample size and broad population coverage, a wide range

of sponsors use CPS supplements to collect data on topics

as diverse as expectation of family size, tobacco use,

computer use, and voting patterns. The supplements are

described in greater detail in Chapter 11.

The CPS questionnaire is a completely computerized

document that is administered by Census Bureau field

1

Beginning with July 2001, the sample size increased to 60,000

occupied households. (See Appendix J for details.) 2

In the month of December, the survey is often conducted 1 week

earlier to avoid conflicting with the holiday season.

1–1
representatives across the country through both personal

and telephone interviews. Additional telephone interviewing also is conducted from the Census Bureau’s three

centralized collection facilities in Hagerstown, Maryland;

Jeffersonville, Indiana; and Tucson, Arizona.

The labor force concepts and definitions used in the

CPS have undergone only slight modification since the

survey’s inception in 1940. Those concepts and definitions

are discussed in Chapter 5.

1–2
Chapter 2.

History of the Current Population Survey

INTRODUCTION

The Current Population Survey (CPS) has its origin in a

program established to provide direct measurement of

unemployment each month on a sample basis. There were

several earlier attempts to estimate the number of unemployed using various devices ranging from guesses to

enumerative counts. The problem of measuring unemployment became especially acute during the Economic Depression of the 1930s.

The Enumerative Check Census, taken as part of the

1937 unemployment registration, was the first attempt to

estimate unemployment on a nationwide basis using probability sampling. During the latter half of the 1930s, the

Work Projects Administration (WPA) developed techniques

for measuring unemployment, first on a local area basis

and later on a national basis. This research combined with

the experience from the Enumerative Check Census led to

the Sample Survey of Unemployment which was started in

March 1940 as a monthly activity by the WPA.

MAJOR CHANGES IN THE SURVEY: A

CHRONOLOGY

In August 1942, responsibility for the Sample Survey of

Unemployment was transferred to the Bureau of the Census, and in October 1943, the sample was thoroughly

revised. At that time, the use of probability sampling was

expanded to cover the entire sample, and new sampling

theory and principles were developed and applied to

increase the efficiency of the design. The households in the

revised sample were in 68 Primary Sampling Units (PSUs)

(see Chapter 3), comprising 125 counties and independent

cities. By 1945, about 25,000 housing units were designated for the sample, of which about 21,000 contained

interviewed households.

One of the most important changes in the CPS sample

design took place in 1954 when, for the same total budget,

the number of PSUs was expanded from 68 to 230, without

any change in the number of sample households. The

redesign resulted in a more efficient system of field organization and supervision and provided more information

per unit of cost. Thus the accuracy of published statistics

improved as did the reliability of some regional as well as

national estimates.

Since the mid-1950s, the CPS’s sample has undergone

major revision after every decennial census. The following

list chronicles the important modifications to the CPS

starting in the mid-1940s:

• July 1945. The CPS questionnaire was revised. The

revision consisted of the introduction of four basic employment status questions. Methodological studies showed

that the previous questionnaire produced results that

misclassified large numbers of part-time and intermittent

workers, particularly unpaid family workers. These groups

were erroneously reported as not active in the labor

force.

• August 1947. The selection method was revised. The

method of selecting sample units within a sample area

was changed so that each unit selected would have the

same basic weight. This change simplified tabulations

and estimation procedures.

• July 1949. Previously excluded dwelling places were

now covered. The sample was extended to cover special

dwelling places—hotels, motels, trailer camps, etc. This

led to improvements in the statistics, (i.e., reduced bias)

since residents of these places often have characteristics

different from the rest of the population.

• February 1952. Document sensing procedures were

introduced in the survey process. The CPS questionnaire

was printed on a document-sensing card. In this procedure, responses were recorded by drawing a line through

the oval representing the correct answer using an electrographic lead pencil. Punch cards were automatically

prepared from the questionnaire by document-sensing

equipment.

• January 1953. Ratio estimates now used data from the

1950 population census. Starting in January 1953, population data from the 1950 census were introduced into

the CPS estimation procedure. Prior to that date, the

ratio estimates had been based on 1940 census relationships for the first-stage ratio estimate, and 1940 population data were used to adjust for births, deaths, etc., for

the second-stage ratio estimate. In September 1953, a

question on ‘‘color’’ was added and the question on

‘‘veteran status’’ was deleted in the second-stage ratio

estimate. This change made it feasible to publish separate, absolute numbers for persons by race; whereas,

only the percentage of distributions had previously been

possible.

• July 1953. The 4-8-4 rotation system was introduced.

This sample rotation system was adopted to improve

measurement over time. In this system households are

interviewed for 4 consecutive months 1 year, leave the

2–1
sample for 8 months, and return for the same period of 4

months the following year. In the previous system, households were interviewed for 6 months and then replaced.

The 4-8-4 system provides some year-to-year overlap,

thus improving estimate of change on both a month-tomonth and year-to-year basis.

• September 1953. High speed electronic equipment was

introduced for tabulations. The introduction of electronic

calculation greatly increased timeliness and led to other

improvements in estimation methods. Other benefits

included the substantial expansion of the scope and

content of the tabulations and the computation of sampling variability. The shift to modern computers was

made in 1959. Keeping abreast of modern computing

has proved a continuous process, and to this day, the

Census Bureau is still updating and replacing its computer environment.

• February 1954. The number of PSUs was expanded to

230. The number of PSUs was increased from 68 to 230

while retaining the overall sample size of 25,000 designated housing units. The 230 PSUs consisted of 453

counties and independent cities. At the same time, a

substantially improved estimation procedure (See Chapter 10, Composite Estimation) was introduced.

Composite estimation took advantage of the large

overlap in the sample from month-to-month. These two

changes improved the reliability of most of the major

statistics by a magnitude that could otherwise be achieved

only by doubling the sample size.

• May 1955. Monthly questions on part-time workers were

added. Monthly questions exploring the reasons for

part-time work were added to the standard set of employment status items. In the past, this information had been

collected quarterly or less frequently and was found to be

valuable in studying labor market trends.

• July 1955. Survey week was moved. The CPS survey

week was moved to the calendar week containing the

12th day of the month to align the CPS time reference

with that of other employment statistics. Previously, the

survey week had been the calendar week containing the

8th day of the month.

• May 1956. The number of PSUs was expanded to 330.

The number of PSUs was expanded from 230 to 330.

The overall sample size also increased by roughly twothirds to a total of about 40,000 households units (about

35,000 occupied units). The expanded sample covered

638 counties and independent cities.

All of the former 230 PSUs were also included in the

expanded sample.

The expansion increased the reliability of the major

statistics by around 20 percent and made it possible to

publish more detailed statistics.

• January 1957. Employment status definition was changed.

Two relatively small groups of persons, both formerly

classified as employed ‘‘with a job but not at work,’’ were

assigned to new classifications. The reassigned groups

were (1) persons on layoff with definite instructions to

return to work within 30 days of the layoff date and (2)

persons waiting to start new wage and salary jobs within

30 days of the interview. Most of the persons in these two

groups were shifted to the unemployed classification.

The only exception was the small subgroup in school

during the survey week who were waiting to start new

jobs; these persons were transferred to ‘‘not in labor

force.’’ This change in definition did not affect the basic

question or the enumeration procedures.

• June 1957. Seasonal adjustment was introduced. Some

seasonally adjusted unemployment data were introduced

early in 1955. An extension of the data—using more

refined seasonal adjustment methods programmed on

electronic computers—was introduced in July 1957. The

new data included a seasonally adjusted rate of unemployment and trends of seasonally adjusted total employment and unemployment. Significant improvements in

methodology emerged from research conducted at the

Bureau of Labor Statistics and the Census Bureau in the

ensuing years.

• July 1959. Responsibility for CPS was moved between

agencies. Responsibility for the planning, analysis, and

publication of the labor force statistics from the CPS was

transferred to the BLS as part of a large exchange of

statistical functions between the Commerce and Labor

Departments. The Census Bureau continued to have

(and still has) responsibility for the collection and computer processing of these statistics, for maintenance of

the CPS sample, and for related methodological research.

Interagency review of CPS policy and technical issues

continues under the aegis of the Statistical Policy Division, Office of Management and Budget.

• January 1960. Alaska and Hawaii were added to the

population estimates and the CPS sample. Upon achieving statehood, Alaska and Hawaii were included in the

independent population estimates and in the sample

survey. This increased the number of sample PSUs from

330 to 333. The addition of these two states affected the

comparability of population and labor force data with

previous years. Another result was in an increase of

about 500,000 in the noninstitutional population of working age and about 300,000 in the labor force, four-fifths of

this in nonagricultural employment. The levels of other

labor force categories were not appreciably changed.

• October 1961. Conversion to the Film Optical Sensing

Device for Input to the Computer (FOSDIC) system. The

CPS questionnaire was converted to the FOSDIC type

used by the 1960 census. Entries were made by filling in

small circles with an ordinary lead pencil. The questionnaires were photographed to microfilm. The microfilms

were then scanned by a reading device which transferred

the information directly to computer tape. This system

permitted a larger form and a more flexible arrangement

2–2
of items than the previous document-sensing procedure

and did not require the preparation of punch cards. This

data entry system was used through December 1993.

• January 1963. New descriptive information was made

available. In response to recommendations of a review

committee, two new items were added to the monthly

questionnaire. The first was an item, formerly carried out

only intermittently, on whether the unemployed were

seeking full- or part-time work. The second was an

expanded item on household relationships, formerly included

only annually, to provide greater detail on the marital

status and household relationship of unemployed persons.

• March 1963. The sample and population data used in

ratio estimates were revised. From December 1961 to

March 1963, the CPS sample was gradually revised.

This revision reflected the changes in both population

size and distribution as established by the 1960 census.

Other demographic changes, such as the industrial mix

between areas, were also taken into account. The overall

sample size remained the same, but the number of PSUs

increased slightly to 357 to provide greater coverage of

the fast growing portions of the country. For most of the

sample, census lists replaced the traditional area sampling. These lists were developed in the 1960 census.

These changes resulted in further gains in reliability of

about 5 percent for most statistics. The census-based

updated population information was used in April 1962

for first- and second-stage ratio estimates.

• January 1967. The sample was expanded to 449 PSUs.

The CPS sample was expanded from 357 to 449 PSUs.

An increase in total budget allowed the overall sample

size to increase by roughly 50 percent to a total of about

60,000 housing units (52,500 occupied units). The expanded

sample had households in 863 counties and independent

cities with at least some coverage in every state.

This expansion increased the reliability of the major

statistics by about 20 percent and made it possible to

publish more detailed statistics.

The concepts of employment and unemployment

were modified. In line with the basic recommendations of

the President’s Committee to Appraise Employment and

Unemployment Statistics (Eckler, 1972), a several-year

study was conducted to develop and test proposed

changes in the labor force concepts. The principal

research results were implemented in January 1967.

The changes included a revised age cutoff in defining

the labor force; and new questions to improve the

information on hours of work, the duration of unemployment, and the self-employed. The definition of unemployment was also revised slightly. The revised definition of

unemployment led to small differences in the estimates

of level and month-to-month change.

• March 1968. Separate age/sex ratio estimation cells

were introduced for Negro and other races. Previously,

the second-stage ratio estimation used non-White and

White race categories by age groups and sex. The

revised procedures allowed for separate ratio estimates

for Negro and Other1 race categories.

This change amounts essentially to an increase in the

number of ratio estimation cells from 68 to 116.

• January 1971 and January 1972. 1970 census occupational classification was introduced. The questions on

occupation were made more comparable to those used

in the 1970 census by adding a question on major

activities or duties of current job. The new classification

was introduced into the CPS coding procedures in January 1971. Tabulated data were produced in the revised

version beginning in January 1972.

• December 1971-March 1973. Sample was expanded to

461 PSUs and data used in ratio estimation were updated.

From December 1971 to March 1973, the CPS sample

was revised gradually to reflect the changes in population size and distribution as described by the 1970

census. As part of an overall sample optimization, the

sample size was reduced slightly (from 60,000 to 58,000

housing units), but the number of PSUs increased to 461.

Also, the cluster design was changed from six nearby

(but not contiguous) to four usually contiguous households. This change was undertaken after research found

that smaller cluster sizes would increase sample efficiency.

Even with the reduction in sample size, this change

led to a small gain in reliability for most characteristics.

The noninterview adjustment and first stage ratio estimate adjustment were also modified to improve the

reliability of estimates for central cities and the rest of the

Standard Metropolitan Statistical Areas (SMSAs).

In January 1972, the population estimates used in the

second-stage ratio estimation were updated to the 1970

census base.

• January 1974. Inflation-deflation method was introduced

for deriving independent estimates of the population. The

derivation of independent estimates of the civilian noninstitutional population by age, race, and sex used in

second-stage ratio estimation in preparing the monthly

labor force estimates now used the inflation-deflation

method (see Chapter 10).

• September 1975. State supplementary samples were

introduced. An additional sample, consisting of about

14,000 interviews each month, was introduced in July

1975 to supplement the national sample in 26 states and

the District of Columbia. In all, 165 new PSUs were

involved. The supplemental sample was added to meet a

specific reliability standard for estimates of the annual

average number of unemployed persons for each state.

1

Other includes American Indian, Eskimo, Aleut, Asian, and Pacific

Islander.

2–3
In August 1976, an improved estimation procedure and

modified reliability requirements led to the supplement

PSUs being dropped from three states.

Thus, the size of the supplemental sample was reduced

to about 11,000 households in 155 PSUs.

• October 1978. Procedures for determining demographic

characteristics were modified. At this time, changes were

made in the collection methods for household relationship, race, and ethnicity. Race was now determined by

the respondent rather than by the interviewer.

Other modifications included the introduction of earnings questions for the two outgoing rotations. New items

focused on usual hours worked, hourly wage rate, and

usual weekly earnings. Earnings items were asked of

currently employed wage and salary workers.

• January 1979. A new two-level, first-stage ratio estimation procedure was introduced. This procedure was

designedtoimprovethe reliabilityofmetropolitan/nonmetropolitan

estimates.

Other newly introduced items were the monthly tabulation of children’s demographic data, including relationship, age, sex, race, and origin.

• September/October 1979. The final report of the National

Commission on Employment and Unemployment Statistics (NCEUS; ‘‘Levitan’’ Commission) (Executive Office of

the President, 1976) was issued. This report shaped

many of the future changes to the CPS.

• January 1980. To improve coverage about 450 households were added to the sample, increasing the number

of total PSUs to 629.

• May 1981. The sample was reduced by approximately

6,000 assigned households bringing the total sample

size to approximately 72,000 assigned households.

• January 1982. The race categories in the second-stage

ratio estimation adjustment were changed from White/NonWhite to Black/Non-Black. These changes were made to

eliminate classification differences in race that existed

between the 1980 census and the CPS. The change did

not result in notable differences in published household

data. Nevertheless, it did result in more variability for

certain ‘‘White,’’ ‘‘Black,’’ and ‘‘Other’’ characteristics.

As is customary, the CPS uses ratio estimates from

the most recent decennial census. Beginning in January

1982, these ratio estimates were based on findings from

the 1980 census. The use of the 1980 census-based

population estimates, in conjunction with the revised

second-stage adjustment, resulted in about a 2 percent

increase in the estimates for total civilian noninstitutional

population 16 years and over, civilian labor force, and

unemployed persons. The magnitude of the differences

between 1970 and 1980 census-based ratio estimates

affected the historical comparability and continuity of

major labor force series; therefore, the BLS revised

approximately 30,000 series back to 1970.

• November 1982. The question series on earnings was

extended to include items on union membership and

union coverage.

• January 1983. The occupational and industrial data

were coded using the 1980 classification systems. While

the effect on industry-related data was minor, the conversion was viewed as a major break in occupationrelated data series. The census developed a ‘‘list of

conversion factors’’ to translate occupation descriptions

based on the 1970 census-coding classification system

to their 1980 equivalents.

Most of the data historically published for the ‘‘Black

and Other’’ population group were replaced by data

which relate only to the ‘‘Black’’ population.

• October 1984. School enrollment items were added for

persons 16-24 years of age.

• April 1984. The 1970 census-based sample was phasedout through a series of changes that were completed by

July 1985. The redesigned sample used data from the

1980 census to update the sampling frame, took advantage of recent research findings to improve the efficiency

and quality of the survey, and used a state-based design

to improve the estimates for the states without any

change in sample size.

• September 1984. Collection of veteran’s data for females

was started.

• January 1985. Estimation procedures were changed to

use data from the 1980 census and new sample. The

major changes were to the second-stage adjustment

which replaced population estimates for ‘‘Black’’ and

‘‘Non-Black’’ (by sex and age groups) with population

estimates for ‘‘White,’’ ‘‘Black,’’ and ‘‘Other’’ population

groups. In addition, a separate, intermediate step was

added as a control to the Hispanic2 population. The

combined effect of these changes on labor force estimates and aggregates for most population groups was

negligible; however, the Hispanic population and associated labor force estimates were greatly affected and

revisions were made back to January 1980 to the extent

possible.

• June 1985. The CPS Computer Assisted Telephone

Interviewing (CATI) facility was opened at Hagerstown,

Maryland. A series of tests over the next few years were

conducted to identify and resolve the operational issues

associated with the use of CATI. Later tests focused on

CATI-related issues, such as data quality, costs, and

mode effects on labor force estimates. Samples used in

these tests were not used as part of the CPS.

• April 1987. First CATI cases were used in CPS monthly

estimates. Initially, CATI started with 300 cases a month.

As operational issues were resolved and new telephone

2

Hispanics may be of any race.

2–4
centers were opened—Tucson, Arizona (May 1992) and

Jeffersonville, Indiana (September 1994)—the CATI workload was gradually increased to about 9,200 cases a

month (January 1995).

• June 1990. The first of a series of experiments to test

alternative labor force questionnaires was started at the

Hagerstown Telephone Center. These tests used random digit dialing and were conducted in 1990 and 1991.

• July 1992. The CATI and Computer Assisted Personal

Interviewing (CAPI) Overlap (CCO) experiments began.

CATI and automated laptop versions of the revised CPS

questionnaire were used in a sample of about 12,000

households selected from the National Crime Victimization Survey sample. The experiment continued through

December 1993.

The CCO ran parallel with the official CPS. The

CCO’s main purpose was to gauge the combined effect

of the new questionnaire and computer-assisted data

collection. It is estimated that the redesign had no

statistically significant effect on the total unemployment

rate, but it did affect statistics related to unemployment,

such as the reasons for unemployment, the duration of

unemployment, and the industry and occupational distribution of the unemployed with previous work experience. It also is estimated that the redesign significantly

increased the employment-to-population ratio and the

labor force participation rate for women, but significantly

decreased the employment-to-population ratio for men.

Along with the changes in employment, it was estimated

that the redesign significantly influenced the measurement of characteristics related to employment, such as

the proportion of employment working part-time, the

proportion working part-time for economic reasons, the

number of individuals classified as self-employed, and

industry and occupational distribution of the employed.

• January 1994. A new questionnaire designed solely for

use in computer assisted interviewing was introduced in

the official CPS. Computerization allowed for the use of a

very complex questionnaire without increasing response

burden, increased consistency by reducing interviewer

error, permitted editing at time of interviewing, and allowed

for the use of dependent interviewing where information

reported in one month (industry/occupation, retired/disabled

statuses, and duration of unemployment) was confirmed

or updated in subsequent months.

Industry and occupation codes from the 1990 census

were introduced. Population estimates were converted

to 1990 census base for use in ratio estimation procedures.

• April 1994. The 16-month phase-in of the redesigned

sample based on the 1990 census began. The primary

purpose of this sample redesign was to maintain the

efficiency of the sampling frames. Once phased-in, this

resulted in a monthly sample of 56,000 eligible housing

units in 792 sample areas. The details of the 1990

sample redesign are described in Chapter 3.

• December 1994. Starting in December 1994, a new set

of response categories was phased in for the relationship

to reference person. This modification was directed at

individuals not formally related to the reference person to

identify unmarried partners in a household. The old

partner/roommate category was deleted and replaced

with the following categories: unmarried partner,

housemate/roommate, and roomer/boarder. This modification was phased in two rotation groups at a time and

was fully in place by March 1995. This change had no

effect on the family statistics produced by CPS.

• January 1996. The 1990 CPS design was changed

because of a funding reduction. The original reliability

requirements of the sample were relaxed, allowing a

reduction in the national sample size from roughly 56,000

eligible housing units to 50,000 eligible housing units.

The reduced CPS national sample contains 754 PSUs.

The details of the sample design changes as of January

1996 are described in Appendix H.

• January 1998. A new two-step composite estimation

method for the CPS was implemented (See Appendix I).

The first step involves computation of composite estimates for the main labor force categories, classified by

important demographic characteristics. The second adjusts

person weights, through a series of ratio adjustments, to

agree with the composite estimates, thus incorporating

the effect of composite estimation into the person weights.

This new technique provides increased operational simplicity for microdata users and improves the accuracy of

labor force estimates by using different compositing

coefficients for different labor force categories. The weighting adjustment method assures additivity while allowing

this variation in compositing coefficients.

• July 2001. Effective with the release of July 2001 data,

official labor force estimates from the CPS and Local

Area Unemployment Statistics (LAUS) program reflect

the expansion of the monthly CPS sample from about

50,000 to about 60,000 eligible households. This expansion of the monthly CPS sample was one part of the

Census Bureau’s plan to meet the requirements of the

State Children’s Health Insurance Program (SCHIP)

legislation. The SCHIP legislation requires the Census

Bureau to improve state estimates of the number of

children who live in low-income families and lack health

insurance. These estimates are obtained from the Annual

Demographic Supplement to the CPS. In September

2000, the Census Bureau began expanding the monthly

CPS sample in 31 states and the District of Columbia.

States were identified for sample supplementation based

on the standard error of their March estimate of lowincome children without health insurance. The additional

10,000 households were added to the sample over a

3-month period. The BLS chose not to include the

2–5
additional households in the official labor force estimates, however, until it had sufficient time to evaluate the

estimates from the 60,000 household sample. See Appendix J, Changes to the Current Population Survey Sample

in July 2001, for details.

REFERENCES

Eckler, R.A. (1972), U.S. Census Bureau, New York:

Praeger.

Executive Office of the President, Office of Management

and Budget, Statistical Policy Division (1976), Federal

Statistics: Coordination, Standards, Guidelines: 1976,

Washington, DC: Government Printing Office.

2–6
Chapter 3.

Design of the Current Population Survey Sample

(See Appendix H for sample design changes as of January 1996 and Appendix J for changes in July 2001.)

INTRODUCTION

For more than five decades, the Current Population

Survey (CPS) has been one of the major sources of

up-to-date information on the labor force and demographic

characteristics of the U.S. population. Because of the

CPS’s importance and high profile, the reliability of the

estimates has been evaluated periodically. The design has

often been under constant and close scrutiny in response

to demand for new data and to improve the reliability of the

estimates by applying research findings and new types of

information (especially census results). All changes are

implemented with concern for minimizing cost and maximizing comparability of estimates across time. A sample

redesign takes place after each census. The most recent

decennial revision which incorporated new information

from the 1990 census was, in the main, complete as of July

1995. Thus, this chapter describes the CPS sample design

as of July 1995.

In January 1996, the CPS design was again modified

because of funding reductions. This time, the redesign was

restricted to the most populous states where survey requirements were relaxed compared to earlier years. These

changes in the sample are not reflected in the main body of

this report, but the reader is referred to Appendix H which

provides an up-to-date and contemporary description of

the changes introduced in 1996. Periodic modifications to

the CPS design are occasionally needed to respond to

growth of the housing stock. Appendix C discusses how

this type of design modification is implemented. Any changes

to the CPS design that postdate publication of the present

document will be described separately in future appendices.

Effective with the release of July 2001 data, official labor

force estimates from the CPS and Local Area Unemployment Statistics (LAUS) program reflect the expansion of

the monthly CPS sample. The State Children’s Health

Insurance Program (SCHIP) legislation requires the Census Bureau to improve state estimates of the number of

children who live in low-income families and lack health

insurance. These estimates are obtained from the Annual

Demographic Supplement to the CPS. States were identified for sample supplementation based on the standard

error of their March estimate of low-income children without

health insurance. See Appendix J for details.

This chapter is directed to a general audience and

presents many topics with varying degrees of detail. The

following section provides a broad overview of the CPS

design and is recommended for all readers. Later sections

of this chapter provide a more in-depth description of the

CPS design and are recommended for readers who require

greater detail.

SURVEY REQUIREMENTS AND DESIGN

Survey Requirements

The following list briefly describes the major characteristics of the CPS sample as of July 1995:

1. The CPS sample is a probability sample.

2. The sample is designed primarily to produce national

and state estimates of labor force characteristics of the

civilian noninstitutional population 16 years of age and

older (CNP16+).

3. The CPS sample consists of independent samples in

each state and the District of Columbia. In other words,

each state’s sample is specifically tailored to the

demographic and labor market conditions that prevail

in that particular state. California and New York State

are further divided into two substate areas that also

have independent designs: the Los Angeles-Long Beach

metropolitan area and the rest of California; New York

City and the rest of New York State. Since the CPS

design consists of independent designs for the states

and substate areas, it is said to be state-based.

4. Sample sizes are determined by reliability requirements which are expressed in terms of the coefficient

of variation, or CV. The CV is a relative measure of the

sampling error and is calculated as sampling error

divided by the expected value of the given characteristic. The specified CV for the monthly unemployment

level for the nation, given a 6 percent unemployment

rate, is 1.8 percent. The 1.8 percent CV is based on

the requirement that a difference of 0.2 percent in the

unemployment rate for two consecutive months be

significant at the 0.10 level.

5. The specified CV for the monthly unemployment level

for 11 states, given a 6 percent unemployment rate, is

8 percent.1 The specified CV for the monthly unemployment level for the California and New York substate areas, given a 6 percent unemployment rate, is 9

1

The 11 states are California, Florida, Illinois, Massachusetts, Michigan, New Jersey, New York, North Carolina, Ohio, Pennsylvania, and

Texas.

3–1
percent. This latter specification leads to California and

New York State having CVs somewhat less than 8

percent.

6. The required CV on the annual average unemployment level for the other 39 states and the District of

Columbia, given a 6 percent unemployment rate, is 8

percent.

Overview of Survey Design

The CPS sample is a multistage stratified sample of

approximately 56,000 housing units from 792 sample areas

designed to measure demographic and labor force characteristics of the civilian noninstitutional population 16

years of age and older. The CPS samples housing units

from lists of addresses obtained from the 1990 Decennial

Census of Population and Housing. These lists are updated

continuously for new housing built after the 1990 census.

The first stage of sampling involves dividing the United

States into primary sampling units (PSUs) — most of which

comprise a metropolitan area, a large county, or a group of

smaller counties. Every PSU falls within the boundary of a

state. The PSUs are then grouped into strata on the basis

of independent information, that is, information obtained

from the decennial census or other sources.

The strata are constructed so that they are as homogeneous as possible with respect to labor force and other

social and economic characteristics that are highly correlated with unemployment. One PSU is sampled per stratum. The probability of selection for each PSU in the

stratum is proportional to its population as of the 1990

census.

In the second stage of sampling, a sample of housing

units within the sample PSUs is drawn. Ultimate sampling

units (USUs) are clusters of about four housing units. The

bulk of the USUs sampled in the second stage consists of

sets of addresses which are systematically drawn from

sorted lists of addresses of housing units prepared as part

of the 1990 census. Housing units from blocks with similar

demographic composition and geographic proximity are

grouped together in the list. In parts of the United States

where addresses are not recognizable on the ground,

USUs are identified using area sampling techniques. Occasionally, a third stage of sampling is necessary when actual

USU size is extremely large. A final addition to the USUs is

a sample of building permits, which compensates for the

exclusion of construction since 1990 in the list of addresses

in the 1990 census.

Each month, interviewers collect data from the sample

housing units. A housing unit is interviewed for 4 consecutive months and then dropped out of the sample for the

next 8 months and is brought back in the following 4

months. In all, a sample housing unit is interviewed eight

times. Households are rotated in and out of the sample in

a way that improves the accuracy of the month-to-month

and year-to-year change estimates. The rotation scheme

ensures that in any 1 month, one-eighth of the housing

units are interviewed for the first time, another eighth is

interviewed for the second time, and so on. That is, after

the first month, 6 of the 8 rotation groups will have been in

the survey for the previous month — there will always be a

75 percent month-to-month overlap. When the system has

been in full operation for 1 year, 4 of the 8 rotation groups

in any month will have been in the survey for the same

month, 1 year ago; there will always be a 50 percent

year-to-year overlap. This rotation scheme fully upholds

the scientific tenets of probability sampling, so that each

month’s sample produces a true representation of the

target population. Also, this rotation scheme is considered

better than other candidate schemes. For example, undue

reporting burden expected of survey respondents if they

were to constitute a permanent panel is avoided. The

properties of the rotation system also show that it could be

used to reduce sampling error by use of a composite

estimation procedure2 and, at slight additional cost, by

increasing the representation in the sample of USUs with

unusually large numbers of housing units.

Each state’s sample design ensures that most housing

units within a state have the same overall probability of

selection. Because of the state-based nature of the design,

sample housing units in different states have different

overall probabilities of selection. It is true that if we considered only the national level, a more efficient design would

result from using the same overall probabilities for all

states. Nevertheless, the current system of state-based

designs ensures that both the state and national reliability

requirements are met.

FIRST STAGE OF THE SAMPLE DESIGN

The first stage of the CPS sample design is the selection

of counties. The purpose of selecting a subset of counties

instead of having all counties in the sample is to reduce

travel costs for the field representatives. Two features of

the first-stage sampling are: (1) to ensure that sample

counties represent other counties with similar labor force

characteristics that are not selected and (2) to ensure that

each field representative is allotted a manageable workload in his/her sample area.

The first stage-sample selection is carried out in three

major steps:

1. Definition of the PSUs.

2. Stratification of the PSUs within each state.

3. Selection of the sample PSUs in each state.

2

The complete estimation procedure results in significant reduction in

the sampling error of estimates of level and change for most items. This

procedure depends on the fact that data from previous months are usually

highly correlated with the corresponding estimates for the current month.

3–2
Definition of the Primary Sampling Units

PSUs are delineated in such a way that they encompass

the entire United States. The land areas within each PSU

are made reasonably compact so they can be traversed by

an interviewer without incurring unreasonable costs. The

population is as heterogeneous with regard to labor force

characteristics as can be made consistent with the other

constraints. Strata are constructed that are homogenous in

terms of labor force characteristics to minimize betweenPSU variance. Between-PSU variance is a component of

total variance which arises from selecting a sample of

PSUs rather than selecting housing units from all PSUs. In

each stratum, a PSU is selected that is representative of

the other PSUs in the same stratum. When revisions are

made in the sample each decade, a procedure used for

reselection of PSUs maximizes the overlap in the sample

PSUs with the previous CPS sample (see Appendix A).

Most PSUs are groups of contiguous counties rather

than single counties. A group of counties is more likely to

have diverse labor force characteristics rather than a single

county. Limits are placed on the geographic size of a PSU

to contain the distance a field representative must travel.

After some empirical research in the late 1940s to help

establish rules, the PSUs were initially established in late

1949 and early 1950. The original definitions were subsequently modified and now conform to the rules listed below.

Rules for Defining PSUs

1. PSUs are contained within state boundaries.

2. Metropolitan areas are defined as separate PSUs

using projected 1990 Metropolitan Statistical Area

(MSA) definitions. (An MSA is defined to be at least

one county.) If an MSA straddles state boundaries,

each state-MSA intersection is a separate PSU.3

3. For most states, PSUs are either one county or two or

more contiguous counties. For the New England states4

and part of Hawaii, minor civil divisions (towns or

townships) define the PSUs. In some states, county

equivalents are used: cities, independent of any county

organization, in Maryland, Missouri, Nevada, and Virginia; parishes in Louisiana; and boroughs and census

divisions in Alaska.

4. The area of the PSU should not exceed 3,000 square

miles except in cases where a single county exceeds

the maximum area.

5. The population of the PSU is at least 7,500 except

where this would require exceeding the maximum area

specified in number 4.

6. In addition to meeting the limitation on total area,

PSUs are formed to limit extreme length in any direction and to avoid natural barriers within the PSU.

Combining counties into PSUs. The PSU definitions are

reviewed each time the CPS sample design is revised.

Before 1980, almost all changes in the composition of the

PSUs were made to reflect changes in definitions of MSAs.

For 1980, revised PSU definitions reflect new MSA definitions and ensure that the PSU definitions were compatible

with a state-based sample design. For 1990, revised PSU

definitions reflect changes in MSA definitions and make the

PSU definitions more consistent with those used by the

other Census Bureau demographic surveys. The following

are steps for combining counties, county equivalents, and

independent cities into PSUs for 1990.

1. The 1980 PSUs are evaluated by incorporating into

the PSU definitions those counties comprising MSAs

that are new or have been redefined.

2. Any single county is classified as a separate PSU,

regardless of its 1990 population, if it exceeds the

maximum area limitation deemed practical for interviewer travel.

3. Other counties within the same state are examined to

determine whether they might advantageously be combined with contiguous counties without violating the

population and area limitations.

4. Contiguous counties with natural geographic barriers

between them are placed in separate PSUs to reduce

the cost of travel within PSUs.

5. The proposed combinations are reviewed. Although

personal judgment can have no place in the actual

selection of sample units, (known probabilities of selection can be achieved only through a random selection

process) there are a large number of ways in which a

given population can be structured and arranged prior

to sampling. Personal judgment legitimately plays an

important role in devising an optimal arrangement; that

is, one designed to minimize the variances of the

sample estimates subject to cost constraints.

These steps result in 2,007 CPS PSUs in the United States

from which to draw a sample.

Stratification of Primary Sampling Units

The CPS sample design calls for combining PSUs into

strata within each state and selecting one PSU from each

stratum. For this type of sample design, sampling theory

3

Final MSA definitions were not available from the Office of Management and Budget when PSUs were defined. Fringe counties having a

good chance of being in final MSA definitions are separate PSUs. Most

projected MSA definitions are the same as final MSA definitions (Executive Office of the President, 1993). 4

The New England states are Rhode Island, Connecticut, Massachusetts, Maine, New Hampshire, and Vermont.

3–3
suggests forming strata with approximately equal population sizes. When the design is self-weighting (same sampling fraction in all strata) and one field representative is

assigned to each sample PSU, equal stratum sizes also

have the advantage of providing equal field representative

workloads (at least during the early years of each decade,

before population growth and migration significantly affect

the PSU population sizes). The objective of the stratification, therefore, is to group PSUs with similar characteristics

into strata having approximately equal 1990 populations.

Sampling theory also dictates that highly populated

PSUs should be selected for sample with certainty. The

rationale is that some PSUs exceed or come close to the

stratum size needed for equalizing stratum sizes. These

PSUs are designated as self-representing (SR); that is,

each of the SR PSUs is treated as a separate stratum and

is included in the sample.

The following describes the steps for stratifying PSUs for

the 1990 redesign.

1. The PSUs required to be SR are identified if the PSU

meets one of the following criteria:

a. The PSU belongs to one of the 150 MSAs with the

largest populations in the 1990 census or the PSU

contains counties which had a good chance of

joining one of these 150 MSAs under final MSA

definitions.

b. The PSU belongs to an MSA that was SR for the

1980 design and among the 150 largest following

the 1980 census.

2. The remaining PSUs are grouped into nonself-representing

(NSR) strata within state boundaries by adhering to

the following criteria:

a. Roughly equal-sized NSR strata are formed within

a state.

b. NSR strata are formed so as to yield reasonable

field representative workloads in an NSR PSU of

roughly 45 to 60 housing units. The number of NSR

strata in a state is a function of 1990 population,

civilian labor force, state CV, and between-PSU

variance on the unemployment level. (Workloads in

NSR PSUs are constrained because one field

representative must canvass the entire PSU. No

such constraints are placed on SR PSUs.)

c. NSR strata are formed with PSUs homogeneous

with respect to labor force and other social and

economic characteristics that are highly correlated

with unemployment. This helps to minimize the

between-PSU variance.

d. Stratification is performed independently of previous CPS sample designs.

Key variables used for stratification are:

• Number of male unemployed.

• Number of female unemployed.

• Number of families with female head of household.

• Ratio of occupied housing units with three or

more persons, of all ages, to total occupied

housing units.

In addition to these, a number of other variables

such as industry and wage variables obtained from

the Bureau of Labor Statistics are used for some

states. The number of stratification variables in a

state ranges from 3 to 12.

Table 3–1 summarizes the number of SR and NSR

strata in each state. (The other columns of the table are

discussed in later sections of this chapter.)

The algorithm for implementing the NSR stratification

criteria for the 1980 and 1990 sample designs is a modified

version of the Friedman-Rubin clustering algorithm (Kostanich, 1981). The algorithm consists of three basic steps:

hillclimbing, size adjustment, and an exchange pass. For

each state, the algorithm identifies a stratification which

meets two criteria: (1) all strata are about the same size

and (2) the value of the objective function—a scaled total

between-PSU variance for all the stratification variables—is

relatively small. Each of the algorithm’s three steps assigns

slightly different priorities to criteria (1) and (2). Before the

start of the first step, the program groups the PSUs within

a state into randomly defined strata. The algorithm then

‘‘swaps’’ PSUs between strata to reduce size disparity

between the strata or to decrease the value of the objective

function. The hillclimbing procedure moves PSUs from

stratum to stratum, subject to loose size constraints, in

order to minimize the between-PSU variance for stratification variables.5 The size adjustment tightens size constraints and adjusts stratum sizes by making moves that

lead to the smallest increases in between-PSU variance.

With tight size constraints, the exchange pass seeks to

further reduce between-PSU variance by exchanging PSUs

between strata.

The algorithm is run several times allowing stratum sizes

to vary by differing degrees. A final stratification is chosen

which minimizes, to the extent possible, variability in stratum workloads and total between-PSU variance for all

stratification variables for the state. If a stratification results

in an NSR PSU being placed in a stratum by itself, the PSU

is then SR. After the strata are defined, some state sample

sizes are adjusted to bring the national CV for unemployment level down to 1.8 percent assuming a 6 percent

unemployment rate. (The stratification procedure for Alaska

takes into account expected interview cost and betweenPSU variance (Ludington, 1992).)

5

Between-PSU variance is the component of total variance arising

from selecting a sample of PSUs from all possible PSUs. For this

stratification process, the between-PSU variance was calculated using

1990 census data for the stratification variables.

3–4
A consequence of the above stratification criteria is that

states that are geographically small, mostly urban, or

demographically homogeneous are entirely SR. These

states are Connecticut, Delaware, Massachusetts, New

Hampshire, New Jersey, Rhode Island, Vermont, and the

District of Columbia.

Selection of Sample Primary Sampling Units

Each SR PSU is in the sample by definition. As shown in

Table 3–1, there are 432 SR PSUs. In each of the

remaining 360 NSR strata, one PSU is selected for the

sample following the guidelines described next.

At each sample redesign of the CPS, it is important to

minimize the cost of introducing a new set of PSUs.

Substantial investment has been made in the hiring and

training of field representatives in the existing sample

PSUs. For each PSU dropped from the sample and

replaced by another in the new sample, the expense of

hiring and training a new field representative must be

accepted. Furthermore, there is a temporary loss in accuracy of the results produced by new and relatively inexperienced field representatives. Concern for these factors is

reflected in the procedure used for selecting PSUs.

Objectives of the selection procedure. The selection of

the sample of NSR PSUs is carried out within the strata

using the 1990 population. The selection procedure accomplishes the following objectives:

1. Select one sample PSU from each stratum with probability proportional to the 1990 population.

2. Retain in the new sample the maximum number of

sample PSUs from the 1980 design sample.

Using the Maximum Overlap procedure described in

Appendix A, one PSU is selected per stratum with probability proportional to its 1990 population. This procedure

uses mathematical programming techniques to maximize

the probability of selecting PSUs that are already in sample

while maintaining the correct overall probabilities of selection.

Calculation of overall state sampling interval. After

stratifying the PSUs within the states, the overall sampling

interval in each state is computed. The overall state

sampling interval is the inverse of the probability of selection of each housing unit in a state for a self-weighting

design. By design, the overall state sampling interval is

fixed, but the state sample size is not fixed allowing growth

of the CPS sample because of housing units built after the

1990 census. (See Appendix C for details on how the

desired sample size is maintained.)

The state sampling interval is designed to meet the

requirements for the variance on an estimate of the unemployment level. This variance can be thought of as a sum of

variances from the first stage and the second stage of

sample selection.6 The first-stage variance is called the

between-PSU variance and the second-stage variance is

called the within-PSU variance. The square of the state CV,

or the relative variance, on the unemployment level is

expressed as

CV2  b

2  w2

Ex

2 3.1

where

b

2 = between-PSU variance contribution to

the variance of the state unemployment

level estimator.

w

2 = within-PSU variance contribution to the

variance of the state unemployment level

estimator.

E(x) = the expected value of the unemployment

level for the state.

The term, w

2 , can be written as the variance assuming a

binomial distribution from a simple random sample multiplied by a design effect

w

2  N2 p q deff

n

where

N = the civilian noninstitutional population, 16

years of age and older (CNP16+), for the

state.

p = proportion of unemployed in the CNP16+

for the state, or x

N.

Substituting.

q = 1 – p.

n = the state sample size.

deff = the state within-PSU design effect. This is a factor

accounting for the difference between the variance calculated from a multistage stratified sample

and that from a simple random sample.

This formula can be rewritten as

w

2  SI x q deff 3.2

where

SI = the state sampling interval, or N

n.

Substituting (3.2) into (3.1) and rewriting in terms of the

state sampling interval gives

SI  CV2

x2  b

2

x q deff

6

The variance of an estimator, u, based on a two-stage sample has the

general form

Varu  VarIEIIu set of sample PSUs  EIVarIIu set of sample PSUs

where I and II represent the first and second stage designs, respectively.

The left term represents the between-PSU variance,b

2

. The right term

represents the within-PSU variance, w

2 .

3–5
Table 3–1. Number and Estimated Population of Strata for 792-PSU Design by State

State

Self-representing (SR) Nonself-representing (NSR)

Overall

sampling

Number of strata interval

Estimated

population1 Number of strata

Estimated

population1

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 141,876,295 360 45,970,646 2,060

Alabama .......................................... 4 1,441,118 10 1,604,901 2,298

Alaska ............................................ 5 287,192 5 91,906 336

Arizona ........................................... 3 2,188,880 4 544,402 2,016

Arkansas . ........................................ 4 612,369 13 1,151,315 1,316

California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 20,866,697 5 1,405,500 2,700

Los Angeles ..................................... 1 6,672,035 0 0 1,691

Remainder of California. . . . . . . . . . . . . . . . . . . . . . . . . . . 20 14,194,662 5 1,405,500 3,132

Colorado.......................................... 5 1,839,521 5 629,640 1,992

Connecticut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2,561,010 0 0 2,307

Delaware ......................................... 3 509,330 0 0 505

District of Columbia ................................ 1 486,083 0 0 356

Florida. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 9,096,732 8 1,073,818 2,176

Georgia. . ......................................... 9 2,783,587 9 2,038,631 3,077

Hawaii ............................................ 3 778,364 1 49,720 769

Idaho . . ........................................... 8 416,284 8 302,256 590

Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 6,806,874 12 1,821,548 1,810

Indiana ........................................... 9 2,215,745 8 1,949,996 3,132

Iowa. ............................................. 5 710,910 11 1,372,871 1,582

Kansas . . . ........................................ 3 920,819 9 906,626 1,423

Kentucky.......................................... 9 1,230,062 9 1,546,770 2,089

Louisiana ......................................... 7 1,638,220 9 1,403,969 2,143

Maine ............................................ 8 685,937 4 247,682 838

Maryland. ......................................... 4 3,310,546 2 352,766 3,061

Massachusetts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4,719,188 0 0 954

Michigan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 5,643,817 11 1,345,074 1,396

Minnesota......................................... 4 2,137,810 7 1,119,797 2,437

Mississippi ........................................ 6 540,351 14 1,334,898 1,433

Missouri .......................................... 7 2,394,137 6 1,457,616 3,132

Montana .......................................... 8 366,320 8 221,287 431

Nebraska ......................................... 2 557,203 9 608,513 910

Nevada ........................................... 4 819,424 2 98,933 961

New Hampshire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 846,029 0 0 857

New Jersey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 6,023,359 0 0 1,221

New Mexico . . . .................................... 6 685,543 8 410,929 867

New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 12,485,029 7 1,414,548 1,709

New York City . . . ................................ 1 5,721,495 0 0 1,159

Remainder of New York . . . . . . . . . . . . . . . . . . . . . . . . . . 12 6,763,534 7 1,414,548 2,093

North Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3,089,176 23 1,973,074 1,095

North Dakota ...................................... 4 232,865 9 235,364 363

Ohio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 6,238,600 13 1,958,138 1,653

Oklahoma......................................... 2 1,244,920 11 1,092,513 1,548

Oregon ........................................... 4 1,396,261 6 761,794 1,904

Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 7,704,963 11 1,507,946 1,757

Rhode Island ...................................... 5 784,090 0 0 687

South Carolina .................................... 7 1,434,621 7 1,161,298 2,291

South Dakota. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 211,146 11 291,546 376

Tennessee . . ...................................... 8 2,502,671 6 1,219,915 3,016

Texas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 8,779,997 20 3,613,170 2,658

Utah. ............................................. 2 888,524 3 251,746 958

Vermont . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 428,263 0 0 410

Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3,093,947 6 1,612,667 3,084

Washington ....................................... 5 2,437,454 6 1,216,557 2,999

West Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 803,797 9 581,544 896

Wisconsin. . ....................................... 7 1,773,109 9 1,889,141 2,638

Wyoming.......................................... 8 227,401 6 98,321 253

1

Estimate of civilian noninstitutional population 16 years of age and older based on preliminary 1990 census counts.

3–6
Generally, this overall state sampling interval is used for

all strata in a state yielding a self-weighting state design.

(In some states, the sampling interval is adjusted in certain

strata to equalize field representative workloads.)

When computing the sampling interval for the current

CPS sample, a 6 percent state unemployment rate is

assumed for 1995. The results are given in Table 3–1,

which was provided earlier.

SECOND STAGE OF THE SAMPLE DESIGN

The second stage of the CPS sample design is the

selection of sample housing units within PSUs. The objectives of within-PSU sampling are to:

1. Select a probability sample that is representative of the

total civilian, noninstitutional population.

2. Give each housing unit in the population one and only

one chance of selection, with virtually all housing units

in a state having the same overall chance of selection.

3. For the sample size used, keep the within-PSU variance on labor force statistics (in particular, unemployment) at as low a level as possible, subject to response

burden, costs, and other constraints.

4. Select enough within-PSU sample for additional samples

that will be needed before the next decennial census.

5. Put particular emphasis on providing reliable estimates of monthly levels and change over time of labor

force items.

USUs are the sample units selected during the second

stage of the CPS sample design. As discussed earlier in

this chapter, most USUs consist of a geographically compact cluster of approximately four addresses, corresponding to four housing units at the time of the census. Use of

housing unit clusters lowers travel costs for field representatives. Clustering slightly increases within-PSU variance

of estimates for some labor force characteristics since

respondents within a compact cluster tend to have similar

labor force characteristics.

Overview of Sampling Sources

To accomplish the objectives of within-PSU sampling,

extensive use is made of data from the 1990 Decennial

Census of Population and Housing and the Building Permit

Survey. The 1990 census collected information on all living

quarters existing as of April 1, 1990, including characteristics of living quarters as well as the demographic composition of persons residing in these living quarters. Data on

the economic well-being and labor force status of individuals were solicited for about 1 in 6 housing units. However,

since the census does not cover housing units constructed

since April 1, 1990, a sample of building permits issued in

1990 and later is used to supplement the census data.

These data are collected via the Building Permit Survey,

which is an ongoing survey conducted by the Census

Bureau. Therefore, a list sample of census addresses,

supplemented by a sample of building permits, is used in

most of the United States. However, where city-type street

addresses from the 1990 census do not exist, or where

residential construction does not need or require building

permits, area samples are sometimes necessary. (See the

next section for more detail on the development of the

sampling frames.)

These sources provide sampling information for numerous demographic surveys conducted by the Census Bureau.7

In consideration of respondents, sampling methodologies

are coordinated among these surveys to ensure a sampled

housing unit is selected for one survey only. Consistent

definition of sampling frames allows for the development of

separate, optimal sampling schemes for each survey. The

general strategy for each survey is to sort and stratify all

the elements in the sampling frame (eligible and not

eligible) to satisfy individual survey requirements, select a

systematic sample, and remove the selected sample from

the frame. Sample is selected for the next survey from what

remains. Procedures are developed to determine eligibility

of sample cases at the time of interview for each survey.

This coordinated sampling approach is computer intensive

and was not possible in previous redesigns.8

Development of Sampling Frames

Results from the 1990 census, the Building Permit

Survey, and the relationship between these two sources

are used in developing sampling frames. Four frames are

created: the unit frame, the area frame, the group quarters

frame, and the permit frame. The unit, area, and group

quarters frames are collectively called old construction. To

describe frame development methodology, several terms

must be defined.

Two types of living quarters were defined for the census.

The first type is a housing unit. A housing unit is a group of

rooms or a single room occupied as a separate living

quarter or intended for occupancy as a separate living

quarter. A separate living quarter is one in which the

occupants live and eat separately from all other persons on

the property and have direct access to their living quarter

7

CPS sample selection was coordinated with the following demographic surveys in the 1990 redesign: the American Housing Survey -

Metropolitan Sample, the American Housing Survey - National sample,

the Consumer Expenditure Survey - Diary sample, the Consumer Expenditure Survey - Quarterly sample, the Current Point of Purchase Survey,

the National Crime Victimization Survey, the National Health Interview

Survey, the Rent and Property Tax Survey, and the Survey of Income and

Program Participation. 8

This sampling strategy is unbiased because if a random selection is

removed from a frame, the part of the frame that remains is a random

subset. Also, the sample elements selected and removed from each

frame for a particular survey have similar characteristics as the elements

remaining in the frame.

3–7
from the outside or through a common hall or lobby as

found in apartment buildings. A housing unit may be

occupied by a family or one person, as well as by two or

more unrelated persons who share the living quarter. About

98 percent of the population counted in the 1990 census

resided in housing units.

The second type of living quarter is a group quarters. A

group quarters is a living quarter where residents share

common facilities or receive formally authorized care.

Examples include college dormitories, retirement homes,

and communes. For some group quarters, such as fraternity and sorority houses and certain types of group houses,

a group quarters is distinguished from a housing unit if it

houses ten or more unrelated people. The group quarters

population is classified as institutional or noninstitutional

and as military or civilian. CPS targets only the civilian

noninstitutional population residing in group quarters. Military and institutional group quarters are included in the

group quarters frame and given a chance of selection in

case of conversion to civilian noninstitutional housing by

the time it is scheduled for interview. Less than 2 percent of

the population counted in the 1990 census resided in group

quarters.

Old Construction Frames

Old construction consists of three sampling frames: unit,

area, and group quarters. The primary objectives in constructing the three sampling frames are maximizing the use

of census information to reduce variance of estimates,

ensuring adequate coverage, and minimizing cost. The

sampling frames used in a particular geographic area take

into account three major address features:

1. Type of living quarters — housing units or group

quarters.

2. Completeness of addresses — complete or incomplete.

3. Building permit office coverage — covered or not

covered.

An address is considered complete if it describes a

specific location; otherwise, the address is considered

incomplete. (When the 1990 census addresses cannot be

used to locate sample units, area listings must be performed in those areas before sample units can be selected

for interview. See Chapter 4 for more detail.) Examples of

a complete address are city delivery types of mailing

addresses composed of a house number, street name, and

possibly a unit designation, such as ‘‘1599 Main Street’’ or

‘‘234 Elm Street, Apartment 601.’’ Examples of incomplete

addresses are addresses composed of postal delivery

information without indicating specific locations, such as

‘‘PO Box 123’’ or ‘‘Box 4’’ on a rural route. Housing units in

complete blocks covered by building permit offices are

assigned to the unit frame. Group quarters in complete

blocks covered by building permit offices are assigned to

the group quarters frame. Other blocks are assigned to the

area frame.

Unit frame. The unit frame consists of housing units in

census blocks that contain a very high proportion of

complete addresses and are essentially covered by building permit offices. The unit frame covers most of the

population. (Although building permit offices cover nearly

all blocks in the unit frame, a few exceptions may slightly

compromise CPS coverage of the target population (see

Chapter 16)). A USU in the unit frame consists of a

compact cluster of four addresses, which are identified

during sample selection. The addresses, in most cases,

are those for separate housing units. However, over time

some buildings may be demolished or converted to nonresidential use, and others may be split up into several

housing units. These addresses remain sample units,

resulting in a small variability in cluster size. Also, USUs

usually cover neighboring housing units, though, occasionally they are dispersed across a neighborhood, resulting in

a USU with housing units from different blocks.

Area frame. The area frame consists of housing units and

group quarters in census blocks that contain a high proportion of incomplete addresses, or are not covered by

building permit offices. A CPS USU in the area frame also

consists of about four housing unit equivalents, except in

some areas of Alaska that are difficult to access where a

USU is eight housing unit equivalents. The area frame is

converted into groups of four housing unit equivalents

called ‘‘measures’’ because the census addresses of individual housing units or persons within a group quarters are

not used in the sampling.

An integer number of area measures is calculated at the

census block level. The number is referred to as the area

block measure of size (MOS) and is calculated as follows:

area block MOS  H

4  GQ block MOS 3.3

where

H = the number of housing units enumerated in the block for the 1990 census.

GQ block MOS = the integer number of group quarters

measures in a block (see equation

3.4).

The first term of equation (3.3) is rounded to the nearest

nonzero integer. When the fractional part is 0.5 and the

term is greater than 1, it is rounded to the nearest even

integer.

Sometimes census blocks are combined with geographically nearby blocks before the area block MOS is calculated. This is done to ensure that newly constructed units

3–8
have a chance of selection in blocks with no housing units

or group quarters at the time of the census and that are not

covered by a building permit office. This also reduces the

sampling variability caused by USU size differing from four

housing unit equivalents for small blocks with fewer than

four housing units.

Depending on whether or not a block is covered by a

building permit office, area frame blocks are classified as

area permit or area nonpermit. No distinction is made

between area permit and area nonpermit blocks during

sampling. Field procedures are developed to ensure proper

coverage of housing units built after the 1990 census in the

area blocks to (1) prevent these housing units from having

a chance of selection in area permit blocks and (2) give

these housing units a chance of selection in area nonpermit blocks. These field procedures have the added benefit

of assisting in keeping USU size constant as the number of

housing units in the block increases because of new

construction.

Group quarters frame. The group quarters frame consists

of group quarters in census blocks that contain a sufficient

proportion of complete addresses and are essentially covered by building permit offices. Although nearly all blocks

are covered by building permit offices, some are not, which

may result in minor undercoverage. The group quarters

frame covers a small proportion of the population. A CPS

USU in the group quarters frame consists of four housing

unit equivalents. The group quarters frame, like the area

frame, is converted into housing unit equivalents because

1990 census addresses of individual group quarters or

persons within a group quarters are not used in the

sampling. The number of housing unit equivalents is computed by dividing the 1990 census group quarters population by the average number of persons per household

(calculated from the 1990 census as 2.63).

An integer number of group quarters measures is calculated at the census block level. The number of group

quarters measures is referred to as the GQ block MOS and

is calculated as follows:

GQ block MOS  NIGQPOP

42.63  MIL  IGQ 3.4

where

NIGQPOP = the noninstitutional group quarters population in the block from the 1990 census.

MIL = the number of military barracks in the

block from the 1990 census.

IGQ = 1 if one or more institutional group quarters are in the block or 0 if no institutional

group quarters are in the block from the

1990 census.

The first term of equation (3.4) is rounded to the nearest

nonzero integer. When the fractional part is 0.5 and the

term is greater than 1, it is rounded to the nearest even

integer.

Only the civilian noninstitutional population is interviewed for CPS. Military barracks and institutional group

quarters are given a chance of selection in case group

quarters convert status over the decade. A military barrack

or institutional group quarters is equivalent to one measure

regardless of the number of people counted there in the

1990 census.

Special situations in old construction. During development of the old construction frames, several situations are

given special treatment. Military and national park blocks

are treated as if covered by a building permit office to

increase the likelihood of being in the unit or group quarters

frames to minimize costs. Blocks in American Indian Reservations are treated as if not covered by a building permit

office and are put in the area frame to improve coverage.

To improve coverage of newly constructed college housing,

special procedures are used so blocks with existing college

housing and small neighboring blocks are in the area

frame. Blocks in Ohio which are covered by building permit

offices that issue permits for only certain types of structures

are treated as area nonpermit blocks. Two examples of

blocks excluded from sampling frames are blocks consisting entirely of docked maritime vessels where crews reside

and street locations where only homeless people were

enumerated in the 1990 census.

The Permit Frame

Permit frame sampling ensures coverage of housing

units built since the 1990 census. The permit frame grows

as building permits are issued during the decade. Data

collected by the Building Permit Survey are used to update

the permit frame monthly. About 92 percent of the population lives in areas covered by building permit offices.

Housing units built since the 1990 census in areas of the

United States not covered by building permit offices have a

chance of selection in the nonpermit portion of the area

frame. Group quarters built since the 1990 census are

generally not covered in the permit frame, although the

area frame does pick up new group quarters. (This minor

undercoverage is discussed in Chapter 16.)

A permit measure which is equivalent to a CPS USU is

formed within a permit date and a building permit office

resulting in a cluster containing an expected four newly

built housing units. The integer number of permit measures

is referred to as the BPOMOS and is calculated as follows:

BPOMOSt  HPt

4 3.5

where

HPt = the total number of housing units for which the

building permit office issues permits for a time

period, t, normally a month; for example, a building permit office issued 2 permits for a total 24

housing units to be built in month t.

3–9
BPOMOS for time period t is rounded to the nearest integer

except when nonzero and less than 1, then it is rounded to

1. Permit cluster size varies according to the number of

housing units for which permits are actually issued. Also,

the number of housing units for which permits are issued

may differ from the number of housing units that actually

get built.

When developing the permit frame, an attempt is made

to ensure inclusion of all new housing units constructed

after the 1990 census. To do this, housing units for which

building permits had been issued but which had not yet

been constructed by the time of the census should be

included in the permit frame. However, by including permits

issued prior to the 1990 census in the permit frame, there

is a risk that some of these units will have been built by the

time of the census and, thus, included in the old construction frame. These units will then have two chances of

selection in the CPS: one in the permit frame and one in the

old construction frames.

For this reason, permits issued too long before the

census should not be included in the permit frame. However, excluding permits issued long before the census

brings the risk of excluding units for which permits were

issued but which had not yet been constructed by the time

of the census. Such units will have no chance of selection

in the CPS, since they are not included in either the permit

or old construction frames. In developing the permit frame,

an attempt is made to strike a reasonable balance between

these two problems.

Summary of Sampling Frames

Before providing a summary of the various sampling

frames, an exception is noted. Census blocks containing

sample selected by the National Health Interview Survey

(NHIS) were also included in the area frame to ensure a

housing unit was in sample for only one demographic

survey. That is, any sample (both housing unit and group

quarters) selected for the NHIS was transferred to the area

frame. Therefore, group quarters were in both the area and

group quarters frames. The NHIS had an all area frame

sample design because it was not conducted under Title

13; thus, it was prohibited from selecting a sample of 1990

census addresses.

Table 3–2 summarizes the features of the sampling

frames and CPS USU size discussed above. Roughly 65

percent of the CPS sample is from the unit frame, 30

percent is from the area frame, and 1 percent is from the

group quarters frame. In addition, about 5 percent of the

sample is from the permit frame initially. The permit frame

has grown, historically, about 1 percent a year. Optimal

cluster size or USU composition differs for the demographic surveys. The unit frame allows each survey a

choice of cluster size. For the area, group quarters, and

permit frames, MOS must be defined consistently for all

demographic surveys.

Selection of Sample Units

The CPS sample is designed to be self-weighting by

state or substate area. A systematic sample is selected

from each PSU at a sampling rate of 1 in k, where k is the

within-PSU sampling interval which is equal to the product

of the PSU probability of selection and the stratum sampling interval. The stratum sampling interval is usually the

overall state sampling interval. (See the earlier section in

this chapter, ‘‘Calculation of overall state sampling interval.’’)

The first stage of selection is conducted independently

for each demographic survey involved in the 1990 redesign. Sample PSUs overlap across surveys and have

different sampling intervals. To make sure housing units get

selected for only one survey, the largest common geographic areas obtained when intersecting each survey’s

sample PSUs are identified. These intersecting areas, as

well as the residual areas of those PSUs, are called basic

PSU components (BPCs). A CPS stratification PSU consists of one or more BPCs. For each survey, a within-PSU

sample is selected from each frame within BPCs. However,

sampling by BPCs is not an additional stage of selection.

After combining sample from all frames for all BPCs in a

PSU, the resulting within-PSU sample is representative of

the entire civilian, noninstitutional population of the PSU.

When CPS is not the first survey to select a sample in a

BPC, the CPS within-PSU sampling interval is decreased

to maintain the expected CPS sample size after other

surveys have removed sampled USUs. When a BPC does

not include enough sample to support all surveys present

in the BPC for the decade, each survey proportionally

reduces its expected sample size for the BPC. This makes

a state no longer self-weighting, but this adjustment is rare.

Table 3–2. Summary of Sampling Frames

Frame Typical characteristics

of frame CPS USU

Unit frame ........... High percentage of

complete addresses in

areas covered by a

building permit office

Compact cluster of four

addresses

Group quarters frame . High percentage of

complete addresses in

areas covered by a

building permit office

Measure containing

group quarters of four

expected housing unit

equivalents

Area frame

Area permit. ....... Many incomplete

addresses in areas

covered by a building

permit office

Measure containing

housing units and

group quarters of

four expected housing

Area nonpermit . . . . Not covered by a unit equivalents

building permit office

Permit frame. ........ Housing units built

since 1990 census in

areas covered by a

building permit office

Cluster of four expected

housing units

3–10
CPS sample is selected separately within each sampling

frame. Since sample is selected at a constant overall rate,

the percentage of sample selected from each frame is

proportional to population size. Although the procedure is

the same for all sampling frames, old construction sample

selection is performed once for the decade while permit

frame sample selection is an ongoing process each month

throughout the decade.

Within-PSU Sort

Units or measures are arranged within sampling frames

based on characteristics of the 1990 census and geography. Sorting minimizes within-PSU variance of estimates

by grouping together units or measures with similar characteristics. The 1990 census data and geography are used

to sort blocks and units. (Sorting is done within BPCs since

sampling is performed within BPCs.) The unit frame is

sorted on block level characteristics, keeping housing units

in each block together, and then by a housing unit identification to sort the housing units geographically. Sorts are

different for each frame and are provided in Tables 3–3a

and 3–3b.

General Sampling Procedure

The CPS sampling is a one-time operation that involves

selecting enough sample for the decade. To accommodate

the CPS rotation system and the phasing in of new sample

designs, 19 samples are selected. A systematic sample of

USUs is selected and 18 adjacent sample USUs identified.

The group of 19 sample USUs is known as a hit string. Due

to the sorting variables, persons residing in USUs within a

hit string are likely to have similar labor force characteristics.

The within-PSU sample selection is performed independently by BPC and frame. Four dependent random numbers (one per frame) between 0 and 1 are calculated for

each BPC within a PSU.9 Random numbers are used to

calculate random starts. Random starts determine the first

sampled USU in a BPC for each frame.

The method used to select systematic samples of hit

strings of USUs within each BPC and sampling frame

follows:

1. Units or measures within the census blocks are sorted

using the within-PSU sort criteria specified in Tables

3–3a and 3–3b.

2. Each successive USU not selected by another survey

is assigned an index number 1 through N.

3. A random start (RS) for the BPC/frame is calculated.

RS is the product of the dependent random number

and the adjusted within-PSU sampling interval (SIw).

4. Sampling sequence numbers are calculated. Given N

USUs, sequence numbers are:

RS, RS  1SIw, RS  2SIw, ..., RS  nSIw

where n is the largest integer such that RS + (n(SIw)) ≤ N.

Sequence numbers are rounded up to the next integer.

Each rounded sequence number represents the first

unit or measure designating the beginning of a hit

string.

5. Sequence numbers are compared to the index numbers assigned to USUs. Hit strings are assigned to

sequence numbers. The USU with the index number

matching the sequence number is selected as the first

9

Random numbers are evenly distributed by frame within BPC and by

BPC within PSU to minimize variability of sample size.

Table 3–3a. Old Construction Within-PSU Sorts

Sort order

Unit and area frames Group quarters frame

Urban PSUs Rural PSUs All PSUs

1.......... Block CBUR classification1 Block CBUR classification District Office

2.......... Proportion of minority renter occupied housing

units to all occupied housing units in the block

Proportion of households headed by females to

all households in the block

Address Register Area

3.......... Proportion of owner occupied housing units to

total population age 16 years and over

Proportion of minority population to total population in the blockCounty code

4.......... Proportion of housing units to population age 16

years and over in the block

Proportion of population age 65 and over to total

population in the block

MCD/CCD code

5.......... Proportion of households headed by females to

all households in the block

Proportion of Black renter occupied housing units

to total Black population in the block

Block CBUR classification

6.......... County code County code Block number

7.......... Tract number Tract number

8.......... Combined block number (area frame only) Combined block number (area frame only)

9.......... Housing unit ID (unit frame only) Housing unit ID (unit frame only)

1

A census block is classified as C, B, U, or R, which means: central city of 1990 MSA (C); balance of 1990 urbanized area (B); other urban (U); or rural

(R).

3–11
sample. The 18 USUs that follow the sequence number are selected as the next 18 samples. This method

may yield hit strings with less than 19 samples (called

incomplete hit strings) at the beginning or end of

BPCs.10 Allowing incomplete hit strings ensures that

each USU has the same probability of selection.

6. A sample designation uniquely identifying 1 of the 19

samples is assigned to each USU in a hit string. For

the 1990 design, sample designations A62 through

A80 are assigned sequentially to the hit string. A62 is

assigned to the first sample; A63 to the second sample;

and assignment continues through A80 for the nineteenth sample. A sample designation suffix, A or B, is

assigned in areas of Alaska that are difficult to access

(in which USUs consist of eight housing unit equivalents).

Example of Within-PSU Sample Selection for

Old Construction

The following example illustrates selection of withinPSU sample for an old construction frame within a BPC.

Assume blocks have been sorted within a BPC. The BPC

contains 18 unsampled USUs (N=18) and each USU is

assigned an index number (1, 2, ..., 18). The dependent

random number is 0.6528 and the SIw is 5.7604. To simplify

this example, four samples are selected and sample designations A1 through A4 assigned.

The random start is RS = 0.6528 x 5.7604 = 3.7604.

Sequence numbers are 3.7604, 9.5208, and 15.2812,

rounding up to 4, 10, and 16. These sequence numbers

represent first samples and correspond to index numbers

assigned to USUs. A hit string is assigned to each sequence

number to obtain the remaining three samples:

4, 10, 16 (first sample),

5, 11, 17 (second sample),

6, 12, 18 (third sample), and

7, 13, 111 (fourth sample).

This example includes an incomplete hit string at the

beginning and end of the BPC. After sample selection,

corresponding sample designations are assigned. Table

3–4 illustrates results from sample selection.

Although this example is for old construction, selecting a

systematic sample for the permit frame is similar except

that sampling is performed on an imaginary universe

(called a skeleton universe) consisting of an estimated

number of USUs within each BPC. As monthly permit

information becomes available, the skeleton universe gradually fills with issued permits and eventually specific sample

addresses are identified.

Table 3–4. Sampling Example Within a BPC for Any

of the Three Old Construction Frames

Census block

USU

number

within the

block

Index

number

Sample

designation

101 101A . . . . . . . . . . . . . . . . . . . . . . 1 1 A4

2 2

3 3

4 4 A1

103 104 . . . . . . . . . . . . . . . . . . . . . . . 1 5 A2

2 6 A3

3 7 A4

4 8

5 9

6 10 A1

7 11 A2

8 12 A3

106 . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 13 A4

107 107A . . . . . . . . . . . . . . . . . . . . . . 1 14

2 15

3 16 A1

108 108D . . . . . . . . . . . . . . . . . . . . . . 1 17 A2

2 18 A3

Assignment of Post-Sampling Codes

Two types of post-sampling codes are assigned to the

sampled units. First, there are the CPS technical codes

used to weight the data, estimate the variance of characteristics, and identify representative subsamples of the

CPS sample units. The technical codes include final hit

number, rotation group, and random group codes. Second,

there are operational codes common to the demographic

household surveys used to identify and track the sample

units through data collection and processing. The operational codes include field PSU, segment number and

segment number suffix.

Final hit number. The final hit number identifies the original within-PSU order of selection. All USUs in a hit string

are assigned the same final hit number. For each PSU, this

code is assigned sequentially starting with one for both the

old construction and the permit frames. The final hit

number is used in the application of the CPS variance

estimation method discussed in Chapter 14.

Rotation group. Sample is partitioned into eight representative subsamples called rotation groups used in the CPS

rotation scheme. All USUs in a hit string are assigned to the

same rotation group. Assignment is performed separately

10When RS + I > SIw, an incomplete hit string occurs at the beginning

of a BPC. When (RS + I) + (n( SIw)) > N, an incomplete hit string occurs

at the end of a BPC (I = 1 to 18). 11Since 3.7604 + I > 5.7604 (RS + I > SIw) where I = 3, an incomplete

hit string occurs at the beginning of the BPC. The sequence number is

calculated as RS + I - SIw.

Table 3–3b. Permit Frame Within-PSU Sort

Sort order All PSUs

1..................................... County code

2..................................... Building permit office

3..................................... Permit date

3–12
for old construction and the permit frame. Rotation groups

are assigned after sorting hits by state, MSA/non-MSA

status (old construction only), SR/NSR status, stratification

PSU, and final hit number. Because of this sorting, the

eight subsamples are balanced across stratification PSUs,

states, and the nation. Rotation group is used in conjunction with sample designation to determine units in sample

for particular months during the decade.

Random group. Sample is partitioned into ten representative subsamples called random groups. All USUs in the

hit string are assigned to the same random group. Assignment is performed separately for old construction and the

permit frame. Since random groups are assigned after

sorting hits by state, stratification PSU, rotation group, and

final hit number, the ten subsamples are balanced across

stratification PSUs, states, and the Nation. Random groups

can be used to partition the sample into test and control

panels for survey research.

Field PSU. A field PSU is usually a single county within a

stratification PSU, except in the New England states and

part of Hawaii where a field PSU is a group of minor civil

divisions. Field PSU definitions are consistent across all

demographic surveys and are more useful than stratification PSUs for coordinating field representative assignments among demographic surveys.

Segment number. A segment number is assigned to each

USU within a hit string. If a hit string consists of USUs from

only one field PSU, then the segment number applies to

the entire hit string. If a hit string consists of USUs in

different field PSUs, then each portion of the hit string/field

PSU combination gets a unique segment number. The

segment number is a four-digit code. The first digit corresponds to the rotation group of the hit. The remaining three

digits are sequence numbers. In any 1 month, a segment

within a field PSU identifies one USU or expected four

housing units that the field representative is scheduled to

visit. A field representative’s workload usually consists of a

set of segments within one or more adjacent field PSUs.

Segment number suffix. Adjacent USUs with the same

segment number may be in different blocks for area and

group quarters sample or in different building permit office

dates or ZIP Codes for permit sample, but in the same field

PSU. If so, an alphabetic suffix appended to the segment

number indicates that a hit string has crossed one of these

boundaries. Segment number suffixes are not assigned to

the unit sample.

Examples of Post-Sampling Code Assignments

Two examples are provided to illustrate assignment of

codes. To simplify the examples, only two samples are

selected, and sample designations A1 and A2 are assigned.

The examples illustrate a stratification PSU consisting of all

sampling frames (which often does not occur). Assume the

index numbers (shown in Table 3–5) are selected in two

BPCs.

These sample USUs are sorted and survey design

codes assigned as shown in Table 3–6.

The example in Table 3–6 illustrates that assignment of

rotation group and final hit number is done separately for

old construction and the permit frame. Consecutive numbers are assigned across BPCs within frames. Although

not shown in the example, assignment of consecutive

rotation group numbers (modulo 8) carries across stratification PSUs. For example, the first old construction hit in

the next stratification PSU is assigned to rotation group 1.

However, assignment of final hit numbers is performed

within stratification PSUs. A final hit number of 1 is assigned

to the first old construction hit and the first permit hit of each

stratification PSU. Operational codes are assigned as

shown in Table 3–7.

After sample USUs are selected and post-sampling

codes assigned, addresses are needed in order to interview sampled units. The procedure for obtaining addresses

differs by sampling frame. For operational purposes, identifiers are used in the unit frame during sampling instead of

actual addresses. The procedure for obtaining unit frame

addresses by matching identifiers to census files is described

in Chapter 4. Field procedures, usually involving a listing

operation, are used to identify addresses in other frames. A

description of listing procedures is also given in Chapter 4.

Illustrations of the materials used in the listing phase are

shown in Appendix B.

Table 3–5. Index Numbers Selected During Sampling for Code Assignment Examples

BPC number Unit frame

Group quarters

frame Area frame Permit frame

1....................................................... 3-4, 27-28, 51-52 10-11 1 (incomplete), 32-33 7-8, 45-46

2....................................................... 10-11, 34-35 none 6-7 14-15

3–13
Table 3–6. Example of Postsampling Survey Design Code Assignments Within a PSU

BPC Frame Index

Sample

designation Final hit number Rotation group

1................................................. Unit 3 A1 1 8

1................................................. Unit 4 A2 1 8

1................................................. Unit 27 A1 2 1

1................................................. Unit 28 A2 2 1

1................................................. Unit 51 A1 3 2

1................................................. Unit 52 A2 3 2

1................................................. Group quarters 10 A1 4 3

1................................................. Group quarters 11 A2 4 3

1................................................. Area 1 A2 5 4

1................................................. Area 32 A1 6 5

1................................................. Area 33 A2 6 5

2................................................. Unit 10 A1 7 6

2................................................. Unit 11 A2 7 6

2................................................. Unit 34 A1 8 7

2................................................. Unit 35 A2 8 7

2................................................. Area 6 A1 9 8

2................................................. Area 7 A2 9 8

1................................................. Permit 7 A1 1 3

1................................................. Permit 8 A2 1 3

1................................................. Permit 45 A1 2 4

1................................................. Permit 46 A2 2 4

2................................................. Permit 14 A1 3 5

2................................................. Permit 15 A2 3 5

Table 3–7. Example of Postsampling Operational Code Assignments Within a PSU

BPC County Frame Block Index

Sample

designation

Final hit

number Field PSU

Segment/

suffix

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Unit 1 3 A1 1 1 8999

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Unit 1 4 A2 1 1 8999

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Unit 2 27 A1 2 1 1999

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Unit 2 28 A2 2 2 1999

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Unit 3 51 A1 3 2 2999

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Unit 3 52 A2 3 2 2999

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Group quarters 4 10 A1 4 1 3599

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Group quarters 4 11 A2 4 1 3599

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Area 5 1 A2 5 1 4699

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Area 6 32 A1 6 1 5699

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Area 6 33 A2 6 1 5699

2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Unit 7 10 A1 7 3 6999

2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Unit 7 11 A2 7 3 6999

2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Unit 8 34 A1 8 3 7999

2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Unit 8 35 A2 8 3 7999

2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Area 9 6 A1 9 3 8699

2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Area 10 7 A2 9 3 8699A

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Permit 7 A1 1 1 3001

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Permit 8 A2 1 1 3001

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Permit 45 A1 2 2 4001

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Permit 46 A2 2 2 4001

2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Permit 14 A1 3 3 5001

2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Permit 15 A2 3 3 5001

THIRD STAGE OF THE SAMPLE DESIGN

Often, the actual USU size in the field can deviate from

what is expected from the computer sampling. Occasionally, the deviation is large enough to jeopardize the successful completion of a field representative’s assignment.

When these situations occur, a third stage of selection is

conducted to maintain a manageable field representative

workload. This third stage is called field subsampling.

Field subsampling occurs when a USU consists of more

than 15 sample housing units identified for interview.

Usually, this USU is identified after a listing operation. (See

Chapter 4 for a description of field listing.) The regional

office staff selects a systematic subsample of the USU to

reduce the number of sample housing units to a more

manageable number, from 8 to 15 housing units. To

facilitate the subsampling, an integer take-every (TE) and

start-with (SW) are used. An appropriate value of the TE

3–14
reduces the USU size to the desired range. For example, if

the USU consists of 16 to 30 housing units, a TE of 2

reduces USU size to 8 to 15 housing units. The SW is a

randomly selected integer between 1 and the TE.

Field subsampling changes the probability of selection

for the housing units in the USU. An appropriate adjustment to the probability of selection is made by applying a

special weighting factor in the weighting procedure. See

‘‘Special Weighting Adjustments’’ in Chapter 10.

ROTATION OF THE SAMPLE

The CPS sample rotation scheme is a compromise

between a permanent sample (from which a high response

rate would be difficult to maintain) and a completely new

sample each month (which results in more variable estimates of change). The CPS sample rotation scheme

represents an attempt to strike a balance in the minimization of the following:

1. Variance of estimates of month-to-month change: threefourths of the sample is the same in consecutive

months.

2. Variance of estimates of year-to-year change: one-half

of the sample is the same in the same month of

consecutive years.

3. Variance of other estimates of change: outgoing sample

is replaced by sample likely to have similar characteristics.

4. Response burden: eight interviews are dispersed across

16 months.

The rotation scheme follows a 4-8-4 pattern. A housing

unit or group quarters is interviewed 4 consecutive months,

not in sample for the next 8 months, interviewed the next 4

months, and then retired from sample. The rotation scheme

is designed so outgoing housing units are replaced by

housing units from the same hit string which have similar

characteristics.

The following summarizes the main characteristics (in

addition to the sample overlap described above) of the

CPS rotation scheme:

1. In any 1 month, one-eighth of the sample housing units

are interviewed for the first time; another eighth is

interviewed for the second time; and so on.

2. The sample for 1 month is composed of units from two

or three consecutive samples.

3. One new sample designation-rotation group is activated each month. The new rotation group replaces

the rotation group retiring permanently from sample.

4. One rotation group is reactivated each month after its

8-month resting period. The returning rotation group

replaces the rotation group beginning its 8-month

resting period.

5. Rotation groups are introduced in order of sample

designation and rotation group:

A62(1), A62(2), ..., A62(8), A63(1), A63(2), ..., A63(8),

..., A80(1), A80(2), ..., A80(8).

The present rotation scheme has been used since 1953.

The most recent research into alternate rotation patterns

was prior to the 1980 redesign when state-based designs

were introduced (Tegels, 1982).

The Rotation Chart

The CPS rotation chart illustrates the rotation pattern of

CPS sample over time. Figure 3–1 presents the rotation

chart beginning in January 1996. The following statements

provide guidance in interpreting the chart:

1. Numbers in the chart refer to rotation groups. Sample

designations appear in column headings. In January

1996, rotation groups 3, 4, 5, and 6 of A64; 7 and 8 of

A65; and 1 and 2 of A66 are designated for interview.

2. Consecutive monthly samples have six rotation groups

in common. The sample housing units in A64(4 - 6),

A65(8), and A66(1-2), for example, are interviewed in

January and February of 1996.

3. Monthly samples 1 year apart have four rotation

groups in common. For example, the sample housing

units in A65(7-8) and A66(1-2) are interviewed in

January 1996 and January 1997.

4. Of the two rotation groups replaced from month-tomonth, one is in sample for the first time and one

returns after being excluded for 8 months. For example,

in October 1996, the sample housing units in A67(3)

are interviewed for the first time and the sample

housing units in A65(7) are interviewed for the fifth

time after last being in sample in January.

Overlap of the Sample

Table 3–8 shows the proportion of overlap between any

2 months of sample depending on the time lag between

them. The proportion of sample in common has a strong

effect on correlation between estimates from different

months and, therefore, on variances of estimates of change.

3–15
Table 3–8. Proportion of Sample in Common

for 4-8-4 Rotation System

Interval (in months)

Percent of

sample in

common

between the

2 months

1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4-8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0

9......................................... 12.5

10. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37.5

12. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

13. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37.5

14. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

15. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5

16 and greater............................. 0

Phase-In of a New Design

When a newly redesigned sample is introduced

into the ongoing CPS rotation scheme, there are a

number of reasons not to discard the old CPS

sample one month and replace it with a completely

redesigned sample the next month. Since redesigned sample contains different sample areas, new

field representatives must be hired. Modifications in

survey procedures are usually made for a redesigned sample. These factors can cause discontinuity in estimates if the transition is made at one time.

Instead, a gradual transition from the old sample

design to the new sample design is undertaken.

Beginning in April 1994, the 1990 census-based

design was phased in through a series of changes

completed in July 1995 (U.S. Department of Labor,

1994).

Figure 3–1. CPS Rotation Chart: January 1996 - April 1998

Sample designation and rotation groups

Year/month A64 A65 A66 A67 A68 A69

1996 Jan 3456 78 12

Feb 4567 8 123

Mar 5678 1234

Apr 678 1 2345

May 78 12 3456

June 8 123 4567

July 1234 5678

Aug 2345 678 1

Sept 3456 78 12

Oct 4567 8 123

Nov 5678 1234

Dec 678 1 2345

1997 Jan 78 12 3456

Feb 8 123 4567

Mar 1234 5678

Apr 2345 678 1

May 3456 78 12

June 4567 8 123

July 5678 1234

Aug 678 1 2345

Sept 78 12 3456

Oct 8 123 4567

Nov 1234 5678

Dec 2345 678 1

1998 Jan 3456 78 12

Feb 4567 8 123

Mar 5678 1234

Apr 678 1 2345

3–16
REFERENCES

Executive Office of the President, Office of Management

and Budget (1993), Metropolitan Area Changes Effective With the Office of Management and Budget’s

Bulletin 93-17, June 30, 1993.

Hansen, Morris H., Hurwitz, William N., and Madow,

William G. (1953), Survey Sample Methods and Theory,

Vol. I, Methods and Applications. New York: John Wiley

and Sons.

Hanson, Robert H. (1978), The Current Population

Survey: Design and Methodology, Technical Paper 40,

Washington, DC: Government Printing Office.

Kostanich, Donna, Judkins, David, Singh, Rajendra, and

Schautz, Mindi (1981), ‘‘Modification of Friedman-Rubin’s

Clustering Algorithm for Use in Stratified PPS Sampling,’’

paper presented at the 1981 Joint Statistical Meetings,

American Statistical Association.

Ludington, Paul W. (1992), ‘‘Stratification of Primary

Sampling Units for the Current Population Survey Using

Computer Intensive Methods,’’ paper presented at the

1992 Joint Statistical Meetings, American Statistical Association.

Statt, Ronald, Vacca, E. Ann, Wolters, Charles, and

Hernandez, Rosa (1981), ‘‘Problems Associated With Using

Building Permits as a Frame of Post-Census Construction:

Permit Lag and ED Identification,’’ paper presented at the

1981 Joint Statistical Meetings, American Statistical Association.

Tegels, Robert, and Cahoon, Lawrence (1982), ‘‘The

Redesign of the Current Population Survey: The Investigation Into Alternate Rotation Plans,’’ paper presented at the

1982 Joint Statistical Meetings, American Statistical Association.

U.S. Department of Labor, Bureau of Labor Statistics

(1994), ‘‘Redesign of the Sample for the Current Population

Survey,’’ Employment and Earnings, Washington, DC:

Government Printing Office, May 1994.

3–17
Chapter 4.

Preparation of the Sample

INTRODUCTION

The sample preparation operations have been developed to fulfill the following goals:

1. Implement the sampling procedures described in Chapter 3.

2. Produce virtually complete coverage of the eligible

population.

3. Ensure that only a trivial number of households will

appear in the sample more than once over the course

of a decade, or will appear in more than one of the

household surveys conducted by the Bureau of the

Census.

4. Provide cost efficient collection by producing most of

the sampling materials needed for both the Current

Population Survey (CPS) and other household surveys in a single, integrated operation.

The CPS is one of many household surveys conducted

on a regular basis by the Census Bureau. Insofar as

possible, Census Bureau programs have been designed

so that survey materials, survey procedures, personnel,

and facilities can be used by as many surveys as possible.

Sharing personnel and sampling material among a number

of programs yields obvious savings. For example, CPS

field representatives can be (and often are) employed on

non-CPS activities because the sampling materials, listing

and coverage instructions, and to a lesser extent, questionnaire content are similar for a number of different

programs. In addition, the sharing of sampling materials

makes it possible to keep respondents from being in more

than one sample.

The postsampling codes described in Chapter 3 identify,

among other information, the sample cases that are scheduled to be interviewed for the first time in each month of the

decade and indicate the types of materials (maps, listing of

addresses, etc.) needed by the census field representative

to locate the sample addresses. This chapter describes

how these materials are put together.

This chapter may occasionally indulge in detail beyond

the interest of the general reader. The next section is an

overview that is recommended for all readers. Subsequent

sections provide a more in-depth description of the CPS

sample preparation and will prove useful to those interested in greater detail.

Census Bureau headquarters are located in the Washington, DC area. Staff at headquarters coordinate CPS

functions ranging from sample design, sample selection,

and resolution of subject matter issues to administration of

the interviewing staffs maintained under the 12 regional

offices and data processing. Census Bureau staff located

in Jeffersonville, IN also participate in CPS planning and

administration. Their responsibilities include preparation

and dissemination of interviewing materials, such as maps

and segment folders. Nonetheless, the successful completion of the CPS data collection operations rests on the

combined efforts of all Census Bureau offices.

Monthly sample preparation of the CPS has three major

components:

1. Identifying addresses.

2. Listing living quarters.

3. Assigning sample to field representatives.

The within-PSU sample described in Chapter 3 is selected

from four distinct sampling frames, not all of which consist

of specific addresses. Since the field representatives need

to know the exact location of the households or group

quarters they are going to interview, much of sample

preparation involves the conversion of selected sample

(e.g., maps, lists of building permits) to a set of addresses.

This conversion is described below.

Address Identification in the Unit Frame

About 65 percent of the CPS sample is selected from the

unit frame. The unit frame sample is selected from a 1990

census file that contains the information necessary for

within-PSU sample selection, but does not contain address

information. The address information from the 1990 census

is stored in a separate file. The addresses of unit segments

are obtained by matching the file of 1990 census information to the file containing the associated 1990 census

addresses. This is a one-time operation for the entire unit

sample and is performed at headquarters. If the addresses

are thought to be incomplete (missing a house number or

street name), the 1990 census information is reviewed in

an attempt to complete the address before sending it to the

field representative for interview.

Address Identification in the Area Frame

About 30 percent of the CPS sample is selected from the

area frame. Measures of expected four housing units are

selected during within-PSU sampling instead of selecting

4–1
housing units because the addresses are not city-style or

there is no building permit office coverage. This essentially

means that no particular housing units are as yet associated with the selected measure. The only information

available is a map of the block which contains the area

segment, the number of measures the block contains, and

which measure is associated with the area segment.

Before the individual housing units associated with the

area segment can be identified, additional procedures are

used to ensure that field representatives can locate the

housing units and that all newly built housing units have a

probability of selection. A field representative will be sent to

visit and canvass the block to create a complete list of the

housing units located in the block. This is referred to as a

listing operation which is described more thoroughly in the

next section. A systematic sampling pattern is applied to

this listing to identify the housing units in the area segment

that are designated for each month’s sample.

Systematic sampling pattern. Applying the systematic

sampling pattern to a listing is referred to as a Start-with/Takeevery procedure. Start-with/Take-every’s are calculated at

headquarters based on the first hit measure in the block

and on the number of measures in the block, respectively.

For more information on hits and blocks, see Chapter 3.

The regional offices use the Start-with/Take-every information to identify a sample housing unit to be interviewed. In

simplistic terms, the Start-with identifies the line number on

the listing sheet where the sampling pattern begins; the

Take-every identifies the frequency with which sample

housing units or lines on the listing sheet are selected.

To apply a Start-with/Take-every for a block, the regional

office would begin with the first listing sheet for the block

counting down the number of lines as indicated by the

Start-with. This resulting line number identifies a selected

sample housing unit. Beginning with the next line listed, the

regional office would count down the number of lines equal

to the Take-every. The resulting line identifies the next

selected sample housing unit. The regional office continues

the above approach until the sampling pattern has been

applied to all listed units. For example, a Start-with/Takeevery of 3/4 indicates that when the regional office applies

the sampling pattern, they would start on line three of the

listing sheet and take every fourth line. Suppose the listing

sheet had 12 listed lines, a Start-with/Take-every results in

the units on lines 3, 7, and 11 being selected for interview.

See Figure4-1.

Address Identification in the Group Quarters

Frame

About 1 percent of the CPS sample is selected from the

group quarters frame. The decennial census files did not

have information on the characteristics of the group quarters. The files contain information about the residents as of

April 1, 1990, but there was no information about their living

arrangements within the group quarters. Consequently, the

census information did not provide a tangible sampling unit

for the CPS. Measures were selected during within-PSU

sampling since there was no way to associate the selected

sample cases with persons to interview at a group quarters. A two-step process is used to identify the group

quarters segment. First, the group quarters addresses are

obtained by matching to the file of 1990 census addresses,

similar to the process for the unit frame. This is a one-time

operation done at headquarters. Before the persons living

at the group quarters associated with the group quarters

segment can be identified, an interviewer visits the group

quarters and creates a complete list of eligible sample units

(consisting of persons, rooms, or beds). This is referred to

as a listing operation. Then a systematic sampling pattern,

as described above, is applied to the listing to identify the

individuals at the group quarters in the group quarters

segment.

Address Identification in the Permit Frame

The proportion of the CPS sample selected from the

permit frame keeps increasing over the decade as new

housing units are constructed. The CPS sample redesigns

are introduced about 4 or 5 years after each decennial

census, and at this time the permit sample makes up about

5 percent of the CPS sample; this has historically increased

about 1 percent a year. Hypothetical measures are selected

during within-PSU sampling (skeleton sampling) in anticipation of the construction of new housing units. Identifying

the addresses for these permit measures involves a listing

operation at the building permit office, clustering of addresses

to form measures, and associating these addresses with

the hypothetical measures (or USUs) in sample.

The Census Bureau conducts the Building Permit Survey which collects information on a monthly basis from

each building permit office in the Nation about the number

of housing units authorized to be built. The Building Permit

Survey results are converted to measures of expected four

housing units. These measures are continuously accumulated and linked with the frame of hypothetical measures

used to select the CPS sample. This matching identifies

which building permit office contains the measure that is in

sample. A field representative then visits the building permit

office to obtain a list of addresses of units that were

authorized to be built; this is the Permit Address List (PAL)

operation. This list of addresses is keyed and transmitted to

headquarters where clusters are formed that correspond

one-to-one with the measures. Using this link, the clusters

of four addresses in each permit segment are identified.

Forming clusters. To help ensure some geographic clustering of addresses within permit measures and to make

PAL listing more efficient, information collected by the

4– 2
Figure 4

– 1. Example of Systematic Sampling

4–3
Survey of Construction (SOC)1 is used to identify many of

the addresses in the permit frame. For building permit

offices included in SOC, the Census Bureau collects

information on the characteristics of units to be built for

each permit issued by the building permit office. This

information is used to form measures in SOC building

permit offices. This is not the case for non-SOC building

permit offices.

1. SOC PALs are listings from building permit offices that

are in the SOC. If a building permit office is in the SOC,

then the actual permits issued by the building permit

office and the number of units authorized by each

permit (though not the addresses) are known in advance

of the match to the skeleton universe. Therefore, the

measures for sampling are formed directly from the

actual permits. The sample permits can then be identified. These sample permits are the only ones for

which addresses are collected.

Because measures for SOC permits were constrained to be within permits, once the listed addresses

are complete, the formation of clusters follows easily.

The measures formed at the time of sampling are, in

effect, the clusters. The sample measures within permits are fixed at the time of sampling; that is, there

cannot be any later rearrangement of these units into

more geographically compact clusters without voiding

the original sampling results.

Even without geographic clustering, there is some

degree of compactness inherent in the manner in

which measures are formed for sampling. The units

within the SOC permits are assigned to measures in

the same order in which they were listed for SOC; thus

units within apartment buildings will normally be in the

same clusters, and permits listed on adjacent lines on

the SOC listing often represent neighboring structures.

2. Non-SOC PALs are listings from building permit offices

not in the SOC. At the time of sampling, the only data

known for non-SOC building permit offices is a cumulative count of the units authorized on all permits for an

office for a given month (or year). Therefore, all

addresses for a building permit office/date are collected, together with the number of apartments in

multiunit buildings. The addresses are clustered using

all units on the PAL.

The purpose of clustering is to group units together

geographically, thus enabling a reduction in field travel

costs. For multiunit addresses, as many whole clusters

as possible are created from the units within each

address. The remaining units on the PAL are clustered

within ZIP Code and permit day of issue.

LISTING ACTIVITIES

When address information from the census is not available or the address information from the census no longer

corresponds to the current address situation, then a listing

of all eligible units must be created. Creating this list of

basic addresses is referred to as listing. Listing can occur

in all four frames: units within multiunit structures, living

quarters in blocks, units or residents within group quarters,

and addresses for building permits issued.

The living quarters to be listed are usually housing units.

In group quarters such as transient hotels, rooming houses,

dormitories, trailer camps, etc., where the occupants have

special living arrangements, the living quarters listed may

be housing units, rooms, beds, etc. In this discussion of

listing, all of these living quarters are included in the term

‘‘unit’’ when it is used in context of listing or interviewing.

Completed listings are sampled by regional office staff.

Performing the listing and sampling in two separate steps

allows each step to be verified and allows a more complete

control of sampling to avoid bias in designating the units to

be interviewed.

The units to be interviewed are selected as objectively

as possible to reduce bias. However, the sample rotation

system used in the CPS tends to remove biases that may

have been introduced in the selection process. For example,

if a unit USU is to be defined within a large structure where

units will be assigned to several USUs, the field representative could conceivably manipulate the order of listing of

the housing units to bias the selection for a given USU.

However, as the CPS rotation system eventually replaces

each USU by others made up of units adjacent on the

listing at the address, the unknown error from the selection

bias is replaced by a (measurable) variance.

In order to ensure accurate and complete coverage of

the area and group quarters segments, updating of the

initial listings is done periodically throughout the decade.

The updating ensures that changes such as units missed in

the initial listing, demolished units, residential/commercial

conversions, and new construction are accounted for.

Listing in the Unit Frame

Listing in the unit frame is usually not necessary. The

only time it is done is when the field representative

discovers that the address information from the 1990

census is no longer accurate for a multiunit structure and

the field representative cannot adequately reconcile the

differences.

For multiunit addresses (addresses where the expected

number or unit designations is two or more), the field

representative receives preprinted (computer-generated)

Unit/Permit Listing Sheets (Form 11-3) showing unit designations as recorded in the 1990 census. For multiunits

containing between two and nine units, the listing sheet

displays the unit designations of all units in the structure

1

The Survey of Construction (SOC) is conducted by the U.S. Census

Bureau in conjunction with the Department of Housing and Urban

Development. It provides current regional statistics on starts and completions of new single-family and multifamily units and sales of new onefamily homes.

4– 4
even if some of the units are not in sample. For multiunit

structures with ten or more units, the listing sheet contains

only the unit designations of sample cases. Other important information on the Unit/Permit Listing Sheet includes:

the address, the expected number of units at the address,

the sample designations, and serial numbers for the selected

sample units, and the 1990 census IDs corresponding to

those units. For an example of a Unit/Permit Listing Sheets

see Appendix B.

The first time a multiunit address enters the sample, the

field representative does one or more of the following:

• Verifies that the 1990 census information on the Unit/Permit

Listing Sheet (Form 11-3) is accurate.

• Corrects the listing sheet when it does not agree with

what the field representative finds at the address.

• Relists the address on a blank Unit/Permit Listing Sheet

(only for some large multiunit listings).

After the field representative has an accurate listing

sheet, he/she conducts an interview for each unit that has

a current (preprinted) sample designation. If an address is

relisted, the field representative provides information to the

regional office on the relisting. The regional office staff will

resample the listing sheet and provide the line numbers for

the specific lines on the listing sheet that identify the units

that should be interviewed.

A regular system of updating the listing in unit segments

is not followed; however, the field representative may

correct an in-sample listing during any visit if a change is

noticed. The change may result in additional units being

added or removed from sample.

For single-unit addresses, a preprinted listing sheet is

not provided to the field representative since only one unit

is expected based on the 1990 census information. If a field

representative discovers other units at the address at the

time of interview, these additional units are interviewed.

Listing in the Area Frame

All blocks that contain area frame sample cases must be

listed. Several months before the first area segment in a

block is to be interviewed, a field representative visits the

block to establish a list of living quarters. Units within the

block are recorded on an Area Segment Listing Sheet

(Form 11-5). The heading entries on the form are all filled

by a regional office clerk, and the field representative

records a systematic list of all occupied and vacant housing

units and group quarters within the block boundaries.

If the area segment is within a jurisdiction where building

permits are issued, housing units constructed since April 1,

1990, are eliminated from the area segment through the

year built procedure to avoid giving an address more than

one chance of selection. This is required because housing

units constructed since April 1, 1990, Census Day, are

represented by segments in the permit frame. When there

has been significant growth in housing units since April 1,

1990, it is necessary to determine the year built for every

structure in the segment at the time of listing. To determine

‘‘year built,’’ the field representative inquires at each listed

unit and circles the appropriate code on the listing sheet.

For an example of an Area Segment Listing Sheet see

Appendix B. The inquiry at the time of listing is omitted in

areas with low new construction activity; in such cases,

new construction units are identified later in completing the

coverage questions during the interview. This process

avoids an extra contact at units built before 1990.

If an area segment is not in a building permit issuing

jurisdiction, then housing units constructed after the 1990

census do not have a chance of being selected for interview in the permit frame. The field representative does not

determine ‘‘year built’’ for units in such blocks.

After the listing of living quarters in the area segment

has been completed, the listing forms are returned to the

regional office. A clerk in the regional office then applies the

sampling pattern to identify the units to be interviewed.

(See the Systematic sampling pattern section above.)

To reflect changes in the number of units in each sample

block, it is desirable to periodically review and correct (i.e.,

update) each segment’s listing so that the current USU,

and later USUs to be interviewed, will represent the current

status. The following rule is used: The USU being interviewed for the first time for CPS must be identified from a

listing that has been updated within the last 24 months.

The area listing sheet is updated by retracing the path of

travel of the original lister, verifying the existence of each

unit on the listing sheet, accounting for units no longer in

existence, and appending any unlisted housing units or

other living quarters that are found at the end of the list. By

extending the original sampling pattern, the appended

units are given their chance of being in sample.

Listing in the Group Quarters Frame

Group quarters addresses in the CPS sample are listed

if they are in the group quarters, area, or unit frames.

Group quarters found in the permit frame are not listed.

Before the first interviews at a group quarters address can

be conducted, a field representative visits the group quarters to establish a list of eligible units at the group quarters.

The same group quarters procedures for creating a list of

eligible units applies for group quarters found in the area or

unit frame. The only difference is the time frame in which

the listing is done. If the group quarters is in the group

quarters or area frame, then the listing is done several

months before interview. If the group quarters is discovered

at the address of unit frame sample, then the listing is done

at the time of interview.

Group Quarters Listing Sheets (Form 11-1) are used to

record the group quarters name, group quarters type,

address, the name and telephone number of a contact

person, and to list the eligible units within the group

4– 5
quarters. Institutional and verified military group quarters

are not listed by a field representative. For an example of a

Group Quarters Listing Sheet, see Appendix B.

Each eligible unit of a group quarters is listed on a

separate line of the group quarters listing sheet, regardless

of the number of units. If there is more than one group

quarters within the block, each group quarters must be

listed on a separate listing sheet. More detailed information

on the listing of group quarters can be found in the Listing

and Coverage Manual for Field Representatives. The rule

for the frequency of updating group quarters listings is the

same as for area segments.

Listing in the Permit Frame

There are two phases of listing in the permit frame. The

first is the PAL operation which establishes a list of

addresses authorized to be built by a building permit office.

This is done shortly after the permit has been issued by the

building permit office and associated with a sample hypothetical measure. The second listing is required when the

field representative visits the unit to conduct an interview

and discovers that the original address information collected through the PAL operation is not accurate and

cannot be reconciled.

PAL operation. For each building permit office containing

a sample measure, a PAL Form (Form 11-193A) is computergenerated. A field representative visits the building permit

office and completes the PALs by listing the necessary

permit and address information. If an address given on a

permit is missing a house number or street name (or

number), then the address is considered incomplete. In this

case, the field representative visits the new construction

site and draws a sketch map showing the location of the

structure and, if possible, completes the address.

Permit listing. Listing in the permit frame is necessary for

all multiunit addresses. The PAL operation does not obtain

unit designations at multiunit addresses. Therefore, listing

is necessary to complete the addresses. Additionally, listing

is required when the addresses obtained through the PAL

operation do not correspond to what the field representative actually finds when visiting the address for the first

interview. When this occurs, the unit frame listing procedures are followed.

THIRD STAGE OF THE SAMPLE DESIGN

(SUBSAMPLING)

Chapter 3 describes a third stage of the sample design.

This third stage is referred to as subsampling. The need for

subsampling is dependent on the results of the listing

operations and the results of the clerical sampling. Subsampling is required when the number of housing units in a

segment for a given sample is greater than 15 or when 1 of

the 4 units in the USU yields more than 4 total units. For

unit segments (and permit segments) this can happen

when more units than expected are found at the address at

the time of the first interview. For more information on

subsampling, see Chapter 3.

INTERVIEWER ASSIGNMENTS

The final stage of sample preparation includes those

operations that are needed in order to break the sample

down into manageable interviewer workloads and to get

the resulting assignments to the field representatives for

interview. At this point, all the sample cases for the month

have been identified and all the necessary information

about these sample cases is available in a central database at headquarters. The listings have been completed,

sampling patterns have been applied, and the addresses

are available. The central database also includes additional

information, such as telephone numbers for those cases

which have been interviewed in previous months. The

rotation of sample used in the CPS is such that seveneighths of the sample cases each month have been in

sample in previous months (see Chapter 3). This central

database is part of an integrated system described briefly

below. This integrated system also affects the management of the sample and the way the interview results are

transmitted to central headquarters, as described in Chapter 8.

Overview of the Integrated System

In recent years, technological advances have changed

the face of data preparation and collection activities at the

Census Bureau. The Census Bureau has been developing

computer-based methods for survey data collection, communications, management, and analysis. Within the Census Bureau, this integrated data collection system is called

the Computer Assisted Interviewing System. The computer

assisted interviewing system has two principal components: computer assisted personal interviewing (CAPI) and

centralized computer assisted telephone interviewing (CATI).

See Chapter 7 on the data collection aspects of this

system. The integrated system is designed to manage

decentralized data collection using laptop computers, a

centralized telephone collection, and a central database for

data management and accounting.

The integrated system is made up of three main parts:

1. Headquarters operations in the central database.

The headquarters operations include transmission of

cases to field representatives, transmission of CATI

cases to the telephone centers, and database maintenance.

2. Regional office operations in the central database.

The regional office operations include sample maintenance, keying of addresses as necessary, preparation

of assignments, determination of CATI assignments,

reinterview selection, and review and reassignment of

cases.

4– 6
3. Field representative case management operations

on the laptop computer. The field representative

operations include receipt of assignments, completion

of interview assignments, and transmittal of completed

work to the central database for processing (see

Chapter 8).

The central database resides at headquarters where the

file of sample cases is maintained. The database stores

field representative data (name, phone number, address,

etc.), information for making assignments (Field PSU, segment, address, etc.), and all the data for cases in sample.

Regional Office Operations

When the information in the database is complete, the

regional offices can begin the assignment preparation

phase. This includes making assignments to field representatives and sending cases to a centralized telephone

facility. Regional offices access the central database to

break down the assignment areas geographically and key

in information that is used to aid in the monthly field

representative assignment operations. The regional office

supervisor considers such characteristics as the size of the

Field PSU, the workload in that Field PSU, and the number

of field representatives working in that Field PSU when

deciding the best geographic method for dividing the

workload in Field PSUs among field representatives.

As part of the process of making field representative

assignments, the CATI assignments are also made. Each

regional office is informed of the recommended number of

cases that should be sent to CATI. The regional office

attempts to assign the recommended number of cases for

centralized telephone interviewing. The selection of cases

for CATI involves several steps. Cases may be assigned to

CATI if several criteria are met. These criteria pertain to the

Field PSU, the household, and the time in sample. In

general terms, the criteria are as follows:

1. The sample case must be in a Field PSU and random

group that is CATI eligible. (The random group restriction is imposed to allow for statistical testing. See

Chapter 3 for further description of random group.)

2. The household must have a telephone and be willing

to accept a telephone interview.

3. First and fifth month cases are generally not eligible for

a telephone or CATI interview.

The regional offices have the ability to temporarily

assign cases to CATI in order to cover their workloads in

certain situations. Each region has provided headquarters

with the number of CATI cases that they could provide to

CATI on a temporary basis each month. This is done

primarily to fill in for field representatives that are ill or on

vacation. When interviewing for the month is completed,

these cases will automatically be reassigned for CAPI

interviewing.

Many of the cases sent to CATI are successfully completed as telephone interviews. Those that cannot be

completed from the telephone centers are returned to the

field prior to the end of the interview period. These cases

are called ‘‘CATI recycles.’’ See Chapter 8 for further detail.

The final step is the certification of assignments. After all

changes to the interview and CATI assignments have been

made, the regional offices certify the assignments. This

must be done after all assignments have been reviewed for

geographic efficiency and have the proper balance among

field representatives.

After assignments are made and certified, the regional

offices transmit the assignments to the central database.

This transmission places the assignments on the telecommunications server for the field representatives and centralized telephone facilities. Prior to the interview period,

field representatives receive their assignments by initiating

a transmission to the telecommunications server at headquarters. Assignments include the instrument (questionnaire and/or supplements) and the cases they must interview that month. These files are copied to the laptop during

the transmission from the server. The instrument includes

the control card or household demographic information and

labor force questions. All data sent and received from the

field representatives pass through the central communications system maintained at headquarters. See Chapter 8

for more information on the transmission of interview

results.

Finally, the regional offices prepare the remaining paper

materials needed by the field representatives to complete

their assignments. The materials include:

1. Field Representative Assignment Listing (CAPI-35).

2. Segment folders for cases to be interviewed (including

maps, listing sheets, and other pertinent information

that will aid the interviewer in locating specific cases).

3. Blank listing sheets, payroll forms, respondent letters,

and other supplies requested by the field representative.

4. Segment folders and listing sheets for blocks and

group quarters to be listed for future CPS samples.

Once the field representative has received the above

materials and has successfully completed a transmission

to retrieve his/her assignments, the sample preparation

operations are complete and the field representative is

ready to conduct the interviews.

4– 7
Chapter 5.

Questionnaire Concepts and Definitions for the Current

Population Survey

INTRODUCTION

An extremely important component of the Current Population Survey (CPS) is the questionnaire, also called the

survey instrument. Although the concepts and definitions of

both labor force items and associated demographic information collected in the CPS have, with a few exceptions,

remained relatively constant over the past several decades,

the survey instrument was radically redesigned in January

1994. The purpose of the redesign was to reflect changes

that have occurred in the economy and to take advantage

of the possibilities inherent in the automated data collection

methods introduced in 1994, that is, computer assisted

personal interviewing (CAPI) and computer assisted telephone interviewing (CATI). This chapter briefly describes

and discusses the current survey instrument: its concepts,

definitions, and data collection procedures and protocols.

STRUCTURE OF THE SURVEY INSTRUMENT

The CPS interview is divided into three basic parts: (1)

household and demographic information, (2) labor force

information, and (3) supplement information in months that

include supplements. Household and demographic information historically was called ‘‘control card information’’

because, in the paper-and-pencil environment, this information was collected on a separate cardboard form. With

electronic data collection, this distinction is no longer

apparent to the respondent or the interviewer. The order in

which interviewers attempt to collect information is as

follows: (1) housing unit data, (2) demographic data, (3)

labor force data, (4) more demographic data, (5) supplement data, and finally (6) more housing unit data.

Only the concepts and definitions of the household,

demographic, and labor force data are discussed below.

(For more information about supplements to the CPS see

Chapter 10.)

CONCEPTS AND DEFINITIONS

Household and Demographic Information

Upon contacting a household, interviewers proceed with

the interview unless there is a clear indication that the case

is a definite noninterview. (Chapter 7 discusses the interview process and explains refusals and other types of

noninterviews.) When interviewing a household for the first

time, interviewers collect information about the housing

unit and all individuals who usually live at the address.

Housing unit information. Upon first contact with a

housing unit, interviewers collect information on the housing unit’s physical address, its mailing address, the year it

was constructed, the type of structure (single or multiple

family), whether it is renter- or owner-occupied, whether

other units or persons are part of the sample unit, whether

the housing unit has a telephone and, if so, the telephone

number.

Household roster. After collecting or updating the housing

unit data, the interviewer either creates or updates a list of

all individuals living in the unit and determines whether or

not they are members of the household. This list is referred

to as the household roster.

Household respondent. One person may provide all of

the CPS data for the entire sample unit, provided that the

person is a household member 15 years of age or older

who is knowledgeable about the household. The person

who responds for the household is called the household

respondent. Information collected from the household respondent for other members of the household is referred to as

proxy response.

Reference person. To create the household roster, the

interviewer asks the household respondent to give ‘‘the

names of all persons living or staying’’ in the housing unit,

and to ‘‘start with the name of the person or one of the

persons who owns or rents’’ the unit. The person whose

name the interviewer enters on line one (presumably one

of the individuals who owns or rents the unit) becomes the

reference person. Note that the household respondent and

the reference person are not necessarily the same. For

example, if you are the household respondent and you give

your name ‘‘first’’ when asked to report the household

roster, then you also are the reference person. If, on the

other hand, you are the household respondent and you

give your spouse’s name ‘‘first’’ when asked to report the

household roster, then your spouse is the reference person. (Sometimes the reference person is referred to as the

‘‘householder.’’)

Household. A household is defined as all individuals

(related family members and all unrelated individuals)

whose usual place of residence at the time of the interview

5–1
is the sample unit. Individuals who are temporarily absent

and who have no other usual address are still classified as

household members even though they are not present in

the household during the survey week. College students

comprise the bulk of such absent household members, but

persons away on business or vacation are also included.

(Not included are persons in institutions or the military.)

Once household/nonhousehold membership has been established for all persons on the roster, the interviewer proceeds to collect all other demographic data for household

members only.

Relationship to reference person. The interviewer will

show a flash card with relationship categories (e.g., spouse,

child, grandchild, parent, brother/sister) to the household

respondent and ask him/her to report each household

member’s relationship to the reference person (the person

listed on line one). Relationship data also are used to

define families, subfamilies, and individuals whose usual

place of residence is elsewhere. A family is defined as a

group of two or more individuals residing together who are

related by birth, marriage, or adoption; all such individuals

are considered members of one family. Families are further

classified either as married-couple families or as families

maintained by women or men without spouses. Subfamilies are defined as families that live in housing units where

none of the members of the family are related to the

reference person. It is also possible that a household

contains unrelated individuals; that is, people who are not

living with any relatives. An unrelated individual may be

part of a household containing one or more families or

other unrelated individuals, may live alone, or may reside in

group quarters, such as a rooming house.

Additional demographic information. In addition to asking for relationship data, the interviewer asks for other

demographic data for each household member including:

birth date, marital status, who a particular person’s parent

is among the members of the household (entered as the

line number of parent) and who a person’s spouse is (if it

cannot be determined from the relationship data), Armed

Forces status, level of education, race, ethnicity, nativity,

and social security number (for those 15 years of age or

older in selected months). Total household income also is

collected. The social security number for each household

member 15 years of age or older is collected so that it is

possible to match the CPS data to other government data

in order to evaluate the accuracy, consistency, and comparability of various statistics derived from CPS data. The

following terms are used to define an individual’s marital

status at the time of the interview: married spouse present,

married spouse absent, widowed, divorced, separated, or

never married. The term ‘‘married spouse present’’ applies

to a husband and wife who both live at the same address,

even though one may be temporarily absent due to business, vacation, a visit away from home, a hospital stay, etc.

The term ‘‘married spouse absent’’ applies to individuals

who live apart for reasons such as marital problems, as

well as husbands and wives who are living apart because

one or the other is employed elsewhere, on duty with the

Armed Forces, or any other reason. The information collected during the interview is used to create three marital

status categories: single never married, married spouse

present, and other marital status. The latter category

includes those who were classified as widowed; divorced;

separated; or married, spouse absent.

Starting in January 1992, educational attainment for

each person in the household age 15 or older was obtained

through a question asking about the highest grade or

degree completed. In January 1996, additional questions

were added for several educational attainment categories

to ascertain the total number of years of school or credit

years completed. (Prior to January 1992, the questions

referred to the highest grade or year of school each person

had attended and, then, if they had completed that grade or

year of school.)

The household respondent identifies his/her own race

as well as that of other household members by selecting a

category from a flash card presented by the interviewer.

The categories on the flash card are: White, Black, American Indian, Aleut, Eskimo, Asian or Pacific Islander, or

Other. The ‘‘nativity’’ items ask about a person’s country of

birth, as well as that of the individual’s parents, and

whether they are American citizens and if so, whether by

birth or naturalization. Persons born outside of the 50

states also are asked date of immigration to the United

States. The nativity items were added in April of 1993.

The additional demographic data collected are used to

classify individuals into racial groups, education categories, and groups based on ethnic origin. Currently, the

racial categories for CPS data are White, Black, and Other,

where the Other group includes American Indians, Alaskan

Natives, and Asians and Pacific Islanders. The data about

individuals’ ethnic origin primarily are collected for the

purpose of identifying Hispanics,1 where Hispanics are

those who identify their origin or descent as Mexican,

Puerto Rican, Cuban, Central or South American, or some

other Hispanic descent. It should be noted that race and

ethnicity are distinct categories. Thus, individuals of Hispanic origin may be of any race.

Labor Force Information

Labor force information is obtained after the household

and demographic information has been collected. One of

the primary purposes of the labor force information is to

classify individuals as employed, unemployed, or not in the

labor force. Other information collected includes hours

worked, occupation, and industry and related aspects of

the working population. It should be noted that the major

labor force categories are defined hierarchically and, thus,

1

Hispanics may be of any race.

5–2
are mutually exclusive. Employed supersedes unemployed

which supersedes not in the labor force. For example,

individuals who are classified as employed, even if they

worked less than full time, are not asked the questions

about having looked for work, and hence cannot be classified as unemployed. Similarly, an individual who is classified as unemployed is not asked the questions used to

determine one’s primary nonlabor market activity. For

instance, retired persons who are currently working are

classified as employed even though they have retired from

previous jobs. Consequently, they are not asked the questions about their previous employment nor can they be

classified as retired. The current concepts and definitions

underlying the collection and estimate of the labor force

data are presented below.

Reference week. The CPS labor force questions ask

about labor market activities for 1 week each month. This

week is referred to as the ‘‘reference week.’’ The reference

week is defined as the 7-day period, Sunday through

Saturday, that includes the 12th of the month.

Civilian noninstitutional population. In the CPS, labor

force data are restricted to persons 16 years of age and

older, who currently reside in 1 of the 50 states or the

District of Columbia, who do not reside in institutions (e.g.,

penal and mental facilities, homes for the aged), and who

are not on active duty in the Armed Forces.

Employed persons. Employed persons are those who,

during the reference week (a) did any work at all (for at

least 1 hour) as paid employees; worked in their own

businesses, professions, or on their own farms; or worked

15 hours or more as unpaid workers in an enterprise

operated by a family member or (b) were not working, but

who had a job or business from which they were temporarily absent because of vacation, illness, bad weather,

childcare problems, maternity or paternity leave, labormanagement dispute, job training, or other family or personal reasons whether or not they were paid for the time off

or were seeking other jobs. Each employed person is

counted only once, even if he or she holds more than one

job. (See the discussion of multiple jobholders below.)

Employed citizens of foreign countries who are temporarily in the United States but not living on the premises of

an embassy are included. Excluded are persons whose

only activity consisted of work around their own house

(painting, repairing, cleaning, or other home-related housework) or volunteer work for religious, charitable, or other

organizations.

The initial survey question, asked only once for each

household, inquires whether anyone in the household has

a business or a farm. Subsequent questions are asked for

each household member to determine whether any of them

did any work for pay (or for profit if there is a household

business) during the reference week. If no work for pay or

profit was performed and a family business exists, respondents are asked whether they did any unpaid work in the

family business or farm.

Multiple jobholders. These are employed persons who,

during the reference week, had either two or more jobs as

wage and salary workers; were self-employed and also

held one or more wage and salary jobs; or worked as

unpaid family workers and also held one or more wage and

salary jobs. A person employed only in private households

(cleaner, gardener, babysitter, etc.) who worked for two or

more employers during the reference week is not counted

as a multiple jobholder since working for several employers

is considered an inherent characteristic of private household work. Also excluded are self-employed persons with

multiple unincorporated businesses and persons with multiple jobs as unpaid family workers.

Since January 1994, CPS respondents have been asked

questions each month to identify multiple jobholders. First,

all employed persons are asked ‘‘Last week, did you have

more than one job (or business, if one exists), including

part-time, evening, or weekend work?’’ Those who answer

‘‘yes’’ are then asked, ‘‘Altogether, how many jobs (or

businesses) did you have?’’ Prior to 1994, this information

had been available only through periodic CPS supplements.

Hours of work. Beginning with the CPS redesign in

January 1994, both actual and usual hours of work have

been collected. Prior to the redesign, only actual hours

were requested for all employed individuals.

Published data on hours of work relate to the actual

number of hours spent ‘‘at work’’ during the reference

week. For example, persons who normally work 40 hours a

week but were off on the Memorial Day holiday, would be

reported as working 32 hours, even though they were paid

for the holiday. For persons working in more than one job,

the published figures relate to the number of hours worked

at all jobs during the week.

Data on persons ‘‘at work’’ exclude employed persons

who were absent from their jobs during the entire reference

week for reasons such as vacation, illness, or industrial

dispute. Data also are available on usual hours worked by

all employed persons, including those who were absent

from their jobs during the reference week.

At work part time for economic reasons. Sometimes

referred to as involuntary part time, this category refers to

individuals who gave an economic reason for working 1 to

34 hours during the reference week. Economic reasons

include slack work or unfavorable business conditions,

inability to find full-time work, and seasonal declines in

demand. Those who usually work part time also must

indicate that they want and are available to work full time to

be classified as being part time for economic reasons.

At work part time for noneconomic reasons. This group

includes those persons who usually work part time and

were at work 1 to 34 hours during the reference week for a

5–3
noneconomic reason. Noneconomic reasons include illness or other medical limitation, childcare problems or

other family or personal obligations, school or training,

retirement or social security limits on earnings, and being in

a job where full-time work is less than 35 hours. The group

also includes those who gave an economic reason for

usually working 1 to 34 hours but said they do not want to

work full time or were unavailable for such work.

Usual full- or part-time status. In order to differentiate a

person’s normal schedule from his/her activity during the

reference week, persons also are classified according to

their usual full- or part-time statuses. In this context,

full-time workers are those who usually work 35 hours or

more (at all jobs combined). This group includes some

individuals who worked less than 35 hours in the reference

week — for either economic or noneconomic reasons — as

well as those who are temporarily absent from work.

Similarly, part-time workers are those who usually work

less than 35 hours per week (at all jobs), regardless of the

number of hours worked in the reference week. This may

include some individuals who actually worked more than

34 hours in the reference week, as well as those who were

temporarily absent from work. The full-time labor force

includes all employed persons who usually work full time

and unemployed persons who are either looking for fulltime work or are on layoff from full-time jobs. The part-time

labor force consists of employed persons who usually work

part time and unemployed persons who are seeking or are

on layoff from part-time jobs.

Prior to 1994, persons who worked full time during the

reference week were not asked about their usual hours.

Rather, it was assumed that they usually worked full time,

and hence they were classified as full-time workers.

Occupation, industry, and class-of-worker.For the employed,

this information applies to the job held in the reference

week. A person with two or more jobs is classified according to the job at which he or she worked the greatest

number of hours. The unemployed are classified according

to their last jobs. The occupational and industrial classification of CPS data is based on the coding systems used in

the 1990 census. A list of these codes can be found in

Alphabetical Index of Industries and Occupations (Bureau

of the Census, January 1992). The class-of-worker classification assigns workers to one of the following categories: wage and salary workers, self-employed workers, and

unpaid family workers. Wage and salary workers are those

who receive wages, salary, commissions, tips, or pay in

kind from a private employer or from a government unit.

The class-of-worker question also includes separate

response categories for ‘‘private for profit company’’ and

‘‘nonprofit organization’’ to further classify private wage and

salary workers (this distinction has been in place since

January 1994). Self-employed persons are those who work

for profit or fees in their own businesses, professions,

trades, or farms. Only the unincorporated self-employed

are included in the self-employed category since those

whose businesses are incorporated technically are wage

and salary workers because they are paid employees of a

corporation. Unpaid family workers are persons working

without pay for 15 hours a week or more on a farm or in a

business operated by a member of the household to whom

they are related by birth or marriage.

Occupation, industry, and class-of-worker on second

job. The occupation, industry, and class-of-worker information for individuals’ second jobs is collected in order to

obtain a more accurate measure of multiple jobholders, to

obtain more detailed information about their employment

characteristics, and to provide information necessary for

comparing estimates of number of employees in the CPS

and in BLS’s establishment survey (the Current Employment Statistics; for an explanation of this survey see BLS

Handbook of Methods, April 1997). For the majority of

multiple jobholders, occupation, industry, and class-ofworker data for their second jobs are collected only from a

quarter of the sample–those in their fourth or eighth monthly

interviews. However, for those classified as ‘‘self-employed

unincorporated’’ on their main jobs, class-of-worker of the

second job is collected each month. This is done because,

according to the official definition, individuals who are

self-employed unincorporated on both of their jobs are not

considered multiple jobholders.

The questions used to determine whether an individual is employed or not, along with the questions an

employed person typically will receive, are presented in

Figure 5–1. A copy of the entire questionnaire can be

obtained from the Internet. The address for this file is:

http://www.bls.census.gov/cps/bqestair.htm.

Earnings. Information on what people earn at their main

jobs is collected only for those who are receiving their

fourth or eighth monthly interviews. This means that earnings questions are asked of only one-fourth of the survey

respondents. Respondents are asked to report their usual

earnings before taxes and other deductions and to include

any overtime pay, commissions, or tips usually received.

The term ‘‘usual’’ is as perceived by the respondent. If the

respondent asks for a definition of usual, however, interviewers are instructed to define the term as more than half

the weeks worked during the past 4 or 5 months. Respondents may report earnings in the time period they prefer—for

example, hourly, weekly, biweekly, monthly, or annually.

(Allowing respondents to report in a periodicity with which

they were most comfortable was a feature added in the

1994 redesign.) Based on additional information collected

during the interview, earnings reported on a basis other

than weekly are converted to a weekly amount in later

processing. Data are collected for wage and salary workers

(excluding the self-employed who respond that their businesses were incorporated). These earnings data are used

to construct estimates of the distribution of usual weekly

earnings and median earnings. Individuals who do not

5–4
report their earnings on an hourly basis are asked if they

are, in fact, paid at an hourly rate and if so, what the hourly

rate is. The earnings of those who reported hourly and

those who are paid at an hourly rate is used to analyze the

characteristics of hourly workers, for example, those who

are paid the minimum wage.

Unemployed persons.All persons who were not employed

during the reference week but were available for work

(excluding temporary illness) and had made specific efforts

to find employment some time during the 4-week period

ending with the reference week are classified as unemployed. Individuals who were waiting to be recalled to a job

from which they had been laid off need not have been

looking for work to be classified as unemployed.

A relatively minor change was incorporated into the

definition of unemployment with the implementation of the

January 1994 redesign. Under the former definition, persons who volunteered that they were waiting to start a job

within 30 days (a very small group numerically) were

classified as unemployed, whether or not they were actively

looking for work. Under the new definition, by contrast,

people waiting to start a new job must have actively looked

for a job within the last 4 weeks in order to be counted as

unemployed. Otherwise, they are classified as not in the

labor force.

As the definition indicates, there are two ways people

may be classified as unemployed. They are either looking

for work (job seekers) or they have been temporarily

separated from a job (persons on layoff). Job seekers must

have engaged in active job search during the above

mentioned 4-week period in order to be classified as

unemployed. (Active methods are defined as job search

methods that have the potential to result in a job offer

without any further action on the part of the job seeker.)

Examples of active job search methods include going to an

employer directly or to a public or private employment

agency, seeking assistance from friends or relatives, placing or answering ads, or using some other active method.

Examples of the ‘‘other active’’ category include being on a

union or professional register, obtaining assistance from a

community organization, or waiting at a designated labor

pickup point. Passive methods, which do not qualify as job

search, include reading (as opposed to answering or

placing) ‘‘help wanted’’ ads and taking a job training course.

The response categories for active and passive methods

are clearly delineated in separately labeled columns on the

interviewers’ computer screens. Job search methods are

identified by the following questions: ‘‘Have you been doing

anything to find work during the last 4 weeks?’’ and ‘‘What

are all of the things you have done to find work during the

last 4 weeks?’’ To ensure that respondents report all of the

methods of job search used, interviewers ask ‘‘Anything

else?’’ after the initial or a subsequent job search method is

reported.

Persons ‘‘on layoff’’ are defined as those who have been

separated from a job to which they are waiting to be

recalled (i.e., their layoff status is temporary). In order to

measure layoffs accurately, the questionnaire determines

whether people reported to be on layoff did in fact have an

expectation of recall; that is, whether they had been given

a specific date to return to work or, at least, had been given

an indication that they would be recalled within the next 6

months. As previously mentioned, persons on layoff need

not be actively seeking work to be classified as unemployed.

Reason for unemployment. Unemployed individuals are

categorized according to their status at the time they

became unemployed. The categories are: (1) Job losers: a

group comprised of (a) persons on temporary layoff from a

job to which they expect to be recalled and (b) permanent

job losers, whose employment ended involuntarily and who

began looking for work; (2)Job leavers: persons who quit or

otherwise terminated their employment voluntarily and

began looking for work; (3)Persons who completed temporary jobs: persons who began looking for work after their

jobs ended; (4)Reentrants: persons who previously worked

but were out of the labor force prior to beginning their job

search; (5)New entrants: persons who never worked before

and who are entering the labor force for the first time. Each

of these five categories of unemployed can be expressed

as a proportion of the entire civilian labor force or as a

proportion of the total unemployed. Prior to 1994, new

entrants were defined as job seekers who had never

worked at a full-time job lasting 2 weeks or longer; reentrants were defined as job seekers who had held a full-time

job for at least 2 weeks and had then spent some time out

of the labor force prior to their most recent period of job

search. These definitions have been modified to encompass any type of job, not just a full-time job of at least 2

weeks duration. Thus, new entrants are now defined as job

seekers who have never worked at all, and reentrants are

job seekers who have worked before but not immediately

prior to their current job search.

Duration of unemployment. The duration of unemployment is expressed in weeks. For individuals who are

classified as unemployed because they are looking for

work, the duration of unemployment is the length of time

(through the current reference week) that they have been

looking for work. For persons on layoff, the duration of

unemployment is the number of full weeks (through the

reference week) they have been on layoff.

The questions used to classify an individual as unemployed can be found in Figure 5–1.

Not in the labor force. Included in this group are all

persons in the civilian noninstitutional population who are

neither employed nor unemployed. Information is collected

on their desire for and availability to take a job at the time

of the CPS interview, job search activity in the prior year,

and reason for not looking in the 4-week period prior to the

survey week. This group includes discouraged workers,

5–5
defined as persons not in the labor force who want and are

available for a job and who have looked for work sometime

in the past 12 months (or since the end of their last job if

they held one within the past 12 months), but are not

currently looking, because they believe there are no jobs

available or there are none for which they would qualify.

(Specifically, the main reason identified by discouraged

workers for not recently looking for work is one of the

following: Believes no work available in line of work or area;

could not find any work; lacks necessary schooling, training, skills, or experience; employers think too young or too

old; or other types of discrimination.)

Data on a larger group of persons outside the labor

force, one that includes discouraged workers as well as

persons who desire work but give other reasons for not

searching (such as childcare problems, family responsibilities, school, or transportation problems) are also published

regularly. This group is made up of persons who want a job,

are available for work, and have looked for work within the

past year. This group is generally described as having

some marginal attachment to the labor force.

Prior to January 1994, questions about the desire for

work among those who were not in the labor force were

asked only of a quarter of the sample. Since 1994, these

questions have been asked of the full CPS sample. Consequently since 1994, estimates of the number of discouraged workers as well as those with a marginal attachment

to the labor force are published monthly rather than just

quarterly.

Additional questions relating to individuals’ job histories

and whether they intend to seek work continue to be asked

only of persons not in the labor force who are in the sample

for either their fourth or eighth month. Data based on these

questions are tabulated only on a quarterly basis.

Estimates of the number of employed and unemployed

are used to construct a variety of measures. These measures include:

• Labor force: The labor force consists of all persons 16

years of age or older classified as employed or unemployed in accordance with the criteria described above.

• Unemployment rate: The unemployment rate represents the number of unemployed as a percentage of the

labor force.

• Labor force participation rate: The labor force participation rate is the proportion of the age-eligible population

that is in the labor force.

• Employment-population ratio:The employment-population

ratio represents the proportion of the age-eligible population that is employed.

Figure 5–1. Questions for Employed and

Unemployed

1. Does anyone in this household have a business or a

farm?

2. LAST WEEK, did you do ANY work for (either) pay (or

profit)?

Parenthetical filled in if there is a business or farm in

the household. If 1 is ‘‘yes’’ and 2 is ‘‘no,’’ ask 3. If 1

is ‘‘no’’ and 2 is ‘‘no,’’ ask 4.

3. LAST WEEK, did you do any unpaid work in the family

business or farm?

If 2 and 3 are both ‘‘no,’’ ask 4.

4. LAST WEEK, ( in addition to the business,) did you

have a job, either full or part time? Include any job from

which you were temporarily absent.

Parenthetical filled in if there is a business or farm in

the household.

If 4 is ‘‘no,’’ ask 5.

5. LAST WEEK, were you on layoff from a job?

If 5 is ‘‘yes,’’ ask 6. If 5 is ‘‘no,’’ ask 8.

6. Has your employer given you a date to return to work?

If ‘‘no,’’ ask 7.

7. Have you been given any indication that you will be

recalled to work within the next 6 months?

If ‘‘no,’’ ask 8.

8. Have you been doing anything to find work during the

last 4 weeks?

If ‘‘yes,’’ ask 9.

9. What are all of the things you have done to find work

during the last 4 weeks?

Individuals are classified as employed if they say ‘‘yes’’

to questions 2, 3 (and work 15 hours or more in the

reference week or receive profits from the business/farm),

or 4.

Individuals who are available to work are classified as

unemployed if they say ‘‘yes’’ to 5 and either 6 or 7, or if

they say ‘‘yes’’ to 8 and provide a job search method that

could have brought them into contact with a potential

employer in 9.

5–6
REFERENCES

U.S. Department of Commerce, U.S. Census Bureau

(1992),Alphabetical Index of Industries and Occupations, January 1992.

U.S. Department of Labor, Bureau of Labor Statistics

(1997), BLS Handbook of Methods, Bulletin 2490, April

1997.

5–7
Chapter 6.

Design of the Current Population Survey Instrument

INTRODUCTION

Chapter 5 describes the concepts and definitions underpinning the Current Population Survey (CPS) data collection instrument. The current survey instrument is the result

of an 8-year research and development effort to redesign

the data collection process and to implement previously

recommended changes in the underlying labor force concepts. The changes described here were introduced in

January 1994. Of particular interest in the redesign was the

wording of the labor force portion of the questionnaire and

the data collection methods. For virtually every labor force

concept the current questionnaire wording is different from

what was used previously. Data collection has also been

redesigned so that the instrument is fully automated and is

administered either on a laptop computer or from a centralized telephone facility.

MOTIVATION FOR THE REDESIGN OF THE

QUESTIONNAIRE COLLECTING OF LABOR

FORCE DATA

The CPS produces some of the most important data

used to develop economic and social policy in the United

States. Although the U.S. economy and society have

undergone major shifts in recent decades, the survey

questionnaire had remained unchanged since 1967. The

growth in the number of service-sector jobs and the decline

in the number of factory jobs have been two key developments. Other changes include the more prominent role of

women in the workforce and the growing popularity of

alternative work schedules. The 1994 revisions were designed

to accommodate these changes. At the same time, the

redesign took advantage of major advances in survey

research methods and data collection technology. Furthermore, recommendations for changes in the CPS had been

proposed in the late 1970s and 1980s, primarily by the

Presidentially-appointed National Commission on Employment and Unemployment Statistics (commonly referred to

as the Levitan Commission). No changes were implemented at that time, however, due to the lack of funding for

a large overlap sample necessary to assess the effect of

the redesign. In the mid-1980s, funding for an overlap

became available. Spurred by all of these developments, it

was decided to redesign the CPS questionnaire.

OBJECTIVES OF THE REDESIGN

There were five main objectives in redesigning the CPS

questionnaire: (1) to better operationalize existing definitions and reduce reliance on volunteered responses; (2) to

reduce the potential for response error in the questionnairerespondent-interviewer interaction and, hence, improve

measurement of CPS concepts; (3) to implement minor

definitional changes within the labor force classifications;

(4) to expand the labor force data available and improve

longitudinal measures; and (5) to exploit the capabilities of

computer assisted interviewing for improving data quality

and reducing respondent burden (see Copeland and Rothgeb (1990) for a fuller discussion).

Enhanced Accuracy

In redesigning the CPS questionnaire, the BLS and

Census Bureau attempted to develop questions that would

lessen the potential for response error.Among the approaches

used were: (1) shorter, clearer question wording; (2) splitting complex questions into two or more separate questions; (3) building concept definitions into question wording;

(4) reducing reliance on volunteered information; (5) explicit

and implicit strategies for the respondent to provide numeric

data on hours, earnings, etc.; and (6) the use of revised

precoded response categories for open-ended questions

(Copeland and Rothgeb, 1990.)

Definitional Changes

The labor force definitions used in the CPS have undergone only minor modifications since the survey’s inception

in 1940, and with only one exception, the definitional

changes and refinements made in 1994 were uniformly

small. The one major definitional change dealt with the

concept of discouraged workers; that is, persons outside

the labor force who are not looking for work because they

believed that there are no jobs available for them. As was

noted in Chapter 5, discouraged workers are similar to the

unemployed in that they are not working and want a job.

Since they are not conducting an active job search, however, they do not satisfy a key element necessary to be

classified as unemployed. The former measurement of

discouraged workers was criticized by the Levitan Commission as too arbitrary and subjective. It was deemed

arbitrary that assumptions about a person’s availability for

work were made from responses to a question on why the

respondent was not currently looking for work. It was

considered too subjective that the measurement was based

on a person’s stated desire for a job regardless of whether

the individual had ever looked for work. A new, more

6–1
precise measurement of discouraged workers was introduced that specifically required that a person had searched

for a job during the prior 12 months and was available for

work. The new questions also enable estimation of the

number of persons outside the labor force who, although

they cannot be precisely defined as discouraged, satisfy

many of the same criteria as discouraged workers and thus

show some marginal attachment to the labor force.

Other minor changes were made to fine-tune the definitions of unemployment, categories of unemployed persons, and persons who were employed part time for

economic reasons.

New Labor Force Information Introduced

With the revised questionnaire, several types of labor

force data became available regularly for the first time. For

example, information is now available each month on

employed persons who have more than one job. Also, by

collecting information on the number of hours multiple

jobholders work on their main job and secondary jobs

separately, estimates of the number of workers who combined two or more part-time jobs into a full-time workweek,

and the number of full- and part-time jobs in the economy

can be made. The inclusion of the multiple jobholding

question also improves the accuracy of answers to the

questions on hours worked and facilitates comparisons of

employment estimates from the CPS with those from the

Current Employment Statistics program, the survey of

nonfarm business establishments (for a discussion of CES

survey see BLS Handbook of Methods, Bureau of Labor

Statistics, April 1997). In addition, beginning in 1994,

monthly data on the number of hours usually worked per

week and data on the number of discouraged workers are

collected from the entire CPS sample rather than from the

one-quarter sample of respondents in their fourth or eighth

monthly interviews.

Computer Technology

A key feature of the redesigned CPS is that the new

questionnaire was designed for a computer assisted interview. Prior to the redesign, CPS data were primarily

collected using a paper-and-pencil form. In an automated

environment, most interviewers now use laptop computers

on which the questionnaire has been programmed. This

mode of data collection is known as computer assisted

personal interviewing (CAPI). Interviewers ask the survey

questions as they appear on the screen of the laptop and

then type the responses directly into the computer. A

portion of sample households—currently about 18 percent—is

interviewed via computer assisted telephone interviewing

(CATI) from three centralized telephone centers located in

Hagerstown, MD; Tucson, AZ; and Jeffersonville, IN.

Automated data collection methods allow greater flexibility in questionnaire designing than paper-and-pencil

data collection methods. Complicated skips, respondentspecific question wording, and carry-over of data from one

interview to the next are all possible with an automated

environment. For example, automated data collection permits capabilities such as (1) the use of dependent interviewing, that is the carrying over of information from the

previous month — for industry, occupation, and duration of

unemployment data and (2) the use of respondent-specific

question wording based on the person’s name, age, and

sex, answers to prior questions, household characteristics,

etc. Furthermore, by automatically bringing up the next

question on the interviewer’s screen, computerization reduces

the probability of an interviewer asking the wrong set of

questions. The computerized questionnaire also permits

the inclusion of several built-in editing features, including

automatic checks for internal consistency, checking for

unlikely responses, and verification of answers. With these

built-in editing features, errors can be caught and corrected

during the interview itself.

Evaluation and Selection of Revised Questions

Planning for the revised CPS questionnaire began in

1986, when BLS and the Census Bureau convened a task

force to identify areas for improvement. Studies employing

methods from the cognitive sciences were conducted to

test possible solutions to the problems identified. These

studies included interviewer focus groups, respondent focus

groups, respondent debriefings, a test of interviewers’

knowledge of concepts, in-depth cognitive laboratory interviews, response categorization research, and a study of

respondents’ comprehension of alternative versions of

labor force questions (Campanelli, Martin, and Rothgeb,

1991; Edwards, Levine, and Cohany, 1989; Fracasso,

1989; Gaertner, Cantor, and Gay,1989; Martin, 1987; Palmisano, 1989).

In addition to the qualitative research mentioned above,

the revised questionnaire, developed jointly by Census

Bureau and BLS staff, used information collected in a large

two-phase test of question wording. During Phase I, two

alternative questionnaires were tested with the then official

questionnaire as the control. During Phase II, one alternative questionnaire was tested with the control. The questionnaires were tested using computer assisted telephone

interviewing and a random digit dialing sample (CATI/RDD).

During these tests, interviews were conducted from the

centralized telephone interviewing facilities of the Census

Bureau.

Both quantitative and qualitative information was used in

the two phases to select questions, identify problems, and

suggestion solutions. Analyses were based on information

from item response distributions, respondent and interviewer debriefing data, and behavior coding of interviewer/

respondent interactions.

Item Response Analysis

The primary use of item response analysis was to

determine whether different questionnaires produce different response patterns, which may, in turn, have affected

6– 2
the labor force estimates. Unedited data were used for this

analysis. Statistical tests were conducted to ascertain

whether response patterns for different questionnaire versions differed significantly. The statistical tests were adjusted

to take into consideration the use of a nonrandom clustered

sample, repeated measures over time, and multiple persons in a household.

Response distributions were analyzed for all items on

the questionnaires. The response distribution analysis indicated the degree to which new measurement processes

produced different patterns of responses. Data gathered

using the other methods outlined above also aided in the

interpretation of the response differences observed. (Response

distributions were calculated on the basis of persons who

responded to the item, excluding those for whom a ‘‘don’t

know’’ or ‘‘refused’’ was obtained.)

Respondent Debriefings

At the end of the interview, respondent debriefing questions were administered to a sample of respondents to

measure respondent comprehension and response formulation. Question-specific probes were used to ascertain

whether certain words, phrases, or concepts were understood by respondents in the manner intended. (Esposito et

al., 1992.). From these data, indicators of how respondents

interpret and answer the questions and some measures of

response accuracy were obtained.

The debriefing questions were tailored to the respondent

and depended on the path the interview had taken. Two

forms of respondent debriefing questions were administered—

probing questions and vignette classification. For example,

those who did not indicate that they had done any work in

the main survey were asked the direct probe ‘‘LAST WEEK

did you do any work at all, even for as little as 1 hour?’’ An

example of the vignettes respondents received is ‘‘Last

week, Amy spent 20 hours at home doing the accounting

for her husband’s business. She did not receive a paycheck.’’ Individuals were asked to classify the person in the

vignette as working or not working based on the wording of

the question they received in the main survey (e.g., ‘‘Would

you report her as working last week not counting work

around the house?’’ if the respondent received the unrevised questionnaire or ‘‘Would you report her as working for

pay or profit last week?’’ if the respondent received the

current, revised questionnaire (Martin and Polivka, 1995).

Behavior Coding

Behavior coding entails monitoring or audiotaping interviews, and recording significant interviewer and respondent behaviors (e.g., minor/major changes in question

wording, probing behavior, inadequate answers, requests

for clarification). During early stages of testing, behavior

coding data were useful in identifying problems with proposed questions. For example, if interviewers frequently

reword a question, this may indicate that the question was

too difficult to ask as worded; respondents’ requests for

clarification may indicate that they were experiencing comprehension difficulties; and interruptions by respondents

may indicate that a question was too lengthy (Esposito et

al., 1992).

During later stages of testing, the objective of behavior

coding was to determine whether the revised questionnaire

improved the quality of interviewer/respondent interactions

as measured by accurate reading of the questions and

adequate responses by respondents. Additionally, results

from behavior coding helped identify areas of the questionnaire that would benefit from enhancements to interviewer

training.

Interviewer Debriefings

The primary objective of interviewer debriefing was to

identify areas of the revised questionnaire or interviewer

procedures that were problematic for interviewers or respondents. The information collected during the debriefings was

useful in identifying which questions needed revision. The

information was also useful in modifying initial interviewer

training and the interviewer manual. A secondary objective

of interviewer debriefing was to obtain information about

the questionnaire, interviewer behavior, or respondent behavior that may help explain differences observed in the labor

force estimates from the different measurement processes.

Two different techniques were used to debrief interviewers. The first was the use of focus groups at the centralized

telephone interviewing facilities and in geographically dispersed regional offices. The focus groups were conducted

after interviewers had at least 3 to 4 months experience

using the revised CPS instrument. Approximately 8 to 10

interviewers were selected for each focus group. Interviewers were selected to represent different levels of experience and ability.

The second technique was the use of a self-administered

standardized interviewer debriefing questionnaire. Once

problematic areas of the revised questionnaire were identified through the focus groups, a standardized debriefing

questionnaire was developed and administered to all interviewers.

HIGHLIGHTS OF THE QUESTIONNAIRE

REVISION

A copy of the questionnaire can be obtained from

the Internet. The address for this file is:

http://www.bls.census.gov/cps/bqestair.htm

General

Definition of reference week. In the interviewer debriefings that were conducted in 13 different geographic areas

during 1988, interviewers reported that the current question 19 (Q19, major activity question) ‘‘What were you

6– 3
doing most of LAST WEEK, working or something else?’’

was unwieldy and sometimes misunderstood by respondents. In addition to not always understanding the intent of

the question, respondents were unsure what was meant by

the time period ‘‘last week’’ (BLS, 1988). A respondent

debriefing conducted in 1988 found that only 17 percent of

respondents had definitions of ‘‘last week’’ that matched

the CPS definition of Sunday through Saturday of the

reference week. The majority (54 percent) of respondents

defined ‘‘last week’’ as Monday through Friday (Campanelli

et al., 1991).

In the revised questionnaire, an introductory statement

was added with the reference period clearly stated. The

introductory statement reads as follows: ‘‘I am going to ask

a few questions about work-related activities LAST WEEK.

By last week I mean the week beginning on Sunday,

August 9 and ending Saturday, August 15.’’ This statement

makes the reference period more explicit to respondents.

Additionally, the former Q19 has been deleted from the

questionnaire. In the past, Q19 had served as a preamble

to the labor force questions, but in the revised questionnaire the survey content is defined in the introductory

statement, which also defines the reference week.

Direct question on presence of business. The definition

of employed persons includes those who work without pay

for at least 15 hours per week in a family business. In the

former questionnaire, there was no direct question on the

presence of a business in the household. Such a question

is included in the revised questionnaire. This question is

asked only once for the entire household prior to the labor

force questions. The question reads as follows: ‘‘Does

anyone in this household have a business or a farm?’’ With

this question it can be determined whether a business

exists and who in the household owns the business. The

primary purpose of this question is to screen for households that may have unpaid family workers, not to obtain an

estimate of household businesses. (See Rothgeb et al.

(1992), Copeland and Rothgeb (1990), and Martin (1987)

for a fuller discussion of the need for a direct question on

presence of a business.)

For households that have a family business, direct

questions are asked about unpaid work in the family

business of all persons who were not reported as working

last week. BLS produces monthly estimates of unpaid

family workers who work 15 or more hours per week.

Employment Related Revisions

Revised ‘‘At Work’’ question. Having a direct question on

the presence of a family business not only improved the

estimates of unpaid family workers, but also permitted a

revision of the ‘‘at work’’ question. In the former questionnaire, the ‘‘at work’’ question read: ‘‘LAST WEEK, did you

do any work at all, not counting work around the house?’’ In

the revised questionnaire, the wording reads, ‘‘LAST WEEK

did you do ANY work for (either) pay (or profit)?’’ (The

parentheticals in the question are read only when there is

a business or farm in the household.) The revised wording

‘‘work for pay (or profit)’’ better captures the concept of

work that BLS is attempting to measure. (See Martin

(1987) or Martin and Polivka (1995) for a fuller discussion

of problems with the concept of ‘‘work.’’)

Direct question on multiple jobholding. In the former

questionnaire, the actual hours question read: ‘‘How many

hours did you work last week at all jobs?’’ During the

interviewer debriefings conducted in 1988, it was reported

that respondents do not always hear the last phrase ‘‘at all

jobs.’’ Some respondents who work at two jobs may have

only reported hours for one job (BLS, 1988). In the revised

questionnaire, a question is included at the beginning of

the hours series to determine whether or not the person is

a multiple jobholder. A followup question also asks for the

number of jobs the multiple jobholder has. Multiple jobholders are asked about their hours on their main job and other

job(s) separately to avoid the problem of multiple jobholders not hearing the phrase ‘‘at all jobs.’’ These new

questions also allow monthly estimates of multiple jobholders to be produced.

Hours series. The old question on ‘‘hours worked’’ read:

‘‘How many hours did you work last week at all jobs?’’ If a

person reported 35-48 hours worked, additional followup

probes were asked to determine whether the person

worked any extra hours or took any time off. Interviewers

were instructed to correct the original report of actual

hours, if necessary, based on responses to the probes. The

hours data are important because they are used to determine the sizes of the full-time and part-time labor forces. It

is unknown whether respondents reported exact actual

hours, usual hours, or some approximation of actual hours.

In the revised questionnaire, a revised hours series was

adopted. An anchor-recall estimation strategy was used to

obtain a better measure of actual hours and to address the

issue of work schedules more completely. For multiple

jobholders, it also provides separate data on hours worked

at a main job and other jobs. The revised questionnaire first

asks about the number of hours a person usually works at

the job. Then, separate questions are asked to determine

whether a person worked extra hours, or fewer hours, and

finally a question is asked on the number of actual hours

worked last week. It also should be noted that the new

hours series allows monthly estimates of usual hours

worked to be produced for all employed persons. In the

former questionnaire, usual hours were obtained only in

the outgoing rotation for employed private wage and salary

workers and were available only on a quarterly basis.

Industry and occupation – dependent interviewing.

Prior to the revision, CPS industry and occupation (I&O)

data were not always consistent from month-to-month for

the same person in the same job. These inconsistencies

6– 4
arose, in part, because the household respondent frequently varies from 1 month to the next. Furthermore, it is

sometimes difficult for a respondent to describe an occupation consistently from month-to-month. Moreover, distinctions at the three-digit occupation and industry level, that is,

at the most detailed classification level, can be very subtle.

To obtain more consistent data and make full use of the

automated interviewing environment, dependent interviewing for the I&O questions was implemented in the revised

questionnaire for month-in-sample 2-4 households and

month-in-sample 6-8 households. Dependent interviewing

uses information collected during the previous month’s

interview in the current month’s interview. (Different variations of dependent interviewing were evaluated during

testing. See Rothgeb et al. (1991) for more detail.)

In the revised CPS, respondents are provided with the

name of their employer as of the previous month and asked

if they still work for that employer. If they answer ‘‘no,’’

respondents are asked the independent questions on

industry and occupation.

If they answer ‘‘yes,’’ respondents are asked ‘‘Have the

usual activities and duties of your job changed since last

month?’’ If individuals say ‘‘yes,’’ their duties have changed,

these individuals are then asked the independent questions on occupation, activities or duties, and class-ofworker. If their duties have not changed, individuals are

asked to verify the previous month’s description through

the question ‘‘Last month, you were reported as (previous

month’s occupation or kind of work performed) and your

usual activities were (previous month’s duties). Is this an

accurate description of your current job?’’

If they answer ‘‘yes,’’ the previous month’s occupation

and class-of-worker are brought forward and no coding is

required. If they answer ‘‘no,’’ persons are asked the

independent questions on occupation activities and duties

and class-of-worker. This redesign permits a direct inquiry

about job change before the previous month’s information

is provided to the respondent.

Earnings. The earnings series in the revised questionnaire is considerably different from that in the former

questionnaire. In the former questionnaire, persons were

asked whether they were paid by the hour, and if so, what

the hourly wage was. All wage and salary workers were

then asked for their usual weekly earnings. In the former

version, earnings could be reported as weekly figures only,

even though that may not have been the easiest way for

the respondent to recall and report earnings. Data from

early tests indicated that a small proportion (14 percent)

(n=853) of nonhourly wage workers were paid at a weekly

rate and less than 25 percent (n=1623) of nonhourly wage

workers found it easiest to report earnings as a weekly

amount.

In the revised questionnaire, the earnings series is

designed to first request the periodicity for which the

respondent finds it easiest to report earnings and then

request an earnings amount in the specified periodicity, as

displayed below. The wording of questions requesting an

earnings amount is tailored to the periodicity identified

earlier by the respondent. (Because data on weekly earnings are published quarterly by BLS, earnings data provided by respondents in periodicities other than weekly are

converted to a weekly earnings estimate later during

processing operations.)

Revised Earnings Series (Selected items)

1. For your (MAIN) job, what is the easiest way for you to

report your total earnings BEFORE taxes or other

deductions: hourly, weekly, annually, or on some other

basis?

2. Do you usually receive overtime pay, tips, or commissions (at your MAIN job)?

3. (Including overtime pay, tips and commissions,) What

are your usual (weekly, monthly, annual, etc.) earnings

on this job, before taxes or other deductions?

As can be seen from the revised questions presented

above, other revisions to the earnings series include a

specific question to determine whether a person usually

receives overtime pay, tips, or commissions. If so, a

preamble precedes the earnings questions that reminds

respondents to include overtime pay, tips, and commissions when reporting earnings. If a respondent reports that

it is easiest to report earnings on an hourly basis, then a

separate question is asked regarding the amount of overtime pay, tips and commissions usually received, if applicable.

An additional question is asked of persons who do not

report that it is easiest to report their earnings hourly. The

question determines whether they are paid at an hourly

rate and is displayed below. This information, which allows

studies of the effect of the minimum wage, is used to

identify hourly wage workers.

‘‘Even though you told me it is easier to report your

earnings annually, are you PAID AT AN HOURLY RATE

on this job?’’

Unemployment Related Revisions

Persons on layoff - direct question. Previous research

(Rothgeb, 1982; Palmisano, 1989) demonstrated that the

former question on layoff status—‘‘Did you have a job or

business from which you were temporarily absent or on

layoff LAST WEEK?’’—was long, awkwardly worded, and

frequently misunderstood by respondents. Some respondents heard only part of the question, while others thought

that they were being asked whether they had a business.

In an effort to reduce response error, the revised questionnaire includes two separate direct questions about

layoff and temporary absences. The layoff question is:

‘‘LAST WEEK, were you on layoff from a job?’’ Questions

asked later screen out those persons who do not meet the

criteria for layoff status.

6– 5
Persons on layoff— expectation of recall. The official

definition of layoff includes the criterion of an expectation of

being recalled to the job. In the former questionnaire,

persons reported to be on layoff were never directly asked

whether they expected to be recalled. In an effort to better

capture the existing definition, persons reported to be on

layoff in the revised questionnaire are asked ‘‘Has your

employer given you a date to return to work?’’ Persons who

respond that their employers have not given them a date to

return are asked ‘‘Have you been given any indication that

you will be recalled to work within the next 6 months?’’ If the

response is positive, their availability is determined by the

question, ‘‘Could you have returned to work LAST WEEK if

you had been recalled?’’ Persons who do not meet the

criteria for layoff are asked the job search questions so they

still have an opportunity to be classified as unemployed.

Job search methods. The concept of unemployment

requires, among other criteria, an active job search during

the past 4 weeks. In the former questionnaire, the following

question was asked to determine whether a person conducted an active job search. ‘‘What has ... been doing in the

last 4 weeks to find work?’’ checked with—

• public employment agency

• private employment agency

• employer directly

• friends and relatives

• placed or answered ads

• nothing

• other

Interviewers were instructed to code all passive job

search methods into the ‘‘nothing’’ category. This included

such activities as looking at newspaper ads, attending job

training courses, and practicing typing. Only active job

search methods for which no appropriate response category exists were to be coded as ‘‘other.’’

In the revised questionnaire, several additional response

categories were added and the response options reordered and reformatted to more clearly represent the

distinction between active job search methods and passive

methods. The revisions to the job search methods question

grew out of concern that interviewers were confused by the

precoded response categories. This was evident even

before the analysis of the CATI/RDD test. Martin (1987)

conducted an examination of verbatim entries for the

‘‘other’’ category and found that many of the ‘‘other’’

responses should have been included in the ‘‘nothing’’

category instead. The analysis also revealed responses

coded as ‘‘other’’ that were too vague to determine whether

or not an active job search method had been undertaken.

Fracasso (1989) also concluded that the current set of

response categories was not adequate for accurate classification of active and passive job search methods.

During development of the revised questionnaire, two

additional passive categories were included: (1)‘‘looked at

ads’’ and (2) ‘‘attended job training programs/courses,’’ and

two additional active categories were included: (1)‘‘contacted school/university employment center’’ and (2)‘‘checked

union/ professional registers.’’ Later research also demonstrated that interviewers had difficulty coding relatively

common responses such as ‘‘sent out resumes’’ and ‘‘went

on interviews’’; thus the response categories were further

expanded to reflect these common job search methods.

Duration of job search and layoff. The duration of

unemployment is an important labor market indicator published monthly by BLS. In the former questionnaire, this

information was collected by the question: ‘‘How many

weeks have you been looking for work?’’ This wording

forced people to report in a periodicity that may not have

been meaningful to them, especially for the longer-term

unemployed. Also, asking for the number of weeks (rather

than months) may have led respondents to underestimate

the duration. In the revised questionnaire, the question

reads: ‘‘As of the end of LAST WEEK, how long had you

been looking for work?’’ Respondents can select the periodicity themselves and interviewers are able to record the

duration in weeks, months, or years.

To avoid clustering of answers around whole months,

the revised questionnaire also asks persons who report

duration in whole months (between 1 and 4 months) a

followup question to obtain an estimated duration in weeks:

‘‘We would like to have that in weeks, if possible. Exactly

how many weeks had you been looking for work?’’ The

purpose of this is to lead people to report the exact number

of weeks instead of multiplying their monthly estimates by

four as was done in an earlier test and may have been

done in the former questionnaire.

As mentioned earlier, the CATI/CAPI technology makes

it possible to automatically update duration of job search

and layoff for persons who are unemployed in consecutive

months. For persons reported to be looking for work for 2

consecutive months or longer, the previous month’s duration is updated without re-asking the duration questions.

For persons on layoff for at least 2 consecutive months, the

duration of layoff is also automatically updated. This revision was made to reduce respondent burden and enhance

the longitudinal capability of the CPS. This revision also will

produce more consistent month-to-month estimates of

duration. Previous research indicates that only about 25

percent of those unemployed in consecutive months who

received the former questionnaire (where duration was

collected independently each month) increased their reported

durations by 4 weeks plus or minus a week. (Polivka and

Rothgeb, 1993; Polivka and Miller, 1995). A very small bias

is introduced when a person has a brief (less than 3 or 4

6– 6
weeks) period of employment in between surveys. However, testing revealed that only 3.2 percent of those who

had been looking for work in consecutive months said that

they had worked in the interlude between the surveys.

Furthermore, of those who had worked, none indicated that

they had worked for 2 weeks or more.

Revisions Included in ‘‘Not in the Labor Force’’

Related Questions

Response options of retired, disabled, and unable to

work at key labor force items. In the former questionnaire, when individuals reported they were retired in response

to any of the labor force items, the interviewer was required

to continue asking whether they worked last week, were

absent from a job, were looking for work, and, in the

outgoing rotation, when they last worked and their job

histories. Interviewers commented that elderly respondents frequently complained that they had to respond to

questions that seemed to have no relevance to their own

situations.

In an attempt to reduce respondent burden, a response

category of ‘‘retired’’ was added to each of the key labor

force status questions in the revised questionnaire. If

individuals 50 years of age or older volunteer that they are

retired, they are immediately asked a question inquiring

whether they want a job. If they indicate that they want to

work, they are then asked questions about looking for work

and the interview proceeds as usual. If they do not want to

work, the interview is concluded and they are classified as

not in the labor force - retired. (If they are in the outgoing

rotation, an additional question is asked to determine

whether they worked within the last 12 months. If so, the

industry and occupation questions are asked about the last

job held.)

A similar change has been made in the revised questionnaire to reduce the burden for individuals reported to be

‘‘unable to work’’ or ‘‘disabled.’’ (Individuals who may be

‘‘unable to work’’ for a temporary period of time may not

consider themselves as ‘‘disabled’’ so both response options

are provided.) If a person is reported to be ‘‘disabled’’ or

‘‘unable to work’’ at any of the key labor force classification

items, a followup question is asked to determine whether

he/she can do any gainful work during the next 6 months.

Different versions of the followup probe are used depending on whether the person is disabled or unable to work.

Dependent interviewing for persons reported to be

retired, disabled, or unable to work. The revised questionnaire also is designed to use dependent interviewing

for persons reported to be retired, disabled, or unable to

work. An automated questionnaire increases the ease with

which information from the previous month’s interview can

be used during the current month’s interview.

Once it is reported that the person did not work during

the current month’s reference week, the previous month’s

status of retired (if a person is 50 years of age or older),

disabled, or unable to work is verified, and the regular

series of labor force questions is not asked. This revision

reduces respondent and interviewer burden.

Discouraged workers. The implementation of the Levitan

Commission’s recommendations on discouraged workers

resulted in one of the major definitional changes in the

1994 redesign. The Levitan Commission criticized the

former definition because it was based on a subjective

desire for work and questionable inferences about an

individual’s availability to take a job. As a result of the

redesign, two requirements were added: For persons to

qualify as discouraged, they must have engaged in some

job search within the past year (or since they last worked if

they worked within the past year), and they must currently

be available to take a job. (Formerly, availability was

inferred from responses to other questions; now there is a

direct question.)

Data on a larger group of persons outside the labor force

(one that includes discouraged workers as well as persons

who desire work, but give other reasons for not searching,

such as child care problems, family responsibilities, school,

or transportation problems) also are published regularly.

This group is made up of persons who want a job, are

available for work, and have looked for work within the past

year. This group is generally described as having some

marginal attachment to the labor force. Also beginning in

1994, questions on this subject are asked of the full CPS

sample rather than limited to a quarter of the sample,

permitting estimates of the number of discouraged workers

to be published monthly rather than quarterly.

From data available during the tests of the revised

questionnaire, it is clear that improvements in data quality

for labor force classification have been obtained as a result

of the redesign of the CPS questionnaire, and in general,

measurement error has been reduced. Data from respondent debriefings, interviewer debriefings, and response

analysis demonstrated that the revised questions are more

clearly understood by respondents and the potential for

labor force misclassification is reduced. Results from these

tests formed the basis for the design of the final revised

version of the questionnaire. This revised version was

tested in a separate year and a half parallel survey prior to

implementation as the official survey in January 1994. In

addition, from January 1994 through May 1994, the unrevised procedures were used with the parallel survey sample.

These parallel surveys were conducted to assess the effect

of the redesign on national labor force estimates. Estimates derived from the initial year and a half of the parallel

survey indicated that the redesign might increase the

unemployment rate by 0.5 percentage points. However,

subsequent analysis using the entire parallel survey indicates that the redesign did not have a statistically significant effect on the unemployment rate. (Analysis of the

effect of the redesign on the unemployment rate and other

labor force estimates can be found in Cohany, Polivka, and

6– 7
Rothgeb (1994). Analysis of the redesign on the unemployment rate along with a wide variety of other labor force

estimates using data from the entire parallel survey can be

found in Polivka and Miller (1995).)

CONTINUOUS TESTING AND IMPROVEMENTS

OF THE CURRENT POPULATION SURVEY AND

ITS SUPPLEMENTS

Experience gained during the redesign of the CPS has

demonstrated the importance of testing questions and

monitoring data quality. The experience, along with contemporaneous advances in research on questionnaire design,

also has helped inform the development of methods for

testing new or improved questions for the basic CPS and

its periodic supplements (Martin, 1987; Oksenberg, Bischoping, K., Cannell and Kalton, 1991; Campanelli, Martin,

and Rothgeb, 1991; Esposito et al., 1992; and Forsyth and

Lessler, 1991). Methods to continuously test questions and

assess data quality are discussed in Chapter 15. It is

important to note, however, that despite the benefits of

adding new questions and improving existing ones, changes

in the CPS should be approached cautiously and the

effects measured and evaluated. When possible, methods

to bridge differences caused by changes or techniques to

avoid the disruption of historical series should be included

in the testing of new or revised questions.

REFERENCES

Bischoping, K. (1989), An Evaluation of Interviewer

Debriefings in Survey Pretests, In C. Cannell et al. (eds.),

New Techniques for Pretesting Survey Questions, Chapter 2.

Campanelli, P.C., Martin, E.A., and Rothgeb, J.M. (1991),

The Use of Interviewer Debriefing Studies as a Way to

Study Response Error in Survey Data, The Statistician,

vol. 40, pp. 253-264.

Cohany, S.R., Polivka, A.E., and Rothgeb, J.M., (1994)

Revisions in the Current Population Survey Effective January 1994, Employment and Earnings, February 1994 vol.

41 no. 2 pp. 1337.

Copeland, K. and Rothgeb, J.M. (1990), Testing Alternative Questionnaires for the Current Population Survey,

Proceedings of the Section on Survey Research Methods, American Statistical Association, pp. 63-71.

Edwards. S.W., Levine, R., and Cohany, S.R., (1989),

Procedures for Validating Reports of Hours Worked and for

Classifying Discrepancies Between Reports and Validation

Totals, Proceedings of the Section on Survey Research

Methods, American Statistical Association.

Esposito, J.L., Rothgeb,J.M., Polivka, A.E., Hess, J.,

and Campanelli, P.C. (1992), Methodologies for Evaluating

Survey Questions: Some Lessons From the Redesign of

the Current Population Survey, Paper presented at the

International Conference on Social Science Methodology,

Trento, Italy, June, 1992.

Forsyth, B.H. and Lessler, J.T. (1991), Cognitive Laboratory Methods: A Taxonomy, in P.P Biemer, R.M. Groves,

L.E. Lyberg, N.A. Mathiowetz, and S. Sudman (eds.),

Measurement Errors in Surveys, New York: Wiley, pp.

393-418.

Fracasso, M.P. (1989), Categorization of Responses to

the Open-Ended Labor Force Questions in the Current

Population Survey, Proceedings of the Section on Survey Research Methods, American Statistical Association,

pp. 481-485.

Gaertner, G., Cantor, D., and Gay, N. (1989), Tests of

Alternative Questions for Measuring Industry and Occupation in the CPS, Proceedings of the Section on Survey

Research Methods, American Statistical Association.

Martin, E.A., (1987), Some Conceptual Problems in the

Current Population Survey, Proceedings of the Section

on Survey Research Methods, American Statistical Association, pp. 420-424.

Martin, E.A. and Polivka, A.E. (1995), Diagnostics for

Redesigning Survey Questionnaires: Measuring Work in

the Current Population Survey, Public Opinion Quarterly,

Winter 1995 vol. 59, no.4, pp. 547-67.

Oksenberg, L., Cannell, C., and Kalton, G. (1991), New

Strategies for Pretesting Survey Questions, Journal of

Official Statistics, vol. 7, no. 3, pp. 349-365.

Palmisano, M. (1989), Respondents Understanding of

Key Labor Force Concepts Used in the CPS, Proceedings

of the Section on Survey Research Methods, American

Statistical Association.

Polivka, A.E. and Miller, S.M. (1998),The CPS After the

Redesign Refocusing the Economic Lens, Labor Statistics Measurement Issues, edited by J. Haltiwanger et al.,

pp. 249-86.

Polivka, A.E. and Rothgeb, J.M. (1993), Overhauling the

Current Population Survey: Redesigning the Questionnaire, Monthly Labor Review, September 1993 vol. 116,

no. 9, pp. 10-28.

Rothgeb, J.M. (1982), Summary Report of July Followup

of the Unemployed, Internal memorandum, December 20,

1982.

Rothgeb, J.M., Polivka, A.E., Creighton, K.P., and Cohany,

S.R. (1992), Development of the Proposed Revised Current Population Survey Questionnaire, Proceedings of

the Section on Survey Research Methods, American

Statistical Association.

U.S. Department of Labor, Bureau of Labor Statistics

(1988), Response Errors on Labor Force Questions Based

on Consultations With Current Population Survey Interviewers in the United States. Paper prepared for the OECD

Working Party on Employment and Unemployment Statistics.

U.S. Department of Labor, Bureau of Labor Statistics

(1997), BLS Handbook of Methods, Bulletin 2490, April

1997.

6– 8
Chapter 7.

Conducting the Interviews

INTRODUCTION

Each month during interview week, field representatives

(FRs) and computer assisted telephone interviewers (CATI)

attempt to contact and interview a responsible person living

in each sample unit to complete a Current Population

Survey (CPS) interview. Typically, the week containing the

19th of the month is the interview week. The week containing the 12th is the reference week (i.e., the week about

which the labor force questions are asked). In December,

the week containing the 12th is used as interview week,

provided the reference week (in this case the week containing the 5th) falls entirely within the month of December.

As outlined in Chapter 3, households are in sample for 8

months. Each month, one-eighth of the households are in

sample for the first time (month-in-sample 1 (MIS-1)),

one-eighth for the second time, etc. Because of this,

different types of interviews (due to differing MIS) are

conducted by each FR within his/her assignment. An

introductory letter is sent to each household in sample prior

to its first and fifth month interviews. The letter describes

the CPS, announces the forthcoming visit, and provides

respondents with information regarding their rights under

the Privacy Act, the voluntary nature of the survey, and the

guarantees of confidentiality for the information they provide. Figure 7–1 shows the introductory letter sent to

sample units in the area administered by the Chicago

Regional Office. A personal visit interview is required for all

first month-in-sample households. This is due to the fact

that the CPS sample is strictly a sample of addresses. The

Census Bureau has no way of knowing who the occupants

of the sample household are, much less whether the

household is occupied or eligible for interview. (Note: In

some MIS-1 households, telephone interviews are conducted. This occurs when, during the initial personal contact, the respondent requests a telephone interview.)

NONINTERVIEWS AND HOUSEHOLD

ELIGIBILITY

The FR’s first task is to establish the eligibility of the

sample address for CPS. There are many reasons an

address may not be eligible for interview. The address may

have been converted to a permanent business, condemned

or demolished, or falls outside the boundaries of the

segment for which it was selected. Regardless of the

reason, such sample addresses are classified as Type C

noninterviews. The Type C units have no chance of becoming eligible for the CPS interview in future months, because

the condition is considered permanent. These addresses

are stricken from the roster of sample addresses and are

never visited again with regard to CPS. All households

classified as Type C undergo a full supervisory review of

the circumstances surrounding the case before such a

determination is made final.

Other sample addresses are also ineligible for CPS

interview. These are units which are intended for occupancy but are not occupied by any eligible individuals.

Reasons for such ineligibility include a vacant housing unit

(either for sale or rent), occupied entirely by individuals

who are not eligible for a CPS labor force interview

(individuals with a usual residence elsewhere (URE), in the

Armed Forces, or children under the age of 15), or other

reasons why a housing unit is temporarily not occupied.

Such units are classified as Type B noninterviews. These

noninterviews are considered unavoidable. Type B noninterview units have a chance of becoming eligible for

interview in future months, because the condition is considered temporary (e.g., a vacant unit could become occupied). Therefore, Type B units are reassigned to FRs in

subsequent months. These sample addresses remain in

sample for the entire 8 months that households are eligible

for interview. Each succeeding month, an FR visits the unit

to determine whether the unit has changed status, and

either continues the Type B classification, revises the

noninterview classification, or conducts an interview as

applicable. Some of these Type B households are found to

be eligible for the Housing Vacancy Survey (HVS). See

Chapter 11 for a description of the HVS.

Additionally, one final set of households is not interviewed for CPS; these are called Type A households.

These are households that the FR has determined as

eligible for a CPS interview, but for which no useable data

were collected. To be eligible the unit has to be occupied by

at least one person eligible for an interview (an individual

who is a civilian, at least 15 years old, and does not have

a usual residence elsewhere). Even though such households are eligible for interview, they are not interviewed

because the household members refuse, are absent during

the interviewing period, or are unavailable for other reasons. All Type A cases are subject to full supervisory review

before such a determination is made final. Every effort is

made to keep such noninterviews to a minimum. All Type A

cases remain in sample and are assigned for interview in

all succeeding months. Even in cases of confirmed refusals

7–1
Figure 7– 1. Introductory Letter

7– 2
(cases that still refuse to be interviewed despite supervisory attempts to convert the case), the FR must verify that

the same household still resides at that address before

submitting a Type A noninterview.

Figure 7– 2 shows how the three types of noninterviews

are classified and the various reasons for reaching such

results. Even if a unit is designated as a noninterview, FRs

are responsible for collecting information about the unit.

Figure 7– 3 lists the main housing unit items that are

collected for noninterviews and summarizes each item

briefly.

INITIAL INTERVIEW

If the unit is not classified as a noninterview, the FR

initiates the CPS interview. The FR attempts to interview a

knowledgeable adult household member (known as the

household respondent). The FRs are trained to ask the

questions exactly as worded as they appear on the computer screen. The interview begins with the verification of

the unit’s address and confirmation of its eligibility for a

CPS interview. Part 1 of Figure 7– 4 shows the household

items asked at the beginning of the interview. Once this is

established, the interview moves into the demographic

portion of the instrument. The primary task of this portion of

the interview is to establish the household’s roster (the

listing of all household residents at the time of the interview). At this point in the interview, the main concern is to

establish an individual’s usual place of residence. (These

rules are summarized in Figure 7– 5.) For all individuals

residing in the household without a usual residence elsewhere, a number of personal and family demographic

characteristics are collected. Part 1 of Figure 7– 6 shows

the demographic items asked in MIS-1 households.

These characteristics are the relationship to the reference person (the person who owns or rents the home),

parent or spouse pointers (if applicable), age, sex, marital

status, educational attainment, veteran’s status, current

Armed Forces status, race, and origin. As discussed in

Figure 7– 7, these characteristics are collected in an interactive format that includes a number of consistency edits

embedded in the interview itself. The goal is to collect as

consistent a set of demographic characteristics as possible. The final steps in this portion of the interview are to

verify the accuracy of the roster. To this end, a series of

questions is asked to ensure that all household members

have been accounted for. Before moving on to the labor

force portion of the interview, the FR is prompted to review

the roster and all data collected up to this point. A chance

is provided for the FR to correct any incorrect or inconsistent information at this time. The instrument then begins the

labor force portion of the interview.

In a household’s initial interview, a few additional characteristics are collected after completion of the labor force

portion of the interview. This information includes a question on family income and data on all household members’

countries of birth (along with the country of birth of his/her

father and mother) and, for the foreign born, data on year

of entry into the United States and citizenship status. See

Part 2 of Figure 7– 6 for a list of these items.

After completing the household roster, the FR collects

the labor force data described in Chapter 6. The labor force

data are collected from all civilian adult individuals (age 15

and older) who do not have a usual residence elsewhere.

To the greatest extent possible, the FR attempts to collect

this information from each eligible individual him/herself. In

the interest of timeliness and efficiency, however, a household respondent (any knowledgeable adult household member) generally is used to collect the data. Just over one-half

of the CPS labor force data are collected by self-response.

The bulk of the remainder is collected by proxy from the

household respondent. Additionally, in certain limited situations, collection of the data from a nonhousehold member

is allowed. All such cases receive direct supervisory review

before the data are accepted into the CPS processing

system.

Figure 7– 2. Noninterviews: Types A, B, and C

Note: See the CPS Interviewing Manual for more details

regarding the answer categories under each type of noninterview. See figure 7– 3 for a list of the main housing unit

items asked for noninterview and a brief description of what

each item asks.

TYPE A

1 No one home

2 Temporarily absent

3 Refusal

4 Other occupied

TYPE B

1 Vacant regular

2 Temporarily occupied by persons with usual residence

elsewhere

3 Vacant -- storage of household furniture

4 Unfit or to be demolished

5 Under construction, not ready

6 Converted to temporary business or storage

7 Unoccupied tent site or trailer site

8 Permit granted, construction not started

9 Other Type B -- specify

TYPE C

1 Demolished

2 House or trailer moved

3 Outside segment

4 Converted to permanent business or storage

5 Merged

6 Condemned

8 Unused line of listing sheet

9 Other -- specify

7– 3
SUBSEQUENT MONTHS’ INTERVIEWS

For households in sample for the second, third, and

fourth months, the FR has the option of conducting the

interview over the telephone. Use of this interviewing mode

must be approved by the respondent. Such approval is

obtained at the end of the first month’s interview upon

completion of the labor force and supplemental, if any,

questions. This is the preferred method for collecting the

data; it is much more time and cost efficient. We obtain

approximately 85 percent of interviews in these 3 monthsin-samples (MIS) via the telephone. See Part 2 of Figure

7– 4 for the questions asked to determine household eligibility and obtain consent for the telephone interview. A

personal visit interview attempt is required for the fifthmonth interview. After this initial attempt, a telephone

interview may be conducted provided the original household still occupies the sample unit. This is after a sample

unit’s 8-month dormant period and is used to reestablish

rapport with the household. Fifth-month households are

more likely than any other MIS household to be a replacement household. A replacement household is one in which

all the previous month’s residents have moved out and

been replaced by an entirely different group of residents.

This can and does occur in any MIS except for MIS 1

households. As with their MIS 2, 3, and 4 counterparts,

households in their sixth, seventh, and eighth MIS are

eligible for telephoning interviewing. Once again we collect

about 85 percent of these cases via the telephone.

The first thing the FR does in subsequent interviews is

update the household roster. The instrument presents a

screen (or a series of screens for MIS 5 interviews) that

Figure 7– 3. Noninterviews: Main Housing Unit Items Asked for Types A, B, and C

Note: This list of items is not all inclusive. The list covers only the main data items and does not include related items used

to arrive at the final response (e.g., probes and verification screens). See CPS Interviewing Manual for illustrations of the

actual instrument screens for all CPS items.

Housing Unit Items for Type A Cases

Item Name Item Asks

1 TYPEA Which specific kind of Type A is the case.

2 ABMAIL What is the property’s mailing address.

3 PROPER If there is any other building in the property (occupied or vacant).

4 ACCES-scr If access to the household is direct or through another unit; this item is answered by the interviewer based on observation.

5 LIVQRT What type of housing unit is it (house/apt., mobile home or trailer, etc.); this item is answered by the interviewer based on

observation.

6 INOTES-1 If the interviewer wants to make any notes about the case that might help with the next interview.

Housing Unit Items for Type B Cases

Item Name Item Asks

1 TYPEB Which specific kind of Type B is the case.

2 ABMAIL What is the property’s mailing address.

3 BUILD If there are any other units (occupied or vacant) in the unit.

4 FLOOR If there are any occupied or vacant living quarters besides this one on this floor.

5 PROPER If there is any other building in the property (occupied or vacant).

6 ACCES-scr If access to the household is direct or through another unit; this item is answered by the interviewer based on observation.

7 LIVQRT What is the type of housing unit (house/apt., mobile home or trailer, etc.); this item is answered by the interviewer based

on observation.

8 SEASON If the unit is intended for occupancy year round, by migratory workers, or seasonally.

9 BCINFO What are the name, title, and phone number of contact who provided Type B or C information; or if the information was

obtained by interviewer observation.

10 INOTES-1 If the interviewer wants to make any notes about the case that might help with the next interview.

Housing Unit Items for Type C Cases

Item Name Item Asks

1 TYPEC Which specific kind of Type C is the case.

2 PROPER If there is any other building in the property (occupied or vacant).

3 ACCES-scr If access to the household is direct or through another unit; this item is answered by the interviewer based on observation.

4 LIVQRT What type of housing unit is it (house/apt., mobile home or trailer, etc.); this item is answered by the interviewer based on

observation.

5 BCINFO What are the name, title, and phone number of contact who provided Type B or C information; or if the information was

obtained by interviewer observation.

6 INOTES-1 If the interviewer wants to make any notes about the case that might help with the next interview.

7– 4
verifies the accuracy of the roster. Since households in MIS

5 are returning to sample after an 8-month hiatus, additional probing questions are asked to correctly establish

the household’s current roster and update some characteristics. See Figure 7– 8 for a list of major items asked in

MIS 5 interviews. If there are any changes, the instrument

goes through the steps necessary to add or delete an

individual(s). Once all the additions/deletions are completed, the instrument then prompts the FR/interviewer to

correct or update any relationship items (e.g., relationship

to reference person, marital status, and parent and spouse

pointers) that may be subject to change. After completion

of the appropriate corrections, the instrument will then take

the interview to any items, such as educational attainment,

that require periodic updating. The labor force interview in

MIS 2, 3, 5, 6, and 7 collects the same information as the

MIS 1 interview. MIS 4 and 8 interviews are different in

several respects. Additional information collected in these

interviews includes a battery of questions for employed

wage and salary workers on their usual weekly earnings at

their only or main job. For all individuals who are multiple

jobholders, information is collected on the industry and

occupation of their second job. For individuals who are not

in the labor force, we obtain additional information on their

previous labor force attachment.

Dependent interviewing is another enhancement contributed to the subsequent months’ interviewing by the

computerization of the labor force interview. Information

collected in the previous month’s interview is imported into

the current interview to ease response burden and improve

the quality of the labor force data. This change is most

noticeable in the collection of main job industry and occupation data. By importing the previous month’s job description into the current month’s interview, we can ascertain

whether an individual has the same job as he/she had the

preceding month. Not only does this enhance analysis of

month-to-month job mobility, it also frees the FR/interviewer

from re-entering the detailed industry and occupation descriptions. This speeds the flow through the labor force interview. Other information collected using dependent interviewing is the duration of unemployment (either job search

or layoff duration), and the not-in-labor-force subgroups of

Figure 7– 4. Interviews: Main Housing Unit Items Asked in MIS 1 and Replacement Households

Note: This list of items is not all inclusive. The list covers only the main data items and does not include related items used

to arrive at the final response (e.g., probes and verification screens). See CPS Interviewing Manual for illustrations of the

actual instrument screens for all CPS items.

Part 1. Items Asked at the Beginning of the Interview

Item Name Item Asks

1 INTRO-b If interviewer wants to classify case as a noninterview.

2 NONTYP What type of noninterview the case is (A, B, or C); asked depending on answer to INTRO-b.

3 VERADD What is the street address (as verification).

4 MAILAD What is the mailing address (as verification).

5 STRBLT If the structure was originally built before or after 4/1/90.

6 BUILD If there are any other units (occupied or vacant) in the building.

7 FLOOR If there are any occupied or vacant living quarters besides the sample unit on the same floor.

8 PROPER If there is any other building in the property (occupied or vacant).

9 TENUR-scrn If unit is owned, rented, or occupied without paid rent.

10 ACCES-scr If access to household is direct or through another unit; this item is answered by the interviewer (not read to the

respondent).

11 MERGUA If the sample unit has merged with another unit.

12 LIVQRT What type of housing unit is it (house/apt., mobile home or trailer, etc.); this item is answered by the interviewer (not read

to the respondent).

13 LIVEAT If all persons in the household live or eat together.

14 HHLIV If any other household on the property lives or eats with the interviewed household.

Part 2. Items Asked at the End of the Interview

Item Name Item Asks

15 TELHH-scrn If there is a telephone in the unit.

16 TELAV-scrn If there is a telephone elsewhere on which people in this household can be contacted; asked depending on answer to

TELHH-scrn.

17 TELWHR-scr If there is a telephone elsewhere, where is the phone located; asked depending on answer to TELAV-scrn.

18 TELIN-scrn If a telephone interview is acceptable.

19 TELPHN What is the phone number and whether it is a home or office phone.

20 BSTTM-scrn When is the best time to contact the respondent.

21 NOSUN-scrn If a Sunday interview is acceptable.

22 THANKYOU If there is any reason why the interviewer will not be able to interview the household next month.

23 INOTES-1 If the interviewer wants to make any notes about the case that might help with the next interview; also asks for a list of

names/ages of ALL additional persons if there are more than 16 household members.

7– 5
retired and disabled. Dependent interviewing is not used in

the MIS 5 interviews nor is it used for any of the data

collected solely in MIS 4 and 8.

Another recent development in the computerization of

the CPS is the use of centralized facilities for computer

assisted telephone interviewing (CATI). CPS has been

experimenting with the use of CATI interviewing since 1983

and has gradually been introducing it into the CPS operations. The first use of CATI in production was the Tri-Cities

Test which started in April 1987. Since that time, more and

more cases have been sent to our CATI facilities for

interviewing. Currently, about 9,200 cases are sent to the

facilities each month. The facilities generally interview about 88-90 percent of

the cases assigned to them. The net

result is that about 15 percent of all

CPS interviews are completed at a CATI

facility.

Three facilities are in use: Hagerstown, MD; Tuscon, AZ; and Jeffersonville, IN. During the time of the initial

phasein of CATI data, and continuing to

this day, there is a controlled selection

criteria (see Chapter 4) that allows the

analysis of any effects of the CATI

collection methodology. See Chapter

16 for a discussion of CATI effects on

the labor force data. One of the main

reasons for using CATI is to ease the

recruiting and hiring effort in hard to

enumerate areas. It is much easier to

hire an individual to work in the CATI

facilities than it is to hire individuals to

work as FRs in most major metropolitan

areas. This is especially true in most

large cities. Most of the cases sent to

CATI are from the major metropolitan

areas. CATI is not used in most rural

areas because the sample sizes in these

areas are small enough to keep a single

FR busy the entire interview week without causing any undo hardship in completing the assignment.Aconcerted effort

is made to hire some Spanish speaking

interviewers in the Tuscon Telephone

Center. This allows us to send Spanish

speaking cases to this facility for interviewing. For obvious reasons stated

above, no MIS 1 or 5 cases are sent to

the facilities.

As stated above, the facilities complete all but 10-12 percent of the cases

sent to them. These uncompleted cases

are recycled back to the field for followup and final determination. For this

reason, the CATI facilities generally cease

interviewing the labor force portions of

the interview on Wednesday of interview week. This allows the field staff 3 to 4 days to check

on the case and complete any required interviewing or

classify the case as a noninterview. The field staff is highly

successful in completing these cases as interviews, generally interviewing about 80-85 percent of the cases. The

cases that are sent to the CATI facilities are selected by the

supervisors in each of the regional offices. The FRs provide

information on a household’s probable acceptance of a

CATI interview, but ultimately it is the supervisor’s decision.

This decision is based on the FR’s input and the need to

balance workloads and meet specific goals on the number

Figure 7– 5. Summary Table for Determining Who Is To Be Included As

a Member of the Household

Include as

member of

household

A. PERSONS STAYING IN SAMPLE UNIT AT TIME OF INTERVIEW

Person is member of family, lodger, servant, visitor, etc.

1. Ordinarily stays here all the time (sleeps here) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes

2. Here temporarily - no living quarters held for person elsewhere . . . . . . . . . . . . . . . . . Yes

3. Here temporarily - living quarters held for person elsewhere . . . . . . . . . . . . . . . . . . . . No

Person is in Armed Forces

1. Stationed in this locality, usually sleeps here . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes

2. Temporarily here on leave - stationed elsewhere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . No

Person is a student - Here temporarily attending school - living quarters held for

person elsewhere

1. Not married or not living with immediate family. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . No

2. Married and living with immediate family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes

3. Student nurse living at school . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes

B. ABSENT PERSON WHO USUALLY LIVES HERE IN SAMPLE UNIT

Person is inmate of institutional special place - Absent because inmate in a

specified institution regardless of whether or not living quarters held for person

here . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . No

Person is temporarily absent on vacation, in general hospital, etc. (including

veterans’ facilities that are general hospitals) - Living quarters held here for person . Yes

Person is absent in connection with job

1. Living quarters held here for person - temporarily absent while ‘‘on the road’’ in

connection with job (e.g., traveling salesperson, railroad conductor, bus driver) . . . Yes

2. Living quarters held here and elsewhere for person but comes here infrequently

(e.g., construction engineer) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . No

3. Living quarters held here at home for unmarried college student working away

from home during summer school vacation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes

Person is in Armed Forces - was member of this household at time of induction

but currently stationed elsewhere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . No

Person is a student in school - away temporarily attending school - living quarters

held for person here

1. Not married or not living with immediate family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes

2. Married and living with immediate family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . No

3. Attending school overseas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . No

4. Student nurse living at school . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . No

C. EXCEPTIONS AND DOUBTFUL CASES

Person with two concurrent residences - determine length of time person has

maintained two concurrent residences

1. Has slept greater part of that time in another locality. . . . . . . . . . . . . . . . . . . . . . . . . . . No

2. Has slept greater part of that time in sample unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes

Citizen of foreign country temporarily in the United States

1. Living on premises of an Embassy, Ministry, Legation, Chancellery, or Consulate . No

2. Not living on premises of an Embassy, Ministry, etc.

a. Living here and no usual place of residence elsewhere in the United States . . . . Yes

b. Visiting or traveling in the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . No

7– 6
Figure 7– 6. Interviews: Main Demographic Items Asked in MIS 1 and Replacement Households

Note: This list of items in not all inclusive. The list covers only the main data items and does not include related items used

to arrive at the final response (e.g., probes and verification screens). See CPS Interviewing Manual for illustrations of the

actual instrument screens for all CPS items.

Part 1. Items Asked at Beginning of Interview

Item Name Item Asks

1 HHRESP What is the line number of the household respondent.

2 RPNAME What is the name of the reference person (i.e., person who owns/rents home, whose name should appear on line number

1 of the household roster).

3 NEXTNM What is the name of the next person in the household (lines number 2 through a maximum of 16).

4 VERURE If the sample unit is the person’s usual place of residence.

5 HHMEM-scrn If the person has his/her usual place of residence elsewhere; asked only when the sample unit is not the person’s usual

place of residence.

6 SEX-scrn What is the person’s sex; this item is answered by the interviewer (not read to the respondent).

7 MCHILD If the household roster (displayed on the screen) is missing any babies or small children.

8 MAWAY If the household roster (displayed on the screen) is missing usual residents temporarily away from the unit (e.g., traveling,

at school, in a hospital).

9 MLODGE If the household roster (displayed on the screen) is missing any lodgers, boarders, or live-in employees.

10 MELSE If the household roster (displayed on the screen) is missing anyone else staying in the unit.

11 RRP-nscr How is the person related to the reference person; the interviewer shows the respondent a flashcard from which he/she

chooses the appropriate relationship category.

12 VR-NONREL If the person is related to anyone else in the household; asked only when the person is not related to the reference person.

13 SBFAMILY Who on the household roster (displayed on the screen) is the person related to; asked depending on answer to

VR-NONREL.

14 PAREN-scrn What is the parent’s line number.

15 BMON-scrn

BDAY-scrn

BYEAR-scrn

What is the month of birth.

What is the day of birth.

What is the year of birth.

16 AGEVR How many years old is the person (as verification).

17 MARIT-scrn What is the person’s marital status; asked only of persons 15+ years old.

18 SPOUS-scrn What is the spouse’s line number; asked only of persons 15+ years old.

19 AFEVE-scrn If the person ever served on active duty in the U.S. Armed Forces; asked only of persons 17+ years old.

20 AFWHE-scrn When did the person serve; asked only of persons 17+ years old who have served in the U.S. Armed Forces.

21 AFNOW-scrn If the person is now in the U.S. Armed Forces; asked only of persons 17+ years old who have served in the U.S. Armed

Forces. Interviewers will continue to ask this item each month as long as the answer is ‘‘yes.’’

22 EDUCA-scrn What is the highest level of school completed or highest degree received; asked only of persons 15+ years old. This item

is asked for the first time in MIS 1, and then verified in MIS 5 and in specific months (i.e., February, July, and October).

23 RACE-scrn What is the person’s race; the interviewer shows the respondent a flashcard from which he/she chooses the appropriate

race category.

24 ORIGI-scrn What is the person’s origin; the interviewer shows the respondent a flashcard from which he/she chooses the appropriate

origin category.

25 SSN-scrn What is the person’s social security number; asked only of persons 15+ years old. This item is asked only from December

through March, regardless of month in sample.

26 CHANGE If there has been any change in the household roster (displayed with full demographics) since last month, particularly in

the marital status.

Part 2. Items Asked at the End of the Interview

Item Name Item Asks

27 NAT1 What is the person’s country of birth.

28 MNAT1 What is his/her mother’s country of birth.

29 FNAT1 What is his/her father’s country of birth.

30 CITZN-scr If the person is a citizen of the U.S.; asked only when neither the person nor both of his/her parents were born in the U.S.

or U.S. territory.

31 CITYA-scr If the person was born a citizen of the U.S.; asked when the answer to CITZN-scr is yes.

32 CITYB-scr If the person became a citizen of the U.S. through naturalization; asked when the answer to CITYA-scr is no.

33 INUSY-scr When did the person come to live in the U.S.; asked of U.S. citizens born outside of the 50 states (e.g., Puerto Ricans,

U.S. Virgin Islanders, etc.) and of non-U.S. citizens.

34 FAMIN-scrn What is the household’s total combined income during the past 12 months; the interviewer shows the respondent a

flashcard from which he/she chooses the appropriate income category.

7– 7
Figure 7– 7. Demographic Edits in the CPS Instrument

Note: The following list of edits is not all inclusive; only the major edits are described. The demographic edits in the CPS

instrument take place while the interviewer is creating or updating the roster. After the roster is in place, the interviewer may

still make changes to the roster (e.g., add/delete persons, change variables) at the Change screen. However, the instrument

does not include any more demographic edits past the Change screen, because inconsistencies—between the data

collected at that point and the data collected earlier in the interview—are difficult to resolve without risking endless loops.

Education Edits

1. The instrument will force interviewers to probe if the education level is inconsistent with the person’s age; interviewers will probe for the correct

response if the education entry fails any of the following range checks:

• If 19 years old, the person should have an education level below the level of a master’s degree (EDUCA-scrn < 44).

• If 16-18 years old, the person should have an education level below the level of a bachelor’s degree (EDUCA-scrn 43).

• If younger than 15 years old, the person should have an education below college level (EDUCA-scrn < 40).

2. The instrument will force the interviewer to probe before it allows him/her to lower an education level reported in a previous month in sample.

Veterans’ Edit

1. The instrument will display only the answer categories that apply (i.e., periods of service in the Armed Forces), based on the person’s age. For

example, the instrument will not display certain answer categories for a 40 year old veteran (e.g., World War I, World War II, Korean war), but it

will display them for a 99 year old veteran.

Nativity Edits

1. The instrument will force the interviewer to probe if the person’s year of entry into the U.S. is earlier than his/her year of birth.

Spouse Line Number Edits

1. If the household roster does not include a spouse for the reference person, the instrument will set the reference person’s SPOUSE line number

equal to zero. It will also omit the first answer category (i.e., married spouse present) when it asks for the marital status of the reference person).

2. The instrument will not ask SPOUSE line number for both spouses in a married couple. Once it obtains the SPOUSE line number for the first

spouse on the roster, it will fill the second spouse’s SPOUSE line number with the line number of the first spouse. Likewise, the instrument will

not ask marital status for both spouses. Once it obtains the marital status for the first spouse on the roster, it will set the second spouse’s marital

status equal to that of his/her spouse.

3. Before assigning SPOUSE line numbers, the instrument will verify that there are opposing sex entries for each spouse. If both spouses are of the

same sex, the interviewer will be prompted to fix whichever one is incorrect.

4. For each household member with a spouse, the instrument will ensure that his/her SPOUSE line number is not equal to his/her own line number,

nor to his/her own PARENT line number (if any). In both cases, the instrument will not allow the interviewer to make the wrong entry and will display

a message telling the interviewer to ‘‘TRY AGAIN.’’

Parent Line Number Edits

1. The instrument will never ask for the reference person’s PARENT line number. It will set the reference person’s PARENT line number equal to

the line number of whomever on the roster was reported as the reference person’s parent (i.e., an entry of 24 at RRP-nscr), or equal to zero if

no one on the roster fits that criteria.

2. Likewise, for each individual reported as the reference person’s child (an entry of 22 at RRP-nscr), the instrument will set his/her PARENT line

number equal to the reference person’s line number, without asking for each individual’s PARENT line number.

3. The instrument will not allow more than two parents for the reference person.

4. If the individual is the reference person’s brother or sister (i.e., an entry of 25 at RRP-nscr), the instrument will set his/her PARENT line number

equal to the reference person’s PARENT line number. However, the instrument will not do so without first verifying that the parent that both siblings

have in common is indeed the one whose line number appears in the reference person’s PARENT line number (since not all siblings have both

parents in common).

5. For each household member, the instrument will ensure that his/her PARENT line number is not equal to his/her own line number. In such a case,

the instrument will not allow the interviewer to make the wrong entry and will display a message telling the interviewer to ‘‘TRY AGAIN.’’

7– 8
of cases sent to the facilities. The selection of cases to

send to CATI also must take into consideration statistical

aspects of the selection. With this constraint in mind,

selection of cases is controlled by a series of random group

numbers assigned at the time of sample selection that

either allow or disallow the assignment of a case to CATI.

This control procedure allows the Census Bureau to continue to analyze statistical differences that may exist between

the data collected in CATI mode versus data collected in

CAPI mode.

Figures 7– 9 and 7– 10 show the results of a typical

month’s (December 1996) CPS interviewing. Figure 7– 9

lists the outcomes of all the households in the CPS sample.

The expectations for normal monthly interviewing are a

Type A rate around 6.0 percent with an overall noninterview

rate in the 20-22 percent range. In December 1996, the

Type A rate was 5.81. For the April 1996 - March 1997

period, the CPS Type A rate was 6.36 percent. The months

of January, February, and March 1996 were not used due

to the effects of the Federal government shut downs. The

overall noninterview rate for December 1996 was 20.34

percent, compared to the 12-month average of 20.75

percent.

Figure 7– 10 shows the rates of personal and telephone

interviewing in December 1996. It is highly consistent with

the usual monthly results for personal and telephone

interviews.

Figure 7– 8. Interviews: Main Items (Housing Unit and Demographic) Asked in MIS 5 Cases

Note: This list of items is not all inclusive. The list covers only the main data items and does not include related items used

to arrive at the final response (e.g., probes and verification screens). See CPS Interviewing Manual for illustrations of the

actual instrument screens for all CPS items.

Housing Unit Items

Item Name Item Asks

1 HHNUM-vr If household is a replacement household.

2 VERADD What is the street address (as verification).

3 CHNGPH If current phone number needs updating.

4 MAILAD What is the mailing address (as verification).

5 TENUR-scrn If unit is owned, rented, or occupied without paid rent.

6 TELHH-scrn If there is a telephone in the unit.

7 TELIN-scrn If a telephone interview is acceptable.

8 TELPHN What is the phone number and whether it is a home or office phone.

9 BSTTM-scrn When is the best time to contact the respondent.

10 NOSUN-scrn If a Sunday interview is acceptable.

11 THANKYOU If there is any reason why the interviewer will not be able to interview the household next month.

12 INOTES-1 If the interviewer wants to make any notes about the case that might help with the next interview; also asks for a list of

names/ages of ALL additional persons if there are more than 16 household members.

Demographic Items

Item Name Item Asks

13 RESP1 If respondent is different from the previous interview.

14 STLLIV If all persons listed are still living in the unit.

15 NEWLIV If anyone else is staying in the unit now.

16 MCHILD If the household roster (displayed on the screen) is missing any babies or small children.

17 MAWAY If the household roster (displayed on the screen) is missing usual residents temporarily away from the unit (e.g., traveling,

at school, in hospital).

18 MLODGE If the household roster (displayed on the screen) is missing any lodgers, boarders, or live-in employees.

19 MELSE If the household roster (displayed on the screen) is missing anyone else staying in the unit.

20 EDUCA-scrn What is the highest level of school completed or highest degree received; asked for the first time in MIS 1, and then verified

in MIS 5 and in specific months (i.e., February, July, and October).

21 CHANGE If, since last month, there has been any change in the household roster (displayed with full demographics), particularly in

the marital status.

7– 9
Figure 7– 9. Interviewing Results (December 1996)

Description

Total Assigned Workload . . . . . . . . . . . . . . . . . . . . . . . . . . . 60,234

Interviewed Cases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47,981

Response Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94.19%

Interviewed CAPI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41,464

CAPI Partial Interviews. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Assigned CATI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,112

Interviewed CATI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,517

CATI Partial Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

CATI Recycles to Region. . . . . . . . . . . . . . . . . . . . . . . . . . . 595

Noninterviews. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12,253

NI Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.34%

Type A Noninterviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,960

Type A Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.81%

No One Home . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 638

Temporarily Absent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292

Refused. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,928

Other Occupied . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Accessed Instr. - No Progress ...................... 3

Type B Noninterviews. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8,921

Type B Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.90%

Armed Forces Occupied or < age 15 . . . . . . . . . . . . . . . . 93

Temp. Occupied With Persons With URE . . . . . . . . . . . . 1,254

Vacant Regular (REG) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,005

Vacant HHLD Furniture Storage. . . . . . . . . . . . . . . . . . . . . 374

Unfit, to be Demolished . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424

Under Construction, Not Ready . . . . . . . . . . . . . . . . . . . . . 259

Converted to Temp. Business or Storage. . . . . . . . . . . . . 186

Unoccupied Tent or Trailer Site . . . . . . . . . . . . . . . . . . . . . 274

Permit Granted, Construction Not Started . . . . . . . . . . . . 37

Other Type B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Type C Noninterviews. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372

Type C Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.62%

Demolished. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

House or Trailer moved . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Outside Segment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Converted to Perm. Business or Storage. . . . . . . . . . . . . 26

Merged . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Condemned ...................................... 1

Built after April 1, 1980. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Unused Serial No#/Listing Sheet Line. . . . . . . . . . . . . . . . 73

Other Type C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Figure 7– 10. Telephone Interview Rates (December 1996)

Total

Telephone Personal Indeterminate

Number Percent Number Percent Number Percent

Total . . . . . . . . . . . . 47,981 34,285 71.5 13,548 28.2 148 0.3

MIS 1&5 . . . . . . . . 11,710 2,677 22.9 8,968 76.6 65 0.6

MIS 2-4, 6-8 . . . . . 36,271 31,608 87.1 4,580 12.6 83 0.2

7– 10
Chapter 8.

Transmitting the Interview Results

INTRODUCTION

With the advent of the completely electronic interviewing

environment, transmission of the interview results took on

a heightened importance to the field representative (FR).

Until recently, the FR dropped the completed interviews in

the mail and waited for the U.S. Postal Service to complete

the task. Now, the data must be prepared for transmission

and sent out via computer. This chapter provides a summary of these procedures and how the data are prepared

for production processing.

The system for transmission of data is centralized at

headquarters. All data transfers must pass through headquarters even if that is not the final destination of the

information. The system was designed this way for ease of

management and to ensure uniformity of procedures within

a given survey and between different surveys. The transmission system was designed to satisfy the following

requirements:

• Provide minimal user intervention.

• Upload and/or download in one transmission.

• Transmit all surveys in one transmission.

• Transmit software upgrades with data.

• Maintain integrity of software and assignment.

• Prevent unauthorized access.

• Handle mail messages.

Another important aspect about the system is the headquarters central database system cannot initiate transmissions. Either the FR or the regional offices (ROs) must

initiate any transmissions. Computers in the field are not

connected to the headquarters computers at all times.

Instead, the field computers contact the headquarters

computers and all data exchanges take place at the time of

such call-ins. The central database system contains a

group of servers that store messages and case information

required by the FRs or the ROs. When an interviewer calls

in, the transfer of data from the FR’s computer to headquarters computers is completed first and then any outgoing data are transferred to the FR’s computer.

A major concern with the use of a electronic method of

transmitting interview data is the need for complete security of these data. Both the Census Bureau and the Bureau

of Labor Statistics (BLS) are required to honor the pledge

of confidentiality given to all Current Population Survey

(CPS) respondents. The system was designed to safeguard this pledge. All transmissions between the headquarters central database and the FR’s computers are compacted and encrypted for this reason. All transmissions

between the headquarters, the ROs, the Centralized Telephone Facilities, and Jeffersonville are over secure telecommunications lines that are leased by the Census

Bureau and are accessible only by Census Bureau employees through their computers.

TRANSMISSION OF INTERVIEW DATA

Telecommunications exchanges between a FR and the

central database system usually take place once per day

during the interview period. Additional transmissions may

be made at any time as needed. Each transmission is a

batch process in which all relevant files are automatically

transmitted and received.

Each FR is expected to make a telecommunications

transmission at the end of every work day during the

interview period. This is usually accomplished by a preset

transmission; that is, each evening the FR sets the FR’s

computer up to transmit the completed work and then

hooks the computer up to a modem within the FR’s

residence. During the night, at a preset time, the computer

automatically dials into the headquarters central database

and transmits the completed cases. At the same time, the

central database returns any messages and other data to

complete the FR’s assignment. It is also possible for a FR

to make an immediate transmission at any time of the day.

The results of such a transmission are identical to a preset

transmission, both in the types and directions of various

data transfers, but allow the FR instant access to the

central database as necessary. This type of procedure is

used primarily around the time of closeout when a FR

might have one or two straggler cases that need to be

received by headquarters before field staff can close out

the month’s workload and production processing can begin.

The RO staff may also perform a daily data transmission

sending in cases that required supervisory review or were

completed at the RO.

Centralized Telephone Facility Transmission

Most of the cases sent to a Centralized Telephone

Facility are successfully completed as computer assisted

telephone interviews (CATI). Those that cannot be completed from the telephone center are transferred to a FR

8–1
prior to the end of the interview period. These cases are

called ‘‘CATI recycles.’’ Each telephone facility makes daily

transmissions of completed cases and recycles to the

headquarters database. All the completed cases are batched

for further processing. Each recycled case is transmitted

directly to the computer of the FR who is assigned the

case. Case notes that include the reason for recycle are

also transmitted to the FR to assist in follow-up. CATI

recycles are placed on the server when a daily transmission is performed for the region. Daily transmissions are

performed automatically for each region every hour during

the CPS interview week. This provides cases to the FRs

that are reassigned or recycled.

The RO staff also monitor the progress of the CATI

recycled cases Recycle Report. All cases that are sent to a

CATI facility are also assigned to a FR by the RO staff. The

RO staff keep a close eye on recycled cases to ensure that

they are completed on time and to monitor the reasons for

recycling so that future recycling can be minimized. The

reason for recycling is also closely monitored to ensure that

recycled cases are properly handled by the CATI facility

and correctly identified as CATI eligible by the FR.

Transmission of Interviewed Data From the Centralized

Database

Each day during the production cycle (see Figure 8-1 for

an overview of the daily processing cycle) the field staff

send to the production processing system at headquarters

four files containing the results of the previous day’s

interviewing. A separate file is received from each of the

CATI facilities, and all data received from the FRs are

batched together and sent as a single file. At this time,

cases requiring industry and occupation coding (I&O) are

identified, and a file of such cases is created. This file is

then used by Jeffersonville coders to assign the appropriate I&O codes. This cycle repeats itself until all data are

received by headquarters, usually Tuesday or Wednesday

of the week after interviewing begins. By the middle of the

interview week, the CATI facilities close down, usually

Wednesday, and only one file is received daily by headquarters production processing system. This continues

until field closeout day when multiple files may be sent to

expedite the preprocessing.

Data Transmissions for I&O Coding

The I&O data are not actually transmitted to Jeffersonville. Rather, the coding staff directly access the data on

headquarters computers through the use of remote monitors in Jeffersonville. When a batch of data has been

completely coded, that file is returned to headquarters, and

the appropriate data are loaded into the headquarters

database. See Chapter 9 for a complete overview of the

I&O coding and processing system.

Once these transmission operations have been completed, final production processing begins. Chapter 9 provides a description of the processing operation.

8– 2
Figure 8– 1. Overview of CPS Monthly Operations

Week 1

(week containing

the 5th)

Week 2

(week containing the 12th)

Week 3

(week containing the 19th)

Week 4

(week containing the 

Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Field reps pick upcomputerizedquestionnaire viamodem

Field reps and CATIinterviewers completepractice interviews andhome studies

Field reps pick upassignments viamodem

CATI interviewing

CATI facilitiestransmitcompleted work toheadquarters nightly

CATIinterviewingends*

Allassignmentscompletedexcept fortelephoneholds

Allinterviewscompleted

ROscompletefinalfieldcloseout CAPI interviewing ● FRs transmit completed work to headquarters nightly

● ROs monitor interviewing assignments

Processing begins● Files received overnight are checked in by ROs and

headquarters daily

● Headquarters performs initial processing and sends eligible

cases to Jeffersonville for industry and occupation coding

Initialprocesscloseout

Jeffersonville sends back completed I&O cases

J’villecloseoutHeadquarters performfinal compuprocessing● Edits● Recode● Weighti

* CATI interviewing is extended in March and certain other months.
Chapter 9.

Data Preparation

INTRODUCTION

For the Current Population Survey (CPS), the goal of

post data collection processing is to transform a raw data

file, as collected by interviewers, into a microdata file that

can be used to produce estimates. There are several

processes needed in order for this to be accomplished. The

raw data files must be read and processed. Textual industry

and occupation responses are coded. Even though some

editing takes place in the instrument at the time of interview

(see Chapter 7), further editing is required once all the data

are received. Editing and imputations are performed to

improve the consistency and completeness of the microdata. New data items are created based upon responses to

multiple questions. All of this is in preparation for weighting

and estimation (see Chapter 10).

DAILY PROCESSING

For a typical month, computer assisted telephone interviewing (CATI) starts on Sunday of the week containing the

19th of the month and continues through Wednesday of the

same week. The answer files representing these interviews

are received on a daily basis from Monday through Thursday of this interview week. Separate files are received for

each of the three CATI facilities: Hagerstown, Tucson, and

Jeffersonville. Computer assisted personal interviewing

(CAPI) also begins on the same Sunday and continues

through Monday of the following week. The CAPI answer

files are received for each day of interviewing, until all the

interviewers and regional offices have transmitted the

workload for the month. This is generally completed by

Wednesday of the following week. These answer files are

read, and various computer checks are performed to

ensure the data can be accepted into the CPS processing

system. These checks include, but are not limited to,

ensuring the successful transmission and receipt of the

files, item range checks, and rejection of invalid cases.

Files containing records needing three-digit industry and

occupation (I&O) codes are electronically sent to Jeffersonville for assignment of these codes. Once Jeffersonville has

completed the I&O coding, the files are electronically

transferred back to headquarters, where the codes are

placed on the CPS production file. When all of the expected

data for the month are accounted for and all of Jeffersonville’s I&O coding files have been returned and placed on

the appropriate records on the data file, editing and imputation are performed.

INDUSTRY AND OCCUPATION (I&O) CODING

The industry and occupation coding operation for a

typical month requires ten coders for a period of just over 1

week to code data from 27,000 individuals.1 At other times,

these same coders are available for similar activities on

other surveys, where their skills can be maintained. The

volume of codes has decreased significantly with the

introduction of dependent interviewing for I&O codes (see

Chapter 6). Only the new monthly CPS cases, as well as

those persons whose industry or occupation has changed

since the previous month of interviewing, are sent to

Jeffersonville to be coded. For those persons whose

industry and occupation have not changed, the three-digit

codes are brought forward from the previous month of

interviewing and require no further coding.

A computer assisted industry and occupation coding

system is used by the Jeffersonville I&O coders. Files of all

eligible I&O cases are sent to this system each day. Each

coder works at a computer terminal where the computer

screen displays the industry and occupation descriptions

that were captured by the field representatives at the time

of the interview. The coder then enters three-digit numeric

industry and occupation codes used in the 1990 census

that represent the industry and occupation descriptions.

A substantial effort is directed at supervision and control

of the quality of this operation. The supervisor is able to

turn the dependent verification setting ‘‘on’’ or ‘‘off’’ at any

time during the coding operation. The ‘‘on’’ mode means

that a particular coder’s work is verified by a second coder.

In addition, a 10 percent sample of each month’s cases is

selected to go through a quality assurance system to

evaluate the work of each coder. The selected cases are

verified by another coder after the current monthly processing has been completed.

After this operation is complete, the batch of records is

electronically returned to headquarters to be used in the

monthly production processing.

EDITS AND IMPUTATIONS

The CPS suffers from two sources of nonresponse. The

largest results from noninterview households. We compensate for this data loss in the weighting where essentially the

1

Because of the CPS sample increase in July 2001 from the State

Children’s Health Insurance Program, the number of cases for I&O coding

has increased to about 30,000.

9–1
weights of noninterviewed households are distributed among

interviewed households (see Chapter 10). The second

source of data loss is from item nonresponse. Item nonresponse occurs when a respondent either does not know

the answer to a question or refuses to provide the answer.

Item nonresponse in the CPS is modest (see Chapter 16).

We compensate for item nonresponse in the CPS by

using 1 of 3 imputation methods. Before the edits are

applied, the daily data files are merged and the combined

file is sorted by state and PSU within state. This sort

ensures that allocated values are from geographically

related records; that is, missing values for records in

Maryland will not receive values from records in California.

This is an important distinction since many labor force and

industry and occupation characteristics are geographically

clustered.

The edits effectively blank all entries in inappropriate

questions and ensure that all appropriate questions have

valid entries. For the most part, illogical entries or out-ofrange entries have been eliminated since the use of

electronic instruments; however, the edits still address

these possibilities due to data transmission problems and

occasional instrument malfunctions. The main purpose of

the edits, however, is to assign values to questions where

the response was ‘‘Don’t know’’ or ‘‘Refused.’’ This is

accomplished by using 1 of 3 imputation techniques described

below.

Before discussing these imputation techniques, it is

important to note that the edits are run in a deliberate and

logical sequence. That is, demographic variables are edited

first because several of those variables are used to allocate

missing values in the other modules. The labor force

module is edited next since labor force status and related

items are used to impute missing values for industry and

occupation codes and so forth.

The three imputation methods used by the CPS edits

are described below.

1. Relational imputation infers the missing value from

other characteristics on the person’s record or within

the household. For instance, if race is missing, it is

assigned based on the race of another household

member or failing that, taken from the previous record

on the file. Similarly, if relationship is missing, it is

assigned by looking at age and sex of the person in

conjunction with the known relationship of other household members. Missing occupation codes are sometimes assigned by viewing the industry codes and vice

versa. This technique is used exclusively in the demographic and industry and occupation edits. If missing

values cannot be assigned using this technique, they

are assigned using 1 of the 2 following methods.

2. Longitudinal edits are used primarily in the labor force

edits. If a question is blank and the record is in the

overlap sample, the edit looks at last month’s data to

determine whether there was a nonallocated entry for

that item. If so, last month’s entry is assigned; otherwise, the item is assigned a value using the appropriate hot deck, as described next.

3. This imputation method is commonly referred to as

‘‘hot deck’’ allocation. This method assigns a missing

value from a record with similar characteristics. Hot

decks are always defined by age, race, and sex . Other

characteristics used in hot decks vary depending on

the nature of the question being referenced. For

instance, most labor force questions use only age,

race, sex, and occasionally another labor force item

such as full- or part-time status. This means the

number of cells in labor force item hot decks are

relatively small, perhaps less than 100. On the other

hand, the weekly earnings hot deck is defined by age

race, sex, usual hours, occupation, and educational

attainment. This hot deck has several thousand cells.

All CPS items that require imputation for missing values

have an associated hot deck . The initial values for the hot

decks are the ending values from the preceding month. As

a record passes through the edits, it will either donate a

value to each hot deck in its path or receive a value from

the hot deck. For instance, in a hypothetical case, the hot

deck for question X is defined by the characteristics

Black/non-Black, male/female, and age 16-25/25+. Further

assume a record has the value of White, male, and age 64.

When this record reaches question X, the edits determine

whether it has a valid entry. If so, that record’s value for

question X replaces the value in the hot deck reserved for

non-Black, male, and age 25+. Comparably, if the record

was missing a value for item X, it would be assigned the

value in the hot deck designated for non-Black, male, and

age 25+.

As stated above the various edits are logically sequenced,

in accordance with the needs of subsequent edits. The

edits and codes, in order of sequence, are:

1. Household edits and codes. This processing step

performs edits and creates recodes for items pertaining to the household. It classifies households as

interviews or noninterviews and edits items appropriately. Hot deck allocations defined by geography are

used in this edit.

2. Demographic edits and codes. This processing step

ensures consistency between all demographic variables for all individuals within a household. It ensures

all interview households have one and only one reference person and that entries stating marital status,

spouse, and parents are all consistent. It also creates

families based upon these characteristics. It uses

longitudinal editing, hot deck allocation defined by

related demographic characteristics, and relational

imputation.

Demographic related recodes are created for both

individual and family characteristics.

9–2
3. Labor force edits and codes. This processing step

first establishes an edited Major Labor Force Recode

(MLR), which classifies adults as either employed,

unemployed, or not in the labor force.

Based upon MLR, the labor force items related to

each series of classification are edited. This edit uses

longitudinal editing and hot deck allocation matrices.

The hot decks are defined by age, race, and sex and,

on occasion, by a related labor force characteristic.

4. Industry and occupation (I&O) edits and codes.

This processing step assigns three-digit industry and

occupation codes to those I&O eligible persons for

which the I&O coders were unable to assign a code. It

also ensures consistency, wherever feasible, between

industry, occupation, and class of worker. I&O related

recodes are also created. This edit uses relational

allocation and hot deck allocation. The hot decks are

defined by employment status, age, sex, race, and

educational attainment.

5. Earnings edits and codes. This processing step

performs edits on the earnings series of items for

earnings eligible individuals. A usual weekly earnings

recode is created to allow earnings amounts to be in a

comparable form for all eligible individuals. There is no

longitudinal editing because this series of questions is

asked only of MIS 4 and 8 households. Hot deck

allocation is used here. The hot deck for weekly

earnings is defined by age, race, sex, major occupation recode, educational attainment, and usual hours

worked. Additional earnings recodes are created.

6. School enrollment edits and codes. School enrollment items are edited for individuals 16-24 years old.

Hot deck allocation based on age, race, and sex is

used.

9–3
Chapter 10.

Estimation Procedures for Labor Force Data

INTRODUCTION

The Current Population Survey (CPS) is a multistage

probability sample of housing units in the United States. It

produces monthly labor force and related estimates for the

total U.S. civilian noninstitutional population and for various

age, sex, race, and ethnic groups. In addition, estimates for

a number of other population subdomains of the nation

(e.g., families, veterans, persons with earnings, households) are produced on either a monthly or quarterly basis.

Each month a sample of eight panels (called rotation

groups) is interviewed, with demographic data collected for

all occupants of the sample housing units. Labor force data

are collected for persons 15 years and older. Each rotation

group is itself a representative sample of the U.S. population. The labor force estimates are derived through a

number of steps in the estimation procedure.

The weighting procedures of the supplements are discussed in Chapter 11. The supplements tend to have

higher nonresponse rates. In addition, many of the supplements apply to specific demographic subpopulations and

thus differ in coverage from the basic CPS universe.

In order to produce national and state estimates from

survey data, a weight for each person in the sample is

developed through the following steps:

• Preparation of simple unbiased estimates from baseweights and special weights derived from CPS sampling

probabilities.

• Adjustment for nonresponse.

• First-stage ratio adjustment to reduce variances due to

the sampling of PSUs.

• Second-stage ratio adjustment to reduce variances by

controlling CPS estimates of population to independent

estimates of the current population.

• Composite estimation which uses survey data from previous months to reduce the variances.

• Seasonal adjustment for key labor force statistics.

In addition to estimates of basic labor force characteristics, several other types of estimates are also produced

either on a monthly or a quarterly basis. These include:

• Household-level estimates and estimates of married

couples living in the same household using household

and family weights.

• Estimates of earnings, union affiliation, and industry and

occupation of second jobs collected from respondents in

the quarter sample using outgoing rotation group weights.

• Estimates of labor force status by age for veterans and

nonveterans using veterans’ weights.

• Estimates of monthly gross flows using longitudinal weights.

The additional estimation procedures provide highly

accurate estimates for particular subdomains of the civilian

noninstitutional population. The processes described in this

chapter have remained essentially unchanged since January 1978. Seasonal adjustment for selected labor force

categories has been a part of the estimation procedure

since June 1975. Some of these processes have been

slightly modified after each decennial census, when the

CPS sample is restratified and a new set of sample PSUs

is identified. Modifications have been made in the noninterview adjustment and the source and scope of the

independent estimates of current population used in the

second-stage ratio adjustment. In January 1998, a new

compositing procedure was introduced. (See Appendix I.)

UNBIASED ESTIMATION PROCEDURE

A probability sample is defined as a sample that has a

known nonzero probability of selection for each sample

unit. With probability samples, unbiased estimators can be

obtained. These are estimates that on average, over

repeated samples, yield the population values.

An unbiased estimator of the population total for any

characteristic investigated in the survey may be obtained

by multiplying the value of that characteristic for each

sample unit (person or household) by the reciprocal of the

probability with which that unit was selected and summing

the products over all units in the sample (Hansen, 1953).

By starting with unbiased estimates from a probability

sample, various kinds of estimation and adjustment procedures (such as for noninterview) can be applied with

reasonable assurance that the overall accuracy of the

estimates will be improved.

In the CPS sample for any given month, not all units

respond, and this nonresponse is a potential source of

bias. This nonresponse averages about 6 to 7 percent.

Other factors, such as occasional errors occurring in the

sample selection procedure and households or persons

missed by interviewers, can also introduce bias. These

10–1
missing households or persons can be considered as

having zero probability of selection. These two exceptions

not withstanding, the probability of selecting each unit in

the CPS is known, and every attempt is made to keep

departures from true probability sampling to a minimum.

If all units in a sample have the same probability of

selection, the sample is called self-weighting, and unbiased estimators can be computed by multiplying sample

totals by the reciprocal of this probability. Most of the state

samples in the CPS come close to being self-weighting.

Basic Weighting

The sample designated for the 792-area design was

selected with probabilities equal to the inverse of the

required state sampling intervals shown in Table 3–1.

These sampling intervals are called the basic weights (or

baseweights). Almost all sample persons within the same

state have the same probability of selection. Exceptions

include sample persons in New York and California, where

households in New York City and Los Angeles-Long Beach

Metropolitan area are selected with higher probability than

those in the remainder of these two states. As the first step

in the estimation procedure, raw counts from the sample

housing units are multiplied by the baseweights. Every

person in the same housing unit receives the same baseweight.

Effect of Sample Reductions on Basic Weights

As time goes on, the number of households and the

population as a whole increases, and continued use of the

original sampling interval leads to a larger sample size

(with an increase in costs). The sampling interval is regularly adjusted (the probability of selection is reduced) in

order to maintain a fairly constant sample size and cost

(see Appendix C).

Special Weighting Adjustments

As discussed in Chapter 3, some ultimate sampling units

(USUs) are subsampled in the field, because their observed

size is much larger than the expected four housing units.

During the estimation procedure, housing units in these

USUs must receive special weighting factors to account for

the change in their probability of selection. For example, an

area sample USU expected to have 4 housing units (HUs)

but found at the time of interview to contain 36 HUs, could

be subsampled at the rate of 1 in 3 to reduce the interviewer’s workload. Each of the 12 designated housing units in

this case would be given a special weighting factor of 3. In

order to limit the effect of this adjustment on the variance of

sample estimates, these special weighting factors are

limited to a maximum value of 4. At this stage of CPS

estimation process, the special weighting factors are multiplied by the baseweights. The resulting weights are then

used to produce ‘‘unbiased’’ estimates. Although this estimate is commonly called ‘‘unbiased,’’ it does still include

some negligible bias because the size of the special

weighting factor is limited to 4. The purpose of this limitation is to achieve a compromise between an increase in the

bias and the variance.

ADJUSTMENT FOR NONRESPONSE

Nonresponse arises when households or other units of

observation that have been selected for inclusion in a

survey fail to yield all or some of the data that were to be

collected. This failure to obtain complete results from all the

units selected can arise from several different sources,

depending upon the survey situation. There are two major

types of nonresponse: item nonresponse and complete (or

unit) nonresponse. Unit nonresponse refers to the failure to

collect any survey data from an occupied sample unit. For

example, data may not be obtained from an eligible household in the survey because of respondent’s absence,

impassable roads, refusal to participate in the interview, or

unavailability of the respondents for other reasons. This

type of nonresponse in the CPS is called a Type A

noninterview. Historically, between 4 and 5 percent of the

eligible units in a given month were Type A noninterviews.

Recently, the Type A rate has risen to between 6 and 7

percent (see Chapter 16). Item nonresponse occurs when

a cooperating unit fails or refuses to provide some specific

items of information. Procedures for dealing with this type

of nonresponse are discussed in Chapter 9.

In the CPS estimation process, the weights for all

interviewed households are adjusted to account for occupied sample households for which no information was

obtained because of unit nonresponse (Type A noninterviews). This noninterview adjustment is made separately

for similar sample areas that are usually, but not necessarily, contained within the same state. Increasing the weights

of interviewed sample units to account for eligible sample

units which are not interviewed assumes that the interviewed units are similar to the noninterviewed units with

regard to their demographic and socioeconomic characteristics. This may or may not be true. Nonresponse bias

results when the nonresponding units differ in relevant

respects from those which respond to the survey or to the

particular items.

Noninterview Clusters and Noninterview

Adjustment Cells

To reduce the size of the bias, the noninterview adjustment is performed within sample PSUs that are similar in

MSA (metropolitan statistical area ) status and MSA size.

These PSUs are grouped together to form noninterview

clusters. In general, PSUs belonging to MSAs of the same

10–2
(or similar) size in the same state belong to the same

noninterview cluster. PSUs classified as MSA are assigned

to MSA clusters. Likewise, non-MSA PSUs are assigned to

non-MSA clusters. Within each cluster, there is a further

breakdown into two noninterview adjustment cells (also

called residence cells). Each MSA cluster is split into

‘‘central city’’ and ‘‘not central city’’ cells. Each non-MSA

cluster is divided into ‘‘urban’’ and ‘‘rural’’ cells, making a

total of 254 adjustment cells (or 127 noninterview clusters).

Computing Noninterview Adjustment Factors

Weighted counts of interviewed and noninterviewed

households are tabulated separately for each noninterview

adjustment cell. The basic weight multiplied by any special

weighting factor is used as the weight for this purpose. The

noninterview factor Fij is computed as:

Fij  ZijNij

Zij

where

Z ij = is the weighted count of interviewed households in

cell j of cluster i, and

N ij  is the weighted count of Type A noninterviewed

households in cell j of cluster i.

These factors are applied to data for each interviewed

person except in cells where either of the following situations occur:

• The computed factor is greater than or equal to 2.0.

• There are fewer than 50 unweighted interviewed households in the cell.

If any of these situations occur, the weighted counts are

combined for the residence cells within the noninterview

cluster. A common adjustment factor is computed and

applied to weights for interviewed persons within the

cluster. Generally, fewer than 10 noninterview clusters

require this type of collapsing in a given month.

Weights After the Noninterview Adjustment

At the completion of the noninterview adjustment procedure, the weight for each interviewed person is:

(baseweight) x (special weighting factor) x

(noninterview adjustment factor)

At this point, records for all individuals in the same household have the same weight, since the adjustments discussed so far depend only on household characteristics.

RATIO ESTIMATION

Distributions of demographic characteristics derived from

the CPS sample in any month will be somewhat different

from the true distributions even for such basic characteristics as age, race, sex, and Hispanic1 ethnicity. These

particular population characteristics are closely correlated

with labor force status and other characteristics estimated

from the sample. Therefore, the variance of sample estimates based on these characteristics can be reduced

when, by the use of appropriate weighting adjustments, the

sample population distribution is brought as closely into

agreement as possible with the known distribution of the

entire population with respect to these characteristics. This

is accomplished by means of ratio adjustments. There are

two ratio adjustments in the CPS estimation process: the

first-stage ratio adjustment and the second-stage ratio

adjustment.

In the first-stage ratio adjustment, weights are adjusted

so that the distribution of Black and non-Black census

population from the sample PSUs in a state corresponds to

the Black/non-Black population distribution from the census for all PSUs in the state. In the second-stage ratio

adjustment, weights are adjusted so that aggregated CPS

sample estimates match independent estimates of population in various age/sex/race and age/sex/ethnicity cells at

the national level. Adjustments are also made so that the

estimated state populations from CPS match independent

state population estimates.

FIRST-STAGE RATIO ADJUSTMENT

Purpose of the First-Stage Ratio Adjustment

The purpose of the first-stage ratio adjustment is to

reduce the contribution to the variance of sample statelevel estimates arising from the sampling of PSUs. That is,

the variance that would still be associated with the statelevel estimates even if we included in the survey all

households in every sample PSU. This is called the

between-PSU variance. For some states, the betweenPSU variance makes up a relatively large proportion of the

total variance, while the relative contribution of the betweenPSU variance at the national level is generally quite small.

As can be seen in Table 14–3 the first-stage ratio adjustment causes a rise in the national relative variance factor,

but the second-stage ratio adjustment decreases the relative variance factor below that of the noninterview adjustment. Further research into the effect of the combined firstand second-stage ratio adjustment is needed to determine

whether the first-stage ratio adjustment is in fact meeting

its purpose.

1

Hispanics may be of any race.

10–3
There are several factors to be considered in determining what information to use in applying the first-stage

adjustment. The information must be available for each

PSU, correlated with as many of the statistics of importance published from the CPS as possible, and reasonably

stable over time so that the gain from the ratio adjustment

procedure does not deteriorate. The basic labor force

categories (unemployed, nonagricultural employed, etc.)

could be considered. However, this information could badly

fail the stability criterion. The distribution of population by

race (Black/non-Black) satisfies all three criteria.

By using the Black/non-Black categories, the first-stage

ratio adjustment compensates for the fact that the racial

composition of an NSR (nonself-representing) sample PSU

could differ substantially from the racial composition of the

stratum it is representing. This adjustment is not necessary

for SR (self-representing) PSUs since they represent only

themselves.

Computing First-Stage Ratio Adjustment Factors

The first-stage adjustment factors are based on 1990

census data and are applied only to sample data for the

NSR PSUs. Factors are computed for the two race categories (Black, non-Black) for each state containing NSR

PSUs. The following formula is used to compute the

first-stage adjustment factors for each state:

FSsj 



i1

n

Csij



k1

m



1

skCskj

where

FSsj  the first-stage factor for state s and race cell j

(j=Black, non-Black).

Csij  the 1990 16+ census population for NSR PSU i

(sample or nonsample) in state s, race cell j.

Cskj  the 1990 16+ census population for NSR sample

PSU k in state s, race cell j.

 sk = 1990 probability of selection for sample PSU k

in state s.

n  total number of NSR PSUs (sample and nonsample) in state s.

m = number of sample NSR PSUs in state s.

The estimate in the denominator of each of the ratios is

obtained by multiplying the 1990 census population in the

appropriate race cell for each NSR sample PSU by the

inverse of the probability of selection for that PSU and

summing over all NSR sample PSUs in the state.

The Black and non-Black cells are collapsed within a

state when a cell meets one of the following criteria:

• The factor (FSsj) is greater than 1.3.

• The factor is less than 1/1.3=.769230.

• There are fewer than 4 NSR sample PSUs in the state.

• There are fewer than ten expected interviews in a race

cell in the state.

Race cells are collapsed within 23 states, resulting in

first-stage factors of 1.0 for these states. Eight states are

self-representing and have first-stage factors of 1.0 by

definition. (See Table 10–1).

Weights After First-Stage Ratio Adjustment

At the completion of the first-stage ratio adjustment, the

weight for each responding person is the product of:

(baseweight) x (special weighting adjustment factor) x

(noninterview adjustment factor) x

(first-stage ratio adjustment factor).

The weight after the first-stage adjustment is called the

first-stage weight. Records for all individuals in the same

household should have the same first-stage weight, except

for mixed-race households having some members who are

Black and others who are non-Black.

10–4
Table 10–1: State First-Stage Factors for CPS 1990

Design (September - December 1995)

State Black Non-Black

Alabama ............................. 0.92986976 1.02360321

Alaska ............................... 1.00000000 1.00000000**

Arizona . . ............................ 1.00000000 1.00000000**

Arkansas . . ........................... 1.03854268 0.99625041

California . . ........................... 0.92824011 1.01550280

Colorado . ............................ 1.00000000 1.00000000**

Connecticut ........................... 1.00000000 1.00000000*

Delaware . . ........................... 1.00000000 1.00000000*

District of Columbia .................... 1.00000000 1.00000000*

Florida ............................... 1.07779736 1.00025192

Georgia . . . ........................... 1.06981965 0.98237807

Hawaii ............................... 1.00000000 1.00000000**

Idaho . ............................... 1.00000000 1.00000000**

Illinois . ............................... 1.01947743 1.00254363

Indiana . . . ............................ 1.16715920 0.99747055

Iowa . . ............................... 1.00000000 1.00000000**

Kansas ............................... 1.00000000 1.00000000**

Kentucky . . . .......................... 1.09352656 0.99897341

Louisiana . ............................ 1.04956759 0.98344772

Maine ................................ 1.00000000 1.00000000**

Maryland ............................. 1.00000000 1.00000000**

Massachusetts . . ...................... 1.00000000 1.00000000*

Michigan ............................. 0.92441097 0.99798724

Minnesota ............................ 1.00000000 1.00000000**

Mississippi ............................ 0.98243024 1.00997154

Missouri . ............................. 1.00000000 1.00000000**

Montana . ............................ 1.00000000 1.00000000**

Nebraska . . . .......................... 1.00000000 1.00000000**

Nevada .............................. 1.00000000 1.00000000**

New Hampshire ....................... 1.00000000 1.00000000*

New Jersey ........................... 1.00000000 1.00000000*

New Mexico .......................... 1.00000000 1.00000000**

New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.83647167 1.00163779

North Carolina ........................ 1.07378643 0.98057928

North Dakota . . ....................... 1.00000000 1.00000000**

Ohio . . ............................... 0.97362223 1.00000085

Oklahoma . . .......................... 1.01775196 1.00400963

Oregon ............................... 1.00000000 1.00000000**

Pennsylvania ......................... 1.17560284 0.99367587

Rhode Island . ........................ 1.00000000 1.00000000*

South Carolina ........................ 0.93971915 1.05454366

South Dakota ......................... 1.00000000 1.00000000**

Tennessee ............................ 1.08638935 0.98680199

Texas ................................ 1.23277658 0.97475648

Utah . . ............................... 1.00000000 1.00000000**

Vermont .............................. 1.00000000 1.00000000*

Virginia ............................... 1.00000000 1.00000000**

Washington ........................... 1.00000000 1.00000000**

West Virginia . . ....................... 1.22587622 0.99959222

Wisconsin . . . ......................... 1.00000000 1.00000000**

Wyoming ............................. 1.00000000 1.00000000**

* These states contain only SR PSUs in the 1990 sample design and

have an implied first-stage factor of 1.000000.

** Race cells were collapsed.

SECOND-STAGE RATIO ADJUSTMENT

The second-stage ratio adjustment decreases the error

in the great majority of sample estimates. Chapter 14

illustrates the amount of reduction in variance for key labor

force estimates. The procedure is also believed to reduce

the bias due to coverage errors (see Chapter 15). The

procedure adjusts the weights for sample records within

each rotation group to control the sample estimates for a

number of geographic and demographic subgroups of the

population to ensure that these sample-based estimates of

population match independent population controls in each

of these categories. These independent population controls are updated each month. Three sets of controls are

used:

• The civilian noninstitutional population 16 years of age

and older for the 50 states and the District of Columbia.

• Total national civilian noninstitutional population for 14

Hispanic and 5 non-Hispanic age-sex categories (see

Table 10–2).

• Total national civilian noninstitutional population for 66

white, 42 Black, and 10 ‘‘Other’’ age-sex categories (see

Table 10–3).

The adjustment is done separately for each of the eight

rotation groups which comprise a monthly sample. Adjusting the weights to match one set of controls can cause

differences in other controls, so an iterative process is used

to simultaneously control all variables. Successive iterations begin with the weights as adjusted by all previous

iterations. A total of six iterations is performed, which

results in virtual consistency between the sample estimatesandpopulationcontrols.Thethree-way(state,Hispanic/sex/age,

race/sex/age) raking ratio estimator is also known as

iterative proportional fitting.

In addition to reducing the error in many CPS estimates

and converging to the population controls within six iterations for most items, the raking ratio estimator has another

desirable property. When it converges, this estimator minimizes the statistic



i

W2iInW2iW1i,

where

W2i  the final weight for the ith sample record, and

W1i  the weight for the ith record after the first-stage

adjustment.

Thus, the raking ratio estimator adjusts the weights of the

records so that the sample estimates converge to the

population controls while, in some sense, minimally affecting the first-stage weights. The reference (Ireland and

Kullback, 1968) provides more details on the properties of

raking ratio estimation.

10–5
Sources of Independent Controls

The independent population controls used in the secondstage ratio adjustment are prepared by projecting forward

the population figures derived from the 1990 decennial

census using information from a variety of other data

sources that account for births, deaths, and net migration.

Subtracting estimated numbers of resident Armed Forces

personnel and institutionalized persons from the resident

population gives the civilian noninstitutional population.

Estimates of net census undercount, determined from the

Post-Enumeration Survey, are added to the population

projections. One should note that, prepared in this manner,

the controls are themselves estimates. However, they are

derived independently of the CPS and provide useful

information for adjusting the sample estimates. See Appendix D for more details on sources and derivation of the

independent controls.

Ages

Hispanic1

Male Female

0-5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6-13 . .................................

*14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

*15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

16-19 .................................

20-29 .................................

30-49 .................................

50+. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

* No distinction is made between sexes.

1

Hispanics may be of any race.

Ages Non-Hispanic

*0-5 ............................................

*6-13 ...........................................

*14. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

*15. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

*16+ ...........................................

*No distinction is made between sexes.

Age Black

male

Black

female

0-1 . . . . . . . . . . . . . . . . . . . . . . .

2-3 . . . . . . . . . . . . . . . . . . . . . . .

4-5 . . . . . . . . . . . . . . . . . . . . . . .

6-7 . . . . . . . . . . . . . . . . . . . . . . .

8-9 . . . . . . . . . . . . . . . . . . . . . . .

10-11 . . ...................

12-13 . . ...................

14 . . . . . . . . . . . . . . . . . . . . . . . .

15 . . . . . . . . . . . . . . . . . . . . . . . .

16-17 . . ...................

18-19 . . ...................

20-24 . . ...................

25-29 . . ...................

30-34 . . ...................

35-39 . . ...................

40-44 . . ...................

45-49 . . ...................

50-54 . . ...................

55-59 . . ...................

60-64 . . ...................

65+ . . . . . . . . . . . . . . . . . . . . . . .

Age White

male

White

female

0......................

1......................

2......................

3......................

4......................

5......................

6......................

7......................

8......................

9......................

10-11 ..................

12-13 ..................

14. . . . . . . . . . . . . . . . . . . . .

15. . . . . . . . . . . . . . . . . . . . .

16. . . . . . . . . . . . . . . . . . . . .

17. . . . . . . . . . . . . . . . . . . . .

18. . . . . . . . . . . . . . . . . . . . .

19. . . . . . . . . . . . . . . . . . . . .

20-24 ..................

25-26 ..................

27-29 ..................

30-34 ..................

35-39 ..................

40-44 ..................

45-49 ..................

50-54 ..................

55-59 ..................

60-62 ..................

63-64 ..................

65-67 ..................

68-69 ..................

70-74 ..................

75+. . . . . . . . . . . . . . . . . . . .

Ages Other1

male

Other1

female

0-5 . . . . . . . . . . . . . . . . . .

6-13 .................

*14 . . . . . . . . . . . . . . . . . .

*15 . . . . . . . . . . . . . . . . . .

16-44................

45+ . . . . . . . . . . . . . . . . .

*No distinction is made between sexes.

1

Other includesAmericanIndian, Eskimo,

Aleut, Asian, and Pacific Islander.

Table 10–2. Initial Cells for Hispanic/Non-Hispanic by Age and Sex

Table 10–3. Initial Cells for Black/White/Other by Age, Race, and Sex

10–6
Computing Initial Second-Stage Ratio

Adjustment Factors

As mentioned before, the second-stage adjustment involves

a three-way rake:

• State

• Hispanic/sex/age

• Race/sex/age

In order to prevent the second-stage adjustment from

increasing the variance of the sample estimates, estimation cells which have zero estimates (i.e., no sample

respondents) or extremely large or small adjustment factors are identified and combined (or collapsed) with others.

No collapsing is done for the state rake. Prior to iteration 1

for the Hispanic/sex/age and the race/sex/age rakes, initial

adjustment factors are computed by rotation group for each

of the cells shown in Tables 10–2 and 10–3.

For any particular cell j, the initial factor is computed as:

Fjk  Cj

 Ejk

where

Cj  the population control for cell j (divided by 8,

because the raking is done for each rotation

group)

Ejk  the CPS estimate for cell j and rotation group k

after the first-stage ratio adjustment.

These initial factors are not used in the estimates. They are

only used to determine whether any cells need to be

collapsed. A cell is combined with adjacent (next higher or

next lower) age cells in the same race/sex or ethnicity/sex

category if:

• It contains no sample respondents (i.e., Ejk = 0).

• Its initial factor is less than or equal to 0.6.

• Its initial factor is greater than or equal to 2.0.

Collapsing cells continues until none of the three criteria

are met or all available cells have been collapsed. Once

cells are collapsed, all cell definitions are maintained

through all six iterations of the raking procedure. In a

typical month approximately 10 cells require collapsing.

Raking

For each iteration of each rake an adjustment factor is

computed for each cell and applied to the estimate of that

cell. The factor is the population control divided by the

estimate of the current iteration for the particular cell. The

three steps are repeated through six iterations. The following simplified example begins after one state rake. The

example shows the raking for two cells in an ethnicity rake

and two cells in a race rake. Age/sex cells and one race cell

(see Tables 10–2 and 10–3) have been collapsed here for

simplification.

10–7
Iteration 1:

State rake

Hispanic rake

Race rake

Example of Raking Ratio Adjustment

Raking Estimates by Ethnicity and Race

Es = Estimate from CPS sample after state rake

Ee = Estimate from CPS sample after ethnicity rake

Er = Estimate from CPS sample after race rake

Fe = Ratio adjustment factor for ethnicity

Fr = Ratio adjustment factor for race

Iteration 1 of the Ethnicity Rake

Non-Hispanic Hispanic Population controls

Non-Black

Es  650

Fe  1050

650180  1.265

Ee  EsFe  822

Es=150

Fe= 250

150+20 = 1.471

Ee= EsFe= 221 1000

Black

Es  180

Fe  1050

650180  1.265

Ee  EsFe  228

Es  20

Fe  250

15020  1.471

Ee  EsFe  29 300

Population

controls 1050 250 1300

Iteration 1 of the Race Rake

Non-Hispanic Hispanic Population controls

Non-Black

Ee  822

Fr  1000

822221  .959

Er  EeFr  788

Ee=221

Fr

= 1000

822+221=.959

Er  EeFr  212 1000

Black

Ee  228

Fr  300

22829  1.167

Er  EeFr  266

Ee  29

Fr  300

22829  1.167

Er  EeFr  34 300

Population

controls 1050 250 1300

Iteration 2 (repeat steps above beginning with sample cell

estimates at the end of iteration 1)

.

.

.

Iteration 6

Note that the matching of estimates to controls for the

race rake causes the cells to differ slightly from the controls

for the ethnicity rake or previous rake. With each rake,

these differences decrease when cells are matched to the

controls for the most recent rake. For the most part, after

six iterations the estimates for each cell have converged to

the population controls for each cell. Thus, the weight for

each record after the second-stage ratio adjustment procedure can be thought of as the weight for the record after

the first-stage ratio adjustment multiplied by a series of 18

adjustment factors (six iterations of three rakes). The

product of these 18 adjustment factors is called the secondstage ratio adjustment factor.

10–8
Weight After the Second-Stage Ratio Adjustment

At the completion of the second-stage ratio adjustment,

the record for each person has a weight reflecting the

product of:

(baseweight) x (special weighting adjustment factor) x

(noninterview adjustment factor) x

(first-stage ratio adjustment factor) x

(second-stage ratio adjustment factor)

The weight after the second-stage ratio adjustment is also

called the final weight. The estimates produced using the

final weights are often called the first- and second-stage

combined (FSC) estimates. The second-stage ratio adjustment factors also provide estimates of CPS coverage (see

Chapter 16).

COMPOSITE ESTIMATOR2

Once each record has a final weight, an estimate of level

for any given set of characteristics identifiable in the CPS

can be computed by summing the final weights for all the

sample cases that have that set of characteristics. The

process for producing this type of estimate has been

variously referred to as a Horvitz-Thompson estimator, a

two-stage ratio estimator, or a simple weighted estimator.

But the estimator actually used for the derivation of most

official CPS labor force estimates that are based upon

information collected every month from the full sample (in

contrast to information collected in periodic supplements or

from partial samples) is a composite estimator. Composite

estimation for the CPS modifies the aggregated FSC

estimates without adjusting the weights of individual sample

records. This is a disadvantage for data users, since

composite estimates for a particular month cannot be

produced from a microdata file for only that month. However, research is being conducted into the possibility of

using a composite estimator from which composite estimates could be produced from a single month’s microdata

file (Lent, Miller, and Cantwell, 1994).

In general, a composite estimate is a weighted average

of several estimates. The composite estimate from the

CPS has historically combined two estimates. The first of

these is the FSC estimate described above. The second

consists of the composite estimate for the preceding month

and an estimate of the change from the preceding to the

current month. The estimate of the change is based upon

data from that part of the sample which is common to the

two months (about 75 percent). The higher month-to-month

correlation between estimates from the same sample units

tends to reduce the variance of the estimate of month-tomonth change. Although the average improvements in

variance from the use of the composite estimator are

greatest for estimates of month-to-month change, improvements are also realized for estimates of change over other

intervals of time and for estimates of levels in a given

month (Breau and Ernst, 1983).

Prior to 1985, the two estimators described in the

preceding paragraph were the only terms in the CPS

composite estimator and were given equal weight. Since

1985, the weights for the two estimators have been unequal

and a third term has been included, an estimate of the net

difference between the incoming and continuing parts of

the current month’s sample. The formula for the composite

estimate of a labor force level Yt

 is

Yt



 1KYˆ t KYt1

 t

 	
ˆ

t

where

Yˆ t  

i1

8

xt,i

t  4

3

is

xt,i  xt1, i1 and


ˆ

t  

is

xt,i  1

3

is

xt,i

i = 1,2,...,8 (month in sample).

xt,i  sum of weights after second-stage ratio adjustment

of respondents in month t, and month in sample i

with characteristic of interest.

S = {2,3,4,6,7,8}.

In the formula above, Yˆ t is the current month’s FSC

estimate, t is the estimate of change from rotation groups

common to months t and t-1, and 
ˆ

t is the estimate of the

net difference between the incoming and continuing part of

the current month’s sample. The third term, 
ˆ

t

, was added

primarily because it was found to keep composited and

uncomposited estimates closer while leaving intact the

variance reduction advantages of the composite estimator.

It also is often characterized as an adjustment term for the

bias associated with rotation groups or time in sample in

the CPS (see Chapter 15). Historically, the incoming parts

of the sample (the rotation groups in their first or fifth

month-in-sample) have tended to have higher labor force

participation and unemployment rates than the continuing

parts (Bailar, 1975). The third term offsets, to some degree,

the effect of this bias on the estimate of change in the

second term. If there was no such bias, then the expected

value of 
ˆ

t would be zero.

The values of the constants K (K=0.4) and A (A=0.2)

were chosen to be approximately optimal for reducing

variances of estimates of labor force characteristics. They

reflect a compromise of optimal values across a variety of

characteristics and types of estimates (Kostanich and

Bettin, 1986). 2

See Appendix I for update.

10–9
The compositing formula is applied only to estimated

levels. Other types of composite estimates (rates, percents, means, medians) are computed from the component

composited levels (e.g., an unemployment rate is computed by taking the composited unemployment level as a

percentage of the composited labor force level).

SEASONAL ADJUSTMENT

A time series is a sequence of observations (measurements or estimates) of a particular measurable phenomenon over time. The use and interpretation of many time

series, including many of the monthly labor force time

series that are based on the CPS, are enhanced by the

process of seasonal adjustment. The objective of seasonal

adjustment is to measure and remove from time series the

effects of normal seasonality caused by such things as

weather, holidays, and school schedules. Seasonality can

account for much of the observed month-to-month change

in estimates, such as those for employment and unemployment, and can obscure the underlying movements associated with long-term cycles and trends that are of great

economic significance for most users. For example, the

unadjusted CPS levels of employment and unemployment

in June are consistently much higher than those for May

because of the influx of students into the labor force. If the

only change that occurred in the unadjusted estimates

between May and June approximated the normal seasonal

change, then the seasonally adjusted estimates for the 2

months should be about the same, indicating that essentially no change occurred in the underlying business cycle

and trend even though there may have been a large

change in the unadjusted data. Changes that do occur in

the seasonally adjusted series reflect changes not associated with normal seasonal change and should provide

information about the direction and magnitude of changes

in the behavior of trend and business cycle effects. They

may, however, also reflect the effects of sampling error and

other irregularities, which are not removed by the seasonal

adjustment process. Change in the seasonally adjusted

series can and occasionally should be in a direction

opposite the movement in the unadjusted series.

Refinements of the methods used for seasonal adjustment have been under development for decades. The

procedure used since 1980 for the seasonal adjustment of

the labor force series is the X-11 ARIMA program, developed by Statistics Canada in the late 1970s as an extension and improvement of the widely used X-11 method

developed at the U.S. Census Bureau in the 1960s. The

X-11 approach to seasonal adjustment is univariate and

nonparametric and involves the iterative application of a

set of moving averages that can be summarized as one

lengthy weighted average (Dagum, 1983). Nonlinearity is

introduced by a set of rules and procedures for identifying

and reducing the effect of ‘‘extreme values.’’ In most uses

of X-11 seasonal adjustment, including that for CPS-based

labor force series, the seasonality is estimated as evolving

rather than fixed over time. A detailed description of the

program is given in Scott and Smith (1974).

The current official practice for the seasonal adjustment

of the labor force series involves the running of all directly

adjusted series through X-11 ARIMA twice each year, after

receipt of June and December data, with 6 months of

projected factors drawn from each run and used in the

subsequent 6 months, and historical revisions drawn from

the end-of-year run. This practice allows, among other

things, the publication of the seasonal factors prior to their

use.

Seasonally adjusted estimates of many labor force

series, including the levels of the civilian labor force,

employment, and unemployment and all unemployment

rates, are derived indirectly by arithmetically combining or

aggregating the series directly adjusted with X-11 ARIMA.

For example, the overall unemployment rate is computed

using 12 directly adjusted components—unemployment,

agricultural employment, and nonagricultural employment

by sex and by age (16-19 and 20+). The principal reason

for doing such indirect adjustment is that it ensures that the

major seasonally adjusted totals will be arithmetically consistent with at least one set of components. If the totals

were directly adjusted along with the components, such

consistency would generally not occur because X-11 is not

a sum- or ratio-preserving procedure. It is not generally

appropriate to apply factors computed for an aggregate

series to the components of the aggregate because various components tend to have significantly different patterns of seasonal variation.

For up-to-date information and a more thorough discussion on the seasonal adjustment of the labor force series,

see the January issues of Employment and Earnings

(U.S. Department of Labor).

MONTHLY STATE AND SUBSTATE ESTIMATES

Employment and unemployment estimates for states

and local areas are key indicators of local economic

conditions. Under a Federal-state cooperative program,

monthly estimates of the civilian labor force and unemployment are prepared for some 6,950 areas, including all

states, metropolitan areas, counties, cities of 25,000 population or more, and all cities and towns in New England.

The Bureau of Labor Statistics is responsible for the

concepts, definitions, technical procedures, validation, and

publication of the estimates which are prepared by state

employment security agencies.

The state and area estimates are used by a wide variety

of customers. Federal programs base allocations to states

and areas on the data, as well as eligibility determinations

for assistance. State and local governments use the estimates for planning and budgetary purposes and to determine the need for local employment and training services.

Private industry and individuals use the data to compare

and assess labor market developments in states and

substate areas.

10–10
The underlying concepts and definitions of all labor force

data developed for state and substate areas are consistent

with those of the Current Population Survey (CPS). Annual

average estimates for all states are derived directly from

the CPS. In addition, monthly estimates for 11 large states

(California, Florida, Illinois, Massachusetts, Michigan, New

Jersey, New York, North Carolina, Ohio, Pennsylvania, and

Texas) and two substate areas (New York City and the Los

Angeles-Long Beach metropolitan area) also come directly

from the CPS; these areas have a sufficiently large sample

to meet the BLS standard of reliability. For the remaining 39

states and the District of Columbia, where the CPS sample

is not large enough to produce reliable monthly estimates,

data are produced using a signal-plus-noise time series

models. These models combine current and historical data

from the CPS, the Current Employment Statistics (CES)

program, and State unemployment insurance (UI) systems

to produce estimates that reflect each state’s individual

economy.

Estimates for substate labor market areas (other than

the two direct-use CPS areas) are produced through a

building block approach which uses data from several

sources, including the CPS, CES, state unemployment

insurance systems, and the decennial census to create

estimates which are then adjusted to the state modelbased measures of employment and unemployment. Below

the labor market area level, estimates are prepared for all

counties and cities with populations of 25,000 or more

using disaggregation techniques based on decennial census and annual population estimates and current UI statistics.

Estimates for States

The employment and unemployment estimates for 11

large states (California, Florida, Illinois, Massachusetts,

Michigan, New Jersey, New York, North Carolina, Ohio,

Pennsylvania, and Texas) are sufficiently reliable to be

taken directly from the Current Population Survey (CPS) on

a monthly basis. These states are termed ‘‘direct-use’’

states.

For the 39 smaller states and the District of Columbia

(called ‘‘nondirect-use’’ states) models based on a signalplus-noise approach (Bell and Hillmer, 1990; Tiller, 1992)

are used to develop employment and unemployment estimates.3 The model of the signal is a time series model of

the true labor force which consists of three components: a

variable coefficient regression, a flexible trend, and a

flexible seasonal component. The regression components

are based on historical and current relationships found

within each state’s economy as reflected in the different

sources of data that are available for each state—the CPS,

the Current Employment Statistics (CES) survey, and the

unemployment insurance (UI) system. The noise component of the models explicitly accounts for autocorrelation in

the CPS sampling error and changes in the average

magnitude of the error. While all the state models have

important components in common, they differ somewhat

from one another to better reflect individual state labor

force characteristics. See Appendix E for more details on

the models.

Two models—one for the employment-to-population ratio

and one for the unemployment rate—are used for each

state. The employment-to-population ratio, rather than the

employment level, and the unemployment rate, rather than

the unemployment level, are estimated primarily because

the hyperparameters used in the state-space models are

easier to estimate and compare across states for ratios

than levels.

The employment-to-population ratio models estimate

the monthly CPS employment-to-population ratio using the

state’s monthly CPS data and employment data from the

CES. The models also include trend and seasonal components to account for different seasonal patterns in the CPS

not captured by the CES series. The seasonal component

accounts for the seasonality in the CPS not explained by

the CES while the trend component adjusts for long-run

systematic differences between the two series (Evans,

Tiller, and Zimmerman, 1993).

The unemployment rate models estimate the monthly

CPS unemployment rate using the state’s monthly unemployment insurance claims data, along with trend and

seasonal components (Zimmerman, Evans, and Tiller, 1993).

Once the estimates are developed from the models, levels

are calculated for the employment, unemployment, and

labor force.

State estimates are seasonally adjusted using the X-11

ARIMA seasonal-adjustment procedure twice a year and

new seasonal adjustment factors are created. The entire

series is used as an input to the seasonal adjustment

process; however, for programming purposes only the last

5 years of data are replaced.

Benchmarking and Population Controls

Once each year, when new population controls are

available from the Census Bureau, CPS labor force estimates for all states and the District of Columbia are

adjusted to these controls. The model-based estimates for

the 39 states and the District of Columbia are reestimated

incorporating the recontrolled CPS labor force estimates

(see above), and these (monthly) estimates are adjusted,

or benchmarked, to the newly population-controlled annual

average CPS estimates. The benchmarking technique

uses a procedure called the Denton Method (Denton,

1971) which adjusts the annual average of the modelbased employment and unemployment levels to equal the

corresponding CPS annual averages, while preserving, as

much as possible, the original monthly seasonal pattern of

the model-based estimates.

3

Because of the January 1996 sample reduction, estimates for all

states are based on a model.

10–11
Substate estimates for previous years are also revised

on an annual basis. The updates incorporate any changes

in the inputs, such as revisions to establishment-based

employment estimates, corrections in claims data, and

updated historical relationships. The revised estimates are

then readjusted to the latest (benchmarked) state estimates of employment and unemployment.

PRODUCING OTHER LABOR FORCE

ESTIMATES

In addition to basic weighting to produce estimates for

persons, several ‘‘special-purpose’’ weighting procedures

are performed each month. These include:

• Weighting to produce estimates for households and

families.

• Weighting to produce estimates from data based on only

2 of 8 rotation groups (outgoing rotation weighting for the

quarter sample data).

• Weighting to produce labor force estimates for veterans

and nonveterans (veterans weighting).

• Weighting to produce estimates from longitudinally-linked

files (longitudinal weighting).

Most of these special weights are based on the final

weight after the second-stage ratio adjustment. Some also

make use of composited estimates. In addition, consecutive monthly estimates are often averaged to produce

quarterly or annual average estimates. Each of these

procedures is described in more detail below.

Family Weight

Family weights are used to produce statistics on families

and family composition. They also provide the basis for

household weights. The family weight is derived from the

final weight of the reference person in each household. In

most households, it is exactly the reference person’s

weight. However, when the reference person is a married

man, for purposes of family weights, he is given the same

weight as his wife. This is done so that weighted tabulations of CPS data by sex and marital status show an equal

number of married women and married men with their

spouses present. If the CPS final weights were used for

this tabulation (without any further adjustment), the estimated numbers of married women and married men would

not be equal, since the second-stage ratio adjustment

tends to increase the weights of males more than the

weights of females. The wife’s weight is usually used as the

family weight, since CPS coverage ratios for women tend

to be higher and subject to less month-to-month variability

than those for men.

Household Weight

The same household weight is assigned to every person

in the same household and is equal to the family weight of

the household reference person. The household weight is

used to produce estimates at the household level, such as

the number of households headed by a female or the

number of occupied housing units.

Outgoing Rotation Weights (Quarter Sample

Data)

Some items in the CPS questionnaire are asked only in

households due to rotate out of the sample temporarily or

permanently after the current month. These are the households in the rotation groups in their fourth or eighth monthin-sample, sometimes referred to as the ‘‘outgoing’’ rotation

groups. Items asked in the outgoing rotations include those

on discouraged workers (through 1993), earnings (since

1979), union affiliation (since 1983), and industry and

occupation of second jobs of multiple jobholders (beginning in 1994). Since the data are collected from only

one-fourth of the sample each month, these estimates are

averaged over 3 months to improve their reliability, and

published quarterly.

Since 1979, most CPS files have included separate

weights for the outgoing rotations. These weights were

generally referred to as ‘‘earnings weights’’ on files through

1993, and are generally called ‘‘outgoing rotation weights’’

on files for 1994 and subsequent years. In addition to ratio

adjustment to independent population controls (in the

second stage), these weights also reflect additional constraints that force them to sum to the composited estimates

of employment, unemployment, and not in labor force each

month. An individual’s outgoing rotation weight will be

approximately four times his or her final weight.

To compute the outgoing rotation adjustment factors, the

final CPS weights of the appropriate records in the two

outgoing rotation groups are tallied. CPS composited estimates from the full sample for the labor force categories of

employed wage and salary workers, other employed, unemployed, and not in labor force by age, race and sex are

used as the controls. The adjustment factor for a particular

cell is the ratio of the control total to the weighted tally from

the outgoing rotation groups.

Collapsing is performed with an adjacent cell (the next

higher or lower age group) if:

• A cell has no sample respondents,

• A composited control total is less than or equal to zero, or

• The corresponding adjustment factor is less than or

equal to 2.0 or greater than or equal to 8.0 (i.e., one-half

or twice the normal adjustment factor of 4).

If a cell requires collapsing, it is collapsed with another

cell (the next higher or lower age group) in the same sex,

race, and labor force category. The adjustment factors are

10–12
recomputed after all collapsing has been performed. The

outgoing rotation weights are obtained by multiplying the

outgoing ratio adjustment factors by the final CPS weights.

For consistency, an outgoing rotation group weight equal to

four times the basic CPS family weight is assigned to all

persons in the two outgoing rotation groups who were not

eligible for this special weighting (military personnel and

persons aged 15 and younger).

Production of monthly, quarterly, and annual estimates

using the quarter sample data and the associated weights

is completely parallel to production of uncomposited, simple

weighted estimates from the full sample—the weights are

summed and divided by the number of months used. The

composite estimator is not applicable for these estimates

because there is no overlap between the quarter samples

in consecutive months. Because the outgoing rotations are

all independent samples within any consecutive 12-month

period, averaging of these estimates on a quarterly and

annual basis realizes relative reductions in variance greater

than those achieved by averaging full sample estimates.

Family Outgoing Rotation Weight

The family outgoing rotation weight is analogous to the

family weight computed for the full sample, except that

outgoing rotation weights are used, rather than the final

weights from the second-stage ratio adjustment.

Veterans’ Weights

Since 1986, CPS interviewers have collected data on

veteran status from all respondents. Veterans’ weights are

calculated for all CPS respondents based on their veteran

status. This information is used to produce tabulations of

employment status for veterans and nonveterans.

The process begins with the final weights after the

second-stage ratio adjustment. Each respondent is classified as a veteran or a nonveteran. Veterans’ records are

classified into six cells: (1) male, pre-Vietnam veteran; (2)

female, pre-Vietnam veteran; (3) male, Vietnam veteran;

(4) female, Vietnam veteran; (5) male, other veteran; and

(6) female, other veteran. The cell definitions change

throughout the decade as the minimum age for nonpeacetime veterans increases.

The final weights for CPS veterans are tallied into

sex/age/type-of-veteran cells using the classifications described

above. Separate ratio adjustment factors are computed for

each cell, using independently established monthly counts

of veterans provided by the Department of Veterans Affairs.

The ratio adjustment factor is the ratio of the independent

control total to the sample estimate. The final weight for

each veteran is multiplied by the appropriate adjustment

factor to produce the veteran’s weight.

To compute veterans’ weights for nonveterans, a table of

composited estimates is produced from the CPS data by

sex, race (White/non-White), labor force status (unemployed, employed, and not in the labor force), and age. The

veterans’ weights produced in the previous step are tallied

into the same cells. The estimated number of veterans is

then subtracted from the corresponding cell entry for the

composited table to produce nonveterans control totals.

The final weights for CPS nonveterans are tallied into the

same sex/race/labor force status/age cells. Separate ratio

adjustment factors are computed for each cell, using the

nonveterans controls derived above. The factor is the ratio

of the nonveterans control total to the sample estimate. The

final weight for each nonveteran is multiplied by the appropriate factor to produce the nonveterans’ weight. A table of

labor force estimates by age status for veterans and

nonveterans is published each month.

Longitudinal Weights

For many years, the month-to-month overlap of 75

percent of the sample households has been used as the

basis for estimating monthly ‘‘gross flow’’ statistics. The

difference or change between consecutive months for any

given level or ‘‘stock’’ estimate is an estimate of net change

that reflects a combination of underlying flows in and out of

the group represented. For example, the month-to-month

change in the employment level is the number of people

who went from not being employed in the first month to

being employed in the second month minus the number

who made the opposite transition. The gross flow statistics

provide estimates of these underlying flows and can provide useful insights to analysts beyond those available in

the stock data.

The estimation of monthly gross flows, and any other

longitudinal use of the CPS, begins with a longitudinal

matching of the microdata (or person-level) records within

the rotation groups common to the months of interest. Each

matched record brings together all the information collected in those months for a particular individual. The CPS

matching procedure uses the household identifier and

person line number as the keys for matching. Prior to 1994,

it was also necessary to check other information and

characteristics, such as age and sex, for consistency to

verify that the match based on the keys was almost

certainly a valid match. Beginning with 1994 data, the

simple match on the keys provides an essentially certain

match. Because the CPS does not follow movers (rather,

the sample addresses remain in sample according to

rotation pattern), and because not all households are

successfully interviewed every month they are in sample, it

is not possible to match interview information for all persons in the common rotation groups across the months of

interest. The highest percentage of matching success is

generally achieved in the matching of consecutive months,

where between 90 and 95 percent of the potentially matchable records (or about 67 to 71 percent of the full sample)

can usually be matched. The use of CATI and CAPI since

1994 has also introduced dependent interviewing which

eliminated much of the erratic differences in response

between pairs of months.

10–13
On most CPS files for 1994 forward, there is a longitudinal weight which allows users to estimate gross labor

force flows by simply summing up the longitudinal weights

after matching. These longitudinal weights reflect the technique that had been used prior to 1994 to inflate the gross

flow estimates to appropriate population levels. That technique simply inflates all estimates or final weights by the

ratio of the current month’s population controls to the sum

of the final weights for the current month in the matched

cases by sex. Although the technique does provide estimates consistent with the population levels for the stock

data in the current month, it does not force consistency with

labor force stock levels in either the current or the previous

month, nor does it control for the effects of the bias and

sample variation associated with the exclusion of movers,

differential noninterview in the matched months, the potential for the compounding of classification errors in flow data,

and the particular rotations that are common to the matched

months. There have been a number of proposals for

improving the estimation of gross labor force flows, but

none have yet been adopted in official practice. See

Proceedings of the Conference on Gross Flows in Labor

Force Statistics (U.S. Department of Commerce and U.S.

Department of Labor, 1985) for information on some of

these proposals and for more complete information on

gross labor force flow data and longitudinal uses of the

CPS.

Averaging Monthly Estimates

CPS estimates are frequently averaged over a number

of months. The most commonly computed averages are (1)

quarterly, which provide four estimates per year by grouping the months of the calendar year in nonoverlapping

intervals of three, and (2) annual, combining all 12 months

of the calendar year. Quarterly and annual averages of

uncomposited data can be computed by summing the

weights for all of the months contributing to each average

and dividing by the number of months involved. Averages

for calculated cells, such as rates, percents, means, and

medians, are computed from the averages for the component levels, not by averaging the monthly values (e.g., a

quarterly average unemployment rate is computed by

taking the quarterly average unemployment level as a

percentage of the quarterly average labor force level, not

by averaging the three monthly unemployment rates together).

Although such averaging multiplies the number of interviews contributing to the resulting estimates by a factor

approximately equal to the number of months involved in

the average, the sampling variance for the average estimate is actually reduced by a factor substantially less than

that number of months. This is primarily because the CPS

rotation pattern and resulting month-to-month overlap in

sample units ensure that estimates from the individual

months are not independent. The reduction in sampling

error associated with the averaging of CPS estimates over

adjacent months was studied using 12 months of data

collected beginning January 1987 (Fisher and McGuinness, 1993). That study showed that characteristics for

which the month-to-month correlation is low, such as

unemployment, are helped considerably by such averaging, while characteristics for which the correlation is high,

such as employment, benefit less from averaging. For

unemployment, variances of national estimates were reduced

by about one-half for quarterly averages and about onefifth for annual averages.

REFERENCES

Bailar, B., (1975) ‘‘The Effects of Rotation Group Bias on

Estimates from Panel Surveys,’’ Journal of the American

Statistical Association, Vol. 70, pp. 23-30.

Bell, W.R., and Hillmer, S.C. (1990), ‘‘The Time Series

Approach to Estimation for Repeated Surveys,’’ Survey

Methodology, 16, pp. 195-215.

Breau, P. and Ernst, L., (1983) ‘‘Alternative Estimators to

the Current Composite Estimator,’’ Proceedings of the

Section on Survey Research Methods, American Statistical Association, pp. 397-402.

Copeland, K. R., Peitzmeier, F. K., and Hoy, C. E.

(1986), ‘‘An Alternative Method of Controlling Current Population Survey Estimates to Population Counts,’’ Proceedings of the Survey Research Methods Section, American Statistical Association, pp.332-339.

Dagum, E.B. (1983), The X-11 ARIMA Seasonal Adjustment Method, Statistics Canada, Catalog No. 12-564E.

Denton, F.T. (1971), ‘‘Adjustment of Monthly or Quarterly

Series to Annual Totals: An Approach Based on Quadratic

Minimization,’’ Journal of the American Statistical Association, 64, pp. 99-102.

Evans, T.D., Tiller, R.B., and Zimmerman, T.S. (1993),

‘‘Time Series Models for State Labor Force Estimates,’’

Proceedings of the Survey Research Methods Section,

American Statistical Association, pp. 358-363.

Fisher, R. and McGuinness R. (1993), ‘‘Correlations and

Adjustment Factors for CPS (VAR80 1).’’ Internal Memorandum for Documentation, January 6th, Demographic

Statistical Methods Division, U.S. Census Bureau.

Hansen, M. H., Hurwitz, W. N., and Madow, W. G.(1953),

Sample Survey Methods and Theory, Vol. II, New York:

John Wiley and Sons.

Harvey, A.C. (1989), Forecasting, Structural Time

Series Models and the Kalman Filter, Cambridge: Cambridge University Press.

Ireland, C.T., and Kullback, S. (1968), ‘‘Contingency

Tables With Given Marginals,’’ Biometrika, 55, pp. 179-

187.

Kostanich, D. and Bettin, P. (1986), ‘‘Choosing a Composite Estimator for CPS,’’ presented for presentation at

the International Symposium on Panel Surveys, Washington, DC.

10–14
Lent, J., Miller, S., and Cantwell, P. (1994), ‘‘Composite

Weights for the Current Population Survey,’’ Proceedings

of the Survey Research Methods Section, American

Statistical Association, pp. 867-872.

Oh, H.L., and Scheuren, F., (1978), ‘‘Some Unresolved

Application Issues in Raking Estimation,’’ Proceedings of

the Section on Survey Research Methods, American

Statistical Association, pp. 723-728.

Proceedings of the Conference on Gross Flows in

Labor Force Statistics (1985), U.S. Department of Commerce and U.S. Department of Labor.

Scott, J.J., and Smith, T.M.F. (1974), ‘‘Analysis of Repeated

Surveys Using Time Series Methods,’’ Journal of the

American Statistical Association, 69, pp. 674678.

Thompson, J.H., (1981), ‘‘Convergence Properties of

the Iterative 1980 Census Estimator,’’ Proceedings of the

American Statistical Association on Survey Research

Methods, American Statistical Association, pp. 182185.

Tiller, R.B. (1989), ‘‘A Kalman Filter Approach to Labor

Force Estimation Using Survey Data,’’ Proceedings of the

Survey Research Methods Section, American Statistical

Association, pp. 16-25.

Tiller, R.B.(1992), ‘‘Time Series Modeling of Sample

Survey Data From the U.S. Current Population Survey,’’

Journal of Official Statistics, 8, pp, 149-166.

U.S. Department of Labor, Bureau of Labor Statistics

(Published monthly), Employment and Earnings, Washington, DC: Government Printing Office.

Young, A.H., (1968), ‘‘Linear Approximations to the

Census and BLS Seasonal Adjustment Methods,’’ Journal

of the American Statistical Association, 63, pp. 445-

471.

Zimmerman, T.S., Evans, T.D., and Tiller, R.B. (1994),

‘‘State Unemployment Rate Time Series,’’ Proceedings of

the Survey Research Methods Section, American Statistical Association, pp. 1077-1082.

10–15
Chapter 11.

Current Population Survey Supplemental Inquiries

INTRODUCTION

In addition to providing data on the labor force status of

the population, the Current Population Survey (CPS) is

used to collect data for a variety of studies on the entire

U.S. population and specific population subsets. Such

studies keep the nation informed of the economic and

social well being of its people and are conducted for use by

Federal and state agencies, private foundations, and other

organizations. Supplemental inquiries take advantage of

several special features of the CPS: large sample size and

general purpose design; highly skilled, experienced interviewing and field staff; and generalized processing systems that can easily accommodate the inclusion of additional questions.

Some CPS supplemental inquiries are conducted annually, others every other year, and still others on a one-time

basis. The frequency and recurrence of a supplement

depend on what best meets the needs of the supplement’s

sponsor. It is important that any supplemental inquiry meet

strict criteria discussed in the next section.

Producing supplemental data from the CPS involves

more than just including additional questions. Separate

data processing is required to edit responses for consistency and to impute missing values. Another weighting

method is often necessary because the supplement targets

a different universe from the basic CPS. A supplement can

also engender a different level of response or cooperation

from respondents.

CRITERIA FOR SUPPLEMENT INQUIRIES

As a basis for undertaking supplements for federal

agencies or other sponsors, a number of criteria which

determine the acceptability of such work have been developed and refined over the years. These criteria were

developed by the Census Bureau in consultation with the

Bureau of Labor Statistics (BLS).

The staff of the Census Bureau, working with the

sponsoring agency, develops the survey design, including

the methodology, questionnaires, pretesting options, interviewer instructions and processing requirements. The Census Bureau provides a written description of the statistical

properties associated with each supplement. The same

standards of quality that apply to the basic CPS apply to

the supplements.

The following criteria are considered before undertaking

a supplement:

1. The subject matter of the inquiry must be in the public

interest.

2. The inquiry must not have an adverse effect on the

CPS or other Census Bureau programs. The questions

must not cause respondents to question the importance of the survey and result in losses of response or

quality. It is essential that the image of the Census

Bureau as the objective fact finder for the Nation is not

damaged. Other important functions of the Census

Bureau, such as the decennial censuses or the economic censuses, must not be affected in terms of

quality or response rates or in congressional acceptance and approval of these programs.

3. The subject matter must be compatible with the basic

CPS survey. This is to avoid introducing a concept that

could affect the accuracy of responses to the basic

CPS information. For example, a series of questions

incorporating a revised labor force concept that could

inadvertently affect responses to the standard labor

force items would not be included.

4. The inquiry must not slow down the work of the basic

survey or impose a response burden that may affect

future participation in the basic CPS. In general, the

supplemental inquiry must not add more than 10

minutes of interview time per respondent or 25 minutes per household. All conflicts or competition for the

use of Census Bureau staff or facilities that arise in

dealing with the supplemental inquiry are resolved by

giving the basic CPS first priority. The Census Bureau

will not jeopardize the schedule for completing CPS or

other Census Bureau work to favor completing a

supplemental inquiry within some specified time frame.

5. The subject matter must not be overly sensitive. This

criterion is imprecise, and its interpretation has changed

over time. For example, the subject of birth expectations, once considered sensitive, has been included as

a CPS supplemental inquiry.

6. It must be possible to meet the objectives of the inquiry

through the survey method. That is, it must be possible

to translate the survey objectives into meaningful

questions, and the respondent must be able to supply

the information required to answer the questions.

11–1
7. If the supplemental information is to be collected

during the CPS interview, the inquiry must be suitable

for the personal visit/telephone procedures used in the

CPS.

8. All data must abide by the Census Bureau’s enabling

legislation, which, in part, ensures that no information

will be released that can identify an individual. Requests

for name, address, social security number, or other

information that can directly identify an individual will

not be included. In addition, information that could be

used to indirectly identify an individual with a high

probability of success (e.g., small geographic areas in

conjunction with income or age) will be suppressed.

9. The cost of supplements must be borne by the sponsor, regardless of the nature of the request or the

relationship of the sponsor to the ongoing CPS.

The questionnaires developed for the supplement are

subject to the Census Bureau’s pretesting policy. This

policy was established in conjunction with other sponsoring

agencies to encourage questionnaire research aimed at

improving data quality.

Even though the proposed inquiry is compatible with the

criteria given in this section, the Census Bureau does not

make the final decision regarding the appropriateness or

desirability of the supplemental survey. The Office of

Management and Budget, through its Statistical Policy

Division, reviews the proposal to make certain it meets

government wide standards regarding the need for the

data and appropriateness of the design and ensures that

the survey instruments, strategy, and response burden are

acceptable.

RECENT SUPPLEMENTAL INQUIRIES

The scope and type of CPS supplemental inquiries vary

considerably from month to month and from year to year.

Generally, in any given month, a respondent who is selected

for the supplement is asked the additional questions that

comprise the supplemental inquiry after completing the

regular part of the CPS. Table 11-1 summarizes CPS

supplemental inquiries that were conducted between September 1994 and December 2001.

The Housing Vacancy Supplement (HVS) is unusual in

that it is the only supplement that is conducted every

month. This supplement collects additional information

(e.g., number of rooms, plumbing, and rental/sales price)

on housing units identified as vacant in the basic CPS.

Probably the most widely used supplement is the Annual

Demographic Survey (ADS) which is conducted every

March. This supplement collects data on work experience,

several sources of income, migration, household composition, health insurance coverage, and receipt of noncash

benefits.

The basic CPS weighting is not always appropriate for

supplements, since supplements tend to have higher nonresponse rates. In addition, supplement universes are

generally different from the basic CPS universe. Thus,

some supplements require weighting procedures different

from the basic CPS. These variations are described for two

of the major supplements, the Housing Vacancy Survey

and the Annual Demographic Survey, in the following

sections.

Housing Vacancy Survey Supplement

Description of supplement

The HVS is a monthly supplement to the CPS sponsored by the Census Bureau. The supplement is administered when the CPS encounters a unit in sample that is

intended for year-round or seasonal occupancy and is

currently vacant, or occupied by persons with a usual

residence elsewhere. The interviewer asks a reliable respondent (e.g., the owner, a rental agent, or a knowledgeable

neighbor) questions on year built; number of rooms, bedrooms, and bathrooms; how long the housing unit has been

vacant; the vacancy status (for rent, for sale, etc); and

when applicable, the selling price or rent amount.

The purpose of the HVS is to provide current information

on the rental and homeowner vacancy rates, home ownership rates, and characteristics of units available for

occupancy in the United States as a whole, geographic

regions, and inside and outside metropolitan areas. The

rental vacancy rate is a component of the index of leading

economic indicators which is used to gauge the current

economic climate. Although the survey is performed monthly,

data for the nation and for Northeast, South, Midwest, and

West regions are released quarterly and annually. The data

released annually include information for states and large

metropolitan areas.

Calculation of vacancy rates

The HVS collects data on year-round and seasonal

vacant units. Vacant year-round units are those intended

for occupancy at any time of the year, even though they

may not be in use year-round. In resort areas, a housing

unit which is intended for occupancy on a year-round basis

is considered a year-round unit; those intended for occupancy only during certain seasons of the year are considered seasonal. Also, vacant housing units held for occupancy by migratory workers employed in farm work during

the crop season are classified as seasonal. The rental and

homeowner vacancy rates are the most prominent HVS

statistics. The vacancy rates are determined using information collected by the HVS and CPS since the formulas

use both vacant and occupied housing units.

The rental vacancy rate is calculated as the ratio of

vacant year-round units for rent to the sum of renter

occupied units, vacant year-round units rented but awaiting

occupancy, and vacant year-round units for rent.

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Table 11–1. Current Population Survey Supplements September 1994 - December 2001

Title Month Purpose Sponsor

Housing Vacancy Monthly Provide quarterly data on vacancy rates and characteristics of vacant

units.

Census

Health/Pension September 1994 Provide information on health/pension coverage for persons 40 years

of age and older. Information includes benefit coverage by former as

well as current employer and reasons for noncoverage, as appropriate. Amount, cost, employer contribution, and duration of benefits are

also measured. Periodicity: As requested.

PWBA

Lead Paint Hazards

Awareness

December 1994

June 1997

Provide information on the current awareness of the health hazards

associated with lead-based paint. Periodicity: As requested.

HUD

Contingent Workers February 1995, 1997, 1999,

2001

Provide information on the type of employment arrangement workers

have on their current job and other characteristics of the current job such

as earnings, benefits, longevity, etc., along with their satisfaction with

and expectations for their current jobs. Periodicity: Biennial.

BLS

Annual Demographic

Supplement

March 1995 - 2001 Data concerning work experience, several sources of income, migration, household composition, health insurance coverage, and receipt of

noncash benefits. Periodicity: Annual.

Census/BLS

Food Security April 1995, September 1996,

April 1997, August 1998, April

1999, September 2000, April

2001, December 2001

Data that will measure hunger and food security. It will provide data on

food expenditures, access to food, and food quality and safety.

FNS

Race and Ethnicity May 1995, July 2000 Use alternative measurement methods to evaluate how best to collect

these types of data.

BLS/Census

Marital History June 1995 Information from ever married persons on marital history. Periodicity: Quinquennial.Census/BLS

Fertility June 1995, 1998, 2000 Data on the number of children that women aged 15-44 have ever had

and the children’s characteristics. Periodicity: Quinquennial.

Census/BLS

Educational Attainment July 1995 Tested several methods of collecting these data. Tested both the current method (highest grade completed or degree received) and the old

method (highest grade attended and grade completed).

BLS/Census

Veterans August 1995, August 1997,

September 1999, August 2001

Data for veterans of the United States on Vietnam-theatre status, serviceconnected income, effect of a service-connected disability on current

labor force participation, and participation in veterans’ programs. Periodicity: Biennial.

BLS

School Enrollment October 1994, 1995, 1996,

1997, 1998, 1999, 2000, 2001

Provide information on school enrollment, junior or regular college attendance, and high school graduation. Periodicity: Annual.BLS/Census/NCES

Tobacco Use September 1995, January 1996,

May 1996, September 1998,

January 1999, May 1999, January 2000, May 2000, June

2001, November 2001

Data for 15+ population on current and former use of tobacco products;

restrictions of smoking in workplace for employed persons; and personal

attitudes toward smoking; a repeat of supplements in 1995/1996. Supplements will be repeated in 1998/1999. Periodicity: As requested.

NCI

Displaced Workers February 1996, 1998, 2000 Provide data on workers who lost a job in the last 5 years because of

plant closing, shift elimination, or other work-related reason. Periodicity: Biennial.

BLS

Job Tenure/Occupational

Mobility

February 1996, 1998, 2000 Collect data that will measure an individual’s tenure with his/her current

employer and in his/her current occupation. Periodicity: As requested.

BLS

Child Support April 1996, 1998, 2000 Identify households with absent parents and provide data on child support arrangements, visitation rights of absent parent, amount and frequency of actual versus awarded child support, and health insurance

coverage. Data are also provided on why child support was not received

and/or awarded. April data will be matched to March data.

OCSE

Voting and Registration November 1994, 1996, 1998,

2000

Provide demographic information on persons who registered and did not

register to vote. Also measures number of persons who voted and reasons for not registering. Periodicity: Biennial.

Census

Work Schedule/

Home-Based Work

May 1997, 2001 Provide information about multiple job holdings and work schedules and

telecommuters who work at a specific remote site.

BLS

Telephone Availability March, July & November

1994-2001

Collect data on whether there is a phone in the HU, on which to contact the person; is a telephone interview acceptable.FCC

Computer Use/Internet

Use

August 2000, September 2001,

November 1994, October 1997

Obtain information about household access to computers and the use

of the internet or worldwide web.

NTIA

11–3
The homeowner vacancy rate is calculated as the ratio

of vacant year-round units for sale to the sum of owner

occupied units, vacant year-round units sold but awaiting

occupancy, and vacant year-round units for sale.

Weighting procedure

Since the HVS universe differs from the CPS universe,

the HVS records require a different weighting procedure

from the CPS records. The HVS records are weighted by

the CPS basic weight, the CPS special weighting factor,

and two HVS adjustments. (Refer to Chapter 10 for a

description of the two CPS weighting adjustments.) The

two HVS adjustments are referred to as the HVS first-stage

ratio adjustment and the HVS second-stage ratio adjustment.

The HVS first-stage ratio adjustment is comparable to

the CPS first-stage ratio adjustment in that it reduces the

contribution to variance from the sampling of PSUs. The

adjustment factors are based on 1990 census data. For

each state, they are calculated as the ratio of the statelevel census count of vacant year-round housing units in all

NSR PSUs to the state-level estimate, using census counts,

of vacant year-round housing units in NSR PSUs.

The first-stage adjustment factors are applied to both

vacant year-round and seasonal housing units in NSR

PSUs.

The HVS second-stage ratio adjustment, which applies

to vacant year-round and seasonal housing units in SR and

NSR PSUs, is calculated as the ratio of the weighted CPS

interviewed housing units after CPS second-stage ratio

adjustment to the weighted CPS interviewed housing units

after CPS first-stage ratio adjustment.

The cells for the HVS second-stage adjustment are

calculated within each month-in-sample by census region

and type of area (metropolitan/nonmetropolitan, central

city/balance of MSA, and urban/rural). This adjustment is

made to all eligible HVS records.

The final weight for each HVS record is determined by

calculating the product of the CPS basic weight, the CPS

special weighting factor, the HVS first-stage ratio adjustment, and the HVS second-stage ratio adjustment. Note

that the occupied units in the denominator of the vacancy

rate formulas use a different final weight since the data

come from the CPS. The final weight applied to the renter

and owner-occupied units is the CPS household weight.

(Refer to Chapter 10 for a description of the CPS household weight.)

Annual Demographic Supplement (March

Supplement)

Description of supplement

The ADS is sponsored by the Census Bureau and the

Bureau of Labor Statistics (BLS). The Census Bureau has

collected data in the ADS since 1947. From 1947 to 1955,

the ADS took place in April and from 1956 to 2001 the ADS

took place in March. In 2002, a sample increase was

implemented requiring more time for data collection. Thus,

additional ADS interviews are now taking place in February

and April. However, even with this sample increase, most

of the data collection still occurs in March.

The supplement collects data on family characteristics,

household composition, marital status, migration, income

from all sources, information on weeks worked, time spent

looking for work or on layoff from a job, occupation and

industry classification of the job held longest during the

year, health insurance coverage, and receipt of noncash

benefits. A major reason for conducting the ADS around the

month of March is to obtain better income data. It was

thought that since March is the month before the deadline

for filing federal income tax returns, respondents were

likely to have recently prepared tax returns or be in the

midst of preparing such returns and could report their

income more accurately than at any other time of the year.

The ADS sample consists of the March CPS sample,

plus additional CPS households identified in the prior

November and following April CPS samples. Starting in

2002, the eligible ADS sample households are:

1. The entire March CPS sample.

2. Hispanic households — identified in November (from

all month-in-sample (MIS) rotation groups) and in April

(MIS 1,5).

3. Non-Hispanic non-White households — identified in

November (MIS 1, 5-8) and April (MIS 1,5).

4. Non-Hispanic White households with children 18 years

or younger — identified in November (MIS 1, 5-8) and

April (MIS 1,5).

Prior to 2002, only the November CPS households

containing at least one person of Hispanic origin were

added to the ADS. The added households in 2002, along

with a general sample increase in selected states (see

Appendix J), are collectively known as the State Children’s

Health Insurance Program (SCHIP) sample expansion.

The added households improve the reliability of the ADS

estimates for the Hispanic households, non-Hispanic nonWhite households, and non-Hispanic White households

with children 18 years or younger.

Because of the characteristics of CPS sample rotation

(see Chapter 3), the additional sample from the November

and April CPS rotation groups are completely different from

those in the March CPS. The additional sample cases

increase the effective sample size of the ADS compared to

the March CPS sample alone. The ADS sample includes

18 MIS rotation groups for Hispanic households, 15 MIS

rotation groups for non-Hispanic non-White households, 15

MIS rotation groups for non-Hispanic White households

with children 18 years or younger, and 8 MIS rotation

groups for all other households.

11–4
The March and April ADS eligible cases are administered the ADS questionnaire in those respective months

(see Table 11-2). The April cases are classified as ‘‘split

path’’ cases because they receive the ADS, while other

households receive the supplement scheduled for April.

The November eligible Hispanic households are administered the ADS questionnaire in March, regardless of rotation group (MIS 1-8). (Note that November MIS 5-8 households have already completed all 8 months of interviewing

for the CPS before March, and the November MIS 1-4

households have an extra contact scheduled for the ADS

before the 5th interview of the CPS later in the year.)

The November eligible non-Hispanic households (in MIS

1, 5-8) are administered the ADS questionnaire in either

February or April. November ADS eligible cases in MIS 1

and 5 are interviewed for the CPS in February (in MIS 4

and 8, respectively) so the ADS questionnaire is administered in February. (These are also split path cases, since

other households in the rotation groups get the regular

supplement scheduled for February). The November MIS

6-8 eligible cases are split between the February and April

CPS interviewing months.

Mover households, defined as households with a different reference person when compared to the November

CPS interview, are found at the time of the ADS interview.

Mover households identified from the November eligible

sample are removed from the ADS sample. Mover households identified in the March and April eligible samples

receive the ADS questionnaire.

The ADS sample universe is slightly different from the

CPS. The CPS completely excludes military personnel

while the ADS includes military personal who live in households with at least one other civilian adult. These differences require the ADS to have a different weighting

procedure from the regular CPS.

Weighting procedure

Prior to weighting, missing supplement items are assigned

values based on hot deck imputation, a system that uses a

statistical matching process. Values are imputed even

when all the supplement data are missing. Thus, there is no

separate adjustment for households that respond to the

basic survey but not to the supplement. The ADS records

are weighted by the CPS basic weight, the CPS special

weighting factor, the CPS noninterview adjustment, the

CPS first-stage ratio adjustment, and the CPS secondstage ratio adjustment procedure. (Refer to Chapter 10 for

a description of these adjustments.) Also there is an

additional ADS noninterview adjustment for the November

ADS sample, a SCHIP Adjustment Factor, a family equalization adjustment, and an adjustment for Armed Forces

members.

The November eligible sample, the March eligible sample,

and the April eligible sample are weighted separately up to

the second-stage weighting adjustment. The samples are

then combined so that one second-stage adjustment procedure is performed. The flowchart in Figure 11-1 illustrates the weighting process for the ADS sample.

Households from November eligible sample. The households from the November eligible sample start with their

basic CPS weight as calculated in November, modified by

their November CPS special weighting factor and November CPS noninterview adjustment. At this point, a second

noninterview adjustment is made for those November

eligible households that are still occupied, but an interview

could not be obtained in the February, March, or April CPS.

Then, the November ADS sample weights are adjusted by

the CPS first-stage adjustment ratio and finally, the weights

are adjusted by the SCHIP Adjustment Factor.

The ADS noninterview adjustment for November eligible

sample. The second noninterview adjustment is applied to

the November eligible sample households to reflect noninterviews of occupied housing units that occur in the February, March, or April CPS. If a noninterviewed household

is actually a Mover household, it would not be eligible for

interview. Since the mover status of noninterviewed households is not known, we assume that the proportion of

Table 11-2. Summary of ADS Interview Month

CPS month/Hispanic status

Mover Nonmover

Month in sample Month in sample

123456781234567 8

November Hispanic1

NI2 March

Non-Hispanic3 Feb. NI2 Feb. Feb./Apr.

March Hispanic1

March March

Non-Hispanic3

April

Hispanic1

Apr. NI2 Apr. NI2 Apr. NI2 Apr. NI2

Non-Hispanic3

1

Hispanics may be of any race.

2

NI - Not interviewed for the ADS.

3

The non-Hispanic group includes both non-Hispanic non-Whites and non-Hispanic Whites with children ≤ 18 years old.

11–5
11–6
mover households is the same for interviewed and noninterviewed households. This is reflected in the noninterview

adjustment. With this exception, the noninterview adjustment procedure is the same as described in Chapter 10.

The weights of the interviewed households are adjusted by

the noninterview factor as described below. At this point,

the noninterviews and those November Mover households

are removed from any further weighting of the ADS. The

noninterview adjustment factor, Fij, is computed as follows:

Fij =

Zij + Nij + Bij

Zij + Bij

where:

Zij = the weighted number of November eligible

sample households interviewed in the February, March, or April CPS in cell j of cluster

i.

Nij = the weighted number of November eligible

sample occupied, noninterviewed housing

units in the February, March, or April CPS in

cell j of cluster i.

Bij = the weighted number of November eligible

sample Mover households identified in the

February, March, or April CPS in cell j of

cluster i.

The weighted counts used in this formula are those after

the November CPS noninterview adjustment is applied.

The clusters refer to the variously defined regions that

compose the United States. These include clusters for the

Northeast, Midwest, South, and West, as well as clusters

for particular cities or smaller areas. Within each of these

clusters is a pair of residence cells. These could be (1)

Central City and balance of MSA, (2) MSA and non-MSA,

or (3) Urban and Rural, depending on the type of cluster.

SCHIP adjustment factor for the November eligible sample.

The SCHIP adjustment factor is applied to Nonmover

eligible households that contain residents who are Hispanic, non-Hispanic non-White, and non-Hispanic Whites

with children 18 years or younger to compensate for the

increased sample in these demographic categories. Hispanic households receive a SCHIP adjustment factor of

8/18 and non-Hispanic non-White households and nonHispanic White households with children 18 years or

younger receive a SCHIP adjustment factor of 8/15. (See

Table 11-3.) After this adjustment is applied, the November

ADS sample is ready to be combined with the March and

April eligible samples for the application of the secondstage ratio adjustment.

Households from April eligible sample. The households

from the April eligible sample start with their basic CPS

weight as calculated in April, modified by their April CPS

special weighting factor, the April CPS noninterview adjustment, and the SCHIP adjustment factor. After the SCHIP

adjustment factor is applied, the April eligible sample is

ready to be combined with the November and March

eligible samples for the application of the second-stage

ratio adjustment.

SCHIP adjustment factor for the April eligible sample. The

SCHIP adjustment factor is applied to April eligible households that contain residents who are Hispanic, non-Hispanic

non-Whites, or non-Hispanic Whites with children 18 years

or younger to compensate for the increased sample in

these demographic categories regardless of mover status.

Hispanic households receive a SCHIP adjustment factor of

8/18 and non-Hispanic non-White households and nonHispanic White households with children 18 years or

younger receive a SCHIP adjustment factor of 8/15. Table

11-3 summarizes these weight adjustments.

Households from the March eligible sample. The March

eligible sample households start with their basic CPS

weight, modified by their CPS special weighting factor, the

March CPS noninterview adjustment, the March CPS

first-stage ratio adjustment (as described in Chapter 10),

and the SCHIP adjustment factor.

SCHIP adjustment factor for the March eligible sample.

The SCHIP adjustment factor is applied to the March

eligible nonmover households that contain residents who

are Hispanic, non-Hispanic non-White, and non-Hispanic

Whites with children 18 years or younger to compensate

for the increased sample in these demographic categories.

Hispanic households receive a SCHIP adjustment factor of

8/18 and non-Hispanic non-White households and nonHispanic White resident households with children 18 years

or younger receive a SCHIP adjustment factor of 8/15.

Mover households and all other households receive a

SCHIP adjustment of 1. Table 11-3 summarizes these

weight adjustments.

Combined household eligible sample from November,

March, and April CPS. At this point, the eligible sample

from November, March, and April are combined. The

remaining adjustments are applied to this combined sample

file.

ADS second-stage ratio adjustment. The second-stage

ratio adjustment adjusts the ADS estimates so that they

agree with independent age, sex, race, and ethnicity

population controls as described in Chapter 10. The same

procedure, used for CPS, is used for the ADS.

Additional ADS weighting. After the ADS weight (the

weight through the second-stage procedure) is determined, the next step is to determine the final ADS weight.

There are two more weighting adjustments applied to the

ADS sample cases. One is for family equalization. Without

this adjustment there would be more married men than

married women. Weights, mostly of males, are adjusted to

11–7
give a husband and wife the same weight, while maintaining the overall age/race/sex/Hispanic control totals. The

second adjustment is for members of the Armed Forces.

Family equalization. The family equalization procedure

categorizes adults (at least 15 years old) into seven groups

based on sex and household composition:

1. Female partners in female/female unmarried partner

households

2. All other civilian females

3. Married males, spouse present

4. Male partners in male/female unmarried partner households

5. Other civilian male heads

6. Male partners in male/male unmarried partner households

7. All other civilian males

Three different methods, dependent on the household

composition, are used to assign the ADS weight to other

members of the household. The methods are 1) assigning

the weight of the householder to the spouse or partner, 2)

averaging the weights of the householder and partner, or 3)

computing a ratio adjustment factor and multiplying the

factor by the ADS weight.

Armed forces. Male and female members of the Armed

Forces living off post or living with their families on post are

included in the ADS as long as there is at least one civilian

adult living in the same household, whereas the CPS

excludes all Armed Forces members. Households with no

civilian adults in the household, i.e., households with all

Armed Forces members, are excluded from the ADS. The

weights assigned to the Armed Forces members, included

in the ADS, is the same weight civilians receive through the

SCHIP adjustment. Control totals, used in the secondstage factor, do not include Armed Forces members so the

Armed Force members do not go through the secondstage ratio adjustment. During family equalization, a male

Armed Forces member with a spouse or partner is reassigned the weight of his spouse/partner.

SUMMARY

Although this discussion focuses on only two CPS

supplements, the HVS and the ADS, every supplement has

its own unique objectives. The additional questions, edits,

and imputations are tailored to each supplement’s data

needs. For many supplements this also means altering the

weighting procedure to reflect a different universe, account

for a modified sample, or adjust for a higher rate of

nonresponse. The weighting revisions discussed here for

HVS and ADS are only indicative of the types of modifications that might be used for a supplement.

Table 11-3. Summary of SCHIP Adjustment Factor

CPS month/Hispanic status

Mover Nonmover

Month in sample Month in sample

123456781234567 8

November Hispanic1

02 8/18

Non-Hispanic3 8/15 02 8/15 8/15

March Hispanic1 1 8/18

Non-Hispanic3 1 8/15

April

Hispanic1 8/18

02 8/1802 8/1802 8/1802

Non-Hispanic3 8/15 8/15 8/15 8/15

1

Hispanics may be of any race.

2

Zero weight indicates the cases are ineligible for the ADS.

3

The non-Hispanic group includes both non-Hispanic non-Whites and non-Hispanic Whites with children ≤ 18 years old.

11–8
Chapter 12.

Data Products From the Current Population Survey

INTRODUCTION

Information collected in the Current Population Survey

(CPS) is made available by both the Bureau of Labor

Statistics and the Census Bureau through broad publication programs which include news releases, periodicals,

and reports. CPS-based information is also available on

magnetic tapes, CD-ROM, and computer diskettes and can

be obtained online through the Internet. This chapter lists

many of the different types of products currently available

from the survey, describes the forms in which they are

available, and indicates how they can be obtained. This

chapter is not intended to be an exhaustive reference for all

information available from the CPS. Furthermore, given the

rapid ongoing improvements occurring in computer technology, it can be expected that greater numbers of the

CPS-based products will be electronically accessible in the

future.

BUREAU OF LABOR STATISTICS

Each month, employment and unemployment data are

published initially in The Employment Situation news release

about 2 weeks after data collection is completed. The

release includes a narrative summary and analysis of the

major employment and unemployment developments together

with tables containing statistics for the principal data series.

The news release is also available electronically on the

Internetandcanbeaccessedathttp://stats.bls.gov:80/newsrels.htm.

Subsequently, more detailed statistics are published in

Employment and Earnings, a monthly periodical. The detailed

tables provide information on the labor force, employment,

and unemployment by a number of characteristics, such as

age, sex, race, marital status, industry, and occupation.

Estimates of the labor force status and detailed characteristics of selected population groups not published on a

monthly basis, such as Vietnam-era veterans and Hispanics1 are published every quarter. Data are also published

quarterly on usual median weekly earnings classified by a

variety of characteristics. In addition, the January issue of

Employment and Earnings provides annual averages on

employment and earnings by detailed occupational categories, union affiliation, and employee absences.

About 25,000 of the monthly labor force data series plus

quarterly and annual averages are maintained in LABSTAT,

the BLS public database, on the Internet. They can be

accessed from http://stats.bls.gov/datahome.htm. In most

cases, these data are available from the inception of the

series through the current month. Approximately 250 of the

most important estimates from the CPS are presented

monthly and quarterly on a seasonally adjusted basis. The

CPS is used also for a program of special inquiries to

obtain detailed information from particular segments or for

particular characteristics of the population and labor force.

About four such special surveys are made each year. The

inquiries are repeated annually in the same month for

some topics, including the earnings and total incomes of

individuals and families (published by the Census Bureau);

the extent of work experience of the population during the

calendar year; the marital and family characteristics of

workers; the employment of school-age youth, high school

graduates and dropouts, and recent college graduates;

and the educational attainment of workers. Surveys are

also made periodically on subjects such as contingent

workers, job tenure, displaced workers, and disabled veterans.

Generally, the persons who provide information for the

monthly CPS questions also answer the supplemental

questions. Occasionally, the kind of information sought in

the special surveys requires the respondent to be the

person about whom the questions are asked. The results of

these special surveys are first published as news releases

and subsequently in the Monthly Labor Review or BLS

reports.

In addition to the regularly tabulated statistics described

above, special data can be generated through the use of

the CPS individual (micro) record files. These files contain

records of the responses to the survey questionnaire for all

individuals in the survey. While the microdata can be used

simply to create additional cross-sectional detail, an important feature of their use is the ability to match the records of

specific individuals at different points in time during their

participation in the survey. (The actual identities of these

individuals are protected on all versions of the files made

available to noncensus staff.) By matching these records,

data files can be created which lend themselves to some

limited longitudinal analysis and the investigation of shortrun labor market dynamics. An example is the statistics on

gross labor force flows, which indicate how many persons

move among the labor force status categories each month.

Microdata files are available for all months since January

1976 and for various months in prior years. These data are

made available on magnetic tape, CD-ROM, or diskette. 1

Hispanics may be of any race.

12–1
Annual averages from the CPS for the four census

regions and nine divisions, the 50 states and the District of

Columbia, 50 large metropolitan areas, and 17 central

cities are published annually in Geographic Profile of

Employment and Unemployment. Data are provided on the

employed and unemployed by selected demographic and

economic characteristics.

Table 12– 1 provides a summary of the CPS data

products available from BLS.

U.S. CENSUS BUREAU

The U.S. Census Bureau has been analyzing data from

the Current Population Survey and reporting the results to

the public for over five decades. The reports provide

information on a recurring basis about a wide variety of

social, demographic, and economic topics. In addition,

special reports on many subjects have also been produced. Most of these reports have appeared in 1 of 3 series

issued by the Census Bureau: P-20, Population Characteristics; P-23, Special Studies; and P-60, Consumer Income.

Many of the reports are based on data collected as part of

the March demographic supplement to the CPS. However,

other reports use data from supplements collected in other

months (as noted in the listing below). A full inventory of

these reports as well as other related products is documented in: Subject Index to Current Population Reports

and Other Population Report Series, CPR P23-192, which

is available from the Government Printing Office, or the

Census Bureau. Most reports have been issued in paper

form; more recently, some have been made available on

the Internet (http://www.census.gov). Generally, reports

are announced by press release, and are released to the

public via the Census Bureau Public Information Office.

Census Bureau Report Series

P-20, Population Characteristics. Regularly recurring reports

in this series include topics such as geographic mobility,

educational attainment, school enrollment (October supplement), marital status, households and families, Hispanic

origin, the Black population, fertility (June supplement),

voter registration and participation (November supplement),

and the foreign-born population.

P-23, Special Studies. Information pertaining to special

topics, including one-time data collections, as well as

research on methods and concepts are produced in this

series. Examples of topics include computer ownership

and usage, child support and alimony, ancestry, language,

and marriage and divorce trends.

P-60, Consumer Income. Regularly recurring reports in

this series include information concerning families, individuals, and households at various income and poverty levels,

shown by a variety of demographic characteristics. Other

reports focus on health insurance coverage and other

noncash benefits.

In addition to the population data routinely reported from

the CPS, Housing Vacancy Survey (HVS) data are collected from a sample of vacant housing units in the Current

Population Survey (CPS) sample. Using these data, quarterly and annual statistics are produced on rental vacancy

rates and home ownership rates for the United States, the

four census regions, location inside and outside metropolitan areas (MAs), the 50 states and the District of Columbia,

and the 75 largest MAs. Information is also made available

on national home ownership rates by age of householder,

family type, race, and Hispanic origin. A press release is

issued each quarter as well as quarterly and annual data

tables on the Internet.

Supplement Data Files

Public use microdata files containing supplement data

are available from the Census Bureau. These files contain

the full battery of basic labor force and demographic data

along with the supplement data. A standard documentation

package containing a record layout, source and accuracy

statement, and other relevant information is included with

each file. (The actual identities of the individuals surveyed

are protected on all versions of the files made available to

noncensus staff.) These files can be purchased through the

Customer Services Branch of the Census Bureau and are

available in either tape or CD-ROM format. The CPS

homepage is the other source for obtaining these files.

Eventually, we plan to add most historical files to the site

along with all current and future files.

12– 2
Table 12– 1. Bureau of Labor Statistics Data Products From the Current Population Survey

Product Description Periodicity Source Cost

News Releases

College Enrollment and Work

Activity of High School

Graduates

An analysis of the college enrollment and work activity of the prior

year’s high school graduates by a variety of characteristics

Annual October CPS

supplement

Free (1

)

Contingent and Alternative

Employment Arrangements

An analysis of workers with ‘‘contingent’’ employment arrangements (lasting less than 1 year) and alternative arrangements

including temporary and contract employment by a variety of

characteristics

Biennial January CPS

supplement

Free (1

)

Displaced Workers An analysis of workers who lost jobs in the prior 3 years due to

plant or business closings, position abolishment, or other reasons

by a variety of characteristics

Biennial February CPS

supplement

Free (1

)

Employment Situation of

Vietnam-Era Veterans

An analysis of the work activity and disability status of persons who

served in the Armed Forces during the Vietnam era

Biennial September CPS

supplement

Free (1

)

Job Tenure of American

Workers

An analysis of employee tenure by industry and a variety of

demographic characteristics

Biennial February CPS

supplement

Free (1

)

State and Regional

Unemployment

An analysis of state and regional employment and unemployment Annual CPS annual

averages

Free (1

)

The Employment Situation Seasonally adjusted and unadjusted data on the Nation’s employed

and unemployed workers by a variety of characteristics

Monthly (2

) Monthly CPS Free (1)

Union Membership An analysis of the union affiliation and earnings of the Nation’s

employed workers by a variety of characteristics

Annual Monthly CPS;

outgoing rotation groups

Free (1

)

Usual Weekly Earnings of Wage

and Salary Workers

Median usual weekly earnings of full- and part-time wage and

salary workers by a variety of characteristics

Quarterly (3

) Monthly CPS;

outgoing rotation groups

Free (1)

Work Experience of the

Population

An examination of the employment and unemployment experience

of the population during the entire preceding calendar year by a

variety of characteristics

Annual March CPS

supplement

Free (1

)

Periodicals

Employment and Earnings A monthly periodical providing data on employment, unemployment, hours, and earnings for the Nation, states, and metropolitan

areas

Monthly (3

) CPS; other

surveys and

programs

$35.00 domestic; $43.75

foreign

Monthly Labor Review A monthly periodical containing analytical articles on employment,

unemployment, and other economic indicators, book reviews, and

numerous tables of current labor statistics

Monthly CPS; other

surveys and

programs

$29.00 domestic; $36.25 foreign

Other Publications

A Profile of the Working Poor An annual report on workers whose families are in poverty by work

experience and various characteristics

Annual March CPS

supplement

Free

Geographic Profile of Employment and UnemploymentAn annual publication of employment and unemployment data for

regions, states, and metropolitan areas by a variety of characteristics

Annual CPS annual

averages

$9.00

Issues in Labor Statistics Brief analysis of important and timely labor market issues Occasional CPS; other

surveys and

programs

Free

12– 3
Table 12– 1. Bureau of Labor Statistics Data Products From the Current Population Survey—Con.

Product Description Periodicity Source Cost

Microdata Files

Job Tenure and Occupational

Mobility

Biennial February CPS

supplement

(

4

)

Displaced Workers Biennial February CPS

supplement

(

4

)

Contingent Work Biennial January CPS

supplement

(

4

)

Annual Demographic Survey Annual March CPS

supplement

(

4

)

Veterans Biennial September CPS

supplement

(

4

)

School Enrollment Annual October CPS

supplement

(

4

)

Work Schedules/Home-Based

Work

Occasional May CPS

supplement

(

4

)

Annual Earnings (outgoing

rotation groups)

Annual Annual CPS (4

)

Time Series (Macro) Files

National Labor Force Data Monthly Labstat(5

) (1)

Regional Data Monthly Labstat(5

) (1)

Unpublished Tabulations

National Labor Force Data Monthly Microfiche Free

Regional, State, and Area Data Monthly Microfiche Free

1 Accessible from the Internet (http://stats.bls.gov:80/newsrels.htm). 2 About 3 weeks following period of reference. 3 About 5 weeks after period of reference. 4 Diskettes ($80); cartridges ($165-$195); tapes ($215-$265); and CD-ROMs ($150). 5 Electronic access via the Internet (http://stats.bls.gov).

Note: Prices noted above are subject to change.

12– 4
Chapter 13.

Overview of Data Quality Concepts

INTRODUCTION

It is far easier to put out a figure than to accompany it

with a wise and reasoned account of its liability to

systematic and fluctuating errors. Yet if the figure is ...

to serve as the basis of an important decision, the

accompanying account may be more important than

the figure itself. John W. Tukey (1949, p. 9)

The quality of any estimate based on sample survey

data can and should be examined from two perspectives.

The first is based on the mathematics of statistical science,

and the second stems from the fact that survey measurement is a production process conducted by human beings.

From both perspectives, survey estimates are subject to

error, and to avoid misusing or reading too much into the

data, we should use them only after their potential error of

both sorts has been examined relative to the particular use

at hand.

In this chapter, we give an overview of these two

perspectives on data quality, discuss their relationship to

each other from a conceptual viewpoint, and define a

number of technical terms. The definitions and discussion

are applicable to all sample surveys, not just the Current

Population Survey (CPS). Succeeding chapters go into

greater detail about the specifics as they relate to CPS.

QUALITY MEASURES IN STATISTICAL SCIENCE

The statistical theory of finite population sampling is

based on the concept of repeated sampling under fixed

conditions. First, a particular method of selecting a sample

and aggregating the data from the sample units into an

estimate of the population parameter is specified. The

method for sample selection is referred to as the sample

design (or just the design). The procedure for producing the

estimate is characterized by a mathematical function known

as an estimator. After the design and estimator have been

determined, a sample is selected and an estimate of the

parameter is computed. The difference between the value

of the estimate and the population parameter is referred to

here as the sampling error, and it will vary from sample to

sample (Särndal, Swensson, and Wretman, 1992, p. 16).

Properties of the sample design-estimator methodology

are determined by looking at the distribution of estimates

that results from taking all possible samples that could be

selected using the specified methodology. The expected

(or mean) value of the squared sampling errors over all

possible samples is known as the mean squared error. The

mean squared error is generally accepted as the standard

overall measure of the quality of a proposed designestimator methodology.

The mean value of the individual estimates is referred as

the expected value of the estimator. The difference between

the expected value of a particular estimator and the value

of the population parameter is known as sampling bias.

When the bias of the estimator is zero, the estimator is said

to be unbiased. The mean value of the squared difference

of the values of the individual estimates and the expected

value of the estimator is known as the sampling variance of

the estimator. The variance measures the magnitude of the

variation of the individual estimates about their expected

value while the mean squared error measures the magnitude of the variation of the estimates about the value of the

population parameter of interest. It can be shown that the

mean squared error can be decomposed into the sum of

the variance and the square of the bias. Thus, for an

unbiased estimator, the variance and the mean squared

error are equal.

Quality measures of a design-estimator methodology

expressed in this way, that is, based on mathematical

expectation assuming repeated sampling, are inherently

grounded on the assumption that the process is correct

and constant across sample repetitions. Unless the measurement process is uniform across sample repetitions, the

mean squared error is not by itself a full measure of the

quality of the survey results.

The assumptions associated with being able to compute

any mathematical expectation are extremely rigorous and

rarely practical in the context of most surveys. For example,

the basic formulation for computing the true mean squared

error requires that there be a perfect list of all units in the

universe population of interest, that all units selected for a

sample provide all the requested data, that every interviewer be a clone of an ideal interviewer who follows a

predefined script exactly and interacts with all varieties of

respondents in precisely the same way, and that all respondents comprehend the questions in the same way and

have the same ability to recall from memory the specifics

needed to answer the questions.

Recognizing the practical limitations of these assumptions, sampling theorists continue to explore the implications of alternative assumptions which can be expressed in

terms of mathematical models. Thus, the mathematical

expression for variance has been decomposed in various

13–1
ways to yield expressions for statistical properties that

include not only sampling variance, but simple response

variance (a measure of the variability among the possible

responses of a particular respondent over repeated administrations of the same question) (Hansen, Hurwitz, and

Bershad, 1961) and correlated response variance, one

form of which is interviewer variance (a measure of the

variability among responses obtained by different interviewers over repeated administrations). Similarly, when a particular design-estimator fails over repeated sampling to

include a particular set of population units in the sampling

frame or to ensure that all units provide the required data,

bias can be viewed as having components such as coverage bias, unit nonresponse bias, or item nonresponse bias

(Groves, 1989). For example, a survey administered solely

by telephone could result in coverage bias for estimates

relating to the total population if the nontelephone households were different than the telephone households with

respect to the characteristic being measured (which almost

always occurs).

One common theme of these types of models is the

decomposition of total mean squared error into one set of

components resulting from the fact that estimates are

based on a sample of units rather than the entire population (sampling error) and another set of components due to

alternative specifications of procedures for conducting the

sample survey (nonsampling error). (Since nonsampling

error is defined negatively, it ends up being a catch-all term

for all errors other than sampling error.) Conceptually,

nonsampling error in the context of statistical science has

both variance and bias components. However, when total

mean squared error is decomposed mathematically to

include a sampling error term and one or more other

‘‘nonsampling error’’ terms, it is often difficult to categorize

such terms as either variance or bias. The term nonsampling error is used rather loosely in the survey literature to

denote mean squared error, variance, or bias in the precise

mathematical sense and to imply error in the more general

sense of process mistakes (see next section).

Some nonsampling error components which are conceptually known to exist have yet to be expressed in practical

mathematical models. Two examples are the bias associated with the use of a particular set of interviewers and the

variance associated with the selection of one of the numerous possible sets of questions. In addition, the estimation

of many nonsampling errors — and sampling bias — is

extremely expensive and difficult or even impossible in

practice. The estimation of bias, for example, requires

knowledge of the truth, which may be sometimes verifiable

from records (e.g., number of hours paid for by employer)

but often is not verifiable (e.g., number of hours actually

worked). As a consequence, survey organizations typically

concentrate on estimating the one component of total

mean squared error for which practical methods have been

developed — variance.

Since a survey is generally conducted only once with

one specific sample of units, it is impossible to compute the

actual sampling variance. In simple cases, it is frequently

possible to construct an unbiased estimator of variance. In

the case of complex surveys like CPS, estimators have

been developed which typically rely on the proposition —

usually well-grounded — that the variability among estimates based on various subsamples of the one actual

sample is a good proxy for the variability among all the

possible samples like the one at hand. In the case of CPS,

160 subsamples or replicates are used in variance estimation for the 1990 design. (For more specifics, see Chapter

14.) It is important to note that the estimates of variance

resulting from the use of this and similar methods are not

merely estimates of sampling variance. The variance estimates include the effects of some nonsampling errors,

such as response variance and intra-interviewer correlation. On the other hand, users should be aware of the fact

that for some statistics these estimates of standard error

might be significant underestimates of total error, an important consideration when making inferences based on survey data.

To draw conclusions from survey data, samplers rely on

the theory of finite population sampling from a repeated

sampling perspective: If the specified sample design-estimator

methodology were implemented repeatedly and the sample

size sufficiently large, the probability distribution of the

estimates would be very close to a normal distribution.

Thus, one could safely expect 90 percent of the estimates to be within two standard errors of the mean of all

possible sample estimates (standard error is the square

root of the estimate of variance) (Gonzalez et al., 1975;

Moore, 1997). However, one cannot claim that the probability is .90 that the true population value falls in a

particular interval. In the case of a biased estimator due to

nonresponse, undercoverage, or other types of nonsampling error, confidence intervals may not cover the population parameter at the desired 90 percent rate. In such

cases, a standard error estimator may indirectly account

for some elements of nonsampling error in addition to

sampling error and lead to confidence intervals having

greater than the nominal 90 percent coverage. On the

other hand, if the bias is substantial, confidence intervals

can have less than the desired coverage.

QUALITY MEASURES IN STATISTICAL

PROCESS MONITORING

The process of conducting a survey includes numerous

steps or components, such as defining concepts, translating concepts into questions, selecting a sample of units

from what may be an imperfect list of population units,

hiring and training interviewers to ask persons in the

sample unit the questions, coding responses to questions

into predefined categories, and creating estimates which

take into account the fact that not everyone in the population of interest had a chance to be in the sample and not all

of those in the sample elected to provide responses. It is a

13– 2
process where the possibility exists at each step of making

a mistake in process specification and in deviating during

implementation from the predefined specifications.

For example, we now recognize that the initial labor

force question used in CPS for many years (‘‘What were

you doing most of last week--...’’) was problematic to many

respondents (see Chapter 6). Moreover, many interviewers

tailored their presentation of the question to particular

respondents, for example, saying ‘‘What were you doing

most of last week—working, going to school, etc.?’’ if the

respondent was of school age. Having a problematic

question is a mistake in process specification; varying

question wording in a way not prespecified is a mistake in

process implementation.

Errors or mistakes in process contribute to nonsampling

error in that they would contaminate results even if the

whole population were surveyed. Parts of the overall

survey process which are known to be prone to deviations

from the prescribed process specifications and thus could

be potential sources of nonsampling error in the CPS are

discussed in Chapter 15, along with the procedures put in

place to limit their occurrence.

A variety of quality measures have been developed to

describe what happens during the survey process. These

measures are vital for managers and staff working on a

survey as reflecting process quality, but they can also be

useful to users of the various products of the survey

process — both individual responses and their aggregations into statistics, since they can aid in determining a

particular product’s potential limitations and whether it is

appropriate for the task at hand. Chapter 16 contains a

discussion of quality indicators and, in a few cases, their

potential relationship to nonsampling errors.

SUMMARY

The quality of estimates made from any survey, including CPS, is a function of innumerable decisions made by

designers and implementers. As a general rule of thumb,

designers make decisions aimed at minimizing mean squared

error within given cost constraints. Practically speaking,

statisticians are often compelled to make decisions on

sample designs and estimators based on variance alone;

however, in the case of CPS, the availability of external

population estimates and data on rotation group bias

makes it possible to do more than that. Designers of

questions and data collection procedures tend to focus on

limiting bias and assume the specification of exact question

wording and ordering will naturally limit the introduction of

variance. Whatever the theoretical focus of the designers,

the accomplishment of the goal is heavily dependent upon

those responsible for implementing the design.

Implementers of specified survey procedures, like interviewers and respondents, are presumably concentrating

on doing the best job they know how to do given time and

knowledge constraints. Consequently, process monitoring

through quality indicators, such as coverage and response

rates, is necessary to determine when additional training or

revisions in process specification are needed. Continuing

process improvement must be a vital component of survey

management if the quality goals set by designers are to be

achieved.

REFERENCES

Gonzalez, M. E., Ogus, J. L., Shapiro, G., and Tepping,

B. J. (1975), ‘‘Standards for Discussion and Presentation of

Errors in Survey and Census Data,’’ Journal of the

American Statistical Association, 70, No. 351, Part II,

5-23.

Groves, R. M. (1989), Survey Errors and Survey

Costs, New York: John Wiley & Sons.

Hansen, M. H., Hurwitz, W. N., and Bershad, M. A.

(1961), ‘‘Measurement Errors in Censuses and Surveys,’’

Bulletin of the International Statistical Institute, 38(2),

pp. 359-374.

Moore, D. S. (1997), Statistics Concepts and Controversies, 4th Edition, New York: W. H. Freeman.

Särndal, C., Swensson, B., and Wretman, J. (1992),

Model Assisted Survey Sampling, New York: SpringerVerlag.

Tukey, J. W. (1949), ‘‘Memorandum on Statistics in the

Federal Government,’’ American Statistician, 3, No. 1,

pp. 6-17; No. 2, pp. 12-16.

13– 3
Chapter 14.

Estimation of Variance

INTRODUCTION

The following two objectives are considered in estimating variances of the major statistics of interest for the

Current Population Survey (CPS):

1. Estimate the variance of the survey estimates for use

in various statistical analyses.

2. Evaluate the effect of each of the stages of sampling

and estimation on the overall precision of the survey

estimates for the evaluation of the survey design.

CPS variance estimates take into account the magnitude of the sampling error as well as the effects of some

nonsampling errors, such as response variance and intrainterviewer correlation. Certain aspects of the CPS sample

design, such as the use of one sample PSU per nonselfrepresenting stratum and the use of systematic sampling

within PSUs, make it impossible to obtain a completely

unbiased estimate of the total variance. The use of ratio adjustments in the estimation procedure also contributes to

this problem. Although imperfect, the current variance estimation procedure is accurate enough for all practical uses

of the data, as well as reflecting the effects of the separate

stages of sample selection and estimation on the total

variance. Variance estimates of selected characteristics

and tables, which also show the effects of estimation steps

on variances, are presented at the end of this chapter.

VARIANCE ESTIMATES BY THE REPLICATION

METHOD

Replication methods are able to provide satisfactory

estimates of variance for a wide variety of designs using

probability sampling, even when complex estimation procedures are used. This method requires that the sample

selection, the collection of data, and the estimation procedures be independently carried through (replicated) several times. The dispersion of the resulting estimates can be

used to measure the variance of the full sample.

The Method

Obviously, one would not consider repeating the entire

Current Population Survey several times each month simply to obtain variance estimates. A practical alternative is to

draw a set of random subsamples from the full sample

surveyed each month, using the same principles of selection as are used for the full sample, and to apply the regular

CPS estimation procedures to these subsamples. We refer

to these subsamples as replicates. The number of replicates to use is based on a trade-off between cost and the

reliability of the variance estimator; that is, increasing the

number of replicates decreases the variance of the variance estimator.

Prior to the design introduced after the 1970 census,

variance estimates were computed using 40 replicates.

The replicates were subjected to only the second-stage

ratio adjustment for the same age-sex-race categories

used for the full sample at the time. The noninterview and

first-stage ratio adjustments were not replicated. Even with

these simplifications, limited computer capacity allowed for

the computation of variances for only 14 characteristics.

For the 1970 design, an adaptation of the Keyfitz method of

calculating variances was used. These variance estimates

were derived using the Taylor approximation, dropping

terms with derivatives higher than the first. By 1980,

improvements in computer memory capacity allowed for

the calculation of variance estimates for many characteristics with replication of all stages of the weighting through

compositing. Note that the seasonal adjustment has not

been replicated. A study from an earlier design indicated

that the seasonal adjustment of CPS estimates had relatively little impact on the variances; however, it is not known

what impact this adjustment would have on the current

design variances.

Starting with the 1980 design, variances were computed

using a modified balanced half-sample approach. The

sample was divided to form 48 replicates that retained all

the features of the sample design, for example, the stratification and the within-PSU sample selection. For total

variance, a pseudo-first-stage design was imposed on the

CPS by dividing large self-representing (SR) PSUs into

smaller areas called Standard Error Computation Units

(SECUs) and combining small nonself-representing (NSR)

PSUs into paired strata or pseudostrata. One NSR PSU

was selected randomly from each pseudostratum for each

replicate. Forming these pseudostrata was necessary since

the first stage of the sample design has only one NSR PSU

per stratum in sample. However, pairing the original strata

for variance estimation purposes creates an upward bias in

the variance estimator. For self-representing PSUs each

SECU was divided into two panels, and one panel was

selected for each replicate. One column of a 48 by 48

Hadamard orthogonal matrix was assigned to each SECU

or pseudostratum. The unbiased weights were multiplied

by replicate factors of 1.5 for the selected panel and 0.5 for

14–1
the other panel in the SR SECU or NSR pseudostratum

(Fay, Dippo, and Morganstein, 1984). Thus the full sample

was included in each replicate, but differing weights for the

half-samples were determined by the matrix. These 48

replicates were processed through all stages of the CPS

weighting through compositing. The estimated variance for

the characteristic of interest was computed by summing a

squared difference between each replicate estimate (Yˆ r

)

and the full sample estimate (Yˆ 0) The complete formula1 is

Var(Yˆ 0) =

4

48 

r=1

48

(Yˆ r - Yˆ 0)

2

.

Due to costs and computer limitations, variance estimates

were calculated for only 13 months (January 1987 through

January 1988) and for about 600 estimates at the national

level. Replication estimates of variances at the subnational

level were not reliable because of the small number of

SECUs available (Lent, 1991). Based on the 13 months of

variance estimates, generalized sampling errors (explained

later) were calculated. (See Wolter 1985 or Fay 1984, 1989

for more details on half-sample replication for variance

estimation.)

METHOD FOR ESTIMATING VARIANCE FOR

1990 DESIGN

The general goal of the current variance estimation

methodology, the method in use since July 1995, is to

produce consistent variances and covariances for each

month over the entire life of the design. Periodic maintenance reductions in the sample size and the continuous

addition of new construction to the sample complicated the

strategy needed to achieve this goal. However, research

has shown that variance estimates are not adversely

affected as long as the cumulative effect of the reductions

is less than 20 percent of the original sample size (Kostanich, 1996). Assigning all future new construction sample to

replicates when the variance subsamples are originally

defined provides the basis for consistency over time in the

variance estimates.

The current approach to estimating the 1990 design

variances is called successive difference replication. The

theoretical basis for the successive difference method was

discussed by Wolter (1984) and extended by Fay and Train

(1995) to produce the successive difference replication

method used for the CPS. The following is a description of

the application of this method. Successive USUs2 (ultimate

sampling units) formed from adjacent hit strings (see

Chapter 3) are paired in the order of their selection to take

advantage of the systematic nature of the CPS within-PSU

sampling scheme. Each USU usually occurs in two consecutive pairs; for example, (USU1, USU2), (USU2, USU3),

(USU3, USU4), etc. A pair then is similar to a SECU in the

1980 design variance methodology. For each USU within a

PSU, two pairs (or SECUs) of neighboring USUs are

defined based on the order of selection — one with the

USU selected before and one with the USU selected after

it. This procedure allows USUs adjacent in the sort order to

be assigned to the same SECU, thus better reflecting the

systematic sampling in our variance estimator. Also, the

large increase in the number of SECUs and in the number

of replicates (160 vs. 48) over the 1980 design increases

the precision of the variance estimator.

Replicate Factors for Total Variance

Total variance is composed of two types of variance, the

variance due to sampling of housing units within PSUs

(within-PSU variance) and the variance due to the selection of a subset of all NSR PSUs (between-PSU variance).

Replicate factors are calculated using a 160 by 1603

Hadamard orthogonal matrix. To produce estimates of total

variance, replicates are formed differently for SR and NSR

sample. Note that between-PSU variance cannot be estimated directly using this methodology. Rather, it is the

difference between the estimates of total variance and

within-PSU variance. NSR strata are combined into pseudostrata within each state, and one NSR PSU from the

pseudostratum is randomly assigned to each panel of the

replicate as in the 1980 design variance methodology.

Replicate factors of 1.5 or 0.5 adjust the weights for the

NSR panels. These factors are assigned based on a single

row from the Hadamard matrix and are further adjusted to

account for the unequal sizes of the original strata within

the pseudostratum (Wolter, 1985). In most cases these

pseudostrata consist of a pair of strata except where an

odd number of strata within a state requires that a triplet be

formed. In this case, two rows from the Hadamard matrix

are assigned to the pseudostratum resulting in replicate

factors of about 0.5, 1.7, and 0.8; or 1.5, 0.3, and 1.2 for the

three PSUs. All USUs in a pseudostratum are assigned the

same row number(s).

For SR sample, two rows of the Hadamard matrix are

assigned to each pair of USUs creating replicate factors,

fr for r = 1,...,160

fir 1  2



3

2 ai1,r  2



3

2 ai2,r

where ai,r equals a number in the Hadamard matrix (+1 or

-1) for the ith USU in the systematic sample. This formula

yields replicate factors of approximately 1.7, 1.0, or 0.3.

1

Usually balanced half-sample replication uses replicate factors of 2

and 0 with the formula,

Var(Yˆ o) =

1

k r=1

k

(Yˆ r - Yˆ o)

2

.

where k is the number of replicates. The factor of 4 in our variance

estimator is the result of using replicate factors of 1.5 and 0.5. 2

An ultimate sampling unit is usually a group of four neighboring

housing units. 3

Rows 1 and 81 have been dropped from the matrix.

14–2
As in the 1980 methodology, the unbiased weights

(baseweight x special weighting factor) are multiplied by

the replicate factors to produce unbiased replicate weights.

These unbiased replicate weights are further adjusted

through the noninterview adjustment, the first-stage ratio

adjustment, the second-stage ratio adjustment, and compositing just as the full sample is weighted. A variance

estimator for the characteristic of interest is a sum of

squared differences between each replicate estimate (Yˆ r

)

and the full sample estimate (Yˆ o) The formula is

Var(Yˆ o) =

4

160 

r=1

160

(Yˆ r - Yˆ o)

2

.

Note that the replicate factors 1.7, 1.0, and 0.3 for the

self-representing portion of the sample were specifically

constructed to yield ‘‘4’’ in the above formula in order that

the formula remains consistent between SR and NSR

areas (Fay and Train, 1995).

Replicate Factors for Within-PSU Variance

The above variance estimator can also be used for

within-PSU variance. The same replicate factors used for

total variance are applied to SR sample. For NSR sample,

alternate row assignments are made for USUs to form

pairs of USUs in the same manner that was used for the

SR assignments. Thus for within-PSU variance all USUs

(both SR and NSR) have replicate factors of approximately

1.7, 1.0, or 0.3.

The successive difference replication method is used to

calculate total national variances and within-PSU variances for some states and metropolitan areas. Improved

reliability for within-PSU subnational variance estimates is

expected due to the increased number of SECUs over the

1980 design. The reliability of between-PSU variance

estimates for subnational variance estimates (e.g., state

estimates) and the reliability of national estimates of variance should be about the same as for the 1980 design. For

more detailed information regarding the formation of replicates, see the internal Census Bureau memorandum (Gunlicks, 1996).

VARIANCES FOR STATE AND LOCAL AREA

ESTIMATES

For estimates at the national level, total variances are

estimated from the sample data by the successive difference replication method previously described. For local

areas that are coextensive with one or more sample PSUs,

a variance estimator can be derived using the methods of

variance estimation used for the SR portion of the national

sample. It is anticipated that these estimates of variance

will be more reliable than the 1980 procedure for areas with

comparable sample sizes due to the change in methodology. However, estimates for states and areas which have

substantial contributions from NSR sample areas have

variance estimation problems that are much more difficult

to resolve.

Complicating Factors for State Variances

Most states contain a small number of NSR sample

PSUs. Pairing them into pseudostrata reduces the number

of NSR SECUs still further and increases reliability problems. Also, the component of variance resulting from

sampling PSUs can be more important for state estimates

than for national estimates in states where the proportion of

the population in NSR strata is larger than the national

average.

Further, creating pseudostrata for variance estimation

purposes introduces a between-stratum variance component that is not in the sample design, causing overestimation of the true variance. The between-PSU variance

which includes the between-stratum component is relatively small at the national level for most characteristics, but

it can be much larger at the state level (Gunlicks, 1993;

Corteville, 1996). Thus, this additional component should

be accounted for when estimating state variances.

Research is in progress to produce improved state and

local variances utilizing the within-PSU variances obtained

from successive difference replication and perhaps some

modeling techniques. These variances will be provided as

they become available.

GENERALIZING VARIANCES

With some exceptions, the standard errors provided with

published reports and public data files are based on

generalized variance functions (GVFs). The GVF is a

simple model that expresses the variance as a function of

the expected value of the survey estimate. The parameters

of the model are estimated using the direct replicate

variances discussed above. These models provide a relatively easy way to obtain an approximate standard error on

numerous characteristics.

Why Generalized Standard Errors Are Used

It would be possible to compute and show an estimate of

the standard error based on the survey data for each

estimate in a report, but there are a number of reasons why

this is not done. A presentation of the individual standard

errors would be of limited use, since one could not possibly

predict all of the combinations of results that may be of

interest to data users. Also, for estimates of differences and

ratios that users may compute, the published standard

errors would not account for the correlation between the

estimates.

Most importantly, variance estimates are based on sample

data and have variances of their own. The variance estimate for a survey estimate for a particular month generally

14–3
has less precision than the survey estimate itself. This

means that the estimates of variance for the same characteristic may vary considerably from month-to-month or for

related characteristics (that might actually have nearly the

same level of precision) in a given month. Therefore, some

method of stabilizing these estimates of variance, for

example, by generalization or by averaging over time, is

needed to improve their reliability.

Experience has shown that certain groups of CPS

estimates have a similar relationship between their variance and expected value. Modeling or generalization may

provide more stable variance estimates by taking advantage of these similarities.

The Generalization Method

The GVF that is used to estimate the variance of an

estimated population total, X, is of the form

VarXˆ ) aX2  bX 14.1

where a and b are two parameters estimated using least

squares regression. The rationale for this form of the GVF

model is the assumption that the variance of Xˆ can be

expressed as the product of the variance from a simple

random sample for a binomial random variable and a

‘‘design effect.’’ The design effect (deff) accounts for the

effect of a complex sample design relative to a simple

random sample. Defining P = X/N as the proportion of the

population having the characteristic X, where N is the

population size, and Q = 1-P, the variance of the estimated

total Xˆ , based on a sample of n individuals from the

population, is

VarXˆ  N2

PQdeff

n 14.2

This can be written as

Var(Xˆ )  deff(

N

n) (X2N )  deff(

N

n) X .

Letting

a  b

N

and

b deffN

n

gives the functional form

VarXˆ  aX2  bX.

We choose

a  b

N

where N is a control total so that the variance will be zero

when X = N.

In generalizing variances, all estimates that follow a

common model such as 14.1 (usually the same characteristics for selected demographic or geographic subgroups)

are grouped together. This should give us estimates in the

same group that have similar design effects. These design

effects incorporate the effect of the estimation procedures,

particularly the second stage, as well as the effect of the

sample design. In practice, the characteristics should be

clustered similarly by PSU, by USU, and among persons

within housing units. For example, estimates of total persons classified by a characteristic of the housing unit or of

the household, such as the total urban population, number

of recent migrants, or persons of Hispanic4 origin, would

tend to have fairly large design effects. The reason for this

is that these characteristics usually appear among all

persons in the sample household and often among all

households in the USU as well. On the other hand, lower

design effects would result for estimates of labor force

status, education, marital status, or detailed age categories, since these characteristics tend to vary among members of the same household and among households within

a USU.

Notice also that for many subpopulations of interest, N is

a control total used in the second-stage ratio adjustment. In

these subpopulations, as X approaches N, the variance of

X approaches zero, since the second-stage ratio adjustment guarantees that these sample population estimates

match independent population controls5 (Chapter 10). The

GVF model satisfies this condition. This generalized variance model has been used since 1947 for the CPS and its

supplements, although alternatives have been suggested

and investigated from time to time (Valliant, 1987). The

model has been used to estimate standard errors of means

or totals. Variances of estimates based on continuous

variables (e.g., aggregate expenditures, amount of income,

etc.) would likely fit a different functional form better.

The parameters, a and b, are estimated by use of the

model for relative variance

Vx2 a 

b

X ,

where the relative variance (Vx2

) is the variance divided by

the square of the expected value of the estimate. The a and

b parameters are estimated by fitting a model to a group of

related estimates and their estimated relative variances.

The relative variances are calculated using the successive

difference replication method.

The model fitting technique is an iterative weighted least

squares procedure, where the weight is the inverse of the

square of the predicted relative variance. The use of these

weights prevents items with large relative variances from

unduly influencing the estimates of the a and b parameters.

4

Hispanics may be of any race.

5

The variance estimator assumes no variance on control totals, even

though they are estimates.

14–4
Usually at least a year’s worth of data are used in this

model fitting process and each group of items should

comprise at least 20 characteristics with their relative

variances, although occasionally fewer characteristics are

used.

Direct estimates of relative variances are required for

estimates covering a wide range, so that observations are

available to ensure a good fit of the model at high, low, and

intermediate levels of the estimates. It must be remembered that, by using a model to estimate the relative

variance of an estimate in this way, we introduce some

error, since the model may substantially and erroneously

modify some legitimately extreme values. Generalized

variances are computed for estimates of month-to-month

change as well as for estimates of monthly levels. Periodically, the a and b parameters are updated to reflect

changes in the levels of the population totals or changes in

the ratio (N/n) which result from sample reductions. This

can be done without recomputing direct estimates of

variances as long as the sample design and estimation

procedures are essentially unchanged (Kostanich, 1996).

How the Relative Variance Function is Used

After the parameters a and b of expression (14.1) are

determined, it is a simple matter to construct a table of

standard errors of estimates for publication with a report. In

practice, such tables show the standard errors that are

appropriate for specific estimates, and the user is instructed

to interpolate for figures not explicitly shown in the table.

However, many reports present a list of the parameters,

enabling data users to compute generalized variance estimates directly. A good example is a recent monthly issue of

Employment and Earnings (U.S. Department of Labor)

from which the following table was taken.

Example:

The approximate standard error, sX

ˆ , of an estimated

monthly level Xˆ can be obtained with a and b from the

above table and the formula

sX

ˆ aXˆ 2  bXˆ

Assume that in a given month there are an estimated

6 million unemployed men in the civilian labor force

(Xˆ = 6,000,000). From Table 14–1

a = -0.000015749 and b = 2464.91 , so

sx 0.0000157496,000,000

2  2464.916,000,000  119,000

An approximate 90-percent confidence interval for the

monthly estimate of unemployed men is between 5,810,000

and 6,190,000 [or 6,000,000 ± 1.6(119,000)].

VARIANCE ESTIMATES TO DETERMINE

OPTIMUM SURVEY DESIGN

The sample design and the estimation procedures used

in the CPS have changed many times since the start of the

survey. These changes result from a continuing program of

research and development with the objective of optimizing

the use of the techniques and resources which are available at a given time. Changes also occur when reliability

requirements are revised. Estimates of the components of

variance attributable to each of the several stages of

sampling and the effect of the separate steps in the

estimation procedure on the variance of the estimate are

needed to develop an efficient sample design.

The tables which follow show variance estimates computed, using replication methods, by type (total and withinPSU), and by stage of estimation. Averages over 6 months

have been used to improve the reliability of the estimated

monthly variances. The 6-month period, July-December

1995, was used for estimation because the sample design

was unchanged throughout the period. Appendix H provides similar information on variances and components of

variance after the January 1996 sample reduction.

Variance Components Due to Stages of

Sampling

Table 14–2 indicates, for the first- and second-stage

combined (FSC) estimate, how the several stages of

sampling affect the total variance of each of the given

characteristics. The FSC estimate is the estimate after the

first- and second-stage ratio adjustments are applied.

Within-PSU variance and total variance are computed as

described earlier in this chapter. Between-PSU variance is

estimated by subtracting the within-PSU variance from the

total variance. Due to variation of the variance estimates,

the between-PSU variance is sometimes negative. The far

right two columns of Table 14–2 show the percentage

within-PSU variance and the percentage between-PSU

variance in the total variance estimate.

For all characteristics shown in Table 14–2, the proportion of the total variance due to sampling housing units

within PSUs (within-PSU variance) is larger than that due

to sampling a subset of NSR PSUs (between-PSU variance). In fact, for most of the characteristics shown, the

within-PSU component accounts for over 90 percent of the

total variance. For civilian labor force and not in labor force

characteristics, almost all of the variance is due to sampling housing units within PSUs. For the total and White

employed in agriculture, the within-PSU component still

Table 14–1. Parameters for Computation of Standard

Errors for Estimates of Monthly Levels1

Characteristic a b

Unemployment:

Total or White. ................... −0.000015749 2464.91

Black ........................... 0.000191460 2621.89

Hispanic origin2 .................. −0.000098631 2704.53

1

Parameters reflect variances for 1995. See Appendix H for variance

estimates since January 1996. 2

Hispanics may be of any race.

14–5
accounts for 60 to 70 percent of the total variance, while

the between-PSU component accounts for the remaining

30 to 40 percent. The between-PSU component is also

larger (between 10 and 20 percent) for employed in

agriculture, Hispanic origin, and employed in nonagriculture, Hispanic origin than for other characteristics shown in

the table.

Because the FSC estimate of total civilian labor force

and total not in labor force must add to the independent

population controls (which are assumed to have no variance), the standard errors and variance components for

these estimated totals are the same. They are not the same

for the teenage category because the estimates do not

completely converge to the control totals during the secondstage ratio adjustment.

TOTAL VARIANCES AS AFFECTED BY

ESTIMATION

Table 14–3 shows how the separate estimation steps

affect the variance of estimated levels by presenting ratios

of relative variances. It is more instructive to compare

ratios of relative variances than the variances themselves,

since the various stages of estimation can affect both the

level of an estimate and its variance (Hanson, 1978; Train,

Cahoon, and Makens, 1978). The unbiased estimate uses

the baseweight with special weighting factors applied. The

noninterview estimate includes the baseweights, the special weighting adjustment, and the noninterview adjustment. The first- and second-stage ratio adjustments are

used in the estimate for the first- and second-stage combined (FSC).

In Table 14–3, the figures for unemployed show, for

example, that the relative variance of the FSC estimate of

level is 3.590 x 10-4 (equal to the square of the coefficient

of variation in Table 14–2). The relative variance of the

unbiased estimate for this characteristic would be 1.06

times as large. If the noninterview stage of estimation is

also included, the relative variance is only 1.05 times the

size of the relative variance for the FSC estimate of level.

Including the first stage of estimation raises the relative

variance factor to 1.13. The relative variance for total

unemployed after applying the second stage without the

first stage is about the same as the relative variance that

results from applying the first- and second-stage adjustments.

The relative variance as shown in the last column of this

table illustrates that the first-stage ratio adjustment has

little effect on the variance of national level characteristics

in the context of the overall estimation process. The

first-stage adjustment was implemented in order to reduce

variances of state-level estimates. Its actual effect on these

state-level estimates has not yet been examined.

The second-stage adjustment, however, appears to

greatly reduce the total variance, as intended. This is

Table 14–2. Components of Variance for FSC Monthly Estimates

[Monthly averages: July - Dec. 1995]

Civilian-noninstitutional population

16 years old and over FSC1 estimate

(x 106)

Standard error

(x 105)

Coefficient of

variation

(percent)

Percent of total variance

Within Between

Unemployed. . ..................................... 7.30 1.38 1.89 98.4 1.6

White........................................... 5.31 1.19 2.24 98.1 1.9

Black . . ......................................... 1.57 0.68 4.32 100.4 −0.4

Hispanic origin2 ................................. 1.15 0.63 5.51 94.2 5.8

Teenage, 16-19 .................................. 1.35 0.57 4.25 98.7 1.3

Employed - Agriculture ............................. 3.47 1.49 4.30 63.2 36.8

White........................................... 3.23 1.43 4.42 66.1 33.9

Black . . ......................................... 0.10 0.19 18.52 105.9 −5.9

Hispanic origin2 .................................. 0.60 0.73 12.27 83.4 16.6

Teenage, 16-19 .................................. 0.29 0.28 9.50 97.0 3.0

Employed - Nonagriculture. ......................... 122.34 3.34 0.27 92.8 7.2

White........................................... 104.05 3.05 0.29 94.4 5.6

Black . . ......................................... 13.29 1.30 0.98 100.3 −0.3

Hispanic origin2 .................................. 10.72 1.43 1.34 86.3 13.7

Teenage, 16-19 .................................. 6.40 0.96 1.50 108.1 −8.1

Civilian labor force . . . .............................. 133.11 2.89 0.22 99.0 1.0

White........................................... 112.58 2.61 0.23 99.3 0.7

Black . . ......................................... 14.97 1.18 0.79 99.9 0.1

Hispanic origin2 .................................. 12.47 1.09 0.87 99.2 0.8

Teenage, 16-19 .................................. 8.04 0.98 1.22 103.5 −3.5

Not in labor force .................................. 65.96 2.89 0.44 99.0 1.0

White........................................... 54.67 2.61 0.48 99.3 0.7

Black . . ......................................... 8.37 1.18 1.41 99.9 0.1

Hispanic origin2 .................................. 6.31 1.09 1.73 99.2 0.8

Teenage, 16-19 .................................. 6.61 1.00 1.52 101.5 −1.5

1

First- and second-stage combined (FSC).

2

Hispanics may be of any race.

14–6
especially true for characteristics that comprise high proportions of age, sex, or race/ethnicity subclasses, such as

White, Black, or Hispanic persons in the civilian labor force

or employed in nonagricultural industries. Without the

second stage, the relative variances of these characteristics would be 5, 6, or even 11 to 12 times as large. For

smaller groups, such as unemployed and employed in

agriculture, the second-stage adjustment does not have as

dramatic an effect.

After the second-stage ratio adjustment, a composite

estimator is used to improve estimates of month-to-month

change by taking advantage of the 75 percent of the total

sample that continues from the previous month (see Chapter 10). Table 14–4 compares the variance and relative

variance of the composited estimates of level to those of

the FSC estimates. For example, the estimated variance of

the composited estimate of unemployed persons of Hispanic origin is 3.659 x 109

. The variance factor for this

characteristic is 0.92, implying that the variance of the

composited estimate is 92 percent of the variance of the

estimate after the first and second stages. The relative

variance, which takes into account the estimate of the

number of people with this characteristic, is 0.94 times the

size of the relative variance of the FSC estimate. The two

factors are similar for most characteristics, indicating that

compositing tends to have a small effect on the level of

most estimates.

DESIGN EFFECTS

Table 14-5 shows the design effects for the total variance for selected labor force characteristics. A design

effect (deff) is the ratio of the variance from complex

sample design or a sophisticated estimation method to the

variance of a simple random sample design. The design

effects in this table were computed by solving equation

14.2 for deff and replacing N/n in the formula with an

estimate of the national sampling interval. Estimates of P

and Q were obtained from the 6 months of data.

For unemployed, the design effect is 1.314 for the uncomposited (FSC) estimate and 1.229 for the composited

estimate. This means that, for the same number of sample

cases, the design of the CPS (including the sample selection, weighting, and compositing) increases the variance

by about 23 percentage points over the variance of

an unbiased estimate based on a simple random sample.

On the other hand, for the civilian labor force the design of

the CPS decreases the variance by about 24 percentage points. Note that the design effects for composited

estimates are generally lower than those for the FSC

estimates indicating again the tendency of the compositing

to reduce the variance of most estimates.

Table 14–3. Effects of Weighting Stages on Monthly Relative Variance Factors

[Monthly averages: July - Dec. 1995]

Civilian-noninstitutional population

16 years old and over

Relative

variance of FSC

estimate of level

(x 10–4)

Relative variance factor1

Unbiased

estimator

Unbiased estimator with–

NI2 NI & FS3 NI & SS4

Unemployed. . ..................................... 3.590 1.06 1.05 1.13 1.00

White........................................... 5.004 1.11 1.11 1.16 1.00

Black . . ......................................... 18.626 1.30 1.28 1.28 1.00

Hispanic origin5 .................................. 30.347 1.14 1.14 1.19 1.00

Teenage, 16-19 .................................. 18.092 1.13 1.13 1.14 0.99

Employed - Agriculture ............................. 18.460 0.98 0.97 1.04 0.96

White........................................... 19.514 0.98 0.97 1.03 0.96

Black . . ......................................... 342.808 1.03 1.02 1.04 1.01

Hispanic origin5 .................................. 150.548 1.01 0.98 1.06 0.95

Teenage, 16-19 .................................. 90.235 1.08 1.08 1.10 1.00

Employed - Nonagriculture .......................... 0.075 4.71 4.47 6.85 0.96

White........................................... 0.086 5.54 5.44 7.55 0.97

Black . . ......................................... 0.957 6.73 6.60 6.61 1.00

Hispanic origin5 .................................. 1.783 3.19 3.18 3.27 0.95

Teenage, 16-19 .................................. 2.264 2.03 2.00 2.05 1.00

Civilian labor force . . . .............................. 0.047 7.28 6.84 10.98 0.99

White........................................... 0.054 8.96 8.74 12.33 0.99

Black . . ......................................... 0.623 9.87 9.62 9.64 1.01

Hispanic origin5 .................................. 0.764 7.03 6.95 7.41 0.99

Teenage, 16-19 .................................. 1.497 2.69 2.64 2.74 0.99

Not in labor force .................................. 0.192 2.79 2.67 4.15 0.99

White........................................... 0.228 3.22 3.16 4.50 0.99

Black . . ......................................... 1.994 4.21 4.11 3.88 1.01

Hispanic origin5 .................................. 2.984 2.99 2.92 3.09 0.99

Teenage, 16-19 .................................. 2.312 2.04 2.03 2.23 1.00

1

Relative variance factor is the ratio of the relative variance of the specified level to the relative variance of the FSC level.

2

NI = Noninterview.

3

FS = First-stage.

4

SS = Second-stage.

5

Hispanics may be of any race.

14–7
Table 14–5. Design Effects for Total Monthly Variances

[Monthly averages: July - Dec. 1995]

Civilian-noninstitutional population

16 years old and over

Design effects for total variance

After first and

second stages After compositing

Unemployed ..................................................................... 1.314 1.229

White ......................................................................... 1.318 1.227

Black ......................................................................... 1.424 1.403

Hispanic origin1 ................................................................ 1.689 1.574

Teenage, 16-19 ................................................................ 1.183 1.191

Employed - Agriculture ............................................................ 3.153 2.958

White ......................................................................... 3.091 2.875

Black ......................................................................... 1.680 1.516

Hispanic origin1 ................................................................ 4.370 3.903

Teenage, 16-19 ................................................................ 1.278 1.188

Employed - Nonagriculture ........................................................ 1.144 0.966

White ......................................................................... 0.904 0.770

Black ......................................................................... 0.659 0.564

Hispanic origin1 ................................................................ 0.976 0.880

Teenage, 16-19 ................................................................ 0.723 0.664

Civilian labor force................................................................ 0.913 0.760

White ......................................................................... 0.672 0.576

Black ......................................................................... 0.487 0.421

Hispanic origin1 ................................................................ 0.491 0.433

Teenage, 16-19 ................................................................ 0.606 0.540

Not in labor force................................................................. 0.913 0.760

White ......................................................................... 0.829 0.709

Black ......................................................................... 0.841 0.723

Hispanic origin1 ................................................................ 0.939 0.824

Teenage, 16-19 ................................................................ 0.763 0.679

1

Hispanics may be of any race.

Table 14–4. Effect of Compositing on Monthly Variance and Relative Variance Factors

[Monthly averages: July - Dec. 1995]

Civilian-noninstitutional population

16 years old and over

Variance of composited

estimate of level

(x 109

) Variance factor1

Relative variance

factor2

Unemployed ....................................................... 17.690 0.93 0.94

White............................................................ 13.003 0.93 0.94

Black ............................................................ 4.448 0.97 1.00

Hispanic origin3................................................... 3.659 0.92 0.94

Teenage, 16-19................................................... 3.279 1.00 1.01

Employed - Agriculture .............................................. 20.789 0.93 0.94

White............................................................ 18.781 0.93 0.93

Black ............................................................ 0.302 0.85 0.95

Hispanic origin3................................................... 4.916 0.91 0.88

Teenage, 16-19................................................... 0.713 0.92 0.93

Employed - Nonagriculture ........................................... 94.359 0.85 0.84

White............................................................ 79.145 0.85 0.85

Black ............................................................ 14.489 0.85 0.85

Hispanic origin3................................................... 18.466 0.90 0.90

Teenage, 16-19................................................... 8.508 0.92 0.92

Civilian labor force .................................................. 69.497 0.83 0.83

White............................................................ 58.329 0.85 0.85

Black ............................................................ 12.037 0.86 0.87

Hispanic origin3................................................... 10.448 0.88 0.88

Teenage, 16-19................................................... 8.607 0.89 0.89

Not in labor force . .................................................. 69.497 0.83 0.83

White............................................................ 58.329 0.85 0.85

Black ............................................................ 12.037 0.86 0.85

Hispanic origin3................................................... 10.448 0.88 0.88

Teenage, 16-19................................................... 9.004 0.89 0.88

1

Variance factor is the ratio of the variance of a composited estimate to the variance of an FSC estimate.

2

Relative variance factor is the ratio of the relative variance of a composited estimate to the relative variance of an FSC estimate.

3

Hispanics may be of any race.

Note: Composite formula constants used: K = 0.4 and A = 0.2.

14–8
REFERENCES

Corteville, J. (1996), ‘‘State Between-PSU Variances

and Other Useful Information for the CPS 1990 Sample

Design (VAR90-16),’’ Memorandum for Documentation,

March 6th, Demographic Statistical Methods Division, U. S.

Census Bureau.

Fay, R.E., (1984) ‘‘Some Properties of Estimates of

Variance Based on Replication Methods,’’ Proceedings of

the Section on Survey Research Methods, American

Statistical Association, pp. 495-500.

Fay, R. E., (1989) ‘‘Theory and Application of Replicate

Weighting for Variance Calculations,’’ Proceedings of the

Section on Survey Research Methods, American Statistical Association, pp. 212-217.

Fay, R., Dippo, C., and Morganstein, D. (1984), ‘‘Computing Variances From Complex Samples With Replicate

Weights,’’Proceedings of the Section on Survey Research

Methods, American Statistical Association, pp. 489-494.

Fay, R., and Train, G. (1995), ‘‘Aspects of Survey and

Model-Based Postcensal Estimation of Income and Poverty Characteristics for States and Counties,’’ Proceedings of the Section on Government Statistics, American

Statistical Association, pp. 154-159.

Gunlicks, C. (1993), ‘‘Overview of 1990 CPS PSU

Stratification (S_S90_DC-11),’’ February 24, Memorandum

for Documentation, Demographic Statistical Methods Division, U.S. Census Bureau.

Gunlicks, C. (1996), ‘‘1990 Replicate Variance System

(VAR90-20),’’ Internal Memorandum for Documentation,

June 4th, Demographic Statistical Methods Division, U.S.

Census Bureau.

Hanson, R. H. (1978), The Current Population Survey:

Design and Methodology, Technical Paper 40, Washington, DC: Government Printing Office.

Kostanich, D. (1996), ‘‘Proposal for Assigning Variance

Codes for the 1990 CPS Design (VAR90-22),’’ Internal

Memorandum to BLS/Census Variance Estimation Subcommittee, June 17th, Demographic Statistical Methods

Division, U.S. Census Bureau.

Kostanich, D., (1996), ‘‘Revised Standard Error Parameters and Tables for Labor Force Estimates: 1994-1995

(VAR80-6),’’ Internal Memorandum for Documentation, February 23rd, Demographic Statistical Methods Division,

U.S. Census Bureau.

Lent, J. (1991), ‘‘Variance Estimation for Current Population Survey Small Area Estimates,’’ Proceedings of the

Section on Survey Research Methods, American Statistical Association, pp. 11-20.

Train, G., Cahoon, L., and Makens, P. (1978), ‘‘The

Current Population Survey Variances, Inter-Relationships,

and Design Effects,’’ Proceedings of the Section on

Survey Research Methods, American Statistical Association, pp. 443-448.

U.S. Dept. of Labor, Bureau of Labor Statistics, Employment and Earnings, 42, p.152, Washington, DC: Government Printing Office.

Valliant, R. (1987), ‘‘Generalized Variance Functions in

Stratified Two-Stage Sampling,’’ Journal of the American

Statistical Association, 82, pp. 499-508.

Wolter, K. (1984), ‘‘An Investigation of Some Estimators

of Variance for Systematic Sampling,’’ Journal of the

American Statistical Association, 79, pp. 781-790.

Wolter, K. (1985), Introduction to Variance Estimation, New York: Springer-Verlag.

14–9
Chapter 15.

Sources and Controls on Nonsampling Error

INTRODUCTION

Nonsampling error can enter the survey process at any

point or stage, and many of these errors are not readily

identifiable. Nevertheless, the presence of these errors can

affect both the bias and variance components of the total

survey error. The effect of nonsampling error on the

estimates is difficult to measure accurately. For this reason,

the most appropriate strategy is to examine the potential

sources of nonsampling error and take steps to prevent

these errors from entering the survey process. This chapter

discusses the various sources of nonsampling error and

the measures taken to control their presence in the Current

Population Survey (CPS).

Many sources of nonsampling error were studied and

well-charted in the pre-January 1994 CPS (e.g., see Brooks

and Bailar, 1978; McCarthy, 1978), but the full extent of

nonsampling error in the post-January 1994 CPS is not

nearly so well examined. It is also unclear how much

previous research on nonsampling error in the CPS is

applicable to the redesigned instrument with computerized

data collection. Although the intent of the new questionnaire and automation was to reduce nonsampling error,

new sources of error may emanate from these methodological advances. Some potential sources of nonsampling

error can be investigated with the available CPS and

reinterview data, but there is only about 2 years of data

available for analyses and comparisons at the time of this

writing, and this may not be sufficient for a full analysis.

Nevertheless, it is clear that there are two main types of

nonsampling error in the CPS. The first is error imported

from other frames or sources of information, such as

Decennial Census omissions. The second type is considered preventable, such as when the sample is not completely representative of the intended population, withinhousehold omissions, respondents not providing true answers

to a questionnaire item, or errors produced during the

processing of the survey data.

Chapter 16 discusses the presence of CPS nonsampling error, but not the effect of the error on the estimates.

The present chapter, however, focuses on the sources and

operational efforts used to control the occurrence of error in

the survey processes. Each section discusses a procedure

aimed to reduce either coverage, nonresponse, response,

or data processing errors. Despite the effort to treat each

control as a separate entity, they nonetheless affect the

survey in a general way. For example, training, even if

focused on a specific problem, can have broadreaching

total survey effects.

An understanding of the sources and causes of nonsampling error is necessary to design procedures to control the

effects of these errors on the estimates. An important

source of nonsampling error that is difficult to understand

and control arises out of the interaction of the people

involved in the survey. The methods by which administrators, analysts, programmers, and field personnel interact

and exchange information are a potential source of nonsampling error. One attempt to reduce this source of

nonsampling error is demonstrated by the use of Deming’s

quality management procedures in the 1990 sample redesign (Waite, 1991). It emphasizes the relationship between

process design and quality improvement. It is a nonbureaucratic approach that has all involved persons working

together toward a common goal. Each person is accountable and responsible for the verification of the consistency,

reliability, and accuracy of his/her output before it is used

as input to a succeeding process or stage. The quality of all

previous processes affects the quality of all succeeding

processes. Communication across all stages is enhanced

by the inclusion of the entire staff involved in the operations, planning, and implementation of the processes up-front,

even before the writing of specifications.

Included in the notions of interaction and exchange of

information across all personnel levels and processes are

feedback, continuous improvement, the introduction of

automation and computerization, and training. Feedback,

when accompanied by corrective measures, is vital and

must flow in all directions between the field representative,

the regional office, headquarters, and the data preparation

office in Jeffersonville, Indiana. (Focus groups are an

example of an effective source of feedback.) Continuous

improvement in every process and stage of the CPS is a

requirement, not an option. Also, automation and computerization are major factors in the control of nonsampling

error, greatly improving uniformity of operations, as well as

data access and control. The degree of automation is

continuously increasing, thus affecting the amount and

allocation of responsibilities. Finally, training is a continuing

task in each regional office, the Jeffersonville office, and

headquarters, and it is a major factor in controlling the

occurrence of nonsampling error. (See Appendix F for a

more detailed discussion on training procedures in the

regional offices.)

This chapter deals with many sources and many controls for nonsampling error. It covers coverage error, error

from nonresponse, response error, and processing errors.

Many of these types of errors interact, and they can occur

15–1
anywhere in the survey process. Although it is believed that

some of the nonsampling errors are small, their full effects

on the survey estimates are unknown. The CPS attempts

to prevent such errors from entering the survey process or

tries to keep them as small as possible.

SOURCES OF COVERAGE ERROR

Coverage error exists when a survey does not completely represent the population of interest. When conducting a sample survey, a primary goal is to give every unit (for

example, person or housing unit) in the target universe a

known probability of being selected into the sample. When

this occurs, the survey is said to have 100 percent coverage. This is rarely the case, however, since errors can

enter the system during almost any phase of the survey

process. A bias in the survey estimates results if characteristics of units erroneously included or excluded from the

survey differ from those correctly included in the survey.

Historically in the CPS, the net effect of coverage errors

has been a loss in population (resulting from undercoverage).

The primary sources of CPS coverage error are:

1. Frame omission. Erroneous frame omission of housing units occurs when the list of addresses is incomplete. This can occur in all of the four sampling frames

(i.e., unit, permit, area, and group quarters; see Chapter 3). Since these erroneously omitted housing units

cannot be sampled, undercoverage of the target population results. Reasons for frame omissions are:

• Census Address List: The census address list may

be incomplete or some units are not locatable. This

occurs when the census lister fails to canvass an

area thoroughly or misses units in multiunit structures.

• New Construction: Some areas of the country are

not covered by building permit offices and are not

recanvassed (unit frame) to pick up new housing.

New housing units in these areas have zero probability of selection.

• Mobile Homes: Mobile homes that move into areas

that are not recanvassed (unit frame) also have a

zero probability of selection.

2. Misclassification of housing units. Erroneous frame

inclusions of housing units occur when housing units

outside an appropriate area boundary are misclassified as inside the boundary. Other erroneous inclusions occur when a single unit is recorded as two units

through faulty application of the housing unit definition.

Since erroneously included housing units can be

sampled, and if the errors are not detected and corrected, overcoverage of the target population results.

Misclassification also occurs when an occupied

housing unit is treated as vacant.

3. Within-housing unit omissions. Undercoverage of

persons can arise from failure to list all usual residents

of a housing unit on the questionnaire and from

misclassifying a household member as a nonmember.

4. Within-housing unit inclusions. Overcoverage can

occur because of the erroneous inclusion of persons

on the roster for a housing unit. For instance, persons

with a usual residence elsewhere are treated as

members of the sample housing unit.

Other sources of coverage error are omission of homeless persons from the frame, unlocatable addresses from

building permit lists, conversion of nonresidential to residential units, and missed housing units due to the start

dates for the sampling of permits.1 Newly constructed

group quarters are another possible source of undercoverage. If noticed on the permit lists, the general rule is to

purposely remove these group quarters; if not noticed and

the field representative discovers a newly constructed

group quarters, the case is stricken from the roster of

sample addresses and never visited again for CPS. For

example, dormitories are group quarters in the 1990 decennial census and are included in the group quarters frame.

They are picked up in the other frames, not in the permit

frame.

The sampling frames are designed to limit missed

housing units in the census frame from affecting coverage

of the CPS frames (see Chapter 3). Through various

studies, it has been determined that the number of housing

unit misses is small; this is because of the way the

sampling frames are designed and because of the routines

in place to ensure that the listing of addresses is performed

correctly. A measure of potential undercoverage is the

coverage ratio which incorporates the completeness of the

coverage because of frame omissions, misclassification of

housing units, and within-housing unit omissions and inclusions. For example, the coverage ratio for the total population, at least 16 years of age, is approximately 92 percent

(January 1997). In general, there is low coverage for

Blacks (about 83 percent), with Black males of ages 25-34

having the lowest coverage. Females have higher coverage. See Chapter 16 for further discussion of coverage

error.

CONTROLLING COVERAGE ERROR

This section focuses on those processes during the

sample design and sample preparation stages which aim

to control housing unit omissions. Other sources of undercoverage can be viewed as response error and are described

in a later section of this chapter.

1

Sampling of permits began with those issued in 1989; the month

varied depending on the size of structure and region. Housing units whose

permits were issued before the start month in 1989 and not built by the

time of the census may be missed.

15–2
Sample Design

The CPS sample selection is coordinated with the

samples of nine other demographic surveys. These surveys undergo the same verification processes, so the

discussion in the rest of this section is in no way unique to

the CPS.

Sampling verification basically consists of testing and

production. They are different in that the intent of the

testing phase is to design the processes better to improve

quality, and the intent of the production phase is to verify or

inspect quality to improve the processes. Both phases are

coordinated across the various surveys. Sampling intervals, expected sample sizes, and the premise that a

housing unit is selected for only one survey for 10 years are

continually verified.

The testing phase tests and verifies the various sampling programs before any sample is selected, ensuring

that the programs work individually and collectively. Smaller

data sets with unusual observations test the performance

of the system in extreme situations. For example, unusual

circumstances encountered in the 1990 decennial census

are used to test the 1990 CPS sampling procedures. These

circumstances include PSUs with no sample, crews of

maritime vessels, group quarters, and Indian Reservations.

Programs are then written to verify that procedures are in

place to handle the problems. Also, actual data from the

1990 decennial census are used to test and verify the

sampling processes.

The production phase of sampling verification treats

output verification, especially expected sample sizes, focusing on whether the system ran successfully on actual data.

The following are illustrations and do not represent any

particular priority or chronology:

1. PSU probabilities of selection are verified; for example,

their sum should be one.

2. Edits are done on file contents, checking for blank

fields, out-of-range data, etc.

3. When applicable, the files are coded to check for

consistency; for example, the number of PSUs ‘‘in’’

should equal the number of PSUs ‘‘out’’ at PSU

selection.

4. Information is centralized; for example, the sampling

rates for all states and across all surveys are located in

one parameter file.

5. As a form of overall consistency verification, the output

at several stages of sampling is compared to that of

the former CPS design.

Sample Preparation

Sample preparation activities, in contrast to those of

sample design, are ongoing. The listing sheet review and

check-in of sample are monthly production processes, and

the listing check is an ongoing field process.

Listing sheet review. The listing sheet review is a check

on each month’s listings to keep the nonsampling error as

low as possible. Its aim is to ensure that the interviews will

be conducted at the correct units and that all units have

one and only one chance of selection. This review also

plays a role in the verification of the sampling, subsampling, and relisting. At present, the timing of the review is

such that not all errors detected in the listing sheet review

are able to be corrected in time for incorporation into CPS.

However, automation of the review is a major advancement

towards improving the timing of the process and, thus, a

more accurate CPS frame.

Listing sheet reviews for the unit, permit, area, and

group quarters samples are performed in varying degrees

by both the regional offices and the Jeffersonville office.

The fact that these reviews are performed across different

parts of the Census Bureau organization is, in itself, a

means of controlling nonsampling error.

In terms of the reviews performed by the regional

offices, of utmost importance is the review of field representative explanations made in the remarks column or

footnotes section of the listing sheet. Other processes that

the regional offices follow to verify and correct the listing

sheets for samples from each type of frame follow.

After a field representative makes an initial visit either to

a multiunit address from a unit frame or to a sample from a

permit frame, the Unit/Permit Listing Sheet is reviewed.

This review occurs the first time the basic address or

sample from a permit frame is in sample for any survey.

However, if major changes occur at an address on a

subsequent visit, a review of the revised listing is done. If

there is evidence that the field representative encountered

a special or unusual situation, the materials are compared

to the instructions in the Listing and Coverage Manual for

Field Representatives (see Chapter 4) to ensure that the

situation was handled correctly. Depending on whether it is

a permit frame sample or a multiunit address from a unit

frame, the following are verified:

1. Were correct entries made on the listing sheet when

either fewer or more units were listed than expected?

2. Was CPS the first survey to interview at the multiunit

address?

3. Were new listing sheets prepared?

4. Were no units listed?

5. Was an extra unit discovered?

6. Was a sample unit added without a serial number?

7. Did the unit designation change?

8. Was a sample unit demolished or condemned?

Errors and omissions are corrected and brought to the

attention of the field representative. This may involve

contacting the field representative for more information.

15–3
The regional office review of Area and Group Quarters

Listing Sheets is performed on newly listed or updated

samples from area or group quarters frames. Basically, this

review is for completeness of identification.

As stated previously, listing sheet reviews are also

performed by the Jeffersonville office. The following are

activities involved in the reviews for the unit sample:

1. Use the mailout control to check-in listing sheets from

the regional offices and followup any that are missing.

2. If subsampling was done, review for accuracy.

3. If any large multiunit addresses were relisted, review

for accuracy.

4. If there were any address changes, key the changes

into the address updates system for future samples at

the same basic address.

5. Keep tallies by regional office and survey of when any

of situations 2 through 4 above were encountered;

provide headquarters with these tallies on a quarterly

basis.

In terms of the review of listing sheets for permit frame

samples, Jeffersonville checks to see whether the number

of units is more than expected and whether there are any

changes to the address. If there are more units than

expected, Jeffersonville determines whether the additional

units already had a chance of selection. If so, the regional

office is contacted with instructions for updating the listing

sheet. If there are any address changes, Jeffersonville

provides updates to headquarters for potential future samples

at the same basic address.

For the area and group quarters sample, the listing

sheets remain in the regional offices. However, Jeffersonville receives a summary of the sampling that was done by

each regional office ensuring that, given the sampling rate,

the regional office sampled the appropriate number of

units. If not, the regional office is contacted and corrections

are made.

The majority of listing sheets are found without any

inaccuracies during the regional office and Jeffersonville

reviews across all frame types. When errors or omissions

are detected, they usually occur in batches, signifying a

misinterpretation of instructions by a regional office or a

newly hired field representative. Therefore, because of

these low error rates during the listing sheet review, the

intent and value of this review are being revisited.

Check-in of sample. Depending on the sampling frame

and mode of interview, the monthly process of check-in of

sample can occur many times as the sample cases progress

through the regional offices, the Jeffersonville office, and

headquarters. Check-in of sample describes the processes

by which the regional offices verify that the field representatives receive all the sample cases that they are supposed

to and that all are completed and returned. Since the CPS

is now conducted entirely by either computer assisted

personal or telephone interview, a closer control of the

tracking of a sample case exists than was ever possible.

Control systems are in place to control and verify the

sample count. The regional offices use these systems to

control the sample during the period when it is active, and

all discrepancies must be resolved before the office is able

to certify that the workload in the database is correct. Since

these systems aid in the balancing of the workload, time

and effort can then be spent elsewhere.

Listing check. The purpose of the regional offices’ listing

check is to provide assurance that quality work is done on

the listing operation and to provide feedback to the field

representatives about their performance, with possible

retraining when a field representative misunderstands or

misapplies a listing concept. In the field, it checks a sample

of each field representative’s work to see whether he/she

began the listing of units at the correct location, listed the

units correctly, stayed in the appropriate geographic boundaries, and included all the units. Since it is not a part of the

monthly production cycle and it can occur either before or

after an interview, its intent is not to capture and correct

data. Rather, it is an attempt, over time, to counteract any

misunderstanding of listing and coverage procedures.

Since January 1993, the listing check has been a

separate operation from reinterview; it is generally combined with some other personal visit field operation, such

as observation. To avoid a bias in the designation of the

units to be interviewed, the listing is performed without any

knowledge of which units will be in the interview or reinterview. A listing check is performed only on area frame

samples for field representatives who list or update. These

field representatives are randomly checked once during

the fiscal year, and depending on how close assignments

are to each other, at least two assignments are checked to

better evaluate the field representative’s listing skills. The

check of the unit frame sample was dropped when address

samples were converted to samples from the unit frame in

the 1990 Sample Redesign. This was done since little of

the unit frame sample requires listing. Also, because permit

and group quarters samples constitute such a small part of

the universe, no formal listing check is done for sample in

these frames.

SOURCES OF ERROR DUE TO NONRESPONSE

There are a variety of sources of nonresponse error in

the CPS. Noninterviews could include housing units that

are vacant, not yet built, demolished, or are not residential

living units; however, these types of noninterviews are not

considered to be nonresponses since they are out-ofscope and not eligible for interview. (See Chapter 7 for a

description of the types of noninterviews.) Nonresponse

error does occur when households that are eligible for

interview are not interviewed for some reason: a respondent refuses to participate in the survey, is incapable of

15–4
completing the interview, or is not available or not contacted by the interviewer during the survey period, perhaps

due to work schedules or vacation. These household

noninterviews are called Type A noninterviews.

There are additional kinds of nonresponse in the CPS.

Individual persons within the household may refuse to be

interviewed, resulting in person nonresponse. Person nonresponse has not been much of a problem in the CPS

because any responsible adult in the household is able to

report for other persons in the household as a proxy

reporter. Also, panel nonresponse exists when those persons who live in the same household during the entire time

they are in the CPS sample may not agree to be interviewed each of the 8 months. Thus, panel nonresponse

can be important if the CPS data are used longitudinally.

Finally, some respondents who complete the CPS interview may be unable or unwilling to answer specific questions resulting in item nonresponse. Imputation procedures

are implemented for item nonresponse. However, because

there is no way of ensuring that the errors of item imputation will balance out, even on an expected basis, item

nonresponse also introduces potential bias into the estimates.

For a sense of the magnitude of the error due to

nonresponse, the household noninterview rate was 6.34

percent in January 1997. For item nonresponse, it is

interesting to note allocation rates of 0.3 percent for the

characteristic of Labor Force Status, 1.4 percent for Race,

and 1.7 percent for Occupation, all using January 1997

weighted data. (See Chapter 16 for discussion on various

quality indicators of nonresponse error.)

CONTROLLING ERROR DUE TO

NONRESPONSE

Field Representative Guidelines2

Response/nonresponse rate guidelines have been developed for field representatives to help ensure the quality of

the data collected. Maintaining high response rates is of

primary importance, and response/nonresponse guidelines

have been developed with this in mind. These guidelines,

when used in conjunction with other sources of information,

are intended to assist supervisors in identifying field representatives needing performance improvement. A field representative whose response rate, household noninterview

rate (Type A), or minutes per case fall below the fully

acceptable range based on one quarter’s work is considered in need of additional training and development, and

the CPS supervisor takes appropriate remedial action.

National and regional response performance data are also

provided to permit the regional office staff to judge whether

their activities are in need of additional attention.

Summary Sheets

Another way to control nonresponse error is the production and review of summary sheets. Although produced by

headquarters after the release of the monthly data products, they are used to detect changes in historical response

patterns. Also, since they are distributed throughout headquarters and the regional offices, other indications of data

quality and consistency can be focused upon. Contents of

selected summary report tables are as follows: noninterview rates by regional office; monthly comparisons to prior

year; noninterview-to-interview conversion rates; resolution status of computer assisted telephone interview cases;

interview status by month-in-sample; daily transmittals;

and coverage ratios.

Headquarters and Regional Offices Working as

a Team

As detailed in a Methods and Performance Evaluation

Memorandum (Reeder, 1997), the Census Bureau and the

Bureau of Labor Statistics formed an interagency work

group to examine CPS nonresponse in detail. One goal

was to share possible reasons and solutions for the

declining CPS response rates. A list of 31 questions was

prepared for the regional offices to help aid in the understanding of CPS field operations, to solicit and share the

regional offices’ views on the causes of the increasing

nonresponse rates, and to evaluate methods to decrease

these rates. All of the answers provide insight on CPS

operations that may affect nonresponse and followup procedures for household noninterviews, but a listing of a few

will suffice:

• The majority of regional offices responded that there is

written documentation of the followup process for CPS

household noninterviews;

• The standard process was that a field representative

must let the regional office know about a possible

household noninterview as soon as possible;

• Most regions attempt to convert confirmed refusals under

certain circumstances;

• All regions provide monthly feedback to their CPS field

representatives on their household noninterview rates;

and

• About half of the offices responded that they provide

specific regional based training/activities for field representatives on converting or avoiding household noninterviews.

Response to one question deserves special attention:

Most offices use letters in a consistent manner for followup

of noninterviews. Most regional offices also include informational brochures tailored to the respondent with the

letters.

2

See Appendix F for a detailed discussion, especially in terms of the

performance evaluation system.

15–5
SOURCES OF RESPONSE ERROR

The survey interviewer asks a question and collects a

response from the respondent. Response error exists if the

response is not the true answer. Reasons for response

error include:

• The respondent misinterprets the question, tries to look

better by inflating or deflating the response, does not

know the true answer and guesses (for example, recall

effects), or chooses a response randomly;

• The interviewer reads the question incorrectly, does not

follow the appropriate skip pattern, misunderstands or

misapplies the questionnaire, or records the wrong answer;

• The data collection modes (for example, personal visit

and telephone) elicit different response rates, as well as

the frequency of interview. Studies have shown that

response rates generally decline as the length of time in

survey increases; and

• The questionnaire does not elicit correct responses, due

to a nonuser-friendly format, complicated skip patterns,

and difficult coding procedures.

Thus, response error can arise from many sources. The

survey instrument, the mode of data collection, the interviewer, and the respondent are the focus of this section3,

as well as their interactions as discussed in The Reinterview Program.

In terms of magnitude, measures of response error are

obtainable through the reinterview program; specifically,

Chapter 16 discusses the index of inconsistency. It is a

ratio of the estimated simple response variance to the

estimated total variance, arising from sampling and simple

response variance. When identical responses are obtained

from trial to trial, both the simple response variance and the

index of inconsistency are zero. Theoretically, the index

has a range of 0 to 100. For example, the index of

inconsistency for the labor force characteristic of unemployed for February through July 1994 is considered moderate at 30.8.

CONTROLLING RESPONSE ERROR

The Survey Instrument4

The survey instrument involves the CPS questionnaire,

the computer software that runs the questionnaire, and the

mode by which the data are collected. The modes are

personal visits or telephone calls made by field representatives and telephone calls made by interviewers at centralized telephone centers. Regardless of the mode, the

questionnaire and the software are basically the same

(See Chapter 7.)

Software. Changes in the early 1990s resulted in the use

of computer assisted interviewing technology in the CPS.

Designed in an automated environment, this technology

allows very complex skip patterns and other procedures

which combine data collection, data input, and a degree of

in-interview consistency editing into a single operation.

This technology provides an automatic selection of

questions for each interview. The screens display response

options, if applicable, and information about what to do

next. The interviewer does not have to worry about skip

patterns, with the possibility of error. Appropriate proper

names, pronouns, verbs, and reference dates are automatically filled into the text of the questions. If there is a refusal

to answer a demographic item, that item is not asked again

in later interviews; rather, it is longitudinally allocated. This

exhibits the trade-off of nonsampling error existing for the

item versus the possibility of a total noninterview. The

instrument provides opportunities for the field representative to review and correct any incorrect/inconsistent information before the next series of questions is asked,

especially in terms of the household roster. In later months,

the instrument passes industry and occupation information

forward to be verified and corrected. In addition to reducing

response and interviewer burden, this also avoids erratic

variations in industry and occupation codes among pairs of

months for people who have not changed jobs, but describe

their industry and occupation differently in the 2 months.

The questionnaire. Beginning in January 1994, the CPS

used a questionnaire in which the wording was different

from what was collected previously for almost every labor

force concept. One of the objectives in redesigning the

CPS questionnaire was to reduce the potential for response

error in the questionnaire-respondent-interviewer interaction and to improve measurement of CPS concepts. The

approaches used to lessen the potential for response error

(i.e., enhanced accuracy) were: shorter and clearer question wording, splitting complex questions into two or more

questions, building concept definitions into question wording, reducing reliance on volunteered information, explicit

and implicit strategies for the respondent to provide numeric

data, and the use of precoded response categories for

open-ended questions. Although they were also used with

the pencil and paper questionnaire, interviewer notes recorded

at the end of the interview are critical to obtaining reliable

and accurate responses.

Mode of data collection. As stated in Chapters 7 and 16,

the first and fifth months’ interviews are done in person

whenever possible, while the remaining interviews may be

conducted via telephone either by the field interviewer or

3

Most discussion in this section is applicable whether the interview is

conducted via computer assisted personal interview, telephone interview

by the field representative or a centralized telephone facility, or computer

assisted telephone interview. 4

Many of the topics in this section are presented in more detail in

Chapter 6.

15–6
an interviewer from a centralized telephone facility. Although

each mode has its own set of performance guidelines that

must be adhered to, similarities do exist. The controls

detailed in The Interviewer of this section, and Error Due to

Nonresponse, which are mainly directed at personal visits,

are basically valid for the calls made from the centralized

facility via the supervisor’s listening in.

Continuous testing and improvements. Experience gained

during the CPS redesign and research on questionnaire

design has assisted in the development of methods for

testing new or revised questions for the CPS. In addition to

reviewing new questions to ensure that they will not

jeopardize the collection of basic labor force information

and to determine whether the questions are appropriate

additions to a household survey about the labor force, the

wording of new questions are tested to gauge whether

respondents are correctly interpreting the questions. Chapter 6 provides an extensive listing of the various methods of

testing and the Census Bureau developed a set of protocols for pretesting demographic surveys.

There is also interest in improving existing questions.

The ‘‘don’t know’’ and refusal rates for specific questions

are monitored, inconsistencies caused by instrument-directed

paths through the survey or instrument-assigned classifications are looked for during the estimation process, and

interviewer notes recorded at the conclusion of the interview are reviewed. Also, focus groups with CPS interviewers and supervisors are periodically conducted.

Despite the benefits of adding new questions and improving existing ones, changes in the CPS are approached

cautiously until the effects are measured and evaluated.

When possible, methods to bridge differences caused by

changes or techniques to avoid the disruption of historical

series are included in the testing. In fact, for the 18 months

prior to the implementation of the 1994 redesigned CPS, a

parallel survey was conducted using the new methodology

and, for the first 5 months after the redesigned survey was

introduced, a parallel survey was conducted using the

unrevised procedures. Results from the parallel survey

were used to anticipate the effect the changes would have

on the survey estimates, nonresponse rates, etc. (Kostanich and Cahoon, 1994; Polivka, 1994; and Thompson,

1994.) Comparable testing and parallel surveys were used

for previous revisions to the questionnaire.

The Interviewer

Interviewer training, observation, monitoring, and evaluation are all methods used to control nonsampling error,

arising from both the frame and data collection. For further

discussion of field representative guidelines, see this chapter’s Error Due to Nonresponse section and Appendix F.

Group training and home study are continuing tasks in

each regional office to control various nonsampling errors,

and they are tailored to the types of duties and length of

service of the interviewer. Observations, including those of

the listing check, are extensions of classroom training and

provide on-the-job training and on-the-job evaluation.

Field observation is one of the methods used by the

supervisor to check and improve the performance of the

field representative. It provides a uniform method for

assessing the field representative’s attitudes toward the job

and use of the computer and evaluating the field representative’s ability to apply CPS concepts and procedures

during actual work situations. There are three types of

observations: initial, general performance review, and special needs. Across all types, the observer stresses good

interviewing techniques: asking questions as worded and

in the order presented on the questionnaire, adhering to

instructions on the instrument and in the manuals, knowing

how to probe, recording answers in the correct manner and

in adequate detail, developing and maintaining good rapport with the respondent conducive to an exchange of

information, avoiding questions or probes that suggest a

desired answer to the respondent, and determining the

most appropriate time and place for the interview. The

emphasis is on correcting habits which interfere with the

collection of reliable statistics.

The Respondent: Self Versus Proxy

The CPS Interviewing Manual (see Chapter 4) states

that any household member 15 years of age or older is

technically eligible to act as a respondent for the household. The field representative attempts to collect the labor

force data from each eligible individual; however, in the

interests of timeliness and efficiency, any knowledgeable

adult household member can provide the information. Also,

the survey instrument is structured so that every effort is

made to interview the same respondent every month. Just

over one-half of the CPS labor force data are collected by

self-response, and most of the remainder is collected by

proxy from a household respondent. The use of a nonhousehold member as a household respondent is only

allowed in certain limited situations; for example, the

household may consist of a single person whose physical

or mental health does not permit a personal interview.

As a caveat, there has been a substantial amount of

research into self versus proxy reporting, including research

involving CPS respondents. Much of the research indicates

that self-reporting is more reliable than proxy reporting,

particularly when there are motivational reasons for self

and proxy respondents to report differently. For example,

parents may intentionally ‘‘paint a more favorable picture’’

of their children than fact supports. However, there are

some circumstances in which proxy reporting is more

accurate, such as in responses to certain sensitive questions.

Interviewer/Respondent Interaction

Rapport with the respondent is a means of improving

data quality. This is especially true for personal visits, which

15–7
are required for months-in-sample one and five whenever

possible. By showing a sincere understanding and interest

in the respondent, a friendly atmosphere is created in

which the respondent can talk honestly and openly. Interviewers are trained to ask questions exactly as worded and

to ask every question. If the respondent misunderstands or

misinterprets a question, the question is repeated as

worded and the respondent is given another chance to

answer; probing techniques are used if a relevant response

is still not obtained. The respondent should be left with a

friendly feeling towards the interviewer and the Census

Bureau, clearing the way for future contacts.

The Reinterview Program - Quality Control and

Response Error5

One of the objectives of the reinterview program is to

evaluate individual field representative performance. Thus,

it is a significant part of quality control. It checks a sample

of the work of a field representative and identifies and

measures aspects of the field procedures which may need

improvement. It is also critical in the identification and

prevention of data falsification.

The reinterview program also provides a measure of

response error. Responses from first and second interviews at selected households are compared and differences are identified and analyzed. This helps to evaluate

the accuracy of the original survey results; as a byproduct,

instructions, training, and procedures are also evaluated.

SOURCES OF MISCELLANEOUS ERRORS

Data processing errors are one focus of this final

section. Their sources can include data entry, industry and

occupation coding, and methodologies for edits, imputations, and weighting. Also, the CPS Population Controls

are not error-free; a number of approximations or assumptions are used in their derivations. Other sources are

composite estimation and modeling errors which may arise

from, for example, seasonally adjusted series for selected

labor force data, and monthly model-based state labor

force estimates.

CONTROLLING MISCELLANEOUS ERRORS

Parallel Survey

Among specific processes that are used to control

nonsampling error in each of these sources, the parallel

survey mentioned in Response Error crosses several sources.

The 18 months of the parallel survey were used to test the

survey instrument and the development of the processing

system. It ensured that the edits, imputations, and weighting program were working correctly. It often uncovered

related problems, such as incorrect skip-patterns. (The

Bureau of Labor Statistics was involved extensively in the

review of the edits.)

Industry and Occupation Coding Verification

To be ultimately accepted into the CPS processing

system, files containing records needing three-digit industry and occupation codes are electronically sent to the

Jeffersonville office for the assignment of these codes (see

Chapter 9). Once completed and transmitted back to

headquarters, the remainder of the production processing,

including edits, weighting, microdata file creation, and

tabulations can begin.

Using on-line industry and occupation reference materials, the Jeffersonville coder enters three-digit numeric

industry and occupation codes that represent the description for each case on the file. If the coder cannot determine

the proper code, the case is assigned a referral code,

which will later be coded by a referral coder. A substantial

effort is directed at the supervision and control of the quality

of this operation. The supervisor is able to turn the dependent verification setting on or off at any time during the

coding operation. (In the on mode, a particular coder’s

work is to be verified by a second coder. Additionally, a 10

percent sample of each month’s cases is selected to go

through a quality assurance system to evaluate each

coder’s work. The selected cases are verified by another

coder after the current monthly processing has been

completed. Upon completion of the coding and possible

dependent verification of a particular batch, all cases for

which a coder assigned at least one referral code must be

reviewed and coded by a referral coder.

Edits, Imputation, and Weighting

As detailed in Chapter 9, there are six edit modules:

household, demographic, industry and occupation, labor

force, earnings, and school enrollment. Each module establishes consistency between logically related items, assigns

missing values using relational imputation, longitudinal

editing, or cross-sectional imputation, and deletes inappropriate entries. Each module also sets a flag for each edit

step that can potentially affect the unedited data.

Consistency is one of the checks used to control nonsampling error. Are the data logically correct, and are the

data consistent within the month? For example, if a respondent says that he/she is a doctor, is he/she old enough to

have achieved this occupation? Are the data consistent

from the previous month and across the last 12 months?

The imputation rates should normally stay about the same

as the previous month’s, taking seasonal patterns into

account. Are the universes verified, and how consistent are

the interview rates? In terms of the weighting, a check is

5

Appendix G provides an overview of the design and methodology of

the entire Reinterview Program.

15–8
made for zero or very large weights. If such outliers are

detected, verification and possible correction follow. Another

method to validate the weighting is to look at coverage

ratios which should fall within certain historical bounds. A

key working document used by headquarters for all of

these checks is a three-sheet tabulation of monthly summary statistics that highlights the six edit modules and the

weighting program for the past 12 months.

The CPS composite estimator is used for the derivation

of most official CPS labor force estimates that are based

upon information collected every month from the full sample

(i.e., not collected in periodic supplements or from partial

samples.) As detailed in Chapter 10, it modifies the aggregated estimates without adjusting the weights of individual

sample records by combining the simple weighted estimate

and the composite estimate for the preceding month plus

an estimate of the change from the preceding to the current

month.

Extensive verification at the time of program development was done to ensure that it was working correctly.

Also, as part of the routine production processing at

headquarters and as part of the routine check-in of the data

by the Bureau of Labor Statistics, both compute identical

tables in composited and uncomposited modes. These

tables are then checked cell for cell to ensure that all

uncomposited cells across the two agencies and that all

composited cells across the two agencies are identical. If

so, and they always have been, the data are deemed to

have been computed correctly.

CPS Population Controls

Total and state-level CPS population controls are developed by the Census Bureau independently from the collection and processing of CPS data. These monthly independent projections of the population are used for the

iterative, second-stage weighting of the CPS data. All of the

estimates start with the last census, with administrative

records and projection techniques providing updates. (See

Appendix D for a detailed discussion of the methodologies.)

As a means of controlling nonsampling error throughout

the processes, numerous internal consistency checks in

the programming are performed. For example, input files

containing age and sex detail are compared to independent files that give age and sex totals. Second, internal

redundancy is intentionally built into the programs that

process the files, as well as in files that contain overlapping/

redundant data. Third, a clerical review (i.e., two-person

review) of all handwritten input data is performed. A final

and extremely important means of assuring that quality

data are input into the CPS population controls is the

continuous research into improvements in methods of

making population estimates and projections.

Modeling Errors

It may be hypothesized that although modeling may

indeed reduce some sampling and nonsampling errors,

other nonsampling error due to the misspecification of the

model may be introduced. Regardless, this final section

focuses on a few of the methods of nonsampling error

reduction, as applied to the seasonal adjustment programs, monthly model-based state labor force estimates,

and composite estimation for selected labor force data.

(See Chapter 10 for a discussion of these procedures.)

Changes that occur in a seasonally adjusted series

reflect changes other than those arising from normal

seasonal change; they are believed to provide information

about the direction and magnitude of changes in the

behavior of trend and business cycle effects. They may,

however, also reflect the effects of sampling and nonsampling errors, which are not removed by the seasonal

adjustment process. Research into the sources of these

irregularities, specifically nonsampling error, can then lead

to controlling their effects and even removal. The seasonal

adjustment programs contain built-in checks as verification

that the data are well-fit and that the modeling assumptions

are reasonable. These diagnostic measures are a routine

part of the output.

The processes for controlling nonsampling error during

the production of monthly model-based state labor force

estimates are very similar to those used for the seasonal

adjustment programs. Built-in checks exist in the programs

and, again, a wide range of diagnostics are produced that

indicate the degree of deviation from the assumptions.

REFERENCES

Brooks, C.A. and Bailar, B.A. (1978), An Error Profile:

Employment as Measured by the Current Population

Survey, Statistical Policy Working Paper 3, Washington,

D.C.: Subcommittee on Nonsampling Errors, Federal Committee on Statistical Methodology, U.S. Department of

Commerce.

Kostanich, D.L. and Cahoon, L.S. (1994), Effect of

Design Differences Between the Parallel Survey and

the New CPS, CPS Bridge Team Technical Report 3, dated

March 4.

McCarthy, P.J. (1978), Some Sources of Error in

Labor Force Estimates from the Current Population

Survey, Background Paper No. 15, Washington, D.C.:

National Commission on Employment and Unemployment

Statistics.

Polivka, A.E. (1994), Comparisons of Labor Force

Estimates from the Parallel Survey and the CPS during

1993: Major Labor Force Estimates, CPS Overlap Analysis Team Technical Report 1, dated March 18.

Reeder, J.E. (1997), Regional Response to Questions

on CPS Type A Rates, Bureau of the Census, CPS Office

Memorandum No. 97-07, Methods and Performance Evaluation Memorandum No. 97-03, January 31, 1997.

15–9
Thompson, J. (1994), Mode Effects Analysis of Major

Labor Force Estimates, CPS Overlap Analysis Team

Technical Report 3, April 14,1994.

Waite, P.J. (1991), Continuing the Project Development Plan, Bureau of the Census internal memorandum,

June 7, 1991.

15–10
Chapter 16.

Quality Indicators of Nonsampling Errors

(Updated coverage ratios, nonresponse rates, and other measures of quality can be found by clicking on ‘‘Quality

Measures’’ at www.bls.census.gov/cps)

INTRODUCTION

Chapter 15 contains a description of the different sources

of nonsampling error in the CPS and the procedures

intended to limit those errors. In the present chapter,

several important indicators of potential nonsampling error

are described. Specifically, coverage ratios, response variance, nonresponse rates, mode of interview, time in sample

biases, and proxy reporting rates are discussed. It is

important to emphasize that unlike sampling error, these

indicators show only the presence of potential nonsampling

error, not the actual degree of nonsampling error present.

Nonetheless, these indicators of nonsampling error are

regularly used to monitor and evaluate data quality. For

example, surveys with high nonresponse rates are judged

to be of low quality, but the actual nonsampling error of

concern is not the nonresponse rate itself, but rather

nonresponse bias, that is, how the respondents differ from

the nonrespondents on the variables of interest. Although it

is possible for a survey with a lower nonresponse rate to

have greater nonresponse bias than a survey that has a

higher nonresponse rate (if the nonrespondents differ to a

greater degree from the respondents in the survey with the

lower nonresponse rate), one would generally expect that

greater nonresponse indicates a greater potential for bias.

Unfortunately, while it is relatively easy to measure nonresponse rates, it is extremely difficult to measure or even

estimate nonresponse bias. Thus, these indicators are

simply a measurement of the potential presence of nonsampling errors. We are not able to quantify the effect the

nonsampling error has on the estimates, and we do not

know the combined effect of all sources of nonsampling

error.

COVERAGE ERRORS

When conducting a sample survey, the primary goal is to

give every person in the target universe a known probability of selection into the sample. When this occurs, the

survey is said to have 100 percent coverage. This is rarely

the case, however. Errors can enter the system during

almost any phase of the survey process, from frame

creation to interviewing. A bias in the survey estimates

results when characteristics of persons erroneously included

or excluded from the survey differ from those correctly

included in the survey. Historically in the CPS, the net effect

of coverage errors has been a loss in population (resulting

from undercoverage).

Coverage Ratios

One way to estimate the coverage error present in a

survey is to compute a coverage ratio. A coverage ratio is

a ratio of the estimated number of persons in a specific

demographic group from the survey over an independent

population total for that group. The CPS coverage ratios

are computed by dividing a CPS estimate using the weights

after the first-stage ratio adjustment by the independent

population controls used to perform the second-stage ratio

adjustment. See Chapter 9 for more information on computation of weights. Population controls are not error free.

A number of approximations or assumptions are required in

deriving them. See Appendix D for details on how the

controls are computed. Chapter 15 highlighted potential

error sources in the population controls. Undercoverage

exists when the coverage ratio is less than 1.0 and an

overcoverage when the ratio is greater than 1.0. Table 16−1

shows the average monthly coverage ratios for January

1996 to April 1996.

It is estimated that the CPS has a 92.6 percent coverage

rate for the first 4 months of 1996. This implies that the

survey is missing approximately 7.4 persons per 100. This

rate has been fairly consistent throughout the 1990s. It is

difficult to compare this coverage rate to prior decades

because of sample design changes and changes in the

method that produces the population totals, but the total

coverage rate was estimated at 96.3 percent during the

mid-1970s (Hanson, 1978).

In terms of race, Whites have the highest coverage ratio

(93.8 percent) while Blacks have the lowest (83.9 percent).

Whites and Other1 races are significantly different from

Blacks, but not significantly different from each other. Black

males age 20-29 have the lowest coverage ratio (66.2

percent) of any race/age group. Females across all races

except Other race have higher coverage ratios than males.

Hispanics2 also have relatively low coverage rates. Historically, Hispanics and Blacks have lower coverage rates than

Whites for each age group, particularly the 20-29 age

group. This is by no fault of the interviewers or the CPS

process. These lower coverage rates for minorities affect

labor force estimates because persons that are missed by

the CPS are almost surely on the average quite different

from those that are included. The persons that are missed

1

Other includes American Indian, Eskimo, Aleut, Asian, and Pacific

Islander. 2

Hispanics may be of any race.

16–1
Table 16–1. CPS Coverage Ratios by Age, Sex, Race, and Ethnicity

[Average for January 1996–April 1996]

Age, sex, race, and

ethnicity

Average

monthly

coverage

ratio

Age, sex, race, and

ethnicity

Average

monthly

coverage

ratio

Age, sex, race, and

ethnicity

Average

monthly

coverage

ratio

Age, sex, race, and

ethnicity

Average

monthly

coverage

ratio

Total1 .................. 0.9255

White.................. 0.9376 Black . . .............. 0.8392 Other2 race............ 0.9255 Hispanic3 ............. 0.8331

Male . .................. 0.9163 Male . . . ............... 0.7727 Male .................. 0.9119 Male .................. 0.7841

16-19 . ................ 0.8974 16-19 . . . ............. 0.8328 16-19 . ............... 0.8754 16-19 ................ 0.8693

20-29 . ................ 0.8406 20-29 . . . ............. 0.6616 20-29 . ............... 0.8615 20-29 ................ 0.7485

30-39 . ................ 0.9091 30-39 . . . ............. 0.7104 30-39 . ............... 0.8623 30-39 ................ 0.7790

40-59 . ................ 0.9357 40-59 . . . ............. 0.8383 40-59 . ............... 0.9616 40-59 ................ 0.7904

60-69 . ................ 0.9705 60-69 . . . ............. 0.8735 60-69 . ............... 1.0110 60-69 ................ 0.7899

70+. . . . . . . . . . . . . . . . . . . 0.9672 70+ .................. 0.8644 70+ .................. 1.0236 70+ . . . ............... 0.7982

Female. ................ 0.9575 Female ................ 0.8931 Female ................ 0.9380 Female ................ 0.8820

16-19 . ................ 0.9385 16-19 . . . ............. 0.8676 16-19 . ............... 0.9071 16-19 ................ 0.8658

20-29 . ................ 0.9130 20-29 . . . ............. 0.8170 20-29 . ............... 0.8966 20-29 ................ 0.9015

30-39 . ................ 0.9501 30-39 . . . ............. 0.8650 30-39 . ............... 0.9023 30-39 ................ 0.8920

40-59 . ................ 0.9613 40-59 . . . ............. 0.9374 40-59 . ............... 0.9743 40-59 ................ 0.8766

60-69 . ................ 0.9681 60-69 . . . ............. 0.9541 60-69 . ............... 1.0185 60-69 ................ 0.8310

70+. . . . . . . . . . . . . . . . . . . 1.0173 70+ .................. 0.9820 70+ .................. 1.0025 70+ . . . .............. 0.8684

1

The estimated standard errors on the Total, White, Black, Other Race, and Hispanic groups are 0.0058, 0.0064, 0.0180, 0.0322, and 0.0198,

respectively. 2

Other includes American Indian, Eskimo, Aleut, Asian, and Pacific Islander.

3

Hispanics may be of any race.

Figure 16–1. Average Yearly Noninterview and Refusal Rates for the CPS 1964-1996

16–2
are accounted for in the CPS, but they are given labor force

characteristics the same as for those that are included in

the CPS. This produces bias in CPS estimates.

NONRESPONSE

As noted in Chapter 15, there are a variety of sources of

nonresponse in the CPS, such as unit or household

nonresponse, panel nonresponse, and item nonresponse.

Unit nonresponse, referred to as Type A noninterviews,

represents households that are eligible for interview but

were not interviewed for some reason. Type A noninterviews occur because a respondent refuses to participate in

the survey, is too ill or is incapable of completing the

interview, is not available or not contacted by the interviewer, perhaps because of work schedules or vacation,

during the survey period. Because the CPS is a panel

survey, households who respond 1 month may not respond

during a following month. Thus, there is also panel nonresponse in the CPS, which can become particularly important if CPS data are used longitudinally. Finally, some

respondents who complete the CPS interview may be

unable or unwilling to answer specific questions in the CPS

resulting in some level of item nonresponse.

Type A Nonresponse

Type A noninterview rate. The Type A noninterview rate

is calculated by dividing the total number of Type A

households (refusals, temporarily absent, noncontacts, and

other noninterviews) by the total number of eligible households (which includes Type As and interviewed households).

As can be seen in Figure 16−1, the noninterview rate for

the CPS has remained relatively stable at around 4 to 5

percent for most of the past 35 years; however, there have

been some changes during this time. It is readily apparent

from Figure 16−1 that there was a major change in the CPS

nonresponse rate in January 1994, which reflects the

launching of the redesigned survey using computer assisted

survey collection procedures. This rise is discussed below.

The end of 1995 and the beginning of 1996 also show a

jump in Type A noninterview rates that were chiefly because

of disruptions in data collection because of shutdowns of

the Federal Government (see Butani, Kojetin, and Cahoon,

1996). The relative stability of the overall noninterview rate

from 1960 to 1994 masks some underlying changes that

have occurred. Specifically, the refusal portion of the

noninterview rate has shown an increase over this same

period with the bulk of the increase in refusals taking place

from the early 1960s to the mid-1970s. In the late 1970s,

there was a leveling off so that refusal rates were fairly

constant until 1994. To compensate for this increase, there

was a corresponding decrease in the rate of noncontacts

and other noninterviews.

There also appears to be seasonal variation in both the

overall noninterview rates and the refusal rates (see Figure

16−2). During the year, the noninterview and refusal rates

have tended to increase after January until they reached a

peak in March or April at the time of the income supplement. At this point, there was a steep drop in noninterview

and refusal rates that extended below the starting point in

January until they bottomed out in July or August. The rates

then increased and approached the initial level. This pattern has been evident most years in the recent past and

appears to be similar for 1994.

Effect of the transition to a redesigned survey with

computer assisted data collection on noninterview

rates. With the transition to the redesigned CPS questionnaire using computerized data collection in January 1994,

there was a noticeable increase in the Type A nonresponse

rates as can be seen in Figures 16−1 and 16−2. As part of

this transition, there were several procedural changes in

the collection of data for the CPS, and the adjustment to

these new procedures may account for this increase. For

example, the CAPI instrument now required the interviewers to go through the entire interview, while previously

some interviewers may have conducted shortened interviews with reluctant respondents, obtaining answers to

only a couple of critical questions. Another change in the

data collection procedures was an increased reliance on

using centralized telephone interviewing in CPS. Households not interviewed by the CATI centers by Wednesday

night of interview week are recycled back to the field

representatives on Thursday morning. These recycled CATI

cases can present difficulties for the field representatives

because there are only a few days left to make contact

before the end of the interviewing period. As depicted in

Figure 16-2, there has been greater variability in the

monthly Type A nonresponse rates in CPS since the

transition in January 1994. In addition, because of events

such as the Federal Government shutdowns that affected

nonresponse rates, it remains to be seen at what level the

Type A nonresponse rates will stabilize for the new CPS.

The annual overall Type A rate, the refusal rate, and

noncontact rate (which includes temporarily absent households and other noncontacts) are shown in Table 16−2 for

the period 1993-1996. Data beyond 1996 were not available at the time of this writing.

Table 16–2. Components of Type A Nonresponse

Rates, Annual Averages for 1993-1996

[Percent distribution]

Nonresponse rate 1993 1994 1995 1996

Overall Type A.............. 4.69 6.19 6.86 6.63

Noncontact. ................ 1.77 2.30 2.41 2.28

Refusal . . .................. 2.85 3.54 3.89 4.09

Other . . . . . . . . . . . . . . . . . . . . . . .13 .32 .34 .25

Panel nonresponse. Households are selected into the

CPS sample for a total of 8 months in a 4-8-4 pattern as

described in Chapter 3. Many families in these households

16–3
may not be in the CPS the entire 8 months because of

moving (movers are not followed, but the new household

members are interviewed). Those who live in the same

household during the entire time they are in the CPS

sample may not agree to be interviewed each month. Table

16−3 shows the percentage of households who were

interviewed 0, 1, 2, ... 8 times during the 8 months that they

were eligible for interview during the period January 1994

to October 1995. These households represent seven rotation groups (see Chapter 3) that completed all of their

rotations in the sample during this period. The vast majority

of households, 82 percent, had completed interviews each

month, and only 2 percent never participated. The remaining 16 percent participated to some degree (for further

information see Harris-Kojetin and Tucker, 1997).

Effect of Type A Noninterviews on Labor Force

Classification.

Although the CPS has monthly measures of Type A

nonresponse, the total effect of nonresponse on labor force

estimates produced from the CPS cannot be calculated

from CPS data alone. It is the nature of nonresponse that

we do not know what we would like to know from the

nonrespondents, and therefore, the actual degree of bias

because of nonresponse is unknown. Nonetheless, because

the CPS is a panel survey, information is often available at

some point in time from households that were nonrespondents at another point. Some assessment can be made of

the effect of nonresponse on labor force classification by

using data from adjacent months and examining the monthto-month flows of persons from labor force categories to

nonresponse as well as from nonresponse to labor force

categories. Comparisons can then be made for labor force

status between households that responded both months

with households that responded 1 month, but failed to

respond in the other month. However, the labor force status

of persons in households that were nonrespondents for

both months is unknown.

Table 16–3. Percentage of Households1 by Number

of Completed Interviews During the

8 Months in the Sample

[January 1994–October 1995]

Number of completed interviews Percent

0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.0

1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.0

5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2

6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5

7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.9

8 .............................................. 82.0

1

Includes only households in the sample all 8 months with only

interviewed and Type A nonresponse interview status for all 8 months, i.e.,

households that were out of scope (e.g., vacant) for any month in sample

were not included in these tabulations. Movers were not included in this

tabulation.

Figure 16–2. Monthly Noninterview and Refusal Rates for the CPS 1984-1996

Note: The break in the refusal rate indicates government furlough.

16–4
Table 16–4. Labor Force Status by Interview/Noninterview Status in Previous and Current Month

[Average January–June 19971 percent distribution]

1st month labor force status Interview in 2nd

month

Nonresponse in 2nd

month Difference

Civilian labor force .................................................. 65.98 68.51 2.53**

Employed . ......................................................... 62.45 63.80 1.35**

Unemployment rate . ................................................ 5.35 6.87 1.52**

2nd month labor force status

Interview in 1st

month

Nonresponse in 1st

month Difference

Civilian labor force .................................................. 65.79 67.41 1.62**

Employed . ......................................................... 62.39 63.74 1.35**

Unemployment rate . ................................................ 5.18 5.48 .30*

** p < .01 * <. 05

1

From Tucker and Harris-Kojetin (1997).

Monthly labor force data were used for each consecutive pair of months for January through June 1997, separately for households that responded for each consecutive

pair of months, and for households that responded only 1

month and were nonrespondents the other month (see

Tucker and Harris-Kojetin, 1997). The top half of Table

16−4 shows the labor force classification in the first month

for persons in households that were respondents the

second month compared to persons who were in households that were noninterviews the second month. Persons

from households that became nonrespondents had significantly higher rates of participation in the labor force,

employment, and unemployment than persons from households that were respondents both months. The bottom half

of Table 16−4 shows the labor force classification for the

second month for persons in households that were respondents in the previous month compared to persons who

were in households that were noninterviews the previous

month. The pattern of differences is similar, but the magnitude of the differences is less. Because the overall Type

A noninterview rate is quite small, the effect of the nonresponding households on the overall unemployment rate is

also relatively small. However, it is important to remember

that the labor force characteristics of the persons in households who never responded are not measured or included

here. In addition, other nonsampling errors are also likely

present, such as those due to repeated interviewing or

month-in-sample effects (described later in this chapter).

Item Nonresponse

Another component of nonresponse is item nonresponse.

Respondents may refuse or may be unable to answer

certain items, but still respond to most of the CPS questions with only a few items missing. To examine the

prevalence of item nonresponse in the CPS, counts were

made of the refusals and ‘‘don’t know’’ responses from 10

demographic items and 94 labor force items (due to skip

patterns only some of these questions were asked of each

respondent) for all interviewed cases from January to

December 1994. The average levels of item-missing data

Table 16–5. Percentage of CPS Items With Missing

Data

Item series Percent

Demographic.................................... 1.54

Labor force ..................................... 1.46

Industry and occupation .......................... 3.76

Earnings........................................ 12.44

from most of the CPS labor force and demographic items

are shown in Table 16−5. It is likely that the bias due to

imputation for the demographic and labor force items

is quite small, but there may be a concern for earnings

data.

RESPONSE VARIANCE

Estimates of the Simple Response Variance

Component

To obtain an unbiased estimate of the simple response

variance, it is necessary to have at least two independent

measurements of the characteristic for each person in a

subsample of the entire sample using the identical measurement procedure on each trial. It is also necessary that

responses to a second interview not be affected by the

response obtained in the first interview.

Two difficulties occur in every attempt to measure the

simple response variance by a reinterview. The first is lack

of independence between the original interview and the

reinterview; a person visited twice within a short period and

asked the same questions may tend to remember his/her

original responses and repeat them. A second difficulty is

that the data collection methods used in the original

interview and in the reinterview are seldom the same. Both

of these difficulties exist in the CPS reinterview program

data used to measure simple response variance. For

example, the interviews in the response variance reinterview sample are conducted by telephone by senior interviewers or supervisory personnel. Also, some characteristics of the reinterview survey process itself introduce

16–5
Table 16–6. Comparison of Responses to the Original

Interview and the Reinterview

Reinterview

Original interview

Unemployed Total

Not

unemployed

Unemployed ....... a b a+b

Not unemployed . . . . c d c + d

Total .............. a+c b+d n=a+b+c+d

unknown biases in the estimates (e.g., a high noninterview

rate.)

It is useful to consider a schematic representation of the

results of the original CPS interview and the reinterview in

estimating, say, the number of persons reported as unemployed (see Table 16-6). In this schematic representation,

the number of identical persons having a difference between

the original interview and the reinterview for the characteristic unemployed is given by (b + c). If these responses are

two independent measurements for identical persons using

identical measurement procedures, then an estimate of

the simple response variance component is given by

(b + c) /2n and

E (

b  c

2n ) = 2

R

and as in formula 14.3 (Hansen, Hurwitz, and Pritzker,

1964)3. We know that (b + c)/2n, using CPS reinterview

data, underestimates 2

R, chiefly because conditioning

sometimes takes place between the two responses for a

specific person resulting in correlated responses, so that

fewer differences (i.e., entries in the b and c cells) occur

than would be expected if the responses were in fact

independent.

Besides its effect on the total variance, the simple

response variance is potentially useful to evaluate the

precision of the survey measurement methods. It can be

used to evaluate the underlying difficulty in assigning

individuals to some category of a distribution on the basis

of responses to the survey instrument. As such, it is an aid

in determining whether a concept is sufficiently measurable

by a household survey technique and whether the resulting

survey data fulfill their intended purpose. For characteristics having ordered categories (e.g., number of hours

worked last week), the simple response variance is helpful

in determining whether the detail of the classification is too

fine. To provide a basis for this evaluation, the estimated

simple response variance is expressed, not in absolute

terms, but as a proportion of the estimated total population

variance. This ratio is called the index of inconsistency and

has a theoretical range of 0.0 to 100.0 when expressed as

a percentage (Hansen, Hurwitz, and Pritzker, 1964). The

denominator of this ratio, the population variance, is the

sum of the simple response variance, 2

R, and the population sampling variance, 2

S. When identical responses are

obtained from trial to trial, the simple response variance is

zero and the index of inconsistency has a value of zero. As

the variability in the classification of an individual over

repeated trials increases (and the measurement procedures become less reliable), the value of the index increases

until, at the limit, the responses are so variable that the

simple response variance equals the total population variance. At the limit, if a single response from N individuals is

required, equivalent information is obtained if one individual is randomly selected and interviewed N times,

independently.

Two important inferences can be made from the index of

inconsistency for a characteristic. One is to compare it to

the value of the index that could be obtained for the

characteristic by best (or preferred) set of measurement

procedures that could be devised. The second is to consider whether the precision of the measurement procedures indicated by the level of the index is still adequate to

serve the purposes for which the survey is intended. In the

CPS, the index is more commonly used for the latter

purpose. As a result, the index is used primarily to monitor

the measurement procedures over time. Substantial changes

in the indices that persist for several months result in

review of field procedures to determine and remedy the

cause.

Table 16−7 provides estimates of the index of consistency shown as percentages for selected labor force

characteristics for the period of February through July

1994.

Table 16–7. Index of Inconsistency for Selected Labor

Force Characteristics February–July 1994

Labor force characteristic

Estimate of

index of

inconsistency

90 percent

confidence

limits

Working, full time . . . . . . . . . . . . . . . . . . . . . . 9.4 8.4 to 10.6

Working, part time ..................... 22.6 20.3 to 25.0

With a job, not at work.................. 30.1 24.5 to 37.0

Unemployed........................... 30.8 25.9 to 36.5

Not in labor force . . . . . . . . . . . . . . . . . . . . . . 8.1 7.1 to 9.2

MODE OF INTERVIEW

Incidence of Telephone Interviewing

As described in Chapter 6, the first and fifth months’

interviews are typically done in person while the remaining

interviews may be done over the telephone either by the

field interviewer or an interviewer from a centralized telephone facility. Although the first and fifth interviews are

supposed to be done in person, the entire interview may

3

The expression (b+c)/n is referred to as the gross difference rate;

thus, the simple response variance is estimated as one-half the gross

difference rate.

16–6
not be completed in person, and the field interviewer may

call the respondent back to obtain missing information. The

CPS CAPI instrument records whether the last contact with

the household was by telephone or personal visit. The

percentage of CAPI cases from each month-in-sample that

were completed by telephone are shown in Table 16−8.

Overall, about 66 percent of the cases are done by

telephone, with almost 85 percent of the cases in monthin-samples 2-4 and 6-8 done by telephone. Furthermore, a

substantial percentage of cases in months 1 and 5 are

obtained by telephone, despite standing instructions to the

field interviewers to conduct personal visit interviews.

Table 16–8. Percentage of Households With

Completed Interviews With Data Collected by Telephone (CAPI Cases Only)

[Percent]

Month-in-sample

Last contact

with household was

telephone

(average,

Jan.–Dec.

1995)

Majority of

data

collected by

telephone

(June 1996)

1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.6 15

2..................................... 80.2 79

3..................................... 83.1 83

4..................................... 83.6 83

5..................................... 23.5 34

6..................................... 83.1 82

7..................................... 84.1 84

8..................................... 84.5 85

Because the indicator variable in the CPS instrument

reflects only the last contact with the household, it may not

be the best indicator of how most of the data were gathered

from a household. For example, an interviewer may obtain

information for several members of the household during

the first month’s personal visit, but may make a telephone

call back to obtain the labor force data for the last household member resulting in the interview being recorded as a

telephone interview. In June 1996, an additional item was

added to the CPS instrument (for that month only) that

asked interviewers whether the majority of the data for

each completed case was obtained by telephone or personal visit. The results using this indicator are presented in

the second column of Table 16-8. It was expected that in

MIS 1 and 5 interviewers would have reported that the

majority of the data was collected though personal visit

more often than was revealed by the last contact with the

household. This would seem likely because, as noted

above, the last contact with a household may be a telephone call back to obtain missing information not collected

at the initial personal visit. However, a higher percentage of

cases were reported with the majority of the data being

collected by telephone than the percentage of cases with

the last contact with the household being by telephone for

MIS 1 and 5. The explanation for this pattern of results is

not clear at the present time.

Effects of Centralized Telephone Interviewing

With the implementation of the revised CPS in January

1994, there was an increased reliance on the use of the

Census Bureau’s centralized telephone centers for conducting CPS interviews. As noted in Chapter 4, only cases

in CATI eligible PSUs can be sent to the CATI centers for

interviewing. Furthermore, all cases within eligible PSUs

were randomly assigned to the CATI panel or the control

(CAPI) panels, so that meaningful comparisons could be

made on the effects of centralized interviewing on estimates from CPS. All cases were interviewed by CAPI in

MIS 1 and 5. In months 2-4 and 6-8, most, but not all of the

cases in the CATI panel were interviewed in the centralized

facilities, because some cases were still interviewed by

field staff in CAPI for a variety of reasons. Nonetheless, to

preserve the integrity of the research design, the results

shown in Table 16−9 reflect the panel that each case was

assigned to, which was also the actual mode of interview

for the vast majority of cases

Table 16−9 shows the results of the comparisons of the

CATI test and control panels. There were significant differences found between the CATI test and control panels in

MIS 2-4 and 6-8 for all groups on the unemployment rate.

There were no significant differences in the unemployment

rate in MIS 1 and 5 (and none would be expected given

random assignment); however, the observed differences

were not necessarily equal to zero. These initial differences

may have had some slight effects on the size of the

differences observed in MIS 2-4 and 6-8 for some of the

groups. There were no significant differences for the civilian labor force participation rate or the employment to

population ratio. Although there are differences in unemployment rates between the centralized CATI and CAPI

interviews as noted above4, the interpretation of the findings is not as clear. One major difference between CATI

and CAPI interviews is that CAPI interviews are likely to be

done by the same interviewer all 8 MIS, while CATI

interviews involve a definite change in interviewers from

MIS 1 to 2 and from MIS 5 to 6. In fact, cases in the CATI

facilities often have a different interviewer each month.

However, it is not clear whether the results from the

centralized CATI or the CAPI interviews are more accurate.

TIME IN SAMPLE

The rotation pattern of the CPS sample was described in

detail in Chapter 3, and the use of composite estimation in

CPS was discussed briefly in Chapter 9. The effects of

interviewing the same respondents for CPS several times

has been discussed for a long time (e.g., Bailar, 1975;

Brooks and Bailar, 1978; Hansen, Hurwitz, Nisselson, and

Steinberg, 1955; McCarthy, 1978; Williams and Mallows,

4

These findings were also similar to those from a test conducted

using the parallel survey (see Thompson, 1994).

16–7
Table 16–9. Effect of Centralized Telephone Interviewing on Selected Labor Force Characteristics

[Average January 1996 - December 1996]

Employment status and sex

MIS 1 and 5 MIS 2-4 and 6-8

CATI test

Control

(CAPI) Difference CATI test

Control

(CAPI) Difference

Standard

error P-value

TOTAL POPULATION, 16 YEARS OLD

AND OVER

Civilian labor force ................... 66.89 67.20 −0.31 66.47 66.44 0.03 1.09 .98

Employment to population ratio ........ 63.20 63.60 −0.40 62.79 63.20 −0.41 1.06 .70

Unemployment rate .................. 5.52 5.36 0.16 5.53 4.87 0.66 0.21 .00

MALES, 16 YEARS OLD AND OVER

Civilian labor force ................... 75.33 76.35 −1.02 74.96 75.31 −0.35 1.71 .84

Employment to population ratio ........ 71.19 72.23 −1.03 70.85 71.63 −0.79 1.66 .64

Unemployment rate .................. 5.49 5.40 0.09 5.49 4.88 0.61 0.28 .03

FEMALES, 16 YEARS OLD AND

OVER

Civilian labor force ................... 59.50 59.19 0.31 59.04 58.68 0.36 1.18 .76

Employment to population ratio ........ 56.20 56.05 0.14 55.75 55.83 −0.08 1.12 .94

Unemployment rate .................. 5.55 5.30 0.25 5.57 4.85 0.72 0.21 .00

WHITE, 16 YEARS OLD AND OVER

Civilian labor force ................... 67.50 67.59 −0.08 67.16 66.89 0.28 1.35 .84

Employment to population ratio ........ 64.22 64.37 −0.15 63.94 63.97 −0.02 1.33 .99

Unemployment rate .................. 4.86 4.76 0.11 4.79 4.37 0.43 0.16 .01

WHITE MALES, 16 YEARS OLD AND

OVER

Civilian labor force ................... 76.44 77.38 −0.93 76.14 76.31 −0.17 1.98 .93

Employment to population ratio ........ 72.67 73.74 −1.07 72.52 72.98 −0.46 1.94 .81

Unemployment rate .................. 4.94 4.70 0.24 4.76 4.37 0.39 0.23 .10

WHITE FEMALES, 16 YEARS OLD

AND OVER

Civilian labor force ................... 59.42 58.80 0.62 59.07 58.43 0.64 1.35 .63

Employment to population ratio ........ 56.59 55.97 0.62 56.22 55.88 0.34 1.29 .79

Unemployment rate .................. 4.78 4.82 −0.05 4.83 4.36 0.47 0.20 .02

BLACKS, 16 YEARS OLD AND OVER

Civilian labor force ................... 63.30 65.44 −2.14 62.12 64.22 −2.10 4.22 .62

Employment to population ratio ........ 56.94 58.85 −1.91 55.55 58.44 −2.89 3.99 .47

Unemployment rate .................. 10.04 10.06 −.02 10.59 9.01 1.58 0.79 .04

BLACK MALES, 16 YEARS OLD AND

OVER

Civilian labor force ................... 67.23 70.24 −3.01 66.45 69.21 −2.75 5.47 .61

Employment to population ratio ........ 60.33 61.76 −1.43 58.73 62.07 −3.34 4.97 .50

Unemployment rate .................. 10.26 12.08 −1.82 11.62 10.31 1.30 0.72 .07

BLACK FEMALES, 16 YEARS OLD

AND OVER

Civilian labor force ................... 60.55 62.06 −1.51 59.03 60.75 −1.72 4.75 .72

Employment to population ratio ........ 54.57 56.81 −2.24 53.27 55.90 −2.63 4.51 .56

Unemployment rate .................. 9.87 8.46 1.41 9.75 7.97 1.79 0.83 .03

1970). It is possible to measure the effect of the time spent

in the sample on labor force estimates from the CPS by

creating a month-in-sample index which shows the relationships of all the month-in-sample groups. This index is

the ratio of the estimate based on the sample units in a

particular month-in-sample group to the average estimate

from all eight month-in-sample groups combined, multiplied by 100. If an equal percentage of people with the

characteristic are present in each month-in-sample group,

then the index for each group would be 100. Table 16−10

shows indices for each group by the number of months

they have been in the sample. The indices are based on

CPS labor force data from September to December 1995.

For the percentage of the total population that is unemployed, the index of 108.62 for the first month households

indicates that the estimate from households in sample the

first month is about 1.0862 times as great as the average

overall eight month-in-sample groups; the index of

99.57 for unemployed for the second month households

16–8
indicates that it is about the same as the average overall

month-in-sample groups.

It should not be assumed that estimates from one of the

month-in-sample groups should be taken as the standard

in the sense that it would provide unbiased estimates, while

the estimates for the other seven would be biased. It is far

more likely that the expected value of each group is

biased—but to varying degrees. Total CPS estimates,

which are the combined data from all month-in-sample

groups (see Chapter 9), are subject to biases that are

functions of the biases of the individual groups. Since the

expected values vary appreciably among some of the

groups, it follows that the CPS survey conditions must be

different in one or more significant ways when applied

separately to these subgroups.

One way in which the survey conditions differ among

rotation groups is reflected in the noninterview rates. The

interviewers, being unfamiliar with households in sample

for the first time, are likely to be less successful in calling

when a responsible household member is available. Thus,

noninterview rates generally start above average with first

month families, decrease with more time in sample, go up

a little for households in sample the fifth month (after the 8

months the household was not in sample), and then

decrease again in the final months. (This pattern may also

reflect the prevalence of personal visit interviews conducted for each of the months-in-sample as noted above.)

An index of the noninterview rate constructed in a similar

manner to the month-in-sample group index can be seen in

Table 16−11. This noninterview rate index clearly shows

that the greatest proportion of noninterviews occurs for

cases that are in the sample for the first month and the

second largest proportion of noninterviews are due to

cases returning to the sample in the fifth month. These

Table 16–10. Month-In-Sample Bias Indexes (and Standard Errors) in the CPS for Selected Labor Force

Characteristics

[Average September - December 1995]

Employment status and sex

Month-in-sample

1234567 8

TOTAL POPULATION, 16 YEARS OLD

AND OVER

Civilian labor force ................... 101.34 100.26 99.93 100.32 99.5 99.5 99.68 99.48

Standard error ..................... (.27) (.26) (.25) (.25) (.28) (.24) (.29) (.30)

Unemployment level .................. 108.62 99.57 98.27 103.33 99.19 99.39 98.74 92.89

Standard error ..................... (2.46) (2.59) (2.28) (2.59) (2.65) (2.42) (2.46) (2.66)

MALES, 16 YEARS OLD AND OVER

Civilian labor force ................... 100.98 100.26 99.87 100.34 99.98 99.55 99.62 99.40

Standard error ..................... (.31) (.31) (.31) (.26) (.34) (.29) (.32) (.34)

Unemployment level .................. 107.65 98.13 97.19 102.94 103.60 99.29 95.60 95.59

Standard error ..................... (3.45) (3.53) (3.03) (3.41) (3.64) (3.22) (3.33) (3.73)

FEMALES, 16 YEARS OLD AND

OVER

Civilian labor force ................... 101.75 100.26 100.00 100.29 98.94 99.44 99.76 99.56

Standard error ..................... (.42) (.38) (.38) (.41) (.39) (.37) (.45) (.47)

Unemployment level .................. 110.09 101.46 99.62 103.53 93.80 99.79 102.25 89.47

Standard error ..................... (3.65) (3.44) (3.17) (3.42) (3.43) (3.61) (3.42) (3.16)

BLACK, 16 YEARS OLD AND OVER

Civilian labor force ................... 102.66 102.82 100.04 98.95 99.19 99.03 99.11 98.21

Standard error ..................... (1.05) (.97) (.99) (.90) (1.07) (1.06) (.90) (1.04)

Unemployment level .................. 116.50 104.13 103.44 106.24 94.56 85.48 92.83 96.82

Standard error ..................... (5.84) (5.41) (5.29) (6.17) (5.46) (5.14) (4.94) (6.00)

BLACK MALES, 16 YEARS OLD AND

OVER

Civilian labor force ................... 101.17 101.40 100.00 100.88 100.31 98.73 99.99 97.52

Standard error ..................... (1.45) (1.34) (1.16) (1.17) (1.29) (1.28) (1.29) (1.52)

Unemployment level .................. 114.46 100.86 100.47 109.39 97.38 80.47 93.32 103.65

Standard error ..................... (9.48) (8.33) (7.49) (8.97) (8.74) (7.19) (6.97) (8.95)

BLACK FEMALES, 16 YEARS OLD

AND OVER

Civilian labor force ................... 104.00 104.13 100.06 97.17 98.16 99.32 98.31 98.86

Standard error ..................... (1.40) (1.31) (1.40) (1.25) (1.45) (1.38) (1.27) (1.30)

Unemployment level .................. 117.30 107.78 106.40 104.24 91.10 90.86 92.62 89.71

Standard error .................... (8.72) (7.00) (7.30) (7.92) (6.76) (6.86) (7.42) (7.49)

16–9
Table 16–11. Month-In-Sample Indexes in the CPS for

Type A Noninterview Rates January–

December 1994

Month-in-sample Noninterview

rate index

1 .............................................. 134.0

2 .............................................. 93.1

3 .............................................. 89.3

4 .............................................. 90.0

5 .............................................. 117.9

6 .............................................. 95.6

7 .............................................. 92.4

8 .............................................. 88.2

Table 16–12. Percentage of CPS Labor Force Reports

Provided by Proxy Reporters

Percent

reporting

All interviews

Proxy reports. ................................. 49.68

Both self and proxy ............................ 0.28

MIS 1 and 5

Proxy reports. ................................. 50.13

Both self and proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.11

MIS 2-4 and 6-8

Proxy reports. ................................. 49.53

Both self and proxy ............................ 0.35

noninterview changes are unlikely to be distributed proportionately among the various labor force categories (see

Table 16−4). Consequently, it is reasonable to assume that

the representation of labor force categories will differ

somewhat among the month-in-sample groups and that

these differences can affect the expected values of these

estimates, which would imply that at least some of the

month-in-sample bias can be attributed to actual differences in response probabilities among the month-in-sample

groups (Williams and Mallows, 1970). Although the individual probabilities are not known, they can be estimated

by nonresponse rates. However, other factors are also

likely to affect the month-in-sample patterns.

PROXY REPORTING

Like many household surveys, the CPS seeks information about all persons in the household whether they are

available for interview or not. CPS field representatives

accept reports from responsible adults in the household

(see Chapter 6 for a discussion of respondent rules) to

provide information about all household members. Respondents who provide labor force information about other

household members are called proxy reporters. Because

some household members may not know or be able to

provide accurate information about the labor force status

and activities of some household members, nonsampling

error may occur because of the use of proxy reporters.

The level of proxy reporting in the CPS had generally

been around 50 percent in the past and continues to be so

in the revised CPS. As can be seen in Table 16−12, the

month-in-sample has very little effect on the level of proxy

reporting. Thus, whether the interview is more likely to be a

personal visit (for MIS 1 and 5) or a telephone interview

(MIS 2-4 and 6-8) has very little effect.

Although one can make overall comparisons of the data

given by self-reporters and proxy reporters, there is an

inherent bias in the comparisons because proxy reporters

were the people more likely to be found at home when the

field representative called or visited. For this reason,

household members with self- and proxy reporters tend to

differ systematically on important labor force and demographic characteristics. In order to compare the data given

by self- and proxy reporters systematic studies must be

conducted that control assignment of proxy reporting status. Such studies have not been carried out.

SUMMARY

This chapter contains a description of several quality

indicators in the CPS, namely, coverage, noninterview

rates, telephone interview rates, and proxy reporting rates.

These rates can be used to monitor the processes of

conducting the survey, and they indicate the potential for

some nonsampling error to enter into the process. This

chapter also includes information on the potential effects of

nonresponse, centralized telephone interviewing, and the

month-in-sample groups on CPS estimates. This research

comes close to identifying the presence and effects of

nonsampling error in the CPS, but the results are far from

conclusive.

The full extent of nonsampling error in the CPS is

unknown. Because of the number and scope of changes

that have occurred in the questionnaire as well as in data

collection, it is also currently unclear how much previous

research on nonsampling error in the CPS is applicable to

the redesigned instrument with computerized data collection. Furthermore, new sources of nonsampling error may

have been introduced by the change in data collection

methodology and with changes in questions. Although

some potential sources of nonsampling error can be investigated with the currently available CPS data and reinterview data, the best source of information on nonsampling

error is often information provided by an external or outside

source or through a special study or experiment. At the

time of this writing, there is currently about 3 years’ data

available for internal analyses and comparison.

REFERENCES

Bailar, B.A. (1975), ‘‘ The Effects of Rotation Group Bias

on Estimates from Panel Surveys,’’ Journal of the American Statistical Association, 70, 23-30.

Brooks, C.A. and Bailar, B.A. (1978). Statistical Policy

Working Paper 3 An Error Profile: Employment as

Measured by the Current Population Survey, Subcommittee on Nonsampling Errors, Federal Committee on

Statistical Methodology, U.S. Department of Commerce,

Washington, DC.

16–10
Butani, S., Kojetin, B.A., and Cahoon, L. (1996), ‘‘Federal Government Shutdown: Options for Current Population Survey (CPS) Data Collection,’’ Paper presented at the

Joint Statistical Meetings of the American Statistical Association, Chicago, Illinois.

Hansen, M.H., Hurwitz, W.N., Nisselson, H., and Steinberg, J. (1955), ‘‘The Redesign of the Current Population

Survey,’’ Journal of the American Statistical Association, 50, 701-719.

Hansen, M.H., Hurwitz, W.N., and Pritzker, L. (1964),

‘‘Response Variance and Its Estimation,’’ Journal of the

American Statistical Association, 59, 1016-1041.

Hanson, R. H. (1978), The Current Population Survey: Design and Methodology, Technical Paper 40,

Washington, DC: Government Printing Office.

Harris-Kojetin, B.A. and Tucker, C. (1997), ‘‘Longitudinal

Nonresponse in the Current Population Survey,’’ Paper

presented at the 8th International Workshop on Household

Survey Nonresponse, Mannheim, Germany.

McCarthy, P.J. (1978), Some Sources of Error in

Labor Force Estimates From the Current Population

Survey, Background Paper No. 15, National Commission

on Employment and Unemployment Statistics, Washington, DC.

Thompson, J. (1994). ‘‘Mode Effects Analysis of Labor

Force Estimates,’’ CPS Overlap Analysis Team Technical Report 3, Bureau of Labor Statistics and U.S. Census

Bureau, Washington, DC.

Tucker, C. and Harris-Kojetin, B.A. (1997), ‘‘The Impact

of Nonresponse on the Unemployment Rate in the Current

Population Survey,’’ Paper presented at the 8th International Workshop on Household Survey Nonresponse,

Mannheim, Germany.

Williams, W.H. and Mallows, C.L., (1970), ‘‘Systematic

Biases in Panel Surveys Due to Differential Nonresponse,’’

Journal of the American Statistical Association, 65,

1338-1349.

16–11
Appendix A.

Maximizing Primary Sampling Unit (PSU) Overlap

INTRODUCTION

The sampling of primary sampling units (PSUs) for the

redesigned CPS was accomplished using linear programming techniques that maximized the overlap with the PSUs

from the former 1980s design while maintaining the correct

overall probabilities of selection for a probability proportional to size (pps) sampling scheme. This strategy enables

the fullest use of field staff who are already hired and

trained and results in substantial cost savings. Procedures

are already in place in the previously existing PSUs for

identifying and sampling new construction housing units. In

addition to the cost savings, a large PSU overlap prevents

overly large breaks in data series that can occur when

many PSUs are changed with the introduction of a new

design.

There were 379 nonself-representing (NSR) PSUs in the

1980 CPS design. (There were some small rotating PSUs,

but only those still in sample at the end of the 1980 design

are counted.) The maximizing procedure retained 249 (66

percent) of these NSR PSUs for the 1990 design. Had

selection for the 1990 design been independent of the

1980 design, only 149 (39 percent) would have been

retained.

Methods of solving the problem of maximizing PSU

overlap, while maintaining the correct unconditional design

probabilities, have evolved over several decades. A limited

solution was presented by Keyfitz (1951) for one PSU per

stratum pps designs similar to the CPS. The definitions of

individual PSUs were held constant, and the strata were

assumed identical. Only the PSU probabilities of selection

were allowed to be different for the former and new

designs, thus responding to relative PSU population size

changes over a decade. Raj (1968) demonstrated that the

problem solved by Keyfitz could be reformulated as a

transportation problem, which is commonly solved using

linear programming techniques. Causey, Cox, and Ernst

(1985) generalized the transportation problem approach to

allow PSU redefinitions, changes in stratum composition,

and more than one sample PSU per stratum. The generalization assumes the former design sampling to be independent within strata, which does not hold for the CPS

since the 1980s design itself had PSU overlap maximized

from the earlier 1970s design. This and other practical

considerations are addressed by various authors, including

Ernst (1986, 1990).

To illustrate the concepts, consider the following example

adapted from Causey et al. (1985). The PSUs and strata do

not change across designs, and a particular stratum has

three PSUs with probabilities p1.=.36, p2.=.24 and p3.=.4 for

the former design. Over the decade, PSUs 1 and 2 have

grown in size relative to PSU 3. Were sampling done anew

without regard for the former design, then p.1=.5, p.2=.3,

and p.3=.2 would be the probabilities for the new design. To

obtain the maximum overlap of PSUs between the former

and new designs, without restraint, we would arbitrarily

choose for the new design any PSU that had been sampled

for the former design. The implication of the arbitrary

strategy for the example would be that PSU 3, which has

been losing in relative size and is now the least populous

PSU in the stratum, would retain the highest probability of

selection (.4 from the former design). In general, the

arbitrary strategy introduces bias by favoring declining

PSUs over other PSUs, thus causing growing PSUs to be

underrepresented.

Keyfitz’s method of maximizing PSU overlap while maintaining the new design probabilities is intuitive. If PSU 1

was actually sampled for the former design, include it for

the new design since the design probability has increased

over the decade from p1.=.36 to p.1=.5. (We say the

conditional probability of choosing PSU 1 for the new

design, given that PSU 1 was sampled for the former

design, is 1.0 or certainty.) Similarly, the design probability

of PSU 2 has increased from p2.=.24 to p.2 =.3, so if it was

actually sampled for the former design then include it for

the new design. (The conditional probability of choosing

PSU 2 for the new design, given that PSU 2 was sampled

for the former design, is 1.0 or certainty.) On the other

hand, the design probability of selection for PSU 3 has

decreased from p3.=.4 to p.3=.2. If PSU 3 was the one

selected for the former design, the probability can be cut

down to size by giving it a 1/2 chance of remaining for the

new design and a 1/2 chance of being replaced. (The

conditional probability of choosing PSU 3 for the new

design, given that PSU 3 was sampled for the former

design, is .5 or 1/2.) The overall probability of PSU 3 being

in the sample for the new design is .4 x 1/2 = .2, which is

exactly the probability required for the new design.

The following tables of conditional and joint probabilities

have been partially completed. (A joint probability pij is the

probability that the ith PSU was sampled for the former

design and the jth PSU for the new design.) The question

marks (?) indicate that if PSU 3 was sampled for the former

design, but not conditionally reselected for the new design,

then one of the other PSUs needs to be selected.

A–1
Conditional probabilities Joint probabilities pij

New PSU j New PSU j

Former PSU i j=1 j=2 j=3 Former PSU i j=1 j=2 j=3

i=1 p1.=.36 1.0 0 0 i=1 p1.=.36 .36 0 0

i=2 p2.=.24 0 1.0 0 i=2 p2.=.24 0 .24 0

i=3 p3.=.4 ? ? .5 i=3 p3.=.4 ? ? .2

p.1=.5 p.2=.3 p.3=.2 p.1=.5 p.2=.3 p.3=.2

diagonal sum = .36 +.24 +.2 = .8

The joint probability table is very easy to construct. The

rows must add to the former design sampling probabilities

and the columns must add to the new design probabilities.

Also, the pij entries must all add to 1.0. The joint probabilities shown are the products of the initial former design

probabilities times the conditional probabilities (diagonal

entries: .36 x 1.0 = .36 for PSU 1; .24 x 1.0 = .24 for PSU

2; .4 x .5 = .2 for PSU 3). For the PSU 1 row, the offdiagonal joint elements are zero; that is, never choose a

different PSU for the new design if PSU 1 was sampled for

the former design. However, in the PSU 1 column something is missing. There is a .36 chance that PSU 1 was

sampled for the former design, then carried over to the new

design, but to give PSU 1 the desired .5 unconditional

probability for the new design, it needs an extra .14 in its

column. Similarly, PSU 2 needs an extra .06 in its column.

The completed tables follow.

Conditional probabilities Joint probabilities pij

New PSU j New PSU j

Former PSU i j=1 j=2 j=3 Former PSU i j=1 j=2 j=3

i=1 p1.=.36 1.0 0 0 i=1 p1.=.36 .36 0 0

i=2 p2.=.24 0 1.0 0 i=2 p2.=.24 0 .24 0

i=3 p3.=.4 .35 .15 .5 i=3 p3.=.4 .14 .06 .2

new p.1=.5 p.2=.3 p.3=.2 p.1= .5 p.2=.3 p.3=.2

diagonal sum = .36+.24 +.2 = .8

The rows and columns of the joint probability table now

add properly. Any conditional probability can be derived by

dividing the corresponding joint probability by the former

design probability of the row (pij/pi.). For example, .35=.14/.4,

.15=.06/.4, and .5=.2/.4 in the PSU 3 row. Any joint

probability can be derived by multiplying the corresponding

joint probability by the former design probability of the row.

Continuing with the PSU 3 row, .14 = .35 x .4, .06 = .15 x

.4, and .2 = .5 x .4.

The diagonal sum of .8 on the joint probability table is

the unconditional probability of including the same PSU in

the sample of both the former and new designs. It is as

large as possible given that additivity to both the former

probabilities (rows) and unconditional new probabilities

must be preserved. That is, the diagonal sum has been

maximized subject to row and column constraints. A mathematical description of the problem is:

maximize i1

3

pii

subject to 

j1

3

pij pi. i1,2,3 and i1

3

pij p .j j1,2,3

This is a special case of the maximizing/minimizing problem that is called a transportation problem. The pij of the

general transportation problem can be any nonnegative

variable, and the pi. and p.j are prespecified constants.

maximize i1

n

j1

m

cij pij

subject to j1

m

pij pi. i1,..,n and i1

n

pij p.j j1,..,m

i1

n

pi. j1

m

p.j

It is this generalized form of the transportation problem

that has been adapted for the CPS. For the 1990s design,

PSUs were redefined. In nonself-representing areas, new

1990s design strata were formed. A new stratum can be

formed from pieces of several already existing strata. Even

a simple example indicates the type of difficulties that may

arise when the stratum structure is changed. Suppose a

stratum is unchanged, except that to one PSU a county has

been added from a previously existing PSU from a different

previously existing stratum. The new design unconditional

probabilities of selection are no more difficult to calculate

than before, but several factors complicate maximizing the

overlap. Now two previously existing strata contribute to

the new stratum. For the unchanged PSUs, there is still a

1-1 matchup between the former design and the new

design, but the changed PSU is linked to two previously

existing PSUs, each of which had a chance of selection.

A–2
Note that the added county had no independent chance of

selection in the former design, but certainly contributed to

the chance of selection of its previously existing PSU. Joint

former/new design probabilities are determined that maximize the overlap between designs, taking into account

such difficulties and maintaining the proper probability

structure. A few numerical examples illustrate how this and

other simple changes in the stratum PSU structure are

dealt with.

ADDING A COUNTY TO A PSU

Suppose a stratum changes between the former and

new designs only by adding a county to one of its three

PSUs. To simplify notation, let the stratum in the former

design consist of PSUs a, b, and cold with probabilities of

selection p(a)=.36, p(b)=.24, and p(cold)=.4. In the new

design, the stratum consists of PSUs a, b, and cnew with

probabilities of selection p(a)=.5, p(b)=.3, and p(cnew)=.2.

In response to a declining population, county cadd has been

added to cold to create cnew. Suppose cadd was part of

former design PSU d, and let p(d)=.2 be the former design

probability of selection for PSU d in its stratum. Also

suppose cadd accounts for one-fourth of the new design

selection size of cnew (cadd has size .05). The Former

Design Probabilities column in the table below gives all of

the former design selection possibilities and their probabilities that sum to 1: either a, b, or cold was sampled; PSU d

may or may not have been sampled. The joint probability

entries are constrained to sum to the probabilities of

selection for both the former design (rows) and the new

design PSUs (columns). The computer programs that solve

the general transportation problem ensure that the maximum sum of joint overlap probabilities is achieved (.77).

The following reasoning can be used to arrive at the

solution.

a only Select PSU a with conditional probability

1.0, since the former design probability

.288 is less than the new probability .5.

Enter .288 as the joint probability.

a and d We want to select PSU a, if possible, since

its relative size (.5) is much larger than the

size of the cadd county of PSU d (.05). Since

the sum of former design probabilities (.288

+ .072) is less than the new design probability .5, PSU a can be selected with

conditional probability 1.0.

b only Select PSU b since .192 is less than the

new probability .3.

b and d Select PSU b since its relative size (.3) is

much larger than the cadd county size (.05),

and .192 + .048 < .3.

cold and d Select PSU cnew. This possibility takes precedence over cold only, since there is more

overlap.

cold only It is advantageous to select cnew, but an

extra joint probability of .12=.2–.08 is all

that can be allowed (the column must add

to .2). The corresponding conditional probability is .375= .12/.32. PSU cold comprises

three-fourths of PSU cnew, and that factor is

entered in parentheses after the joint probability. The entries in the PSU a and b

columns ensure the proper additivity of the

joint probabilities, and the (0) after the joint

probabilities indicate no overlap.

Former design probabilities Conditional probabilities Joint probabilities

New PSU New PSU

a bcnew a bcnew

p(a only) = .36(1-.2) =.288 1.0 0 0 .288 (1) 0 0

p(a and d)= .36(.2) =.072 1.0 0 0 .072 (1) 0 0

p(b only) = .24(1-.2) =.192 0 1.0 0 0 .192 (1) 0

p(b and d)= .24(.2) =.048 0 1.0 0 0 .048 (1) 0

p(cold and d)= .4(.2) =.08 0 0 1.0 0 0 .08 (1)

p(cold only)= .4(1-.2) =.32 .4375 .1875 .375 .14 (0) .06 (0) .12 (3/4)

p(a)= .5 p(b)=.3 p(cnew)=.2 p(a)= .5 p(b)=.3 p(cnew)=.2

overlap sum = .288 +.072 + .192 + .048 + .08 + .12 (3/4) = .77

DROPPING A COUNTY FROM A PSU

Suppose a stratum changes between the former and

new designs only by dropping a county from one of its three

PSUs. Let the stratum in the former design consist of PSUs

a, b, and cold with probabilities of selection p(a)=.36,

p(b)=.24, and p(cold)=.4. In the new design, the stratum

consists of PSUs a, b, and cnew with probabilities of

selection p(a)=.5, p(b)=.3, and p(cnew)=.2. County cdrop has

been dropped from cold to create cnew. Note that dropping

A–3
a county explains part of the former-to-new design decrease

in probability for PSU c, but the solution for maximum

overlap is really the same as for the first example in this

appendix (see the table below). The (1) after the .2 joint

probability entry means that (1) if cold was sampled for the

stratum in the former design and (2) cnew is selected for the

stratum in the new design, then(3) all of cnew overlaps with

the former design.

Former design probabilities Conditional probabilities Joint probabilities

New PSU New PSU

a bcnew a bcnew

p(a)= .36 1.0 0 0 .36 (1) 0 0

p(b)= .24 0 1.0 0 0 .24 (1) 0

p(cold)= .4 .35 .15 .5 .14 (0) .06 (0) .2 (1)

p(a)=.5 p(b)=.3 p(cnew)=.2 p(a)=.5 p(b)=.3 p(cnew)=.2

Overlap sum = .36 +.24 + .2 = .8

DROPPING AND ADDING PSU’S

Suppose one PSU is dropped from a stratum and

another added. Let the stratum in the former design consist

of PSUs a, b, and c with probabilities of selection p(a)=.36,

p(b)=.24, and p(c)=.4. In the new design, the stratum

consists of PSUs b, c, and d (drop a; add d) with probabilities of selection p(b)=.3, p(c)=.2, and p(d)=.5. In the former

design p(d)=.5 in its stratum. The Former Design Probabilities column in the table below gives all of the former design

selection possibilities and their probabilities that sum to 1:

either a, b or c was sampled in the previously existing

stratum; PSU d may or may not have been sampled. The

joint probability entries are constrained to sum to the

probabilities of selection for both the former design (rows)

and the new design PSUs (columns). The computer programs that solve the general transportation problem ensure

that the maximum sum of joint overlap probabilities is

achieved (.82). The following reasoning can be used to

arrive at the solution.

d only Select PSU d with conditional probability

1.0, since the former design probability .18

is less than the new probability .5.

b only Select PSU b with conditional probability

1.0, since the former design probability .12

is less than the new probability .3.

c only Select PSU c with conditional probability

1.0, since the former design probability .2

does not exceed the new probability .2.

d and c Select PSU d with conditional probability

1.0. PSU c cannot be specified since its

joint probability column already adds to the

new probability .2.

d and b Select PSU d with conditional probability

1.0. PSU b could have been specified, but

selecting PSU d provides more overlap with

the former design.

none It is possible (probability .18) that none of

the new stratum PSUs were sampled for

the former design. Selecting PSU b with

conditional probability 1.0 ensures that all

the joint probability columns sum to the new

design PSU probabilities. The (0) after the

joint probability indicates that there is no

overlap.

Former design probabilities Conditional probabilities Joint probabilities

New PSU New PSU

dbc db c

p(d only) = .5(1-.24-.4) =.18 1.0 0 0 .18 (1) 0 0

p(b only)= .24(1-.5) =.12 0 1.0 0 0 .12 (1) 0

p(c only) = .4(1-.5) =.2 0 0 1.0 0 0 .2(1)

p(d and c)= .5(.4) =.2 1.0 0 0 .2 (1) 0 0

p(d and b)= .5(.24) =.12 1.0 0 0 .12 (1) 0 0

p(none)= (1-.5)(1-.24-.4) =.18 0 1.0 0 0 .18 (0) 0

p(d)=.5 p(b)=.3 p(c)=.2 p(d)=.5 p(b)=.3 p(c)=.2

overlap sum = .18 +.12 + .2 + .2 + .12 = .82

A–4
REFERENCES

Causey, B. D., Cox, L.H., and Ernst, L.K. (1985), ‘‘Applications of Transportation Theory to Statistical Problems,’’

Journal of the American Statistical Association, 80,

903-909.

Ernst, L. (1986), ‘‘Maximizing the Overlap Between

Surveys When Information Is Incomplete,’’ European Journal of Operations Research, 27, 192-200.

Ernst, L. (1990), ‘‘Formulas for Approximating Overlap

Probability When Maximizing Overlap,’’ U.S. Census Bureau,

Statistical Research Division, Technical Note Series, SRD

Research Report Number Census/SRD/TN-90/01.

Gunlicks, C. (1992), ‘‘PSU Stratification and Selection

Documentation: Comparison of the Overlap Program and

the Expected Number of 1980 PSUs Selected Without the

Overlap Program in the 1990 Redesign PSU Selection,’’

(S-S90 DC-6), Memorandum to documentation, July 14,

1992.

Keyfitz, N. (1951), ‘‘Sampling With Probabilities Proportional to Size: Adjustment for Changes in Probabilities,’’

Journal of the American Statistical Association, 46,

105-109.

Perkins, W. M. (1970), ‘‘1970 CPS Redesign: Proposed

Method for Deriving Sample PSU Selection Probabilities

Within 1970 NSR Strata,’’ Memorandum to J. Waksberg,

U.S. Census Bureau, August 5, 1970.

Perkins, W. M. (1971), ‘‘1970 CPS Redesign: Illustration

of Computation of PSU Probabilities Within a Given 1970

Stratum,’’ Memorandum to J. Waksberg, U.S. Census

Bureau, February 19, 1971.

Raj, D. (1968), Sampling Theory, New York: McGraw

Hill.

Roebuck, M. (1992), ‘‘1990 Redesign: PSU Selection

Specifications’’ (Document #2.4-R-7), Memorandum to B.

Walter through G. Shapiro and L. Cahoon, September 25,

1992.

A–5
Appendix B.

Sample Preparation Materials

INTRODUCTION

Despite the conversion of CPS to an automated interviewing environment, a number of paper materials are still

in use. These materials include segment folders, listing

sheets, maps, etc. Regional office staff and field representatives use these materials as aids in identifying and

locating selected sample housing units. This appendix

provides illustrations and explanations of these materials

by frame (unit, area, group quarters, and permit). The

information provided here should be used in conjunction

with the information in Chapter 4 of this document.

UNIT FRAME MATERIALS

Segment Folders, (BC-1669 (CPS)) Illustration 1

A field representative receives a segment folder for each

segment assigned for listing, updating, or interviewing.

Segment folders were designed to:

1. Hold all materials needed to complete an assignment.

2. Provide instructions on listing, updating, and sampling.

The segment folder cover provides the following information about a segment:

1. Identifying information about the segment such as the

regional office code, the field primary sampling unit

(PSU), place name, sample designations for the segment, and basic geography.

2. Instructions for listing and updating. Part I of the

segment folder contains ‘‘Field Representative Listing

and Updating Instructions.’’ This information is provided by the regional office and varies by segment

type.

3. Sampling instructions. Part II on the segment folder is

used only for area and group quarters segments. The

regional offices use this section, ‘‘Regional Office

Sampling Instructions,’’ to record Start-with/Take-everys

as described in Chapter 4.

4. Instructions for precanvassing and determining year

built in area segments. Part III on the segment folder is

reserved for stamps that provide the field representatives with information on whether or not to precanvass

and whether or not to determine the year a structure

was built.

5. Helpful information about the segment entered by the

regional office or the field representative in the Remarks

Section.

A segment folder for a unit segment may contain some

or all of the following: Unit/Permit Listing Sheets (Form

11-3), Incomplete Address Locator Actions (Form BC-1718

(ADP)), 1990 Combined Reference File (CRF) listings, and

1990 Census Maps (county or census spotted).

A segment folder for an area segment may contain some

or all of the following: Area Segment Listing Sheets (Form

11-5), Group Quarters Listing Sheets (Form 11-1), Area

Segment Maps, Segment Locator Maps, County Maps,

and Map Legends.

A segment folder for a group quarters segment may

contain some or all of the following: Group Quarters Listing

Sheets (Form 11-1), Incomplete Address Locator Actions

(Form BC-1718 (ADP)), 1990 Census Maps (county or

census spotted), and 1990 Combined Reference File listings.

A segment folder for a permit segment may contain

some or all of the following: Unit/Permit Listing Sheets

(Form 11-3), photocopies of the listed Permit Address List

(PAL) (Form 11-193A), and Permit Sketch Maps (Form

11-187).

Unit/Permit Listing Sheets (Form 11-3)

Illustrations 2–4

Unit/Permit Listing Sheets are provided for all multiunit

addresses in unit segments. For each sample housing unit

at a multiunit address, the field representative receives a

preprinted, computer generated listing. This listing aids the

field representative in locating the sample units and, in

most cases, will eliminate the need to relist the entire

multiunit address.

Listings for multiunit addresses are sorted into two

categories: small and large. The type of multiunit listing is

identified below the listing sheet title. The differences

between these listings are identified in Table B–1.

B–1
Illustration 1. Segment Folder, BC-1669(CPS)

B–2
Table B–1. Listings for Multiunit Addresses

Listing type Expected number of units Listing characteristics Illustration number

Small 2-9 Lists all expected units and could have some

units with missing and/or duplicate unit designations as recorded in the 1990 census

2

Large 10 or more Lists sample units only. On rare occasions, lists

both sample and nonsample units

3

Large (Special Exception) 10 or more No unit designations are listed and the Footnotes

Section instructs the field representative to ‘‘List

all units at the basic address.’’

4

The field representative also receives blank Unit/Permit

Listing Sheets for use when an address needs relisting or

when the field representative finds more units than expected

causing him/her to run out of lines on the preprinted listing

sheet.

Single-unit addresses in unit segments are not listed on

Unit/Permit Listing Sheets.

Incomplete Address Locator Actions

(Form BC-1718(ADP)) Illustration 5

The Incomplete Locator Actions form is used when the

information obtained from the 1990 census is in some way

incomplete (i.e., missing a house number, unit designation,

etc.). The form provides the field representative with additional information that can be used to locate the incomplete

address. The information on the Incomplete Address Locator Actions includes:

1. The address as it appeared in the Combined Reference File and possibly a complete address resulting

from research conducted by Census Bureau staff;

2. A list of additional materials provided to the field

representative to aid in locating the address; and

3. An outline of the actions the field representative should

take to locate the address.

After locating the address, the field representative completes or corrects the basic address on the Incomplete

Address Locator Actions form.

1990 Combined Reference File Listings

For each assigned segment that has at least one

incomplete basic address, the field representative receives

a computer-generated printout of addresses from the 1990

Combined Reference File. Each printout contains one or

more incomplete addresses along with complete addresses

in the same block.

The field representative uses the Combined Reference

File listing by finding the incomplete basic address on the

listing. Then the field representative uses the addresses

listed directly before and after it to physically locate the

incomplete address.

1990 Census Maps (County or Census Spotted)

For each assigned segment that has at least one

incomplete basic address, the field representative receives

a 1990 county or census-spotted map. For each incomplete basic address, the following control numbers are

entered in red at the top of the map: field PSU number,

sample designation, segment number, and serial number.

The map spot of the incomplete address will be circled in

red. The field representative uses the surrounding map

spots to locate the incomplete address.

AREA FRAME MATERIALS

Segment Folder (BC-1669(CPS)) Illustration 1

See the description and illustration in the Unit Frame

Materials above.

Area Segment Listing Sheets (Form 11-5)

Illustration 6

Area Segment Listing Sheets are used to record information about housing units within the assigned block and

to record information on the year a structure was built

(when necessary). A field representative receives several

Area Segment Listing Sheets for a block since the actual

number of housing units in that block is unknown until the

listing is complete. Only one of the listing sheets has the

heading items filled in by the regional office. The heading

items include: regional office code, field PSU, segment

number, and survey acronym.

Group Quarters Listing Sheets (Form 11-1)

Illustration 10

See the description and illustration provided in the

Group Quarters Frame Materials below.

Area Segment Map Illustration 7

An Area Segment Map is a large scale map of the area

segment, but it does not show roads or features outside of

the segment boundaries. Within the segment, the map

B–3
Illustration 2. Unit/Permit Listing Sheet (Unit Segment)

B–4
Illustration 3. Unit/Permit Listing Sheet (Multiunit Structure)

B–5
Illustration 4. Unit/Permit Listing Sheet (Large Special Exception)

B–6
Illustration 5. Incomplete Address Locator Actions, BC-1718 (ADP)

B–7
Illustration 6. Area Segment Listing Sheet

B–8
includes such features as highways, streets, roads, trails,

walkways, railroads, bodies of water, military installations,

and parks. The area segment map is used for:

1. Determining the exact location and boundaries of the

area segment, and

2. Mapspotting the locations of housing units when listing

the area segment.

Segment Locator Map Illustration 8

Segment Locator Maps are used to aid in the location of

the area segment. The map identifies street patterns and

names surrounding the exterior of the area segment. The

location of the area segment is a shaded area, centered on

the segment locator map.

County Map

Field representatives receive county maps for each field

PSU in which they work. The map is divided into blocks or

grids. Each segment locator map is one or more blocks or

grids from the county map.

Map Legend Illustration 9

Map Legends are used to help in the identification of

boundaries and features from the symbols and names

printed on the area segment map and the segment locator

map.

GROUP QUARTERS FRAME MATERIALS

Segment Folder (BC-1669(CPS)) Illustration 1

See the description and illustration in the Unit Frame

Materials above.

Group Quarters Listing Sheets (Form 11-1)

Illustration 10

The Group Quarters Listing Sheet is used to record the

name, type and address of the group quarters, the name

and telephone number of the contact person at the group

quarters and to list the eligible units within the group

quarters.

In group quarters segments, the field representative

receives group quarters listing sheets with certain information preprinted by the computer. This information includes:

group quarters name and address, group quarters type,

group quarters ID number (from the 1990 census), regional

office code, field PSU, segment number, etc.

In area segments, field representatives receive blank

group quarters listing sheets since at the time of the initial

listing, it is not known whether or not the block contains any

group quarters.

Incomplete Address Locator Actions (Form

BC-1718(ADP)) Illustration 3

See the description and illustration in the Unit Frame

Materials above.

1990 Census Maps (County or Census Spotted)

See the description in the Unit Frame Materials above.

1990 Combined Reference File Listings

See the description in the Unit Frame Materials above.

PERMIT FRAME MATERIALS

Segment Folder (BC-1699(CPS)) Illustration 1

See the description and illustration in the Unit Frame

Materials above.

Unit/Permit Listing Sheets (Form 11-3)

Illustration 11

For each permit address in sample for the segment, the

field representative receives a Unit/Permit Listing Sheet

with heading information, sample designations, and serial

numbers preprinted on it. The field representative also

receives blank Unit/Permit Listing Sheets in case there are

not enough lines on the preprinted listing sheet(s) to list all

units at a multiunit address.

Permit Address List (Form 11-193A)

Illustration 12

The permit address list (PAL) form is computer generated for each of the Building Permit Offices in sample. It is

used to obtain necessary address information for individual

permits issued. The PALs for Survey of Construction (SOC)

building permit offices and non-SOC building permit offices

are identical with one exception: For SOC building permit

offices, the sample permit numbers, the date issued, and

the number of housing units for each sample permit are

preprinted on the PALs. The field representatives obtain

the address information for these sample permits only. For

B–9
Illustration 7. Area Segment Map

B–10
Illustration 8. Segment Locator Map

B–11
Illustration 9. Map Legend Current Population Survey

B–12
Illustration 10. Group Quarters Listing Sheet

B–13
Illustration 11. Unit/Permit Listing Sheet

B–14
Illustration 12. Permit Address List

B–15
Illustration 13. Permit Sketch Map

B–16
non-SOC building permit offices, the PALs do not contain

any preprinted permit numbers. Field representatives list

all residential permits issued by the building permit office

for a specific month.

Permit Sketch Map (Form 11-187) Illustration 13

When completing a PAL form, if the address given on a

building permit is incomplete, the field representative attempts

to obtain a complete address. If a complete address cannot

be obtained, the field representative visits the new construction site and draws a Permit Sketch Map. The map

shows the location of the construction site, streets in the

vicinity of the site, and the directions and distance from the

construction site to the nearest town.

B–17
Appendix C.

Maintaining the Desired Sample Size

INTRODUCTION

The Current Population Survey (CPS) sample is continually updated to include housing units built after the most

recent census. If the same sampling rates were used

throughout the decade, the growth of the U.S. housing

inventory would lead to increases in the CPS sample size

and, consequently, to increases in cost. To avoid exceeding

the budget, the sampling rate is periodically reduced to

maintain the desired sample size. Referred to as maintenance reductions, these changes in the sampling rate are

implemented in a way that retains the desired set of

reliability requirements.

These maintenance reductions are different from changes

to the base CPS sample size resulting from modifications

in the CPS funding levels. The methodology for designing

and implementing this type of sample size change is

generally dictated by new requirements specified by the

Bureau of Labor Statistics (BLS). For example, the sample

reduction implemented in January 1996 was due to a

reduction in CPS funding and new design requirements

were specified (see Appendix H). As a result of the January

1996 reduction, however, any impending maintenance

reduction to the 1990 design sample because of sample

growth was preempted.

MAINTENANCE REDUCTIONS

Developing the Reduction Plan

The CPS sample size for the United States is projected

forward for about a year using linear regression based on

previous CPS monthly sample sizes. The future CPS

sample size must be predicted because CPS maintenance

reductions are gradually introduced over 16 months and

operational lead time is needed to prevent dropped cases

from being interviewed.

The growth is examined in all states and major substate

areas to determine whether it is uniform or not. The states

with faster growth are candidates for a possible maintenance reduction. The remaining sample must be sufficient

to maintain the individual state and national reliability

requirements. Generally, the sample in a state is reduced

by the same proportion in all frames in all primary sampling

units (PSUs) to maintain the self-weighting nature of the

state design.

Implementing the Reduction Plan

Reduction groups. The CPS sample size is reduced by

deleting one or more subsamples of ultimate sampling

units (USUs) from both the old construction and permit

frames. The original sample of USUs is partitioned into 101

subsamples called reduction groups; each is representative of the overall sample. The decision to use 101 subsamples is somewhat arbitrary. A useful attribute of the

number used is it is prime to the number of rotation groups

(eight) so that reductions have a uniform effect across

rotations. A number larger than 101 would allow greater

flexibility in pinpointing proportions of sample to reduce.

However, a large number of reduction groups can lead to

imbalances in sample cut distribution across PSUs since

small PSUs may not have enough sample to have all

reduction groups represented. That is, when there are a

large number of reduction groups randomly assigned, a

smaller PSU may or may not experience reductions and

quite possibly it may undergo too much reduction in the

PSU.

All USUs in a hit string have the same reduction group

number (see Chapter 3). For the unit, area, and group

quarters (GQ) frames, hit strings are sorted and then

sequentially assigned a reduction group code from 1

through 101. The sort sequence is:

1. State or substate.

2. Metropolitan statistical area/nonmetropolitan statistical area status.

3. Self-representing/nonself-representing status.

4. Stratification PSU.

5. Final hit number, which defines the original order of

selection.

For the permit frame, a random start is generated for each

stratification PSU and permit frame hits are assigned a

reduction group code from 1 through 101 following a

specific, nonsequential pattern. This method of assigning

reduction group code is used to improve balancing of

reduction groups because of small permit sample sizes in

some PSUs and the uncertainty of which PSUs will actually

have permit samples over the life of the design. The sort

sequence is:

1. Stratification PSU.

2. Final hit number.

C–1
The state or national sample can be reduced by deleting

USUs from both the old construction and permit frames in

one or more reduction groups. If there are k reduction

groups in sample, the sample may be reduced by 1/k by

deleting one of k reduction groups. For the first reduction

applied to redesigned samples, each reduction group

represents roughly 1 percent of the sample. Reduction

group numbers are chosen for deletion in a specific sequence

designed to maintain the nature of the systematic sample

to the extent possible.

For example, suppose a state has an overall state

sampling interval of 500 at the start of the 1990 design.

Suppose the original selection probability of 1 in 500 is

modified by deleting 5 of 101 reduction groups. The

resulting overall state sampling interval (SI) is

SI  500 x

101

101 5

 526.0417

This makes the resulting overall selection probability in the

state 1 in 526.0417. In the subsequent maintenance reduction, the state has 96 reduction groups remaining. A

reduction of 1 in 96 can be accomplished by deleting 1 of

the remaining 96 reduction groups.

The resulting overall state sampling interval is the new

basic weight for the remaining uncut sample.

Introducing the reduction. A maintenance reduction is

implemented only when a new sample designation is

introduced and is gradually phased in with each incoming

rotation group to minimize the effect on survey estimates

and reliability and to prevent sudden changes to interviewer workloads. The basic weight applied to each incoming rotation group reflects the reduction. Once this basic

weight is assigned, it does not change until future sample

changes are made. In all, it takes 16 months for a maintenance sample reduction and new basic weights to be fully

reflected in all eight rotation groups interviewed for a

particular month. During the phase-in period, rotation groups

have different basic weights; consequently, the average

weight over all eight rotation groups changes each month.

After the phase-in period, all eight rotation groups have the

same basic weight.

Maintenance Reductions and GVFs

National generalized variance parameters are not updated

for maintenance reductions because the purpose of these

reductions is to maintain the sample size. However, the

national generalized parameters should be updated to

reflect any changes in sample size. For example, the

parameters were updated for the January 1996 reduction

since survey design parameters changed. (See Chapter 14

for more details.)

C– 2
Appendix D.

Derivation of Independent Population Controls

INTRODUCTION

Each month, for the purpose of the iterative, secondstage weighting of the Current Population Survey (CPS)

data, independent projections of the eligible population are

produced by the Census Bureau’s Population Division.

While the CPS provides the fundamental reason for the

existence of these monthly projections, other survey-based

programs sponsored by the Bureau of the Census, the

Bureau of Labor Statistics, the National Center for Health

Statistics, and other agencies use these independent projections to calibrate surveys. These projections consist of

the adjusted civilian noninstitutional population of the United

States, distributed by demographic characteristics in three

ways: (1) age, sex, and race, (2) age, sex, and Hispanic1

origin, and (3) state of residence, restricted to the population 16 years of age and over. They are produced in

association with the Population Division’s population estimates and projections programs, which provide published

estimates and long-term projections of the population of

the United States, the 50 states and District of Columbia,

the counties, and subcounty jurisdictions.

Organization of This Appendix

The CPS population controls, like other population estimates and projections produced by the Census Bureau,

are based on a demographic framework of population

accounting. Under this framework, time series of population estimates and projections are anchored by decennial

census enumerations, with populations for dates between

two previous censuses, since the last census, or in the

future derived by the estimation or projection of population

change. The method by which population change is estimated depends on the defining demographic and geographic characteristics of the population. The issue of

adjustment for net underenumeration in the census is

handled outside of this demographic framework, and is

applied to the CPS control series as a final step in its

production.

This appendix seeks first to systematically present this

framework, defining terminology and concepts, then to

describe data sources and their application within the

framework. The first subsection is operational and is devoted

to the organization of the chapter and a glossary of terms.

The second and third subsections deal with two broad

conceptual definitions. The second subsection distinguishes

population estimates from population projections and describes

how monthly population controls for surveys ‘‘fit’’ into this

distinction. The third subsection defines the ‘‘CPS control

universe,’’ the set of inclusion and classification rules

specifying the population to be projected for the purpose of

weighting CPS data. The fourth subsection, ‘‘Calculation of

Population Projections for the CPS Control Universe,’’

comprises the bulk of the appendix. It provides the mathematical framework and data sources for updating total

population from a census date via the demographic components of change and how each of the requisite inputs to

the mathematical framework is measured. This is presented separately for the total population of the United

States, its distribution by demographic characteristics, and

its distribution by state of residence. The fifth subsection,

‘‘Adjustment of CPS Controls for Net Underenumeration in

the 1990 Census,’’ describes the application of data from

the 1990 Post-Enumeration Survey (PES) to include an

estimate of net underenumeration in the 1990 census. The

final two sections are, once again, operational. A subsection, ‘‘The Monthly and Annual Revision Process for Independent Population Controls’’ describes the protocols for

incorporating new information in the series through revision. We conclude with a brief section, ‘‘Procedural Revisions,’’ containing an overview of various technical problems for which solutions are being sought.

Terminology Used in This Appendix

The following is an alphabetical list of terms used in this

appendix, essential to understanding the derivation of

census-based population controls for surveys, but not

necessarily prevalent in the literature on survey methodology.

An adjusted (as opposed to unadjusted) population

estimate or projection includes an allowance for net undercoverage (underenumeration) in a census. In this appendix

(as in other Census Bureau publications), the term, when

applied to series based on the 1990 census, refers to

incorporation of the results of the 1990 Post-Enumeration

Survey (PES).

The population base, (or base population) for a population estimate or projection is the population count or

estimate to which some measurement or assumption of

population change is added to yield the population estimate or projection. 1

Hispanics may be of any race.

D–1
A census-level population estimate or projection is one

that can be linked to a census count through an estimate or

projection of population change; specifically, it does not

include an adjustment for underenumeration in the census.

The civilian population is the portion of the resident

population not in the active-duty military. Active-duty military, used in this context, refers to persons defined to be in

active duty by one of the five branches of the Armed

Forces, as well as persons in the National Guard or the

reserves actively participating in certain training programs.

Components of population change are any subdivisions

of the numerical population change over a time interval.

The demographic components often cited in the literature

consist of births, deaths, and net migration. In this appendix, reference is also made to changes in the institutional

and active-duty Armed Forces populations, which affect the

CPS control universe.

The CPS control universe is characterized by three

attributes: (1) restriction to the civilian noninstitutional

population, (2) modification of census data by age, race,

and sex (MARS), and (3) adjustment for net census

underenumeration. Each of the three defining concepts

appears separately in this glossary.

The Demographic Analysis Population (‘‘DA’’ population)

is a distribution of population by characteristics—in the

present case age, sex, and race—developed by observation of births, deaths by age, and international migration by

age, over a period of time for each sex and race category.

It is derived independent of census data. Thus, the number

of persons x years of age of a given sex and race is

determined by the number of births x years before the

reference date, as well as the number of deaths and net

migration of persons born the same year during the x-year

interval from birth date to reference date. In this text, we

also refer to the ‘‘DA/MARS population.’’ This is a hybrid

distribution based on demographic analysis for age, sex,

and three race groups, but on the census for Hispanic

origin detail within these groups. ‘‘MARS’’ refers to the fact

that the census detail is based on modified age and race,

as defined elsewhere in this glossary.

Emigration is the permanent departure of a person from

a country of residence, in this case the United States. In the

present context, it refers to the departure of persons legally

resident in the United States, but not confined to legally

permanent residents (immigrants). Departures of undocumented residents are excluded, because they are included

in the definition of net undocumented migration (elsewhere

in this glossary).

Population estimates are population figures that do not

arise directly from a census or count, but can be determined from available data (e.g., administrative); hence,

they are not based on assumptions or modeling. Population estimates discussed in this appendix stipulate an

enumerated base population, coupled with estimates of

population change from the date of the base population to

the date of the estimate.

Immigration is the acquisition, by a foreign national, of

legal permanent resident status in the United States.

Hence, an immigrant may or may not reside in the United

States prior to immigration. This definition is consistent with

the parlance of the Immigration and Naturalization Service

(INS), but differs from popular usage, which associates

immigration with an actual change of residence.

The institutional population refers to a population universe consisting of inmates of CPS-defined institutions,

such as prisons, nursing homes, juvenile detention facilities, or residential mental hospitals.

Internal consistency of a population time series occurs

when the difference between any two population estimates

in the series can be construed as population change

between the reference dates of the two populations estimates.

International migration is generally the change of a

person’s residence from one country to another. In the

present context, this concept includes change of residence

into the United States by non-U.S. citizens and by previous

residents of Puerto Rico or the U.S. outlying areas, as well

as the change of residence out of the United States by

persons intending to live permanently abroad, in Puerto

Rico, or the outlying areas.

Legal permanent residents are persons whose right to

reside in the United States is legally defined either by

immigration or by U.S. citizenship. In the present context,

the term generally refers to noncitizen immigrants.

Modified age, race, and sex, abbreviated MARS, describes

the census population after the definition of age and race

has been aligned with other administrative sources (or with

OMB Directive 15, in the case of race).

The natural increase of a population over a time interval

is the number of births minus the number of deaths during

the interval.

The Post-Enumeration Survey is a survey of households

conducted after the 1990 population census in a sample of

blocks to estimate the net underenumeration in the 1990

census.

Population projections are population figures relying on

modeled or assumed values for some or all of their

components, therefore not entirely calculated from actual

data. Projections discussed in this appendix stipulate a

base population that may be an estimate or count, and

components of population change from the reference date

of the base population to the reference date of the projection.

The reference date of an estimate or projection is the

date to which the population figure applies. The CPS

control reference date, in particular, is the first day of the

month in which the CPS data are collected.

The resident population of the United States is the

population usually resident in the 50 states and District of

Columbia. For the census date, this population matches

the total population in census decennial publications, although

D–2
applications to the CPS series beginning in 1995 include

some count resolution corrections to the 1990 census not

included in original census publications.

Undocumented migration (more precisely, net undocumented migration) is the net increase of population brought

about by the residential migration of persons in and out of

the country who have no legal basis for residence in the

United States.

A population universe is a set of rules or criteria defining

inclusion and exclusion of persons from a population, as

well as rules of classification for specified geographic or

demographic characteristics within the population. Examples

include the resident population universe and the civilian

population universe, both of which are defined elsewhere

in this glossary. Frequent reference is made to the ‘‘CPS

control universe,’’ defined separately in this glossary.

CPS Population Controls: Estimates or

Projections?

Throughout this appendix, the independent population

controls for the CPS will be cited as population projections.

Throughout much of the scientific literature on population,

demographers take care to distinguish the concepts of

‘‘estimate’’ and ‘‘projection’’; yet, the CPS population controls lie perilously close to a somewhat ragged line of

distinction. Generally, population estimates relate to past

dates, and in order to be called ‘‘estimates,’’ must be

supported by a reasonably complete data series. If the

estimating procedure involves the computation of population change from a base date to a reference date, the

inputs to the computation must have a solid basis in data.

Projections, on the other hand, allow the replacement of

unavailable data with assumptions.

The reference date for CPS population controls is

indeed in the past, relative to their date of production.

However, the past is so recent—3 to 4 weeks prior to

production—that for all intents and purposes, CPS controls

are projections. No data relating to population change are

available for the month prior to the reference date; very

little data are available for 3 to 4 months prior to the

reference date; no data for state-level geography are

available past July 1 of the year prior to the current year.

Hence, we refer to CPS controls as projections, despite

their frequent description as estimates in the literature.

Be this as it may, CPS controls are founded primarily on

a population census and on administrative data. Analysis of

coverage rates has shown that even without adjustment for

underenumeration in the controls, the lowest coverage

rates of the CPS often coincide with those age-sex-raceHispanic segments of the population having the lowest

coverage in the census. Therefore, the contribution of

independent population controls to the weighting of CPS

data is highly productive. This holds, despite the use of

modeling for missing data.

THE POPULATION UNIVERSE FOR CPS

CONTROLS

In the concept of ‘‘population universe,’’ as used in this

appendix, are not only the rules specifying what persons

are included in the population under consideration, but also

the rules specifying their geographic locations and their

relevant characteristics, such as age, sex, race, and Hispanic origin. In this section we consider three population

universes; the resident population universe defined by the

1990 census, the resident population universe used for

official population estimates, and the CPS control universe

that relates directly to the calculation of CPS population

controls. These three universes are distinct from one

another; each one is derived from the one that precedes it;

hence, the necessity of considering all three.

The primacy of the decennial census in the definition of

these three universes is a consequence of its importance

within the Federal statistical system. This, in turn, is a result

of the extensive detail that it provides on the geographic

distribution and characteristics of the U.S. population, as

well as the legitimacy accorded it by the U.S. Constitution.

Later in this appendix, we cite another population universe—the

Demographic Analysis population—that may be technically

more appropriate as a base for estimates and projections

than the 1990 census in some regards, mainly in the

continuity of the age distribution and the conformity of

racial categories with other administrative data sources.

Because the census defines the universe for estimates, the

use of the DA population in the process is intended to

ensure the continuity of census-related biases over time in

the estimates, not to eliminate them.

The universe defined by the 1990 census is the U.S.

resident population, including persons resident in the 50

states and the District of Columbia. This definition excludes

residents of the Commonwealth of Puerto Rico, and residents of the outlying areas under U.S. sovereignty or

jurisdiction (principally American Samoa, Guam, Virgin

Islands of the United States, and Commonwealth of the

Northern Mariana Islands). The definition of residence

conforms to the criterion used in the census, which defines

a resident of a specified area as a person ‘‘usually resident’’

in the area. For the most part, ‘‘usual residence’’ is defined

subjectively by the census respondent; it is not defined by

de facto presence in a particular house or dwelling, nor is

it defined by any de jure (legal) basis for a respondent’s

presence. There are two exceptions to the subjective

character of the residence definition.

1. Persons living in military barracks, prisons, and some

other types of residential group-quarters facilities are

generally reported by the administration of their facility,

and their residence is generally the location of the

facility. Naval personnel aboard ships reside at the

home port of the ship, unless the ship is deployed to

the overseas fleets, in which case they are not U.S.

residents. Exceptions may occur in the case of Armed

D–3
Forces personnel who have an official duty location

different from the location of their barracks, in which

case their place of residence is their duty location.

2. Students residing in dormitories report their own residences, but are instructed on the census form to report

their dormitories, rather than their parental homes, as

their residences.

The universe of interest to the official estimates of

population that the Census Bureau produces is the resident population of the United States, as it would be

counted by the last census (1990) if this census had been

held on the reference date of the estimates. However, this

universe is distinct from the census universe in two regards,

neither of which affects the total population.

1. The estimates differ from the census in their definition

of race, with categories redefined to be consistent with

other administrative-data sources. Specifically, persons enumerated in the 1990 census who gave responses

to the census race question not codable to White,

Black, American Indian, Eskimo, Aleut, or any of

several Asian and Pacific Islander categories were

assigned a race within one of these categories. Most

of these persons (9.8 million nationally) were persons

of Hispanic origin who gave a Hispanic origin response

to the race question. Publications from the 1990

census do not incorporate this modification, while all

official population estimates incorporate it.

2. Respondents’ ages are modified to compensate for

the aging of census respondents from the census date

(April 1, 1990) to the actual date that the census form

was completed. This modification was necessitated by

the fact that no printed instruction on the census form

required respondents to state their age as of the

census date. They therefore tended to state their age

at the time of completion of the form, or possibly their

next anticipated birthday. This modification is not included

in census publications, which tabulate respondents’

age as stated. It is applied to all population estimates

and projections, including controls for the CPS.

The universe underlying the CPS sample is confined to

the civilian noninstitutional population. Thus, independent

population controls are sought for this population, which

defines the CPS control universe. The previously mentioned alternative definitions of race and age, associated

with the universe for official estimates, are carried over to

the CPS control universe. Four additional changes are

incorporated, which distinguish the CPS control universe

from the universe for official estimates.

1. The CPS control universe excludes active-duty Armed

Forces personnel, including those stationed within the

United States, while these are included in the resident

population. ‘‘Active duty’’ is taken to refer to personnel

reported in military strength statistics of the Departments of Defense (Army, Navy, Marines, and Air

Force) and Transportation (Coast Guard), including

reserve forces on 3- and 6-months’ active duty for

training, National Guard reserve forces on active duty

for 4 months or more, and students at military academies. Reserve forces not active by this definition are

included in the CPS control universe, if they meet the

other inclusion criteria.

2. The CPS control universe excludes persons residing

in institutions, such as nursing homes, correctional

facilities, juvenile detention facilities, and long-term

mental health care facilities. The resident population,

on the other hand, includes the institutional population.

3. The CPS control universe, like the resident population

base for population estimates, includes students residing in dormitories. Unlike the population estimates

base, however, it accepts as state of residence a

family home address within the United States, in

preference to the address of the dormitory. This affects

estimation procedures at the state level, but not at the

national level.

4. An added difference between the census official

estimates and the CPS population controls after January 1, 1994, is that the former do not include adjustment for undercoverage in the 1990 census based on

the Post-Enumeration Survey (PES) while the latter

do. Details of this adjustment are given later in this

appendix.2

CALCULATION OF POPULATION PROJECTIONS

FOR THE CPS UNIVERSE

The present section is concerned with the methodology

for computing population projections for the CPS control

universe used in the actual calibration of the survey. Three

subsections are devoted to the calculation of (i) the total

population, (ii) its distribution by age, sex, race, and

Hispanic origin, and (iii) its distribution by state of residence.

Total Population

Projections of the population to the CPS control reference date (the first of each month) are determined by a

base population (either estimated or enumerated) and a

2

Whether the presence of an adjustment for census undercount

implies an actual change in universe, as opposed to the correction of a

measurement problem, is a legitimate subject for dispute. In the context of

the census itself, adjustment might be viewed as a measurement issue

relative to the population of a ‘‘true’’ universe of U.S. residents. It is treated

here as a technical attribute of the universe, since unadjusted population

estimates are specifically adapted to remain consistent with the census in

regard to coverage. Hence ‘‘census-level’’ and ‘‘adjusted’’ define distinct

population universes.

D–4
projection of population change. The latter is generally an

amalgam of components measured from administrative

data and components determined by projection. The ‘‘balancing equation of population change’’ used to produce

estimates and projections of the U.S. population states the

population at some reference date as the sum of the base

population and the net of various components of population

change. The components of change are the sources of

increase or decrease in the population from the reference

date of the base population to the date of the estimate. The

exact specification of the balancing equation depends on

the population universe, and the extent to which components of population change are disaggregated. For the

resident population universe, the equation in its simplest

form is given by

Rt1  Rt0 B  D NM (1)

where:

Rt1 = Resident population at time t1 (the reference

date),

Rt0 = Resident population at time t0 (the base date),

B = births to U.S. resident women, from time t0 to time

t1,

D = deaths of U.S. residents, from time t0 to time t1,

NM = net migration to the United States (migration into

the United States minus migration out of the

United States) from time t0 to time t1.

It is essential that the components of change in a

balancing equation represent the same population universe as the base population. If we derive the current

population controls for the CPS independently, from a base

population in the CPS universe, civilian noninstitutional

deaths would replace resident deaths, ‘‘net migration’’

would be confined to civilian migrants, but would incorporate net recruits to the Armed Forces and net admissions to

the institutional population. The equation would thus appear

as follows:

CNPt1  CNPt0 B  CND (NCM  NRAF  NEI) (2)

where:

CNPt1 = civilian noninstitutional population, time t1,

CNPt0 = civilian noninstitutional population, time t0,

B = births to the U.S. resident population, time t0

to time t1,

CND = deaths of the civilian noninstitutional population, time t0 to time t1,

NCM = net civilian migration, time t0 to time t1,

NRAF = net recruits (inductions minus discharges) to

the U.S. Armed Forces from the domestic

civilian population, from time t0 to time t1,

NEI = net admissions (enrollments minus discharges)

to institutions, from time t0 to time t1.

In the actual estimation process, it is not necessary to

directly measure the variables CND, NRAF, and NEI in the

above equation. Rather, they are derived from the following

equations:

CND  D  DRAF  DI (3)

where D = deaths of the resident population, DRAF =

deaths of the resident Armed Forces, and DI = deaths of

inmates of institutions (the institutional population)

NRAF  WWAFt1  WWAFt0 DRAF DAFO  NRO (4)

where WWAFt1 and WWAFt0 represent the U.S. Armed

Forces worldwide at times t1 and t0, DRAF represents

deaths of the Armed Forces residing in the United States

during the interval, DAFO represents deaths of the Armed

Forces residing overseas, and NRO represents net recruits

(inductions minus discharges) to the Armed Forces from

the overseas civilian population

NEI  CIPt1  CIPt0 DI (5)

where CIPt1 and CIPt0 represent the civilian institutional

population at times t1 and time t0, and DI represents

deaths of inmates of institutions during the interval. The

appearance of DI in both equations (3) and (5) with

opposite sign, and DRAF in equations (3) and (4) with

opposite sign, ensures that they will cancel each other

when applied to equation (2). This fact obviates the need to

measure institutional inmate deaths or deaths of the Armed

Forces residing in the United States.

A further disaggregation to facilitate the description of

how the components on the right side of equation (2) are

measured as follows:

NCM  IM CCM (6)

where IM = net international migration, and CCM = net

movement of civilian citizens to the United States from

abroad, in the time interval (t0,t1).

At this point, we restate equation (2) to incorporate

equations (3), (4), (5), and (6), and simplify. The resulting

equation, which follows, is the operational equation for

population change in the civilian noninstitutional population

universe.

CNPt1  CNPt0 B  D IM CCM 

WWAFt1  WWAFt0 DAFO  NRO  CIPt1  CIPt0 (7)

Equation (7) identifies all the components that must be

separately measured or projected to update the total

civilian noninstitutional population from a base date to a

later reference date.

Aside from forming the procedural basis for all estimates

and projections of total population (in whatever universe),

balancing equations (1), (2), and (7) also define a recursive

concept of internal consistency of a population series

within a population universe. We consider a time series of

population estimates or projections to be internally consistent if any population figure in the series can be derived

D–5
from any earlier population figure as the sum of the earlier

population and the components of population change for

the interval between the two reference dates.

Measuring the Components of the Balancing

Equation

Balancing equations such as equation (7) requires a

base population, whether estimated or enumerated, and

information on the various components of population change.

Because a principal objective of the population estimating

procedure is to uphold the integrity of the population

universe, the various inputs to the balancing equation need

to agree as much as possible with respect to geographic

scope, treatment of underenumeration of population or

underregistration of events, and residential definition, and

this is a major aspect of the way they are measured. A

description of the individual inputs to equation (7), which

applies to the CPS control universe, and their data sources

follows.

The census base population (CNPt0). While any base

population (CNPt0, in equation (7)), whether a count or an

estimate, can give rise to estimates and projections for

later dates, the original base population for all unadjusted

postcensal estimates and projections is the enumerated

population from the last census. Although survey controls

are currently adjusted for underenumeration in the 1990

census, the census population is itself not adjusted for

underenumeration in the census; rather, an adjustment

difference is added in a later step. After the 2000 census, it

is expected that official estimates will be adjusted for

undercoverage, so that the adjustment can be incorporated

in the base population. However, the census base population does include minor revisions arising from count resolution corrections. As these corrections arise entirely from

the tabulation of data from the enumerated population, they

are unrelated to adjustment for underenumeration. As of

the end of 1994, count resolution corrections incorporated

in the estimates base population amounted to a gain of

8,418 persons from the originally published count.

In order to apply equation (7), which is derived from the

balancing equation for the civilian noninstitutional population, the resident base population enumerated by the

census must be transformed into a base population consistent with this universe. The transformation is given as

follows:

CNP0  R0  RAF0  CIP0 (8)

where R0 = the enumerated resident population, RAF0 =

the Armed Forces resident in the United States on the

census date, and CIP0 = the civilian institutional population

residing in the United States on the census date. This is

consistent with previous notation, but with the stipulation

that t0 = 0, since the base date is the census date, the

starting point of the series. The transformation can also be

used to adapt any postcensal estimate of resident population to the CPS universe for use as a population base in

equation (7). In practice, this is what occurs when the

monthly CPS population controls are produced.

Adjustment for underenumeration, while essential to the

CPS universe, is handled independently of the process of

producing projections. It is added after the population is

updated. Hence, adjustment will be discussed later in this

appendix.

Births and deaths (B, D). In estimating total births and

deaths of the resident population (B and D, in equation (7)),

we assume the population universe for vital statistics to

match the population universe for the census. If we define

the vital statistics universe to be the population subject to

the natural risk of giving birth or dying, and having the

event recorded by vital registration systems, the assumption implies the match of this universe with the census-level

resident population universe. We relax this assumption in

the estimation of some characteristic detail, and this will be

discussed later in the appendix.

The numbers of births and deaths of U.S. residents are

supplied by the National Center for Health Statistics (NCHS).

These are based on reports to NCHS from individual state

and local registries; the fundamental unit of reporting is the

individual birth or death certificate. For years in which

reporting is considered final by NCHS, the birth and death

statistics are considered final; these generally cover the

period from the census until the end of the calendar year 3

years prior to the year of the CPS series. For example, the

last available final birth and death statistics available in

time for the 1998 CPS control series were for calendar year

1995. Final birth and death data are summarized in NCHS

publications (see Ventura et al., 1997a, Anderson et al.,

1997). Monthly births and deaths for calendar year(s) up to

2 years before the CPS reference dates (e.g., 1996 for

1998 controls) are based on provisional estimates by

NCHS (Ventura et al., 1997b). For the year before the CPS

reference date through the last month before the CPS

reference date, births and deaths are projected based on

the population by age according to current estimates and

age-specific rates of fertility and mortality and seasonal

distributions of births and deaths observed in the preceding

year.

Various concerns exist relative to the consistency of the

NCHS vital statistics universe with the census resident

universe and the CPS control universe derived from it.

1. Births and deaths can be missed entirely by the

registration system. In the accounting of the national

population without adjustment for underenumeration,

underregistration of vital events is tacitly assumed to

match underenumeration in the census. While we

have found no formal investigation of the underregistration of deaths, we surmise that it is likely to be far

less than the rate of underenumeration in the census

because of the requisite role of local public agencies in

D–6
the processing of bodies for cremation or burial. A test

of the completeness of birth registration conducted by

the Census Bureau in 1964-1968, applied to the

distribution of 1992 births by race and whether born in

hospital, implied an underregistration of 0.7 percent,

far less than most estimates of underenumeration in

recent censuses, but probably greater than the underregistration of deaths.

2. Birth and death statistics obtained from NCHS exclude

those events occurring to U.S. residents while outside

the United States. The resulting slight downward bias

in the numbers of births and deaths would be partially

compensatory.

These two sources of bias, which are assumed to be

minor, affect the accounting of total population. Other, more

serious comparability problems exist in the consistency of

NCHS vital statistics with the CPS control universe, with

respect to race, Hispanic origin, and place of residence

within the United States. These will be discussed in later

sections of this appendix.

International migration (IM). The objective of the current

procedures to estimate international migration (IM, in equation (7)), is to transform the number of legal immigrants—

for whom records of migration are available—into an

estimate of the net of persons who become ‘‘usual residents’’ of the United States according to the census

residency definition, and those who cease to be usual

residents because they move out of the United States. This

objective is met primarily by the availability of administrative data from Federal agencies. However, it is beset by

five fundamental problems.

1. A substantial number of foreign-born persons increase

the resident population each year, either by overstaying legal, nonimmigrant visas or by entering the country without inspection. Those that have not returned to

their country of origin (either voluntarily or by force of

law) by a CPS date are eligible for interview by the

CPS and must be included in the CPS controls.

2. Because the geographic limits of the census universe

do not include Puerto Rico or outlying areas under

U.S. jurisdiction, persons who enter or leave the

country from or to these areas must be treated as

international migrants. However, migrants to or from

these areas are generally U.S. citizens and need not

produce any administrative record of their moves.

3. Legal residents of the United States departing for

residence abroad are not required to provide any

administrative record of departure. Hence, there exist

no current data on emigration.

4. Some persons who enter the country legally, but

temporarily (e.g., foreign students, scholars, and members of certain professions), are eligible for enumeration in the census as usual residents and should be

included in population estimates. While administrative

records exist for the arrival of such persons, there is no

adequate source of data for their departures or their

lengths of stay.

5. The census definition of usual residence is subjective

and depends on the interpretation of census respondents. Immigration data generally assume a legal,

rather than a subjective concept of residence (e.g.,

legal permanent resident, nonresident alien) that does

not match the census concept. Thus, an alien could be

legally resident in the United States, but perceive their

usual residence to be elsewhere when responding to a

census or survey.

For purposes of population accounting, migration to the

United States is assumed to include persons arriving in the

United States who, based on their arrival status, appear to

be new census-defined residents, meaning they would

report their usual residence in a census as being inside the

United States. The accounting should thus include all legal

permanent residents, refugees, and undocumented migrants

that do not return and are not deported. It should also

include arrivals (such as foreign students and foreign

scholars) that generally assume steady residence in the

United States for the duration of their stay and would,

therefore, be enumerated in a census. Tourists and business travelers from abroad are assumed not to be U.S.

residents and are, therefore, excluded from the international migration tally.

The core data source for the estimation of international

migration is the Immigration and Naturalization Service

(INS) public use immigrant file, which is issued yearly for

immigrants (year defined by the Federal fiscal year). This

file contains records for citizens of foreign countries immigrating or establishing legal permanent residence in the

United States and accounts for the majority of international

migration to the United States. Data from the immigrant file

are summarized by the INS in its statistical yearbooks (for

example, U.S. Immigration and Naturalization Service,

1997). However, the file contains no information on emigration, undocumented immigration, or nonimmigrant moves

into the country. Moreover, immigrants do not always

change residence at time of immigration; they may in fact

already reside in the United States. INS immigrant data

are, therefore, partitioned into four categories based on

INS class of admission and each category is treated

differently.

The first category consists of all persons classed as

‘‘new arrivals,’’ meaning their last legal entry into the U.S.

coincided with their immigration or acquisition of legal

permanent resident status. These persons are simply

included in the migration component for the month and

year of their immigration.

The second category consists of persons already resident in the United States who are adjusting their status

from nonimmigrant (e.g., temporary resident) to immigrant

D–7
and who are not refugees. They include a large component

of persons who are nonimmigrant spouses or children of

U.S. citizens or legal permanent residents. We cannot

account for these persons at their time of arrival through

current data. We could account for these persons retrospectively at the earlier year of their arrival from INS

records. However, this would result in a serious downward

bias for the most recent years, since we would miss similar

persons, currently entering the United States, who will

immigrate in future years. For this reason, we accept the

number of adjustees in this category as a proxy for the

number of future adjustees physically entering the country

in the current year. This assumption is robust over time

provided the number of persons in this category remains

stable from year to year.3

A third category consists of persons who entered the

country as refugees and are currently adjusting their status

to immigrant. The assumption of year-to-year stability of

the flow, made for the previous category, would generally

be inadequate for refugees because they tend to enter the

country in ‘‘waves’’ depending on emigration policies or

political upheavals in foreign countries. Hence, the time

series of their entry would be poorly reflected by the time

series of their conversion to immigrant status. The Office of

Refugee Resettlement (ORR) maintains monthly data on

the arrival of refugees (as well as some special-status

entrants from Haiti and Cuba) by country of citizenship.

Refugees adjusting to immigrant status are thus excluded

from the accounting of immigrants but included as refugees

at their time of arrival based on the ORR series.

The fourth and final category consists of persons living

in the country illegally—either as nonimmigrant visa overstayers or ‘‘entered without inspection’’—who adjust to

legal immigrant status by proving a history of continuous

residence in the United States. Since fiscal year 1989, the

largest portion of these persons has been admitted under

the Immigration Reform and Control Act of 1986 (IRCA),

which required proof of permanent residence since 1982.

Generally, these persons should qualify as census-defined

residents from the date of their arrival, since they do not

generally expect to return to their country of origin. Because

their arrival occurred before the 1990 census and current

undocumented migration is separately accounted, immigrating IRCA adjustees are excluded from the accounting

of migration altogether for post-1990 estimates.

The separate accounting of net undocumented migration cannot be based on registration data because, by

definition, it occurs without registration. Research conducted at the Census Bureau (Robinson, 1994) has produced an allowance of 225,000 net migration per year,

based on observation of the late 1980s, with the universe

confined (appropriately) to those counted in the 1990

census. This number is a consensus estimate, based partly

on a specific derivation and partly on a review of other

estimates for the same period. The derivation is based on

a residual method, according to which the number of

foreign-born persons enumerated in 1990 by period of

arrival is compared to net legal migration measured for the

same period. Currently, all Census Bureau estimates and

projections of the population after 1990 incorporate this

annual allowance as a constant.

The remaining category of foreign-born migration to the

United States, not included in the immigrant data, is the

flow of legal temporary residents; persons who reside in

the country long enough to consider themselves ‘‘usual

residents’’ while they are here, but who do not have

immigrant visas. These include foreign students, scholars,

some business persons (those who establish residence),

and some professionals who are provided a special allowance to work. INS data on nonimmigrants would provide

records of admission for temporary visa holders; however,

there would be no reliable data source for their departures.

The stock of this category of foreign-born persons enumerated in the 1990 census was estimated at 488,000, based

on various characteristics measured by the census ‘‘long

form’’ (1 in 6 households). This migration flow is currently

assumed to maintain this stock at 488,000, This tells us

that net migration (arrivals minus departures) equals the

estimated number of deaths occurring to the group while in

the United States. The overall net migration of this group is

rather trivial (less than a thousand per year); far more

important is the implied distribution of migration by age,

which will be discussed later in this appendix.

A major migratory movement across the U.S. frontier,

not directly measurable with administrative data, is the

emigration of legal permanent residents of the United

States to abroad. The current projection is a constant

222,000 per year, of whom 195,000 are foreign-born

(Ahmed and Robinson, 1994). Like the allowance for

undocumented immigration, the method of derivation is a

residual method. In this case, the comparison is between

foreign-born persons enumerated in the 1980 census and

foreign-born persons enumerated in the 1990 census who

gave a year of arrival prior to 1980. In theory, the latter

number would be smaller, with the difference attributed to

either death or emigration. The number of deaths was

estimated through life tables, leaving emigration as a

residual. This analysis was carried out by country of birth,

facilitating detailed analysis of possible biases arising from

differential reporting in the two censuses. The annual

estimate of 195,000 was projected forward as a constant,

for purposes of producing post-1990 estimates and projections.

The remaining annual emigrant allowance of 27,000 is

based on an estimate of native-born emigrants carried out

during the early 1980s which employed data on U.S.-born

3

In fiscal year 1995, the assumption of a stable flow of nonimmigrants

adjusting to immigrant status was seriously undermined by a legal

change. For the first time, persons qualifying for legal immigrant status

were allowed to do so without leaving the country. As a result, a rapid

increase in applications to INS resulted in an increase in the backlog of

unprocessed applications. It was, therefore, necessary to adapt the

accounting for the increase in applications, rather than assuming a

one-for-one relationship of current adjustees to future adjustees currently

entering the country.

D–8
persons enumerated in foreign censuses, as well as historical evidence from the period before 1968, when permanent departures from the United States were registered by

INS.

A final class of international migration that can only be

estimated roughly as a net flow is the movement of persons

from Puerto Rico and the outlying areas to the United

States. No definitive administrative data exist for measuring volume, direction, or balance of these migration flows.

The migratory balance from all areas except Puerto Rico is

assumed to be zero. For Puerto Rico, we assume an

allowance of roughly 7,000 net migration (arrivals minus

departures) per year. This projection is based on an

imputation of net out-migration from Puerto Rico during the

1980s, based on a comparison of the island’s natural

increase (births minus deaths) with the population change

between the 1980 and 1990 censuses.

There is considerable variation in the currency of data

on international migration, depending on the source. The

INS public use immigrant file is generally available during

the summer following the fiscal year of its currency, so

projections for control of the CPS have access to final

immigration data through September, 2 years prior to the

reference date of the projections (e.g., 1995 for 1997

controls). INS provides a provisional monthly series for the

period from October through June for the following year,

that is, through the middle of the calendar year before the

CPS reference date. From that point until the CPS reference date itself, the immigrant series is projected. The

Office of Refugee Resettlement data on refugees follows

roughly the same timetable, except the preliminary series

is ongoing and is generally current through 4 to 5 months

before the CPS reference date. Net undocumented immigration, emigration of legal residents, and net migration

from Puerto Rico are projected as constants from the last

decennial census, as no current data are available. The

determination of the base series for the projection requires

considerable research, so the introduction of data for a new

decade generally does not occur until about the middle of

the following decade.

Net migration of Federally affiliated civilian U.S. citizens (CCM). While the approach to estimating international migration is to measure the migration flows for

various types of migration, no data exist on flows for civilian

U.S. citizens between the United States and abroad (except

with respect to Puerto Rico, which is treated here as

international); this is the variable CCM in equation (7).

Because U.S. citizens are generally not required to report

changes of address to or from other countries, there is no

registration system from which to obtain data. Fortunately,

there are data sources for measuring the current number

(or stock) of some classes of civilian U.S. citizens residing

overseas, including persons affiliated with the Federal

government, and civilian dependents of Department of

Defense employees, both military and civilian. The estimation procedure used for the movement of U.S. citizens thus

relies on an analysis of the change in the stock of overseas

U.S. citizens over time. We assume the net migration of

non-Federally affiliated U.S. citizens to be zero.

Imputation of migration from stock data on overseas population is by the following formula:

CCM   (OCPt1  OCPt0  OB OD) (9)

where OCPt1 and OCPt0 represent the overseas civilian

citizen population (the portion that can be estimated) at the

beginning of two consecutive months; OB is the number of

births, and OD the number of deaths of this population

during the month. This equation derives from the balancing

equation of population change and states the net migration

to the overseas civilian population to be its total change

minus its natural increase.

Federally affiliated civilian U.S. citizens residing overseas for whom we have data, or the basis for an estimate,

are composed of three categories of persons; dependents

of Department of Defense (DOD) personnel, civilian Federal employees, and dependents of non-DOD civilian Federal employees. The number of civilian dependents of DOD

military and civilian personnel overseas are published

quarterly by DOD; the number of Federal employees—total

and DOD civilian—located overseas at the beginning of

each month is published by the Office of Personnel Management (OPM). We estimate the number of dependents of

non-DOD civilian Federal employees by assuming the ratio

of dependents to employees for non-DOD personnel to

match the ratio for civilian DOD personnel. Quarterly data

(e.g., DOD dependents) are converted to monthly by linear

interpolation.

Estimates of the numbers of births and deaths of this

population (OB and OD, in equation (9)) depend almost

entirely on a single statistic, published quarterly by DOD:

the number of births occurring in United States military

hospitals overseas. This number (apportioned to months

by linear interpolation) is adopted as an estimate of overseas births, on the reasoning that most births to Federally

affiliated citizens or their dependents would occur in military hospitals. We estimate deaths by assuming deaths of

persons 1 year of age or older to be nil; we estimate infant

deaths by applying a life table mortality rate to the number

of births. The likely underestimate of both births and deaths

would tend to compensate each other. While this method of

estimating the natural increase of overseas civilian citizens

is very approximate, the numbers involved are very small,

so the error is unlikely to have a serious effect on estimates

of civilian citizen migration.

Data from DOD and OPM used to estimate civilian

citizen migration are generally available for reference dates

until 6 to 9 months prior to the CPS reference date. For the

months not covered by data, the estimates rely on projected levels of the components of the overseas population

and overseas births.

D–9
Net recruits to the Armed Forces from the civilian

population (NRAF). The net recruits to the worldwide U.S.

Armed Forces from the U.S. resident civilian population is

given by the expression

(WWAFt1 - WWAFt0 + DRAF + DAFO - NRO)

in equation (4). The first two terms represent the change in

the number of Armed Forces personnel worldwide. The

third and fourth represent deaths of the Armed Forces in

the U.S. and overseas, respectively. The fifth term represents net recruits to the Armed Forces from the overseas

civilian population. While this procedure is indirect, it allows

us to rely on data sources that are consistent with our

estimates of the Armed Forces population on the base

date.

Most of the information required to estimate the components of this expression is supplied directly by the Department of Defense, generally through a date 1 month prior to

the CPS control reference date; the last month is projected,

for various detailed subcomponents of military strength.

The military personnel strength of the worldwide Armed

Forces, by branch of service, is supplied by personnel

offices in the Army, Navy, Marines and Air Force, and the

Defense Manpower Data Center supplies total strength

figures for the Coast Guard. Participants in various reserve

forces training programs (all reserve forces on 3- and

6-month active duty for training, National Guard reserve

forces on active duty for 4 months or more, and students at

military academies) are treated as active-duty military for

purpose of estimating this component, and all other applications related to the CPS control universe, although the

Department of Defense would not consider them to be in

active duty.

The last three components of net recruits to the Armed

Forces from the U.S. civilian population, deaths of the

resident Armed Forces (DRAF), deaths of the Armed

Forces overseas (DAFO) and net recruits to the Armed

Forces from overseas (NRO) are usually very small and

require indirect inference. Four of the five branches of the

Armed Forces (those in DOD) supply monthly statistics on

the number of deaths within each service. Normally, deaths

are apportioned to the domestic and overseas component

of the Armed Forces relative to the number of the domestic

and overseas military personnel. If a major fatal incident

occurs for which an account of the number of deaths is

available, these are assigned to domestic or overseas, as

appropriate, before application of the pro rata assignment.

Lastly, the number of net recruits to the Armed Forces from

the overseas population (NRO) is computed annually, for

years from July 1 to July 1, as the difference between

successive numbers of Armed Forces personnel giving a

‘‘home of record’’ outside the 50 states and District of

Columbia. To complete the monthly series, the ratio of

persons with home of record outside the U.S. to the

worldwide military is interpolated linearly, or extrapolated

as a constant from the last July 1 to the CPS reference

date. These monthly ratios are applied to monthly worldwide Armed Forces strengths; successive differences of

the resulting estimates of persons with home of record

outside the U.S. yield the series for net recruits to the

Armed Forces from overseas.

Change in the institutional population (CIPt1 - CIPt0).

The change in the civilian population residing in institutions is measured by a report of selected group quarters

facilities carried out by the Census Bureau, in conjunction

with state governments, through the Federal State Cooperative Program for Population Estimates (FSCPE). Information is collected on the type of facility and the number of

inhabitants, and these data are tabulated by institutional

and noninstitutional. The change in the institutional population from the census date to the reference date of the

population estimates, measured by the group quarters

report, is used to update the institutional population enumerated by the census. Certain facilities, such as military

stockades and resident hospitals on military bases, are

excluded from the institutional segment, as most of their

inhabitants would be military personnel; hence, the resulting institutional population is assumed to be civilian.

The last available group quarters data refer to July 1,

2 years prior to the reference year of the CPS series

(July 1, 1995, for 1997 CPS controls). Institutional population from this date forward must be projected. Censusbased institutional participation rates are computed for the

civilian population by type of institution, age, sex, race, and

Hispanic origin. These are used to produce estimates for

the group quarters report dates, which are then proportionally adjusted to sum to the estimated totals from the report.

Participation rates are recomputed, and the resulting rates,

when applied to the monthly estimate series for the civilian

population by characteristic, produce projections of the

civilian institutional population. These estimates produce

the last two terms on the right side of equation (7).

Population by Age, Sex, Race, and Hispanic

Origin

The CPS second-stage weighting process requires the

independent population controls to be disaggregated by

age, sex, race, and Hispanic origin. The weighting process,

as currently constituted, requires cross-categories of age

group by sex and by race, and age group by sex and by

Hispanic origin, with the number of age groups varying by

race and Hispanic origin. Three categories of race (White,

Black, and all Other4), and two classes of ethnic origin

(Hispanic, non-Hispanic) are required, with no cross-classifications

of race and Hispanic origin. Beginning in 1993, the population projection program adopted the full cross-classification

of age by sex and by race and by Hispanic origin into its

4

Other includes American Indian, Eskimo, Aleut, Asian, and Pacific

Islander.

D–10
monthly series, with 101 single-year age categories (single

years from 0 to 99, and 100 and over), two sex categories

(male, female), four race categories (White; Black; American Indian, Eskimo, and Aleut; and Asian and Pacific

Islander), and two Hispanic origin categories (not Hispanic,

Hispanic).5 The resulting matrix has 1,616 cells (101x2x

4 x 2), which are then aggregated to the distributions used

as controls for the CPS.

In discussing the distribution of the projected population

by characteristic, we will stipulate the existence of a base

population and components of change, each having a

distribution by age, sex, race, and Hispanic origin. In the

case of the components of change, ‘‘age’’ is understood to

be age at last birthday as of the estimate date. The present

section on the method of updating the base population with

components of change is followed by a section on how the

base population and component distributions are measured.

Update of the Population by Sex, Race, and

Hispanic Origin

The full cross-classification of all variables except age

amounts to 16 cells, defined by variables not naturally

changing over time (2 values of sex by 4 of race by 2 of

Hispanic origin). The logic for projecting the population of

each of these cells follows the same logic as the projection

of the total population and involves the same balancing

equations. The procedure, therefore, requires a distribution

by sex, race, and Hispanic origin to be available for each

component on the right side of equation (7). Similarly, the

derivation of the civilian noninstitutional base population

from the census resident population follows equation (8).

Update of the Age Distribution: The

Inflation-Deflation Method

Having produced population series for 16 cells of sex,

race, and Hispanic origin, it remains to distribute each cell

to 101 age groups. Age differs fundamentally from other

demographic characteristics because it changes over time.

The balancing equations described in earlier sections rely

on the premise that the defining characteristics of the

population being estimated remain fixed, hence the procedure must be adapted to allow for the fact that age does not

meet this criterion. We present the method of estimating

the age distribution through the equation for the resident

population, analogous to equation (1) for the total population, for reasons discussed at the end of this section. The

mathematical logic producing the civilian noninstitutional

population (CNP) by age from the resident population, the

active-duty resident Armed Forces, and the civilian institutional population is precisely the same for any age group as

for the total population, so we omit its discussion. Of

course, it is necessary to know the age distribution of the

resident Armed Forces population and the civilian institutionalized population in order to derive the CNP by age

from the resident population by age.

Under ideal circumstances, a very standard demographic procedure—the method of cohort components—would

be employed to update the population age distribution. The

cohort component method yields the population of a given

birth cohort (persons born in a given year) from the same

cohort in an earlier year, incremented or decremented by

the appropriate components of change. To take a simple

case, we assume the time interval to be 1 year, from the

beginning of year t to the beginning of year t+1, and the age

distribution being updated is a distribution of the resident

population by single year of age. We can derive the number

of persons age x (for most values of x) by the equation

Rx,t1  Rx1,t  Dx,t NMx,t (10)

where:

Rx,t+1 = resident population aged x (last birthday), at

time t+1

Rx-1,t = resident population aged x-1, at time t

Dx,t = deaths during the time interval from t to t+1,

of persons who would have been age x at

time t+1

NMx,t = net migration during the time interval from t

to t+1, of persons who would be age x at

time t+1

For the special case where x=0, the equation (10) becomes

R0,t1  Bt  D0,t NM0,t (11)

with variable definitions analogous to equation (10), except

that Bt equals live births during the time interval from t to

t+1. For the special case where the population estimated

comprises an ‘‘open’’ age category, like 100 and over, the

equation becomes

RX,t1  Rx1,t RX,t DX,t NMX,t (10)

where RX,t+1 and RX,t designate persons age x and older, at

time t+1 and time t, respectively, and DX,t and NMX,t

designate deaths and net migration, respectively, from t to

t+1 of persons who would be age x and older at time t+1.

Here, Rx-1,t is defined as in equation (10).

A fundamental property of this method is its tracking of a

birth cohort, or persons born in a given year, to a later date

through aging; hence it optimizes the comparability of the

population of birth cohorts over time. But, in producing a

time series of projections or estimates of a population age

distribution, the objective is rather to maintain consistency

from month-to-month in the size of age groups, or the

number of persons in a specific, unchanging age range. As

5

Throughout this appendix, ‘‘American Indian’’ refers to the aggregate

of American Indian, Eskimo, and Aleut; ‘‘API’’ refers to Asian and Pacific

Islander.

D–11
a result of this, the cohort component method of updating

age distributions is highly sensitive to discontinuities from

age group to age group in the base population. Unfortunately, two attributes of the census base population render

the assumption of continuity across age groups untenable.

First, underenumeration in the census is highly age-specific.

This problem is most egregious for age categories of Black

males in the age range from 25 to 40, which represent a

‘‘trough’’ in census coverage. It is lowest near the middle of

the age range, as is evident from the age pattern of

census-based sex ratios within the range. Consequently,

consecutive years of age are inconsistent with respect to

the proportion of persons missed by the census. The

second troublesome attribute of the census base population is the presence of distinct patterns of age reporting

preference, as manifested by a general tendency to underreport birth years ending in one and overreport birth years

ending in zero. Because the census date falls in the first

half of a decade year, this results in an exaggeration of the

number of persons with age ending in nine, and an

understatement of the number with age ending in eight.

Were the cohort component method applied to this distribution without adaptation, these perturbations in the age

distribution would progress up the age distribution with the

passage of time, impairing the consistency of age groups in

the time series.

The solution to this problem, first adopted in the 1970s,

is an adaptation of the cohort-component method that

seeks to prevent the aging of spurious elements of the

base population age structure. The resulting method is

known as ‘‘inflation-deflation.’’ In principle, the method

depends on the existence of an alternative base population

distribution by age, sex, race, and origin, for the census

date, which is free of age-specific underenumeration and

inconsistent age reports, but as consistent as possible with

the census in every other aspect of the population universe. Under ideal circumstances, the alternative base

distribution would be the population that would have been

enumerated by the census, if the census had been free of

undercount and age misreporting, although this requirement is neither realistic nor essential. It is essential,

however, that single-year-of-age categories be consistent

with one another, in the sense that they share the same

biases. The correctness of the overall level of the alternative base population (absence of biases in the total population of all ages) is unimportant. Once such a base

population distribution is available, it can be updated,

recursively, from the census date to the estimates reference date using the cohort component logic of equations

(10), (11), and (12) without concern for bias arising from

discontinuities in the population by age.

To update a census-level population from one date to

another, ‘‘inflation-deflation factors’’ are computed as the

ratio of each single-year age group in the census base

population to the same age group in the alternative base

population. If the alternative population represents a higher

degree of coverage than the census base population, the

factors can be expected to have values somewhat less

than one. If the alternative population is free of age

misreporting, then overreported ages in the census may

have factors greater than one, while underreported ages

will have factors less than one. If, as in the present

application, the calculations are done within categories of

sex, race, and Hispanic origin, the factors can take on

unnatural values if there is relative bias between the

census and the alternative population in the reporting of

these variables. Such is the case with the current application for ‘‘all other races,’’ the racial category combining

American Indian, Eskimo, Aleut, Asian, and Pacific Islanders. This group tends to be more heavily reported in the

census than in other administrative data sources.

The census-level population at the base date is ‘‘inflated,’’

through multiplication by the reciprocal of the factors, to be

consistent with the alternative population base. The resulting distribution is updated via cohort component logic

(equations (10) through (12)), and the factors are multiplied

by the same age groups (not the same birth cohorts) on the

reference date, thereby deflating the estimates back to

‘‘census level.’’ Because the inflation-deflation factors are

held constant with respect to age rather than birth cohort,

those aspects of the base age distribution that should not

increase in age from year-to-year are embodied in the

inflation-deflation factors, and those aspects that should

indeed ‘‘age’’ over time are embodied in the alternative

base distribution. A mathematical representation of this

procedure is given by the following equation, analogous to

equation (10) for the cohort component method

R

 ́

x,t1  Rx,0

Rx,0

A (

Rx—1,0

A

Rx1,0

Rx—1,t — Dx,t

A NMx,tA NMx,tA ) (13)

where R ́

x,t1 is the census-level estimate of population age

x at time t+1, R ́

x1,tis the same for age x-1 at time t, Rx,0

A is

the resident population age x in the alternative distribution

at the census date, Rx—1,0

A is the same for age x-1, and

Dx,t

A and NMx,tA are deaths and net migration for the interval

beginning at time t, respectively, consistent with the alternative population, age x at time t+1. Note that Rx,0  Rx,0

A is

the inflation-deflation factor for age x. A similar adaptation

can be applied to equations (11) and (12) to obtain

expressions for estimates for age 0 and the open age

category, respectively.

The inflation-deflation procedure does not preserve exact

additivity of the age groups to the external sex-race-origin

total Rt

. It is therefore necessary, in order to preserve the

balancing equation for the population totals, to introduce a

final proportional adjustment. This can be expressed by the

formula

Rx,t  R

 ́

x,t

Rt



y=0

100

R

 ́

y,t

(14)

D–12
which effectively ensures the additivity of the resident

population estimates Rx,t by age (x) to the external total Rt

.

The alternative base population for the census date

used in the Bureau’s population estimates program, including the survey control projections, is known as the Demographic Analysis (DA) resident population. Originally devised

to measure underenumeration in the census, this population is developed, up to age 65, from an historical series of

births, adjusted for underregistration. This series is updated

to a population on April 1, 1990, by sex and race using

cumulative data on deaths and net migration from date of

birth to the census date. For ages 65 and over, the

population is based on medicare enrollees, as this population is considered more complete than the census enumeration with respect to coverage and age reporting. This

population is assumed to possess internal consistency with

respect to age superior to that of the census because births

and deaths, which form the mainstay of the estimates of

age categories, are based on administrative data series

rather than subjective reporting of age from an underenumerated population. Some adaptation of the administrative

series was necessary to standardize the registration universe for the time series of births and deaths, which

changed, as various states were added to the central

registration network. For a description of this method, see

Fay, Passel and Robinson (1988).

One might reasonably ask why the results of the PostEnumeration Survey (PES), used to adjust the CPS controls for underenumeration, were not adopted as the alternative population. The reason is that the PES age distribution

was based on application of adjustment factors to the

census population defined only for large age categories.

This method does not address census-based inconsistencies among single years of age within the categories;

moreover, it introduces serious new inconsistencies between

ages close to the edges of the categories. Hence, its use

as an alternative base for inflation-deflation would be

inappropriate. This fact does not impair the appropriateness of PES-based adjustment as a means of addressing

undercoverage in the completed survey controls, since the

results may be superior in their treatment of other dimensions of the population distribution, such as state of residence, race, origin, or large aggregates of age.

Having noted the relative appropriateness of the DA

base population for the inflated series, it is not without

technical limitations.

1. Because of its dependency on historical vital statistics

data, it can only be generated for three categories of

race—White, Black, and all other. The distribution of

the third category to American Indian and API assumes

the distribution of the census base for each age-sex

group. Therefore, spurious disturbances in the census

age distributions that differ for these two groups remain

uncorrected in the DA base population.

2. For the same reason, the two categories of Hispanic

origin cannot be generated directly in the DA base. The

practical solution was to assume that the proportion

Hispanic in each age, sex, and race category matched

the 1990 census base population. Because of this

assumption, no distinctly Hispanic properties of the

age discontinuity in the census population could be

reflected in the DA base employed in the estimates.

3. The reliance by the DA procedure on medicare enrollee

data for the population 65 years of age and over, while

defensible as a basis for adjusting the elderly population, leaves open the possibility of inconsistency in the

age distribution around age 65.

4. The DA procedure assumes internally consistent registration of deaths and an accounting of net migration

in the historical series, although adaptation had to be

made for the changing number of states reporting

registered births and deaths to the Federal government. Deviations from this assumption could affect the

internal consistency of the age distribution, especially

for the older ages under 65.

5. Because the DA population is based primarily on vital

registration data, race reporting does not always match

race reporting in the census. In particular, births and

deaths reported to NCHS are less likely to be coded

American Indian and Asian and Pacific Islander than

respondents in the census. While this has no direct

effect on estimated distributions of population by race

(inflation-deflation only affects age within sex-race

totals), the population defining the ‘‘ageable’’ attributes

of the base age distribution for these groups is smaller

(roughly 15 percent for most ages) than the base used

to compute sex-race-origin-specific population totals.

6. The inflation-deflation method, as currently implemented,

assumes current deaths and all the components of

migration to match in the DA universe and the census

base universe by sex, race, and origin. This amounts

to saying that Dx,t

A and NMx,tA in equation (13) sum to D

and NM in equation (1), respectively. In the case of

births, we assume births adjusted for underregistration

to be consistent with the DA population, and births

without adjustment to be consistent with the censuslevel population. In principle, the application of components of change to a DA-consistent population in

equation (13) roughly parallels the derivation of the DA

population.6 However, the components used to update

the census-level population in equations (1) or (7)

6

One deviation from this rule occurs in the treatment of births by race.

The DA population defines race of child by race of father; the postcensal

update currently assigns race of child by race of mother. This practice will

likely be modified in the near future. A second deviation occurs in the

accounting of undocumented immigration and the emigration of legal

residents. As of 1995, the postcensal estimates had incorporated revised

assumptions, which had not been reflected in the DA base population;

moreover, the revised assumptions were conspicuously ‘‘census-level,’’

rather than ‘‘DA-level’’ assumptions.

D–13
should theoretically exclude the birth, death, or migration of persons who would not be enumerated, if the

census-level character of the estimates universe is to

be maintained.

Finally, we note the DA population can be estimated only

for the resident population universe, so the process of

estimating age distributions must be carried out on the

resident population. The accounting of total population, on

the other hand, is for the civilian noninstitutional universe.

The resident universe must then be adapted to the civilian

noninstitutional population by subtracting resident Armed

Forces and civilian institutional population estimates and

projections by age, sex, and race, from the resident

population, as of the estimate or projection reference date.

Fortunately, our sources of information for the institutional

and Armed Forces populations yield age distributions for

any date for which a total population is available; hence,

this adaptation is uncomplicated.

Distribution of Census Base and Components of

Change by Age, Sex, Race, and Hispanic Origin

Our discussion until now has addressed the method of

estimating population detail in the CPS control universe,

and has assumed the existence of various data inputs. The

current section is concerned with the origin of the inputs.

Modification of the census race and age distributions.

The distribution of sex, race, and Hispanic origin in the

census base population is determined by the census, but

with some adaptation. The 1990 census included questions

on sex, race, and Hispanic origin, as was the case in 1980.

The race question elicited responses of White, Black,

American Indian, Eskimo, Aleut, and ten Asian and Pacific

Islander categories, including ‘‘other API.’’ It also allowed

respondents to write-in a race category for ‘‘other race.’’

The Hispanic origin question elicited responses of ‘‘No (not

Spanish/Hispanic),’’ and a series of ‘‘Yes’’ answers, including Mexican, Puerto Rican, Cuban, and other Spanish/Hispanic,

with the last category offering a possibility of write-in. While

the initial census edit process interpreted the write-in

responses to the race question, it left a substantial number

of responses not directly codable to any White, Black,

American Indian, Eskimo, Aleut, or Asian and Pacific

Islander group. In 1980 this ‘‘other or not specified’’ category comprised 6.7 million persons; in 1990, it comprised

9.8 million persons. The overwhelming majority, in both

cases, consisted of persons who gave a positive response

to the Hispanic origin question, and/or a Hispanic origin

response to the race question. However, the Hispanic

component of the ‘‘other or not specified’’ race category

comprised somewhat less than half of the total Hispanic

population (based on the Hispanic origin response), with

most of the balance classified as White.

The existence of a residual race category with these

characteristics was inconsistent with other data systems

essential to the estimation process, namely vital statistics

and Armed Forces strength statistics. It also stood at

variance with Office of Management and Budget Directive

15, a 1977 ruling intended to standardize race and ethnicity

reporting across Federal data systems. This directive specified an exhaustive distribution of race that could either be

cross-classified with Hispanic origin, into a 4-by-2 matrix,

or combined into a single-dimensional variable with five

categories, including American Indian, Eskimo, and Aleut

(one category), Asian and Pacific Islander (one category),

non-Hispanic White, non-Hispanic Black, and Hispanic

origin. It was thus necessary to adapt the census distribution in such a way as to eliminate the residual race

category. The Bureau opted to follow the 4-by-2 crossclassification in 1990. Prior to 1990, the American Indian

and Asian and Pacific Islander categories were combined

into a single category in all Census Bureau population

estimates, and the race distribution included persons of

Hispanic origin separately, with race unspecified. In 1990-

based estimates, these two racial categories were separated, and the procedure was expanded to estimate the full

race-Hispanic cross-classification, although at present this

disaggregation is recombined for the purpose of weighting

the CPS.

The modification of the 1990 race distribution occurred

at the level of individual microdata records. Persons of

unspecified race were assigned to a racial category using

a pool of ‘‘race donors.’’ This pool was derived from

persons with a specified race response, and the identical

response to the Hispanic origin question (e.g., Mexican,

Puerto Rican, Cuban, other Hispanic, or not Hispanic).

Thus, a respondent’s race (if initially unspecified) was, in

expectation, determined by the racial distribution of persons with the same Hispanic origin response who resided

in the vicinity of the respondent.

The distribution by age from the 1990 census also

required modification. The 1990 census form asked respondents to identify their ages and their years of birth. While

the form provided explicit direction to include only those

persons who were alive and in the household on the

census date, there was no parallel instruction to state the

ages of household members as age on April 1, 1990. It was

apparent that many respondents reported age at time of

completion of the form, time of interview by an enumerator,

or time of their next anticipated birthday. Any of these could

occur several months after the April 1 reference date. As a

result, age was biased upward, a fact most noticeable

through gross understatement of the under 1-year-of-age

category. While this distribution was published in most

1990 census publications and releases, it was considered

inadequate for postcensal estimates and survey controls.

The resulting modification was based on reported year of

birth. Age was respecified by year of birth, with allocation to

first quarter (persons aged 1990 minus year of birth) and

last three quarters (aged 1989 minus year of birth) based

on a historical series of birth by month derived from birth

registration data. This methodology is detailed in U.S.

Bureau of the Census (1991).

D–14
As was the case with race, the recoding of age occurred

directly on the individual records, after preliminary edits to

eliminate reports of year of birth or age inconsistent with

other variables (such as household relationship). The

tabulations required for the base distribution for estimates

could thus be obtained by simple frequency distributions

from the individual-record file. There was also a trivial

modification of the distribution by sex that came about

because the assignment of a very small percentage of

census respondents of unknown sex was linked to respondent’s age. The modification of the age distribution will be

discussed under ‘‘Distribution of Population Projections by

Age.’’ The resulting distribution is known to the Census

Bureau estimates literature as ‘‘MARS,’’ an acronym for

‘‘modified age, race, and sex.’’

We note parenthetically that the Demographic Analysis

(DA) population, used as the alternative base for the

inflation-deflation method, did not require modifying with

respect to age and race because it was derived primarily

from vital statistics and immigration. However, the historical

distribution of international migrants by race was based on

the MARS distribution by race within country of birth. More

importantly, the need to expand the race detail of the

original DA population required introducing the MARS

distribution to the split between American Indians and API,

as well as any indirect effect of MARS on the distribution by

Hispanic origin within race.

Distribution of births and deaths by sex, race, and

Hispanic origin. The principal source of information on

sex and race for births and deaths is the coding of sex and

race on birth and death certificates (race of mother, in the

case of births). These results are coded by NCHS on detail

files of individual birth and death records. Hispanic origin is

also coded on most vital records, but a substantial number

of events did not receive a code, in some cases because

they occurred in states that do not code Hispanic origin on

birth and death certificates. For births, the unknown category was small enough to be distributed to Hispanic and

not Hispanic in proportion to the MARS distribution of

persons aged under 1 year. For deaths, the number of

unknowns was sufficiently large (in some years, exceeding

the number of deaths known to be Hispanic) to discourage

the use of the NCHS distribution. Life tables were applied

to a projected Hispanic origin population by age and sex;

the resulting distribution was aggregated to produce totals

for Hispanic and not Hispanic by sex. While the resulting

distributions of births and deaths resemble closely the

MARS distribution of the base population, a few discrepancies remained. Some are of sufficient concern to prompt

adaptations of the distributions.

1. NCHS final data through 1992 included, for both births

and deaths, a small category of ‘‘other races,’’ not

coded to one of the categories in the MARS (OMB)

distribution. The practice to date has been to include

these in the Asian and Pacific Islander category,

although a closer examination of the characteristics of

these events in 1994 suggested some should be

coded elsewhere in the distribution, principally Hispanic origin, with race other than API.

2. The reporting of American Indian and Asian and Pacific

Islander categories in the census, hence also in the

MARS base population, has tended to exceed the

reporting of these categories in birth and death data.

This fact is symptomized by unrealistically low fertility

and mortality rates when MARS-consistent population

estimates are used as denominators. This fact has not

been addressed in estimates and projections to date,

but is currently under review for possible future adaptation.

3. Persons of Hispanic origin and race other than White

are substantially more numerous in the MARS base

population than in the vital statistics universe. This can

best be explained by the procedure used to code race

in the base population. The initial edit of write-in

responses to the census race question was done

independently of the Hispanic origin response. Consequently, a considerable number of write-in responses

allocated to non-White racial categories were of Hispanic origin. While the subsequent allocation of the

‘‘other (unspecified) race’’ category in the MARS procedure did indeed reflect Hispanic origin responses, it

was possible for ‘‘race donors’’ selected for this procedure to have been persons of previously assigned

race. Because the census (MARS) base population

defines the population universe for estimates and

projections, the non-White race categories of the Hispanic origin population have been estimated using a

race-independent assumption for Hispanic age-specific

fertility and mortality rates, applied to MARS-consistent

projected populations. The NCHS race totals for Hispanic and non-Hispanic combined have been considered sufficiently close to consistency with MARS to

warrant their adoption for the population estimates;

hence, the non-Hispanic component of each racial

group is determined by subtraction of Hispanic from

the total. Hence, this adaptation will have no direct

effect on second stage controls to the CPS, as long as

the control procedure does not depend on the crossclassification of race with Hispanic origin.

A further issue that has not been fully addressed is the

possible effect on the race distribution of the practice of

identifying the race of a child by the race of its mother.

Research is currently in progress to define race and

Hispanic origin of child by a method that recognizes the

race and origin of both parents, using census results on the

reported race of children of biracial parentage.

Deaths by age. The distribution of deaths by age presents

yet another need for adaptation of the population universe.

However, its use for census-based estimates requires the

D–15
application of projected rates to the oldest age groups,

rather than a simple decrement of the population by deaths

in an age group. Because a cohort-component method (or

in the present case, inflation-deflation) is required to estimate the age distribution of the population, the procedure

is extremely sensitive to the reporting of age on death

certificates and in the Demographic Analysis base population among elderly persons. We recall that medicare enrollees, not vital statistics, form the basis for the DA age

distribution of the elderly. Because deaths of the oldest age

groups may represent a substantial portion of the living

population at the beginning of an age interval, relatively

small biases caused by differential age reporting can

produce large cumulative biases in surviving populations

over a few years’ time. This, in fact, occurred for estimates

produced during the 1980s. It was the practice then to

decrement the population at all ages using data on numeric

deaths. Because there were not enough deaths to match

the extreme elderly population, the population in the extreme

elderly groups grew rapidly in the estimates. This was most

noted in the population over 100 years of age; while this is

of no direct concern to the CPS, which does not disaggregate age above 85, the situation provoked legitimate

concern that an overestimate of the population 85 and over

was also occurring, albeit of lesser proportion. At the

opposite extreme, the application of numeric deaths for the

oldest age groups could produce negative populations, if

the number of deaths in a cohort exceeded the living

population at the beginning of the interval.

This problem was solved in 1992 by developing a

schedule of age-specific death rates using life tables for

racial and Hispanic origin categories. When life table death

rates are applied to a population, the fact that proportions

of the living population in each age group must die each

year ensures the timely demise of the oldest birth cohorts.

Of course, the life tables are themselves reliant on the

assumed compatibility of the numerators and denominators of the death rates. However, the fact that the population estimates underlying the rates had incorporated earlier

death rates was sufficient to limit any cumulative error from

differential age reporting. The current practice is to incorporate numeric deaths from NCHS through 69, apply life

table-based death rates to ages 70 and over by single year

of age, then proportionately adjust deaths to ages 70 and

over to sum to NCHS-based totals by sex and race. Thus,

the only cumulative bias entering the series affecting the

extreme elderly would arise from differential reporting of

age by under or over 70 years, which is deemed to be of

minor concern.

International migration by age, sex, race, and Hispanic

origin. Legal international migration to the U.S. is based on

actual records of immigration from INS or refugee arrivals

from ORR, so the statistics include direct reporting of sex

and country of birth or citizenship as well as age. Race and

Hispanic origin are not reported. The procedure to obtain

the race and Hispanic origin variables depends on a

distribution from the 1990 census sample edited detail file

(1 in 6 households or ‘‘long form’’ sample) of foreign-born

persons arrived since the beginning of 1985, by sex, race

(MARS categories) Hispanic origin, and country of birth.

This distribution is used to distribute male and female

immigrants and refugees by race and Hispanic origin. This

procedure is very robust for most country-of-birth categories, since migrants from most foreign countries are heavily

concentrated in a single race-origin category (the notable

exception being Canada).

For the remaining components of international migration, indirect methods are required to produce the distribution by age, sex, race, and origin.

1. For annual emigration of legal residents (222,000

total), the foreign-born and native-born were distributed separately. For emigration of the foreign-born, the

age-sex distribution is a byproduct of the determination of the annual allowance of 195,000 persons.

Because emigration is computed as a residual of the

foreign-born from two censuses (with adjustment for

age-specific mortality), their age and sex distribution

can rest on the censuses themselves. This logic also

yields country of birth, so race and origin are imputed

in the same manner as for legal immigrants and

refugees, previously described. The 27,000 annual

allowance of native-born emigrants is assigned a

distribution by all characteristics matching the nativeborn population from the 1990 census.

2. For net undocumented migration, we rely primarily on

a distribution supplied by INS, giving the distribution by

age, sex, country of birth, and period of arrival, for

persons who legalized their residence under provisions of the Immigration Reform and Control Act

(IRCA). Age is back-dated from date of legalization to

date of arrival, since undocumented migrants are

assumed to be residents of the United States from the

time they arrive. An allowance is made, based on a life

table application, for persons who died before they

could be legalized. Once again, country of birth was

used to assign race and Hispanic origin based on data

from the 1990 census on the race and Hispanic origin

of foreign-born migrants by country of birth. This

assumption is subject to two foreseeable biases; (1) it

takes no account of undocumented migrants who

never legalized their status, and who may have different characteristics than those who legalized; and (2) it

takes no account of persons who entered and departed

prior to legalizing, who would (if nothing else) tend to

make the migration flow more youthful since such

persons are older upon departure than upon entry.

3. The distribution by age and sex of the net migration

flow from Puerto Rico arises from the method of

imputation. The method of cohort survival was used to

estimate the net migration out of Puerto Rico between

the 1980 and 1990 census dates. This method yields

D–16
an age distribution as a byproduct. Race and Hispanic

origin were not asked in the Puerto Rican census

questionnaire and could not be considered in the

census survival method. The distribution within each

age-sex category is imputed from the one-way flow of

persons from Puerto Rico to the United States from

1985 to 1990, based on the 1990 census of the 50

states and the District of Columbia.

4. As previously noted, we assume the overall net migration of legal temporary residents other than refugees to

be constant and of a magnitude and distribution necessary to maintain a constant stock of legal temporary

residents in the United States. The consideration of

this component in the accounting system is of virtually

no importance to population totals, but is quite important to the age distribution. Were it not considered, the

effect would be to age temporary residents enumerated in the census through the distribution; whereas, in

reality they are more likely replaced by other temporary residents of similar age. This stock distribution of

488,000 persons enumerated in the 1990 census is

derived by identifying characteristics, measured by the

census ‘‘long form’’ (20 percent sample) data, resembling those that qualify for various nonimmigrant visa

categories. The largest such category is foreign students. We estimate an annual distribution of net migration by computing the difference of two distributions of

population stock. The first is simply the distribution of

the 488,000 persons from the 1990 census. The

second is the distribution of the same population after

the effects of becoming 1 year older, including losses

to mortality, estimated from a life table. Thus, the

migration distribution effectively negates the effects of

cumulative aging of these persons in the cohortcomponent and inflation-deflation methods.

Migration of Armed Forces and civilian citizens by age,

sex, race, and Hispanic origin. Estimation of demographic detail for the remaining components of change

requires estimation of the detail of the civilian citizen

population residing overseas, as well as the Armed Forces

residing overseas and Armed Forces residing in the United

States. The first two are necessary to assign demographic

detail to the net migration of civilian citizens; the third is

required to assign detail to the effects of Armed Forces

recruitment on the civilian population.

Distributions of the Armed Forces by branch of service,

age, sex, race/origin, and location inside or outside the

United States are provided by the Department of Defense,

Defense Manpower Data Center. The location-and-servicespecific totals closely resemble those provided by the

individual branches of the services for all services except

the Navy. For the Navy, it is necessary to adapt the location

distribution of persons residing on board ships to conform

to census definitions, which is accomplished through a

special tabulation (also provided by Defense Manpower

Data Center) of persons assigned to sea duty. These are

prorated to overseas and U.S. residence, based on the

distribution of the total population afloat by physical location, supplied by the Navy.

In order to incorporate the resulting Armed Forces

distributions in estimates, the race-origin distribution must

also be adapted. The Armed Forces ‘‘race-ethnic’’ categories supplied by the Defense Manpower Data Center treat

race and Hispanic origin as a single variable, with the

Hispanic component of Black and White included under

‘‘Hispanic,’’ and the Hispanic components of American

Indian and API assumed to be nonexistent. There also

remains a residual ‘‘other race’’ category. The method of

converting this distribution to consistency with MARS employs,

for each age-sex category, the 1990 census MARS distribution of the total population (military and civilian) to supply

all MARS information missing in the Armed Forces raceethnic categories. Hispanic origin is thus prorated to White

and Black according to the total MARS population of each

age-sex category in 1990; American Indian and API are

similarly prorated to Hispanic and non-Hispanic. As a final

step, the small residual category is distributed as a simple

prorata of the resulting Armed Forces distribution, for each

age-sex group. Fortunately, this adaptation is very robust;

it requires imputing the distributions of only very small race

and origin categories.

The overseas population of Armed Forces dependents

is distributed by age and sex, based on an overseas

census conducted in connection with the 1970 census, of

U.S. military dependents. While this source obviously

makes no claim to currency, it reflects an age distribution

uniquely weighted in favor of young adult females and

children. Race and origin are based on the distribution of

overseas Armed Forces, with cross-categories of age-sex

with race-origin determined by the marginals. Detail for

civilian Federal employees overseas has been provided by

the Office of Personnel Management for decennial dates,

and assumed constant; their dependents are assumed to

have the same distribution as Armed Forces dependents.

Having determined these distributions for populations,

the imputation of migration of civilian citizens previously

described (equation (9)) can be carried out specifically for

all characteristics. The only variation occurs in the case of

age, where it is necessary to carry out the imputation by

birth cohort, rather than by age group.

The two very small components of deaths of the Armed

Forces overseas and net recruits to the Armed Forces from

the overseas population, previously described, are also

assigned the same level of demographic detail. Deaths of

the overseas Armed Forces are assigned the same demographic characteristics as the overseas Armed Forces. Net

recruits from overseas are assigned race and origin, based

on an aggregate of overseas censuses, in which Puerto

Rico dominates numerically. The age and sex distribution

follows the worldwide Armed Forces.

D–17
The civilian institutional population by age, sex, race,

and origin. The fundamental source for the distribution of

the civilian institutional population by age, sex, race, and

origin is a 1990 census/MARS tabulation of institutional

population by age, sex, race, origin, and type of institution.

The last variable has four values: nursing home, correctional facility, juvenile facility, and a small residual. From

this table, participation rates are computed for the civilian

population. These rates are applied in each year to the

current estimated distribution of the civilian population. As

previously observed, the institutional population total is

updated annually, until July 1 of the year 2 years prior to the

CPS reference date, by the results of a special report on

selected group quarters facilities. The report provides an

empirical update of the group quarters populations by type,

including these four institutional types. The demographic

detail can then be proportionately adjusted to sum to the

type-specific totals. For dates beyond the last group quarters survey, the detailed participation rates are recomputed

for the last available date, and these are simply applied to

the estimated or projected civilian population distribution.

Population Controls for States

The second-stage weighting procedure for the CPS

requires a distribution of the national civilian noninstitutional population ages 16 and over by state. This distribution is determined by a linear extrapolation through two

population estimates for each state and the District of

Columbia. The reference dates are July 1 of the years that

are 2 years and 1 year prior to the reference date for the

CPS population controls. The extrapolated state distribution is forced to sum to the national total population ages 16

and over by proportional adjustment. For example, the

state distribution for June 1, 1996, was determined by

extrapolation along a straight line determined by two data

points (July 1, 1994, and July 1, 1995) for each state. The

resulting distribution for June 1, 1996, was then proportionately adjusted to the national total for ages 16 and over,

computed for the same date. This procedure does not allow

for any difference among states in the seasonality of

population change, since all seasonal variation is attributable to the proportional adjustment to the national population.

The procedure for state estimates (e.g., through July 1

of the year prior to the CPS control reference date), which

form the basis for the extrapolation, differs from the nationallevel procedure in a number of ways. The primary reason

for the differences is the importance of interstate migration

to state estimates and the need to impute it by indirect

means. Like the national procedure, the state-level procedure depends on fundamental demographic principles embodied in the balancing equation for population change; however, they are not applied to the total resident or civilian

noninstitutional population but to the household population

under 65 years of age. Estimates of the group quarters

population under 65 and the population 65 and over

employ a different logic, based (in both cases) on independent sources of data for population size, benchmarked to

the results of the last census. Furthermore, while nationallevel population estimates are produced as of the first of

each month, state estimates are produced at 1-year intervals, with July 1 reference dates.

The Population 65 Years of Age and Over

The base population by state (which can be either a

census enumeration or an earlier estimate) is divided into

three large categories; the population 65 years of age and

over, the group quarters population under 65, and the

household (nongroup quarters) population under 65. The

first two categories are estimated directly by stock-based

methodologies; that is, the population change is estimated

as the change in independent estimates of population for

the categories. In the case of the population ages 65 and

over, the principal data source is a serial account of

medicare enrollees, available by county of residence.

Because there is a direct incentive for eligible persons to

enroll in the medicare program, the coverage rate for this

source is very high. To adapt this estimate to 1990 censuslevel, the change in the medicare population from census

date to estimate date is added to the 1990 census enumeration of the population 65 and over.

The Group Quarters Population Under 65 Years of

Age

The group quarters population is estimated annually

from the same special report on selected group quarters

facilities, conducted in cooperation with the Federal and

State Cooperative Program for Population Estimates (FSCPE),

used to update national institutional population estimates.

Because this survey is conducted at the level of the actual

group quarters facility, it provides detail at any level of

geography. Its results are aggregated to seven group

quarters types: the four institutional types previously discussed, and three noninstitutional types: college dormitories, military barracks, and a small noninstitutional group

quarters residual. Each type is disaggregated to the population over and under 65 years of age, based on 1990

census distributions of age by type. The change in the

aggregate group quarters population under 65 forms the

basis for the update of the group quarters population from

the census to the estimate date.

The Household Population Under 65 Years of Age

The procedure for estimating the nongroup quarters

population under 65 can be analogized to the national-level

procedures for the total population, with the following major

differences.

D–18
1. Because the population is restricted to persons under

65 years of age, the number of persons advancing to

age 65 must be subtracted, along with deaths of

persons under 65. This component is composed of the

64-year-old population at the beginning of each year,

and is measured by projecting census-based ratios of

the 64-year-old to the 65-year-old population by the

national-level change, along with proration of other

components of change to age 64.

2. All national-level components of population change

must be distributed to state-level geography, with

separation of each state total into the number of

events to persons under 65 and persons 65 and over.

Births and deaths are available from NCHS by state of

residence and age (for deaths). Similar data can be

obtained—often on a more timely basis—from FSCPE

state representatives. These data, after appropriate

review, are adopted in place of NCHS data for those

states. For legal immigration (including refugees), ZIP

Code of intended residence is coded by INS on the

immigrant public use file; this is converted to county

and state geography by a program known as ZIPCRS,

developed cooperatively by the Census Bureau and

the U.S. Postal Service (Sater, 1994). Net undocumented migration is distributed by state on the basis of

data from INS on the geographic distribution of undocumented residents legalizing their status under the

Immigration Reform and Control Act (IRCA). The emigration of legal residents is distributed on the basis of

the foreign-born population enumerated in the 1990

census.

3. The population updates include a component of change

for net internal migration. This is obtained through the

computation for each county of rates of out-migration

based on the number of exemptions under 65 years of

age claimed on year-to-year matched pairs of IRS tax

returns. Because the IRS codes tax returns by ZIP

Code, the previously mentioned ZIPCRS program is

used to identify county and state of residence on the

matched returns. The matching of returns allows the

identification for any pair of states or counties of the

number of ‘‘stayers’’ who remain within the state or

county of origin and the number of ‘‘movers’’ who

change address between the two. This identification is

based on the number of exemptions on the returns and

the existence or nonexistence of a change of address.

Dividing the number of movers for each pair by the

sum of movers and stayers for the state of origin yields

a matrix of out-migration rates from each state to each

of the remaining states. The validity of these interstate

migration rates depends positively on the level of

coverage (the proportion of the population included on

two consecutive returns). It is negatively affected by

the difference between tax filers and tax nonfilers with

respect to migration behavior, since nonfilers are assumed

to migrate at the same rate as filers.

The Exclusion of the Population Under 16 Years of

Age, Armed Forces, and Inmates of Civilian

Institutions

The next step in the state-level process is the estimation

of the age-sex distribution, which allows exclusion of the

population under 16. The major input to this procedure is a

nationwide inquiry to state governments and some private

and parochial school authorities for data on school enrollment. The number of school-aged children (exact age 6.5

to 14.5) for each annual estimate date is projected, without

migration, from the census date by the method of cohort

survival. This method begins with the number of persons

enumerated in the last census destined to be of school age

on the estimate date, known as the school-age cohort. For

reference dates more than 6.5 years from the census, the

enumerated cohort must be augmented with a count of

registered births occurring between the census date and

the estimate date minus 6.5 years. For example, if the

estimate reference date is July 1, 1995, the enumerated

cohort will consist of persons age 1.25 to 9.25, since the

census of April 1, 1990, is 5.25 years prior to the estimate

date. On the other hand, if the estimate reference date is

July 1, 1998, the cohort will consist of the census population aged under 6.25 years, plus registered births occurring

from April 1, 1990, through December 31, 1991. The

number of deaths of this cohort of children from the census

date to the estimate date (estimated from vital registration

data) is then subtracted to yield a projection of the schoolage population on the estimate reference date.

Subtracting the resulting projection from actual estimates of school-age population, determined by updating

the census school-age population via school enrollments,

yields estimates of cumulative net migration rates for

school-age cohorts for each state. These estimates are

used to estimate migration rates for each age group up to

age 18, using historical observation of the relationship of

the migration of each age group to the migration of the

school-age population. Populations by age are proportionately adjusted to sum to the national total, yielding estimates of the population under age 16. These are subtracted from the total to yield the population ages 16 and

over, required to produce the population of the CPS control

universe.

Once the resident population 16 years of age and older

has been estimated for each state, the population must be

restricted to the civilian noninstitutional universe. Armed

Forces residing in each state are excluded, based on

reports of location of duty assignment from the branches of

the Armed Forces (including the homeport and projected

deployment status of ships, for the Navy). The group

quarters report is used to exclude the civilian institutional

population. The group quarters data and some of the

Armed Forces data (especially information on deployment

of naval vessels) must be projected from the last available

date to the later annual estimate dates.

D–19
Dormitory Adjustment

The final step in the production of the CPS base series

for states is the adjustment of the universe from the census

universe to the CPS control universe with respect to the

geographic location of college students living in dormitories. The decennial census form specifically directs students living in dormitories to report the address of their

dormitories, while CPS interviews identify the address of

the family home. A dormitory adjustment for each state is

defined as the number of students with family home

address in the state residing in dormitories (in any state)

minus the number of students residing in dormitories in the

state. The latter estimate is a product of the previously

mentioned group quarters report, restricted to college

dormitories. The former is the same group quarters dormitory estimate, but adjusted by a ratio of students by family

resident state to students by college enrollment state,

which is computed from data from the National Center for

Education Statistics (NCES). Adding the dormitory adjustment (which may be negative or positive) to the censuslevel civilian noninstitutional population ages 16 and over

yields the population ages 16 and over for a universe

consistent with the CPS in every regard except adjustment

for underenumeration.

ADJUSTMENT OF CPS CONTROLS FOR NET

UNDERENUMERATION IN THE 1990 CENSUS

Beginning in 1995, the CPS controls were adjusted for

net undercoverage in the 1990 census. This adjustment

was based on the results of the Post-Enumeration Survey

(PES) carried out in the months following the census. From

this, census coverage ratios were obtained for various

large ‘‘poststrata’’ or cross-categories of a few variables

determined to be most related to underenumeration. The

present section addresses briefly the methodology of this

survey, the application of coverage ratios for poststrata to

obtain numerical levels of underenumeration in the CPS

universe, and the application of the resulting numerical

adjustment to the CPS population controls.

Important to the rationale behind the adjustment of CPS

controls is the origin of the decision to adjust them for

underenumeration, given that population estimates published by the Census Bureau are not. The Post-Enumeration

Survey was, in its design, conceived as a method of

providing an adjustment for net underenumeration in the

1990 census. In the year following the 1990 census date,

estimates of net underenumeration were produced. In a

decision effective July 15, 1991, Secretary Robert Mosbacher announced a decision not to adjust the census for

undercount or overcount (Federal Register, 1991). Cited in

the decision was a large amount of research pointing to

improvement in the estimates of the national population

resulting from incorporation of the Post-Enumeration Survey, coupled with a lessening of the accuracy of estimates

for some states and metropolitan areas. The decision also

called for further research into the possibility of incorporating PES results in the population estimates programs.

Research conducted over the following 18 months under

the auspices of the Committee on Adjustment of Postcensal Estimates (CAPE), a committee appointed by the

Director of the Census Bureau, provided a description of

the PES methodology, and a detailed analysis of the effect

of adjustment on national, state, and county estimates.

This research is summarized in a report, which forms the

basis for all technical information regarding the PES discussed here (U.S. Census Bureau, 1992). After extensive

public hearings, including testimony by members of Congress, Barbara Bryant, then Director of the Census Bureau,

decided, effective December 30, 1992, not to incorporate

PES results in the population estimates programs, but

offered to calibrate Federally sponsored surveys, conducted by the Census Bureau, to adjusted population data

(Federal Register, 1993). The decision cited the finding by

CAPE that estimates of large geographic aggregates were

improved by inclusion of PES estimates, while the same

finding could not be determined for some states and

substate areas. In the course of 1993, the Bureau of Labor

Statistics opted to include the 1990 census, with adjustment based on the Post-Enumeration Survey, in the controls for the CPS, beginning January 1994.

We stress that the intent of the PES from its inception

was to serve as a device for adjusting the census, not for

the calibration of surveys or population estimates. Its

application to population controls for the CPS and other

surveys was based on an analysis of its results. Essential

to its usefulness is the primacy of national-level detail in the

evaluation of survey controls, coupled with the apparent

superior performance of the PES for large geographic

aggregates.

THE POST-ENUMERATION SURVEY AND

DUAL-SYSTEM ESTIMATION

The PES consisted of a reinterview, after the 1990

census, of all housing units within each of a sample of

small geographic units. The geographic unit chosen was

the census block, a small polygon of land surrounded by

visible features, frequently four-sided city blocks bounded

by streets. The sampling universe of all such blocks was

divided into 101 strata, based on certain variables seen to

be related to the propensity to underenumeration. These

consisted of geography, city size, racial and ethnic composition, and tenure of housing units (owned versus rented).

The strata were sampled; the entire sample consisted of

more than 5,000 blocks.

The reinterview and matching procedures consisted of

an interview with all households within each of the sampled

blocks followed by a comparison of the resulting PES

observations with original census enumeration records of

individual persons. Care was taken to ensure the independence of the reinterview process from the original enumeration, through use of different enumerators, as well as

D–20
independent local administration of the activity. Through

the comparison of census and PES records of individual

persons, matches were identified, as were nonmatches

(persons enumerated in one survey but not in the other),

and erroneous enumerations (persons who either should

not have been enumerated, or should not have been

enumerated in that block). Where necessary, follow-up

interviews were conducted of census and PES households

to determine the status of each individual with respect to

his/her match between census and PES or his/her possible

erroneous enumeration.

This matching procedure yielded a determination for

each block of the number of persons correctly enumerated

by the census only, the number of persons enumerated by

the PES only, and the number enumerated by both. The

number of persons missed by both census and survey

could then be imputed by observing the relationship—within

the PES sample—of the number missed by the census to

the number enumerated, and generalizing this relationship

to those missed by the PES sample. The resulting estimate

was called a ‘‘dual-system estimate,’’ because of its reliance on two independent enumeration systems. In most

cases, the dual-system estimate was higher than the

original enumeration, implying a net positive undercount in

the census. For some blocks, the dual-system estimate

was lower than the census count, meaning the number of

incorrect enumerations exceeded the number of persons

imputed by the matching process, resulting in a net overcount (negative undercount).

Having established dual-system estimates of the sampled

blocks, a new stratification—-this time of individual persons

(rather than blocks)—was defined; the resulting strata were

dubbed ‘‘poststrata,’’ as they were used for retrospective

analysis of the survey results (as opposed to being part of

the sampling design). These poststrata formed the basic

unit for which census coverage ratios were computed.

They were defined on data for characteristics considered

relevant to the likelihood of not being enumerated in the

census, including large age group, sex, race (including

residence on a reservation, if American Indian), Hispanic

origin, housing tenure (living in an owned versus rented

dwelling), large city, urban, or rural residence, and geographic region of residence. The choice of categories of

these variables was intended to balance two conflicting

objectives. The first was to define strata that were homogeneous with respect to the likelihood of enumeration in

the census. The second was to ensure that each stratum

was large enough to avoid spuriousness in the estimation

of census coverage. As a result, the categories were

asymmetric with respect to most of the variables: the

choice of categories of one variable depended on the value

of another. Seven categories of age and sex (four age

categories by two sex categories, but with males and

females combined for the youngest category) were crossed

with 51 categories of the other variables to produce a total

of 357 poststrata.

The percentages of undercount or overcount were computed for each poststratum by dividing the difference

between the dual-system estimate of population and the

census enumeration for the sampled population by the

census enumeration. Applying these ratios to the poststrata, this time defined for the entire noninstitutional

population of the United States enumerated in the 1990

census, yielded estimates of undercoverage. Among variables tabulated in the CPS, there was considerable variation in the adjustment factors by race, since race was an

important source of heterogeneity among poststrata. Moreover, as a result of the small number of age categories

distinguished in the poststrata, adjustment factors tended

to vary sharply between single year age groups close to the

limits of neighboring age categories.

Application of Coverage Ratios to the MARS

Population

Undercount ratios were applied to the 1990 MARS

distribution by age, sex, race, and origin. Because the

poststrata included, in their definition, one racial category

(‘‘Other’’) that did not exist in the MARS distribution, undercount ratios for census race-origin categories were imputed

to the corresponding MARS categories. These undercount

ratios were implemented in the MARS microdata file, which

was then tabulated to produce resident population by age,

sex, race, Hispanic origin, and state. ‘‘Difference matrices’’

were obtained by subtracting the unadjusted MARS from

the adjusted MARS distribution. This was done for both the

national age-sex-race-origin distribution, and the 51-state

distribution of the civilian noninstitutional population ages

16 years and over.

An important assumption in the definition of the difference matrix was the equality of the adjustment for resident

population and civilian noninstitutional population. This

amounted to an assumption of zero adjustment difference

for the active-duty military and civilian institutional populations. The assumption was natural in the case of the

institutional population, because this population was expressly

excluded from the PES. In the case of the Armed Forces,

the population stock is estimated, for both the nation and

for states, from data external to the census; hence, there

was no basis on which to estimate PES-consistent undercount for the Armed Forces population.

Once derived, the differences have been added to all

distributions of population used for independent controls

for the CPS produced since January 1, 1995. While it

would have been technically more satisfying to apply

undercount ratios, as defined by the PES, directly to the

CPS population controls, this would have required projections of the poststrata for CPS control reference dates, for

which no methodology has been developed.

This adjustment of the CPS universe is the final step in

the process of computing independent population controls

for the second-stage weighting of the Current Population

Survey.

D–21
A question that frequently arises regarding adjustment

for underenumeration is how it relates to inflation-deflation,

as previously discussed. We need to inflate estimated

populations to an ‘‘adjusted level’’ in order to apply cohortcomponent logic to the estimation of the age distribution.

Why then is it necessary to deflate to census level, then

inflate again to adjust for underenumeration? The answer

to this lies in the relationship of the purpose of the

inflation-deflation method to the method of adjustment. In

the case of inflation-deflation, the sole objective is to

provide an age distribution that is unaffected by differential

undercount and age reporting bias, from single year to

single year of age throughout the distribution. This is

necessary because cohort component logic advances the

population 1 year of age for each 1-year time interval. The

Demographic Analysis population meets this objective well,

as it is developed, independently of the census, from series

of vital and migratory events that are designed to be

continuous, based on time series of administrative data.

However, the definition of poststrata for dual-system estimates in the PES is based on the application of uniform

adjustment factors to large age categories, with large

differences at the limits of the categories. This would not

serve the needs of inflation-deflation, even though the PES

has been assumed to be a superior means of adjusting the

census for underenumeration (at least for subnational

geographic units).

THE MONTHLY AND ANNUAL REVISION

PROCESS FOR INDEPENDENT POPULATION

CONTROLS

Each month a projection of the civilian noninstitutional

population of the United States is produced for the CPS

control reference date. Each projection is derived from the

population at some base date and the change in the

population from the base date to the reference date of the

projection. In every month except January, the base date

(the date after which new data on population change can

be incorporated in the series) is 2 months prior to the CPS

reference date. In January, the entire monthly series back

to the last census is revised, meaning the base date is the

date of the last census (currently April 1, 1990). Early in the

decade (in the current decade, January 1, 1994), the

population from the most recent decennial census is introduced for the first time as a base for the January revision.

As a consequence of the policy of ongoing revision of a

month back, for monthly intervals from January 1 to

December 1, the monthly series of population figures

produced each month for 1 month prior to the CPS

reference date is internally consistent for the year of

reference dates from December 1 to November 1—meaning

the month-to-month change is determined by the measurement of population change. For actual CPS reference

dates from January 1 to December 1, the series is not

strictly consistent; there is a small amount of ‘‘slippage’’ in

the month-to-month intervals, which is the difference between:

1. The population change during the 1 month immediately preceding the CPS reference date, as measured

a month after the CPS control figure is produced, and

2. The population change in the month preceding the

CPS date, associated with the actual production of the

CPS controls.

This slippage is maintained for the purpose of preventing cumulative error, since the measurement of population

change in the last month at the time of measurement (2) is

based entirely on projection (no data are available to

measure the change); whereas, some key administrative

data (e.g., preliminary estimates of births and deaths) are

generally available for the month before the last (1). The

slippage rarely exceeds 20,000 persons for the population

ages 16 and over, compared to a monthly change in this

population that varies seasonally from roughly 150,000 to

260,000 persons, based on calendar year 1994. Because

the numerically largest source of slippage is usually the

projection of births, most of the slippage is usually confined

to the population under 1 year of age, hence, of no concern

to labor force applications of the survey.

Monthly population controls for states, by contrast,

normally contribute no slippage in the series from January

to December, because they are projections from July 1 of

the previous year. Generally, any slippage in the state

series is a consequence of their forced summation to the

national total for persons ages 16 and over. The annual

revision of the population estimates to restart the national

series each January affects states as well as the Nation, as

it involves not only revisions to the national-level estimates

of population change, but also the distribution of these

components to states, and an additional year of internal

migration data.

For the revision of the entire series each January,

preliminary estimates of the cumulative slippage for the

national population by demographic characteristic (not the

states) are produced in November and reviewed for possible effect on the population control series. Annual slippage (the spurious component of the difference between

December 1 and January 1 in CPS production figures) is

generally confined to less than 250,000 in a year, although

larger discrepancies can occur if the revision to the series

entails cumulative revision of an allowance for an unmeasured component (such as undocumented immigration),

the introduction of a new census, or a redefinition of the

universe (e.g., with respect to adjustment for undercoverage).

PROCEDURAL REVISIONS

The process of producing estimates and projections, like

any research activity, is in a constant state of flux. Various

technical issues are addressed with each cycle of estimates for possible implementation in the population estimating procedures, which carry over to the CPS controls.

These procedural revisions tend to be concentrated early

in the decade, because of the introduction of data from a

D–22
new decennial census, which can be associated with new

policies regarding population universe or estimating methods. However, they may occur at any time, depending

either on new information obtained regarding the components of population change, or the availability of resources

to complete methodological research.

REFERENCES

Ahmed, B. and Robinson, J. G. (1994), Estimates of

Emigration of the Foreign-Born Population: 1980-1990,

U.S. Census Bureau, Population Division Technical Working Paper No. 9, December, 1994.

Anderson, R. N., Kochanek, K. D., Murphy, S. L. (1997),

‘‘Report of Final Mortality Statistics, 1995,’’ Monthly Vital

Statistics Report, National Center for Health Statistics,

Vol. 45, No. 11, Supplement 2.

Fay, R. E., Passel, J. S., and Robinson, J. G. (1988),

The Coverage of the Population in the 1980 Census,

1980 Census of Population and Housing, U.S. Census

Bureau, Vol. PHC–E–4, Washington, DC: U.S. Government Printing Office.

Federal Register, Vol. 56, No. 140, Monday, July 22,

1991.

Federal Register, Vol, 58, No. 1, Monday, January 4,

1993.

Robinson, J. G. (1994), ‘‘Clarification and Documentation of Estimates of Emigration and Undocumented Immigration,’’ unpublished U.S. Census Bureau memorandum.

Sater, D. K. (1994), Geographic Coding of Administrative Records— Current Research in the ZIP/SECTORto-County Coding Process, U.S. Census Bureau, Population Division Technical Working Paper No. 7, June, 1994.

U.S. Census Bureau (1991), Age, Sex, Race, and

Hispanic Origin Information From the 1990 Census: A

Comparison of Census Results With Results Where

Age and Race Have Been Modified, 1990 Census of

Population and Housing, Publication CPH-L-74.

U.S. Census Bureau (1992), Assessment of Adjusted

Versus Unadjusted 1990 Census Base for Use in Intercensal Estimates: Report of the Committee on Adjustment of Postcensal Estimates, unpublished, August 7,

1992.

U.S. Immigration and Naturalization Service (1997),

Statistical Yearbook of the Immigration and Naturalization Service, 1996, Washington, DC: U.S. Government

Printing Office.

Ventura, S. J., Martin, J. A., Curtin, S. C., Mathews, T.J.

(1997a), ‘‘Report of Final Natality Statistics, 1995,’’ Monthly

Vital Statistics Report, National Center for Health Statistics, Vol. 45, No. 11, supplement.

Ventura, S. J., Peters, K. D., Martin, J. A., and Maurer, J.

D. (1997b), ‘‘Births and Deaths: United States, 1996,’’

Monthly Vital Statistics Report, National Center for Health

Statistics, Vol. 46, No. 1.

SUMMARY LIST OF SOURCES FOR CPS

POPULATION CONTROLS

The following is a summary list of agencies providing

data used to calculate independent population controls for

the CPS. Under each agency are listed the major data

inputs obtained.

1. The U.S. Census Bureau (Department of Commerce)

The 1990 census population by age, sex, race,

Hispanic origin, state of residence, and household or

type of group quarters residence

Sample data on distribution of the foreign-born

population by sex, race, Hispanic origin, and country of

birth

The population of April 1, 1990, estimated by the

Method of Demographic Analysis

Decennial census data for Puerto Rico

2. National Center for Health Statistics (Department of

Health and Human Services)

Live births by age of mother, sex, race, and Hispanic

origin

Deaths by age, sex, race, and Hispanic origin

3. The U.S. Department of Defense

Active-duty Armed Forces personnel by branch of

service

Personnel enrolled in various active-duty training

programs

Distribution of active-duty Armed Forces Personnel

by age, sex, race, Hispanic origin, and duty location

(by state and outside the United States)

Deaths to active-duty Armed Forces personnel

Dependents of Armed Forces personnel overseas

Births occurring in overseas military hospitals

4. The Immigration and Naturalization Service (Department of Justice)

Individual records of persons immigrating to the

United States, month and year of immigration, including age, sex, country of birth, and ZIP Code of intended

residence, and year of arrival (if different from year of

immigration)

5. Office of Refugee Resettlement (Department of Health

and Human Services)

Refugee arrivals by month, age, sex, and country of

citizenship

6. Department of State

Supplementary information on refugee arrivals

7. Office of Personnel Management

Civilian Federal employees overseas in overseas

assignments

D–23
Appendix E.

State Model-Based Labor Force Estimation

INTRODUCTION

Small samples in each state and the District of Columbia

result in unacceptably high variation in the monthly CPS

composite estimates of state employment and unemployment. The table below gives the sample sizes, the standard

errors, and coefficient of variation (CVs) for unemployment

and employment assuming an unemployment rate of 6

percent for the states and the Nation as a whole. These

numbers are based on the current design which was in

effect January 1996 through July 2001.1

Table E–1. Reliability of CPS Estimators Under the

Current Design

Unemployment

rate

Employment-topopulation

Number

of households in

CV STD CV STD sample

Nation ........... 1.90 0.11 0.25 0.16 50,000

States . . ......... 15.70 0.94 1.98 1.25 546-4055

In an effort to produce less variable labor force estimates, BLS introduced time series models to ‘‘borrow

strength’’ over time. The models are based on a signalplus-noise approach to small area estimation where the

monthly CPS estimates are treated as stochastically varying labor force values obscured by sampling error (Tiller,

1992). Given a model for the true labor force values and

sampling error variance-covariance information, signalextraction techniques are used to estimate the true labor

force values. This approach was first suggested by Scott

and Smith (1974) and has been more fully developed by

Bell and Hillmer (1990), Binder and Dick (1990) and

Pfefferman (1992).

TIME SERIES COMPONENT MODELING

For each state, separate models for the CPS employmentto-population ratio and the unemployment rate are developed. In the signal-plus-noise model, the CPS labor force

estimate at time t, denoted by y(t), is represented as the

sum of two independent processes

yt t et(1)

where trepresents the systematic part of the true labor

force value and etis the sampling error. The basic

objective is to produce an estimator for tthat is less

variable than the CPS composite estimator. The signal

component, θ(t), represents the variation in the sample

values due to the stochastic behavior of the population.

The sampling error component, e(t), consists of variation

arising from sampling only a portion of the population.

The Signal Component

In the state labor force models, the signal component is

represented by an unobserved variance component model

with explanatory variables (Harvey, 1989)

t 

t t

where



t Xtt Tt St(2)

X(t) = vector of known explanatory variables

β(t)= random coefficient vector

T(t) = trend component

S(t) = periodic or seasonal component

η(t)= residual noise.

The stochastic properties of each of these components are

determined by one or more normally distributed, mutually

independent white noise disturbances

	j

t
 NID0,	j

2

(3)

where j indexes the individual components.

Regression component. The regression component

consists of explanatory variables, X(t), with time-varying

coefficients, β(t)

Mt Xtt. (4)

This component accounts for variation in the CPS

estimates that can be explained by a set of observable

economic variables developed from auxiliary data sources

that are independent of the CPS sampling error. A common

core of state-specific explanatory variables has been developed: unemployment insurance claims from the FederalState Unemployment Insurance System (Blaustein, 1979),

1

They do not reflect the increased reliability after the sample expansion in July 2001 due to the State Children’s Health Insurance Program.

(See Appendix J.)

E–1
a nonagricultural payroll employment estimate from the

BLS Current Employment Statistics (CES) program (US

DOL, 1997), and intercensal population estimates developed by the Census Bureau. The explanatory variable

included in the employment model is the CES estimate,

adjusted for strikes, expressed as a percent of the state’s

population. For the unemployment rate model the explanatory variable is the claims rate defined as the ratio of the

worker claims for unemployment insurance (UI) benefits to

CES employment.

The regression coefficients are modeled as a random

walk process

t t  1 	t, 	t
 NID0,

2

. (5)

The effect of 	tis to allow the coefficients to evolve

slowly over time. A zero variance, 

2  0, results in a fixed

regression coefficient.

Time series components. The explanatory variables in

the models account for an important part of the variation in

the CPS estimates but, because of definitional and other

differences, leave significant amounts of the nonsampling

error variation in the CPS estimates unexplained (Tiller,

1989). Therefore, stochastic trend and seasonal components are added to the model to adjust the explanatory

variables for differences from the CPS measure of state

employment and unemployment. Since the explanatory

variables account for a portion of the trend and seasonal

variation in the CPS, these stochastic time series components are residuals; they do not account for all the trend

and seasonal variation in the CPS.

Residual trend component. The trend component is represented by a local approximation to a linear trend with a

random level, Tt, and slope, R(t)

Tt Tt1 Rt1 	


*

t

and

Rt Rt1 	Rt

where

	


*

t k



1

m

kkt 	Tt

and

kt 

1 if t  tk

0 if t  tk. (6)

External shocks which cause permanent shifts in the

level of the trend are specified in the disturbance term

associated with the trend level, 	Tt. The coefficient, k,

represents the impact of the shock at time tk.

The disturbance terms 	Ttand 	Rtare assumed to be

mutually uncorrelated white noise random variates with

zero means and constant variances

	Tt
 NID0,T

2

(7)

	Rt
 NID0,R

2 

E 	T



t	T

*

t  0.

The larger the variances, the greater the stochastic

movements in the trend. The effect of 	Ttis to allow the

level of the trend to shift up and down, while 	Rtallows the

slope to change. This two-parameter trend model allows for

a variety of patterns. If the variances of the two disturbances are both zero, then this component reduces to a

fixed linear trend. A random walk results when the level

variance is positive and the slope is identically zero and,

when explanatory variables are included in the model,

results in an intercept which varies over time.

Residual seasonal component. This component is the sum

of up to six trigonometric terms associated with the 12-month

frequency and its five harmonics

St 

j1

6

Sj

t. (8)

Each frequency component is represented by a pair of

stochastic variables

Sj

t cosjSjt1 sinjSj

*

t1 	sjt(9)

Sj

*

t sinjSjt1 cosjSj

*

t1 	s

*

j

t

j = 2j

12

where 	sj and 	s

*

j are zero mean white noise processes

which are uncorrelated with each other, and have a common variance s

2

. The white noise disturbances are assumed to have a common variance, so that the change in

the seasonal pattern depends upon a single parameter. If

the common variance is zero then the seasonal pattern is

fixed over time. The expected values of the seasonal

effects add to zero over a 12-month period.

Residual noise component. The residual noise component consists of random variation unaccounted for by other

components plus unusually large transitory fluctuations or

outliers

t t Ot. (10)

Irregular component, Ι(t). In some cases after estimating

the signal and sampling error components, we find that the

signal still contains significant irregular movements, not

attributable to sampling error, which tend to average to

zero over a short period of time. In this case an additional

irregular component is added to the model specification to

E–2
further smooth the estimates of the signal. This component

is specified as consisting of a single white noise disturbance with a zero mean and constant variance

t 	t, 	t
 N0,

2

. (11)

If the variance for this component is zero, then the

irregular is identically zero and can be dropped from the

model.

Additive outlier component, O(t). An additive outlier represents a one-period transitory shift in the level of the

observed series

Ot 

j

jj

t

where

j

t  1 if t  j

0 otherwise. (12)

The coefficient j is the change in the level of the series at

time j. Time series are typically influenced by exogenous

disturbances that affect specific observations. These outliers may occur because an unusually nonrepresentative

sample of households was selected or because some real,

nonrepeatable event occurred in the population. Irrespective of its origin, an outlier of this type affects only a single

observation at a time and is unrelated to the time series

model of the signal. Because an outlier represents a

sudden, large, temporary change in the level of the CPS,

the model may attempt to initially adjust to this break as if

it were permanent. Accordingly, it is important to identify

such outliers and then discount them when estimating the

signal.

While the general model of the signal, just described, is

very flexible, not all of the components discussed above

are necessarily needed. The seasonal component may

have less than 6 frequency components, depending upon

the seasonal nature of the series being modeled. The

regressor variables may be able to explain a substantial

amount of variation in the observed series with fixed

coefficients. On average, the CES explains 11 percent of

the month-to-month variation in the CPS employment-topopulation ratio (CPSEP) while the UI data account for

about 15 percent of the monthly variation in the CPS

unemployment rate (CPSRT). If good regressor variables

are not available, the signal may be represented by just the

time series component; this results in a univariate analysis.

Sampling error component. Sampling error is defined as

the difference between the population value, θ(t) , and the

survey estimate

et yt t(13)

where e(t) has the following properties:2

Eet  0 (14)

Varet  e

2

t(15)

el Eetetl

e

2

t(16)

The CPS estimates contain measurement error which is

very difficult to quantify. We ignore this source of error by

treating our target variable as the set of values the CPS

would produce with a complete census of the population.

The sampling error is, therefore, assumed to have a zero

expectation. While the variances and, hence, the covariances change over time, the autocorrelations are treated

as stationary (see below.)

When modeling the sampling error, it is important to

account for sample design features which are likely to have

a major effect on the error structure e(t). The autocorrelation structure of the CPS sampling error depends strongly

upon the CPS rotating panel design and population characteristics (see Chapter 3). Another important characteristic of the state CPS estimator is its changing reliability over

time. That is, the absolute size of the sampling error is not

fixed but changes because of redesigns, sample size

changes, and variation in labor force levels. Prior to 1985,

some state estimates were produced from a national

design. Special sample supplementations under the old

national design also had an effect on the reliability of

selected state samples in the late 1970s and early 1980s.

A state-based design was phased in during 1984/85 along

with improved procedures for noninterviews, ratio adjustments, and compositing. This redesign had a major effect

on the reliability of many of the state CPS estimates. Even

with a fixed design and sample size, the variance of the

sampling error component changes because it is also a

function of the size of the labor force characteristics of the

population being measured. Since the CPS variance changes

across time and the autocorrelation structure is stable over

time, we express e(t) in multiplicative form as

et te*t. (17)

This expression allows us to capture the autocorrelated

and heteroscedastic structure of e(t). The variance inflation

factor, t, accounts for heteroscedasticity in the CPS and

is defined by

t et

e* (18)

where etis the GVF estimate of the standard error for the

CPS estimate and e* is the standard error of the autoregressive moving average (ARMA) process, e*t.

2

In the context of time series analysis, we view the variances and

covariances of the CPS estimators as properties of the sampling error

component.

E–3
The CPS autocorrelation structure is captured through

e*twhich is modeled as an ARMA process

e*t 1

LL	e*t(19)

where L is the lag operator, that is, Lk

Xt Xtk so that

L 1  1L2L2  ... pLp

and

L 1  1L2L2  ... qLq. (20)

The parameters, 1, 2 , . . ., p, and 1, 2, . . ., q are

estimated from the sampling error lag correlations (Dempster and Hwang, 1990) through the autocorrelation function

e*l LL1



LL1

(21)

where we set e*  l e*l. The coefficients θ and φ are

then used to compute the impulse response weights {gk}

through the generating function

gL 1

LL. (22)

The variance of the ARMA process e*tis computed as

e*

2 =

k=0



gk

2

. (23)

We can then compute the variance inflation γ(t) factor

using CPS standard error estimates which are obtained

from generalized variance function (see Chapter 13) for

et.

ESTIMATION

The parameters of the noise component are derived

directly from design-based variance-covariance information. The state CPS variance estimates are obtained

through the method of generalized variance functions (see

Chapter 13). State level autocorrelations of the sampling

error are based on research conducted by Dempster and

Hwang (1990) that used a variance component model to

compute autocorrelations for the sampling error. After the

unobserved signal and noise components are put into the

state-space form, the unknown parameters of the variance

components of the signal are estimated by maximum

likelihood using the Kalman filter (Harvey, 1989). Given

these parameter values, the filter calculates the expected

value of the signal and the noise components at each point

of time conditional on the observed data up to the given

time point. As more data become available, previous

estimates are updated by a process called smoothing

(Maybeck, 1979). For more details, see Tiller (1989).

DIAGNOSTIC TESTING

A model should adequately represent the main features

of movements in the CPS. An analysis of the model’s

prediction errors is the primary tool for assessing goodness

of fit. This is an indirect test of the model. The actual model

error is the difference between the true value of the signal

and the model’s estimate of that value. Since we do not

observe the true values, but only the CPS, which contains

sampling error, we cannot compute the actual model error.

The overall model, however, provides an estimate of the

signal and sampling error, which sum to an estimate of the

CPS. We may, therefore, use the model to predict new

CPS observations. If the model’s prediction errors are

larger than expected or do not average to zero, then this

suggests that the signal and/or noise components may be

misspecified. In this way, an examination of the model

errors in predicting the CPS provides an overall test of its

consistency with the CPS data.

The prediction errors are computed as the difference

between the current values of the CPS and the predictions

of the CPS made from the model, based on data prior to

the current period. Since these errors represent movements not explained by the model, they should not contain

any systematic information about the behavior of the signal

or noise component of the CPS. Specifically, the prediction

errors, when standardized, should approximate a randomly

distributed normal variate with zero mean (unbiased) and

constant variance. The models are subjected to a battery of

diagnostic tests to check the prediction errors for departure

from these properties.

MONTHLY PROCESSING

State agency staff prepare their official monthly estimates using software developed by BLS that implements

the KF. The state model-based labor force estimates are

generally released 2 weeks after the release of the national

labor force estimates. The KF algorithm is particularly well

suited for the preparation of current estimates as they

become available each month. Since it is a recursive data

processing algorithm, it does not require all previous data

to be kept in storage and reprocessed every time a new

sample observation becomes available. All that is required

is an estimate of the state vector and its covariance matrix

for the previous month. The computer interface used by the

states to make, review, and transmit their model estimates

is a system of interactive programs called STARS (State

Time Series Analysis and Review System). The software is

interactive, querying users for their UI and CPS data and

then combining these data with CPS estimates to produce

model-based estimates.

END-OF-THE-YEAR PROCESSING

At the end of the year, a number of revisions are made

to the model estimates. The model estimates are first

re-estimated to incorporate new population estimates obtained

E–4
from the Census Bureau and revisions to state-supplied

data. The revised estimates are then smoothed and benchmarked to CPS annual averages. Finally, the benchmarked

series is then seasonally adjusted using the X-11 ARIMA

seasonal adjustment procedure.

Re-Estimation and Benchmarking

After the Census Bureau provides new population controls to BLS, the state CPS labor force estimates are

adjusted to these controls. Similarly, state agencies revise

their CES and UI claims data as more information about

the series becomes available throughout the year. Using

the revised state CPS and input data, the KF produces a

revised estimate for the current year. The revised estimates

are smoothed, using a fixed interval smoothing algorithm,

to incorporate data accumulated during the current year.

A benchmarking process follows the smoothing of the

forward filter estimates. Since the CPS state samples were

designed to produce reliable annual average estimates,

the annual average of the smoothed estimates is forced to

equal the CPS annual average. The method used during

the benchmarking procedure is called the Denton method.

It forces the average of the monthly smoothed estimates to

equal the CPS state annual averages while minimizing

distortions in the month-to-month changes of the smoothed

estimates.

Seasonal Adjustment

The model estimates are seasonally adjusted using the

X-11 ARIMA seasonal adjustment procedure. Seasonal

factors for the first half of the current year, January through

June, are based on the historical series. Factors for July

through December are calculated from a data series comprising the historical series and the forward filter estimates

for the first half of the current year. The models are

designed to suppress sampling error but not to decompose

the series into seasonal and nonseasonal variation. This

preprocessed series can then be adequately decomposed

by the X-11 filters (Tiller, 1996).

RESULTS

Using the basic model structure described above, employment and unemployment models were developed for all of



















      	  
       	  
   


  
 






 	

 



E–5
the states. Auxiliary variables, available by state, were

used in the regression components: Current Employment

Statistics survey employment (CESEM), worker claims for

unemployment insurance benefits (UI), and population

estimated by the Census Bureau. The general form for

each model is

CPSEPt tCESEPt Trendt Seasonalt Noiset

(24)

CPSRTt tCLRSTt Trendt Seasonalt Noiset

where:

CESEP = 100(CESEM/POP)

CLRST = 100(UI/CESEM)

POP = noninstitutional civilian 16+ population

The basic signal component consists of a regression

component with a time varying coefficient, trend level and

slope, and six seasonal frequencies, irregular variation and

outliers. The basic noise component consists of sampling

error. Once estimated, these models were subjected to

diagnostic testing. In a well-specified model, the standardized one-step-ahead prediction errors should behave approximately as white noise, that is, be uncorrelated with a zero

mean and fixed variance. To be acceptable, the final model

was required to show no serious departures from the white

noise properties. Once satisfactory results were obtained,

further decisions were based on goodness of fit measures

and Akaike’s Information Criterion (Harvey,1989) and on

subject matter knowledge.

Often, one or more of these components could be

simplified. For the signal, the trend slope could often be

dropped and the number of seasonal frequencies reduced.

In many cases, the estimated variance of the irregular was

close to zero, allowing it to be dropped from the model.

A graph of the CPS unemployment rate and the signal

from a state unemployment rate model is on page E–5. The

signal is considerably smoother than the CPS. Elimination

of the sampling error from the CPS by signal extraction

removed about 65 percent of the monthly variation in the

series.

REFERENCES

Bell, W.R. and Hillmer, S.C. (1990), ‘‘The Time Series

Approach to Estimation for Repeated Surveys,’’ Survey

Methodology, 16, 195-215.

Binder, D.A. and Dick, J.P. (1989),‘‘Modeling and Estimation for Repeated Surveys,’’ Survey Methodology, 15,

29-45.

Blaustein, S.J. (1979), paper prepared for the National

Commission on Employment and Unemployment Statistics.

Dempster, A.P., and Hwang, J.S. (1990), ‘‘A Sampling

Error Model of Statewide Labor Force Estimates From the

CPS,’’ Paper prepared for the U.S. Bureau of Labor

Statistics.

Evans, T., Tiller, R., and Zimmerman, T. (1993), ‘‘Time

Series Models for State Labor Force Series,’’ Proceedings

of the Survey Research Methods Section, American

Statistical Association.

Harvey, A.C. (1989), Forecasting, Structural Time

Series Models and the Kalman Filter, Cambridge: Cambridge University Press.

Maybeck, P.S. (1979), Stochastic Models, Estimation,

and Control (Vol. 2), Orlando: Academic Press.

Pfeffermann, D. (1992), ‘‘Estimation and Seasonal Adjustment of Population Means Using Data From Repeated

Surveys,’’ Journal of Business and Economic Statistics, 9, 163-175.

Scott,A.J., and Smith, T.M.F. (1974), ‘‘Analysis of Repeated

Surveys Using Time Series Methods,’’ Journal of the

American Statistical Association, 69, 674-678.

Scott, A.J., Smith, T.M.F., and Jones, R.G. (1977), ‘‘The

Application of Time Series Methods to the Analysis of

Repeated Surveys,’’ International Statistical Review, 45,

13- 28.

Thisted, R.A. (1988), Elements of Statistical Computing: Numerical Computation, New York: Chapman and

Hall.

Tiller, R. (1989), ‘‘A Kalman Filter Approach to Labor

Force Estimation Using Survey Data,’’ Proceedings of the

Survey Research Methods Section, American Statistical

Association, pp. 16-25.

Tiller, R. (1992), ‘‘An Application of Time Series Methods

to Labor Force Estimation Using CPS Data,’’ Journal Of

Official Statistics, 8, 149-166.

U.S. Department of Labor, Bureau of Labor Statistics

(1997), BLS Handbook of Methods, Bulletin 2490.

Zimmerman, T., Tiller, R., and Evans, T. (1994), ‘‘State

Unemployment Rate Time Series Models,’’ Proceedings

of the Survey Research Methods Section, American

Statistical Association.

E–6
Appendix F.

Organization and Training of the Data Collection Staff

INTRODUCTION

The data collection staff for all Census Bureau programs

is directed through 12 regional offices (ROs) and 3 telephone centers (computer assisted telephone interviewing -

CATI). The ROs collect data in two ways; CAPI (computer

assisted personal interviewing) and PAPI (paper-and-pencil

interviewing). The 12 ROs report to the Chief of the Field

Division whose headquarters is located in Washington, DC.

The three CATI facility managers report to the Chief of the

National Processing Center (NPC).

ORGANIZATION OF REGIONAL OFFICES/CATI

FACILITIES

The staffs of the ROs and CATI facilities carry out the

Census Bureau’s field data collection programs, both sample

surveys and censuses. Currently, the ROs supervise about

6,000 part-time and intermittent field representatives (FRs)

who work on continuing current programs and one-time

surveys. Approximately 2,100 of these FRs work on the

Current Population Survey (CPS). When a census is being

taken, the field staff increases greatly.

The location of the ROs and the boundaries of their

responsibilities are displayed in Figure F–1. RO areas were

originally defined to evenly distribute the office workloads

for all programs. Table F–1 shows the average number of

CPS units assigned for interview per month in each RO.

A regional director is in charge of each RO. Program

coordinators report to the director through an assistant

regional director. The CPS is the responsibility of the

demographic program coordinator who has one or two

CPS program supervisors on staff. The program supervisor

has a staff of one or two office clerks working essentially full

time. Most of the clerks are full-time civil servants who work

in the RO. The typical RO employs about 100 to 250 FRs

who are assigned to the CPS. Most FRs also work on other

surveys. The RO usually has 15 to 18 senior field representatives (SFRs) who act as team leaders to FRs. Each

team leader is assigned 6 to 10 FRs. The primary function

of the team leader is to assist the program supervisors with

training and supervising the field interviewing staff. In

addition, the SFRs conduct response follow-up with eligible

households. Like other FRs, the SFR is a part-time or

intermittent employee who works out of his or her home.

Despite the geographic dispersion of the sample areas,

there is a considerable amount of personal contact between

the supervisory staff and the FRs. This is accomplished

mainly through the training programs and various aspects

of the quality control program. For some of the outlying

PSUs, it is necessary to use the telephone and written

communication to keep in continual touch with all FRs.

With the introduction of CAPI, the ROs also communicate

with the FRs using e-mail. Assigning new functions, such

as team leaders, also improves communications between

the ROs and the interviewing staff. In addition to communications relating to the work content, there is a regular

system for reporting progress and costs.

The CATI centers are staffed with one facility manager

who directs the work of two to three supervisory survey

statisticians. Each supervisory survey statistician is in

charge of about 15 supervisors and between 100-200

interviewers.

A substantial portion of the budget for field activities is

allocated to monitoring and improving the quality of the

FRs’ work. This includes FRs group training, monthly home

studies, personal observation, and reinterview. Approximately 25 percent of the CPS budget (including travel for

training) was allocated to quality enhancement. The remaining 75 percent of the budget went to FR and SFR salaries,

all other travel, clerical work in the ROs, recruitment, and

the supervision of these activities.

TRAINING FIELD REPRESENTATIVES

Approximately 20 to 25 percent of the CPS FRs leave

the staff each year. As a result, the recruitment and training

of new FRs is a continuing task in each RO. To be selected

as a CPS FR, a candidate must pass the Field Employee

Selection Aid test on reading, arithmetic, and map reading.

The FR is required to live in the Primary Sampling Unit

(PSU) in which the work is to be performed and have a

residence telephone and in most situations, an automobile.

As a part-time or intermittent employee, the FR works 40

hours or less per week or month. In most cases, new FRs

are paid at the GS-3 level and are eligible for payment at

the GS-4 scale after 1 year of fully successful or better

work. FRs are paid mileage for the use of their own cars

while interviewing and for commuting to classroom training

sites. They also receive pay for completing their home

study training packages.

FIELD REPRESENTATIVE TRAINING

PROCEDURES

Initial training for new field representatives. Each FR,

when appointed, undergoes an initial training program prior

to starting his/her assignment. The initial training program

F–1
Figure F–1. Map

F–2
consists of up to 20 hours of pre-classroom home study,

3.5 to 4.5 days of classroom training (dependent upon the

trainee’s interview experience) conducted by the program

supervisor or coordinator, and as a minimum, an on-the-job

field observation by the program supervisor or SFR during

the FRs first 2 days of interviewing. The classroom training

includes comprehensive instruction on the completion of

the survey using the laptop computer. In classroom training, special emphasis is placed on the labor force concepts

to ensure that the new FRs fully grasp these concepts

before conducting interviews. In addition, a large part of the

classroom training is devoted to practice interviews that

reinforce the correct interpretation and classification of the

respondents’ answers.

Each FR completes a home study exercise before the

second month’s assignment and, during the second month’s

interview assignment, is observed for at least 1 full day by

the program supervisor or the SFR who gives supplementary training, as needed. The FR also completes a home

study exercise and a final review test prior to the third

month’s assignment.

Training for all field representatives. As part of each

monthly assignment, FRs are required to complete a home

study exercise which usually consists of questions concerning labor force concepts and survey coverage procedures.

Once a year, the FRs are gathered in groups of about 12 to

15 for 1 or 2 days of refresher training. These sessions are

usually conducted by program supervisors with the aid of

SFRs. These group sessions cover regular CPS and

supplemental survey procedures.

Training for interviewers at the CATI centers. Candidates selected to be CATI interviewers receive 3 days of

training on using the computer. This initial training does not

cover subject matter material. While in training, new CATI

interviewers are monitored for a minimum of 5 percent of

their time on the computer. This is compared to 2.5 percent

monitoring time for experienced staff. In addition, once a

CATI interviewer has been assigned to conduct interviews

on the CPS, she/he receives an additional 3 1/2 days of

classroom training.

FIELD REPRESENTATIVE PERFORMANCE

GUIDELINES

Performance guidelines have been developed for CPS

CAPI FRs for response/nonresponse and production.

Response/nonresponse rate guidelines have been developed to ensure the quality of the data collected. Production

guidelines have been developed to assist in holding costs

within budget and to maintain an acceptable level of

efficiency in the program. Both sets of guidelines are

intended to help supervisors analyze activities of individual

FRs and to assist supervisors in identifying FRs who need

to improve performance.

Each CPS supervisor is responsible for developing each

employee to his/her fullest potential. Employee development can only be accomplished by providing meaningful

feedback on a continuous basis. By acknowledging strong

points and highlighting areas for improvement, the CPS

supervisor can monitor an employee’s progress and take

appropriate steps to improve weak areas.

FR performance is measured by a combination of the

following: response rates, production rates, supplement

response, reinterview results, observation results, submitting accurate payrolls on time, meeting deadlines, reporting

to team leaders, and attending training sessions.

The most useful tool to help supervisors’ evaluate the

FRs performance is the CPS 11-39 form. These reports are

generated monthly and are produced using data from the

ROSCO and CARMN systems.

This report provides the supervisor with information on:

Workload and number of interviews

Response rate and adjective rating

Noninterview results (counts)

Production rate, adjective rating, and mileage

Observation and reinterview results

Meeting transmission goals

Rate of personal visit telephone interviews

Supplement response rate

Refusal/Don’t Know counts and rates

Industry and Occupation (I&O) entries Jeffersonville could

not code.

Table F–1. Average Monthly Workload by Regional Office: 2001

Regional office Base workload CATI workload Total workload Percent

Total . .................................................. 66,576 5,640 72,216 100.00

Boston . . ................................................. 8,914 615 9,529 13.20

New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,887 361 3,248 4.50

Philadelphia .............................................. 5,915 786 6,701 9.28

Detroit . .................................................. 5,006 286 5,292 7.33

Chicago.................................................. 4,430 525 4,955 6.86

Kansas City . . . ........................................... 6,566 450 7,016 9.71

Seattle . .................................................. 5,147 529 5,676 7.86

Charlotte ................................................. 5,310 366 5,677 7.86

Atlanta . .................................................. 4,853 376 5,230 7.24

Dallas . . ................................................. 4,094 320 4,414 6.11

Denver................................................... 9,681 815 10,495 14.53

Los Angeles .............................................. 3,773 212 3,984 5.52

F–3
EVALUATING FIELD REPRESENTATIVE

PERFORMANCE

Census Bureau headquarters, located in Suitland, Maryland provides guidelines to the ROs for developing performance standards for FRs response and production rates.

The ROs have the option of using the guidelines, modifying

them, or establishing a completely different set of standards for their FRs. If the RO establishes their own

standards, the RO must notify the FRs of the standards.

Maintaining high response rates is of primary importance to the Census Bureau. The response rate is defined

as the proportion of all sample households eligible for

interview that are actually interviewed. It is calculated by

dividing the total number of interviewed households by the

sum of interviewed households and the number of refusals,

those that are temporarily absent, noncontacts, and noninterviewed households for other reasons. (All of these

noninterviews are referred to as Type A noninterviews.)

Type A cases do not include vacant units, those that are

used for nonresidential purposes, or other addresses that

are not eligible for interview.

Production guidelines. The production guidelines used in

the CPS CAPI program are designed to measure the

efficiency of individual FRs and the RO field functions.

Efficiency is measured by total minutes per case which

includes interview time and travel time. It is calculated by

dividing total time reported on payroll documents by total

workload. The standard acceptable minutes per case rate

for FRs varies with the characteristics of the PSU. When

looking at an FRs production, a program supervisor must

consider extenuating circumstances, such as:

• Unusual weather conditions such as floods, hurricanes,

or blizzards.

• Extreme distances between sample units, or assignment

covers multiple PSUs.

• Large number of inherited or confirmed refusals.

• Working part of another FRs assignment.

• Inordinate number of temporarily absent cases.

• High percentage of Type B/C noninterviews that decrease

the base or nonresponse rate.

• Other substantial changes in normal assignment conditions.

Supplement response rate. The supplement response

rate is another measure that CPS program supervisors

must use in measuring the performance of their FR staff.

Transmittal rates. The ROSCO system allows the supervisor to monitor transmittal rates of each CPS FR. A daily

receipts report is printed each day showing the progress of

each case on CPS.

Observation of field work. Field observation is one of the

methods used by the supervisor to check and improve

performance of the FR staff. It provides a uniform method

for assessing the FRs attitudes toward the job, use of the

computer and evaluating the extent to which FRs apply

CPS concepts and procedures during actual work situations. There are three types of observations:

1. Initial observations.

2. General performance review.

3. Special needs.

Initial observations are an extension of the initial classroom

training for new hires and provides on-the-job training for

FRs new to the survey. They also allow the survey supervisor to assess the extent to which a new CPS CAPI FR

grasps the concepts covered in initial training and, therefore, are an integral part of the initial training given to all

FRs. A 2-day initial observation (N1) is scheduled during

the FRs first CPS CAPI assignment. A second 1-day initial

observation (N2) is scheduled during the FRs second CPS

CAPI assignment. A third 1-day initial observation (N3) is

scheduled during the FRs fourth through sixth CPS CAPI

assignment.

General performance review observations are conducted

at least annually and allow the supervisor to provide

continuing developmental feedback to all CPS CAPI FRs.

Each CPS CAPI FR is regularly observed at least once a

year.

Special-needs observations are made when there is

evidence of a FR having problems or poor performance.

The need for a special-needs observation is usually detected

by other checks on the FR’s work. For example, specialneeds observations are conducted if a FR has a high Type

A noninterview rate, a high minutes per case rate, a failure

on reinterview, an unsatisfactory on a previous observation, a request for help, or for other reasons related to the

FR’s performance.

An observer accompanies the FR for a minimum of 6

hours during an actual work assignment. The observer

takes note of the FR’s performance including how the

interview is conducted and how the computer is used. The

observer stresses good interviewing techniques: asking

questions as worded and in the order presented on the

CAPI screen, adhering to instructions on the instrument

and in the manuals, knowing how to probe, recording

answers correctly and in adequate detail, developing and

maintaining good rapport with the respondent conducive to

an exchange of information, avoiding questions or probes

that suggest a desired answer to the respondent, and

determining the most appropriate time and place for the

interview.

The observer reviews the FR’s household performance

and discusses the FR’s strong and weak points with an

emphasis on correcting habits that interfere with the collection of reliable statistics. In addition, the FR is encouraged to ask the observer to clarify survey procedures not

F–4
fully understood and to seek the observer’s advice on

solving other problems encountered.

Unsatisfactory performance. When the performance of a

FR is at the unsatisfactory level over any period (usually 90

days), he/she may be placed in a trial period for 30 to 90

days. Depending on the circumstances, the FR will be

issued a letter stating that he/she is being placed in a

Performance Opportunity Period (POP) or a Performance

Improvement Period (PIP). These administrative actions

warn the FR that his/her work is substandard, makes

specific suggestions on ways to improve performance,

alerts the FR to actions that will be taken by the survey

supervisor to assist the FR to improve his/her performance,

and notifies the FR that he/she is subject to separation if

the work does not show improvement in the allotted time.

F–5
Appendix G.

Reinterview: Design and Methodology

INTRODUCTION

A continuing program of reinterviews on subsamples of

Current Population Survey (CPS) households is carried out

every month. Reinterview involves a second interview

where all the labor force questions are repeated. The

reinterview program is one of our major tools for limiting the

occurrence of nonsampling error and is a critical part of the

CPS program. The CPS reinterview program has been in

place since 1954. Reinterviewing for CPS serves two main

purposes: as a quality control (QC) tool to monitor the work

of the field representatives (FRs) and to evaluate data

quality via the measurement of response error (RE).

Prior to the automation of CPS in January 1994, the

reinterview consisted of one sample selected in two stages.

The FRs were primary sampling units and the households

within the FRs assignments were secondary sampling

units. In 75 percent of the reinterview sample differences

between original and reinterview responses were reconciled, and the results were used both to monitor the FRs

and to estimate response bias; that is, the accuracy of the

original survey responses. In the remaining 25 percent of

the sample the differences were not reconciled and were

used only to estimate simple response variance; that is, the

consistency in response between the original interview and

reinterview. Because the one sample approach did not

provide a monthly reinterview sample that was fully representative of the original survey sample for estimating

response error, the decision was made to separate the RE

and QC reinterview samples beginning with the introduction of the automated system in January 1994.

As a QC tool, reinterviewing is used to deter and detect

falsification. As such, it provides a means of limiting

nonsampling error as described in Chapter 15. The RE

reinterview currently measures simple response variance

or reliability.

The measurement of simple response variance in the

reinterview assumes an independent replication of the

interview. However, this assumption does not always hold,

since the respondent may remember his or her interview

response and repeat it in the reinterview (conditioning).

Also the fact that the reinterview is done by telephone for

personal visit interviews may violate this assumption. In

terms of the measurement of RE, errors or variations in

response may affect the accuracy and reliability of the

results of the survey. Responses from interviews and

reinterviews are compared and differences identified and

analyzed. (See Chapter 16 for a more detailed description

of response variance.)

Sample cases for QC and RE reinterviews are selected

by different methods and have somewhat different field

procedures. To minimize response burden, a household is

only reinterviewed one time (or not at all) during its life in

sample. This rule applies to both the RE and QC samples.

Any household contacted for reinterview is ineligible for

selection during its remaining months in sample. An analysis of respondent participation in later months of the CPS

showed that the reinterview had no significant effect on the

respondent’s willingness to respond (Bushery, Dewey, and

Weller, 1995). Sample selection for reinterview is done

immediately after the monthly assignments are certified

(see Chapter 4).

RESPONSE ERROR SAMPLE

The regular RE sample is selected first. It is a systematic

random sample across all households eligible for interview

each month. It includes households assigned to both the

telephone centers and the regional offices. Only households which can be reached by telephone and for which a

completed or partial interview is obtained are eligible for

RE reinterview. This restriction introduces a small bias into

the RE reinterview results because households without

‘‘good’’ telephone numbers are made ineligible for the RE

reinterview. About 1 percent of CPS households are assigned

for RE reinterview each month.

QUALITY CONTROL SAMPLE

The QC sample is selected next. The QC sample has

the FRs in the field as its first stage of selection. The QC

sample does not include interviewers at the telephone

centers. It is felt that the monitoring operation at the

telephone centers sufficiently serves the QC purpose. The

QC sample uses a 15-month cycle. The FRs are randomly

assigned to 15 different groups. Both the frequency of

selection and the number of households within assignments are based upon the length of tenure of the FRs.

Through a falsification study it was determined that a

relationship exists between tenure and both frequency of

falsification and the percentage of assignment falsified

(Waite, 1993 and 1997). Experienced FRs (those with at

least 5 years of service) were found less likely to falsify.

Also, experienced FRs who did falsify were more circumspect, falsifying fewer cases within their assignments than

inexperienced FRs (those with under 5 years of service).

G–1
Because inexperienced FRs are more likely to falsify,

more of them are selected for reinterview each month:

three groups of inexperienced FRs to two groups of

experienced FRs. On the other hand, since inexperienced

FRs falsify a greater percentage of cases within their

assignments, fewer of their cases are needed to detect

falsification. For inexperienced FRs, five households are

selected for reinterview. For experienced FRs, eight households are selected. The selection system is set up so that

an FR is in reinterview at least once, but no more than four

times within a 15-month cycle.

A sample of households assigned to the telephone

centers is selected for QC reinterview, but these households become eligible for reinterview only if recycled to the

field and assigned to FRs already selected for reinterview.

Recycled cases are included in reinterview because recycles

are more difficult to interview and may be more subject to

falsification.

All cases, except noninterviews for occupied households, are eligible for QC reinterview: completed and partial

interviews and all other types of noninterviews (vacant,

demolished, etc.). FRs are evaluated on their rate of

noninterviews for occupied households. Therefore, FRs

have no incentive to misclassify a case as a noninterview

for an occupied household.

Approximately 2 percent of CPS households are assigned

for QC reinterview each month.

REINTERVIEW PROCEDURES

QC reinterviews are conducted out of the regional

offices by telephone, if possible, but in some cases by

personal visit.1 They are conducted on a flow basis extending through the week following the interview week. They

are conducted mostly by senior FRs and sometimes by

program supervisors. For QC reinterviews, the reinterviewers are instructed to try to reinterview the original household respondent, but are allowed to conduct the reinterview

with another eligible household respondent.

The QC reinterviews are computer assisted and are a

brief check to verify the original interview outcome. The

reinterview asks questions to determine if the FR conducted the original interview and followed interviewing

procedures. The labor force questions are not asked.2 The

QC reinterview instrument also has a special set of outcome codes to indicate whether or not any noninterview

misclassifications occurred or any falsification is suspected.

RE reinterviews are conducted only by telephone from

both the telephone centers and out of the regional offices

(ROs). Cases interviewed at the telephone centers are

reinterviewed from the telephone centers, and cases interviewed in the field are reinterviewed in the field. At the

telephone centers reinterviews are conducted by the telephone center interviewing staff on a flow basis during

interview week. In the field they are conducted on a flow

basis and by the same staff as the QC reinterviews. The

reinterviewers are instructed to try to reinterview the original household respondent, but are allowed to conduct the

reinterview with another knowledgeable adult household

member.3 All RE reinterviews are computer assisted and

consist of the full set of labor force questions for all eligible

household members. The RE reinterview instrument dependently verifies household membership and asks the industry and occupation questions exactly as they are asked in

the original interview.4 Currently, no reconciliation is conducted.5

SUMMARY

Periodic reports on the QC reinterview program are

issued showing the number of FRs determined to have

falsified data. Since FRs are made aware of the QC

reinterview program and are also informed of the results

following reinterview, falsification is not a major problem in

the CPS. Only about 0.5 percent of CPS FRs are found to

falsify data. These FRs either resign or are terminated.

From the RE reinterview, results are also issued on a

periodic basis. They contain response variance results for

the basic labor force categories and for certain selected

other questions.

REFERENCES

Bushery, J. M., Dewey, J. A., and Weller, G. (1995),

‘‘Reinterview’s Effect on Survey Response,’’ Proceedings

of the 1995 Annual Research Conference, U.S. Census

Bureau, pp. 475-485.

U.S. Census Bureau (1996), Current Population Survey Office Manual, Form CPS-256, Washington, DC:

Government Printing Office, ch. 6.

U.S. Census Bureau (1996), Current Population Survey SFR Manual, Form CPS-251, Washington, DC: Government Printing Office, ch. 4.

U.S. Census Bureau (1993), ‘‘Falsification by Field

Representatives 1982-1992,’’ memorandum from Preston

Jay Waite to Paula Schneider, May 10, 1993.

U.S. Census Bureau (1997), ‘‘Falsification Study Results

for 1990-1997,’’ memorandum from Preston Jay Waite to

Richard L. Bitzer, May 8, 1997.

1

Because the mail component was ineffective in detecting falsification,

it was stopped as of March 1998. 2

Prior to October 2001, QC reinterviews asked the full set of labor

force questions for all eligible household members.

3

Beginning in February 1998, the RE reinterview respondent rule was

made the same as the QC reinterview respondent rule. 4

Prior to October 2001, RE reinterviews asked the industry and

occupation questions independently. 5

Reconciled reinterview, which was used to estimate reponse bias and

provide feedback for FR performance, was discontinued in January 1994.

G–2
Appendix H.

Sample Design Changes of the Current Population Survey:

January 1996

(See Appendix J for sample design changes in July 2001.)

INTRODUCTION

As of January 1996, the 1990 Current Population Survey

(CPS)1 sample changed because of a funding reduction.

The budget made it necessary to reduce the national

sample size from roughly 56,000 eligible housing units to

50,000 eligible housing units and from 792 sample areas to

754 sample areas. The U.S. Census Bureau and the

Bureau of Labor Statistics (BLS) decided to achieve the

budget reduction by eliminating the oversampling in CPS in

seven states and two substate areas that made it possible

to produce reliable monthly estimates of unemployment

and employment in these areas. In effect, this decision

produced the least possible damage to the precision of

national estimates with a sample reduction of this size. It

was a confirmation of the high priority attached to national

statistics and data by demographic and social subgroups

as compared to geographic detail. The four largest states

had not required any oversampling for the production of

monthly estimates, so no reductions were made in their

sample sizes. The sample was reduced in seven states:

Illinois, Massachusetts, Michigan, New Jersey, North Carolina, Ohio, and Pennsylvania; and two substate areas: Los

Angeles and New York City.

Survey Requirements

The original survey requirements stated in Chapter 3

remain unchanged except for the reliability requirements.

The new reliability requirements are:

1. The coefficient of variation (CV) on the monthly unemployment level for the Nation, given a 6 percent

unemployment rate, is 1.9 percent.

2. The CV on the annual average unemployment level

given a 6 percent unemployment rate for each of the

50 states and the District of Columbia is 8 percent or

lower.

These requirements effectively eliminated substate

area reliability requirements and monthly CV requirements for 11 large states: California, Florida, Illinois,

Massachusetts, Michigan, New Jersey, New York,

North Carolina, Ohio, Pennsylvania, and Texas. The

39 other states and the District of Columbia were not

affected by this sample reduction. These states kept

their original designs which meet an 8 percent CV

requirement on the annual average estimates of unemployment, assuming a 6 percent unemployment rate.

Strategy for Reducing the Sample

The sample reduction plan was designed to meet both

the national and state reliability requirements. The sample

sizes in all states and the District of Columbia, other than

the 11 largest, were already at the levels necessary to meet

the national reliability requirements, so changes in their

sizes were not required. The sample sizes in 7 of the 11

largest states and in New York City and Los Angeles were

greater than necessary to meet the new requirements, and

the sample sizes in these areas were reduced. The sample

sizes in the four most populous states — Florida, Texas,

and the parts of California and New York outside Los

Angeles and New York City — were also greater than the

precision requirements necessitated, but reductions in their

sample sizes would have created problems in achieving

the national goals. The sample sizes in these four areas

used in the redesign, therefore, were retained.

For the redesigned sample, an attempt was made to

allocate sample proportionally to the state populations in

the remaining large states and substate areas: Illinois,

Massachusetts, Michigan, New Jersey, North Carolina,

Ohio, Pennsylvania, Los Angeles, and New York City. Such

an approach would yield the same sampling rate in all of

these areas. The use of a consistent sampling rate would

tend to minimize the increase in national variance resulting

from the reduction. However, it was necessary to allow for

a fair amount of variation in the sampling rates among

these areas in order to meet the reliability requirement for

annual average data for each of the 50 states (see Table

H–2). This design results in variations in the precision

among the individual states resulting in increased expected

annual average CVs on estimates of unemployment ranging from 2.9 to 5.7 percent. The proportion of sample cut

because of the reduction and the expected CVs before and

after the reduction is shown in Table H–1. All 11 large states

have better expected reliability than the 39 other states and

the District of Columbia.

1

The CPS produced by the redesign after the 1990 census is termed

the 1990 CPS.

H–1
Table H–1. The Proportion of Sample Cut and Expected CVs Before and After the January 1996 Reduction

State

Proportion of

sample cut

Expected CV (percent)

Before reduction After reduction

Monthly Annual Monthly Annual

United States. .................................... 0.107 1.8 0.9 1.9 * 0.9

California ......................................... 0.024 6.2 2.9 6.3 2.9

Los Angeles .................................... 0.059 9.2 4.1 9.5 4.3

Remainder of state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . – 7.9 3.7 7.9 3.7

Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . – 8.2 3.7 8.2 3.7

Illinois . . . ......................................... 0.130 7.9 3.7 8.8 4.2

Massachusetts .................................... 0.478 7.9 3.5 11.1 4.2

Michigan ......................................... 0.320 7.8 3.6 9.7 4.5

New Jersey . . . .................................... 0.348 8.0 3.6 9.8 4.4

New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.195 6.6 3.0 7.1 3.2

New York City . . ................................ 0.348 9.1 4.1 11.5 5.2

Remainder of state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . – 8.9 4.1 8.9 4.1

North Carolina . . .................................. 0.458 8.0 3.9 11.1 5.7

Ohio ............................................. 0.240 7.9 3.6 9.4 4.4

Pennsylvania . . . .................................. 0.154 8.0 3.6 8.8 4.1

Texas ............................................ 0.000 7.7 3.6 7.7 3.6

* The national CV increased after the reduction but was within the rounding error.

CHANGES TO THE CPS SAMPLE DESIGN

Implementing the January 1996 sample reduction involved

modifying the original 1990 CPS state designs described in

Chapter 3. Both the first-stage and second-stage sample

designs were changed to realize major cost savings. This

differed from the maintenance reductions described in

Appendix C which affected only the second-stage design.

In addition, unlike the maintenance reductions which are

implemented over a period of 16 months, this reduction

was fully implemented in January 1996.

Many characteristics of the original design were retained.

Some of the major characteristics are:

• State-based design.

• PSU definitions.

• Self-representing PSU requirements.

• One PSU per stratum selected to be in sample.

• Systematic sample of ultimate sampling units consisting

of clusters of four housing units.

• Rotation group and reduction group assignments.

First-Stage of the Sample Design

New first-stage designs were developed by restratifying

the original 1990 design strata. First, the 1990 design

strata that were required to be self-representing (SR)

remain SR for the new designs2. For each state, any

nonrequired SR strata and all of the nonself-representing

(NSR) strata were grouped into super-strata of two strata

(and one group of three if there was an odd number of

strata in the state.) The super-strata were formed using a

linear programming technique to minimize the betweenstratum variation on the 1990 census levels of unemployment and civilian labor force. Then, one stratum was

selected from the super-strata with probability proportional

to the 1990 population. The sample PSU from this selected

stratum remained in sample while the sample PSU from

the nonselected stratum was dropped.

The first-stage probability of selection for the PSUs in

the new design is the product of the probability of selecting

the stratum from the super-stratum and the probability of

selecting the PSU from the stratum.

Pfs  Pstratumsuper-stratum x PPSU  stratum

 Nstratum

Nsuper-stratum

x

NPSU

Nstratum

(H.1)

 NPSU

Nsuper-stratum

where

Pfs is the first-stage unconditional probability of selection of

the PSU from the super-stratum,

P(stratum|super-stratum) is the probability of selection of

the stratum from the super-stratum,

P(PSU|stratum) is the probability of selection of the PSU

from the stratum,

NPSU, Nstratum, and Nsuper-stratum are the corresponding

1990 populations.

The restratifications were developed for five states:

Illinois, Michigan, North Carolina, Ohio, and Pennsylvania.

Two states, Massachusetts and New Jersey, and two

substate areas, Los Angeles and New York City, are

entirely SR and for the most part required to be SR so no

2 See Chapter 3 ‘‘Stratification of Primary Sampling Units’’ for the

areas which are required to be SR.

H–2
PSUs were eliminated. The reduced CPS national design,

as of January 1996, contains 754 stratification PSUs in

sample of which 428 are SR and 326 are NSR. Table H–2

contains the number of strata by state for the nine states in

which reductions were made.

Table H–2. January 1996 Reduction: Number and Estimated Population of Strata for 754-PSU Design for the

Nine Reduced States

State

Self-representing (SR) Nonself-representing (NSR)

State

sampling

Strata interval

Estimated

population1 Strata

Estimated

population1

California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 20,866,697 5 1,405,500 2,745

Los Angeles .................................... 1 6,672,035 0 0 1,779

Remainder of state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 14,194,662 5 1,405,500 3,132

Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 6,806,874 6 1,821,548 2,227

Massachusetts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4,719,188 0 0 1,853

Michigan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 5,643,817 5 1,345,074 2,098

New Jersey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 6,023,359 0 0 1,841

New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 12,485,029 7 1,414,548 1,970

New York City . . ................................ 1 5,721,495 0 0 1,774

Remainder of state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 6,763,534 7 1,414,548 2,093

North Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2,840,376 13 2,221,874 1,986

Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 6,238,600 6 1,958,138 2,343

Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 7,584,129 6 1,628,780 2,149

1

Projected 1995 estimates of civilian noninstitutional population ages 16 and over.

Note: This results in an overall national sampling interval of 2,255 based on the 1995 population distribution.

Table H–3. Reduction Groups Dropped From SR

Areas

State

Number of

reduction groups

dropped

California ...................................... –

Los Angeles ................................... 5

Remainder of state ............................. 0

Illinois . . . ...................................... 3

Massachusetts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Michigan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

New Jersey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –

New York City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Remainder of state ............................. 0

North Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Pennsylvania ................................... 6

Second-Stage of the Sample Design

New second-stage designs were developed for SR

PSUs but not NSR PSUs. The NSR PSUs remaining in

sample were originally designed to have a manageable

workload for a single field representative. Cutting sample

housing units for NSR PSUs would make workloads inefficient for these PSUs.

Within SR PSUs, the within-PSU sampling intervals

were adjusted and subsamples of ultimate sampling units

(USU) were dropped from sample. The subsample of

USUs to drop was identified by reduction groups (see

Appendix C). The number of reduction groups to drop from

the SR PSUs in each state was a function of the state

sampling interval required to achieve the national reliability

given the new first-stage designs. The reduction groups

were deleted in a specified order so that the nature of the

systematic sample was retained to the extent possible.

Within each state, the number of reduction groups dropped

was uniform for all SR PSUs. Table H–3 gives the number

of reduction groups dropped from each SR PSU by state.

Calculation of Sampling Intervals

The design changes do not maintain the self-weighting

nature of the original designs. The stratum sampling intervals differ for the SR and NSR portions of the sample. In

other words, the probability of selecting a housing unit

varies by stratum. The stratum sampling interval is calculated as

SIstratum  SIPSU

Pfs

x

101

101  RGD (H.2)

where

SIstratum is the stratum sampling interval,

SIPSU is the original within-PSU sampling interval,

Pfs is the first-stage probability of selection as defined for

formula (H.1), and

RGD is the integer number of reduction groups dropped

(0 ≤ RGD ≤ 101).

Each NSR PSU has a first-stage probability of selection

calculated in formula (H.1), and the number of reduction

H–3
groups dropped equals zero. For NSR strata, formula (H.2)

reduces to

SINSR stratum  SIPSU

Pfs

(H.3)

SR strata have a first-stage probability of selection equal

to one, and the number of reduction groups dropped is

found in Table H–3. For SR strata, formula (H.2) reduces to

SISR stratum  SIPSU x

101

101  RGD (H.4)

The state sampling interval is a weighted average of the

stratum sampling intervals using 1990 census stratum

populations

SI 



h1

H

Nh SIh



h1

H

Nh

(H.5)

where

SI is the state sampling interval,

Nh is the 1990 census population of the hth stratum in the

state,

SIh is the stratum sampling interval for the hth stratum in the

state, and

H is the total number of SR and NSR strata in the state.

Table H–2 gives the resulting state sampling intervals.

Since Massachusetts and New Jersey contain only SR

strata, they continue to have a self-weighting design.

REVISIONS TO CPS ESTIMATION

Three revisions were made to CPS estimation because

of the January 1996 sample reduction, but the general

estimation methodology was not changed. The revisions

affected only the seven states and two substate areas

affected by the reduction. The stages of weighting affected

were the basic weight, adjustment for nonresponse, and

the first-stage ratio adjustment (see Chapter 10).

Basic Weight Changes

The new basic weights for the remaining sample are the

stratum sampling intervals given in formula (H.2). These

stratum sampling intervals are the inverse of the probability

of selecting a housing unit from the stratum.

Adjustment for Nonresponse Changes

In New Jersey and North Carolina, two noninterview

clusters within each state were combined due to small

sample sizes. For January 1996 and subsequent months,

there are 125 noninterview clusters for the Nation.

First-Stage Ratio Adjustment Changes

The first-stage factors for CPS were recomputed in the

five states where PSUs were dropped. The first-stage ratio

adjustment factors were revised to account for the new

first-stage designs in the following states: Illinois, Michigan,

North Carolina, Ohio, and Pennsylvania. The factors did

not change for California, Massachusetts, New Jersey, and

New York. Table H–4 contains the revised CPS first-stage

factors.

Table H–4. State First-Stage Factors for CPS January

1996 Reduction

[Beginning January 1996]

State

CPS

Black Non-Black

Illinois ................................ 0.96670 0.99477

Michigan ............................. 1.00000 ** 1.00000

North Carolina ........................ 1.04952 1.00641

Ohio ................................. 0.92339 0.99953

Pennsylvania ......................... 1.17540 0.99157

** Race cells were collapsed.

Monthly State and Substate Estimate Change

After the January 1996 sample reduction, the 11 large

states and 2 substate areas had insufficient sample to

produce reliable monthly estimates of employment and

unemployment. Now these estimates are produced using

the same ‘‘signal-plus-noise’’ time series model used for

the 39 small states and the District of Columbia discussed

in Chapter 10.

ESTIMATION OF VARIANCES

Changing the CPS design because of the January 1996

sample cut required changes to the CPS variance estimation procedures, but the variance estimation methodology

did not change. These revisions affected only the seven

states and two substate areas where the reduction occurred.

A consequence of these changes is that consistent estimates of covariances between December 1995 (and previous months) and January 1996 (and subsequent months)

cannot be produced. See Chapter 14 for a description of

the CPS variance estimation methodology.

Included in this appendix are estimates of variance

properties of the CPS design following the January 1996

reduction. These estimates replace those in Chapter 14 as

measures of the current CPS design variance properties3.

3

The chapters of this document are written as of the conclusion of the

implementation of the 1990 CPS design, through December, 1995. Many

of the appendixes are written to provide updates that began in January,

1996.

H–4
Changes to the Variance Estimation Procedure

Three changes were made to the variance estimation

procedure affecting replicate sample composition: new

standard error computation units (SECUs) were defined

(pairs of USUs), the NSR strata were combined into new

pseudostrata, and associated replicate factors were calculated using new Hadamard matrix row assignments. New

SECUs were defined in all seven states and two substate

areas because the second-stage reduction in SR PSUs

resulted in many SECUs with only one USU. NSR strata

were combined into new pseudostrata only in the five

states with new first-stage designs. These changes resulted

in a break in consistent replicate sample definitions between

December 1995 and January 1996.

CPS Variance Estimates - 754 PSU Design

Monthly levels. The estimated variance properties on

monthly levels of select labor force characteristics from the

CPS design following the January 1996 reduction are

provided in Tables H–5 to H–8. These tables replace the

corresponding Tables 14–2 to 14–5 in Chapter 14 as

indicators of the variance properties of the current CPS

design. (See previous footnote and see Chapter 14 for

description of table headings.) The variance estimates in

Tables H–5 to H–8 are averages over 12 months (January

to December 1996), while Tables 14–2 to 14–5 are averages over 6 months, making the Appendix H tables more

stable than the Chapter 14 tables.

Overall, the reduction was expected to increase CVs by

about 4 percent, based on the ratio of the square root of the

national sampling rates before and after the reduction. The

estimated CVs in Table H–5 are larger than those estimated prior to the reduction (Table 14–2) for all characteristics but Hispanic4 origin. The reliability of Hispanic origin

characteristics is also adversely affected by the reduction.

However, the variance on the Hispanic origin CVs is too

large to measure this effect.

The effect of the reduction on the contribution of the

within- and between-PSU variances to the total variance

(Table H–5) is even more difficult to assess because of the

variance on the variance estimates. In general, no noticeable effect on the variance estimates is found in the

contribution from the first- and second-stages of selection.

Tables H–6 and H–7 show that the sample reduction did

not appear to affect the variance properties related to

estimation. The new estimated design effects in Table H–8

appear to have increased slightly.

Month-to-month change in levels. The estimated variance properties on month-to-month change in levels of

select labor force characteristics from the CPS design

following the January 1996 reduction are in Tables H–9 and

H–10. These variance estimates are averages of 11 monthto-month change estimates.

Table H–9 indicates how the two stages of sampling

affect the variance of the composited estimates of monthto-month change. The last two columns of the table show

the percentage of the total variance due to sampling

housing units within PSUs (within-PSU variance) and the

variance due to sampling a subset of NSR PSUs (betweenPSU variance). For all characteristics in the table, the

largest component of the total variance is due to withinPSU sampling. The Hispanic origin characteristics and

teenage unemployed have smaller within-PSU components than most of the other characteristics. Total and

White employed in agriculture also have relatively small

within-PSU variance components. Negative numbers in the

last column of the table imply negative estimates of the

between-PSU variance. Because this component is relatively small and estimated by subtraction, some negative

estimates are expected. Since variances cannot be negative, the variance on the estimated variance components is

what is actually reflected in these negative estimates.

The reduction in the variance of month-to-month change

estimates due to the compositing is shown in Table H–10.

The variance factor is the variance of the composited

estimate of month-to-month change divided by the firstand second-stage combined (FSC) estimate of month-tomonth change. The factors are less than one for all but one

of the characteristics shown in the table, indicating that

compositing reduces the variance of most month-to-month

change estimates. 4

Hispanics may be of any race.

H–5
Table H–5. Components of Variance for FSC Monthly Estimates

[Monthly average: January - December 1996]

Civilian noninstitutional population, ages 16 and older FSC1

estimate

(x 106)

Standard error

(x 105)

Coefficient of

variation

(%)

Percent of total variance

Within Between

Unemployed . ..................................... 7.31 1.48 2.02 97.4 2.6

White .......................................... 5.34 1.26 2.36 95.2 4.8

Black . . ........................................ 1.61 0.71 4.40 102.8 −2.8

Hispanic origin2 ................................. 1.14 0.62 5.45 94.1 5.9

Teenage, 16-19 . . ............................... 1.32 0.59 4.51 97.1 2.9

Employed - agriculture . . ........................... 3.45 1.61 4.68 70.7 29.3

White .......................................... 3.29 1.56 4.78 69.9 30.1

Black . . ........................................ 0.10 0.19 19.82 95.3 4.7

Hispanic origin2 ................................. 0.61 0.73 12.08 91.0 9.0

Teenage, 16-19 . . ............................... 0.26 0.27 11.03 104.1 −4.1

Employed - nonagriculture .......................... 123.37 3.55 0.29 90.2 9.8

White .......................................... 104.62 3.13 0.30 91.6 8.4

Black . . ........................................ 13.46 1.34 1.00 100.2 −0.2

Hispanic origin2 ................................. 11.06 1.43 1.30 92.1 7.9

Teenage, 16-19 . . ............................... 6.26 0.99 1.58 103.5 −3.5

Civilian labor force . . .............................. 134.13 3.18 0.24 93.9 6.1

White .......................................... 113.25 2.73 0.24 96.1 3.9

Black . . ........................................ 15.17 1.26 0.83 95.4 4.6

Hispanic origin2 ................................. 12.80 1.14 0.89 98.0 2.0

Teenage, 16-19 . . ............................... 7.84 1.00 1.30 105.0 −5.0

Not in labor force .................................. 66.46 3.18 0.48 93.9 6.1

White .......................................... 55.07 2.73 0.50 96.1 3.9

Black . . ........................................ 8.43 1.26 1.50 95.4 4.6

Hispanic origin2 ................................. 6.41 1.14 1.79 98.0 2.0

Teenage, 16-19 . . ............................... 7.09 1.04 1.48 104.5 −4.5

1

FSC = first- and second-stage combined.

2

Hispanics may be of any race.

H–6
Table H–6. Effects of Weighting Stages on Monthly Relative Variance Factors

[Monthly averages: January - December 1996]

Civilian noninstitutional population, ages 16 and older

Relative

variance of FSC

estimate of level

(x10-4)

Relative variance factor1

Unbiased

estimator

Unbiased estimator with—

NI2 NI & FS3 NI & SS4

Unemployed ....................................... 4.109 1.03 1.03 1.10 1.00

White ........................................... 5.594 1.05 1.04 1.10 1.00

Black ........................................... 19.392 1.16 1.16 1.20 0.99

Hispanic origin5 ................................... 30.261 1.15 1.14 1.21 1.00

Teenage, 16-19 ................................... 19.959 1.13 1.13 1.16 1.00

Employed - agriculture ................................ 21.763 1.00 0.98 1.10 0.93

White ........................................... 22.695 1.00 0.98 1.09 0.93

Black ........................................... 385.214 1.09 1.08 1.06 1.02

Hispanic origin5 ................................... 144.816 1.05 1.03 1.20 0.95

Teenage, 16-19 ................................... 106.648 1.06 1.05 1.07 0.99

Employed - nonagriculture ............................. 0.083 4.41 4.20 5.48 0.96

White ........................................... 0.090 5.61 5.46 6.48 0.95

Black ........................................... 0.998 5.88 5.76 5.54 1.01

Hispanic origin5 ................................... 1.674 3.33 3.34 3.60 0.97

Teenage, 16-19 ................................... 2.499 1.97 1.96 2.01 1.00

Civilian labor force ................................... 0.056 6.23 5.88 8.24 0.99

White ........................................... 0.058 8.38 8.10 10.20 0.99

Black ........................................... 0.693 7.82 7.66 7.45 1.01

Hispanic origin5 ................................... 0.800 6.65 6.61 7.34 1.00

Teenage, 16-19 ................................... 1.653 2.47 2.46 2.57 1.00

Not in labor force .................................... 0.230 2.88 2.74 3.74 0.99

White ........................................... 0.247 3.24 3.12 3.98 0.99

Black ........................................... 2.245 3.98 3.88 3.41 1.00

Hispanic origin5 ................................... 3.193 2.57 2.52 2.76 1.00

Teenage, 16-19 ................................... 2.154 2.06 2.03 2.26 1.00

1

Relative Variance Factor is the ratio of the relative variance of the specified level to the relative variance of FSC level.

2

NI = Noninterview

3

FS = First Stage

4

SS = Second Stage

5

Hispanics may be of any race.

Table H–7. Effect of Compositing on Monthly Variance and Relative Variance Factors

[Monthly averages: January - December 1996]

Civilian noninstitutional population, ages 16 and older

Variance of composited

estimate of level

(x 109

) Variance factor1

Relative variance

factor2

Unemployed ........................................................ 21.063 0.96 0.98

White ............................................................ 15.487 0.97 0.99

Black ............................................................ 4.712 0.93 0.96

Hispanic origin3 .................................................... 3.887 0.99 1.00

Teenage, 16-19 .................................................... 3.429 0.99 1.01

Employed - agriculture ................................................ 23.478 0.91 0.91

White ............................................................ 22.151 0.90 0.91

Black ............................................................ 0.325 0.85 0.87

Hispanic origin3 .................................................... 4.764 0.89 0.89

Teenage, 16-19 .................................................... 0.645 0.88 0.89

Employed - nonagriculture .............................................. 109.120 0.86 0.86

White ............................................................ 85.476 0.87 0.87

Black ............................................................ 15.741 0.87 0.87

Hispanic origin3 .................................................... 18.009 0.88 0.88

Teenage, 16-19 .................................................... 8.787 0.90 0.90

Civilian labor force .................................................... 86.616 0.85 0.86

White ............................................................ 64.105 0.86 0.86

Black ............................................................ 14.260 0.89 0.90

Hispanic origin3 .................................................... 11.474 0.87 0.88

Teenage, 16-19 .................................................... 9.163 0.90 0.91

Not in labor force ..................................................... 86.616 0.85 0.85

White ............................................................ 64.105 0.86 0.85

Black ............................................................ 14.260 0.89 0.89

Hispanic origin3 .................................................... 11.474 0.87 0.87

Teenage, 16-19 .................................................... 9.696 0.89 0.89

1

Variance Factor is the ratio of the variance of the composited level to the variance of the FSC level.

2

Relative Variance Factor is the ratio of the relative variance of the composited level to the relative variance of the FSC level.

3

Hispanics may be of any race.

Note: Composite formula constants used: K = 0.4 and A = 0.2

H–7
Table H–8. Design Effects for Total Monthly Variance

[Monthly averages: January - December 1996]

Civilian noninstitutional population, ages 16 and older

Design effect

Total variance Within-PSU variance

Not composited Composited Not composited

Unemployed ....................................................... 1.382 1.339 1.346

White . . ......................................................... 1.362 1.331 1.296

Black ........................................................... 1.398 1.323 1.437

Hispanic origin1 .................................................. 1.539 1.531 1.448

Teenage, 16-19 .................................................. 1.173 1.171 1.139

Employed - agriculture .............................................. 3.390 3.076 2.396

White . . ......................................................... 3.362 3.048 2.350

Black ........................................................... 1.702 1.465 1.622

Hispanic origin1 .................................................. 3.916 3.482 3.564

Teenage, 16-19 .................................................. 1.243 1.097 1.294

Employed - nonagriculture .......................................... 1.181 1.018 1.065

White . . ......................................................... 0.870 0.757 0.797

Black ........................................................... 0.639 0.557 0.640

Hispanic origin1 .................................................. 0.869 0.766 0.800

Teenage, 16-19 .................................................. 0.716 0.645 0.742

Civilian labor force ................................................. 1.014 0.863 0.952

White . . ......................................................... 0.674 0.576 0.648

Black ........................................................... 0.504 0.452 0.481

Hispanic origin1 .................................................. 0.485 0.425 0.476

Teenage, 16-19 .................................................. 0.598 0.542 0.628

Not in labor force .................................................. 1.014 0.863 0.952

White . . ......................................................... 0.832 0.710 0.799

Black ........................................................... 0.876 0.780 0.835

Hispanic origin1 .................................................. 0.938 0.816 0.919

Teenage, 16-19 .................................................. 0.702 0.625 0.734

1

Hispanics may be of any race.

Table H–9. Components of Variance for Composited Month-to-Month Change Estimates

[Change averages: January - December 1996]

Civilian noninstitutional population, ages 16 and older

Estimate of

change1

(x106)

Standard error of

change2

(x105)

Percent of total variance3

Within Between

Unemployed ............................................. 0.29 1.59 98.0 2.0

White ................................................. 0.23 1.30 102.7 −2.7

Black ................................................. 0.08 0.80 100.3 −0.3

Hispanic origin4 ........................................ 0.06 0.67 96.9 3.1

Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.11 0.73 93.7 6.3

Employed - agriculture .................................... 0.14 0.82 92.6 7.4

White ................................................. 0.13 0.79 92.6 7.4

Black ................................................. 0.01 0.16 103.7 −3.7

Hispanic origin4 ........................................ 0.04 0.42 88.8 11.2

Teenage, 16-19 ........................................ 0.05 0.24 101.5 −1.5

Employed - nonagriculture ................................. 0.60 2.40 102.5 −2.5

White ................................................. 0.47 2.11 105.3 −5.3

Black ................................................. 0.12 1.00 100.7 −0.7

Hispanic origin4 ........................................ 0.10 0.97 94.8 5.2

Teenage, 16-19 ........................................ 0.35 0.91 100.6 −0.6

Civilian labor force ........................................ 0.77 2.30 106.0 −6.0

White ................................................. 0.60 2.00 106.0 −6.0

Black ................................................. 0.17 0.98 98.7 1.3

Hispanic origin4 ........................................ 0.12 0.89 97.5 2.5

Teenage, 16-19 ........................................ 0.46 0.99 101.9 −1.9

Not in labor force ......................................... 0.76 2.30 106.0 −6.0

White ................................................. 0.59 2.00 106.0 −6.0

Black ................................................. 0.18 0.98 98.7 1.3

Hispanic origin4 ........................................ 0.10 0.89 97.5 2.5

Teenage, 16-19 ........................................ 0.45 1.00 100.9 −0.9

1

The arithmetic mean of the absolute values of the 11 month-to-month changes.

2

The square root of the arithmetic mean of the variances of the 11 month-to-month changes.

3

The percent of the estimated total variance attributed to the two stages of sampling.

4

Hispanics may be of any race.

H–8
Table H–10. Effect of Compositing on Month-to-Month Change Variance Factors

[Change averages: January - December 1996]

Civilian noninstitutional population, ages 16 and older

Variance of composited

estimate of change1

(x 109) Variance factor2

Unemployed . . . ................................................................. 25.124 0.90

White . . ...................................................................... 16.800 0.88

Black ......................................................................... 6.409 0.94

Hispanic origin3 ............................................................... 4.484 0.90

Teenage, 16-19 ................................................................ 5.322 0.96

Employed - agriculture . . ......................................................... 6.717 0.77

White . . ...................................................................... 6.244 0.77

Black ......................................................................... 0.249 0.80

Hispanic origin3 ............................................................... 1.796 0.67

Teenage, 16-19 ................................................................ 0.556 0.82

Employed - nonagriculture ........................................................ 57.684 0.70

White . . ...................................................................... 44.718 0.68

Black ......................................................................... 10.007 0.76

Hispanic origin3 ............................................................... 9.397 0.70

Teenage, 16-19 ................................................................ 8.228 1.02

Civilian labor force ............................................................... 52.997 0.70

White . . ...................................................................... 39.834 0.68

Black ......................................................................... 9.582 0.87

Hispanic origin3 ............................................................... 7.919 0.70

Teenage, 16-19 ................................................................ 9.782 0.98

Not in labor force . ............................................................... 52.997 0.70

White . . ...................................................................... 39.834 0.68

Black ......................................................................... 9.582 0.87

Hispanic origin3 ............................................................... 7.919 0.70

Teenage, 16-19 ................................................................ 9.907 0.94

1

The arithmetic mean of the 11 variance estimates.

2

Variance Factor is the ratio of the variance of the composited estimate to the variance of the FSC estimate.

3

Hispanics may be of any race.

H–9
Appendix I.

New Compositing Procedure

INTRODUCTION

Effective with the release of January 1998 data, BLS

implemented a new composite estimation method for the

Current Population Survey. The new technique provides

increased operational simplicity for microdata users and

allows optimization of compositing coefficients for different

labor force categories.

In the CPS estimation procedure, described in Chapter

10, a separate weight for each person in the CPS sample

is computed and ratio adjusted through a sequence of

weighting steps. These adjustments are followed by a

composite estimation step that improves the accuracy of

current estimates by incorporating information gathered in

previous months, taking advantage of the fact that 75

percent of sample households are common in each pair of

consecutive months. Under the old procedure, composite

estimation was performed at the macro level. The composite estimator for each tabulated cell was a function of

aggregated weights for sample persons contributing to that

cell in current and prior months. The different months of

data were combined together using compositing coefficients. Thus, microdata users needed several months of

CPS data to compute composite estimates. To ensure

consistency, the same coefficients had to be used for all

estimates. The values of the coefficients selected were

much closer to optimal for unemployment than for employment or labor force totals.

The new composite weighting method involves two

steps: (1) the computation of composite estimates for the

main labor force categories, classified by important demographic characteristics and (2) the adjustment of the microdata weights, through a series of ratio adjustments, to

agree with these composite estimates, thus incorporating

the effect of composite estimation into the microdata

weights. Under this procedure, the sum of the composite

weights of all sample persons in a particular labor force

category equals the composite estimate of the level for that

category. To produce a composite estimate for a particular

month, a data user may simply access the microdata file for

that month and compute a weighted sum. The new composite weighting approach also improves the accuracy of

labor force estimates by using different compositing coefficients for different labor force categories. The weighting

adjustment method assures additivity while allowing this

variation in compositing coefficients.

COMPOSITE ESTIMATION IN THE CPS

As described in Chapter 10, eight panels or rotation

groups, approximately equal in size, make up each monthly

CPS sample. Due to the 4-8-4 rotation pattern, six of these

panels (three-quarters of the sample) continue in sample

the following month and one-half of the households in a

given month’s sample will be back in the sample for the

same calendar month 1 year later. The sample overlap

improves estimates of change over time. Through composite estimation, the positive correlation among CPS estimators for different months is increased. This increase in

correlation improves the accuracy of monthly labor force

estimates.

The CPS AK composite estimator for a labor force total

(e.g., the number of persons unemployed) in month t is

given by

Y ́t  1KYˆ t  KY ́t1t

  Aˆ

t

where:

Yˆ t  

i1

8

xt,i

t  4

3 

is

xt,i  xt1,i1 and

ˆ

t

 

is

xt,i

1

3 

is

xt,i

i = 1,2,...,8 month in sample

xt,i = sum of weights after second-stage ratio adjustment

of respondents in month t, and month-in-sample i

with characteristic of interest

S = {2,3,4,6,7,8} sample continuing from previous month

K = 0.4 for unemployed

0.7 for employed

A = 0.3 for unemployed

0.4 for employed

The values given above for the constant coefficients A

and K are close to optimal–with respect to variance–for

month-to-month change estimates of unemployment level

and employment level. The coefficient K determines the

weight, in the weighted average, of each of two estimators

I–1
for the current month: (1) the current month’s ratio estimator Yˆ t and (2) the sum of the previous month’s composite

estimator Y ́t1 and an estimator t of the change since the

previous month. The estimate of change is based on data

from sample households in the six panels common to

months t and t-1. The coefficient A determines the weight of

ˆ

t

, an adjustment term that reduces both the variance of

the composite estimator and the bias associated with time

in sample. (See Breau and Ernst 1983, Bailar 1975.)

Before January 1998, a single pair of values for K and A

was used to produce all CPS composite estimates. Optimal

values of the coefficients, however, depend on the correlation structure of the characteristic to be estimated. Research

has shown, for example, higher values of K and A result in

more reliable estimates for employment levels because the

ratio estimators for employment are more strongly correlated across time than those for unemployment. The new

composite weighting approach allows use of different compositing coefficients, thus improving the accuracy of labor

force estimates, while ensuring the additivity of estimates.

For a more detailed description of the selection of compositing parameters, see Lent et al. (1997).

COMPUTING COMPOSITE WEIGHTS

Composite weights are produced only for sample persons age 16 or older. As described in Chapter 10, the CPS

estimation process begins with the computation of a ‘‘baseweight’’ for each adult in the survey. The baseweight– the

inverse of the probability of selection– is adjusted for nonresponse, and two successive stages of ratio adjustments

to population controls are applied. The second-stage raking procedure, performed independently for each of the

eight sample rotation groups, ensures that sample weights

add to independent population controls for states, as well

as for age/sex/ethnicity groups and age/sex/race groups,

specified at the national level.

The post-January 1998 method of computing composite

weights for the CPS imitates the second-stage ratio adjustment. Sample person weights are raked to force their sums

to equal control totals. Composite labor force estimates are

used as controls in place of independent population estimates. The composite raking process is performed separately within each of the three major labor force categories:

employed, unemployed, and those not in the labor force.

Adjustment of microdata weights to the composite estimates for each labor force category proceeds as follows.

For simplicity, we describe the method for estimating the

number of people unemployed (UE); analogous procedures are used to estimate the number of people employed

and the number not in the labor force. Data from all eight

rotation groups are combined for the purpose of computing

composite weights.

1. For each state and the District of Columbia (51 cells) j,

the direct (optimal) composite estimate of UE,

comp(UEj

), is computed as described above. Similarly,

direct composite estimates of UE are computed for 9

National age/sex/ethnicity cells (8 Hispanic1 age/sex

cells and 1 non-Hispanic cell) and 66 National age/sex/race

cells (38 White age/sex cells, 24 Black age/sex cells,

and 4 other age/sex cells). These computations use

cell definitions specified in the second-stage ratio

adjustment (Tables 10– 2 and 10– 3), excluding cells

containing only persons aged 15 or under. Coefficients

K=0.4 and A=0.3 are used for all UE estimates in all

categories (state, age/sex/ethnicity, and age/sex/race).

2. Sample records are classified by state. Within each

state j, a simple estimate of UE, simp(UEj

), is computed by adding the weights of all unemployed sample

persons in the state.

3. Within each state j, the weight of each unemployed

sample person in the state is multiplied by the following ratio: comp(UEj

) / simp(UEj).

4. Sample records are cross-classified by age, sex, and

ethnicity. Within each cross-classification cell, a simple

estimate of UE is computed by adding the weights (as

adjusted in step 3) of all unemployed sample persons

in the cell.

5. Weights are adjusted within each age/sex/ethnicity cell

in a manner analogous to step 3.

6. Steps 4 and 5 are repeated for age/sex/race cells.

7. Steps 2-6 are repeated five more times for a total of six

iterations.

An analogous procedure is done for estimating the

number of people employed using coefficients K=0.7 and

A=0.4.

For not in labor force (NILF) the same raking steps are

performed, but the controls are obtained as the residuals

from the population controls and the direct composite

estimates for employed (E) and unemployed (UE). The

formula is NILF = Population – (E + UE). During computation of composite weights for persons who are unemployed, some further collapsing of cells is needed where

cells contain insufficient sample. Any such collapsing of

cells for UE leads to a similar collapsing for NILF.

A NOTE ABOUT FINAL WEIGHTS

Prior to the introduction of composite weights the weight

after the second-stage ratio adjustment was called the final

weight, as stated in Chapter 10. This is no longer true since

the weight after compositing is now the final weight. In

addition, since data from all eight rotation groups are

combined for the purpose of computing composite weights,

summations of final weights within each panel do not

match independent population controls. However, summations of final weights for the entire sample still match these

independent population controls.

1

Hispanics may be of any race.

I– 2
REFERENCES

Breau, P., and L. Ernst, (1983). ‘‘Alternative Estimators

to the Current Composite Estimator.’’ Proceedings of the

Section on Survey Research Methods, American Statistical Association, pp. 397-402.

Bailar, B., (1975). ‘‘The Effects of Rotation Group Bias

on Estimates From Panel Surveys.’’ Journal of the American Statistical Association, 70, pp. 23-30.

Lent, J., S. Miller, P. Cantwell, and M. Duff, (1999).

‘‘Effect of Composite Weights on Some Estimates From the

Current Population Survey.’’ Journal of Official Statistics,

to appear.

I– 3
Appendix J.

Changes to the Current Population Survey Sample

in July 2001

INTRODUCTION

In 1999, Congress allocated $10 million annually to the

Census Bureau to ‘‘make appropriate adjustments to the

annual Current Population Survey . . . in order to produce

statistically reliable annual state data on the number of

low-income children who do not have health insurance

coverage, so that real changes in the uninsured rates of

children can reasonably be detected.’’ (Public Law 106-

113)

The health insurance coverage estimates are derived

from the Annual Demographic Supplement (ADS) which is

discussed in Chapter 11. Briefly, the ADS is a series of

questions asked each March in addition to the regular CPS

questions. Consequently, the ADS is also known as the

‘‘March Supplement.’’

To achieve the objectives of the new law, the Census

Bureau added sample to the ADS in three different ways:

• General sample increase in selected states

• ‘‘Split-path’’ assignments

• Month-in-sample (MIS) 9 assignments

These changes are collectively known as the State

Children’s Health Insurance Program (SCHIP) sample

expansion. The procedures used to implement the SCHIP

sample expansion were chosen in order to minimize the

effect on the basic CPS. The general sample increase will

be explained first. Later sections of this appendix provide

details on the split-path and MIS 9 assignments.

GENERAL SAMPLE INCREASE IN SELECTED

STATES

The first part of the SCHIP plan expanded basic monthly

CPS sample in selected states, using retired1 sample from

CPS. Sample was identified using CPS sample designation, rotation group codes and all four frames. Expanding

the monthly CPS was necessary, rather than simply interviewing many more cases in March, because of the

difficulty in managing a large spike in the sample size for a

single month in terms of data quality and staffing.

A new sample designation code beginning with ‘‘B’’ was

assigned to cases included in the general sample increase

to distinguish SCHIP cases from regular CPS sample

cases, which use an ‘‘A.’’ The rotation group code was not

reassigned in order to maintain the structure of the sample.

In states targeted for the general sample increase, sample

was added in all existing CPS sample areas.

The general sample increase differs from the other parts

of the SCHIP expansion in two ways:

• It affects CPS every month of the year; and

• It is implemented differently for different states. More

sample was added in same states, while other states

received no general sample increase.

For the other two parts of the SCHIP expansion, each

state’s sample size was increased proportional to the

amount of CPS sample already there.

Allocating the Sample to States (Round 1)

The estimates to be improved by the SCHIP sample

expansion are 3-year averages of the percentage of lowincome children without health insurance. Low-income

children are defined, for SCHIP purposes, as those in

households with incomes at or below 200 percent of the

poverty level. To determine the states where added sample

would be of the most benefit, the detectable difference of

two consecutive 3-year averages was calculated (example:

comparing the 1998-2000 3-year average to the 1999-

2001 3-year average). The detectable difference is the

smallest percentage difference between the two estimates

that is statistically significant at the 90-percent confidence

level.

To reassign only portions of retired CPS sample, the

natural units to use were reduction groups. The assignment

of reduction group codes is explained in Appendix C. Using

reduction group codes, the sample was divided into eight

approximately equal parts called expansion groups. This

was done to simplify the iteration process performed to

reassign sample. Each expansion group contains 12 to 13

reduction groups. In most states, only some of the eight

expansion groups were reassigned sample designations.

Reassigning all eight expansion groups is roughly equivalent to doubling the sample size, meaning that all expired

sample from the old CPS sample designations and rotation

groups is brought back to be interviewed again.

1

‘‘Retired sample’’ consists of households that finished their series of

eight attempted CPS interviews.

J-1
The uninsured estimates used for allocating the sample

can be found at

http://www.census.gov/hhes/hlthins/liuc98.html.

To find the current detectable difference for each state, a

variation of a formula found in the Source and Accuracy

statement (http://www.bls.census.gov/cps/ads/1998/ssrcacc.htm)

for the March Supplement was used:

se x,p  bf 2

x p 100  p (1)

where: sex,p = standard error of the proportion

x = the total number of children meeting the

low-income criteria

p = the percentage of low-income children without health insurance

b = a general variance function (GVF) parameter

f

2 = state factor to adjust for differences in the

CPS sample design between states

The same proportion (p) was used for all of the states

(the 3-year national average from 1996 to 1998, 24.53

percent); this is consistent with CPS procedures to not

favor one state over another, based upon rates that may

change. Because the estimates are 3-year averages, some

manipulation was necessary to find the standard error of

the difference from one year to the next. To do so, the

following formula was used:

sep234  p123  1

3 se p4

2  se p12 (2)

where p123 is the average estimate for years 1, 2, and 3,

and p123 is the average for years 2, 3, and 4. Since the

single year standard error estimates were not available, the

standard errors calculated using formula (1) were used for

both standard errors beneath the radical. The detectable

difference was calculated by finding the coefficient of

variation (by dividing by 24.53 percent), then multiplying by

1.645 to create a 90-percent confidence interval. The

resulting detectable difference is proportional to the standard errors calculated.

Of the total annual funds allocated for this project, $7

million was budgeted for the general sample increase. This

translated into approximately 12,000 additional assigned

housing units each month. The 12,000 additional housing

units were allocated among states by applying the formula

below over several iterations, with the goal of lowering

each state’s detectable difference on the health insurance

coverage rate for poor children:

SS ’  SS * 

DD

DD ’

2

(3)

where: SS’ = new state sample size

SS = old state sample size

DD = old detectable difference for the state

DD’ = new detectable difference for the state (The

number being decreased with each iteration.)

An increase of 12,000 housing units was reached when

the state detectable differences were lowered to a maximum of 10.01 percent. There were some states that did not

achieve this threshold, even after the sample size was

doubled. For example, Connecticut started with a detectable difference of 14.51 percent. When lowering the desired

detectable difference by iteration, the necessary sample

size there doubled when the detectable difference reached

10.25 percent. No more sample was added there because

of the decision to no more than double the existing sample

in any state. The iterative process continued to lower the

detectable difference (and raise the sample size) for the

remainder of the states, however.

Alabama started with a detectable difference of 10.81

percent. After adding one expansion group, the detectable

difference was 10.19 percent. After adding two, the detectable difference was 9.67 percent. Since the limit was 10.01

percent, only two expansion groups were added for Alabama. Arizona started with a detectable difference of 8.34

percent. Since the limit was 10.01 percent, no sample was

added there.

Round 2 of State Allocation

The proposed allocation of sample to the states was

then sent to the Census Bureau’s Field Division for approval.

Based upon their comments, the proposed sample increases

were scaled back in Maryland, Virginia, Delaware, and the

District of Columbia. After setting limits on the amount of

sample to be added to these areas, the iteration process

was continued to get back to roughly 12,000 housing units

added to sample. All states where four or more expansion

groups had already been added were also removed from

consideration. These states were prohibited from getting

further sample increases because of the proportionally

large increases they had already received. After these final

iterations, an extra expansion group was assigned in Iowa,

North Dakota, Nevada, Oklahoma, Oregon, Utah, West

Virginia, and Wyoming. These were states where three or

fewer expansion groups had been added in Round 1. Table

J-1 shows the final allocation of the general sample increase

to states.

Phase-in of the General Sample Increase

Successful implementation of the other two parts of the

SCHIP expansion required that the general sample increase

be fully implemented by November 2000. To provide enough

time for listing and keying the new sample assignments,

SCHIP interviews could not begin until September 2000

(for more details on these processes, see Chapter 4). It

was advantageous to begin adding sample as soon as

possible to make the additions as gradual as possible for

the field staff.

J-2
Table J-1. Size of SCHIP General Sample Increase and Effect on Detectable Difference by State

State

Original

detectable

difference

(percent)

After first iteration After final iteration

Expansion

groups

added

Detectable

difference

(percent)

Expansion

groups

added

Detectable

difference

(percent)

Alabama ................................. 10.81 2 9.67 2 9.67

Alaska ................................... 11.54 3 9.84 3 9.84

Arizona .................................. 8.34 0 8.34 0 8.34

Arkansas ................................. 8.87 0 8.87 0 8.87

California. . . .............................. 4.00 0 4.00 0 4.00

Colorado ................................. 12.68 5 9.95 5 9.95

Connecticut. . ............................. 14.51 8 10.26 8 10.26

Delaware................................. 12.59 5 9.88 3 10.74

District of Columbia. . . ..................... 11.31 3 9.65 2 10.12

Florida . .................................. 5.93 0 5.93 0 5.93

Georgia .................................. 8.88 0 8.88 0 8.88

Hawaii ................................... 12.11 4 9.89 4 9.89

Idaho .................................... 9.30 0 9.30 0 9.30

Illinois. . .................................. 6.57 0 6.57 0 6.57

Indiana................................... 12.76 5 10.01 5 10.01

Iowa . . . .................................. 11.38 3 9.71 4 9.29

Kansas................................... 11.75 4 9.60 4 9.60

Kentucky ................................. 10.79 2 9.65 2 9.65

Louisiana. . ............................... 9.25 0 9.25 0 9.25

Maine. . . ................................. 13.64 7 9.96 7 9.96

Maryland . . . .............................. 14.65 8 10.36 5 11.50

Massachusetts ............................ 9.40 0 9.40 0 9.40

Michigan ................................. 7.23 0 7.23 0 7.23

Minnesota . .............................. 11.82 4 9.65 4 9.65

Mississippi................................ 8.92 0 8.92 0 8.92

Missouri. . ................................ 11.84 4 9.67 4 9.67

Montana . . ............................... 9.23 0 9.23 0 9.23

Nebraska................................. 11.31 3 9.64 3 9.64

Nevada .................................. 11.71 3 9.98 4 9.56

New Hampshire . . ......................... 14.75 8 10.43 8 10.43

New Jersey............................... 8.53 0 8.53 0 8.53

New Mexico .............................. 7.96 0 7.96 0 7.96

New York. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.71 0 4.71 0 4.71

North Carolina ............................ 8.17 0 8.17 0 8.17

North Dakota ............................. 10.93 2 9.78 3 9.32

Ohio . . . .................................. 7.01 0 7.01 0 7.01

Oklahoma ................................ 9.97 0 9.97 1 9.40

Oregon. .................................. 11.35 3 9.68 4 9.27

Pennsylvania . . ........................... 6.93 0 6.93 0 6.93

Rhode Island . . ........................... 14.93 8 10.56 8 10.56

South Carolina ........................... 11.19 3 9.55 3 9.55

South Dakota . ............................ 11.28 3 9.62 3 9.62

Tennessee ............................... 10.01 1 9.44 1 9.44

Texas . . .................................. 4.78 0 4.78 0 4.78

Utah . . . .................................. 9.78 0 9.78 1 9.22

Vermont. ................................. 13.40 7 9.79 7 9.79

Virginia................................... 12.02 4 9.81 2 10.75

Washington. . ............................. 12.28 5 9.63 5 9.63

West Virginia ............................. 10.62 1 10.01 2 9.50

Wisconsin ............................... 12.59 5 9.88 5 9.88

Wyoming ................................. 10.44 1 9.84 2 9.33

J-3
Table J-2 shows the phase-in of the expanded sample.

The sample designation/rotation groups in italics did not

enter sample MIS 1 or 5.2 B73(1) entered at MIS 3, and

B71(5) entered at MIS 8. Once introduced, these cases

followed the regular CPS rotation pattern. By November

2000, the phase-in of the general sample increase was

completed. However, because of the 4-8-4 rotating design

of the CPS sample, it was necessary to continue adding

new SCHIP cases to MIS 1 and 5 assignments for the next

8 months. In other words, for the next 8 months, SCHIP

sample was being interviewed for the first time in both MIS

1 and MIS 5, rather than just MIS 1. New SCHIP cases

were added only to MIS 1 assignments after July 2001.

A rotation chart for SCHIP follows (Figure J-1), for those

who are more familiar with this method of visualizing

survey sample. An explanation of rotation charts is found in

Chapter 3.

Use of General Sample Increase in Official

Labor Force Estimates

Although the original purpose of the SCHIP general

sample increase focused on improving the reliability of

estimates of low-income uninsured children, the Bureau of

Labor Statistics (BLS) saw the increase in the monthly CPS

sample size as an opportunity to improve the reliability of

labor force estimates, particularly at the state level. However, the added SCHIP sample initially was not used in the

official CPS labor force estimates, for a couple of reasons:

• To give Field Division the time to train the new field

representatives, and

• To analyze the labor force estimates both with and

without the new sample, ensuring that no bias was

introduced into the official time series.

In order to gauge the effect of the additional sample on

estimates, CPS data were weighted both with and without

the expanded sample beginning in November 2000. The

analysis mentioned compared the estimates as calculated

using both sets of weights. CPS weights for all SCHIP

sample are 0, while combined CPS/SCHIP weights are the

same for SCHIP sample and CPS sample cases within

each Primary Sampling Unit (PSU).

Weighting the CPS sample with the SCHIP sample included

was done using this formula:

BWCPSSCHIP  BWCPS * ( RGCPS

RGCPS  RGSCHIP

) (4)

2

To maintain the known properties of the sample rotation pattern, it is

desirable for CPS sample to begin interviewing in MIS 1. If sample cannot

be ready (for example, permit sample that does not finish processing) by

MIS 1, then it is deferred to MIS 5, thus getting four interviews in

consecutive months. However, in this case there was no choice but to add

sample at other points in the CPS interview cycle, because of the short

time frame available for phase-in.

Table J-2. Phase-in Table (First 11 Months of Sample Expansion)

New first month in sample Old sample

designation

New sample

designation

Rotation group

(both old and

new)

Last month in

sample

previously

MIS upon return

to sample

Sep. 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A63 B71 6 Aug. 1995 5

A65 B73 2 Aug. 1996 1

Oct. 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A63 B71 7 Sep. 1995 5

A65 B73 1 July 1996 3

A65 B73 3 Sep. 1996 1

Nov. 2000......................................... A63 B71 5 July 1995 8

A63 B71 8 Oct. 1995 5

A65 B73 4 Oct. 1996 1

Dec. 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A64 B72 1 Nov. 1995 5

A65 B73 5 Nov. 1996 1

Jan. 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A64 B72 2 Dec. 1995 5

A65 B73 6 Dec. 1996 1

Feb. 2001. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A64 B72 3 Jan. 1996 5

A65 B73 7 Jan. 1997 1

Mar. 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A64 B72 4 Feb. 1996 5

A65 B73 8 Feb. 1997 1

Apr. 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A64 B72 5 Mar. 1996 5

A66 B74 1 Mar. 1997 1

May 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A64 B72 6 Apr. 1996 5

A66 B74 2 Apr. 1997 1

Jun. 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A64 B72 7 May 1996 5

A66 B74 3 May 1997 1

Jul. 2001. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A64 B72 8 June 1996 5

A66 B74 4 June 1997 1

Aug. 2001. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A66 B74 5 July 1997 1

J-4
Figure J-1. SCHIP Rotation Chart: September 2000 to December 2002

J-5
where: BWCPS/SCHIP = baseweight of combined CPS and

SCHIP sample

BWCPS = baseweight of CPS sample alone

RGSCHIP = reduction groups added in SCHIP

expansion (as part of appropriate

expansion groups)

RGCPS = reduction groups currently in CPS

sample (usually 101)

The updated baseweights (or sampling intervals) can be

found in Table J-4, along with updated coefficients of

variation. For more details on weighting, see Chapter 10.

After analyzing several months of data, with and without

the added SCHIP sample, BLS decided to include the

SCHIP data in the official monthly CPS estimates, beginning with the July 2001 data release. The national unemployment rates were virtually identical when estimated from

the sample with and without the additional SCHIP cases.

The SCHIP expansion did not result in systematically

higher or lower labor force estimates across states, although

there were a few statistically significant differences in some

months in a few states. Results for a typical month are

shown in Table J-3.

The additional sample will slightly improve the reliability

of national labor force estimates, and will more substantially improve the state estimates for states in which SCHIP

sample was added (see U.S. Department of Labor, Employment & Earnings, August 2001, for more details). Table J-4

shows the effect of the general sample increase on state

sampling intervals and expected coefficients of variation

(CVs) for the annual average estimated levels of unemployed assuming a 6 percent unemployment rate. The CVs

will be improved (made smaller) by the additional sample in

some states. The first row of the table shows the effect on

the monthly national CV on unemployment level, which is

expected to drop from about 1.9 percent to 1.8 percent.

For purposes of comparison, the state sampling intervals and CVs are included, as estimated following a sample maintenance reduction which began in December

1999. (Details on how maintenance reductions are accomplished are found in Appendix C.) All type A rates, design

effects, estimated state populations, and civilian labor force

levels used to estimate the new CVs are the same as those

used for the maintenance reduction, so the differences in

CVs shown are resulting only from the changes in the

sampling intervals due to the SCHIP general sample

increase.

Summary of CPS/SCHIP Sample Design and

Reliability Requirements

The current sample design, introduced in July 2001,

includes about 72,000 assigned housing units from 754

sample areas (see Table J-5). Sufficient sample is allocated to maintain, at most, a 1.9 percent CV on national

monthly estimates of unemployment level, assuming a

6-percent unemployment rate. This translates into a change

of 0.2 percentage point in the unemployment rate being

significant at a 90-percent confidence level. For each of the

50 states and for the District of Columbia, the design

maintains a CV of at most 8 percent on the annual average

estimate of unemployment level, assuming a 6-percent

unemployment rate. About 60,000 assigned housing units

are required in order to meet the national and state

reliability criteria. Due to the national reliability criterion,

estimates for several large states are substantially more

reliable than the state design criterion requires. Annual

average unemployment estimates for California, Florida,

New York, and Texas, for example, carry a CV of less than

4 percent.

Table J-3. Effect of SCHIP on National Labor Force Estimates: June 2001

Estimate Population segment CPS only

CPS/SCHIP

combined Difference

Labor force participation rate .......................... Total, 16 years and over 67.4 67.4 0.0

16 to 19 years 58.1 58.5 –0.4

Men, 20 years and over 76.5 76.6 –0.1

Women, 20 years and over 60.5 60.4 0.1

White 67.6 67.7 –0.1

Black 66.2 66.1 0.1

Hispanic origin 67.9 67.8 0.1

Employment-population ratio .......................... Total, 16 years and over 64.2 64.2 0.0

16 to 19 years 48.5 48.6 –0.1

Men, 20 years and over 73.6 73.7 –0.1

Women, 20 years and over 58.0 58.0 0.0

White 64.8 64.9 –0.1

Black 60.4 60.5 –0.1

Hispanic origin 63.4 63.4 0.0

Unemployment rate . ................................. Total, 16 years and over 4.7 4.7 0.0

16 to 19 years 16.6 16.8 –0.2

Men, 20 years and over 3.8 3.8 0

Women, 20 years and over 4.0 4.0 0

White 4.1 4.1 0

Black 8.7 8.6 0.1

Hispanic origin 6.6 6.6 0

J-6
Table J-4. Effect of SCHIP on State Sampling Intervals and Coefficients of Variation

State

Sampling intervals

Coefficients of variation (CVs)

of annual average level of

unemployed (percent)

Following 1996

reduction

(Appendix H)

Following 1999

maintenance

reduction

With SCHIP

sample included

Following 1999

maintenance

reduction

With SCHIP

sample included

Total ........................... 2,255 2,366 2,128 1.9 1.8

Alabama ........................... 2,298 2,443 1,955 7.2 6.5

Alaska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 336 244 7.4 6.5

Arizona ............................ 2,238 2,238 2,238 7.0 7.0

Arkansas ........................... 1,316 1,316 1,316 7.2 7.2

California. . . ........................ 2,745 3,018 3,018 2.9 2.9

Los Angeles . ..................... 1,779 1,919 1,919 4.3 4.3

Balance . . ........................ 3,132 3,438 3,438 3.7 3.7

Colorado ........................... 1,992 2,163 1,331 7.0 5.8

Connecticut. . ....................... 2,307 2,307 1,154 7.7 5.4

Delaware. . . . . . . . . . . . . . . . . . . . . . . . . . . 505 505 367 7.4 6.3

District of Columbia. . . . . . . . . . . . . . . . . . 356 356 285 8.0 7.1

Florida . ............................ 2,176 2,338 2,338 3.7 3.7

Georgia ............................ 3,077 3,451 3,451 6.3 6.3

Hawaii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769 769 513 7.8 6.4

Idaho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 590 608 608 6.8 6.8

Illinois. . ............................ 2,227 2,227 2,227 4.2 4.2

Indiana............................. 3,132 3,132 1,927 7.0 5.7

Iowa . . . ............................ 1,582 1,582 1,055 7.2 6.2

Kansas............................. 1,423 1,452 968 7.3 6.2

Kentucky ........................... 2,089 2,153 1,722 7.2 6.5

Louisiana. . ......................... 2,143 2,186 2,186 7.0 7.0

Maine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 838 838 447 7.6 5.7

Maryland . . . ........................ 3,061 3,061 1,884 7.1 5.7

Massachusetts ...................... 1,853 1,927 1,927 5.0 5.0

Michigan ........................... 2,098 2,181 2,181 4.5 4.5

Minnesota . . ........................ 2,437 2,437 1,625 7.3 6.3

Mississippi.......................... 1,433 1,508 1,508 7.3 7.3

Missouri. . .......................... 3,132 3,132 2,088 7.1 5.9

Montana . . . . . . . . . . . . . . . . . . . . . . . . . . . 431 463 463 7.2 7.2

Nebraska. . . . . . . . . . . . . . . . . . . . . . . . . . . 910 910 662 7.2 6.3

Nevada . . . . . . . . . . . . . . . . . . . . . . . . . . . . 961 1,067 711 7.0 5.8

New Hampshire . . . . . . . . . . . . . . . . . . . . . 857 892 446 7.5 5.3

New Jersey......................... 1,841 1,898 1,898 4.4 4.4

New Mexico . . . . . . . . . . . . . . . . . . . . . . . . 867 922 922 7.2 7.2

New York. . . . . . . . . . . . . . . . . . . . . . . . . . . 1,970 2,034 2,034 3.2 3.2

New York City..................... 1,774 1,951 1,951 5.2 5.2

Balance . . ........................ 2,093 2,093 2,093 4.3 4.3

North Carolina ...................... 1,986 2,145 2,145 5.6 5.6

North Dakota . . . . . . . . . . . . . . . . . . . . . . . 363 363 264 7.4 6.5

Ohio . . . ............................ 2,343 2,376 2,376 4.4 4.4

Oklahoma .......................... 1,548 1,629 1,448 7.4 7.1

Oregon. ............................ 1,904 2,046 1,364 7.3 6.1

Pennsylvania . . ..................... 2,149 2,149 2,149 4.1 4.1

Rhode Island . . . . . . . . . . . . . . . . . . . . . . . 687 687 344 7.8 5.5

South Carolina . ..................... 2,291 2,385 1,735 7.4 6.4

South Dakota . . . . . . . . . . . . . . . . . . . . . . . 376 376 273 7.1 6.3

Tennessee.......................... 3,016 3,140 2,791 7.0 6.6

Texas . . ............................ 2,658 2,796 2,796 3.6 3.6

Utah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 958 1,063 945 7.1 6.8

Vermont. . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 436 232 7.3 5.4

Virginia............................. 3,084 3,268 2,619 6.8 6.2

Washington. . ....................... 2,999 3,329 2,048 7.6 6.3

West Virginia . . . . . . . . . . . . . . . . . . . . . . . 896 896 717 7.1 6.4

Wisconsin .......................... 2,638 2,638 1,623 7.4 6.3

Wyoming . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 253 202 7.3 6.7

J-7
In support of the SCHIP, about 12,000 additional housing units are allocated to the District of Columbia and 31

states. These are generally the states with the smallest

samples after the 60,000 housing units are allocated to

satisfy the national and state reliability criteria.

In the first stage of sampling, the 754 sample areas are

chosen. In the second stage, ultimate sampling unit clusters composed of about four housing units each are

selected.3 Each month, about 72,000 housing units are

assigned for data collection, of which about 60,000 are

occupied and thus eligible for interview. The remainder are

units found to be destroyed, vacant, converted to nonresidential use, to contain persons whose usual place of

residence is elsewhere, or to be ineligible for other reasons. Of the 60,000 eligible housing units, about 7.5

percent are not interviewed in a given month due to

temporary absence (such as a vacation), other failures to

make contact after repeated attempts, inability of persons

contacted to respond, unavailability for other reasons, and

refusals to cooperate (about half of the noninterviews).

Information is obtained each month for about 112,000

persons 16 years of age or older.

OTHER ASPECTS OF THE SCHIP EXPANSION

Although the general sample increase was the most

difficult to implement of the three parts of the SCHIP

expansion, the other two parts actually provide more

sample for the March Supplement. The following paragraphs provide more details on these.

Split-Path Assignments

As part of the SCHIP expansion, some CPS sample

from February and April will receive the March CPS Supplement. This will take the place of the regularly scheduled

supplement for that month. Only households that would

ordinarily not receive the March Supplement will receive it.

This means that February households in MIS 4 and 8 will

receive the March Supplement in February, and April MIS 1

and 5 households will receive it in April.

Households from MIS 1 and 5 in the previous November

will be screened at that time based upon whether they have

children (18 or younger) or non-White members. Those

that do meet these criteria will receive the March CPS

basic instrument and supplement in February, when they

are MIS 4 and 8, respectively. Households with Hispanic

residents will be excluded from this screening, since they

will already be interviewed as part of the November Hispanic oversampling. The March supplement will take the

place of the February supplement for these households,

effectively lowering the February supplement’s sample size.

In April, sample cases from MIS 1 and 5 will be screened

in the regular CPS interview for presence of children or

Hispanic or non-White inhabitants. Those who meet any of

these criteria will be given the March supplement instead of

the April supplement. See Chapter 11 for more details on

CPS supplements.

Month-in-Sample 9 Assignments

CPS sample from MIS 6, 7, and 8 in November will be

assigned for interview in February or April using the March

CPS basic instrument and income supplement. Before

interviewing, these households will be screened, using

CPS data, so that each household with at least one child

age 18 or younger or a non-White member will be in the

sample. (Hispanics will already be interviewed as a result

of the Hispanic oversampling of the March supplement;

see Chapter 11.) To the extent possible, these cases will be

assigned for interview through one of the telephone centers, but recycles will be interviewed in the field during the

week of the appropriate month in which CPS interviews are

conducted.

EFFECT OF SCHIP EXPANSION ON STANDARD

ERRORS OF ESTIMATES OF UNINSURED

LOW-INCOME CHILDREN

Table J-6 shows the effect of the SCHIP sample expansion on the standard errors of the state-level estimates of

3

In states with the SCHIP general sample increase, the size of these

ultimate clusters can be between four and eight housing units.

Table J-5. CPS Sample Housing Unit Counts With and

Without SCHIP

Housing unit categories CPS only

CPS and

SCHIP

Assigned housing units ................. 60,000 72,000

Eligible (occupied) ................... 50,000 60,000

Interviewed........................ 46,800 55,500

Type A noninterviews . . ............. 3,200 4,500

J-8
Table J-6. Standard Errors on Percentage of Uninsured, Low-Income Children Compared at Stages

of the SCHIP Expansion1

[In percent]

State

Before SCHIP

expansion

(percent)

After general

sample increase

(percent)

After SCHIP

expansion

(percent)2

Alabama . .......................................................... 1.61 1.44 1.22

Alaska............................................................. 1.72 1.47 1.30

Arizona ............................................................ 1.24 1.24 1.14

Arkansas .......................................................... 1.32 1.32 1.17

California .......................................................... 0.60 0.60 0.53

Colorado........................................................... 1.89 1.48 1.32

Connecticut . . ...................................................... 2.16 1.53 1.34

Delaware .......................................................... 1.88 1.60 1.41

District of Columbia ................................................. 1.69 1.51 1.29

Florida............................................................. 0.88 0.88 0.80

Georgia............................................................ 1.32 1.32 1.12

Hawaii............................................................. 1.81 1.47 1.18

Idaho. . . ........................................................... 1.39 1.39 1.24

Illinois ............................................................. 0.98 0.98 0.86

Indiana ............................................................ 1.90 1.49 1.33

Iowa . ............................................................. 1.70 1.39 1.24

Kansas ............................................................ 1.75 1.43 1.26

Kentucky .......................................................... 1.61 1.44 1.28

Louisiana .......................................................... 1.38 1.38 1.17

Maine . . . .......................................................... 2.03 1.49 1.36

Maryland .......................................................... 2.19 1.71 1.48

Massachusetts ..................................................... 1.40 1.40 1.26

Michigan........................................................... 1.08 1.08 0.94

Minnesota ......................................................... 1.76 1.44 1.25

Mississippi ......................................................... 1.33 1.33 1.15

Missouri ........................................................... 1.77 1.44 1.29

Montana ........................................................... 1.38 1.38 1.23

Nebraska .......................................................... 1.69 1.44 1.24

Nevada . . . ......................................................... 1.75 1.43 1.30

New Hampshire .................................................... 2.20 1.56 1.37

New Jersey ........................................................ 1.27 1.27 1.12

New Mexico........................................................ 1.19 1.19 1.09

New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.70 0.70 0.62

North Carolina...................................................... 1.22 1.22 1.07

North Dakota ....................................................... 1.63 1.39 1.22

Ohio . ............................................................. 1.05 1.05 0.91

Oklahoma.......................................................... 1.49 1.40 1.23

Oregon . ........................................................... 1.69 1.38 1.24

Pennsylvania . . ..................................................... 1.03 1.03 0.92

Rhode Island ....................................................... 2.23 1.57 1.40

South Carolina ..................................................... 1.67 1.42 1.23

South Dakota ...................................................... 1.68 1.43 1.23

Tennessee ......................................................... 1.49 1.41 1.25

Texas ............................................................. 0.71 0.71 0.64

Utah . ............................................................. 1.46 1.38 1.20

Vermont ........................................................... 2.00 1.46 1.34

Virginia . . . ......................................................... 1.79 1.60 1.39

Washington . . ...................................................... 1.83 1.44 1.24

West Virginia ....................................................... 1.58 1.42 1.28

Wisconsin.......................................................... 1.88 1.47 1.32

Wyoming . . . ....................................................... 1.56 1.39 1.24

1

Standard error on difference of two consecutive 3-year averages.

2

Includes MIS 9 and split-path cases.

J-9
the proportions of low-income children without health

insurance. These standard errors:

• Refer to the difference between two consecutive 3-year

averages,

• Are calculated after 4 years of the SCHIP expansion;

i.e. all 3 years of both estimates are expanded sample,

• Use the same rate of low-income children without

health insurance for all states.

• Are based on expected, not actual, sample sizes.

The values in the middle column (After General Sample

Increase) are proportional to the final detectable differences found in Table J-1. Included in the last column in the

table is an estimated standard error for each state after the

Split-path and MIS 9 cases are taken into account.

REFERENCES

Cahoon, L. (2000), ‘‘CPS 1990 Design Reassignment

#1 - September 2000 (SCHIP90-2, SAMADJ90-20),’’ Memorandum for J. Mackey Lewis, April 11th, Demographic

Statistical Methods Division, U.S. Census Bureau.

Rigby, B. (2000), ‘‘SCHIP CPS Sample Expansion used

to Quantify Sample Expansion Among the States (SCHIP1),’’ Memorandum for Documentation, March 29th,

Demographic Statistical Methods Division, U.S. Census

Bureau.

Shoemaker, H. (2000), ‘‘Effect of SCHIP/CPS General

Sample Expansion on State Sampling Intervals and Coefficients of Variation (SAMADJ90-22, SCHIP90-5),’’ Memorandum for Distribution List, Demographic Statistical Methods Division, U.S. Census Bureau.

U.S. Department of Labor, Bureau of Labor Statistics,

Employment and Earnings, Vol.48, No.8, Washington,

DC: Government Printing Office.

U.S. Public Law 106-113.

J-10
Acronyms

ADS Annual Demographic Supplement

API Asian and Pacific Islander

ARMA Autoregressing Moving Average

BLS Bureau of Labor Statistics

BPC Basic PSU Components

CAPE Committee on Adjustment of Postcensal

Estimates

CAPI Computer Assisted Personal Interviewing

CATI Computer Assisted Telephone Interviewing

CCM Civilian U.S. Citizens Migration

CCO CATI and CAPI overlap

CES Current Employment Statistics

CESEM Current Employment Statistics Survey

Employment

CNP Civilian Noninstitutional Population

CPS Current Population Survey

CPSEP CPS Employment-to-Population Ratio

CPSRT CPS Unemployment Rate

CV Coefficient of Variation

DA Demographic Analysis

DAFO Deaths of the Armed Forces Overseas

DEFF Design Effect

DOD Department of Defense

DRAF Deaths of the Resident Armed Forces

FOSDIC Film Optical Sensing Device for Input to the

Computer

FR Field Representative

FSC First- and Second-Stage Combined

FSCPE Federal State Cooperative Program for

Population Estimates

GQ Group Quarters

GVF Generalized Variance Function

HVS Housing Vacancy Survey

I&O Industry and Occupation

IM International Migration

INS Immigration and Naturalization Service

IRCA Immigration Reform and Control Act

LAUS Local Area Unemployment Statistics

MARS Modified Age, Race, and Sex

MIS Month-in-Sample

MLR Major Labor Force Recode

MOS Measure of Size

MSA Metropolitan Statistical Area

NCEUS National Commission on Employment and

Unemployment Statistics

NCHS National Center for Health Statistics

NHIS National Health Interview Survey

NPC National Processing Center

NRAF Net Recruits to the Armed Forces

NRO Net Recruits to the Armed Forces from

Overseas

NSR Nonself-Representing

OB Number of Births

OD Number of Deaths

OMB Office of Management and Budget

ORR Office of Refugee Resettlement

PAL Permit Address List

PES Post-Enumeration Survey

PIP Performance Improvement Period

POP Performance Opportunity Period

PSU Primary Sampling Unit

QC Quality Control

RDD Random Digit Dialing

RE Response Error

RO Regional Office

SCHIP State Children’s Health Insurance Program

SECU Standard Error Computation Unit

SFR Senior Field Representative

SMSA Standard Metropolitan Statistical Area

SOC Survey of Construction

SR Self-Representing

STARS State Time Series Analysis and Review

System

SW Start-With

TE Take-Every

UE Number of People Unemployed

UI Unemployment Insurance

URE Usual Residence Elsewhere

USU Ultimate Sampling Unit

WPA Work Projects Administration

ACRONYMS–1
Index

A

Active duty military personnel D-4

Address identification 4-1–4-4

Address lists, See Permit address lists

Adjusted population estimates definition D-1

ADS, See Annual Demographic Supplement

Age

base population by state D-18

deaths by D-15–D-16

institutional population D-18

international migration D-16–D-17

migration of Armed Forces and civilian citizens D-17

modification of census distributions D-14–D-15

population updates by D-10–D-14

Alaska

addition to population estimates 2-2

American Indian Reservations 3-9

Annual Demographic Supplement 11-2–11-8, J-1–J-10

Area frame

address identification 4-1–4-2

Area Segment Listing Sheets B-3, B-8

Area Segment Maps B-3, B-9–B-10

county maps B-9

description of 3-8–3-9

Group Quarters Listing Sheets B-3

listing in 4-5

Map Legends B-9, B-12

materials B-3, B-9

segment folders B-3

Segment Locator Maps B-9, B-11

Area Segment Maps B-3, B-9–B-10

ARMA, See Autoregressive moving average process

Armed Forces members D-4, D-10, D-17, 11-5–11-6,

11-8

Autoregressive moving average process E-3–E-4

B

Base population

definition D-1

Baseweights (see also basic weights) I-2, 10-2

Basic PSU components 3-10–3-11

Basic weights (see also baseweights) H-4, 10-2

Behavior coding 6-3

Between-PSU variances 10-3

Births

definition D-6

distribution by sex, race, and Hispanic origin D-15

BLS, See U.S. Bureau of Labor Statistics

BPCs, See Basic PSU components

BPO, See Building permit offices

Building permit offices B-9, 3-9

Building Permit Survey 3-7, 3-9, 4-2

Building permits

permit frame 3-9–3-10

sampling 3-2

Bureau of Labor Statistics, See U.S. Bureau of Labor

Statistics

Business presence 6-4

C

California

substate areas 3-1

CAPE, See Committee on Adjustment of Postcensal

Estimates

CAPI, See Computer Assisted Personal Interviewing

CATI, See Computer Assisted Telephone Interviewing

CATI recycles 4-7, 8-2

CCM, See Civilian U.S. citizens migration

CCO, See Computer Assisted Telephone Interviewing and

Computer Assisted Personal Interviewing Overlap

Census base population D-6

Census Bureau, See U.S. Census Bureau

Census Bureau report series 12-2

Census maps B-3, B-9

Census-level population

definition D-2

CES, See Current Employment Statistics

CESEM, See Current Employment Statistics survey

employment

Check-in of sample 15-4

Civilian housing 3-8

Civilian noninstitutional population D-11, 3-1, 5-3, 10-5

Civilian population

definition D-2

migration by age, sex, race, and Hispanic origin D-17

Civilian U.S. citizens migration D-9

Class-of-worker classification 5-4

Cluster design

changes in 2-3

Clustering, geographic 4-2, 4-4

Clustering algorithm 3-4

CNP, See Census base population; Civilian noninstitutional

population

Coding data 9-1

INDEX–1
Coefficients of variation

calculating 3-1

changes in requirements H-1—H-2

changes resulting from SCHIP J-6–J-8

Collapsing cells 10-7, 10-12

College students

state population adjustment D-20

Combined Reference File listing B-3, B-9

Committee on Adjustment of Postcensal Estimates D-20

Complete nonresponse 10-2

Components of population change

definition D-2

Composite estimation

introduction of 2-2

new procedure for I-1–I-2

Composite estimators 10-9–10-10

Computer Assisted Interviewing System 2-4–2-5, 4-6

Computer Assisted Personal Interviewing

daily processing 9-1

description of 4-6–4-7, 6-2

effects on labor force characteristics 16-7–16-8

implementation of 2-5

nonsampling errors 16-3, 16-7–16-8

Computer Assisted Telephone Interviewing

daily processing 9-1

description of 4-6–4-7, 6-2, 7-6, 7-9

effects on labor force characteristics 16-7–16-8

implementation of 2-4–2-5

nonsampling errors 16-3, 16-7–16-8

training of staff F-1–F-5

transmission of 8-1–8-3

Computer Assisted Telephone Interviewing and Computer

Assisted Personal Interviewing Overlap 2-5

Computerization

electronic calculation system 2-2

questionnaire development 2-5

Conditional probabilities A-1–A-4

Consumer Income report 12-2

Control card information 5-1

Counties

combining into PSUs 3-3

selection of 3-2

County maps B-9

Coverage errors

controlling 15-2—15-4

coverage ratios 16-1—16-3

sources of 15-2

CPS, See Current Population Survey

CPS control universe

definition D-2

CPS employment-to-population ratio E-3

CPS unemployment rate E-3

CPSEP, See CPS employment-to-population ratio

CPSRT, See CPS unemployment rate

Current Employment Statistics E-1–E-2, 1-1, 10-11

Current Employment Statistics survey employment E-6

Current Population Survey

data products from 12-1–12-4

decennial and survey boundaries F-2

design overview 3-1–3-2

history of revisions 2-1–2-5

overview 1-1

participation eligibility 1-1

reference person 1-1, 2-5

requirements H-1, 3-1

sample design H-1–H-9, 3-2–3-15

sample rotation 3-15–3-16

supplemental inquiries 11-1–11-8

CV, See Coefficients of variation

D

DA population, See Demographic analysis population

DAFO, See Deaths of the Armed Forces overseas

Data collection staff

organization and training F-1–F-5

Data preparation 9-1–9-3

Data quality 13-1–13-3

Deaths

defining D-6

distribution by age D-15–D-16

distribution by sex, race, and Hispanic origin D-15

Deaths of the Armed Forces overseas D-10

Deaths of the resident Armed Forces D-10

deff, See Design effects

Demographic analysis population

definition D-2

Demographic data

for children 2-4

codes 9-2

edits 7-8, 9-2

interview information 7-7, 7-9

procedures for determining characteristics 2-4

questionnaire information 5-1–5-2

Denton Method 10-11

Department of Defense D-9, D-10

Dependent interviewing 7-5–7-6

Design effects 14-7–14-8

Direct-use states 10-11

Disabled persons

questionnaire information 6-7

Discouraged workers

definition 5-5–5-6, 6-1–6-2

questionnaire information 6-7–6-8

Document sensing procedures 2-1

DOD, See Department of Defense

Dormitory adjustment D-20

DRAF, See Deaths of the resident Armed Forces

Dual-system estimation D-20–D-22

Dwelling places 2-1

INDEX–2
E

Earnings

edits and codes 9-3

information 5-4–5-5, 6-5

weights 10-12

Edits 9-2–9-3, 15-8–15-9

Educational attainment categories 5-2

Electronic calculation system 2-2

Emigration

definition D-2

Employed persons

definition 5-3

Employment and Earnings 12-1, 14-5

The Employment Situation 12-1

Employment statistics, See Current Employment Statistics

Employment status, See also Unemployment

full-time workers 5-4

layoffs 2-2, 5-5, 6-5–6-7

monthly estimates 10-10–10-11

not in the labor force 5-5–5-6

occupational classification additions 2-3

part-time workers 2-2, 5-3–5-4

seasonal adjustment E-5, 2-2, 10-10

state model-based labor force estimation E-1–E-6

Employment-population ratio

definition 5-6

Employment-to-population ratio models 10-11

Enumerative Check Census 2-1

Estimates population

definition D-2

Estimating variance, See Variance estimation

Estimators E-1–E-6, 10-9–10-10, 13-1

Expected values 13-1

F

Families

definition 5-2

Family businesses

presence of 6-4

Family equalization 11-5

Family weights 10-12

Federal State Cooperative Program for Population

Estimates D-10, D-18

Field PSUs 3-13

Field representatives, See also Interviewers

conducting interviews 7-1, 7-3–7-6

evaluating performance F-4–F-5

performance guidelines F-3

training procedures F-1–F-2

transmitting interview results 8-1–8-3

Field subsampling 3-14–3-15, 4-6

Film Optical Sensing Device for Input to the

Computer 2-2–2-3

Final hit numbers 3-12

Final weight 10-9

First- and second-stage combined estimates 10-9, 14-5–14-7

First-stage weights 10-4, 11-4

FOSDIC, See Film Optical Sensing Device for Input to the

Computer

4-8-4 rotation system 2-1–2-2, 3-15

Friedman-Rubin clustering algorithm 3-4

FRs, See Field representatives

FSC, See First- and second-stage combined estimates

FSCPE, See Federal State Cooperative Program for

Population Estimates

Full-time workers

definition 5-4

G

Generalized variance functions C-2, 14-3–14-5

Geographic clustering 4-2, 4-4

Geographic Profile of Employment and

Unemployment 12-2

GQ, See Group quarters frame

Group Quarters frame

address identification 4-2

base population by state D-18

census maps B-9

Combined Reference File listing B-9

description of 3-7–3-9

Group Quarters Listing Sheets B-3, B-9, B-13, 4-5–4-6

Incomplete Address Locator Actions form B-9

listing in 4-5–4-6

materials B-9

segment folder B-9

GVFs, See Generalized variance functions

H

Hadamard matrix 14-2

Hawaii

addition to population estimates 2-2

Hispanic origin

distribution of births and deaths D-15

institutional population D-18

international migration D-16–D-17

migration of Armed Forces and civilian citizens D-17

mover and nonmover households 11-5–11-8

population updates by D-10–D-11

Hit strings 3-11–3-12

Homeowner vacancy rates 11-2

Horvitz-Thompson estimators 10-9

‘‘Hot deck’’ allocations 9-2–9-3, 11-5

Hours of work

definition 5-3

questionnaire information 6-4

Households

base population by state D-18–D-19

definition 5-1–5-2

edits and codes 9-2

eligibility 7-1, 7-3

mover and nonmover households 11-5–11-8

questionnaire information 5-1–5-2

INDEX–3
Households—Con.

respondents 5-1

rosters 5-1, 7-3, 7-6

weights 10-12

Housing units

definition 3-7

frame omission 15-2

interview information 7-4–7-5, 7-9

measures 3-8

misclassification of 15-2

questionnaire information 5-1

selection of sample units 3-7

ultimate sampling units 3-2

vacancy rates 11-2–11-4

Housing Vacancy Survey 7-1, 11-2–11-4

HUs, See Housing units

HVS, See Housing Vacancy Survey

I

IM, See International migration

Immigration

definition D-2

Immigration and Naturalization Service D-2, D-7, D-16

Immigration Reform and Control Act D-8, D-16

Imputation techniques 9-2–9-3, 15-8–15-9

Incomplete Address Locator Actions forms B-3, B-7, B-9

Independent population controls, See Population controls

Industry and occupation data

coding of 2-4, 8-2, 9-1, 9-3, 15-8

edits 9-3

questionnaire information 5-4, 6-4–6-5

verification 15-8

Inflation-deflation method D-11–D-14, 2-3

Initial adjustment factors 10-7

INS, See Immigration and Naturalization Service

Institutional housing 3-8

Institutional population

changes in D-10

definition D-2

population by age, sex, race, and Hispanic origin D-18

Internal consistency of a population

definition D-2

International migration

by age, sex, race, and Hispanic origin D-16–D-17

definition D-2

population projections D-7–D-9

Internet

CPS reports 12-2

employment statistics 12-1

questionnaire revision 6-3

Interview week 7-1

Interviewers, See also Field representatives

assignments 4-6–4-7

controlling response error 15-7

debriefings 6-3

interaction with respondents 15-7–15-8

Spanish-speaking 7-6

Interviews

beginning questions 7-5, 7-7

dependent interviewing 7-5–7-6

ending questions 7-5, 7-7

household eligibility 7-1, 7-3

initial 7-3

noninterviews 7-1, 7-3, 7-4

reinterviews D-20–D-22, G-1–G-2, 15-8

results 7-10

telephone interviews 7-4–7-10

transmitting results 8-1–8-3

Introductory letters 7-1, 7-2

I&O, See Industry and occupation data

IRCA, See Immigration Reform and Control Act

Item nonresponse 9-2, 10-2, 16-5

Item response analysis 6-2–6-3

Iterative proportional fitting 10-5

J

Job leavers 5-5

Job losers 5-5

Job seekers

definition 5-5

duration of search 6-6–6-7

methods of search 6-6

Joint probabilities A-1–A-4

L

Labor force

adjustment for nonresponse 10-2–10-3

composite estimators 10-9–10-10

definition 5-6

edits and codes 9-2–9-3

effect of TypeA noninterviews on classification 16-4–16-5

family weights 10-12

first-stage ratio adjustment 10-3–10-5

gross flow statistics 10-13–10-14

household weights 10-12

interview information 7-3

longitudinal weights 10-13–10-14

monthly estimates 10-10–10-11, 10-14

new composite estimation method I-1–I-2

outgoing rotation weights 10-12–10-13

questionnaire information 5-2–5-6, 6-2

ratio estimation 10-3

seasonal adjustment 10-10

second-stage ratio adjustment 10-5–10-9

state model-based estimation E-1–E-6

unbiased estimation procedure 10-1–10-2

veterans’ weights 10-13

Labor force participation rate

definition 5-6

LABSTAT 12-1

Large special exceptions

Unit/Permit Listing Sheets B-6

Layoffs 2-2, 5-5, 6-5–6-7

INDEX–4
Legal permanent residents

definition D-2

Levitan Commission 2-4, 6-1, 6-7

Listing activities 4-4–4-6

Listing checks 15-4

Listing Sheets

Area Segment B-3, B-8

Group Quarters B-3, B-9, B-13, 4-5–4-6

reviews 15-3–15-4

Unit/Permit B-1, B-3–B-6, B-9, B-14, 4-4–4-5

Longitudinal edits 9-2

Longitudinal weights 10-13–10-14

M

Maintenance reductions C-1–C-2

Major Labor Force Recode 9-2–9-3

Map Legends B-9, B-12

March Supplement (see also Annual Demographic

Supplement) 11-2–11-8

Marital status categories 5-2

MARS, See Modified age, race, and sex

Maximum Overlap procedure A-1–A-4, 3-6

Mean squared error 13-1–13-2

Measure of size 3-8

Metropolitan Statistical Areas 3-3, 10-2–10-3

Microdata files 12-1–12-2, 12-4

Military housing 3-8, 3-9

MIS, See Month-in-sample

MLR, See Major Labor Force Recode

Modeling errors 15-9

Modified age, race, and sex

application of coverage ratios D-21–D-22

definition D-2

Month-in-sample 7-1, 7-4–7-5, 11-4–11-5, 11-8, 16-7–16-9

Month-In-Sample 9 J-1, J-9–J-10

Monthly Labor Review 12-1

MOS, See Measure of size

Mover households 11-5–11-8

MSAs, See Metropolitan Statistical Areas

Multiple jobholders

definition 5-3

questionnaire information 6-2, 6-4

Multiunit structures

Unit/Permit Listing Sheets B-5

Multiunits 4-4–4-5

N

National Center for Education Statistics D-20

National Center for Health Statistics D-6

National Commission on Employment and Unemployment

Statistics 2-4, 6-1, 6-7

National Health Interview Survey 3-10

National Park blocks 3-9

National Processing Center F-1

Natural increase of a population

definition D-2

NCES, See National Center for Education Statistics

NCEUS, See National Commission on Employment and

Unemployment Statistics

NCHS, See National Center for Health Statistics

Net recruits to the Armed Forces D-10

Net recruits to the Armed Forces from overseas D-10

New entrants 5-5

New York

substate areas 3-1

NHIS, See National Health Interview Survey

Nondirect-use states 10-11

Noninstitutional housing 3-8

Noninterview

adjustments 10-3, 11-4

clusters 10-2–10-3

factors 10-3

Noninterviews 7-1, 7-3–7-4, 9-1, 16-2–16-5

Nonmover households 11-5–11-8

Nonresponse H-4, 9-2, 10-2, 15-4–15-5

Nonresponse errors

item nonresponse 16-5

mode of interview 16-6–16-7

proxy reporting 16-10

response variance 16-5–16-6

time in sample 16-7–16-10

Type A noninterviews 16-3–16-5

Nonsampling errors

controlling response error 15-6–15-8

coverage errors 15-2–15-4, 16-1

definitions 13-2–13-3

miscellaneous errors 15-8–15-9

nonresponse errors 15-4–15-5, 16-3–16-5

overview 15-1–15-2

response errors 15-6

Nonself-representing primary sampling units 3-4–3-6,

10-4, 14-1–14-3

NPC, See National Processing Center

NRAF, See Net recruits to the Armed Forces

NRO, See Net recruits to the Armed Forces from overseas

NSR PSUs, See Nonself-representing primary sampling

units

O

OB, See Overseas births

Occupational data

coding of 2-4, 8-2, 9-1

questionnaire information 5-4, 6-4–6-5

OD, See Overseas deaths

Office of Management and Budget D-2, 2-2

Office of Refugee Resettlement D-8

Old construction frames 3-8–3-9, 3-11–3-12

OMB, See Office of Management and Budget

ORR, See Office of Refugee Resettlement

Outgoing rotation weights 10-12–10-13

Overseas births D-9

Overseas deaths D-9

INDEX–5
P

PALs, See Permit Address Lists

Part-time workers

definition 5-4

economic reasons 5-3

monthly questions 2-2

noneconomic reasons 5-3–5-4

Performance Improvement Period F-5

Performance Opportunity Period F-5

Permit address lists

forms B-9, B-15, B-17, 4-6

operation 4-2, 4-4, 4-6

Permit frames

address identification 4-2, 4-4

description of 3-9–3-11

listing in 4-6

materials B-9, B-17

permit address list forms B-9, B-15, B-17

Permit Sketch Map B-16–B-17

segment folder B-9

Unit/Permit Listing Sheets B-9, B-14

Permit Sketch Maps B-16–B-17

Persons on layoff

definition 5-5, 6-5–6-7

PES, See Post-Enumeration Survey

PIP, See Performance Improvement Period

POP, See Performance Opportunity Period

Population base

definition D-1

Population Characteristics report 12-2

Population controls

adjustment for net underenumeration D-20

annual revision process D-22

dual-system estimation D-20–D-22

monthly revision process D-22

nonsampling error 15-9

population projection calculations D-4–D-20

the population universe D-3–D-4

procedural revisions D-22–D-23

sources D-23, 10-6

for states D-18–D-20

terminology D-1–D-3

Population data revisions 2-3

Population estimates

definition D-2

Population projections

calculation of D-4–D-20

definition D-2

total D-4–D-10

Population universe

for CPS controls D-3–D-4

definition D-3

Post-Enumeration Survey D-1, D-2, D-20–D-22

Post-sampling codes 3-12–3-14

pps, See Probability proportional to size

President’s Committee to Appraise Employment and

Unemployment Statistics 2-3

Primary Sampling Units

adding a county A-3

basic PSU components 3-10–3-11

changes in variance estimates H-5

definition 3-2–3-3

division of states 3-2

dropping a county A-3–A-4

dropping and adding A-4

field PSUs 3-13

increase in number of 2-1–2-4

Maximum Overlap procedure A-1–A-4, 3-6

noninterview clusters 10-2–10-3

nonself-representing 3-4–3-6, 10-4, 14-1–14-3

rules for defining 3-3

selection of 3-4–3-6

self-representing 3-3–3-5, 10-4, 14-1–14-3

stratification of 3-3–3-5

Probability proportional to size (see also Chapter 3) A-1

Probability samples 10-1–10-2

Projections, population

calculation of D-4–D-20

definition D-2

total D-4–D-10

Proxy reporting 16-10

PSUs, See Primary Sampling Units

Publications 12-1–12-4

Q

QC, See Quality control

Quality control

reinterview program G-1–G-2, 15-8

Quality measures

of statistical process 13-1–13-3

Questionnaires, See also Interviews

controlling response error 15-6

demographic information 5-1–5-2

household information 5-1–5-2

labor force information 5-2–5-6

revision of 2-1, 6-1–6-8

structure of 5-1

R

Race

category modifications 2-1, 2-3–2-4

determination of 2-4

distribution of births and deaths D-15

institutional population D-18

international migration D-16–D-17

migration of Armed Forces and civilian citizens D-17

modification of census distributions D-14–D-15

population updates by D-10–D-11

racial categories 5-2

Raking ratio 10-5, 10-6–10-8

Random digit dialing sample 6-2

Random groups 3-12–3-13

INDEX–6
Random starts 3-11

Ratio adjustments

first-stage 10-3–10-5

second-stage 10-5–10-9

Ratio estimates

revisions to 2-1–2-4

two-level, first-stage procedure 2-4

RDD, See Random digit dialing sample

RE, See Response error

Recycle Reports 8-2

Reduction groups C-1–C-2

Reduction plans C-1–C-2, H-1

Reentrants 5-5

Reference dates

definition D-2

Reference persons 1-1, 2-5, 5-1–5-2

Reference week 5-3, 6-3–6-4, 7-1

Referral codes 15-8

Refusal rates 16-2–16-4

Regional offices

operations of 4-7

organization and training of data collection staff F-1–F-5

transmitting interview results 8-1–8-3

Regression components E-1–E-2

Reinterviews D-20–D-22, G-1–G-2, 15-8

Relational imputation 9-2

Relationship information 5-2

Relative variances 14-4–14-5

Rental vacancy rates 11-2

Replicates

definition 14-1

Replication methods 14-1–14-2

Report series 12-2

Residence cells 10-3

Resident population

definition D-2–D-3

Respondents

controlling response error 15-7

debriefings 6-3

interaction with interviewers 15-7–15-8

Response distribution analysis 6-2–6-3

Response error G-1, 6-1, 15-6–15-8

Response variance 13-2, 16-5–16-6

Retired persons

questionnaire information 6-7

ROs, See Regional offices

Rotation chart 3-15–3-16, J-4–J-5

Rotation groups 3-12, 10-1, 10-12

Rotation system 2-1–2-2, 3-2, 3-15–3-16

RS, See Random starts

S

Sample characteristics 3-1

Sample data revisions 2-3

Sample design

changes due to funding reduction H-1–H-9

controlling coverage error 15-3

county selection 3-2–3-6

definition of the PSUs 3-2–3-3

field subsampling 3-14–3-15, 4-6

old construction frames 3-8–3-9

permit frame 3-9–3-10

post-sampling code assignment 3-12–3-14

quality measures 13-1–13-3

sample housing unit selection 3-7–3-14

sample unit selection 3-10

sampling frames 3-7–3-8, 3-10

sampling procedure 3-11–3-12

sampling sources 3-7

selection of the sample PSUs 3-4–3-6

stratification of the PSUs 3-3–3-5

variance estimates 14-5–14-6

within-PSU sample selection for old construction 3-12

within-PSU sort 3-11

Sample preparation

address identification 4-1–4-4

area frame materials B-3, B-9

components of 4-1

controlling coverage error 15-3–15-4

goals of 4-1

group quarters frame materials B-9

interviewer assignments 4-6–4-7

listing activities 4-4–4-6

permit frame materials B-9, B-17

unit frame materials B-1–B-3, B-4–B-8

Sample redesign 2-5

Sample size

determination of 3-1

maintenance reductions C-1–C-2

reduction in C-1–C-2, H-1, 2-3–2-5

Sample Survey of Unemployment 2-1

Sampling

bias 13-1–13-2

error 13-1–13-2

frames 3-7–3-8

intervals H-3–H-4, 3-6, J-6–J-7

variance 13-1–13-2

SCHIP, See State Children’s Health Insurance Program

School enrollment

edits and codes 9-3

School enrollment data 2-4

Seasonal adjustment of employment E-5, 2-2, 10-10

Second jobs 5-4

Second-stage adjustment 11-4–11-8

Security

transmitting interview results 8-1

SECUs, See Standard Error Computation Units

INDEX–7
Segment folders

area frames B-3

group quarters frames B-9

permit frames B-9

unit frame B-1–B-2

Segment Locator Maps B-9, B-11

Segment number suffixes 3-13

Segment numbers 3-13

Selection method revision 2-1

Self-employed

definition 5-4

Self-representing primary sampling units 3-3–3-5, 10-4,

14-1–14-3

Self-weighting samples 10-2

Senior field representatives F-1

Sex

distribution of births and deaths D-15

institutional population D-18

international migration D-16–D-17

migration of Armed Forces and civilian citizens D-17

population updates by D-10–D-11

SFRs, See Senior field representatives

Signal components E-1

Signal-plus-noise models E-1, 10-11

Simple response variance 16-5–16-6

Simple weighted estimators 10-9

Skeleton sampling 4-2

Sketch maps B-16–B-17

SMSAs, See Standard Metropolitan Statistical Areas

SOC, See Survey of Construction

Spanish-speaking interviewers 7-6

Special Studies report 12-2

Split-path J-8

SR PSUs, See Self-representing primary sampling units

Staff

organization and training F-1–F-5

Standard Error Computation Units H-5, 14-1–14-2

Standard Metropolitan Statistical Areas 2-3

State Children’s Health Insurance Program 1-1, 2-5,

3-1, 9-1, 11-4–11-8, E-1, H-1, J-1–J-10

STARS, See State Time Series Analysis and Review

System

Start-with procedure 3-14–3-15, 4-2

State sampling intervals 3-6

State supplementary samples 2-3–2-4

State Time Series Analysis and Review System E-4

State-based design 2-4

States

model-based labor force estimation E-1–E-6

monthly employment and unemployment estimates 10-10–

10-11

population controls for D-18–D-20

Statistical Policy Division 2-2

Statistics

quality measures 13-1–13-3

Subfamilies

definition 5-2

Subsampling 3-14–3-15, 4-6

Successive difference replication 14-2–14-3

Supplemental inquiries

Annual Demographic Supplement 11-2–11-8

criteria for 11-1–11-2

Housing Vacancy Survey 11-2–11-4, 11-8

Survey, See Current Population Survey

Survey instrument, See Questionnaires

Survey of Construction B-9, 4-4

Survey week 2-2

SW, See Start-with procedure

Systematic sampling 4-2–4-3

T

Take-every procedure 3-14–3-15, 4-2

TE, See Take-every procedure

Telephone interviews 7-4–7-10, 16-6–16-8

Temporary jobs 5-5

Time series component modeling E-1–E-4

Two-stage ratio estimators 10-9

Type A noninterviews 7-1, 7-3–7-4, 10-2, 16-3–16-5

Type B noninterviews 7-1, 7-3–7-4

Type C noninterviews 7-1, 7-3–7-4

U

UI, See Unemployment insurance

Ultimate sampling units

area frames 3-8–3-9

description 3-2

hit strings 3-11–3-12

reduction groups C-1–C-2

selection of 3-7

special weighting adjustments 10-2

variance estimates 14-1–14-4

Unable to work persons

questionnaire information 6-7

Unbiased estimation procedure 10-1–10-2

Unbiased estimators 13-1

Undocumented migration

definition D-3

Unemployment

classification of 2-2

definition 5-5

duration of 5-5

early estimates of 2-1

employment search information 2-3

household relationships and 2-3

monthly estimates 10-10–10-11

questionnaire information 6-5–6-7

reason for 5-5

seasonal adjustment E-5, 2-2, 10-10

state model-based labor force estimation E-1–E-6

variance on estimate of 3-6

Unemployment insurance E-1, E-6, 10-11

Unemployment rate

definition 5-6

models 10-11

INDEX–8
Union membership 2-4

Unit frame

address identification 4-1

census maps B-3

Combined Reference File listing B-3

description of 3-8

Incomplete Address Locator Actions form B-3, B-7

listing in 4-4–4-5

materials B-1–B-8

segment folders B-1–B-2

Unit/Permit Listing Sheets B-1, B-3–B-6

Unit nonresponse 10-2, 16-3–16-5

Unit segments

Unit/Permit Listing Sheets B-4

Unit/Permit Listing Sheets B-1, B-3–B-6, B-9, B-14,

4-4–4-5

Universe population

definition D-3

URE, See Usual residence elsewhere

U.S. Bureau of Labor Statistics E-1, 1-1, 2-2, 12-1–12-2

U.S. Census Bureau 1-1, 2-2, 2-5, 4-1, 12-2

Usual residence elsewhere 7-1

USUs, See Ultimate sampling units

V

Vacancy rates 11-2

Variance estimation

changes in H-4–H-9

Variance estimation—Con.

definitions 13-1–13-2

design effects 14-7–14-8

determining optimum survey design 14-5–14-6

generalizing 14-3–14-5

objectives of 14-1

replication methods 14-1–14-2

for state and local area estimates 14-3

successive difference replication 14-2–14-3

total variances as affected by estimation 14-6–14-7

Veterans

data for females 2-4

Veterans’ weights 10-13

W

Web sites

CPS reports 12-2

employment statistics 12-1

questionnaire revision 6-3

Weighting samples I-2, 10-2–10-3, 11-3–11-4, 15-8–15-9

Work Projects Administration 2-1

Workers, See Employment status

WPA, See Work Projects Administration

X

X-11 ARIMA program E-5, 10-10, 10-11

INDEX–9