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This Current Population Survey Technical Paper (CPS TP66) is an updated version of TP63RV. Previous

chapters and appendices have been brought up-to-date to reflect the 2000 Current Surveys’ Sample

Redesign and current U.S. Census Bureau confidentiality concerns. This included the deletion of five

appendices. However, most of the design and methodology of the 2000 CPS design and methodology

remains the same as that of January 1994, which is documented in CPS TP63RV. As updates in the

design and methodology occur, they will be posted on the Internet version.

There were many individuals who contributed to the publication of the CPS TP63 and TP63RV. Their

valued expertise and efforts laid the basis for TP66. However, only those involved with this current

version are listed in these Acknowledgments. Some authored sections/chapters/appendices, some

reviewed the text for technical, procedural, and grammatical accuracy, and others did both. Some are

still with their agencies and some have since left.

This technical paper was written under the coordination of Andrew Zbikowski and Antoinette

Lubich of the U.S. Census Bureau. Tamara Sue Zimmerman served as the coordinator/contact for the

Bureau of Labor Statistics.

It has been produced through the combined efforts of many individuals. Contributing from the U.S.

Census Bureau were Samson A. Adeshiyan, Adelle Berlinger, Sam T. Davis, Karen D. Deaver,

John Godenick, Carol Gunlicks, Jeffrey Hayes, David V. Hornick, Phawn M. Letourneau, Zijian

Liu, Antoinette Lubich, Khandaker A. Mansur, Alexander Massey, Thomas F. Moore, Richard C.

Ning, Jeffrey M. Pearson, Benjamin Martin Reist, Harland Shoemaker, Jr., Bonnie S. Tarsia, Bac

Tran, Alan R. Tupek, Cynthia L. Wellons-Hazer, Gregory D. Weyland, and Andrew Zbikowski.

Lawrence S. Cahoon and Marjorie Hanson served as editorial reviewers of the entire document.

Contributing from the Bureau of Labor Statistics were Sharon Brown, Shail Butani, Sharon R.

Cohany, Samantha Cruz, John Dixon, James L. Esposito, Thomas Evans, Howard V. Hayghe,

Kathy Herring, Diane Herz, Vernon Irby, Sandi Mason, Brian Meekins, Stephen M. Miller,

Thomas Nardone, Kenneth W. Robertson, Ed Robison, John F. Stinson, Jr., Richard Tiller,

Clyde Tucker, Stephanie White, and Tamara Sue Zimmerman.

Catherine M. Raymond, Corey T. Beasley, Theodora S. Forgione, and Susan M. Kelly 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 James R. Clark, Assistant

Division Chief, Wanda K. Cevis, Chief, Publications Services Branch, and Everett L. Dove, Chief,

Printing Section.

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.

ACKNOWLEDGMENTS
	












	




	
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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.

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. In January 2003, the CPS

adopted the 2002 census industry and occupation classification systems, derived from the 2002

North American Industry Classification System and the 2000 Standard Occupational Classification

System.

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.

Charles Louis Kincannon

Director

U.S. Census Bureau

May 2006

Kathleen P. Utgoff

Commissioner

U.S. Bureau of Labor Statistics

May 2006

Current Population Survey TP66 Foreword iii

U.S. Bureau of Labor Statistics and U.S. Census Bureau
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.................................... 2–7

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 ...................... 3–6

Third Stage of the Sample Design ....................... 3–13

Rotation of the Sample ............................. 3–13

References.................................... 3–15

Chapter 4. Preparation of the Sample

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

Listing Activities ................................ 4–3

Third Stage of the Sample Design (Subsampling) ............... 4–5

Interviewer Assignments ............................ 4–5

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.................................... 5–6

Chapter 6. Design of the Current Population Survey Instrument

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

Motivation for Redesigning the Questionnaire Collecting 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

Chapter 8. Transmitting the Interview Results

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

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

CONTENTS

Current Population Survey TP66 Contents v

U.S. Bureau of Labor Statistics and U.S. Census Bureau
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. Weighting and Seasonal Adjustment 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

National Coverage Adjustment ......................... 10−4

State Coverage Adjustment........................... 10−6

Second-Stage Ratio Adjustment ........................ 10−7

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

Producing Other Labor Force Estimates .................... 10–13

Seasonal Adjustment .............................. 10–15

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

Chapter 11. Current Population Survey Supplemental Inquiries

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

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

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

Summary .................................... 11–10

Chapter 12. Data Products From the Current Population Survey

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

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

Census Bureau Products ............................ 12–1

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 and 2000 Designs ......... 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

Chapter 15. Sources and Controls on Nonsampling Error

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

Sources of Coverage Error ........................... 15–1

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

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

Controlling Nonresponse Error ......................... 15–5

Sources of Response Error ........................... 15–5

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

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

CONTENTS

vi Contents Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Chapter 15.—Con.

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

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

Chapter 16. Quality Indicators of Nonsampling Errors

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

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

Nonresponse .................................. 16–2

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

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

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

Proxy Reporting ................................. 16–9

Summary .................................... 16–9

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

Appendix A. Sample Preparation Materials

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

Unit Frame Materials .............................. A–1

Area Frame Materials .............................. A–1

Group Quarters Frame Materials ........................ A–1

Permit Frame Materials ............................. A–1

Appendix B. Maintaining the Desired Sample Size

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

Maintenance Reductions ............................ B–1

Appendix C. Derivation of Independent Population Controls

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

Population Universe for CPS Controls ..................... C–3

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

Procedural Revisions .............................. C–11

Summary List of Sources for CPS Population Controls ............. C–11

References.................................... C–12

Appendix D. Organization and Training of the Data Collection Staff

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

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

Training Field Representatives ......................... D–1

Field Representative Training Procedures ................... D–1

Field Representative Performance Guidelines ................. D–3

Evaluating Field Representative Performance ................. D–4

Appendix E. Reinterview: Design and Methodology

Introduction ................................... E−1

Response Error Sample ............................. E−1

Quality Control Sample ............................. E−1

Reinterview Procedures ............................. E–2

Summary .................................... E–2

References.................................... E–2

Acronyms .................................... Acronyms−1

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

CONTENTS

Current Population Survey TP66 Contents vii

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Figures

3–1 CPS Rotation Chart: January 2006−April 2008 ............. 3–14

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 Items of Housing Unit Information 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 (September 2004)................. 7–10

7–10 Telephone Interview Rates (September 2004) ............. 7–10

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

11–1 Diagram of the ASEC Weighting Scheme ................ 11–9

16–1 CPS Total Coverage Ratios: September 2001−September 2004,

National Estimates ........................... 16–2

16–2 Average Yearly Type A Noninterview and Refusal Rates for the CPS

1964−2003, National Estimates .................... 16–3

16−3 CPS Nonresponse Rates: September 2003−September 2004,

National Estimates ........................... 16−3

16−4 Basic CPS Household Nonresponse by Month in Sample,

July 2004−September 2004, National Estimates ........... 16−9

D−1 2000 Decennial and Survey Boundaries ................ D−2

Illustrations

1 Segment Folder, BC-1669 (CPS) ..................... A–3

2 Multi-Unit Listing Aid, Form 11-12 ................... A–4

3 Unit/Permit Listing Sheet, 11-3 (Blank) ................. A–5

4 Incomplete Address Locator Actions Form, NPC 1138 ......... A–6

5 Unit/Permit Listing Sheet, 11-3 (Single unit in Permit Frame) ..... A–7

6 Unit/Permit Listing Sheet, 11-3 (Multi-unit in Permit Frame)...... A−8

7 Permit Sketch Map, Form 11-187 .................... A−9

Tables

3–1 Estimated Population in Sample Areas for 824-PSU Design by State. . 3–5

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

3–3 Index Numbers Selected During Sampling for Code Assignment

Examples ................................ 3–12

3–4 Example of Post-Sampling Survey Design Code Assignments Within

a PSU .................................. 3–12

3–5 Example of Post-Sampling Operational Code Assignments Within a

PSU ................................... 3–13

3–6 Proportion of Sample in Common for 4-8-4 Rotation System ..... 3–14

10–1 National Coverage Adjustment Cell Definitions ............ 10–5

10−2 State Coverage Adjustment Cell Definitions .............. 10−6

10–3 Second-Stage Adjustment Cell by Ethnicity, Age, and Sex ....... 10–7

10–4 Second-Stage Adjustment Cell by Race, Age, and Sex ......... 10–8

CONTENTS

viii Contents Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Tables—Con.

10–5 Composite National Ethnicity Cell Definition .............. 10–12

10–6 Composite National Race Cell Definition ................ 10–12

11–1 Current Population Survey Supplements September 1994−December

2004 .................................. 11–3

11–2 MIS Groups Included in the ASEC Sample for Years 2001, 2002,

2003, and 2004 ............................ 11–6

11–3 Summary of 2004 ASEC Interview Months ............... 11–7

11–4 Summary of ASEC SCHIP Adjustment Factor for 2004 ......... 11–10

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 SS 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 and Within-PSU Monthly Variances ...... 14–8

16–1 Components of Type A Nonresponse Rates, Annual Averages for

1993−1996 and 2003, National Estimates .............. 16–4

16–2 Percentage of Households by Number of Completed Interviews

During the 8 Months in the Sample, National Estimates ....... 16–4

16–3 Labor Force Status by Interview/Noninterview Status in Previous and

Current Month, National Estimates .................. 16–5

16–4 CPS Items With Missing Data (Allocation Rates, %), National

Estimates ............................... 16–5

16–5 Comparison of Responses to the Original Interview and the

Reinterview, National Estimates .................... 16–6

16–6 Index of Inconsistency for Selected Labor Force Characteristics in

2003, National Estimates ....................... 16–6

16–7 Percentage of Households With Completed Interviews With Data

Collected by Telephone (CAPI Cases Only), National Estimates ... 16–7

16–8 Month-in-Sample Bias Indexes (and Standard Errors) in the CPS for

Selected Labor Force Characteristics ................. 16–8

16–9 Month-in-Sample Indexes in the CPS for Type A Noninterview Rates

January−December 2004 ....................... 16–9

16–10 Percentage of CPS Labor Force Reports Provided by Proxy Reporters . 16–9

D–1 Average Monthly Workload by Regional Office: 2004 ......... D–3

CONTENTS

Current Population Survey TP66 Contents ix

U.S. Bureau of Labor Statistics and U.S. Census Bureau
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 primary source of labor force statistics for

the population of the United States. The CPS is the source

of numerous high-profile economic statistics, including

the national unemployment rate, and provides data on a

wide range of issues relating to employment and earnings.

The CPS also collects extensive demographic data that

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

population groups, in the states and in substate areas.

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. Although labor market information is

central to the CPS, the survey provides a wealth of other

social and economic data that are widely used in both the

public and private sectors. In addition, because of its long

history and the quality of its data, the CPS has been a

model for other household surveys, both in the United

States and in other countries.

The CPS is a source of information not only for economic

and social science research, but also for the study of survey methodology. This report provides 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

probability selected sample of about 60,000 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 one month to the next (as well as over the

year). The 4-8-4 sampling scheme has the added benefit

of allowing the constant replenishment of the sample

without excessive burden to respondents.

The CPS questionnaire is a completely computerized document that is administered by Census Bureau field representatives across the country through both personal and

telephone interviews. Additional telephone interviewing is

conducted from the Census Bureau’s three centralized collection facilities in Hagerstown, Maryland; Jeffersonville,

Indiana; and Tucson, Arizona.

To be eligible to participate in the CPS, individuals must be

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

People in institutions, such as prisons, long-term care hospitals, and nursing homes are ineligible to be interviewed

in the CPS. In general, the BLS publishes labor force data

only for people aged 16 and over, since those under 16

are 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 people who are not working. 1 The sample size was increased from 50,000 occupied households in July 2001. (See Chapter 2 for details.) 2 In the month of December, the survey is often conducted 1

week earlier to avoid conflicting with the holiday season.

Current Population Survey TP66 Background 1–1

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

1–2 Background Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
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. Several earlier

efforts attempted 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 it 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 sample has undergone major

revision on a regular basis. The following list chronicles

the important modifications to the CPS starting in the mid1940s:

• 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 chance of selection. 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 that are different from the rest of the population.

• February 1952. Document-sensing procedures were

introduced into 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 documentsensing 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 individuals by race

whereas only the percentage of distributions had previously been published.

• 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 during 1 year,

leave the sample for 8 months, and return for the same

Current Population Survey TP66 History of the Current Population Survey 2–1

U.S. Bureau of Labor Statistics and U.S. Census Bureau
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-toyear overlap, thus improving estimates of change on

both a month-to-month 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 is

a continuous process, and the Census Bureau regularly

updates 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. The definition of employment status

was changed. Two relatively small groups of people,

both formerly classified as employed ‘‘with a job but not

at work,’’ were assigned to new classifications. The

reassigned groups were (1) people on layoff with definite instructions to return to work within 30 days of the

layoff date and (2) people waiting to start new wage

and salary jobs within 30 days of the interview. Most of

the people 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 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 (BLS) and the

Census Bureau in the following 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 to be the responsibility 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 noninstitutionalized 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

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
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 of items than the previous documentsensing procedure and did not require the preparation

of punch cards. This data entry system was used

through December 1993.

• January 1963. 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 people.

• 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 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 (U.S. Department of Commerce, 1976), 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-tomonth change.

• March 1968. Separate age/sex ratio estimation cells

were introduced for Negro1 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 separate ratio estimates for

Negro and Other2 race categories.

This change amounted essentially to an increase in the

number of ratio estimation cells from 68 to 116.

• January 1971 and January 1972. The 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. The sample was

expanded to 461 PSUs and the 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

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. The inflation-deflation method was

introduced for deriving independent estimates of the

population. The derivation of independent estimates of

the civilian noninstitutionalized population by age, race,

1 Negro was the race terminology used at that time. 2 Other includes American Indian, Eskimo, Aleut, Asian, and

Pacific Islander.

Current Population Survey TP66 History of the Current Population Survey 2–3

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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 people for each state.

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 data. From

now on, race was 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

designed to improve the reliability of metropolitan/

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/Non-White 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 censusbased population estimates, in conjunction with the

revised second-stage adjustment, resulted in about a 2

percent increase in the estimates for total civilian noninstitutionalized population 16 years and over, civilian

labor force, and unemployed people. 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 going 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 that

relate only to the ‘‘Black’’ population.

• October 1984. School enrollment items were added for

people 16−24 years of age.

• April 1984. The 1970 census-based sample was

phased out 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 veterans’ data for

females was started.

• January 1985. Estimation procedures were changed to

use data from the 1980 census and the 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

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
was added as a control to the Hispanic3 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 dramatically 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 in 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 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.

• January 1992. Industry and occupation codes from the

1990 census were introduced. Population estimates

were converted to the 1990 census base for use in ratio

estimation procedures.

• 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 to 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 data,

the redesign significantly influenced the measurement

of characteristics related to employment, such as the

proportion of the employed 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 the

use of a very complex questionnaire without increasing

respondent burden, increased consistency by reducing

interviewer error, permitted editing at time of interviewing, and allowed 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.

CPS data used by the BLS were adjusted to reflect an

undercount in the 1990 decennial census. Quantitative

measures of this undercount are derived from a postenumeration survey. Because of reliability issues associated with the post-enumeration survey for small areas

of geography (i.e., places with populations of less than

one million), the undercount adjustment was made only

to state and national level estimates. While the undercount varied by geography and demographic group, the

overall undercount was estimated to be slightly more

than 2 percent for the total 16 and over civilian noninstitutionalized population.

• 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 TP63RV.

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

of response categories was phased in for the relationship to reference person question. This modification

was directed at individuals not formally related to the

reference person to identify whether there were 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

for 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 3 Hispanics may be any race.

Current Population Survey TP66 History of the Current Population Survey 2–5

U.S. Bureau of Labor Statistics and U.S. Census Bureau
units. The reduced CPS national sample contained 754

PSUs. The details of the sample design changes as of

January 1996 are described in Appendix H, TP63RV.

• January 1998. A new two-step composite estimation

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

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

key demographic characteristics. The second adjusted

person-weights, through a series of ratio adjustments,

to agree with the composite estimates, thus incorporating the effect of composite estimation into the personweights. This new technique provided increased operational simplicity for microdata users and improved the

accuracy of labor force estimates by using different

compositing coefficients for different labor force categories. The weighting adjustment method assured 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 the 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 low-income 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 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.

• January 2003. The 2002 Census Bureau occupational

and industrial classification systems, which are derived

from the 2000 Standard Occupational Classification

(SOC) and the 2002 North American Industry Classification System (NAICS), were introduced into the CPS. The

composition of detailed occupational and industrial classifications in the new systems was substantially

changed from the previous systems, as was the structure for aggregating them into broad groups. This created breaks in existing data series at all levels of aggregation.

Questions on race and ethnicity were modified to comply with new federal standards. Beginning in January

2003, individuals are asked whether they are of Hispanic ethnicity before being asked about their race.

Individuals are now asked directly if they are Spanish,

Hispanic, or Latino. With respect to race, the response

category of Asian and Pacific Islanders was split into

two categories: a) Asian and b) Native Hawaiian or

Other Pacific Islanders. The questions on race were

reworded to indicate that individuals could select more

than one race and to convey more clearly that individuals should report their own perception of what their

race is. These changes had little or no impact on the

overall civilian noninstitutionalized population and civilian labor force but did reduce the population and labor

force levels of Whites, Blacks or African Americans, and

Asians beginning in January 2003. There was little or no

impact on the unemployment rates of these groups. The

changes did not affect the size of the Hispanic or Latino

population and had no significant impact on the size of

their labor force, but did cause an increase of about half

a percentage point in their unemployment rate.

New population controls reflecting the results of Census

2000 substantially increased the size of the civilian

noninstitutionalized population and the civilian labor

force. As a result, data from January 2000 through

December 2002 were revised. In addition, the Census

Bureau introduced another large upward adjustment to

the population controls as part of its annual update of

population estimates for 2003. The entire amount of

this adjustment was added to the labor force data in

January 2003. The unemployment rate and other ratios

were not substantially affected by either of these population control adjustments.

The CPS program began using the X-12 ARIMA software

for seasonal adjustment of time series data with release

of the data for January 2003. Because of the other revisions being introduced with the January data, the

annual revision of 5 years of seasonally adjusted data

that typically occurs with the release of data for December was delayed until the release of data for January. As

part of the annual revision process, the seasonal adjustment of CPS series was reviewed to determine if additional series could be adjusted and if the series currently adjusted would pass a technical review. As a

result of this review, some series that were seasonally

adjusted in the past are no longer adjusted.

Improvements were introduced to both the secondstage and composite weighting procedures. These

changes adapted the weighting procedures to the new

race/ethnic classification system and enhanced the stability over time of national and state/substate labor

force estimates for demographic groups.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
More detailed information on these changes and an

indication of their effect on national labor force estimates appear in ‘‘Revisions to the Current Population

Survey Effective in January 2003’’ in the February 2003

issue of Employment and Earnings, available on the

Internet at <http://www.bls.gov/cps/rvcps03.pdf>.

• January 2004. Population controls were updated to

reflect revised estimates of net international migration

for 2000 through 2003. The updated controls resulted

in a decrease of 560,000 in the estimated size of the

civilian noninstitutionalized population for December

2003. The civilian labor force and employment levels

decreased by 437,000 and 409,000 respectively. The

Hispanic or Latino population and labor force estimates

declined by 583,000 and 446,000 respectively and Hispanic or Latino employment was lowered by 421,000.

The updated controls had little or no effect on overall

and subgroup unemployment rates and other measures

of labor market participation.

More detailed information on the effect of the updated

controls on national labor force estimates appears in

‘‘Adjustments to Household Survey Population Estimates

in January 2004’’ in the February 2004 issue of Employment and Earnings, available on the Internet at

<http://www.bls.gov/cps/cps04adj.pdf>.

Beginning with the publication of December 2003 estimates in Janaury 2004, the practice of concurrent seasonal adjustment was adopted. Under this practice, the

current month’s seasonally adjusted estimate is computed using all relevant original data up to and including those for the current month. Revisions to estimates

for previous months, however, are postponed until the

end of the year. Previously, seasonal factors for the CPS

labor force data were projected twice a year. With the

introduction of concurrent seasonal adjustment, BLS will

no longer publish projected seasonal factors for CPS

data. More detailed information on concurrent seasonal

adjustment is available in the January 2004 issue of

Employment and Earnings in ‘‘Revision of Seasonally

Adjusted Labor Force Series,’’ available on the Internet

at <http://www.bls.gov/cps/cpsrs2004.pdf>.

In addition to introducing population controls that

reflected revised estimates of net international migration for 2000 through 2003, in January 2004, the LAUS

program introduced a linear wedge adjustment to CPS

16+ statewide estimates of the population, labor force,

employment, unemployment, and unemployment rate.

This adjustment linked the 1990 decennial censusbased CPS estimates, adjusted for the undercount (see

January 1994), to the 2000 decennial census-based CPS

estimates. This adjustment provided consistent estimates of statewide labor force characteristics from the

1990s to the 2000s. It also provided consistent CPS

series for use in the LAUS program’s econometric models that are used to produce the official labor force estimates for states and selected sub-state areas, which use

CPS employment and unemployment estimates as

dependent variables.

• April 2004. The 16-month phase-in of the redesigned

sample based on the 2000 census began. This is the

sample design documented in this technical paper.

• September 2005. Hurricane Katrina made landfall on

the Gulf Coast after the August 2005 survey reference

period. The data produced for the September reference

period were the first from the CPS to reflect any impacts

of the storm. The Census Bureau attempted to contact

all sample households in the disaster areas except those

areas under mandatory evacuation at the time of the

survey. Starting in October, all areas were surveyed. In

accordance with standard procedures, uninhabitable

households, and those for which the condition was

unknown, were taken out of the CPS sample universe.

People in households that were successfully interviewed

were given a higher weight to account for those missed.

Also starting in October, BLS and the Census Bureau

added several questions to identify persons who were

evacuated from their homes, even temporarily, due to

Hurricane Katrina. Beginning in November 2005, state

population controls used for CPS estimation were

adjusted to account for interstate moves by evacuees.

This had a negligible effect on estimates for the total

United States. The CPS will continue to identify Katrina

evacuees monthly, possibly through December 2006.

REFERENCES

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.

U.S. Department of Commerce, Bureau of the Census, and

U.S. Department of Labor, Bureau of Labor Statistics. ‘‘Concepts and Methods Used in Labor Force Statistics Derived

from the Current Population Survey,’’ Current Population

Reports. Special Studies Ser. P23, No. 62. Washington,

DC: Government Printing Office, 1976.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
Chapter 3.

Design of the Current Population Survey Sample

INTRODUCTION

For more than six 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 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. The methods used to select the sample

households for the survey are evaluated after each decennial census. Based on these evaluations, the design of the

survey is modified and systems are put in place to provide

sample for the following decode. The most recent decennial revision incorporated new information from Census

2000 and was complete as of July 2005.

This chapter describes the CPS sample design as of July

2005. It 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 2005:

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 noninstitutionalized population 16 years of

age and older (CNP16+).

3. The CPS sample consists of independent samples in

each state and the District of Columbia. Each state

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: Los Angeles County and the rest of

California; New York City and the rest of New York

State.1 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 that 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 requirement for the monthly

unemployment level for the nation, given a 6 percent

unemployment rate, is 1.9 percent. The 1.9 percent

CV is based on the requirement that a difference of

0.2 percentage points in the unemployment rate for

two consecutive months be statistically significant at

the 0.10 level.

5. The required CV on the annual average unemployment

level for each state, substate area, 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 72,000 assigned housing units from 824

sample areas designed to measure demographic and labor

force characteristics of the civilian noninstitutionalized

population 16 years of age and older. Approximately

12,000 of the assigned housing units are sampled under

the State Children’s Health Insurance Program (SCHIP)

expansion that has been part of the official CPS sample

since July 2001. The CPS samples housing units from lists

of addresses obtained from the 2000 Decennial Census of

Population and Housing. The sample is updated continuously for new housing built after Census 2000. 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 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

1 New York City consists of Bronx, Kings, New York, Queens,

and Richmond Counties.

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social and economic characteristics that are highly correlated with unemployment. One PSU is sampled in each

stratum. The probability of selection for each PSU in the

stratum is proportional to its population as of Census

2000.

In the second stage of sampling, a sample of housing

units within the sample PSUs is drawn. Ultimate sampling

units (USUs) are small groups of housing units. The bulk of

the USUs sampled in the second stage consist of sets of

addresses that are systematically drawn from sorted lists

of blocks prepared as part of Census 2000. 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. The CPS sample is usually described as a

two-stage sample, but occasionally, a third stage of sampling is necessary when actual USU size is extremely large.

In addition, a sample of building permits is selected to

provide coverage of construction since 2000. The sample

of building permits is based on listings of new construction obtained from local jurisdictions in sample PSUs.

Each month, interviewers collect data from the sample

housing units. A housing unit is interviewed for 4 consecutive months, dropped out of the sample for the next 8

months, and interviewed again 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 single 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

upholds the scientific tenets of probability sampling and

each month’s sample produces a true representation of

the target population. The rotation system makes it possible to reduce sampling error by using 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. The system of statebased 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 minimize

the cost of the survey. This is done mainly by minimizing

the number of field representatives needed to conduct the

survey, and reducing the travel cost incurred in visiting

the sample housing units. 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 or 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.

Definition of the Primary Sampling Units

PSUs are delineated so that they encompass the entire

United States. The land area covered by each PSU is made

reasonably compact so it 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 that arises from selecting a sample of PSUs

rather than selecting 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.

Most PSUs are groups of contiguous counties rather than

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

single county to have diverse labor force characteristics.

Limits are placed on the geographic size of a PSU to contain the distance a field representative must travel.

Rules for Defining PSUs

1. Each PSU is contained within the boundary of a single

state.

2. Metropolitan statistical areas (MSAs) are defined as

separate PSUs using projected 2003 Core-Based Statistical Area (CBSA) definitions. CBSAs are defined as

2 See Chapter 10: Estimation Procedures for Labor Force Data

for more information on the composite estimation procedure.

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metropolitan or micropolitan areas and include at least

one county. Micropolitan areas and areas outside of

CBSAs are considered nonmetropolitan areas. If any

metropolitan area crosses state boundaries, each

state-metropolitan area intersection is a separate PSU.3

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

more contiguous counties. 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.

The PSU definitions are revised each time the CPS sample

design is revised. Revised PSU definitions reflect changes

in metropolitan area definitions and an attempt to have

PSU definitions consistent with other U.S. Census Bureau

demographic surveys. The following are steps for combining counties, county equivalents, and independent cities

into PSUs for the 2000 design:

1. The 1990 PSUs are evaluated by incorporating into the

PSU definitions those counties comprising metropolitan areas that are new or have been redefined.

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

regardless of its 2000 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.

These steps created 2,025 PSUs in the United States from

which to draw the sample for the CPS when it was redesigned after the 2000 decennial census.

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

and cost considerations suggest 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 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 populations in 2000.

Sampling theory and costs dictate that highly populated

PSUs should be selected for sample with certainty. The

rationale is that some PSUs exceed or come close to the

population 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 2000 redesign:

1. A PSU which consists of at least one county that is

likely to be part of the 151 most populous metropolitan areas based on projected definitions and Census

2000 population is required to be SR.

2. The remaining PSUs are grouped into non-selfrepresenting (NSR) strata within state boundaries. In

each NSR stratum, one PSU will be selected to represent all of the PSUs in the stratum. They are formed 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 35 to 55 housing units. The number of NSR

strata in a state is a function of the 2000 population, civilian labor force, state CV, and between-PSU

variance4 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.) In Alaska, the

strata are also a function of expected interview

cost.

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.

3 Final metropolitan area 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 CBSA definitions are separate PSUs. Most projected CBSA definitions are the

same as final CBSA definitions (Executive Office of the President,

2003).

4 Between-PSU variance is the component of total variance arising from selecting a sample of PSUs from all possible PSUs.

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d. Stratification is performed independently of previous

CPS sample designs.

Key variables used for stratification are:

• Number of males unemployed.

• Number of females unemployed.

• Number of families with female head of household.

• Ratio of occupied housing units with three or

more people, 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 7.

e. In states with SCHIP sample, the self-representing

PSUs are the same for both CPS and SCHIP. In most

states, the same non-self-representing sample PSUs

are in sample for both surveys. However, to improve

the reliability of the SCHIP estimates in Maine, Maryland, and Nevada, the SCHIP non- self-representing

PSUs are selected independent of the CPS sample

PSUs, with replacement. The methodology for the

stratification of PSUs for SCHIP in these states is similar to the other stratifications, except that the stratification variable used is the number of people under

age 18 with a household income below 200 percent

of poverty.

Table 3−1 summarizes the percentage of the targeted

population in SR and sampled NSR areas by state.

Several current surveys, including CPS, use the Stratification Search Program (SSP) created by the Demographic Statistical Methods Division of the Census Bureau to perform

the PSU stratification. CPS strata in all states except Alaska

are formed by the SSP. (A separate program performs the

stratification for Alaska.) The SSP classifies certain PSUs as

SR, using the criteria mentioned previously, and creates

NSR strata. First, initial parameter sets for each stratification area (i.e., state or substate area) are formed by creating unique combinations of the number of NSR PSUs, the

number of SR PSUs, the number of strata that must be

formed, and the average monthly workload for NSR PSUs

and SR PSUs. Non-self-representing PSUs are reclassified as

SR if additional SR PSUs are needed to provide adequate

samples and if they are among the most populous PSUs in

the stratification area. Some NSR PSUs are reclassified SR if

they are not similar enough to other NSR PSUs to produce

a favorable stratification. Some of the created parameter

sets are eliminated because of unsatisfactory PSU workloads or lack of a self-weighting design.

Next, random stratifications for each parameter set are

formed. NSR PSUs are moved from one stratum to another

to even out the size of the strata. Stratifications are evaluated based on the criteria in the previous section. A

national stratification is then chosen by selecting one

stratification from each state. The national stratification is

refined through a series of moves and swaps to minimize

the difference in workloads among NSR PSUs and the CV

for unemployment level for each stratification area.

After the strata are defined, some state sample sizes are

increased to bring the national CV for unemployment level

down to 1.9 percent assuming a 6 percent unemployment

rate.

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, Hawaii, 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. There are currently 446 SR PSUs. In each of the remaining 378 NSR

strata, one PSU is selected for the sample following the

guidelines described next. Four of the NSR strata only contain SCHIP sample.

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 hiring and training

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 Census 2000 population. The selection procedure accomplishes the following objectives:

1. Select one sample PSU from each stratum with probability proportional to the 2000 population.

2. Retain in the new sample the maximum number of

sample PSUs from the 1990 design sample.

Using a procedure designed to maximize overlap, one PSU

is selected per stratum with probability proportional to its

2000 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.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
Table 3–1. Estimated Population in Sample Areas for 824-PSU Design by State

State

Self-representing

(SR) areas

Non-self-representing

(NSR) areas Total sample areas

Population1

in SR areas Percentage

Population1

in NSR sample

areas Percentage

Population1

in sample areas Percentage

Total. . . .............. 161,282,405 76.1 18,957,760 8.9 180,240,165 85.0

Alabama ..................... 1,556,994 46.2 746,019 22.1 2,303,013 68.3

Alaska ....................... 336,349 77.2 28,541 6.5 364,890 83.7

Arizona ...................... 3,076,477 80.5 421,667 11.0 3,498,144 91.5

Arkansas ..................... 876,805 43.4 388,413 19.2 1,265,218 62.6

California. . . .................. 23,690,332 94.6 587,004 2.3 24,277,336 96.9

Los Angeles .............. 7,040,665 100.0 0 0.0 7,040,665 100.0

Remainder of California .... 16,649,667 92.5 587,004 3.3 17,236,671 95.8

Colorado ..................... 2,444,539 75.4 401,785 12.4 2,846,324 87.8

Connecticut. . ................. 2,588,699 100.0 0 0.0 2,588,699 100.0

Delaware..................... 595,030 100.0 0 0.0 595,030 100.0

District of Columbia. . . ......... 457,495 100.0 0 0.0 457,495 100.0

Florida . ...................... 11,247,999 90.5 702,456 5.7 11,950,455 96.1

Georgia ...................... 3,921,187 64.7 730,347 12.1 4,651,534 76.8

Hawaii ....................... 902,559 100.0 0 0.0 902,559 100.0

Idaho ........................ 582,187 61.5 128,137 13.5 710,324 75.0

Illinois. . ...................... 7,459,357 79.9 875,368 9.4 8,334,725 89.3

Indiana....................... 2,504,432 54.6 705,866 15.4 3,210,298 69.9

Iowa . . . ...................... 718,299 32.2 590,359 26.5 1,308,658 58.7

Kansas....................... 1,028,154 51.5 294,193 14.7 1,322,347 66.2

Kentucky ..................... 1,414,196 45.9 525,654 17.1 1,939,850 63.0

Louisiana. . ................... 1,779,331 54.1 570,528 17.4 2,349,859 71.5

Maine. . . ..................... 831,273 83.7 104,708 10.5 935,981 94.2

Maryland . . . .................. 3,635,366 91.2 249,759 6.3 3,885,125 97.5

Massachusetts ................ 4,915,261 100.0 0 0.0 4,915,261 100.0

Michigan ..................... 5,708,265 76.1 788,772 10.5 6,497,037 86.6

Minnesota . . .................. 2,534,229 68.2 293,420 7.9 2,827,649 76.1

Mississippi.................... 658,381 31.4 518,634 24.8 1,177,015 56.2

Missouri. . .................... 2,592,889 61.3 532,452 12.6 3,125,341 73.9

Montana . . ................... 449,818 65.6 60,735 8.9 510,553 74.4

Nebraska..................... 669,967 52.3 166,160 13.0 836,127 65.2

Nevada ...................... 1,361,183 90.3 73,785 4.9 1,434,968 95.2

New Hampshire . . ............. 946,316 100.0 0 0.0 946,316 100.0

New Jersey................... 6,424,830 100.0 0 0.0 6,424,830 100.0

New Mexico .................. 869,341 64.9 195,258 14.6 1,064,599 79.4

New York. . . . . . . . . . . . . . . . . . . . . 12,976,943 89.4 604,945 4.2 13,581,888 93.6

New York City............. 6,197,673 100.0 0 0.0 6,197,673 100.0

Remainder of New York .... 6,779,270 81.5 604,945 7.3 7,384,215 88.8

North Carolina ................ 3,227,560 53.0 1,112,224 18.2 4,339,784 71.2

North Dakota ................. 258,088 53.2 97,567 20.1 355,655 73.3

Ohio . . . ...................... 6,512,613 75.7 597,297 6.9 7,109,910 82.6

Oklahoma .................... 1,453,949 56.4 288,633 11.2 1,742,582 67.6

Oregon. ...................... 1,803,289 68.5 255,641 9.7 2,058,930 78.2

Pennsylvania . . ............... 7,456,685 78.7 877,672 9.3 8,334,357 88.0

Rhode Island . . ............... 810,041 100.0 0 0.0 810,041 100.0

South Carolina . ............... 1,923,917 63.7 330,931 11.0 2,254,848 74.7

South Dakota . ................ 320,381 57.2 105,941 18.9 426,322 76.1

Tennessee.................... 2,449,113 56.4 761,816 17.5 3,210,929 73.9

Texas . . ...................... 11,598,894 76.6 947,146 6.3 12,546,040 82.9

Utah . . . ...................... 1,228,567 78.1 157,145 10.0 1,385,712 88.0

Vermont. ..................... 472,874 100.0 0 0.0 472,874 100.0

Virginia....................... 3,750,284 70.9 460.699 8.7 4,210,983 79.6

Washington. . ................. 3,226,608 72.5 505,437 11.4 3,732,045 83.9

West Virginia ................. 778,715 54.5 256,355 17.9 1,035,070 72.4

Wisconsin .................... 2,021,672 49.6 877,326 21.5 2,898,998 71.1

Wyoming ..................... 234,672 63.3 40,965 11.0 275,637 74.3

1 Civilian noninstitutionalized population 16 years of age and over based on Census 2000.

Current Population Survey TP66 Design of the Current Population Survey Sample 3–5

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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 Census 2000. (See Appendix B 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.5 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 = σb

2

+ σw

2

[E(x)]2 (3.1)

where

σb

2

= between-PSU variance contribution to the

variance of the state unemployment level

estimator.

σw

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, σw

2

, can be written as the variance assuming a

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

σw

2

= N2 p q (deff)

n

where

N = the civilian noninstitutionalized population, 16 years of age and older (CNP16+),

for the state.

p = proportion of unemployed in the

CNP16+ for the state, or x

N.

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.

Substituting.

q = 1 – p.

This formula can be rewritten as

σw

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 = CV2x2 − σb

2

x q (deff)

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 2005. Table 3-1 provides information on the proportion of the population in sample areas for each state.

The SCHIP sample is allocated among the states after the

CPS sample is allocated. A sampling interval accounting

for both the CPS and SCHIP samples can be computed as:

SICOMB  SICPS

1  SISCHIP1 

1

The between-PSU variance component for the combined

sample in the three states which were restratified for

SCHIP can be estimated using a weighted average of the

individual CPS and SCHIP between-PSU variance. The

weight is the proportion of the total state sample

accounted for by each individual survey:

B,COMB

2  (SICOMBSICPS)B,CPS2  (1  SICOMBSICPS)B,SCHIP2

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 civilian noninstitutionalized population.

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

one chance of selection, with virtually all housing

units in a state or substate area 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 respondent burden, cost, and other constraints.

4. Select within-PSU sample units for additional samples

that will be needed before the next decennial census.

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

has the general form:

Varu  VarIEIIuset of sample PSUs  EIVarIIuset of sample PSUs

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

respectively. The left term represents the between-PSU variance,

σb

2

. The right term represents the within-PSU variance, σw

2

.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
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 2000 Decennial

Census of Population and Housing and the Building Permit

Survey. Census 2000 collected information on all living

quarters existing as of April 1, 2000, including characteristics of living quarters as well as the demographic composition of people 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, 2000, a sample of building permits

issued in 2000 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 Census 2000 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.6 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 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.7

Development of Sampling Frames

Results from Census 2000 and the Building Permit Survey,

and the relationship between these two sources, are used

to develop 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 people on the

property and have direct access to their living quarter

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 people who share the living quarter. About

97 percent of the population counted in Census 2000

resided in housing units.

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

group quarter 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 quarter is distinguished from a housing

unit if it houses ten or more unrelated people. The group

quarters population is classified as institutional or noninstitutionalized and as military or civilian. CPS targets only

the civilian noninstitutionalized 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 noninstitutionalized housing by the time it is scheduled for

interview. Less than 3 percent of the population counted

in Census 2000 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

6 CPS sample selection is coordinated with the following

demographic surveys in the 2000 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.

7 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.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
use of census information to reduce variance of estimates,

ensure adequate coverage, and minimize 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 of the area—covered

or not covered.

An address is considered complete if it describes a specific

location; otherwise, the address is considered incomplete.

(When Census 2000 addresses cannot be used to locate

sample units, area listings must be performed 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. A USU in the unit frame consists of a geographically

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.

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 people within a group quarter 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 Census 2000.

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

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 when USU size differs 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 Census 2000

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 Census 2000 addresses of individual group

quarters or people within a group quarter are not used in

the sampling. The number of housing unit equivalents is

computed by dividing the Census 2000 group quarters

population by the average number of people per household (calculated from Census 2000 as 2.59).

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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

(4)(2.59) + MIL + IGQ (3.4)

where

NIGQPOP = the noninstitutionalized group quarters

population in the block from Census

2000.

MIL = the number of military barracks in the

block from Census 2000.

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 Census 2000.

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 noninstitutionalized 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 Census 2000.

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

are given special treatment. 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 Census 2000.

Permit Frame

Permit frame sampling ensures coverage of housing units

built since Census 2000. 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 Census 2000 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 Census 2000 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.

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

Census 2000. 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 Census 2000 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.

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Summary of Sampling Frames

Table 3–2 summarizes the features of the sampling frames

and CPS USU size discussed above. Roughly 80 percent of

the CPS sample is from the unit frame, 12 percent is from

the area frame, and less than 1 percent is from the group

quarters frame. In addition, about 6 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.

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

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 unit

Area nonpermit . . Not covered by a equivalents

building permit office

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

since 2000 census in

areas covered by a

building permit office

Cluster of four

expected housing units

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 2000 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, noninstitutionalized 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.

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 Census 2000 and geography.

Sorting minimizes within-PSU variance of estimates by

grouping together units or measures with similar characteristics. The Census 2000 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.

General Sampling Procedure

The CPS sampling in the unit frame and GQ frame is a onetime operation that involves selecting enough sample for

the decade. In the area and permit frames, sampling is a

continuous operation. To accommodate the CPS rotation

system and the phasing in of new sample designs, 21

samples are selected. A systematic sample of USUs is

selected and 20 adjacent sample USUs identified. The

group of 21 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.8 Random numbers are used to calculate random starts. Random starts determine the first

sampled USU in a BPC for each frame.

8 Random numbers are evenly distributed by frame within BPC

and by BPC within PSU to minimize variability of sample size.

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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 within-PSU sort criteria.

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 + (1(SIw)), RS + (2(SIw)), ..., RS + (n(SIw))

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

sample. The 20 USUs that follow the sequence number

are selected as the next 20 samples. This method may

yield hit strings with fewer than 21 samples (called

incomplete hit strings) at the beginning or end of

BPCs.9 Allowing incomplete hit strings ensures that

each USU has the same probability of selection.

6. A sample designation uniquely identifying 1 of the 21

samples is assigned to each USU in a hit string. The 21

samples are designated A77 through A97 for the CPS,

and B77 through B97 for the SCHIP.

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 1 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 15.

Rotation group. The 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 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. The 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 a single county within a stratification PSU. 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

an 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.

9 When 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 20).

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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 3–3)

are selected in two BPCs.

These sample USUs are sorted and survey design codes

assigned as shown in Table 3–4.

The example in Table 3–4 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 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 3–5.

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 3–3. 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

Table 3–4. Example of Post-Sampling Survey Design Code Assignments Within a PSU

BPC number 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

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Table 3–5. Examples of Post-Sampling Operational Code Assignments Within a PSU

BPC number 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

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 startwith (SW) are used. An appropriate value of the TE 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 for more on

this procedure.

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:

three-fourths 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 single 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.

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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:

A77(1), A77(2), ..., A77(8), A78(1), A78(2), ..., A78(8),

..., A97(1), A97(2), ..., A97(8).

This 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).

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 2006. 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

2006, rotation groups 3, 4, 5, and 6 of A79; 7 and 8

of A80; and 1 and 2 of A81 are designated for interview.

2. Consecutive monthly samples have six rotation

groups in common. The sample housing units in

A79(4−6), A80(8), and A81(1−2), for example, are

interviewed in January and February of 2006.

3. Monthly samples 1 year apart have four rotation

groups in common. For example, the sample housing

units in A80(7−8) and A81(1−2) are interviewed in

January 2006 and January 2007.

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 2006, the sample housing units

in A82(3) are interviewed for the first time and the

sample housing units in A80(7) are interviewed for the

fifth time after last being in sample in January.

Figure 3–1. CPS Rotation Chart:

January 2006−April 2008

Sample designation and rotation groups

Year/month A/B79 A/B80 A/B81 A/B82 A/B83 A/B84

2006 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

2007 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

2008 Jan 3456 78 12

Feb 4567 8 123

Mar 5678 1234

Apr 678 1 2345

Overlap of the Sample

Table 3–6 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.

Table 3-6. 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

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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

2004, the 2000 census-based design was phased in

through a series of changes completed in July 2005 (U.S.

Department of Labor, 2004).

REFERENCES

Cahoon, L. (2002), ‘‘Specifications for Creating a Stratification Search Program for the 2000 Sample Redesign (3.1-S1),’’ Internal Memorandum, Demographic Statistical Methods Division, U.S. Census Bureau.

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., William N. Hurwitz, and William G.

Madow, (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, David Judkins, Rajendra Singh, and

Mindi Schautz, (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, E. Ann Vacca, Charles Wolters, and Rosa Hernandez, (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 Lawrence Cahoon, (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, December 2004.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
Chapter 4.

Preparation of the Sample

INTRODUCTION

The Current Population Survey (CPS) 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 CPS sample more than once over the

course of a decade, or in more than one of the household surveys conducted by the U. S. Census Bureau.

4. Provide cost-efficient data collection by producing

most of the sampling materials needed for both the

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 a number of benefits. For example, training

costs are reduced when CPS field representatives 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, sharing sampling

materials helps ensure that respondents will be in only

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.

The next section is an overview, while subsequent sections provide a more in-depth description of the CPS

sample preparation.

The successful completion of the CPS data collection rests

on the combined efforts of headquarters and regional

staff. 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. The regional offices

coordinate the interview activities of the interviewing

staff.

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 information (e.g., maps, lists of building permits)

to a set of addresses. This conversion is described below.

Address Identification in the Unit Frame

About 80 percent of the CPS sample is selected from the

unit frame. The unit frame sample is selected from a 2000

census file that contains the information necessary for

within-PSU sample selection, but does not contain address

information. The address information from the 2000 census is stored in a separate file. The addresses of unit segments are obtained by matching the file of 2000 census

information to the file containing the associated 2000 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 2000 census information is

reviewed in an attempt to complete the address before

sending it to the field representative for interview. In sampling operations, this facilitates the formation of clusters

of about 4 housing units, called Ultimate Sampling Units

(USUs), as described in Chapter 3.

Address Identification in the Area Frame

About 12 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 individual

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housing units. This is because many of the addresses in

the area frame 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 an

automated map of the blocks that contain the area segment, the addresses within the block(s) that are in the

2000 census file, the number of measures the block contains, and which measure is associated with the area segment. Before the individual housing units in 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

canvass the block to create a complete list of the housing

units located in the block. This activity is called 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 will be designated for each month’s sample.

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, 2000, but there is insufficient information

about their living arrangements within the group quarters

to provide a tangible sampling unit for the CPS. Measures

are selected during within-PSU sampling since there is no

way to associate the selected sample cases with people 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

2000 census addresses, similar to the process for the unit

frame. This is a one-time operation done at headquarters.

Before the individuals 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

people, rooms, or beds) or obtains a count of eligible

sample units from a usable register. This is referred to as a

listing operation. Then a systematic sampling pattern is

applied to the listing to identify the individuals to be interviewed at the group quarters facilities.

Address Identification in the Permit Frame

The proportion of the CPS sample selected from the permit

frame increases over the decade as new housing units are

constructed. The CPS sample is redesigned about 4 or 5

years after each decennial census, and at this time the permit sample makes up about 6 percent of the CPS sample;

this proportion has historically increased about 1 percent

a year. Hypothetical measures are selected during withinPSU sampling in anticipation of the construction of new

housing units. Identifying the addresses for these new

units involves a listing operation at the building permit

office, clustering addresses to form measures, and associating these addresses with the hypothetical measures (or

USUs) in the sample.

The Census Bureau conducts the Building Permit Survey,

which collects information on a monthly basis from each

building permit office (BPO) nationwide 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 BPO contains the measure that is in sample.

Using an automated instrument, a field representative visits the BPO to list addresses of units that were authorized

to be built; this is the Permit Address Listing (PAL) operation. This list of addresses is transmitted to headquarters,

where clusters are formed that correspond one-to-one

with the measures. Using this link between addresses and

measures, the clusters of four addresses to be interviewed

in each permit segment are identified.

Forming clusters. To ensure some geographic clustering

of addresses within permit measures and to make PAL listing more efficient, information collected by the Survey of

Construction (SOC)1 is used to identify many of the

addresses in the permit frame. The Census Bureau collects

information on the characteristics of units to be built for

each permit issued by BPOs that are in the SOC. This information is used to form measures in SOC building permit

offices. This data is not collected for non-SOC building

permit offices.

1. SOC PALs are listings from BPOs that are in the SOC. If

a BPO is in the SOC, then the actual permits issued by

the BPO 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 identified 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.

1 The Survey of Construction (SOC) is conducted by the Census

Bureau in conjunction with the U.S. 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 one-family homes.

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Even without geographic clustering, there is some

degree of compactness inherent in the manner in

which measures are formed for sampling. The units

drawn from the SOC permits are assigned to measures

in the same order in which they were listed in the

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 BPOS not in the SOC.

At the time of sampling, the only data known for nonSOC BPOs is a cumulative count of the units authorized on all permits from a particular office for a given

month (or year). Therefore, all addresses for a

BPO/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 without addresses 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 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 at headquarters. Performing the listing and sampling in two separate steps allows

each step to be verified and allows more complete control

of sampling procedures to avoid bias in designating the

units to be interviewed.

In order to ensure accurate and complete coverage of the

area and group quarters segments, the initial listing is

updated 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 2000 census is no

longer accurate for a multiunit structure and the field representative cannot adequately correct the information.

For multiunit addresses (addresses where the expected

number of unit designations is two or more), the field representative receives a segment folder containing a preprinted (computer-generated) Multi-Unit Listing Aid

(MULA), Form 11-12, showing unit designations for the

segment as recorded in the 2000 census. The MULA displays the unit designations of all units in the structure,

even if some of the units are not in sample. Other important information on the MULA includes: the address, the

expected number of units at the address, the sample designations, and serial numbers for the selected sample

units. If the address is incomplete (missing house number,

street name), the field representative receives an Incomplete Address Locator Action Form, which provides additional information to locate the address.

The first time a multiunit address enters the sample, the

field representative does one or more of the following:

• For addresses with 2−4 units, verifies that the 2000

census information on the MULA is accurate and corrects the listing sheet when it does not agree with what

he/she finds at the address.

• For larger addresses of 5 or more units, resolves missing and duplicate unit designations only for addresses

with missing or duplicate unit designations that are in

any sample.

• If the changes are so extensive that a MULA cannot

handle the corrections, relists the address on a blank

Unit/Permit Listing Sheet.

After the field representative has an accurate MULA or listing sheet, he/she conducts an interview for each unit that

has a current 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 (using the sampling pattern

on the MULA) 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 necessary. The field representative may correct an

in-sample listing during any visit if a change is noticed.

For single-unit addresses, a preprinted listing sheet is not

provided to the field representative since only one unit is

expected, based on the 2000 census information. If the

address is incomplete (missing house number, no street

name), the field representative receives an Incomplete

Address Locator Form, which provides additional information to locate the address. If a field representative discovers other units at the address at the time of interview,

he/she prepares a Unit/Permit Listing Sheet and lists the

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extra units. These additional units, up to and including 15,

are interviewed. If the field representative discovers more

than 15 units, he/she must contact the regional office for

subsampling instructions. For an example of a segment

folder, MULA, Unit/Permit Listing Sheet, and Incomplete

Address Locator Actions Form, see Appendix A.

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 an accurate list of living quarters.

This is a dependent operation conducted via a laptop computer using the Automated Listing and Mapping Instrument (ALMI) software. The field representative starts with

a list of the addresses within the block and a digitized

map of the block with some addresses mapspotted onto

it. The addresses come from the Master Address File

(MAF), which is a list of 2000 census addresses, updated

periodically through other operations. The digitized map

is downloaded from the Census Bureau Topological Integrated Geographic Encoding Reference (TIGER®

) system.

The field representative updates the block information by

matching the living quarters and block features found to

the list of addresses and map on the laptop, and makes

additions, deletions, or other corrections as necessary.

The field representative collects additional data for any

group quarters in the block using the Group Quarters

Automated Instrument for Listing (GAIL) software.

If the area segment is within a jurisdiction where building

permits are issued, housing units constructed since April

1, 2000, 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, 2000, Census

Day, are already represented by segments in the permit

frame. The field representative determines the year each

unit was built except for: units in the 2000 census, mobile

homes and trailers, group quarters, and nonstructures

(buses, boats, tents). To determine ‘‘year built,’’ the field

representative inquires at each appropriate unit and enters

the appropriate information on the computer.

If an area segment is not in a building-permit-issuing jurisdiction, then housing units constructed after the 2000

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 files are transmitted to headquarters

where staff then apply the sampling pattern and identify

the units to be interviewed.

Periodic updates of the listing are done to reflect change

in the housing inventory in the listed block. 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 field representative updates the area block by verifying the existence of

each unit and map feature, accounting for units or features no longer in existence, and adding any new units or

features.

Listing in the Group Quarters Frame

The listing procedure is applied to group quarters

addresses in the CPS sample that are in the group quarters

or area frames. 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

(rooms, beds, persons, etc.) at the group quarters. The

same procedures apply for group quarters found in the

area frame.

Group quarters listing is an independent listing operation

conducted via a laptop computer. The instrument, referred

to as the Group Quarters Automated Instrument for Listing

(GAIL), is 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 quarters, or to obtain a count of the number of eligible units from a register (card file, computer printout, or

file) located at the group quarters. Field representatives do

not list institutional and military groups quarters; however, they verify that the status has not changed from

institutional or military. If it has changed, the field representative will list the addresses of noninstitutional units.

After listing group quarters units or obtaining a count of

eligible units from a register, the files are transmitted to

headquarters, where staff then apply the sampling pattern

to identify the group quarters unit(s) (or units corresponding to sample line(s) within a register) to be interviewed.

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 BPO. This is done

shortly after the permit has been issued by the BPO and is

associated with a sample hypothetical measure. The second listing is required when the field representative visits

the unit to conduct an interview.

PAL operation. For each BPO containing a sample measure, a field representative visits the BPO and lists the necessary permit and address information using a laptop

computer. 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, a field representative visits the new construction site and draws a

Permit Sketch Map showing the location of the structure

and, if possible, completes the address.

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Permit listing. Listing in the permit frame is necessary

for all permit segments. Prior to interviewing, the field

representative will receive a segment folder containing a

Unit/Permit Listing Sheet (Form 11−3) and, possibly, a Permit Sketch Map to help locate the address. For an example

of a segment folder, Unit/Permit Listing Sheets, and a Permit Sketch Map, see Appendix A.

At the time of the interview, the field representative verifies or corrects the basic address. Since the PAL operation

does not capture unit designations at a multiunit address,

the field representative will list the unit designations prior

to interviewing. At both single and multiunit addresses,

the field representative will also note any relevant information about the address that would affect sampling or

interviewing, such as conversions, abandoned permits,

construction-not-started situations, and more units found

than expected for the permit address.

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 more than 15 units

are in sample, the field representative must contact their

regional office for subsampling instructions.

The listing of permit segments is not updated systematically; 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.

THIRD STAGE OF THE SAMPLE DESIGN

(SUBSAMPLING)

Although the CPS sample is often characterized as a twostage sample, chapter 3 describes a third stage of the

sample design, referred to as subsampling. The need for

subsampling depends 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 the operations that break the sample down into manageable interviewer workloads and transmit 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 cases which have been interviewed in

previous months. The rotation of sample used in the CPS

is such that seven-eighths of the sample cases each month

have been in the 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

Technological advances have changed data preparation

and collection activities at the Census Bureau. Since the

mid-1980s, 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. This system

has two principal components: computer-assisted personal

interviewing (CAPI) and centralized computer-assisted telephone interviewing (CATI). Chapter 7 explains 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 loading

the monthly sample into the central database, transmission of CATI cases to the telephone centers, and

database maintenance.

2. Regional office operations in the central database. The regional office operations include preparation of assignments, transmission of cases to field

representatives, determination of CATI assignments,

reinterview selection, and review and reassignment of

cases.

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 that are in

the sample.

Regional Office (RO) Operations

Once the monthly sample has been loaded into the central

database, the ROs can begin the assignment preparation

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phase. This includes making assignments to field representatives and selecting cases for the three telephone

facilities. Regional offices access the central database to

break down the assignment areas geographically and key

in information that is used to aid the monthly field representative assignment operations. The RO 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.

The CATI assignments are also made at this time. Each RO

selects at least 10 percent of the sample for centralized

telephone interviewing. The selection of cases for CATI

involves several steps. Cases may be assigned to CATI if

several criteria are met, pertaining to the household and

the time in sample. In general terms, the criteria are as

follows:

1. The household must have a telephone and be willing

to accept a telephone interview.

2. The field representative may recommend that the case

be sent to CATI.

3. First and fifth month cases are generally not eligible

for a telephone or CATI.

The ROs may temporarily assign cases to CATI in order to

cover their workloads in certain situations, primarily to fill

in for field representatives who are ill or on vacation.

When interviewing for the month is completed, these

cases will automatically be reassigned for CAPI.

The majority 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

details.

The final step is the releasing of assignments. After all

changes to the interview and CATI assignments have been

made, and all assignments have been reviewed for geographic efficiency and the proper balance among field representatives, the ROs release the assignments. The release

of assignments by all ROs signals the Master Control in

the central data to create the CATI workload files.

After assignments are made and released, the ROs transmit the assignments to the central database, which places

the assignments on the telecommunications server for the

field representatives. 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 files include

the household demographic information and labor force

data collected in previous interviews. 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 ROs 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, respondent letters, and other supplies requested by the field representative.

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.

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Chapter 5.

Questionnaire Concepts and Definitions for the Current

Population Survey

INTRODUCTION

An important component of the Current Population Survey

(CPS) is the questionnaire, also called the survey instrument. The survey instrument utilizes automated data collection methods; that is, computer-assisted personal interviewing (CAPI) and computer-assisted telephone

interviewing (CATI). This chapter describes and discusses

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. Supplemental questions are added to the CPS in most months and

cover a number of different topics. The order in which

interviewers attempt to collect information is: (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.

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 11.)

CONCEPTS AND DEFINITIONS

Household and Demographic Information

Upon contacting a household, interviewers proceed with

the interview unless 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

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 1 (presumably one of

the individuals who owns or rents the unit) becomes the

reference person. 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 are also 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.

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 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 compose the bulk of such absent household

members, but people away on business or vacation are

also included. (Not included are individuals in institutions

or the military.) Once household/nonhousehold membership has been established for all people 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

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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 present. Subfamilies are defined as families

that live in housing units where none of the members of

the family are related to the reference person. A household may contain 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, 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 is also collected. 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.

Educational attainment for each person in the household

15 or older is obtained through a question asking about

the highest grade or degree completed. Additional questions are asked for several educational attainment categories to ascertain the total number of years of school or

credit years completed.

Questions on race and Hispanic origin comply with federal

standards. Respondents are asked a question to determine

if they are Hispanic, which is considered an ethnicity

rather than a race. The question asks if the individual is

Spanish, Hispanic, or Latino, and is placed before the

question on race. Next, all respondents, including those

who identify themselves as Hispanic, are asked to choose

which of the following races they consider themselves to

be: White, Black or African American, American Indian or

Alaska Native, Asian, or Native Hawaiian or Other Pacific

Islander. Responses of ‘‘other’’ are accepted and allocated

among the race categories. Respondents may choose

more than one race.

Labor Force Information

To avoid any chance of misunderstanding, it is emphasized here that the CPS provides a measure of monthly

employment—not jobs.

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, industry and related aspects of the

working population. The major labor force categories are

defined hierarchically and, thus, 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

during the reference week (defined below), are not asked

the questions about having looked for work, and 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 people 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 not in the labor force. 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 noninstitutionalized population. In the CPS,

labor force data are restricted to people 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 people. Employed people 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, labor-management

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.)

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
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 people 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 people 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

people with multiple unincorporated businesses and

people with multiple jobs as unpaid family workers.

CPS respondents are asked questions each month to identify multiple jobholders. First, all employed people 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?’’

Hours of work. Information on both actual and usual

hours of work have been collected. Published data on

hours of work relate to the actual number of hours spent

‘‘at work’’ during the reference week. For example, people

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

people working in more than one job, the published figures relate to the number of hours worked at all jobs during the week.

Data on people ‘‘at work’’ exclude employed people 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 people, 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 people who usually work part-time and

were at work 1 to 34 hours during the reference week for

a 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, people also are classified according to

their usual full- or part-time status. In this context, fulltime workers are those who usually work 35 hours or

more (at all jobs combined). This group includes some

individuals who worked fewer 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 fewer 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 fulltime labor force includes all employed people who usually

work full-time and unemployed people who are either

looking for full-time work or are on layoff from full-time

jobs. The part-time labor force consists of employed

people who usually work part-time and unemployed

people who are seeking or are on layoff from part-time

jobs.

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

largest 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 Census 2000. A list of these codes can be

found in the Alphabetical Index of Industries and

Occupations at <http://www.census.gov/hhes/www

/ioindex/view.html>. 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.

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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. The self-employed are those who

work for profit or fees in their own businesses, professions, trades, or farms. Only the unincorporated selfemployed 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 individuals

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, marriage, or adoption.

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 at <http://www.bls.gov/opub/hom

/homch1_a.htm>). For the majority of multiple jobholders,

occupation, industry, and class-of-worker data for their

second jobs are collected only from one-fourth of the

sample—those in their fourth or eighth monthly interview.

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 ‘‘selfemployed 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 at the end of this chapter.

Earnings. Information on what people earn at their main

job 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 each month. 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’’ means as perceived by

the respondent. If the respondent asks for a definition of

usual, 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, and for self-employed people

whose businesses are incorporated; earnings data are not

collected for self-employed people whose businesses are

unincorporated. (Earnings data are not edited and are not

released to the public for the ‘‘self-employed incorporated.’’) These earnings data are used to construct estimates of the distribution of usual weekly earnings and

median earnings. Individuals who do not 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 people. All people 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.

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 (people on layoff). Job seekers must have

engaged in an 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 ‘‘help wanted’’ ads and taking a job

training course, as opposed to actually answering ‘‘help

wanted’’ ads or placing ‘‘employment wanted’’ ads. 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

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
‘‘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,

people 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 composed of (a) people 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: people who

quit or otherwise terminated their employment voluntarily

and began looking for work; (3)People who completed temporary jobs: individuals who began looking for work after

their jobs ended; (4)Reentrants: people who previously

worked but were out of the labor force prior to beginning

their job search; (5)New entrants: individuals 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.

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 people 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

members of the civilian noninstitutionalized 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, defined as those 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 people outside the labor force,

one that includes discouraged workers as well as those

who desire work but give other reasons for not searching

(such as childcare problems, school, family responsibilities, or transportation problems) are also published regularly. This group is made up of people 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.

Questions about the desire for work among those who are

not in the labor force are asked of the full CPS sample.

Consequently, estimates of the number of discouraged

workers as well as those with a marginal attachment to

the labor force are published monthly rather than quarterly.

Additional questions relating to individuals’ job histories

and whether they intend to seek work continue to be

asked only of people 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 people 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 employmentpopulation ratio represents the proportion of the ageeligible population that is employed.

Current Population Survey TP66 Questionnaire Concepts and Definitions for the Current Population Survey 5–5

U.S. Bureau of Labor Statistics and U.S. Census Bureau
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.

REFERENCES

U.S. Department of Commerce, U.S. Census Bureau (1992),

Alphabetical Index of Industries and Occupations,

from <http://www.census.gov/hhes/www/ioindex/>.

U.S. Department of Labor, U.S. Bureau of Labor Statistics,

BLS Handbook of Methods, from <http://www.bls.gov

/opub/hom/>.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
Chapter 6.

Design of the Current Population Survey Instrument

INTRODUCTION

Chapter 5 describes the current 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. For virtually every labor force concept, the current questionnaire wording is different from

what was used previously. Data collection was redesigned

so that the instrument is fully automated and is administered either on a laptop computer or from a centralized

telephone facility. This chapter describes the work on the

data collection instrument and changes that were made as

a result of that work.

MOTIVATION FOR REDESIGNING THE

QUESTIONNAIRE COLLECTING 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

remained unchanged from 1967 to 1994. The growth in

the number of service-sector jobs and the decline in the

number of factory jobs were 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. Recommendations for changes in the CPS had been proposed in the late

1970s and 1980s, primarily by the Presidentiallyappointed 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 sample

became available. Spurred by all of these developments,

the decision was made 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

questionnaire-respondent-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 U.S. Bureau of

Labor Statistics (BLS) and U.S. Census Bureau developed

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 small. The

one major definitional change dealt with the concept of

discouraged workers; that is, people outside the labor

force who are not looking for work because they believe

that there are no jobs available for them. As 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

because 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 because 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 precise measurement of discouraged workers was

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introduced that specifically asked if 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 people 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 people,

and people 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 people who have more than one job. Also, by

separately collecting information on the number of hours

multiple jobholders work on their main job and secondary

jobs, estimates of the number of workers who combined

two or more part-time jobs into a full-time work week, and

the number of full- and part-time jobs in the economy can

be made. The inclusion of the multiple job 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 the 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 of respondents who are 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 design than paper-and-pencil data collection methods. Complicated skips, respondent-specific

question wording, and carry-over of data from one interview to the next are all possible in an automated environment. For example, automated data collection allows

capabilities such as (1) the use of dependent interviewing,

that is carrying over 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. By automatically bringing up the next question on the

interviewer’s screen, computerization reduces the probability that an interview will ask the wrong set of questions. The computerized questionnaire also permits the

inclusion of several built-in editing features, including

automatic checks for internal consistency and 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 qualitative research, 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 using 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

suggest solutions. Analyses were based on information

from item response distributions, respondent and interviewer debriefing data, and behavior coding of

interviewer/ respondent interactions. For more on the

evaluation methods used for redesigning the questions,

see Esposito and Rothgeb (1997).

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Item Response Analysis

The primary use of item response analysis was to determine whether different questionnaires produce different

response patterns, which may, in turn, affect the labor

force estimates. Unedited data were used for this analysis.

Statistical tests were conducted to ascertain whether diferences among the response patterns of different questionnaire versions were statistically significant. 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 interpretation of the response differences observed. (Response

distributions were calculated on the basis of people who

responded to the item, excluding those whose response

was ‘‘don’t know’’ or ‘‘refused.’’)

Respondent Debriefings

At the end of the test interview, respondent debriefing

questions were administered to a sample of respondents

to measure respondent comprehension and response formulation. 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. 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). For example, those who did not indicate in the

main survey that they had done any work 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 the 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 was used to identify questions that needed revision, and to modify initial

interviewer training and the interviewer manual. A secondary objective was to obtain information about the questionnaire, interviewer behavior, or respondent behavior

that would 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. See Esposito and Hess (1992) for more information on interviewer debriefing.

HIGHLIGHTS OF THE QUESTIONNAIRE REVISION

A copy of the questionnaire can be obtained from the

Internet at <http://www.bls.census.gov/cps/bqestair

.htm>.

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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 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

new introductory statement reads, ‘‘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,

‘‘Does anyone in this household have a business or a

farm?’’ This question determines 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

by all people 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 a business or farm is 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

holds multiple jobs. A follow-up 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

follow-up 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. The new hours

series allows monthly estimates of usual hours worked to

be produced for all employed people. 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.

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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

arose, in part, because the household respondent frequently varies from one 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 question which uses

information collected during the previous month’s interview in the current month’s interview was implemented in

the revised questionnaire for month-in-sample 2−4 households and month-in-sample 6−8 households. (Different

variations of dependent interviewing were evaluated during testing. See Rothgeb et al. [1991] for more detail.)

In the revised CPS, respondents are provided 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-of-worker. 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,’’ they 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 people 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

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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 who do not meet the criteria for layoff status.

People 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,

people reported being on layoff were never directly asked

whether they expected to be recalled. In an effort to better

capture the existing definition, people reported being on

layoff in the revised questionnaire are asked ‘‘Has your

employer given you a date to return to work?’’ People 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?’’ People 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 previous 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?’’ Responses that could be

checked included:

• 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 were 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. 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.’’

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 those who report duration

in whole months (between 1 and 4 months) a follow-up

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 people who are unemployed in consecutive

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months. For people 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 those 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 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, 1998). A

very small bias is introduced when a person has a brief

(less than 3 or 4 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 to ‘‘Not-in-the-Labor-Force’’ Questions

Response options of retired, disabled, and unable

to work. 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 situation.

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 follow-up question is asked to determine whether he/she can do any gainful work during the

next 6 months. Different versions of the follow-up probe

are used depending on whether the person is disabled or

unable to work.

Dependent interviewing for people reported to be

retired, disabled, or unable to work. The revised

questionnaire also is designed to use dependent interviewing for individuals 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 people outside the labor force

(one that includes discouraged workers as well as those

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 people 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 a quarter of the sample,

permitting estimates of the number of discouraged workers to be published monthly rather than quarterly.

Tests of the revised questionnaire showed that the quality

of labor force data improved as a result of the redesign of

the CPS questionnaire, and in general, measurement error

diminished. 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

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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-ahalf 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 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. Despite the benefits of adding new questions and

improving existing ones, changes to the CPS should be

approached cautiously and the effects measured and

evaluated. When possible, methods to bridge differences

caused by changes and 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., E. A. Martin, and J. M. Rothgeb (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., A. E. Polivka, and J. M. Rothgeb (1994), Revisions in the Current Population Survey Effective January

1994, Employment and Earnings, February 1994 vol.

41 no. 2 pp. 1337.

Copeland, K. and J. M. Rothgeb (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., R. Levine, and S. R. Cohany (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., and J. Hess (1992), ‘‘The Use of Inteviewer

Debriefings to Identify Problematic Questions on Alternate

Questionnaires,′′ Paper presented at the Annual Meeting of

the American Association for Public Opinion Research, St.

Petersburg, FL.

Esposito, J. L., J. M. Rothgeb, A. E. Polivka, J. Hess, and P.

C. Campanelli (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.

Esposito, J. L. and J. M. Rothgeb (1997), ‘‘Evaluating Survey

Data: Making the Transition From Pretesting to Quality

Assessment,’’ in Survey Measurement and Process Quality,

New York: Wiley, pp.541−571.

Forsyth, B. H. and J. T. Lessler (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., D. Cantor, and N. Gay (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 A. E. Polivka (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.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
Oksenberg, L., C. Cannell, and G. Kalton (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 S. M. Miller (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 J. M. Rothgeb (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., A. E. Polivka, K. P. Creighton, and S. R.

Cohany (1992), Development of the Proposed Revised Current Population Survey Questionnaire, Proceedings of

the Section on Survey Research Methods, American

Statistical Association, pp. 56−65.

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.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
Chapter 7.

Conducting the Interviews

INTRODUCTION

Each month during interview week, field representatives

(FRs) and computer-assisted telephone interviewers

attempt to contact and interview a responsible person living in each sample unit selected 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-insample 1 [MIS 1]), one-eighth for the second time, etc.

Because of this schedule, different types of interviews

(due to differing MIS) are conducted by each FR within

his/her weekly assignment. An introductory letter is sent

to each sample household 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 Atlanta Regional Office. A

personal-visit interview is required for all first month-insample households because the CPS sample is strictly a

sample of addresses. The U.S. Census Bureau has no way

of knowing who the occupants of the sample household

are, or even whether the household is occupied or eligible

for interview. (Note: For some MIS 1 households, telephone interviews are conducted if, 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 the CPS. There are many reasons an

address may not be eligible for interview. For example, the

address may have been converted to a permanent business, condemned or demolished, or it may be outside the

boundaries of the area 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 the determination is made final.

Type B ineligibility includes units that 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), units occupied entirely by

individuals who are not eligible for a CPS labor force interview (individuals with a usual residence elsewhere (URE),

or in the Armed Forces). Such units are classified as Type B

noninterviews. 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 interviews. 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), described in Chapter 11.

Additionally, one final set of households not interviewed

for CPS are Type A households. These are households that

the FR has determined are 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, 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 the determination is made final.

Every effort is made to keep such noninterviews to a minimum. All Type A cases remain in the sample and are

assigned for interview in all succeeding months. Even in

cases of confirmed refusals (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.

Current Population Survey TP66 Conducting the Interviews 7–1

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Figure 7−1. Introductory Letter

7–2 Conducting the Interviews Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Figure 7–2 shows how the three types of noninterviews

are classified and the various reasons that define each category. 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.

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. Figure 7–3 shows the main housing unit information gathered for each noninterview category and a brief

description of what each item covers.

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 Entire household in the Armed Forces

10 Entire household under age 15

11 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

7 Removed during subsampling

8 Unit already had a chance of selection

9 Unused line of listing sheet

10 Other—specify

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 worded exactly 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 information collected from 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, and race and ethnic 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.

The FR has an opportunity 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, information about a few

additional characteristics are collected after completion of

the labor force portion of the interview. This information

includes questions on family income and on all household

members’ countries of birth (and the country of birth of

the member’s father and mother) and, for the foreign born,

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 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) often provides 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.

Current Population Survey TP66 Conducting the Interviews 7–3

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Figure 7–3. Noninterviews: Main Items of Housing Unit Information Asked for Types A, B, and C

Note: This list of items is not inclusive. The list covers only the main data items and does not include related items used to arrive at the

final categories (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 TYPE A 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 on 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 is the type of housing unit (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 TYPE B 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 on 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 TYPE C Which specific kind of Type C is the case.

2 PROPER If there is any other building on 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 is the type of housing unit (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.

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 any supplemental questions. Telephone interviewing 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

months-in-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. FRs must attempt to conduct a personal-visit interview for the fifth-month interview. After one attempt, a

telephone interview may be conducted provided the original household still occupies the sample unit. This fifthmonth interview follows 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, that

is, a replacement household 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 telephone

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

prompts the FR to verify 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

7–4 Conducting the Interviews Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Figure 7–4. Interviews: Main Housing Unit Items Asked in MIS 1 and Replacement Households

Note: This list of items is not inclusive. The list covers only the main data items and does not include related items used to identify 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 4/1/00.

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 on 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 is the type of housing unit (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.

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 making the appropriate corrections, the instrument will take the interviewer

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 made

possible by the computerization of the labor force interview. Information collected in the previous month’s interview, like the household roster and demographic data, 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. Importing the previous

month’s job description into the current month’s interview

shows 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 labor force interview and reduces respondent burden. Other information

collected using dependent interviewing is the duration of

unemployment (either job search or layoff duration), and

data on the not-in-labor-force subgroups of retired and disabled. Dependent interviewing is not used in the MIS 5

interviews or for any of the data collected solely in MIS 4

and 8 interviews.

Current Population Survey TP66 Conducting the Interviews 7–5

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Figure 7–5. Summary Table for Determining Who is to be Included as a Member of the Household

Include asmemberofhousehold

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

Another milestone in the computerization of the CPS is the

use of centralized facilities for computer- assisted telephone interviewing (CATI). The CPS has been experimenting with the use of CATI since 1983. 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 CATI facilities for interviewing, currently numbering about 7,000 cases each month. The facilities generally

interview about 80 percent of the cases assigned to them.

The net result is that about 12 percent of all CPS interviews are completed at a CATI facility.

Three CATI facilities are in use: Hagerstown, MD; Tucson,

AZ; and Jeffersonville, IN. During the time of the initial

phase-in of CATI data, use of a controlled selection criteria

(see Chapter 4) allowed the analysis of the effects of the

CATI collection methodology. Chapter 16 provides 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, particularly most large cities. Most of the cases

sent to CATI are from major metropolitan areas. CATI is

not used in most rural areas because the small sample

sizes in these areas do not cause the FRs undo hardship. A

concerted effort is made to hire some Spanish speaking

interviewers in the Tucson Telephone Center, enabling

Spanish interviews to be conducted from this facility. No

MIS 1 or 5 cases are sent to the facilities, as explained

above.

The facilities complete all but 20 percent of the cases sent

to them. These uncompleted cases are recycled back to

the field for follow-up and final determination. For this

7–6 Conducting the Interviews Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
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 HISPNON-R What is the person’s origin; the interviewer shows the respondent a flashcard from which he/she chooses the

appropriate origin categories.

24 RACE*-R What is the person’s race; the interviewer shows the respondent a flashcard from which he/she chooses the

appropriate race 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.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
reason, the CATI facilities generally cease conducting the

labor force portions of the interview on Wednesday of

interview week, so the field staff has 3 to 4 days to check

on the cases and complete any required interviewing or

classify the cases as noninterviews. The field staff is

highly successful in completing these cases as interviews,

generally interviewing about 85−90 percent of them. The

cases that are sent to the CATI facilities are selected by the

supervisors in each of the regional offices, based on the

FR’s analysis of a household’s probable acceptance of a

CATI interview, and the need to balance workloads and

meet specific goals on the number of cases sent to the

facilities.

Figure 7–7. Demographic Edits in the CPS Instrument

Note: The following list of edits is not 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 demographic edits

past the Change screen.

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’ Edits

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.’’

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
Figure 7–8. Interviews: Main Items (Housing Unit and Demographic) Asked in MIS 5 Cases

Note: This list of items is not 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.

Figures 7–9 and 7–10 show the results of a typical

month’s (September 2004) 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 7.5 percent with an overall noninterview rate in the 21−23 percent range. In September

2004, the Type A rate was 7.56 percent. For the April

2003−March 2003 period, the CPS Type A rate was 7.25

percent. The overall noninterview rate for September 2004

was 22.98 percent, compared to the 12-month average of

23.19 percent.

Current Population Survey TP66 Conducting the Interviews 7–9

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Figure 7–9. Interviewing Results (September 2004)

Description Result

Total HHLD 71,575

Eligible HHLD 59,641

Interviewed HHLD 55,130

Response rate 92.44%

Noninterviews 16,445

Rate 22.98%

Type A 4,511

Rate 7.56%

No one home 1,322

Temporarily absent 432

Refused 2,409

Other—specify 348

Callback needed—no progress 0

Type B 11,522

Rate 16.19%

Entire HH Armed Forces 130

Entire HH under 15 3

Temp. occupied with persons with URE 1,423

Vacant regular (REG) 7,660

Vacant HHLD furniture storage 514

Unfit, to be demolished 428

Under construction, not ready 429

Converted to temp. business or storage 167

Unoccupied tent or trailer site 354

Permit granted, construction not started 50

Other Type B 364

Type C 412

Rate 0.58%

Demolished 82

House or trailer moved 45

Outside segment 7

Converted to permanent business or storage 48

Merged 26

Condemned 9

Built after April 1, 2000 14

Unused serial no./listing Sheet Llne 54

Removed during subsampling 0

Unit already had a chance of selection 4

Other Type C 123

Figure 7–10. Telephone Interview Rates

(September 2004)

Note: Figure 7−10 shows the rates of personal and telephone

interviewing in September 2004. It is highly consistent with the

usual monthly results for personal and telephone interviews.

Characteristic

Total

Telephone Personal

Number Percent Number Percent

Total....... 55,130 35,566 64.5 19,564 35.5

MIS 1&5 . . . . . . . . 13,515 2,784 20.6 10,731 79.4

MIS 2−4, 6−8 . . . 41,615 32,782 78.8 8,833 21.2

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Chapter 8.

Transmitting the Interview Results

INTRODUCTION

With the advent of completely electronic interviewing,

transmission of the interview results took on a heightened

importance to the field representative (FR). The data transmissions between headquarters, the regional field office,

and the FRs is all done electronically. 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 U.S.

Census Bureau 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 the software and the assignment.

• Prevent unauthorized access.

• Handle mail messages.

The central database system at headquarters cannot initiate transmissions. Either the FR or the regional offices

(ROs) must initiate any transmissions. Computers in the

field are not continuously connected to the headquarters

computers. Instead, the field computers contact the headquarters computers to exchange data using a toll free 800

number. 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 an electronic method of

transmitting interview data is the need for complete security. 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 FRs’ computers are compacted and

encrypted. All transmissions between headquarters, the

ROs, the Centralized Telephone Facilities, and the National

Processing Center (NPC) 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 an FR and the

central database usually take place once per day during

the survey 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 up his/her laptop

computer to transmit the completed work. During the

night, at a preset time, the computer modem 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 an 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 the FR has instant access to the central

database as necessary. This type of procedure is used primarily around the time of closeout when an FR might have

one or two straggler cases that need to be received by

headquarters before the 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 require supervisory review or were

completed at the RO.

Centralized Telephone Facility Transmission

Most of the cases sent to the Census Bureau’s Centralized

Telephone Facilities are successfully completed as

computer-assisted telephone interviews (CATI). Those that

cannot be completed from the telephone center are transferred to an FR prior to the end of the interview period.

These cases are called ‘‘CATI recycles.’’ Each telephone

facility daily transmits both completed cases and recycles

to the headquarters database. All the completed cases are

batched for further processing. Each recycled case is

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
transmitted directly to the computer of the FR who has

been assigned the case. Case notes that include the reason for recycle are also transmitted to the FR to assist in

follow-up. Daily transmissions are performed automatically for each region every hour during the CPS interview

week, sending reassigned or recycled cases to the FRs.

The RO staff also monitor the progress of the CATI

recycled cases with the Recycle Report. All cases that are

sent to a CATI facility are also assigned to an FR by the RO

staff. The RO staff keep a close eye on recycled cases to

ensure that they are completed on time, to monitor the

reasons for recycling so that future recycling can be minimized, and 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 NPC coders to assign the appropriate I&O codes.

This cycle repeats itself until all data are received by headquarters, usually on 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 the 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

the NPC. 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 Transmitting the Interview Results Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
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 26th) Week 5

Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat

SunthruThu Fri

Field reps pick upcomputerizedquestionnaireviamodem.

Field reps and CATI interviewers complete practiceinterviews and homestudies.

Field reps pick upassignments viamodem.

CATI interviewingCATIfacilitiestransmitcompletedworkto headquartersnightly.

CATIinterviewingends*.

All assignments completedexceptfortelephoneholds.

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 NPC for industry and occupation coding.

Initialprocesscloseout.

NPC sends back completed I&O cases.

NPCcloseout.Headquartersperformsfinal computer processing● Edits● Recodes● Weighting

Censusdeliversdatato BLS.

BLSanalyzesdata.

Resultsreleasedtopublicat8:30a.m.byBLS.

* CATI interviewing is extended in March and certain other months.

Current Population Survey TP66 Transmitting the Interview Results 8–3

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Chapter 9.

Data Preparation

INTRODUCTION

For the Current Population Survey (CPS), post datacollection activities transform a raw data file, as collected

by interviewers, into a microdata file that can be used to

produce estimates. Several processes are needed for this

transformation. The raw data files must be read and processed. Textual industry and occupation responses must

be coded. Even though some editing takes place in the

instrument at the time of the interview (see Chapter 7),

further editing is required once all the data are received.

Editing and imputations, explained below, are performed

to improve the consistency and completeness of the

microdata. New data items are created based upon

responses to multiple questions. These activities prepare

the data for weighting and estimation procedures,

described in 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 from these interviews

are sent to headquarters on a daily basis from Monday

through Thursday of this interview week. One file is

received for all of the three CATI facilities: Hagerstown,

MD; Tucson, AZ; and Jeffersonville, IN. Computer-assisted

personal interviewing (CAPI) also begins on the same Sunday and continues through Monday of the following week.

The CAPI answer files are again sent to headquarters daily

until all the interviewers and regional offices have transmitted the workload for the month. This phase is generally completed by Wednesday of the following week.

The 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, confirming the item range checks, and

rejecting invalid cases. Files containing records needing

four-digit industry and occupation (I&O) codes are electronically sent to Jeffersonville for assignment of these

codes. Once the Jeffersonville staff 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 operation to assign the I&O codes for a typical month

requires ten coders for a period of just over 1 week to

code data from 30,000 individuals. Sometimes the 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 new

monthly CPS cases and those where a person’s industry or

occupation has changed since the previous month of interviewing, are sent to Jeffersonville to be coded. For those

whose industry and occupation have not changed, the

four-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 the four-digit

numeric industry and occupation codes used in the 2000

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, the batch of records is electronically

returned to headquarters for the next stage of monthly

production processing.

EDITS AND IMPUTATIONS

The CPS is subject to two sources of nonresponse. The

largest is noninterview households. To compensate for

this data loss, the weights of noninterviewed households

are distributed among interviewed households, as

explained in Chapter 10. The second source of data loss is

from item nonresponse, which occurs when a respondent

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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, Table 16−4).

One of three imputation methods are used to compensate

for item nonresponse in the CPS. 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 (e.g., followed incorrect path of questions) and

ensure that all appropriate questions have valid entries.

For the most part, illogical entries or out-of-range entries

have been eliminated with the use of electronic instruments; however, the edits still address these possibilities,

which may arise from 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 the 3 imputation techniques

described below.

The edits are run in a deliberate and logical sequence.

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 data is

missing, it is assigned by looking at the age and sex

of the person in conjunction with the known relationship of other household members. Missing occupation

codes are sometimes assigned by analyzing the industry codes and vice versa. This technique is used as

appropriate across all edits. If missing values cannot

be assigned using this technique, they are assigned

using one of the two following methods.

2. Longitudinal edits are used in most of the labor force

edits, as appropriate. If a question is blank and the

individual is in the second or later month’s interview,

the edit procedure looks at last month’s data to determine whether there was an 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. The third imputation method is commonly referred to

as ‘‘hot deck’’ allocation. This method assigns a missing value from a record with similar characteristics,

which is the hot deck. Hot decks are defined by variables such as age, race, and sex. Other characteristics

used in hot decks vary depending on the nature of the

unanswered question. For instance, most labor force

questions use age, race, sex, and occasionally another

correlated labor force item such as full- or part-time

status. This means the number of cells in labor force

hot decks are relatively small, perhaps fewer 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 editing procedures, 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 and other

related variables are used in this edit.

2. Demographic edits and codes. This processing

step ensures consistency among all demographic variables for all individuals within a household. It ensures

all interviewed 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.

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Demographic-related recodes are created for both

individual and family characteristics.

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/or sex and,

possibly, by a related labor force characteristic.

4. I&O edits and codes. This processing step assigns

four-digit industry and occupation codes to those I&O

eligible individuals for whom 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 recode guidelines are also

created. This edit uses longitudinal editing, relational

allocation, and hot deck allocation. The hot decks are

defined by such variables as age, sex, race, and educational attainment.

5. Earnings edits and codes. This processing step

edits 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 and other related

variables is used.

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Chapter 10.

Weighting and Seasonal Adjustment 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 noninstitutionalized population and provides details by age, sex, race, and Hispanic origin. In

addition, estimates for a number of other population subdomains (e.g., families, veterans, people 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 people 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 weighting steps in the estimation procedure.1

In addition, the weighting at each step is replicated in

order to derive variances for the labor force estimates.

(See Chapter 14 for details.)

The weighting procedures of the CPS supplements are discussed in Chapter 11. Many of the supplements apply to

specific demographic subpopulations and differ in coverage from the basic CPS universe. The supplements tend to

have higher nonresponse rates.

In order to produce national and state estimates from survey data, a statistical weight for each person in the sample

is developed through the following steps, each of which is

explained below:

• 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 primary sampling units (PSUs).

• National and state coverage adjustments to improve

CPS coverage.

• Second-stage ratio adjustment to reduce variances by

controlling CPS estimates of the population to independent estimates of the current population.

• Composite estimation using estimates from previous

months to reduce the variances.

• Seasonally-adjusted estimates 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. Each of these

involve additional weighting steps to produce the final

estimate. The types of characteristics 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 the outgoing rotation

group’s 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

noninstitutionalized population. Although the processes

described in this chapter have remained essentially

unchanged since January 1978, and seasonal adjustment

has been part of the estimation process since June 1975,

modifications have been made in some of the procedures

from time-to-time. For example, in January 1998, a new

compositing procedure was introduced; in January 2003,

new race cells for the first-stage, second-stage, national,

and state coverage steps were added; in January 2005, the

number of cells used in the national coverage adjustment

and in the second-stage ratio adjustment was expanded to

improve the estimates of children.

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’s 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

1 Weights are needed when the sampled elements are selected

by unequal probability sampling. They are also used in poststratification and in making adjustments for nonresponse.

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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,

Hurwitz, and Madow, 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 between 7 and 8 percent.

Other factors, such as occasional errors caused by the

sample selection procedure or the omission of households

or individuals missed by interviewers, can also introduce

bias. These omitted households or people can be considered as having zero probability of selection. These two

exceptions notwithstanding, 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 current design used in the

CPS was selected with probabilities equal to the inverse of

the required state sampling intervals. These sampling

intervals are called the basic weights (or baseweights).

Almost all sample persons within the same state have the

same probability of selection. 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. New sample is continually

selected and added to the CPS to provide coverage for

newly constructed housing units. This results in a larger

sample size with an associated increase in costs. Small

maintenance sample reductions are implemented on a

periodic basis to offset the increasing sample size. (See

Appendix B.)

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 expected. During the estimation

procedure, housing units in these USUs must receive special weighting factors (also called weighting control 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 a reduction in the bias and an increase in

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 provide 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. Item nonresponse occurs when a cooperating HU fails or refuses to provide some specific items

of information. Procedures for dealing with this type of

nonresponse are discussed in Chapter 9. Unit nonresponse

refers to the failure to collect any survey data from an

occupied sample HU. For example, data may not be

obtained from an eligible household in the survey because

of impassable roads, a respondent’s absence or refusal to

participate in the interview, or unavailability of the respondent for other reasons. This type of nonresponse in the

CPS is called a Type A noninterview. Recently, the Type A

rate has averaged to between 7 and 8 percent (see Chapter 16).

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 that are not interviewed is valid if 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 is present

in CPS estimates when the nonresponding units differ in

relevant respects from those that respond to the survey or

to the particular items.

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Noninterview Clusters and Noninterview

Adjustment Cells

To reduce the size of the estimate’s bias, the noninterview

adjustment is performed based on sample PSUs that are

similar in metropolitan status and population size. These

PSUs are grouped together to form noninterview clusters.

In general, PSUs with a metropolitan status of the same (or

similar) size in the same state belong to the same noninterview cluster. PSUs classified as metropolitan are

assigned to metropolitan clusters. Nonmetropolitan PSUs

are assigned to nonmetropolitan clusters. Within each metropolitan cluster, there is a further breakdown into two

noninterview adjustment cells (also called residence cells).

Each is split into ‘‘central city’’ and ‘‘not central city’’ cells.

The nonmetropolitan clusters are not divided further, making a total of 214 adjustment cells from 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 = Zij+ Nij

Zij

where

Zij = the weighted count of interviewed households in

cell j of cluster i, and

Nij = 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

occurs:

• The computed factor is greater than or equal to 2.0.

• There are fewer than 50 unweighted interviewed households in the cell.

• The cell contains only Type A noninterviewed households and no interviewed households.

If any one of these situations occurs, 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 people within the

cluster. If after collapsing, any of the cells still meet any of

the situations above, the cell is output to an ‘‘extreme cell

file’’ that is created for review each 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 the 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 Hispanic origin.2

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 five ratio adjustments in the CPS

estimation process: the first-stage ratio adjustment, the

national coverage adjustment, the state coverage adjustment, the second-stage ratio adjustment, and the composite ratio adjustment leading to the composite estimator.

In the first-stage ratio adjustment, weights are adjusted so

that the distribution of the single-race Black population

and the population that is not single-race Black (based on

the census) in the sample PSUs in a state corresponds to

the same population groups’ census distribution in all

PSUs in the state. In the national-coverage ratio adjustment, weights are adjusted so that the distribution of agesex-race-ethnicity3 groups match independent estimates

of the national population. In the state-coverage ratio

adjustment, weights are adjusted so that the distribution

of age-sex-race groups match independent estimates of

the state population. 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 the CPS match independent

state population estimates by age and sex.

FIRST-STAGE RATIO ADJUSTMENT

Purpose of the First-Stage Ratio Adjustment

The purpose of the first-stage ratio adjustment is to

reduce the variance of sample state-level estimates caused

by the sampling of PSUs, that is, the variance that would

2 Hispanics may be any race. 3 Although ethnicity, in this chapter, only means Hispanic or

non-Hispanic origin, the word ethnicity will be used.

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still be associated with the state-level estimates even if

the survey included all households in every sample PSU.

This is called the between-PSU variance. For some states,

the between-PSU variance makes up a relatively large proportion of the total variance. The relative contribution of

the between-PSU variance at the national level is generally

quite small.

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 accuracy gained 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 the population by race (Black alone4/non-Black

alone5) by age groups 0−15 and 16+ satisfies all three criteria.

By using the Black alone/non-Black alone categories, the

first-stage ratio adjustment compensates for the fact that

the racial composition of an NSR (non-self-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 Census

2000 data and are applied only to sample data for the NSR

PSUs. Factors are computed for the two race categories

(Black alone/non-Black alone) 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 age/race cell

j (j=1, 2, 3, 4)

Csij = the Census 2000 civilian noninstitutional population for NSR PSU i (sample or nonsample) in state

s, age/race cell j

Cskj = the Census 2000 civilian noninstitutional population for NSR sample PSU k in state s, age/race cell

j

π sk = 2000 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 Census 2000 civilian noninstitutionalized population in the appropriate age/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 alone and non-Black alone 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 an

age/race cell in the state.

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 factor)

x (noninterview adjustment factor)

x (first-stage ratio adjustment factor)

The weight after the first-stage adjustment is called the

first-stage weight.

NATIONAL COVERAGE ADJUSTMENT

Purpose of the National Coverage Adjustment

The purpose of the national coverage adjustment is to correct for interactions between race and ethnicity that are

not addressed in the second-stage weighting. For

example, research has shown that the undercoverage of

certain combinations (e.g., non-Black Hispanic) cannot be

corrected with the second-stage adjustment alone. The

national coverage adjustment also helps to speed the convergence of the second-stage adjustment (Robison, Duff,

Schneider, and Shoemaker, 2002).

Computing the National Coverage Adjustment

The national coverage adjustment factors are based on

independently derived estimates of the population. (See

Appendix C.) Person records are grouped into four pairs

based on months-in-sample (MIS) (MIS 1 and 5, MIS 2 and

6, MIS 3 and 7, and MIS 4 and 8). This increases cell size

4 Alone is defined as single race. 5 Non-Black alone can be defined as everyone in the population who is not of the single race Black.

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and preserves the structure needed for composite weighting. Each MIS pair is then adjusted to age/sex/race/ethnicity population controls (see Table 10−1) using the following formula:

Fjk = Cj

Ejk

where

Fjk = national coverage adjustment factor for cell j and

month-in-sample pair k

Cj = national coverage adjustment control for cell j;

national current population estimate for cell j

Ejk = weighted tally for the cell j and month-in-sample

pair k (using weights after the first-stage adjustment)

For example, in October 2005, 80 percent of the respondents who were adjusted by a national coverage adjustment factor, received a Fjkvalue between 0.9868 and

1.2780. The factor Fjk is computed for each of the cells

listed in Table 10−1.

The independent population controls used for the national

coverage adjustment are from the same source as those

for the second-stage ratio adjustment.

Extreme cells are identified for any of the following criteria:

• Cell contains less than 20 persons.

• National coverage adjustment factor is greater than or

equal to 2.0.

• National coverage adjustment factor is less than or

equal to 0.6.

No collapsing is performed because the cells were created

in order to minimize the number of extreme cells.

Table 10−1: National Coverage Adjustment Cell Definitions

Black alone

non-Hispanic

White alone

non-Hispanic

White alone

Hispanic

Non-White alone

Hispanic

Asian alone

non-Hispanic

Residual race

non-Hispanic

Age Male Female Age Male Female Age Male Female Age Male Female Age Male Female Age Male Female

0−1 0 0 0−15 0−4 0−4

2−4 1 1

5−7 2 2 5−9 5−9

8−9 3 3

10−11 4 4 10−15 10−15

12−13 5 5

14 6 6

15 7 7

16−19 8 8 16+ 16−24 16−24

20−24 9 9

25−29 10−11 10−11 25−34 25−34

30−34 12−13 12−13

35−39 14 14 35−44 35−44

40−44 15 15

45−49 16−19 16−19 45−54 45−54

50−54 20−24 20−24

55−64 25−29 25−29 55−64 55−64

65+ 30−34 30−34 65+ 65+

35−39 35−39

40−44 40−44

45−49 45−49

50−54 50−54

55−59 55−64

60−62 65+

63−64

65−69

70−74

75+

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Weights After National Coverage Adjustment

After the completion of the national coverage adjustment,

the weight for each person is the product of:

(baseweight) x (special weighting factor)

x (noninterview adjustment factor)

x (first-stage ratio adjustment factor)

x (national coverage adjustment factor)

This weight will usually vary within households due to different household members having different demographic

characteristics.

STATE COVERAGE ADJUSTMENT

Purpose of the State Coverage Adjustment

The purpose of the state coverage adjustment is to adjust

for state-level differences in sex, age, and race coverage.

Research has shown that estimates of characteristics of

certain racial groups (e.g., Blacks) can be far from the

population controls if a state coverage step is not used.

Computing the State Coverage Adjustment

The state coverage adjustment factors are based on independently derived estimates of the population. Except for

the District of Columbia (DC), person records for the nonBlack-alone population are grouped into four pairs based

on MIS (MIS 1 and 5, MIS 2 and 6, MIS 3 and 7, and MIS 4

and 8). This increases cell size and preserves the structure

needed for composite weighting. Person records for the

Black-alone population for all states and the non-Blackalone population for DC are formed at the state level with

all months-in-sample combined. For the Black-alone component of the adjustment, states were assigned to different tables (Tables 10−2A, 10−2B and 10−2C) based on the

expected number of sample records in each age/sex cell.

For the non-Black-alone component, all states except DC

were assigned to Table 10−2D. (DC was assigned to Table

10−2C.) Each cell is then adjusted to age/sex/race population controls in each state6 using the following formula:

Fjk = Cj

Ejk

where

Fjk = state coverage adjustment factor for cell j and MIS

pair k.

Cj = state coverage adjustment control for cell j; state

current population estimate for cell j.

Ejk = weighted tally for the cell j and MIS pair k.

For example, in October 2005, 80 percent of the respondents who were adjusted by a state coverage adjustment

factor, received a Fjk value between 0.8451 and 1.1517.

The independent population controls used for the state

coverage adjustment are from the same source as those

for the second-stage ratio adjustment. (See next section

for details on the second-stage adjustment.)

Extreme cells are identified for any of the following criteria:

• Cell contains less than 20 persons.

• State coverage adjustment factor is greater than or

equal to 2.0.

• State coverage adjustment factor is less than or equal to

0.6.

No collapsing is performed because the cells were created

in order to minimize the number of extreme cells.

Table 10−2: State Coverage Adjustment Cell

Definitions

Table 10−2A — Black Alone

Age Male and female combined

0+

States assigned to Table 10−2A: HI, ID, IA, ME, MT, ND, NH, NM,

OR, SD, UT, VT, WY

(all rotation groups combined)

Table 10−2B — Black Alone

Age Male Female

0+

States assigned to Table 10−2B: AK, AZ, CO, IN, KS, KY, MN, NE,

NV, OK, RI, WA, WI, WV

(all rotation groups combined)

Table 10−2C — Black Alone and Non-Black Alone for DC

Age Male Female

0−15

16−44

45+

States assigned to Table 10−2C: Black Alone: AL, AR, CT, DC, DE,

FL, GA, IL, LA, MA, MD, MI, MO, MS, NC, NJ, OH, PA, SC, TN, TX,

VA, LA county, bal CA, NYC, bal NY; Non-Black Alone: DC

(all rotation groups combined)

Table 10−2D — Non-Black Alone

Age Male Female

0−15

16−44

45+

States assigned to Table 10−2D: Non-Black Alone: All states + LA

county + NYC + bal CA + bal NY (DC is excluded)

(by rotation group pair)

6 In the state coverage adjustment, California is split into two

parts and each part is treated like a state—Los Angeles County

and the balance of California. Similarly, New York is split into two

parts with each part being treated as a separate state—New York

City (New York, Queens, Bronx, Kings and Richmond Counties)

and the balance of New York.

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Weights After State Coverage Adjustment

After the completion of the state coverage adjustment, the

weight for each person is the product of:

(baseweight) x (special weighting factor)

x (noninterview adjustment factor)

x (first-stage ratio adjustment factor)

x (national coverage adjustment factor)

x (state coverage adjustment factor)

This weight will usually vary within households due to different household members having different demographic

characteristics.

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

month-in-sample pair 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 noninstitutionalized population for the

50 states and the District of Columbia by sex and age

(0−15, 16−44, 45 and older).

• Total national civilian noninstitutionalized population

for 26 Hispanic and 26 non-Hispanic age-sex categories

(see Table 10–3).

• Total national civilian noninstitutionalized population

for 56 White, 36 Black, and 34 ‘‘Residual Race’’ age-sex

categories (see Table 10–4).

The adjustment is done separately for each MIS pair (MIS 1

and 5, MIS 2 and 6, MIS 3 and 7, and MIS 4 and 8). 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 ten iterations is performed, which

results in virtual consistency between the sample estimates and population controls. The three-way (state,

Hispanic/sex/age, race/sex/age) raking is also known as

iterative proportional fitting or raking ratio estimation.

In addition to reducing the error in many CPS estimates

and converging to the population controls within ten 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 weight for the ith sample record after the

second-stage adjustment, and

W1i = the weight for the ith record after the first-stage

adjustment.

Thus, the raking adjusts the weights of the records so that

the sample estimates converge to the population controls

while minimally affecting the weights after the state coverage adjustment. The article by Ireland and Kullback (1968)

provides more details on the properties of raking ratio

estimation.

Table 10−3: Second-Stage Adjustment Cell by Ethnicity, Age, and Sex

Hispanic Non-Hispanic

Age Male Female Age Male Female

0−4 0−4

5−9 5−9

10−15 10−15

16−19 16−19

20−24 20−24

25−29 25−29

30−34 30−34

35−39 35−39

40−44 40−44

45−49 45−49

50−54 50−54

55−64 55−64

65+ 65+

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Table 10−4: Second-Stage Adjustment Cell by Race, Age, and Sex

Black alone White alone Residual race

Age Male Female Age Male Female Age Male Female

0−1 0 0−1

2−4 1 2−4

5−7 2 5−7

8−9 3 8−9

10−11 4 10−11

12−13 5 12−13

14 6 14−15

15 7 16−19

16−19 8 20−24

20−24 9 25−29

25−29 10−11 30−34

30−34 12−13 35−39

35−39 14 40−44

40−44 15 45−49

45−49 16−19 50−54

50−54 20−24 55−64

55−64 25−29 65+

65+ 30−34

35−39

40−44

45−49

50−54

55−59

60−62

63−64

65−69

70−74

75+

Sources of Independent Controls

The independent population controls used in the secondstage ratio adjustment and in the coverage adjustment

steps are prepared by projecting forward the population

figures derived from Census 2000 using information from

a variety of other sources that account for births, deaths,

and net migration. Subtracting estimated numbers of resident Armed Forces personnel and institutionalized people

from the resident population gives the civilian noninstitutionalized population. 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 C for

more details on sources and derivation of the independent

controls.

Computing Initial Second-Stage Ratio Adjustment

Factors

As mentioned before, the second-stage adjustment

involves a three-way rake:

• State/sex/age

• Hispanic/sex/age

• Race/sex/age

There is no collapsing done for the second-stage adjustment. The cells are designed to avoid having small cells.

Instead, a small or extreme cell is identified as follows:

• It contains fewer than 20 people.

• It has an adjustment factor greater than or equal to 2.0.

• It has an adjustment factor less than or equal to 0.6.

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.

These three steps are repeated through ten 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.

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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

650+180 = 1.265

Ee = EsFe = 822

Es =150

Fe = 250

150+20 = 1.471

Ee = EsFe= 221

1000

Black

Es = 180

Fe = 1050

650+180 = 1.265

Ee = EsFe = 228

Es = 20

Fe = 250

150+20 = 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

822+221 = 0.959

Er = EeFr = 788

Ee=221

Fr= 1000

822+221=0.959

Er = EeFr = 212 1000

Black

Ee = 228

Fr = 300

228+29 = 1.167

Er = EeFr = 266

Ee = 29

Fr = 300

228+29 = 1.167

Er = EeFr = 34 300

Population

controls 1050 250 1300

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Iteration 2: (Repeat steps above beginning with sample

cell estimates at the end of iteration 1.)

•

•

•

Iteration 10:

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 ten 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 30 adjustment factors (ten iterations of three

rakes). The product of these 30 adjustment factors is

called the second-stage ratio adjustment factor.

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 factor)

x (noninterview adjustment factor)

x (first-stage ratio adjustment factor)

x (national coverage adjustment factor)

x (state coverage adjustment factor)

x (second-stage ratio adjustment factor)

COMPOSITE ESTIMATOR

Once each record has a second-stage weight, an estimate

of level for any given set of characteristics identifiable in

the CPS can be computed by summing the second-stage

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.

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 estimate at the completion of the second-stage ratio

adjustment which is 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 the part of the sample that 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.

Effective with the release of January 1998 data, the Bureau

of Labor Statistics (BLS) implemented a new composite

estimation method for the CPS. The new technique provides increased operational simplicity for microdata users

and allows optimization of compositing coefficients for

different labor force categories.

Under the procedure, weights are derived for each record

that, when aggregated, produce estimates consistent with

those produced by the composite estimator. Under the

previous 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.

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Composite Estimation in CPS

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 (about 75 percent of

the sample) continue in the 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

one 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 people unemployed) in month t is

given by

Yt



 1KYˆ t  KYt1

  t

  A	ˆ

t

where

Yˆ t  i1

8

xt,i

t  4

3is

xt,i  xt1, i1 and

	ˆ

t  is

xt,i  1

3is

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 for the current month: (1) the current month’s ratio

estimator Yˆt and (2) the sum of the previous month’s composite estimator Y/

t-1 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 Mansur and Shoemaker,

1999, Breau and Ernst, 1983, Bailar, 1975.) Also, see section ‘‘Time in Sample’’ of Chapter 16. ‘‘Time in Sample’’ is

concerned with the effects on labor force estimates from

the CPS as the result of interviewing the same CPS respondents several times.

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.(1999).

Computing Composite Weights

Composite weights are produced only for sample people

aged 16 or older. As described in previous sections, 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 four successive stages of

ratio adjustments to population controls are applied. The

second-stage raking procedure ensures that sample

weights add to independent population controls for states

by sex and age, 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 the 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.

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1. For each state7 and the District of Columbia (53 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 20

national age/sex/ethnicity cells and 46 national age/

sex/race cells. These computations use cell definitions

specified in Tables 10−5 and 10−6. Coefficients K =

0.4 and A = 0.3 are used for all UE estimates in all categories.

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 nine more times for a total of

10 iterations.

An analogous procedure is done for estimating the number of people employed using coefficients K = 0.7 and A =

0.4.

For the not-in-labor force category (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).

Extreme cells are identified if a cell size is less than 10, or

an adjustment factor is greater than 1.3 or less than 0.7.

Final Weights

For people 16 years and older, the composite weights are

the final weights. Since the procedure does not apply to

persons under age 16, their final weights are the secondstage weights.

Table 10−5: Composite National Ethnicity Cell

Definition

Hispanic

Age Male Female

16−19

20−24

25−34

35−44

45+

Non-Hispanic

Age Male Female

16−19

20−24

25−34

35−44

45+

Table 10−6: Composite National Race Cell

Definition

Black alone

Age Male Female

16−19

20−24

25−29

30−34

35−39

40−44

45+

White alone

Age Male Female

16−19

20−24

25−29

30−34

35−39

40−44

45−49

50−54

55−59

60−64

65+

Residual race

Age Male Female

16−19

20−24

25−34

35−44

45+

Since data for all eight rotation groups are combined for

the purpose of computing composite weights, summations of final weights within rotation group will not match

independent population controls for people 16 years and

older. Summations of final weights for the entire sample

will be consistent with these second-stage controls, but

will only match a selected number of those controls. If the

7 California is split into two parts and each part is treated like

a state—Los Angeles County and the balance of California. Similarly, New York is split into two parts with each part being treated

as a separate state—New York City (New York, Queens, Bronx,

Kings and Richmond Counties) and the balance of New York.

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composite ethnicity x gender x age detail or race x gender

x age detail is the same as the second-stage detail, then

there will be a match. The composite-age detail is coarser

than the second-stage age detail. For example, the composite procedure controls for White alone, male, ages

60−64; the second stage has more age detail (60−62 and

63−64). Summations of final (composite) weights for all

White alone males by age will not match the corresponding second-stage population control for either the 60−62

or the 63−64 age group. However, the summed final

weights for White alone, males, ages 60−64 will match the

sum of the two second-stage population controls.

PRODUCING OTHER LABOR FORCE ESTIMATES

In addition to basic weighting to produce estimates for

individuals, 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 longitudinallylinked files (longitudinal weighting).

Most of these special weights are based on the 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 estimates related to

families and family composition. They also provide the

basis for household weights. The family weight is derived

from the second-stage 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 second-stage

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 secondstage ratio adjustment tends to increase the weights of

males more than the weights of females. This is because

there is better coverage of females than for males. 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

can be used to produce estimates at the household level,

such as the number of households headed by a woman.

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

month- in-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 they are 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

second-stage 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.

The outgoing rotation weights are obtained by multiplying

the outgoing ratio adjustment factors by the second-stage

weights. For consistency, an outgoing rotation group

weight equal to four times the basic CPS family weight is

assigned to all people in the two outgoing rotation groups

who were not eligible for this special weighting (mainly

military personnel and those aged 15 and younger).

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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

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 composite weights. Each

respondent is classified as a veteran or a nonveteran. Veterans’ records are classified into cells based on veteran

status (Vietnam-era, Not Vietnam-era or Peactime), age and

sex.

The composite weights for CPS veterans are tallied into

type-of-veteran/sex/age 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 composite 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 alone/non-White alone), labor force status

(unemployed, employed, and not-in-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 nonveteran control

totals. The composite 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 nonveteran controls derived above. The factor is the ratio of the nonveteran control total to the

sample estimate. The composite weight for each nonveteran is multiplied by the appropriate factor to produce the

nonveteran weight.

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 beyond those available in the stock

data.

The estimation of monthly gross flows, and any other longitudinal use of the CPS data, 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 the sample according to the

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 people

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.

On most CPS files from 1994 forward, a longitudinal

weight allows users to estimate gross labor force flows by

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

inflates all estimates or final weights by the ratio of the

current month’s population controls to the sum of the

second-stage weights for the current month in the

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matched cases by sex. Although the technique provides

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 has 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. For information about

more recent work on gross flows estimation methodology,

refer to two articles in the Monthly Labor Review, accessible online through <http://www.bls.gov/opub/mlr

/mlrhome.htm>. (See Frazis, Robison, Evans and Duff,

2005; Ilg, 2005.) Test tables produced by the BLS using

the methodology described in the two articles cannot be

reproduced using the longitudinal weights specified in this

section.

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 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 one-fifth

for annual averages.

SEASONAL ADJUSTMENT

Short-run movements in labor-force time series are

strongly influenced by seasonality, which refers to periodic fluctuations that are associated with recurring

calendar-related events such as weather, holidays, and the

opening and closing of schools. Seasonal adjustment is

the process of estimating and removing these fluctuations

to yield a seasonally-adjusted series. The reason for doing

so is to make it easier for data users to observe fundamental changes in the level of the series, particularly

those associated with general economic expansions and

contractions.

For example, the unadjusted CPS levels of employment

and unemployment in June are consistently higher than

those for May because of the influx of students into the

labor force. If the only change that occurs in the unadjusted estimates between May and June approximates the

normal seasonal change, then the seasonally-adjusted estimates for the two 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 often will be in a direction opposite to the movement in the unadjusted series.

Refinements of the methods used for seasonal adjustment

have been under development for decades. The current

procedure used for the seasonal adjustment of national

CPS series is the X-12-ARIMA program from the U.S.

Census Bureau. This program is an enhanced version of

earlier programs utilizing the widely used X-11 method,

first developed by the Census Bureau and later modified

by Statistics Canada. 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

outliers. In most uses of the X-11 method, including that

Current Population Survey TP66 Weighting and Seasonal Adjustment for Labor Force Data 10–15

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for national CPS labor force series, the seasonality is estimated as evolving rather than fixed over time. A detailed

description of the X-12-ARIMA program is given in the U.

S. Census Bureau (2002) reference.

The current official practice for the seasonal adjustment of

CPS national labor force data is to run the X-12-ARIMA program monthly as new data become available, which is

referred to as concurrent seasonal adjustment.8 The season factors for the most recent month are produced by

applying a set of moving averages to the entire data set,

including data for the current month. While all previousmonth seasonally-adjusted data are revised in this process, no revisions are made during the year. Revisions are

applied at the end of each year for the most recent five

years of data.

Seasonally-adjusted estimates of many national labor force

series, including the levels of the civilian labor force, total

employment and total unemployment, and all unemployment rates, are derived indirectly by arithmetically combining the series directly adjusted with X-12-ARIMA. For

example, the overall national unemployment rate is computed using eight directly-adjusted series: females aged

16−19, males aged 16−19, females aged 20+, and males

aged 20+ for both employment and unemployment. 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 are directly adjusted along with the

components, such consistency would generally not occur,

since X-11 is not a sum- or ratio-preserving procedure. It is

not generally appropriate to apply factors computed for an

aggregate series to its components because various components tend to have statistically significant different patterns of seasonal variation.

For up-to-date information and a more thorough discussion on seasonal adjustment of national labor force series,

see any monthly issue of Employment and Earnings

(U.S. Department of Labor).

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 S. C. Hillmer (1990), ‘‘The Time Series

Approach to Estimation for Repeated Surveys,’’ Survey

Methodology, 16, pp. 195−215.

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.

Copeland, K. R., F. K. Peitzmeier, and C. E. Hoy (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., R. B. Tiller, and T. S. Zimmerman (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 R. McGuinness (1993), ‘‘Correlations and

Adjustment Factors for CPS (VAR80 1),’’ Internal Memorandum for Documentation, January 6th, Demographic Statistical Methods Division, U.S. Census Bureau.

Frazis, H.J., E.L. Robison, T.D. Evans, and M.A. Duff (2005),

‘‘Estimating Gross Flows Consistent With Stocks in the

CPS,’’ Monthly Labor Review, September, Vol. 128, No.

9, pp. 3−9.

Hansen, M. H., W. N. Hurwitz, and W. G. Madow (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.

Ilg, R. (2005), ‘‘Analyzing CPS Data Using Gross Flows,’’

Monthly Labor Review, September, Vol. 128, No. 9, pp.

10−18.

Ireland, C. T., and S. Kullback (1968), ‘‘Contingency Tables

With Given Marginals,’’ Biometrika, 55, pp. 179−187.

Kostanich, D. and P. Bettin (1986), ‘‘Choosing a Composite

Estimator for CPS,’’ presented at the International Symposium on Panel Surveys, Washington, DC.

Lent, J., S. Miller, and P. Cantwell (1994), ‘‘Composite

Weights for the Current Population Survey,’’ Proceedings

of the Survey Research Methods Section, American

Statistical Association, pp. 867−872.

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, Vol.

15, No. 3, pp. 431−438.

Mansur, K.A. and H. Shoemaker (1999), ‘‘The Impact of

Changes in the Current Population Survey on Time-inSample Bias and Correlations Between Rotation Groups,’’

Proceedings of the Survey Research Methods Section, American Statistical Association, pp. 180−183.

8 Seasonal adjustment of the data is performed by the Bureau

of Labor Statistics, U.S. Department of Labor.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
Oh, H. L. and F. Scheuren (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.

Robison, E., M. Duff, B. Schneider, and H. Shoemaker

(2002), ‘‘Redesign of Current Population Survey Raking to

Control Totals,’’ Proceedings of the Survey Research

Methods Section, American Statistical Assocation.

Scott, J. J., and T. M. F. Smith (1974), ‘‘Analysis of Repeated

Surveys Using Time Series Methods,’’ Journal of the

American Statistical Association, 69, pp. 674−678.

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. 182−185.

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. Census Bureau (2002), X-12-ARIMA Reference Manual

(Version 0.2.10), Washington, DC.

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., T. D. Evans, and R. B. Tiller (1994),

‘‘State Unemployment Rate Time Series,’’ Proceedings of

the Survey Research Methods Section, American Statistical Association, pp. 1077−1082.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
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. These

studies keep the nation informed of the economic and

social well-being of its people and are used 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. In addition, any supplemental inquiry must 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. An additional weighting

method is often necessary because the supplement targets

a different universe from that of the basic CPS. A supplement can also engender a different level of response or

cooperation from respondents.

CRITERIA FOR SUPPLEMENTAL INQUIRIES

A number of criteria to determine the acceptability of

undertaking supplements for federal agencies or other

sponsors have been developed and refined over the years

by the U.S. Census Bureau, in consultation with the U.S.

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 and not introduce 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 allowed.

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. Competing requirements for the use of

Census Bureau staff or facilities that arise in dealing

with a supplemental inquiry are resolved by giving the

basic CPS first priority. The Census Bureau will not

jeopardize the schedule for completing the CPS or

other Census Bureau work to favor completing a

supplemental inquiry within a specified time frame.

5. The subject matter must not be 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 supplemental survey’s objectives into meaningful questions, and the respondent

must be able to supply the information required to

answer the questions.

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.

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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 a person’s 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.

The Census Bureau does not make the final decision

regarding the appropriateness or utility of the supplemental survey. The Office of Management and Budget (OMB),

through its Statistical Policy Division, reviews the proposal

to make certain it meets government-wide standards

regarding the need for the data and the 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 a supplement is asked the additional questions that are included in 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 2004.

The Housing Vacancy Supplement (HVS) and American

Time Use Surveys (ATUS) are unusual in that they are separate survey operations that base their sample on the

results of the basic CPS interview. The HVS supplement

collects additional information (e.g., number of rooms,

plumbing, and rental/sales price) on housing units identified as vacant in the basic CPS and the ATUS collects information about how people spend their time. The HVS is collected at the time of the basic CPS interview, while the

ATUS is collected after the household’s last CPS interview.

Probably the most widely used supplement is the Annual

Social and Economic (ASEC) Supplement, 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 may be

different from the basic CPS universe. Thus, some supplements require weighting procedures that are different

from those of the basic CPS. These variations are

described for three of the major supplements—the Housing Vacancy Survey (HVS), the American Time Use Survey

(ATUS), and the Annual Social and Economic (ASEC)

supplement—in the following sections.

Housing Vacancy Survey (HVS) Supplement

Description of supplement

The Housing Vacancy Survey (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 people

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 the 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 that 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 statistical 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 2004

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, December 1999

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 Social and

Economic Supplement

(formally known as the

AnnualDemographic

Supplement)

March 1995−2004 Provide data concerning family characteristics, household composition, marital

status, education attainment, health insurance coverage, foreign-born population, previous year’s income from all sources, work experience, receipt of

noncash benefit, poverty, program participation, and geographic mobility. Periodicity: Annual

Census/BLS

Food Security April 1995, September 1996,

April 1997, August 1998, April

1999, September 2000, April

2001, December 2001, 2002,

2003, 2004

Provide data that will measure hunger and food security. It will provide data on

food expenditure, access to food, and food quality and safety.

FNS

Race and Ethnicity May 1995, July 2000, May

2002

Test alternative measurement methods to evaluate how best to collect these

types of data.

BLS/Census

Marital History June 1995 Collect information from ′′ever-married′′ persons on marital history. Census/BLS

Fertility June 1998, 2000, 2002, 2004 Provide data on the number of children that women aged 15-44 have ever had

and the children’s characteristics. Periodicity: Biennial.

Census/BLS

Educational Attainment July 1995 Test several methods of collecting these data. Test 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, September 1997,

1999, August 2001, 2003

Provide data for veterans of the United States on Vietnam-theater and Persian

Gulf-theater status, service-connected income, effect of a service-connected

disability on current labor force participation and participation in veterans’

programs. Periodicity: Biennial.

BLS

School Enrollment October 1994−2004 Provide information on population 3 years old and older on school enrollment,

junior or regular college attendance, and high school graduation. Periodicity:

Annual.

BLS/Census/

NCES

Tobacco Use September1995,January1996,

May 1996, September 1998,

January 1999, May 1999, January 2000, May 2000, June

2001, November 2001, February 2002, February 2003,

June 2003, November 2003

Provide data for population 15 years and older on current and former use of

tobacco products; restrictions of smoking in workplace for employed persons;

and personal attitudes toward smoking. Periodicity: As requested.

NCI

Displaced Workers February 1996, 1998, 2000,

January 2002, 2004

Provide data on workers who lost a job in the last 5 years due to plant closing,

shift elimination, or other work-related reason. Periodicity: Biennial.

BLS

Job Tenure/

Occupational Mobility

February 1996, 1998, 2000,

January 2002, 2004

Provide 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, 2002,

2004

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 or awarded. April data will be

matched to March data.

OCSE

Voting and Registration November 1994, 1996, 1998,

2000, 2002, 2004

Provide demographic information on persons who did 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, 2004 Provide information about multiple job holdings and work schedules and

telecommuters who work at a specific remote site.

BLS

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Table 11–1. Current Population Survey Supplements September 1994−December 2004—Con.

Title Month Purpose Sponsor

Computer Use/Internet

Use

November 1994, October 1997,

December 1998,August 2000,

September 2001, October 2003

Provide information about household access to computers and the use of the

Internet or World Wide Web.

NTIA

Participation in the Arts August 2002 Provide data on the type and frequency of adult participation in the arts; training

and exposure (particularly while young); and their musical artistic activity

preferences.

NEA

Volunteers September 2002, 2003, 2004 Provide a measurement of participation in volunteer service, specifically about

frequency of volunteer activity, the kinds of organizations volunteered with, and

types of activities chosen. Among nonvolunteers, questions identify what barriers

were experienced in volunteering, or what encouragement is needed to increase

participation.

USA

Freedom

Corps

Cell Phone Use February 2004 Provide data about household use of regular landline telephones and household

use of cell phones. If both were used in the household, it asked about the amount

of cell phone usage.

BLS/Census

The homeowner vacancy rate is calculated as the ratio of

vacant year-round units for sale to the sum of owneroccupied 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, two HVS

adjustments and a regional housing unit (HU) adjustment.

(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 2000 census data. There

are separate first-stage factors for year-round and seasonal housing units. For each state, they are calculated as

the ratio of the state-level census count of vacant yearround or seasonal housing units in all NSR PSUs to the corresponding state-level estimate of vacant year-round or

seasonal housing units from the NSR PSUs in sample.

The appropriate first-stage adjustment factor is applied to

every vacant year-round and seasonal housing units in the

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 regional HU adjustment is the final stage in the HVS

weighting procedure. The factor is calculated as the ratio

of the HU control estimates by the four major geographic

regions of the United States (Northeast, South, Midwest,

and West) supplied by the Population Division, to the sum

of estimated occupied (from the CPS) plus vacant HUs,

through the HVS second stage adjustment.1 This factor is

applied to both occupied and vacant housing units.

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. 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.)

American Time Use Survey (ATUS)

Description

The American Time Use Survey (ATUS) collects data each

month on how people spend their time. Data collection for

the ATUS began in January 2003. There are seventeen

major categories for activities such as work, sleep, eating

and drinking, leisure and sports. Data are tabulated by

variables such as sex, race/ethnicity, age, and education.

Sample

Approximately 2,200 households are selected for ATUS

each month, and each household is interviewed just once.

The ATUS sample comes from CPS sample households that

1 The estimate of occupied housing units from the CPS is

obtained by aggregating the family weight of each interviewed

CPS household.

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have completed their eighth and final CPS interview (i.e.,

CPS MIS 8). There is a two-month lag from the last CPS

interview, to initial contact with the household for an ATUS

interview. For example, households selected from the

January 2005 CPS sample would be part of the March

2005 ATUS sample.

The CPS households from which the ATUS sample is

selected are stratified by race/ethnicity and presence of

children. Non-White households are sampled at a higher

rate to insure that valid comparisons can be made across

major race/ethnicity groups. Half of the households are

assigned to report on Saturdays and Sundays, and half are

assigned to report on weekdays. One person 15 years or

older is selected from each ATUS sample household for

participation in the ATUS.

Weighting procedure

The basic weight for each ATUS record is the product of

the CPS first-stage weight, an adjustment for the CPS

state-based design, the within-stratum sampling interval

and a household size factor. The ATUS basic weight is then

adjusted by an ATUS noninterview factor, plus a set of

population control adjustments; the population control

adjustments are based on sex, age, race/ethnicity, education, labor force status, and presence of children in the

household. A day adjustment factor is computed separately for weekdays, Saturdays, and Sundays. This factor

accounts for increased numbers of weekend interviews,

and for varying frequencies of each day of the week for a

particular month.

Annual Social and Economic Supplement (ASEC)

Description of supplement

The ASEC is sponsored by the Census Bureau and the BLS.

The Census Bureau has collected data in the ASEC since

1947. From 1947 to 1955, the ASEC took place in April,

and from 1956 to 2001 the ASEC took place in March.

Prior to 2003, the ASEC was known as the Annual Demographic Supplement or the March Supplement. In 20012, a

sample increase was implemented that required more time

for data collection. Thus, additional ASEC interviews are

now taking place in February and April. 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, education attainment, health insurance coverage, the foreign-born population, work experience, income from all sources, receipt of

noncash benefit, poverty, program participation, and geographic mobility. A major reason for conducting the ASEC

in 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 universe for the ASEC is slightly different from that for

the basic CPS. It includes certain members of the armed

forces in the estimates. This requires some minor changes

to the sampling procedures and to the weighting methodology.

The ASEC sample consists of the March CPS sample, plus

additional CPS households identified in prior CPS samples

and the following April CPS sample. Table 11−2 shows the

months when the eligible sample is identified for years

2001 through 2004. Starting in 2004, the eligible ASEC

sample households are:

1. The entire March CPS sample.

2. Hispanic households—identified in November (from all

month-in-sample (MIS) groups) and in April (from MIS

1 and 5 groups).

3. Non-Hispanic non-White households—identified in

August (MIS 8), September (MIS 8), October (MIS 8),

November (MIS 1 and 5), and April (MIS 1 and 5).

4. Non-Hispanic White households with children 18 years

or younger—identified in August (MIS 8), September

(MIS 8), October (MIS 8), November (MIS 1 and 5), and

April (MIS 1 and 5).

Prior to 1976, no additional sample households were

added. From 1976 to 2001, only the November CPS

households containing at least one person of Hispanic origin were added to the ASEC. The households added in

2001, along with a general sample increase in selected

states, are collectively known as the State Children’s

Health Insurance Program (SCHIP) sample expansion. The

added households improve the reliability of the ASEC 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 cases from the August, September, October, November and April CPS are completely different from those in the March CPS. The additional sample

cases increase the effective sample size of the ASEC compared with the March CPS sample alone. The ASEC sample

includes 18 MIS groups for Hispanic households, 15 MIS

groups for non-Hispanic non-White households, 15 MIS

groups for non-Hispanic White households with children

18 years or younger, and 8 MIS groups for all other households.

2 The expanded sample was first used in 2001 for testing and

was not included in the official ADS statistics for 2001. The statistics from 2002 are the first official set of statistics published

using the expanded sample. The 2001 expanded sample statistics

were released and are used for comparing the 2001 data to the

official 2002 statistics.

Current Population Survey TP66 Current Population Survey Supplemental Inquiries 11–5

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Table 11−2. MIS Groups Included in the ASEC Sample for Years 2001, 2002, 2003, and 2004

CPS month/Hispanic status

Month in sample

1234567 8

August

Hispanic1 NI2

NonHispanic3 NI2 2004

September

Hispanic1 NI2

NonHispanic3 NI2 2004

October

Hispanic1 NI2

NonHispanic3 NI2 2003

2004

November

Hispanic1

2001

2002

2003

2004

NonHispanic3

2001

NI2

2001 2001 2001 2001

2002 2002 2002 2002 2002

2003 2003 2003 2003

2004 2004

March

Hispanic1 2001

2002

NonHispanic3

2003

2004

April

Hispanic1 2001

NI2

2001

NI 2002 2002 2

NonHispanic1

2003 2003

2004 2004

1 Hispanics may be any race. 2 NI - Not interviewed for the ASEC. 3 The non-Hispanic group includes both non-Hispanic non-Whites and non-Hispanic Whites with children 18 years old or younger.

The March and April ASEC eligible cases are administered

the ASEC questionnaire in those respective months (see

Table 11−3). The April cases are classified as ‘‘split path’’

cases because some households receive the ASEC supplement questionnaire, while other households receive the

supplement scheduled for April. The November eligible

Hispanic households are administered the ASEC questionnaire in February for MIS groups 1 and 5, during their

regular CPS interviewing time, and the remaining MIS

groups (MIS 2−4 and 6−8) receive the ASEC interview in

March. (November MIS 6−8 households have already completed all 8 months of interviewing for the CPS before

March, and the November MIS 2−4 households have an

extra contact scheduled for the ASEC before the 5th interview of the CPS later in the year.)

The August, September, October, and November eligible

non-Hispanic households are administered the ASEC questionnaire in either February or April. November ASEC eligible cases in MIS 1 and 5 are interviewed for the CPS in

February (in MIS 4 and 8, respectively), so the ASEC questionnaire is administered in February. (These are also split

path cases, since households in other rotation groups get

the regular supplement scheduled for February). The

August, September, and October MIS 8 eligible cases are

split between the February and April CPS interviewing

months.

Mover households are defined at the time of the ASEC

interview as households with a different reference person

when compared to the previous CPS interview, or the person causing the household to be eligible has moved out

(i.e., the Hispanic person or other race minority moved

out, or a single child aged the household out of eligibility.)

Mover households identified from the August, September,

October, and November eligible sample are removed from

the ASEC sample. Mover households identified in the

March and April eligible samples receive the ASEC questionnaire.

The ASEC sample universe is slightly different from that in

the CPS. The CPS completely excludes military personnel

while the ASEC includes military personnel who live in

households with at least one other civilian adult. These

differences require the ASEC 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 ASEC

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. (Chapter 10 contains a

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
Table 11−3. Summary of 2004 ASEC Interview Months

CPS month/Hispanic status

Mover Nonmover

Month in sample Month in sample

123456781234567 8

August

Hispanic1 NI2 NI2

Non-Hispanic3 NI2 NI2 Feb.

September

Hispanic1 NI2 NI2

Non-Hispanic3 NI2 NI2 Feb.

October

Hispanic1 NI2 NI2

Non-Hispanic3 NI2 NI2 Apr.

November

Hispanic1

NI2 Feb March Feb March

Non-Hispanic3 Feb. NI2 Feb. NI2

March

Hispanic1

March March

Non-Hispanic3

April

Hispanic1

Apr. NI2 Apr. NI2 Apr. NI2 Apr. NI2

Non-Hispanic3

1 Hispanics may be any race. 2 NI - Not interviewed for the ASEC. 3 The non-Hispanic group includes both non-Hispanic non-Whites and non-Hispanic Whites with children 18 years old or younger.

description of these and the following adjustments.) The

ASEC also receives an additional noninterview adjustment

for the August, September, October, and November ASEC

sample, a SCHIP Adjustment Factor, a family equalization

adjustment, and weights applied to Armed Forces members.

The August, September, October, and November eligible

samples are weighted individually through the CPS noninterview adjustment and then combined. A noninterview

adjustment for the combined samples and the CPS firststage ratio adjustments are applied before the SCHIP

adjustment is applied.

The March eligible sample, and the April eligible sample

are also weighted separately before the second-stage

weighting adjustment. All 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 ASEC sample.

Households from August, September, October, and

November eligible samples: The households from the

August, September, October, and November eligible samples start with their basic CPS weight as calculated in the

appropriate month, modified by the appropriate CPS special weighting factor and appropriate CPS noninterview

adjustment. At this point, a second noninterview adjustment is made for eligible households that are still occupied, but for which an interview could not be obtained in

the February, March, or April CPS. Then, the ASEC sample

weights for the prior sample are adjusted by the CPS firststage adjustment ratio and the SCHIP Adjustment Factor.

The ASEC noninterview adjustment for the August, September, October, and November eligible sample. The second noninterview adjustment is applied to the August,

September, October, and 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 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 mover households receive no further ASEC

weighting. The noninterview adjustment factor, Fij, is computed as follows:

Fij = Zij + Nij + Bij

Zij + Bij

where:

Zij = the weighted number of August, September, October, and November eligible

sample households interviewed in the

February, March, or April CPS in cell j of

cluster i.

Current Population Survey TP66 Current Population Survey Supplemental Inquiries 11–7

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Nij = the weighted number of August, September, October, and 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 August, September, October, and 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 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 August, September,

October, and 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 11−4.) After this adjustment is applied, the

August, September, October, and November eligible

sample households are ready to be combined with the

March and April eligible samples for the application of

the second-stage ratio adjustment.

Eligible households from the April sample: The

households in 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 size 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 non-Hispanic White households with children 18 years or younger receive a SCHIP

adjustment factor of 8/15. Table 11−4 summarizes

these weight adjustments.

Eligible households from the March 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 size in these demographic categories. Hispanic households receive a

SCHIP adjustment factor of 8/18 and non-Hispanic nonWhite households and non-Hispanic 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 11-4 summarizes these weight adjustments.

Combined sample of eligible households from

the August, September, October, November,

March, and April CPS: At this point, the eligible

samples from August, September, October, November,

March, and April are combined. The remaining adjustments are applied to this combined sample file.

ASEC second-stage ratio adjustment: The secondstage ratio adjustment adjusts the ASEC estimates so

that they agree with independent age, sex, race, and

Hispanic-origin population controls as described in

Chapter 10. The same procedure used for CPS is used

for the ASEC.

Additional ASEC weighting: After the ASEC weight

through the second-stage procedure is determined, the

next step is to determine the final ASEC weight. There

are two more weighting adjustments applied to the

ASEC sample cases. The first is applied to the Armed

Forces members. The Armed Forces adjustment assigns

weights to the eligible Armed Forces members so they

are included in the ASEC estimates. The second adjustment is for family equalization. Without this adjustment, there would be more married men than married

women. Weights, mostly of males, are adjusted to give

a husband and wife the same weight, while maintaining

the overall age/race/sex/Hispanic control totals.

Armed Forces. Male and female members of the Armed

Forces living off post or living with their families on

post are included in the ASEC as long as at least one

civilian adult lives in the same household, whereas the

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
Current Population Survey TP66 Current Population Survey Supplemental Inquiries 11–9

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Table 11−4. Summary of ASEC SCHIP Adjustment Factor for 2004

CPS month/Hispanic status

Mover Nonmover

Month in sample Month in sample

123456781234567 8

August

Hispanic1 02 02

NonHispanic3 02 02

8/15

September

Hispanic1 02 02

NonHispanic3 02 02

8/15

October

Hispanic1 02 02

NonHispanic3 02 02

8/15

November

Hispanic1

02

8/18

NonHispanic3 8/15 02 8/15 8/15

March

Hispanic1

1

8/18

NonHispanic3 8/15

April

Hispanic1 8/18

02

8/18

02

8/18

02

8/18

02

NonHispanic3 8/15 8/15 8/15 8/15

1 Hispanics may be any race. 2 Zero weight indicates the cases are ineligible for the ASEC. 3 The non-Hispanic group includes both non-Hispanic non-Whites and non-Hispanic Whites with children 18 years old or younger.

CPS excludes all Armed Forces members. Households

with no civilian adults in the household, i.e., households with only Armed Forces members, are excluded

from the ASEC. The weights assigned to the Armed

Forces members included in the ASEC are the same

weights civilians receive through the SCHIP adjustment.

Control totals, used in the second-stage factor, do not

include Armed Forces members, so Armed Forces members do not go through the second-stage ratio adjustment. During family equalization, the weight of a male

Armed Forces member with a spouse or partner is reassigned to the weight of his spouse/partner.

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 of households

6. Male partners in male/male unmarried partner

households

7. All other civilian males

Three different methods, depending on the household

composition, are used to assign the ASEC 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 ASEC weight.

SUMMARY

Although this discussion focuses on only three CPS

supplements, the HVS, the ATUS, and the ASEC Supplement, every supplement has its own unique objectives.

The particular 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, ATUS, and ASEC indicate

only the types of modifications that might be used for a

supplement.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
Chapter 12.

Data Products From the Current Population Survey

INTRODUCTION

Information collected in the Current Population Survey

(CPS) is made available by both the U.S. Bureau of Labor

Statistics and the U.S. Census Bureau through broad publication programs that 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, more CPS-based products will be electronically

accessible in the future.

BUREAU OF LABOR STATISTICS PRODUCTS

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 also is available electronically on the Internet and can be accessed at

<http://www.bls.gov/news.release/empsit.toc.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 Asians and Hispanics,1 are published every quarter. Data also are 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 10,000 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://www.bls.gov/data/>. 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 also is used to collect detailed information on particular segments or 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 extent of work

experience of the population during the calendar year; the

marital and family characteristics of workers; and the

employment of school-age youth, high school graduates

and dropouts, and recent college graduates. Surveys are

also made periodically on subjects such as contingent

workers, job tenure, displaced workers, disabled veterans,

and volunteers. 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 and can be used to create

additional cross-sectional detail. The actual identities of

the individuals are protected on all versions of the files

made available to noncensus staff. 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.

Annual averages from the CPS for the four census regions

and nine census 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. The publication is available electronically on the Internet and can be accessed at

<http://www.bls.gov/opub/gp/laugp.htm>.

Table 12–1 provides a summary of the CPS data products

available from BLS.

CENSUS BUREAU PRODUCTS

The 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, 1 Hispanics may be any race.

Current Population Survey TP66 Data Products From the Current Population Survey 12–1

U.S. Bureau of Labor Statistics and U.S. Census Bureau
demographic, and economic topics. In addition, special

reports on many subjects also have 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 news 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 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, locations

inside and outside metropolitan areas, the 50 states and

the District of Columbia, and the 75 largest metropolitan

areas. Information is also made available on national home

ownership rates by age of householder, family type, race,

and Hispanic ethnicity. A quarterly news release and quarterly and annual data tables are released on the Internet.

Supplemental 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 non-census 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 <http://www.bls.census.gov/cps/>. The Census

Bureau plans to add most historical files to the site along

with all current and future files.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
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

)

EmploymentandUnemployment

Among Youth in the Summer

An analysis of the labor force, employment, and unemployment characteristics of 16- to 24-year-olds between April

and July with the focus on July

Annual Monthly CPS Free (1

)

Employment Characteristics

of Families

An analysis of employment and unemployment by family

relationship and the presence and age of children

Annual Monthly CPS Free (1

)

Employment Situation

of Veterans

An analysis of the work activity and disability status of

persons who served in the Armed Forces

Biennial August CPS

supplement

Free (1

)

Job Tenure of American

Workers

An analysis of employee tenure by industry and a variety of

demographic characteristics

Biennial January CPS

supplement

Free (1

)

Labor Force Characteristics of

Foreign-Born Workers

An analysis of the labor force characteristics of foreigh-born

workers and a comparison with the labor force characteristics of their native-born counterparts

Annual Monthly CPS 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)

VolunteeringintheUnitedStates An analysis of the incidence of volunteering and the characteristics of volunteers in the United StatesAnnual September CPS

supplement

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 Annual Social

and Economic

Supplement

to the CPS

(February−April)

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

$53.00 domestic;

$74.20 foreign;

per year

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

$49.00 domestic;

$68.60 foreign;

per year

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 Annual Social

and Economic

Supplement

to the CPS

(February−April)

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

$25.00 domestic;

$35.00 foreign

Current Population Survey TP66 Data Products From the Current Population Survey 12–3

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Table 12–1. Bureau of Labor Statistics Data Products From the Current Population Survey—Con.

Product Description Periodicity Source Cost

Highlights of Women’s Earnings An analysis of the hourly and weekly earnings of women by

a variety of characteristics

Annual Monthly CPS;

outgoing

rotation groups

Free

Issues in Labor Statistics Brief analysis of important and timely labor market issues Occasional CPS; other

surveys and

programs

Free

Microdata Files

Annual Demographic Survey Annual Annual Social

and Economic

Supplement

to the CPS

(February−April)

(

4

)

Contingent Work Biennial January CPS

supplement

(

4

)

Displaced Workers Biennial February CPS

supplement

(

4

)

Job Tenure and

Occupational Mobility

Biennial January CPS

supplement

(

4

)

School Enrollment Annual October CPS

supplement

(

4

)

Usual Weekly Earnings

(outgoing rotation groups)

Annual Monthly CPS;

outgoing

rotation groups

(

4

)

Veterans Biennial August CPS

supplement

(

4

)

Work Schedules/

Home-Based Work

Occasional May CPS

supplement

(

4

)

Time Series (Macro) Files

National Labor Force Data Monthly Labstat(5

) (1)

Unpublished Tabulations

National Labor Force Data Monthly Electronic files Free

1 Accessible from the Internet <http://www.bls.gov/bls/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://www.bls.gov>.

Note: Prices noted above are subject to change.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
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

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 for error

from both sources has been examined relative to the particular use at hand.

This chapter provides an overview of how these two

sources of potential error can affect data quality, discusses

their relationship to each other from a conceptual viewpoint, and defines 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 the 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 would result from taking all possible samples that

could be selected using the specified 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 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. The mean squared

error is 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 that can be expressed in

terms of mathematical models. Thus, the mathematical

expression for variance has been decomposed in various

ways to yield expressions for statistical properties that

include not only sampling variance but also simple

response variance (a measure of the variability among the

possible responses of a particular respondent over

repeated administrations of the same question) (Hansen,

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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 from 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 two sets

of components, one resulting from the fact that estimates

are based on a sample of units rather than the entire

population (sampling error) and the other 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, and can

include issues such as individual behavior.) 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.

It is frequently possible to construct an unbiased estimator of variance. In the case of complex surveys like the

CPS, estimators have been developed that 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 the CPS, 160 subsamples or replicates are used

in variance estimation for the 2000 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 statistically 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 designestimator 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 people in the

sample unit the questions, coding responses into predefined categories, and creating estimates that 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

process where the possibility exists at each step of making a mistake in process specification and deviating during

implementation from the predefined specifications.

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For example, we now recognize that the initial labor force

question used in the 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 that 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 to help managers and staff working on

a survey understand the process is quality. They can also

aid users of the various products of the survey process

(both individual responses and their aggregations into statistics) 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

the CPS, is a function of 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. In the case of the

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, assuming that 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 concentrating on doing the

best job possible. Process monitoring through quality indicators, such as coverage and response rates, can determine when additional training or revisions in process

specification are needed. Continuing process improvement

is a vital component for achieving the survey’s quality

goals.

REFERENCES

Gonzalez, M. E., J. L. Ogus, G. Shapiro, and B. J. Tepping

(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., W. N. Hurwitz, and M. A. Bershad (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., B. Swensson, and J. Wretman (1992), Model

Assisted Survey Sampling, New York: Springer-Verlag.

Tukey, J. W. (1949), ‘‘Memorandum on Statistics in the Federal Government,’’ American Statistician, 3, No. 1, pp.

6−17; No. 2, pp. 12−16.

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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. Analyze the survey design by evaluating the effect of

each of the stages of sampling and estimation on the

overall precision of the survey estimates.

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. Chapter 13 provides additional

information on these topics. Certain aspects of the CPS

sample design, such as the use of one sample PSU per

non-self-representing 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, and captures the effects of sample

selection and estimation on the total variance. Variance

estimates of selected characteristics and tables, which

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 out (replicated) several

times. The dispersion of the resulting estimates can be

used to measure the variance of the full sample.

Method

One would not likely repeat the entire CPS 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 those used for the full sample, and

to apply the regular CPS estimation procedures to these

subsamples, which are called replicates. Determining the

number of replicates to use involves balancing the cost

and the reliability of the variance estimator; because

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 firststage ratio adjustments were not replicated. Even with

these simplifications, limited computer capacity allowed

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 the

calculation of variance estimates for many characteristics

with replication of all stages of the weighting through

compositing. 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 non-self-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 the 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

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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 the matrix determined differing weights for the half samples. 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 (Yˆr) and the full

sample estimate (Yˆ0) 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

below) 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

AND 2000 DESIGNS

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 and 2000

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 the 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 samples.

Between-PSU variance cannot be estimated directly using

this methodology; it is the difference between the estimates of total variance and within-PSU variance. NSR

strata are combined into pseudo- strata 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, for the 1990 design, 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 assuming roughly equal sizes of the

original strata. However, for the 2000 design, these factors were further adjusted to account for the unequal

sizes of the original strata within the pseudostratum. All

USUs in a pseudostratum are assigned the same row number(s).

For an SR sample, two rows of the Hadamard matrix are

assigned to each pair of USUs creating replicate factors,

fr for r = 1,...,160

1 Usually balanced half-sample replication uses replicate factors of 2 and 0 with the formula,

Var(Yˆ 0) =

1

k r=1

k

(Yˆ r  Yˆ 0)

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.

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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.

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, national and state coverage adjustments, the

second-stage ratio adjustments, 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 (Yˆr) and the full sample

estimate (Yˆ0). The formula is

Var(Yˆ 0) =

4

160 r=1

160

(Yˆ r  Yˆ 0)

2

.

The replicate factors 1.7, 1.0, and 0.3 for the selfrepresenting portion of the sample were specifically constructed to yield ‘‘4’’ in the above formula in order that the

formula remain 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 withinPSU variance. The same replicate factors used for total

variance are applied to an SR sample. For an 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. For more detailed

information regarding the formation of replicates, see the

internal Census Bureau memoranda (Gunlicks, 1996, and

Adeshiyan, 2005).

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. However, estimates for states and areas that have

substantial contributions from NSR sample areas have

variance estimation problems that are more difficult to

resolve.

Complicating Factors for State Variances

Most states contain a small number of NSR sample PSUs,

so variances at the state level are based on fairly small

sample sizes. Pairing these PSUs into pseudostrata further

reduces the number of NSR SECUs 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.

Some research has been done to produce improved state

and local variances obtained directly from successive difference replication and the modified balanced half-sample

methods. Griffiths and Mansur (2000a, 2000b, 2001a and

2001b) and Mansur and Griffiths (2001) examine methods

based on times series and generalized linear modeling

techniques to address the small sample size problem and

the bias induced by collapsing NSR strata to estimate

between-PSU variances.

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 has less precision than the survey estimate itself. This

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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 provides

more stable variance estimates by taking advantage of

these similarities.

Generalization Method

The GVF that is used to estimate the variance of an estimated population total, Xˆ, is of the form

Var(Xˆ ) aXˆ 2  bXˆ 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 (

Xˆ 2

N )  deff

N

n

Xˆ .

Letting

a  b

N

and

b deffN

n

gives the functional form

VarXˆ  aXˆ 2  bXˆ .

We choose

a  b

N

where N is a control total so that the variance will be zero

when Xˆ = 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

individuals within housing units. For example, estimates

of total people classified by a characteristic of the housing

unit or of the household, such as the total urban population, number of recent migrants, or people of Hispanic4

origin, would tend to have fairly large design effects. The

reason is that these characteristics usually appear among

all people 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.

For many subpopulations of interest, N is a control total

used in the second-stage ratio adjustment. In these subpopulations, as Xˆ approaches N, the variance of Xˆ

approaches zero, since the second-stage ratio adjustment

guarantees that these sample population estimates match

independent population controls (Chapter 10).5 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

Vx

2 a 

b

X ,

where the relative variance (Vx

2

) 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 any race. 5 The variance estimator assumes no variance on control

totals, even though they are estimates.

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Usually at least a year’s worth of data is 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. Using a model to

estimate the relative variance of an estimate in this way

introduces 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 estimates 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.

Table 14–1.Parameters for Computation of

Standard Errors for Estimates of

Monthly Levels

Characteristic a b

Unemployed:

Total or White............... −0.000016350 3095.55

Black ...................... 0.000151396 3454.72

Hispanic origin1 ............. −0.000141225 3454.72

1 Hispanics may be any race.

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

9 million unemployed men in the civilian labor force

(Xˆ = 9,000,000). From Table 14–1

a = -0.000016350 and b = 3095.55 , so

sx 0.0000163509,000,000

2  3095.559,000,000  163,000

An approximate 90-percent confidence interval for the

monthly estimate of unemployed men is between

8,739,200 and 9,260,800 [or 9,000,000 ± 1.6(163,000)].

VARIANCE ESTIMATES TO DETERMINE OPTIMUM

SURVEY DESIGN

The following tables show variance estimates computed

using replication methods by type (total and within-PSU),

and by stage of estimation. The estimates presented are

based on the 1990 sample design and new weighting procedures introduced in January 2003. Updated estimates

based on the 2000 design will be provided when they are

available.

Averages over 13 months have been used to improve the

reliability of the estimated monthly variances. The

13-month period, March 2003−March 2004, was used for

estimation because the sample design was essentially

unchanged throughout this period (there was a maintenance reduction for CPS in April 2003). Data from January

and February 2003 were not used in order to allow new

weighting procedures to be fully reflected in the composite estimation step.

Variance Components Due to Stages of Sampling

Table 14–2 indicates, for the estimate after the secondstage (SS) adjustment, how the several stages of sampling

affect the total variance of each of the given characteristics. The SS estimate is the estimate after the first-stage,

national coverage, state coverage 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 estimate from the total variance estimate. 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 withinPSU 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 population and White-alone

population employed in agriculture, the within-PSU component still accounts for 70 to 80 percent of the total variance, while the between-PSU component accounts for the

remaining 20 to 30 percent.

Current Population Survey TP66 Estimation of Variance 14–5

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Table 14–2. Components of Variance for SS Monthly Estimates

[Monthly averages: March 2003−March 2004]

Civilian noninstitutionalized

population

16 years old and over

SS1 estimate

(x 106

)

Standard error

(x 105)

Coefficient of

variation

(percent)

Percent of total variance

Within Between

Unemployed, total........... 8.81 1.70 1.93 98.7 1.3

White alone ................... 6.34 1.43 2.25 99.0 1.0

Black alone.................... 1.80 0.76 4.20 100.6 −0.6

Hispanic origin2 .............. 1.46 0.75 5.16 101.0 −1.0

Teenage, 16−19 ............... 1.24 0.56 4.49 99.5 0.5

Employed—agriculture,

total........................ 2.26 1.12 4.98 75.4 24.6

White alone ................... 2.12 1.10 5.18 74.7 25.3

Black alone.................... 0.06 0.15 23.04 94.8 5.2

Hispanic origin2 ............... 0.45 0.59 13.06 86.2 13.8

Teenage, 16−19 ............... 0.12 0.19 16.67 88.3 11.7

Employed—nonagriculture,

total........................ 135.76 3.84 0.28 97.4 2.6

White alone ................... 112.34 3.29 0.29 102.2 −2.2

Black alone.................... 14.66 1.41 0.96 94.9 5.1

Hispanic origin2 ............... 17.01 1.53 0.90 92.7 7.3

Teenage, 16−19 ............... 5.84 1.04 1.77 97.6 2.4

Civilian labor force, total . . . 146.83 3.56 0.24 92.3 7.7

White alone ................... 120.80 3.07 0.25 95.9 4.1

Black alone.................... 16.52 1.31 0.79 93.7 6.3

Hispanic origin2 ............... 18.92 1.32 0.70 91.3 8.7

Teenage, 16−19 ............... 7.19 1.09 1.52 93.7 6.3

Not-in-labor force, total ..... 74.79 3.56 0.48 92.3 7.7

White alone ................... 60.77 3.07 0.50 95.9 4.1

Black alone.................... 9.24 1.31 1.42 93.7 6.3

Hispanic origin2 ............... 8.74 1.32 1.51 91.3 8.7

Teenage, 16−19 ............... 8.93 1.09 1.22 93.7 6.3

1 Estimates after the second-stage ratio adjustments (SS). 2 Hispanics may be any race.

Because the SS estimate of the total civilian labor force

and total not-in-labor-force populations 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.

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 weighting control factors applied.

The noninterview estimate includes the baseweights, the

weighting control adjustment, and the noninterview

adjustment. The SS estimate includes baseweights, the

weighting control adjustment, the noninterview adjustment, the first-stage, national and state coverage adjustments, and second-stage ratio adjustments.

In Table 14–3, the figures for unemployed show, for

example, that the relative variance of the SS estimate of

level is 3.741 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 1.11

times as large. If the noninterview stage of estimation is

also included, the relative variance is 1.10 times the size

of the relative variance for the SS estimate of level. Including the first stage of estimation maintains the relative variance factor at 1.11. The relative variance for total unemployed after applying the second-stage adjustment

without the first-stage adjustment is about the same as

the relative variance that results from applying the firstand 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. However,

as illustrated in Rigby (2000), removing the first-stage

adjustment increased the variances of some state-level

estimates.

The second-stage adjustment, however, appears to greatly

reduce the total variance, as intended. This is especially

true for characteristics that belong to high proportions of

age, sex, or race/ethnicity subclasses, such as White

alone, Black alone, or Hispanic people in the civilian labor

force or employed in nonagricultural industries. Without

the second-stage adjustment, the relative variances of

these characteristics would be 5−10 times as large. For

14–6 Estimation of Variance Current Population Survey TP66

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

[Monthly averages: March 2003−March 2004]

Civilian noninstitutionalized

population

16 years old and over

Relative variance

of SS estimate of

level (x 10–4)

Relative variance factor1

Unbiased

estimator

Unbiased estimator with—

NI2 NI & FS3 NI & SS4

Unemployed, total ............... 3.741 1.11 1.10 1.10 1.00

White alone ........................ 5.065 1.12 1.11 1.11 1.00

Black alone ........................ 17.654 1.27 1.26 1.25 1.00

Hispanic origin5.................... 26.600 1.28 1.27 1.27 1.00

Teenage, 16−19.................... 20.186 1.11 1.11 1.11 1.00

Employed—agriculture, total .... 24.847 0.99 0.99 1.00 0.99

White alone ........................ 26.829 0.99 0.99 0.99 1.00

Black alone ........................ 530.935 1.07 1.07 1.04 1.00

Hispanic origin5.................... 170.521 1.08 1.09 1.10 1.00

Teenage, 16−19.................... 277.985 1.03 1.05 1.05 1.00

Employed—nonagriculture,

total ............................ 0.080 6.53 6.34 6.14 1.00

White alone ........................ 0.086 8.16 8.00 7.70 1.00

Black alone ........................ 0.920 6.67 6.57 5.89 1.00

Hispanic origin5.................... 0.813 6.48 6.43 6.42 1.00

Teenage, 16−19.................... 3.146 1.79 1.78 1.77 1.00

Civilian labor force, total........ 0.059 8.28 8.01 7.75 1.00

White alone ........................ 0.064 10.38 10.15 9.76 1.00

Black alone ........................ 0.631 9.02 8.87 7.86 1.00

Hispanic origin5.................... 0.487 10.45 10.35 10.34 1.00

Teenage, 16−19.................... 2.310 2.04 2.03 2.01 1.00

Not-in-labor force, total.......... 0.226 2.67 2.55 2.50 1.00

White alone ........................ 0.254 3.01 2.91 2.80 1.00

Black alone ........................ 2.015 3.84 3.73 3.10 1.00

Hispanic origin5.................... 2.282 3.58 3.54 3.54 1.00

Teenage, 16−19.................... 1.499 2.64 2.60 2.60 1.00

1 Relative variance factor is the ratio of the relative variance of the specified level to the relative variance of the SS level. 2 NI = Noninterview. 3 FS = First-stage. 4 SS = Estimate after Second-stage when the First-stage adjustment is skipped. 5 Hispanics may be any race.

smaller groups, such as the unemployed and those

employed in agriculture, the effect of second-stage adjustment is not as dramatic.

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 SS estimates. For example, the estimated variance of

the composited estimate of unemployed Hispanics is

5.445 x 109

. The variance factor for this characteristic is

0.96, implying that the variance of the composited estimate is 96 percent of the variance of the estimate after

the second-stage adjustments. The relative variance of the

composite estimate of unemployed Hispanics, which takes

into account the estimate of the number of people with

this characteristic, is about the same as the size of the

relative variance of the SS estimate (hence, the relative

variance factor of 0.99). 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 and

within-PSU variances 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 (SRS)

design. The design effects in this table were computed by

solving the 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 13

months of data.

For the unemployed, the design effect for total variance is

1.377 for the uncomposited (SS) estimate and 1.328 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 total variance by about 32.8 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 total

variance by about 20.6 percentage points. The design

effects for composited estimates are generally lower than

those for the SS estimates, indicating again the tendency

Current Population Survey TP66 Estimation of Variance 14–7

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Table 14–4. Effect of Compositing on Monthly Variance and Relative Variance Factors

[Monthly averages: March 2003−March 2004]

Civilian noninstitutionalized

population, 16 years old and over

Variance of composited

estimate of level (x 109

) Variance factor1 Relative variance factor2

Unemployed, total 27.729 0.96 0.97

White alone 19.462 0.96 0.97

Black alone ....................... 5.163 0.90 0.93

Hispanic origin3 .................. 5.445 0.96 0.99

Teenage, 16−19 .................. 3.067 0.98 1.01

Employed—agriculture, Total . . 12.446 0.98 0.99

White alone....................... 11.876 0.98 1.00

Black alone ....................... 0.213 1.01 1.00

Hispanic origin3 .................. 3.425 0.99 1.00

Teenage, 16−19 .................. 0.354 0.95 0.99

Employed—nonagriculture,

total........................... 115.860 0.79 0.79

White alone....................... 87.363 0.81 0.81

Black alone ....................... 15.624 0.79 0.79

Hispanic origin3 .................. 20.019 0.85 0.86

Teenage, 16−19 .................. 9.604 0.90 0.92

Civilian labor force, total ...... 98.354 0.78 0.78

White alone....................... 74.009 0.79 0.79

Black alone ....................... 14.172 0.82 0.82

Hispanic origin3 .................. 14.277 0.82 0.82

Teenage, 16−19 .................. 10.830 0.91 0.93

Not-in-labor force, total ........ 98.354 0.78 0.77

White alone....................... 74.009 0.79 0.78

Black alone ....................... 14.172 0.82 0.83

Hispanic origin3 .................. 14.277 0.82 0.80

Teenage, 16−19 .................. 10.830 0.91 0.88

1 Variance factor is the ratio of the variance of a composited estimate to the variance of an SS estimate. 2 Relative variance factor is the ratio of the relative variance of a composited estimate to the relative variance of an SS estimate.

3 Hispanics may be any race.

Table 14–5. Design Effects for Total and Within-PSU Monthly Variances

[Monthly averages: March 2003−March 2004]

Civilian noninstitutionalized

population, 16 years old and over

Design effects for total variance Design effects for within-PSU variance

After second stage After compositing After second stage After compositing

Unemployed, total ............... 1.377 1.328 1.359 1.259

White alone ........................ 1.325 1.277 1.312 1.220

Black alone ........................ 1.283 1.180 1.291 1.195

Hispanic origin1.................... 1.567 1.530 1.583 1.533

Teenage, 16−19.................... 1.012 1.008 1.007 1.000

Employed—agriculture, Total.... 2.271 2.248 1.712 1.703

White alone ........................ 2.304 2.281 1.722 1.715

Black alone ........................ 1.344 1.351 1.274 1.279

Hispanic origin1.................... 3.089 3.071 2.662 2.658

Teenage, 16−19.................... 1.292 1.255 1.141 1.112

Employed—nonagriculture,

total ............................ 1.124 0.883 1.094 0.804

White alone ........................ 0.782 0.632 0.799 0.590

Black alone ........................ 0.579 0.456 0.550 0.403

Hispanic origin1.................... 0.601 0.513 0.557 0.478

Teenage, 16−19.................... 0.756 0.688 0.738 0.655

Civilian labor force, total........ 1.024 0.794 0.946 0.693

White alone ........................ 0.685 0.540 0.657 0.471

Black alone ........................ 0.452 0.371 0.423 0.323

Hispanic origin1.................... 0.404 0.332 0.369 0.319

Teenage, 16−19.................... 0.689 0.633 0.645 0.589

Not-in-labor force, total.......... 1.025 0.795 0.946 0.693

White alone ........................ 0.854 0.671 0.819 0.586

Black alone ........................ 0.780 0.643 0.730 0.559

Hispanic origin1.................... 0.833 0.676 0.761 0.650

Teenage, 16−19.................... 0.560 0.501 0.524 0.466

1 Hispanics may be any race.

14–8 Estimation of Variance Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
of the compositing to reduce the variance of most estimates. Since the same denominator (SRS variance) is used

in computing deff for both the total and within-PSU variances, deff is directly proportional to the respective variances. As a result, the total variance design effects tend to

be higher than the within-PSU variances. This is consistent

with results from Table 14−2 where total variance estimates are expected to be higher than within-PSU variance

estimates.

REFERENCES

Adeshiyan, S. (2005), ‘‘2000 Replicate Variance System

(VAR2000−1),’’ Internal Memorandum for Documentation,

Demographic Statistical Methods Division, U.S. Census

Bureau.

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., C. Dippo, and D. Morganstein, (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 G. Train, (1995), ‘‘Aspects of Survey and ModelBased Postcensal Estimation of Income and Poverty Characteristics for States and Counties,’’ Proceedings of the

Section on Government Statistics, American Statistical

Association, pp. 154−159.

Griffiths, R. and K. Mansur, (2000a), ‘‘Preliminary Analysis

of State Variance Data: Sampling Error Autocorrelations

(VAR90−36),’’ Internal Memorandum for Documentation,

May 31st, Demographic Statistical Methods Division, U.S.

Census Bureau.

Griffiths, R. and K. Mansur, (2000b), ‘‘Preliminary Analysis

of State Variance Data: Autoregressive Integrated Moving

Average Model Fitting and Grouping States (VAR90−37),’’

Internal Memorandum for Documentation, June 15th,

Demographic Statistical Methods Division, U.S. Census

Bureau.

Griffiths, R. and K. Mansur, (2001a), ‘‘Current Population

Survey State-Level Variance Estimation,’’ paper prepared

for presentation at the Federal Committee on Statistical

Methodology Conference, Arlington, VA, November 14−16,

2001.

Griffiths, R. and K. Mansur, (2001b), ‘‘The Current Population Survey State Variance Estimation Story (VAR90−38),’’

Internal Memorandum for Documentation, October 29th,

Demographic Statistical Methods Division, U.S. Census

Bureau.

Gunlicks, C. (1993), ‘‘Overview of 1990 CPS PSU Stratification (S−S90−DC−11),’’ Internal Memorandum for Documentation, February 24th, 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, D.C.: 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.

Mansur, K. and R. Griffiths, (2001), ‘‘Analysis of the Current Population Survey State Variance Estimates,’’ paper

presented at the 2001 Joint Statistical Meetings, American

Statistical Association, Section on Survey Research Methods.

Rigby, B. (2000), ‘‘The Effect of the First Stage Ratio Adjustment in the Current Population Survey,’’ paper presented

at the 2000 Joint Statistical Meetings, American Statistical

Association, Proceedings of the Section on Survey

Research Methods.

Train, G., L. Cahoon, and P. Makens, (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, D.C.: Government Printing Office.

Valliant, R. (1987), ‘‘Generalized Variance Functions in

Stratified Two-Stage Sampling,’’ Journal of the American

Statistical Association, 82, pp. 499−508.

Current Population Survey TP66 Estimation of Variance 14–9

U.S. Bureau of Labor Statistics and U.S. Census Bureau
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.

14–10 Estimation of Variance Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Chapter 15.

Sources and Controls on Nonsampling Error

INTRODUCTION

For a given estimator, the difference between the estimate

that would result if the sample were to include the entire

population and the true population value being estimated

is known as nonsampling error. 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 to 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).

Sources of nonsampling error can include the following:

1. Inability to obtain information about all sample cases

(unit nonreponse).

2. Definitional difficulties.

3. Differences in the interpretation of questions.

4. Respondent inability or unwillingness to provide correct information.

5. Respondent inability to recall information.

6. Errors made in data collection, such as recording and

coding data.

7. Errors made in processing the data.

8. Errors made in estimating values for missing data.

9. Failure to represent all units with the sample (i.e.,

undercoverage).

It is clear that there are two main types of nonsampling

error in the CPS. The first type is error imported from

other frames or sources of information, such as decennial

census omissions, errors from the Master Address File or

its extracts, and errors in other sources of information

used to keep the sample current, such as building permits.

All nonsampling errors that are not of the first type are

considered preventable, such as when the sample is not

completely representative of the intended population,

within-household omissions, respondents not providing

true answers to a questionnaire item, proxy response, 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 focuses on the sources and operational

efforts used to control the occurrence of error in the survey processes. Each section discusses a procedure aimed

at reducing 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 broad-reaching effects on the

total survey.

This chapter deals with many sources of and controls on

nonsampling error, but it is not exhaustive. It includes

coverage error, error from nonresponse, response error,

and processing errors. Many of these types of errors interact, and they can occur anywhere in the survey process.

Although the full effects of nonsampling error on the survey estimates are unknown, research in this area is being

conducted (e.g., latent class analysis; Tran and Mansur,

2004). Ultimately, the CPS attempts to prevent such errors

from entering the survey process and tries to keep these

that occur 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 (e.g.,

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.

On the other hand, 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 an undercount of population (resulting

from undercoverage).

The primary sources of CPS coverage error are:

1. Frame omission. Frame omission occurs when the

list of addresses used to select the sample is incomplete. This can occur in any of the four sampling

frames (i.e., unit, permit, area, and group quarters;

see Chapter 3). Since these erroneously omitted units

cannot be sampled, undercoverage of the target population results. Reasons for frame omissions are:

Current Population Survey TP66 Sources and Controls on Nonsampling Error 15–1

U.S. Bureau of Labor Statistics and U.S. Census Bureau
• Master Address File (MAF): The MAF is a list of

every living quarters nationwide and their geographic locations. The MAF is updated throughout

the decade to provide addresses for delivery of

Census 2000 questionnaires, to serve as the sampling frame for the Census Bureau’s demographic

surveys, and to support other Census Bureau statistical programs. The MAF may be incomplete or contain some units that are not locatable. This occurs

when the decennial census lister fails to canvass an

area thoroughly or misses units in multiunit structures.

• New construction: This is a sampling problem.

Some areas of the country are not covered by building permit offices and are not surveyed (unit frame)

to pick up new housing. New housing units in these

areas have zero probability of selection. (Based on

initial results, the level of errors from this source of

frame omission is expected to be quite small in the

2000 CPS Sample Redesign.)

• Mobile homes: This is also a sampling problem.

Mobile homes that move into areas that are not surveyed (unit frame) also have a zero probability of

selection.

• Group quarters: The information used to identify

group quarters may be incomplete, or new group

quarters may not be identified.

2. Erroneous frame inclusion. Erroneous frame inclusion of housing units occurs when any of the four

frames or the MAF (extracts) contains units that do not

exist on the ground; for example, a housing unit is

demolished, but still exists in the frame. 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, overcoverage of the target population

results if the errors are not detected and corrected.

3. Misclassification errors. Misclassification errors

occur when out-of-scope units are misclassified as

in-scope or vice versa. For example, a commercial unit

is misclassified as a residential unit, an institutional

group quarter is misclassified as a noninstitutional

group quarter, or an occupied housing unit with no

one at home is misclassified as vacant. Errors can also

occur when units outside an appropriate area boundary are misclassified as inside the boundary or vice

versa. These errors can cause undercoverage or overcoverage of the target population.

4. Within-housing unit omissions. Undercoverage of

individuals can arise from failure to list all usual residents of a housing unit on the household roster or

from misclassifying a household member as a nonmember.

5. Within-housing unit inclusions. Overcoverage can

occur because of the erroneous inclusion of people on

the roster for a household, for instance, when people

with a usual residence elsewhere are incorrectly

treated as members of the sample housing unit.

Other sources of coverage error are omission of homeless

people from the frame, unlocatable addresses from building permit lists, and missed housing units due to the start

dates for the sampling of permits.1 For example, housing

units whose permits were issued before the start month

and not built by the time of the census may be missed.

Newly constructed group quarters are another possible

source of undercoverage. If they are noticed on the permit

lists, the general rule is purposely to remove these group

quarters; if they are not noticed and the field representative (FR) discovers a newly constructed group quarters,

the case is stricken from the roster of sample addresses

and never visited again for the CPS until possibly the next

sample redesign.

Through various studies, it has been determined that the

number of housing units missed is small 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 that attempts to quantify the

overall coverage of the survey, despite its coverage errors.

For example, the coverage ratio for the total population

that is at least 16 years old has been approximately 88

percent since the CPS started phasing in the new 2000-

based sample in April 2004. See Chapter 16 for further

discussion of coverage error.

CONTROLLING COVERAGE ERROR

This section focuses on those processes during the sample

selection and sample preparation stages that control housing unit omissions. Other sources of undercoverage can

be viewed as response error and are described in a later

section of this chapter.

Sample Selection

The CPS sample selection is coordinated with and does not

duplicate the samples of at least five other demographic

surveys. These surveys basically undergo the same verification processes, so the discussion in the rest of this section is not unique to the CPS.

Sampling verification consists of testing and production.

The intent of the testing phase (unit testing and system

testing) is to design the processes better in order to

1 For the 2000 Sample Redesign, sampling of permits began

with those issued in 1999; the month varied depending on the

size of the structure and region. Housing units whose permits

were issued before the start month in 1999 and not built by the

time of the census may be missed.

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improve quality, and the intent of the production phase is

to verify or inspect the quality of the final product in order

to improve the processes. Both phases are coordinated

across the various surveys. Sampling intervals, measures

of expected sample sizes, and the premise that a housing

unit is selected for only one survey for 10 years are all

part of the sample selection process. Verification in each

of the testing and production phases is done independently by two verifiers.

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 are used to test the performance of the system in extreme situations.

The production phase of sampling verification consists of

verification of output using the actual sample, focusing on

whether the system ran successfully. The following are

illustrations and do not represent any particular priority or

chronology:

1. PSU probabilities of selection are verified; for example,

their sum within a stratum should be one.

2. Edits are done on file contents, checking for blank

fields and out-of-range data.

3. When applicable, the files are coded to check for logic

and consistency.

4. Information is centralized; for example, the sampling

rates for all states and substate areas for 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 Preparation2

Sample preparation activities, in contrast to those of initial

sample selection, are ongoing. The listing review3 and

check-in of sample are monthly production processes, and

the listing check is an ongoing field process.

Listing review. The listing 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. Automation of the review is a major advancement in improving the timing of the process and, thus, a

more accurate CPS frame.

Various listing reviews are performed by both the regional

offices (ROs) and the National Processing Center (NPC) in

Jeffersonville, IN. Performing these reviews in different

parts of the Census Bureau is, in itself, a means of controlling nonsampling error.

After an FR makes an initial visit either to a multiunit

address from the unit frame or to a sample address from

the permit frame, the RO staff reviews the Multiunit Listing

Aid or the Permit Listing Sheet, respectively. (Refer to

Appendix A for a description of these listing forms.) This

review occurs the first time the address is in the sample

for any survey. However, if major changes appear at an

address on a subsequent visit, the revised listing is

reviewed. If there is evidence that the FR encountered a

special or unusual situation, the materials are compared to

the instructions in the manual ‘‘Listing and Coverage: A

Survival Guide for the Field Representative’’ (see Chapter

4) to ensure that the situation was handled correctly.

Depending on whether it is a permit-frame sample address

or a multiunit address from the unit frame, the following

are verified:

1. Were correct entries made on the listing when either

fewer or more units were listed than expected?

2. Did the FR relist the address if major structural

changes were found?

3. Were no units listed (for the permit frame)?

4. Was an extra unit discovered (for the permit frame)?

5. Were additional units interviewed if listed on a line

with the current CPS sample designation?

6. Did the unit designation change?

7. Was a sample unit demolished or condemned?

Errors and omissions are corrected and brought to the

attention of the FR. This may involve contacting the FR for

more information.

The Automated Listing and Mapping Instrument (ALMI)

features an edit that prevents duplication of units in area

frame blocks4. The RO review of area-frame block updates

is performed for new FRs or those with limited listing

experience. This review focuses on deletions, moved

units, added units, and house number changes. There is 2 See Chapter 4, Preparation of the Sample, specifically its section Listing Activities, for more details. Also see Appendix A,

Sample Preparation Materials. 3 Because of the automated instrument, it is appropriate to use

the generic term ‘‘listing review” for the area, unit, and group

quarters frames. ‘‘Listing sheet” and ‘‘listing sheet review” are

appropriate for the permit frame. Regardless, these sections will

use the generic terms.

4

Other features of the ALMI are: pick lists that minimize the

amount of data entry and, therefore, keying errors; edit checks

that check the logic of the data entry and identify missing/critical

data.

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no RO review of group quarters listings in the area and

group quarters frames; however, basic edits are included

with the Group Quarters Automated Instrument for Listing

(GAIL).

As stated previously, listing reviews are also performed by

the NPC. The following are activities involved in the

reviews for the unit-frame sample:

1. Verify that the FR resolved all unit designations that

were missing or duplicated in Census 2000; i.e., if any

sample units contained missing or duplicate unit designations.

2. Review for accuracy any large multiunit addresses that

were relisted.

3. Check whether additional units (found during listing)

already had a chance of selection. If so, the RO is contacted for instructions for correcting the Multiunit Listing Aid.

In terms of the review of listing sheets for permit frame

samples, the NPC 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, the NPC determines whether the additional

units already had a chance of selection. If so, the RO is

contacted with instructions for updating the listing sheet.

The majority of listings are found to be correct during the

RO and NPC reviews. When errors or omissions are

detected, they usually occur in batches, signifying a misinterpretation of instructions by an RO or a newly hired FR.

Check-in of sample. Depending on the sampling frame

and mode of interview, the monthly process of check-in of

the sample occurs as the sample cases progress through

the ROs, the NPC, and headquarters. Check-in of sample

describes the processes by which the ROs verify that the

FRs receive all their assigned sample cases and that all are

completed and returned. Since the CPS is now conducted

entirely by computer-assisted personal or telephone interview, it is easier than ever before to track a sample case

through the various systems. Control systems are in place

to control and verify the sample count. The ROs monitor

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. A check-in also occurs when the cases

are transmitted to headquarters.

Listing check. Listing check is a quality assurance program for the Demographic Area Address Listing (DAAL). It

uses the same instrument as the area-frame listing, i.e.,

the ALMI. Each month, a random sample of FRs who have

completed sufficient listing work is selected for a listing

check. The goal of this sampling process is to check each

FR’s work at least once a year. If the FR is selected for a

listing check, then a sample of the work performed by this

FR is selected and is checked by a senior staff member.

Various types of errors, including coverage errors, content

errors, and mapping errors, are recorded during the listing

check. Classification of errors is completely automated

within the ALMI instrument. The results are then compared

against the 3-month aggregate average error rate for each

RO to determine a ‘‘pass” or ‘‘fail” status for the FR. This

allows ROs to monitor the performance of FRs and provides information about the overall quality of the DAAL

that allows continual improvements of the listing process.

Depending on the severity and type of errors, the ROs

must give feedback to FRs who failed the listing check and

retrain them as necessary. The automated system will

warn the supervisors if they try to assign work to an FR

who failed a listing check but has not been retrained.

SOURCES OF NONRESPONSE ERROR

There are three main sources of nonresponse error in the

CPS: unit nonresponse, person nonresponse, and item

nonresponse. Unit nonresponse error occurs when households that are eligible for interview are not interviewed for

some reason: a respondent refuses to participate in the

survey, is incapable of 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. The weights of eligible households who do

respond to the survey are increased to account for those

who do not, but nonresponse error can be introduced if

the characteristics of the interviewed households differ

from those that are not interviewed.

Individuals 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 others in the household as a proxy reporter.

Panel nonresponse exists when those who live in the same

household during the entire time they are in the CPS

sample do not agree to be interviewed in any 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 (explained in Chapter 9) 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.

One example of the magnitude of the error due to nonresponse is the national Type A household noninterview

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rate, which was 7.70 percent in July 20045. For item nonresponse, a few of the average allocation rates in July

2004, by topical module were: 0.52 percent for household, 1.99 percent for demographic, 2.35 percent for

labor force, 9.98 percent for industry and occupation, and

18.46 percent for earnings. (See Chapter 16 for discussion

of various quality indicators of nonresponse error.)

CONTROLLING NONRESPONSE ERROR

Field Representative Guidelines6

Response/nonresponse rate guidelines have been developed for FRs 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 FRs needing

performance improvement. An FR whose response rate,

household noninterview rate (Type A), or minutes-per-case

falls below the fully acceptable range based on one quarter’s work is considered in need of additional training and

development. The CPS supervisor then takes appropriate

remedial action. National and regional response performance data are also provided to permit the RO staff to

judge whether their activities are in need of additional

attention.

Summary Reports

Another way to monitor and control nonresponse error is

the production and review of summary reports. Produced

by headquarters after the release of the monthly data

products, they are used to detect changes in historical

response patterns. Since they are distributed throughout

headquarters and the ROs, other indications of data quality and consistency can be focused upon. The contents of

some of the summary report tables are: noninterview rates

by RO for both the basic CPS and its supplements,

monthly comparisons to prior year, noninterview-tointerview conversion rates, resolution status of computerassisted telephone interview cases, interview status by

month-in-sample, daily transmittals, percent of personalvisit cases actually conducted in person, allocation rates

by topical module, 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 to help the ROs understand CPS field operations, to

solicit and share the ROs’ views on the causes of the

increasing nonresponse rates, and to evaluate methods to

decrease these rates. All of the answers provide insight

into the CPS operations that may affect nonresponse and

follow-up procedures for household noninterviews. A few

are:

1. The majority of ROs responded that there is written

documentation of the follow-up process for CPS

household noninterviews.

2. The standard process is that an FR must let the RO

know about a possible household noninterview as

soon as possible.

3. Most regions attempt to convert confirmed refusals to

interviews under certain circumstances.

4. All regions provide monthly feedback to their FRs on

their household noninterview rates.

5. About half of the regions responded that they provide

specific region-based training/activities for FRs on

converting or avoiding household noninterviews.

Much research has been distributed regarding whether

and how to convince someone who refused to be

interviewed to change their mind.

Most offices use letters in a consistent manner to

follow-up with noninterviews. Most ROs also include informational brochures about the survey with the letters, and

they are tailored to the respondent.

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 can include:

1. The respondent misinterprets the question, does not

know the true answer and guesses (e.g., recall

effects), exaggerates, has a tendency to give an

answer that appears more ‘socially desireable,’ or

chooses a response randomly.

2. The interviewer reads the question incorrectly, does

not follow the appropriate skip pattern, misunderstands or misapplies the questionnaire, or records the

wrong answer.

3. A proxy responder (i.e., a person who answers on

someone else’s behalf) provides an incorrect response.

4. The data collection modes (e.g., personal visit and

telephone) elicit different responses.

5 In April 2004, CPS started phasing in the new 2000-based

sample. 6 See Appendix D for a detailed discussion, especially in terms

of the performance evaluation system.

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5. The questionnaire does not elicit correct responses

due to a format that is not easy to understand, or has

complicated or incorrect skip patterns or 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 section,7

while their interactions are discussed in the section The

Reinterview Program—Quality Control and Response

Error.8

In terms of magnitude, measures of response error are

obtainable through the reinterview program, specifically,

the index of inconsistency. This index 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 interview to interview, 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 2003 is considered moderate at 37.9. Other

statistical techniques being used to measure response

error include latent class analysis (Tran and Mansur, 2004),

which is being used to look at how responses vary across

time-in-sample.

CONTROLLING RESPONSE ERROR

Survey Instrument9

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 FRs and telephone

calls made by interviewers at centralized telephone centers. Regardless of the interview mode, the questionnaire

and the software are basically the same (see Chapter 7).

Software. Computer-assisted interviewing technology in

the CPS allows very complex skip patterns and other procedures that 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 balances nonsampling error existing for

the item with the possibility of a total noninterview. The

instrument provides opportunities for the FR 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 to the FR to

be verified and corrected. In addition to reducing response

and interviewer burden, the instrument avoids erratic

variations in industry and occupation codes among pairs

of months for people who have not changed jobs but who

describe their industry and occupation differently in the 2

months.

Questionnaire. Two objectives of the design of the CPS

questionnaire are 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) are: short and clear question

wording, splitting complex questions into two or more

questions, building concept definitions into question

wording, reducing reliance on volunteered information,

using explicit and implicit strategies for the respondent to

provide numeric data, and using precoded response categories for open-ended questions. Interviewer notes

recorded at the end of the interview are critical to obtaining reliable and accurate responses.

Modes 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 FR or by an

interviewer from a centralized telephone facility. (In July

2004, nationally, 81 percent and 62.8 percent of personal

visit cases were actually conducted in person in monthsin-sample 1 and 5, respectively.) Although each mode has

its own set of performance guidelines that must be

adhered to, similarities do exist. The controls detailed in

The Interviewer and Error Due to Nonresponse sections

are mainly directed at personal visits but are also basically

valid for the calls made from the centralized facility via

the supervisor’s listening in.

Continuous testing and improvements. Insights

gained from past research on questionnaire design have

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 is tested to gauge whether respondents are

7 Most discussion in this section is applicable whether the

interview is conducted via computer-assisted personal interview

or computer-assisted telephone interview by the FR or in a centralized telephone facility. 8 Appendix E provides an overview of the design and methodology of the entire reinterview program. 9 Many of the topics in this section are presented in more

detail in Chapter 6.

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correctly interpreting the questions. Chapter 6 provides an

extensive list of the various methods of testing. The Census Bureau has also developed a set of protocols for pretesting demographic surveys.

To improve 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 the CPS interviewers

and supervisors are conducted periodically.

Despite the benefits from adding new questions and

improving existing ones, changes to the CPS are

approached cautiously until the effects are measured and

evaluated. In order to avoid the distruption of historical

series, methods to bridge differences caused by changes

or techniques are included in the testing whenever possible. In the past, for example, parallel surveys have been

conducted using the revised and unrevised procedures.

Results from the parallel survey have been used to anticipate the effect the changes would have on the survey’s

estimates and nonresponse rates (Kostanich and Cahoon,

1994; Polivka, 1994; and Thompson, 1994).

Interviewer

Interviewer training, observation, monitoring, and evaluation are all methods used to control nonsampling error,

arising from inaccuracies in both the frame and data collection activities. For further discussion, see this chapter’s

section Field Representative Guidelines and Appendix D.

Group training and home study are continuing efforts in

each RO to control various nonsampling errors, and they

are tailored to the types of duties and length of service of

the interviewer.

Field observation is an extension of classroom training

and provides on-the-job training and on-the-job evaluation. It is one of the methods used by the supervisor to

check and improve the performance of the FR. It provides

a uniform method for assessing the FR’s attitudes toward

the job and use of the computer, and evaluates the FR’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 such as the following:

1. Asking questions as worded and in the order presented on the questionnaire.

2. Adhering to instructions on the instrument and in the

manuals.

3. Knowing how to probe.

4. Recording answers in the correct manner and in

adequate detail.

5. Developing and maintaining good rapport with the

respondent conducive to an exchange of information.

6. Avoiding questions or probes that suggest a desired

answer to the respondent.

7. Determining the most appropriate time and place for

the interview.

The emphasis is on correcting habits that interfere with

the collection of reliable statistics.

Respondent: Self Versus Proxy

The CPS Interviewing Manual states that any household

member 15 years of age or older is technically eligible to

act as a respondent for the household. The FR attempts to

collect the labor force data from each eligible individual;

however, in the interests of timeliness and efficiency, any

knowledgeable adult household member is allowed to provide the information. Also, the survey instrument is structured so that every effort is made to interview the same

respondent every month. The majority of the CPS labor

force data is 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.

There has been a substantial amount of research into selfversus-proxy reporting, including research involving CPS

respondents (Kojetin and Mullin, 1995; Tanur, 1994). Much

of the research indicates that self-reporting is more reliable than proxy reporting, particularly when there are

motivational reasons for proxy and self-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

are required for months-in-sample 1 and 5 whenever possible. By showing a sincere understanding of 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

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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.

Reinterview Program—Quality Control and

Response Error10

The reinterview program has two components: the Quality

Control Reinterview Program and the Response Error Reinterview Program. One of the objectives of the Quality Control Reinterview Program is to evaluate individual FR’s performance. It checks a sample of the work of an FR and

identifies and measures aspects of the field procedures

that may need improvement. It is also critical in the detection and prevention of data falsification.

The Response Error Reinterview Program 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 survey’s original results; as a

by-product, 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 potential 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

Industry and Occupation Coding Verification

To be accepted into the CPS processing system, files containing records needing three-digit industry and occupation codes are electronically sent to the NPC 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 online industry and occupation reference materials,

the NPC 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 and 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 editing 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 with that 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 made for zero weighting 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

four-page tabulation of monthly summary statistics that

highlights the six edit modules and the two main weighting programs for the current month and the preceding 12

months. This is called the ‘‘CPS Monthly Check-in Sheet.”

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 agencies compute

numerous tables in composited and uncomposited modes.

These tables are then checked cell-by-cell to ensure that

the cell entries across the two agencies are identical. If so,

the data are deemed to have been computed correctly.

Extensive verification is done every time any change is

made to any part of the CPS estimation process and its

impact is evaluated. Examples are weighting changes,

10 Appendix E provides an overview of the design and methodology of the entire reinterview program.

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changes in replication methodology, and the introduction

of new noninterview cluster codes and transitional baseweights during the phasing-out of one sample design and

the phasing-in of another design. In fact, there are several

guidelines and specifications that deal solely with CPS

verification roles and responsibilities.

CPS Population Controls

National and state-level CPS population controls are developed by the Census Bureau independently from the collection and processing of the CPS data. These monthly and

independent projections of the population are used for the

iterative, second-stage and composite weighting of the

CPS data. All of the estimates start with data from the last

census (currently Census 2000), and use administrative

records and projection techniques to provide updates. (See

Appendix C 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 details 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 are files that contain overlapping/

redundant data. Third, a two-person clerical review of all

data with comments/notes is performed. An important

means of assuring that quality data are used as input into

the CPS population controls is continuous research into

improvements in methods of making population estimates

and projections.

Modeling Errors

This section focuses on a few of the methods to reduce

nonsampling error that are applied to the seasonal adjustment programs and monthly model-based state labor

force estimates. (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

Kojetin, B.A. and P. Mullin (1995), ‘‘The Quality of Proxy

Reports on the Current Population Survey (CPS),’’ Proceedings of the Section on Survey Research Methods,

American Statistical Association, pp. 1110−1115.

Kostanich, D.L. and L.S. Cahoon, (1994), Effect of

Design Differences Between the Parallel Survey and

the New CPS, CPS Bridge Team Technical Report 3, dated

March 4.

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.

Tanur, J.M. (1994), ‘‘Conceptualizations of Job Search: Further Evidence From Verbatim Responses,’’ Proceedings

of the Section on Survey Research Methods, American Statistical Association, pp. 512−516.

Thompson, J. (1994), Mode Effects Analysis of Major

Labor Force Estimates, CPS Overlap Analysis Team

Technical Report 3, April 14.

Tran, B. and K. Mansur, (2004), ‘‘Analysis of the Unemployment Rate in the Current Population Survey—A Latent

Class Approach,” Journal of the American Statistical

Association, August 12.

Current Population Survey TP66 Sources and Controls on Nonsampling Error 15–9

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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-insample 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 an 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 a larger nonresponse bias than a survey that has a

higher nonresponse rate (if the difference between respondents and nonrespondents is larger in the survey with the

lower nonresponse rate than it is in the survey with the

higher nonresponse rate), one would generally expect that

larger nonresponse indicates a greater potential for bias.

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 being selected for 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 people erroneously

included or excluded from the survey differ from those of

individuals correctly included in the survey. Historically in

the CPS, the net effect of coverage errors has been an

underestimate of the size of the total population for most

major demographic population subgroups before the

population controls are applied (known as undercoverage).

Coverage Ratios

One way to estimate the coverage error present in a survey is to compute a coverage ratio. A coverage ratio is the

outcome of dividing the estimated number of people in a

specific demographic group from the survey by 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 national

and state coverage adjustments and the second-stage

ratio adjustment. See Chapter 10 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 C 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 overcoverage exists when the ratio is greater than

1.0. Figure 16−1 shows the average monthly coverage

ratios for September 2001 through September 2004.

In terms of race, Whites have the highest coverage ratio

(90.7 percent), while Blacks have the lowest (82.2 percent). Females across all races have higher coverage ratios

than males. Hispanics1 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 people who are

missed by the CPS are on the average likely to be different

from those who are included. People who are missed are

accounted for in the CPS, but they are given the same

labor force characteristics as those of the people who are

included. This produces bias in the CPS estimates.

This graph, as well as two other graphs of coverage ratios

by race and gender, can be found at <http://www.bls

.census.gov/cps/basic/perfmeas/coverage.htm>. (Their

updates, with more current data, will be posted on this

site as they are made available.) The three graphs provide

1

Hispanics may be any race.

Current Population Survey TP66 Quality Indicators of Nonsampling Errors 16–1

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Figure 16−1.

CPS Total Coverage Ratios: September 2001−September 20041, National Estimates

1 There is a drop in January 2003. This is when the new definitions for race and ethnicity were introduced, as well as some adjusted population

controls based on Census 2000.

a picture of coverage for the population at least 16 years

old in the CPS from September 2001 through September

2004. The first one gives the coverage ratios for racial

groups. Traditionally, Blacks and Hispanics have been the

most underrepresented groups in the CPS. The other two

graphs show the coverage ratios by race and gender. Coverage ratios are lowest for Black and Hispanic males.

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, or 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 that respond in one 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 seen in Figure 16−2, the noninterview rate for the CPS

remained relatively stable at around 4 to 5 percent for

most of 1964 through 1993; however, there have been

some changes since 1993. Figure 16−2 shows 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 was chiefly

because of disruptions in data collection due to shutdowns of the federal government (see Butani, Kojetin, and

Cahoon, 1996). After 1994, refusals, noninterviews, and

noncontacts increased. The relative stability of the overall

noninterview rate from 1960 to 1994 masked some underlying changes that occurred. Specifically, the refusal portion of the noninterview rate increased over this 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. A corresponding decrease in the rate of noncontacts and other noninterviews compensated for this

increase in refusals.

Seasonal variation also appears in both the overall noninterview rates and the refusal rates (see Figure 16−3). During the year, the noninterview and refusal rates have

0.70

0.75

0.80

0.85

0.90

0.95

1.00

Coverage Ratio

Sep-01 Mar-02 Sep-02 Mar-03 Sep-03 Mar-04 Sep-04 Dec-01 Jun-02 Dec-02 Jun-03 Dec-03 Jun-04

Total White Black Other Hispanic

16–2 Quality Indicators of Nonsampling Errors Current Population Survey TP66

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Figure 16−2.

Average Yearly Type A Noninterview and Refusal Rates for the CPS 1964−2003, National Estimates

Figure 16−3.

CPS Nonresponse Rates: September 2003−September 2004, National Estimates

tended to increase after January until they reach a peak in

March or April, at the time of the Annual Social and Economic Supplement. At this point, there is a drop in noninterview and refusal rates that extends below the starting

point in January until they bottom out in July or August.

The rates then increase and approach the initial level. This

pattern has been evident most years in the recent past

and appears to be similar for 2003−2004. The noncontact

rates are higher during the winter holidays and the summer, when some household members are either away from

home or difficult to contact.

Effect on noninterview rates of the transition to a

redesigned survey with computer-assisted data collection. With the transition to the redesigned CPS questionnaire using computerized data collection in January

1994, Type A nonresponse rates increased as seen in Figure 16−2. This transition included several procedural

changes in the collection of the data, and the adjustment

to these new procedures may account for this increase.

For example, the computer-assisted personal interviewing

(CAPI) instrument requires the interviewers to go through

the entire interview, while previously some interviewers

may have conducted shortened interviews with reluctant

Rate (Percent)

Type A Noninterview Rate

Refusal Rate

1

2

3

4

5

6

7

8

1964 1970 1976 1982 1988 1994 2000 1967 1973 1979 1985 1991 1997 2003

Rate (Percent)

Overall Type A Rate

Refusal Rate

Noncontact Rate

2

4

6

8

10

Sep-03 Nov-03 Jan-04 Mar-04 May-04 July-04 Sep-04 Oct-03 Dec-03 Feb-04 Apr-04 Jun-04 Aug-04

Current Population Survey TP66 Quality Indicators of Nonsampling Errors 16–3

U.S. Bureau of Labor Statistics and U.S. Census Bureau
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. Households not interviewed by the

computer-assisted telephone interviewing (CATI) centers

are recycled back to the field representatives continuously

during the survey week. However, cases recycled late in

the survey week (some reach the field as late as Friday

morning) 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. The annual overall Type A

rate, the refusal rate, and noncontact rate (which includes

temporarily absent households and other noncontacts) are

shown in Table 16−1 for the period 1993−1996 and 2003.

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

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 16−2 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, about 82 percent, completed interviews

each month, and only 2 percent never participated (for further information, see Harris-Kojetin and Tucker, 1997).

Dixon (2000) compared those who moved out to those

who moved in. Out-movers were more likely to be unemployed but more likely to respond compared with

in-movers. Unemployment may be slightly underestimated

due to the combination of these two effects.

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 month-to-month flows of people 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 and households that responded one month

but failed to respond in the other month. However, the

labor force status of people in households that were nonrespondents for both months is unknown.

Monthly labor force data were used for each consecutive

pair of months for January through June 1997, for households whose members responded for each consecutive

pair of months and separately for households whose

respondents responded only one month and were nonrespondents the other month (see Tucker and Harris-Kojetin,

1997). The top half of Table 16−3 shows the labor force

classification in the first month for people in households

who were respondents the second month compared with

people who were in households that were noninterviews

the second month. People from households that became

nonrespondents had higher rates of participation in the

labor force, employment, and unemployment than those

from households that responded in both months. The bottom half of Table 16−3 shows the labor force classification

for the second month for people in households that were

respondents in the previous month compared with people

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

Table 16−1. Components of Type A Nonresponse Rates,

Annual Averages for 1993−1996 and 2003,

National Estimates

[Percent distribution]

Nonresponse rate 1993 1994 1995 1996 2003

Overall Type A ......... 4.69 6.19 6.86 6.63 7.25

Noncontact ............ 1.77 2.30 2.41 2.28 2.58

Refusal ................ 2.85 3.54 3.89 4.09 4.10

Other . . . . . . . . . . . . . . . . . . .13 .32 .34 .25 .57

Table 16–2. Percentage of Households by Number of

Completed Interviews During the 8 Months

in the Sample, National Estimates1

[January 1994−October 1995]

Number of

completed interviews

Percent

1994−1995

0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.0

1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.5

2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.5

3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.6

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

they were in the sample were not included in these tabulations. Movers

were not included in this tabulation.

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Type A noninterview rate is quite small, the effect of the

nonresponding households on the overall unemployment

rate is also relatively small. However, the labor force characteristics of the people 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).

Dixon (2001) found similar effects in data from 1996−

1999, and further found that the bias came from noncontacts more than refusals. These results were supported by

an analysis of data from the CPS matched to the long form

of Census 2000 (Dixon, 2004), where nonresponders (and

their census employment status) in the CPS could be identified. This addressed the problem of those who never

responded to the CPS. The estimates were in the same

direction as found by Tucker and Kojetin, although the

bias was higher for the 2000 data.

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, allocation rates of all

interviewed cases were examined from both January 1997

and January 2004. The average levels of item-missing data

are represented by the allocation rates in Table 16−4. It

is likely that the bias due to imputation or allocation for

the demographic and labor force items is quite small, but

there may be a concern for industry/occupation and earnings data. Allocation rates have increased over the years,

as indicated by differences between 1997 and 2004.

Dixon (2002) found that item refusals predicted unit refusals and were negatively related to unemployment. Respondents who refused to answer particular items (especially

items related to income data) were more likely to refuse to

participate in subsequent surveys and were less likely to

be unemployed.

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

unknown biases in the estimates (e.g., a high noninterview rate.)

Table 16–3. Labor Force Status by Interview/Noninterview Status in Previous and Current Month,

National Estimates

[Average January–June 19971 percent distribution]

First month labor force status Interview in second month Nonresponse in secondmonth 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

Second month labor force status Interview in first month Nonresponse in first 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).

Table 16–4. CPS Items With Missing Data (Allocation Rates, %), National Estimates

Month/year

Household Demographic Labor force I&O Earnings

Range Mean Range Mean Range Mean Range Mean Range Mean

Jan. 1997...... 0.16−5.84 0.41 0.08−9.2 1.35 0.15−12.0 1.57 2.12−5.0 4.09 0.99−32.0 11.02

Jan. 2004...... 0.01−3.42 0.56 0.12−19.0 2.41 0.21−15.0 2.18 5.43−10.0 8.74 0.01−36.0 17.68

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
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 people reported as unemployed (see Table 16−5). In this schematic representation,

the number of identical people giving a different response

in the original interview and the reinterview for the characteristic unemployed is given by (b + c). If these

responses are two independent measurements for identical people using identical measurement procedures, then

an estimate of the simple response variance component

(2

R) is given by (b + c)/2n and

E (

b c

2n ) = 2

R

and as in formula 14.3 (Hansen, Hurwitz, and Pritzker,

1964)2. 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 to

determine 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 the 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 will

trigger a review of field procedures to determine and remedy the cause.

Table 16−6 provides estimates of the index of inconsistency shown as percentages for selected labor force characteristics for 2003.

MODE OF INTERVIEW

Incidence of Telephone Interviewing

As described in Chapter 7, 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

2 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.

Table 16–5. Comparison of Responses to the

Original Interview and the Reinterview,

National Estimates

Reinterview

Original interview

Total

Unemployed

Not

unemployed

Unemployed ...... a b a+b

Not unemployed . . c d c + d

Total .............. a+c b+d n=a+b+c+d

Table 16–6. Index of Inconsistency for Selected Labor

Force Characteristics in 2003,

National Estimates

Laborforcecharacteristic Estimate of index

of inconsistency

90-percent

confidence limits

Employed at work......... 14.0 19.0−20.8

Employed absent ......... 50.4 45.7−55.0

Unemployed layoff........ 60.8 49.8−71.8

Unemployed looking ...... 38.5 34.8−42.1

Not-in-labor force retired . . 7.8 6.8−8.6

Not-in-labor force disabled. 21.1 18.4−24.0

Not-in-labor force other . . . 31.1 29.4−32.8

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supposed to be done in person, the entire interview may

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 is shown in Table 16−7.

Overall, about 66 percent of the cases are done by

telephone, with almost 80 percent of the cases in monthin-samples (MIS) 2−4 and 6−8 done by telephone. Furthermore, a number of cases in months 1 and 5 are completed

by telephone, despite standing instructions to the field

interviewers to conduct personal visit interviews.

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, causing the interview to be

recorded as a telephone interview. In June 1996, an additional item was added to the CPS instrument 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 16−7. 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 data on 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 to obtain missing information not collected at

the initial personal visit. However, the percentage of cases

where the majority of the data was reported to have been

collected by telephone was larger than the percentage of

cases where the last contact with the household was by

telephone for MIS 1 and 5. The percentage increased in

2004 compared with 1995. The explanation for this pattern of results is not clear at the present time. Some reasons hypothesized are that some respondents may be

more comfortable over the telephone, or using CATI may

solve a difficult scheduling problem.

TIME IN SAMPLE

The rotation pattern of the CPS sample was described in

detail in Chapter 3, and the use of composite estimation in

the 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,

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 that shows the relationships of all the month-in-sample groups. This index is

the ratio of the estimate based on a particular month-insample 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 16−8 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 March 2003 to February 2004. For the percentage of

the total population that is unemployed, the index of

106.26 for the first-month households indicates that the

estimate from households in the sample in the first month

is about 1.0626 times the average overall eight month-insample groups; the index of 99.29 for unemployed for the

fourth-month households indicates that it is about the

same as the average overall month-in-sample groups.

Estimates from one of the month-in-sample groups should

not be taken as the standard, in the sense that they 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 that are in

the sample for the first time, are likely to be less successful at calling when a responsible household member is

available. Thus, noninterview rates generally start above

average with first-month households, decrease with more

time in the sample, go up a little for households in the

fifth month of the sample (after 8 months, the household

Table 16–7. Percentage of Households With Completed

Interviews With Data Collected by

Telephone (CAPI Cases Only),

National Estimates

[Percent]

Month in sample

Last contact with household was telephone

(average,Jan.–Dec.2004)

Majority of data

collected by telephone

(average,Jan.−Dec.2004)

1............... 13.5 20.5

2............... 73.0 75.6

3............... 76.0 78.5

4............... 76.3 79.0

5............... 28.3 37.9

6............... 76.2 78.8

7............... 77.8 80.0

8............... 77.7 80.5

Current Population Survey TP66 Quality Indicators of Nonsampling Errors 16–7

U.S. Bureau of Labor Statistics and U.S. Census Bureau
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.) Figure 16-4, showing

the Type A noninterview rate by month-in-sample for

July−September 2004, illustrates this pattern. An index of

the noninterview rate constructed in a similar manner to

the month-in-sample group index can be seen in Table

16−9. This noninterview rate index 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 noninterview

changes are unlikely to be distributed proportionately

among the various labor force categories (see Table 16−3).

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-insample 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.

Table 16–8. Month-in-Sample Bias Indexes (and Standard Errors) in the CPS for Selected Labor Force

Characteristics

[Average March 2003−February 2004]

Employment status and sex

Month-in-sample

1234567 8

TOTAL POPULATION,

16 YEARS AND OLDER

Civilian labor force .............. 101.00 100.97 100.64 100.50 99.74 98.95 99.15 99.06

Standard error ................ 0.34 0.33 0.36 0.36 0.37 0.40 0.38 0.36

Unemployment level ............ 106.26 103.07 100.56 99.29 101.37 99.14 95.64 94.67

Standard error ................ 1.33 1.41 1.32 1.58 1.04 1.40 1.29 1.40

MALES,

16 YEARS AND OLDER

Civilian labor force .............. 100.73 101.04 100.66 100.37 99.86 98.92 99.36 99.07

Standard error ................ 0.39 0.40 0.40 0.41 0.43 0.43 0.40 0.38

Unemployment level ............ 105.69 102.44 102.43 101.90 99.66 97.42 96.07 94.39

Standard error ................ 1.65 1.63 1.64 1.92 1.49 1.74 1.89 1.95

FEMALES,

16 YEARS AND OLDER

Civilian labor force .............. 101.32 100.89 100.61 100.64 99.60 98.99 98.90 99.04

Standard error ................ 0.39 0.35 0.43 0.44 0.40 0.45 0.44 0.43

Unemployment level ............ 106.98 103.87 98.22 96.03 103.49 101.29 95.10 95.02

Standard error ................ 1.77 1.98 2.09 2.30 1.43 1.72 1.31 2.06

BLACK POPULATION,

16 YEARS AND OLDER

Civilian labor force .............. 101.87 100.75 101.37 100.76 99.17 98.62 98.12 99.35

Standard error ................ 1.08 1.15 1.18 1.19 1.18 1.29 1.28 1.34

Unemployment level ............ 112.74 105.44 103.25 98.69 96.63 99.99 93.23 90.03

Standard error ................ 3.41 3.44 3.04 3.21 3.14 3.39 2.94 3.23

BLACK MALES,

16 YEARS AND OLDER

Civilian labor force .............. 101.69 100.70 101.14 100.05 98.76 98.55 99.42 99.68

Standard error ................ 1.23 1.42 1.42 1.50 1.51 1.60 1.63 1.61

Unemployment level ............ 109.55 105.16 103.97 99.15 97.13 97.03 98.56 89.46

Standard error ................ 4.45 4.78 4.50 4.56 4.30 4.55 4.20 4.44

BLACK FEMALES,

16 YEARS AND OLDER

Civilian labor force .............. 102.03 100.79 101.57 101.38 99.52 98.68 96.99 99.05

Standard error ................ 1.29 1.30 1.34 1.33 1.25 1.39 1.40 1.52

Unemployment level ............ 115.86 105.72 102.55 98.25 96.13 102.89 88.01 90.59

Standard error ................ 4.10 4.09 4.43 4.53 3.55 4.32 3.21 4.46

16–8 Quality Indicators of Nonsampling Errors Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Figure 16−4.

Basic CPS Household Nonresponse by Month in Sample, July 2004−September 2004,

National Estimates

PROXY REPORTING

Like many household surveys, the CPS seeks information

about all people in the household, whether they are available for an 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 other household members, nonsampling error may occur because of the use of proxy

reporters.

The level of proxy reporting in the CPS was generally

around 50 percent in the past and continues to be so in

the revised CPS. As can be seen in Table 16−10, the

month-in-sample has very little effect on the level of proxy

reporting and the levels have remained the same over

time. 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 on proxy use.

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.

Nonresponse Rate in Percent

1 23 4 5 67

0

2

4

6

8

10

12

8

Jul-04

Aug-04

Sep-04

Month in Sample

Table 16–9. Month-In-Sample Indexes in the CPS for

Type A Noninterview Rates

January−December 2004

Month in sample Noninterview rate index

1..................................... 129.1

2..................................... 93.7

3..................................... 89.1

4..................................... 88.6

5..................................... 119.8

6..................................... 98.2

7..................................... 94.2

8..................................... 87.1

Table 16–10. Percentage of CPS Labor Force Reports

Provided by Proxy Reporters

Percent reporting 1995 2004

All Interviews

Proxy reports ........... 49.68 48.84

Both self and proxy ..... 0.28 1.14

MIS 1 and 5

Proxy reports ........... 50.13 48.98

Both self and proxy ..... 0.11 0.78

MIS 2−4 and 6−8

Proxy reports ........... 49.53 48.79

Both self and proxy ..... 0.35 1.25

Current Population Survey TP66 Quality Indicators of Nonsampling Errors 16–9

U.S. Bureau of Labor Statistics and U.S. Census Bureau
These rates can be used to monitor the processes of conducting the survey, and they indicate the potential for

some nonsampling error to enter the process. This chapter

also includes information on the potential effects on the

CPS estimates due to nonresponse, centralized telephone

interviewing, and the duration that groups are in the CPS

sample. This research comes close to identifying the presence and effects of nonsampling error in the CPS, but the

results are not conclusive.

The full extent of nonsampling error in the CPS cannot be

known without an external source for comparison. Recent

work cited here indicates undercoverage has increased

since 2000, and nonresponse continues to be a concern.

However, the effect of nonresponse bias is considered to

be small. The month-in-sample bias persists, and item

nonresponse has increased over the last ten years. On the

other hand, there is evidence that, while some inconsistency in reporting exists, the measurement of employment

status is on a sounder conceptual footing than ever

before. (See Biemer (2004), Miller and Polivka (2004),

Tucker (2004), and Vermunt (2004).)

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.

Biemer, P. P. (2004), ‘‘An Analysis of Classification Error for

the Revised Current Population Survey Employment Questions,’’ Survey Methodology, Vol. 30, 2, 127−140.

Brooks, C. A. and B. A. Bailar (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., B. A. Kojetin, and L. Cahoon (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.

Dixon, J. (2000), ‘‘The Relationship Between Household

Moving, Nonresponse, and Unemployment Rate in the Current Population Survey.’’ Paper presented at the Joint Statistical Meetings of the American Statistical Association,

Indianapolis, Indiana.

Dixon, J. (2001), ‘‘Relationship Between Household Nonresponse, Demographics, and Unemployment Rate in the

Current Population Survey.’’ Paper presented at the Joint

Statistical Meetings of the American Statistical Association,

Atlanta, Georgia.

Dixon, J. (2002), ‘‘The Effects of Item and Unit Nonresponse on Estimates of Labor Force Participation.’’ Paper

presented at the Joint Statistical Meetings of the American

Statistical Association, New York City, New York.

Dixon, J. (2004), ‘‘Using Census Match Data to Evaluate

Models of Survey Nonresponse.’’ Paper presented at the

Joint Statistical Meetings of the American Statistical Association, Toronto, Canada.

Hansen, M. H., W. N. Hurwitz, H. Nisselson, and J. Steinberg (1955), ‘‘The Redesign of the Current Population Survey,’’ Journal of the American Statistical Association,

50, 701−719.

Hansen, M. H., W. N. Hurwitz, and L. Pritzker (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, U.S. Census Bureau, Washington, DC.

Harris-Kojetin, B. A. and C. Tucker (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.

Miller, S. H. and A. E. Polivka (2004), ‘‘Comment,’’ Survey

Methodology, Vol. 30, 2, 145−150.

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 B. A. Harris-Kojetin (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.

Tucker, C. (2004), ‘‘Comment,’’ Survey Methodology. Vol.

30, 2, 151−153.

Vermunt, J. K. (2004), ‘‘Comment,’’ Survey Methodology,

Vol. 30, 2, 141−144.

Williams, W. H. and C. L. Mallows (1970), ‘‘Systematic

Biases in Panel Surveys Due to Differential Nonresponse,’’

Journal of the American Statistical Association, 65,

1338−1349, 16−11.

16–10 Quality Indicators of Nonsampling Errors Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Appendix A

Sample Preparation Materials

INTRODUCTION

Despite the conversion of CPS to an automated listing and

interviewing environment, some paper materials are still

in use. These materials include segment folders, listing

sheets, 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 Folder, BC−1669 (CPS)

Illustration 1

A field representative receives a segment folder for each

unit segment with incomplete addresses or multi-unit

addresses.

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 designation for the segment, and basic geography.

2. 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: Multi-UnitListing Aids (MULA) (Form

11-12), Unit/Permit Listing Sheets (Form 11-3), and

Incomplete Address Locator materials.

Multi-Unit Listing Aids (Form 11−12)

Illustration 2

MULAs are provided for all unit segments with multi-unit

addresses. For each multi-unit,the field representative

receives a preprinted listing that shows addresses and unit

information as recorded from Census 2000.

Unit/Permit Listing Sheets (Form 11−3)

Illustration 3

Unit/Permit Listing Sheets are used only when the entire

address needs to be relisted and the field representative

prefers not to relist directly on the MULA. The field representative has a supply of blank Unit/Permit Listing Sheets

for this use.

Incomplete Address Locator Action Form (Form

NPC−1138)

Illustration 4

The Incomplete Address Locator Action Form is used when

the information obtained from Census 2000 is in some

way incomplete (i.e. missing a house number, unit designation, etc.). The form provides the field representative

the additional information that can be used to locate the

incomplete address. The information on the form includes:

1. The address as it appeared in Census 2000 and

possibly a suggested 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 that the field representative

should take to locate the address.

After locating the address, the field representative completes or corrects the basic address in the laptop case

management (for single and multi-units)and on the MULA

(for multi-units).

AREA FRAME MATERIALS

There are no longer paper sample preparation materials

for the area frame. Sample preparation has been fully

automated.

GROUP QUARTERS FRAME MATERIALS

There are no longer paper sample preparation materials

for the group quarters frame. Sample preparation has

been fully automated.

PERMIT FRAME MATERIALS

Segment Folder, BC−1669 (CPS)

Illustration 1

See the description and illustration in the Unit Frame Materials above. A field representative receives a segment

folder for every permit segment. The folder will contain a

Unit/Permit Listing Sheet (Form 11-3) and possibly a Permit Sketch Map.

Unit/Permit Listing Sheets (Form 11−3)

Illustration 3, 5, and 6

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

Current Population Survey TP66 Sample Preparation Materials A–1

U.S. Bureau of Labor Statistics and U.S. Census Bureau
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 sheets(s) to list

all units at a multi-unitaddress.

Permit Sketch Map (Form 11−187)

Illustration 7

When completing the permit address list (PAL) operation, 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 and streets in the vicinity of the

site.

A–2 Sample Preparation Materials Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Illustration 1. Segment Folder, BC-1669 (CPS) 

FORM BC-1669 (CPS)SEGMENT FOLDER (SURVEY CPS) (BAR CODE INFORMATION) 

 DATE 

MO/YR MO/YR MO/YR MO/YR RO PSU SEG# 

REDUCED ZIP FRAME (Unit or Permit) REINSTATED PLACE NAME or MCD NAME 

 REASSIGNED COUNTY 

REMARKS SPECIAL INSTRUCTIONS 

PSU

 SEG # 

(fold is here). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

 Current Population Survey TP66 Sample Preparation Materials A-3 

 U.S. Bureau of Labor Statistics and U. S. Census Bureau 
 

Illustration 2. Multi-Unit Listing Aid, Form 11-12 

RO 

32

PSU 

06037

Segment 

1999

Survey: 

CPS

FORM 11-12 U.S. DEPARTMENT OF COMMERCE 

U.S. CENSUS BUREAU

MULTI-UNIT LISTING AID (MULA) Tract: 

004893 

Block 

3005 

Expected Number of Units 

16

Address 

* 4827 Labor Force Ave. 

North Hollywood, CA 91601

State 

CA

County 

Los Angeles

Line 

No.

Unit Designation 

(or apartment number)

Sample 

Desig.

Serial 

Number

Line 

No.

Unit Designation 

(or apartment number)

Sample 

Desig.

Serial 

Number

1 M 21 A78 

2 M A79 03 22 

3 # 101 A78 04 23 A77 

4 # 102 A77 01 24 A79 

5 # 103 D A78 02 25 

6 Apt. 103 D 26 A78 

7 # 104 A77 04 27 A79 

8 # 105 A79 02 28 A77 

9 # 201 29 

10 # 202 A78 03 30 A79 

11 # 204 A79 01 31 A78 

12 # 205 A77 03 32 A77 

13 #301 33 

14 #302 A79 04 34 A79 

15 #303 A78 01 35 A78 

16 #304 A77 02 36 A77 

17 37 A78 

18 A79 38 

19 A78 39 A77 

20 A77 40 A79 

Contact Person:

Title:

Footnotes:

Telephone:

FR Code 

FR Initials 

Resolve all Missing Unit Designations. 

Resolve all Duplicate Unit Designations.

Month/Year 

BSAID: 0002 Date Printed: 05/09/04 

ESD within segment: 200408 Page: 1 of 1 

A-4 Sample Preparation Materials Current Population Survey TP66 

 U.S. Bureau of Labor Statistics and U.S. Census Bureau
Illustration 3. Unit/Permit Listing Sheet, 11-3 (Blank) 

RO PSU Segment

 

FORM 11-3 U.S. DEPARTMENT OF COMMERCE Survey name

(6-11-2001) Economics and Statistics Administration 

 U.S. CENSUS BUREAU 

UNIT/PERMIT LISTING SHEET Type of segment Expected no. of units

Address Permit office name

Post office State ZIP Code Urban/Rural Permit date of issue

------------------------

Year * Month * Day

PAL sequence/line no.

 

County Permit number (or BSAID orSPID/GQID) Combined 

address

PAL keyed remarks 

Line 

No. 

(1)

Unit Designation 

(or apartment number) 

Sample 

Desig. 

Serial 

Number 

Remarks 

(Reason and date for changes)

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

19. Multi-Units 20. Listed and Updated 

FR Code 

FR Initials 

Month/Year 

Name of Complex: 

Contact Person:

Title: 

Phone No. 

Total No. of 

Units 

Footnotes

ESD: 200411 Date Printed: 08/13/04 Sheet 1 of 1 Sheets 

Illustration 4 Incomplete Address Locator Actions Form, NPC 1138 

 Current Population Survey TP66 Sample Preparation Materials A-5 

 U.S. Bureau of Labor Statistics and U.S. Census Bureau 
 

Illustration 4. Incomplete Address Locator Actions Form, NPC 1138

NPC-1138 (March 19, 2004) U.S. Department of Commerce 

Economics and Statistics Administration 

U.S. Census Bureau

2000 SAMPLE REDESIGN 

INCOMPLETE ADDRESS LOCATOR ACTIONS 

SECTION I – IDENTIFICATION 

Survey Sample County BSAID Block MAFID 

Sample Date Tract/BNA RO PSU Segment Serial# 

SECTION II - NPC LOCATOR REVIEW/RESULTS 

1. Address AFTER NPC Review: Expected 

Name: # units: 

Address: 

Post Office: ST: ZIP: 

2. Research 

[ ] Future File: 

[ ] MAF Browser: 

[ ] Power Finder 2000 

[ ] Internet 

[ ] Zip +4: 

[ ] Directory Assistance 

[ ] Post Office: 

[ ] Outcome Code: 

 (NPC use only) 

SECTION III - ACTIONS THE RO/FR MUST PERFORM 

To locate the sample unit: 

[ ] Use Locator Materials Enclosed 

[ ] Refer to attached list.. 

[ ] Use Multi-Unit Listing Sheet Enclosed. 

[ ] Use Multi-Unit Listing Sheet, previously sent to you for: 

 Sample: Sample Date: 

[ ] Other – Specify: 

REMARKS/COMMENTS 

 A-6 Sample Preparation Materials Current Population Survey TP66 

 U.S. Bureau of Labor Statistics and U.S. Census Bureau 
Illustration 5. Unit/Permit Listing Sheet, 11-3 (Single unit in Permit Frame) 

RO

30

PSU

48155

Segment

 2001 

Survey name

 CPS

FORM 11-3 U.S. DEPARTMENT OF COMMERCE 

(6-11-2001) Economics and Statistics Administration 

 U.S. CENSUS BUREAU 

UNIT/PERMIT LISTING SHEET Type of segment

 PERMIT

Expected no. of units

1

Address

350 POPULATION PLACE

Permit office name 

 WALKER

Post office

 WALKER 

State

 TX 

ZIP Code

 77484 

Urban/Rural

 RURAL 

Permit date of issue

------------------------

Year 2000 * Month 10* Day 01

PAL sequence/line no.

 70035215/11 

County FOARD COUNTY Permit number (or BSAID orSPID/GQID) 

0256

Combined 

address

NO

PAL keyed remarks

Line 

No. 

(1)

Unit Designation 

(or apartment number) 

Sample 

Desig. 

Serial 

Number 

Remarks 

(Reason and date for changes)

1 A79

2 A79

3 A79

4 A79

5 A79

6 A79

7 A79

8 A79

9 A79

10 A79

11 A79

12 A79

13 A79

14 A79

15 A79

19. Multi-Units 20. Listed and Updated 

FR Code 

FR Initials 

Month/Year 

Name of Complex: 

Contact Person:

Title: 

Phone No. 

Total No. of 

Units 

Footnotes

ESD: 200409 Date Printed: 06/10/04 Sheet 1 of 1 Sheets 

Current Population Survey TP66 Sample Preparation Materials A-7 

U.S. Bureau of Labor Statistics and U.S. Census Bureau
 

Illustration 6. Unit/Permit Listing Sheet, 11-3 (Multi-unit in Permit Frame) 

RO

23

PSU

42101

Segment

 5001 

Survey name

 CPS

FORM 11-3 U.S. DEPARTMENT OF COMMERCE 

(6-11-2001) Economics and Statistics Administration 

 U.S. CENSUS BUREAU 

UNIT/PERMIT LISTING SHEET Type of segment

 PERMIT

Expected no. of units

71

Address

2100 SURVEY ST

Permit office name 

 PHILADELPHIA

Post office

 PHILADELPHIA 

State

 PA 

ZIP Code

 19001 

Urban/Rural

 URBAN 

Permit date of issue

------------------------

Year 2002 * Month 07* Day 21

PAL sequence/line no.

 45639851/0001 

County PHILADELPHIA COUNTY Permit number (or BSAID orSPID/GQID) 

78126

Combined 

address

NO

PAL keyed remarks 

Line 

No. 

(1)

Unit Designation 

(or apartment number) 

Sample 

Desig. 

Serial 

Number 

Remarks 

(Reason and date for changes)

1 A78 01 

2 A78 02 

3 A78 03 

4 A78 04 

5 A79 01 

6 A79 02 

7 A79 03 

8 A78 

9 A78 

10 A78 

11 A78 

12 A79 

13 A79 

14 A79 

15 A78 

19. Multi-Units 20. Listed and Updated 

FR Code 

FR Initials 

Month/Year 

Name of Complex: 

Contact Person:

Title: 

Phone No. 

Total No. of 

Units 

Footnotes

ESD: 200404 Date Printed: 01/09/04 Sheet 1 of 1 Sheets 

 A-8 Sample Preparation Materials Current Population Survey TP66 

 U.S. Bureau of Labor Statistics and U.S. Census Bureau
Illustration 7. Permit Sketch Map, Form

 11-187 

1. RO 

27

2. PSU 

06703

3. Permit month/year

07/2005

4. Sequence number 

69003100

5. Permit Office name

Alameda County

6. Locality (Post Office) 

Oakland City

7. County 

Alameda

FORM 11-187 U.S. DEPARTMENT OF COMMERCE 

(10-15-02) Economics and Statistics Administration 

 U.S. CENSUS BUREAU 

PERMIT SKETCH MAP

PERMIT ADDRESS 

LIST OPERATION

8. State

CA

9. ZIP Code 

20709

10. Permit number 

47325

11. PAL line 

32

ATTENTION 

REGIONAL OFFICE

Keep both copies in the 

Regional Office files

12. U/R 

Rural 

13. Field Representative name 

Sue Hood

Code 

M5

Notes

 Red brick house, garage on left side.

 U.S.GOVERNMENT PRINTING OFFICE 1992-750-111/40529 

Current Population Survey TP66 Sample Preparation Materials A-9 

U.S. Bureau of Labor Statistics and U.S. Census Bureau 

X 1 mile 

Quimby Street 

Hwy 64 

N 
Appendix B.

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. The most recent sampling maintenance reductions were implemented in 1999 and 2003.

These maintenance reductions are different from changes

to the base CPS sample size resulting from modifications

to 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.

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 so that dropped cases will

not be interviewed.

Housing 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 maintenance

reduction. The post-reduction 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-level 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 that 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 the sample

to reduce. However, a large number of reduction groups

can lead to imbalances in the distribution of sample cuts

across PSUs, since small PSUs may not have enough

sample to have all reduction groups represented.

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/non-self-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 about which PSUs will actually have permit samples over the life of the design. The

sort sequence is:

1. Stratification PSU.

2. Basic PSU Component (BPC)

3. Original hit number.

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 the 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

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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 2000 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

further 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 it is gradually phased in with each incoming rotation group to minimize the effect on survey estimates and reliability and to prevent sudden changes to

the 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.

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Appendix C.

Derivation of Independent Population Controls

INTRODUCTION

Each month, for the purpose of the iterative, second-stage

weighting of the Current Population Survey (CPS) data,

independent projections of the eligible population are produced by the Census Bureau’s Population Division. In addition to the CPS, 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 civilian noninstitutionalized population of the United States and the 50

states (including the District of Columbia). The projections

for the country are distributed by demographic characteristics in two ways: (1) age, sex, and race, and (2) age, sex,

and Hispanic origin.1 The projections for the states are

distributed by race (Black only and all other race groups

combined), age (0−15, 16−44, and 45 and over), and sex.

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 the 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 measurements of 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.

This appendix seeks first to present this framework systematically, 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 the total population from a census

date via the demographic components of change and

explains how each of the requisite inputs to the mathematical framework is measured. This analysis is presented separately for the total population of the United

States and its distribution by demographic characteristics

and by state of residence by demographic characteristics.

The final two sections are, 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. The final section, ‘‘Procedural Revisions,’’ contains 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, which are essential to understanding the derivation of census-based population controls for surveys but

not necessarily prevalent in the literature on survey methodology.

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.

A census-level population estimate or projection is an estimate or projection of population change that can be linked

to a census count.

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 people defined to be on

active duty by one of the five branches of the Armed

Forces, as well as people 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 1 Hispanics may be any race.

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demographic components often cited in the literature consist of births, deaths, and net migration. This appendix

also refers to changes in the institutional and active-duty

Armed Forces populations, which affect the CPS control

universe.

The CPS control universe is characterized by two

attributes: (1) restriction to the civilian noninstitutionalized population, and (2) modification of census data by

race. Each of these defining concepts appears separately

in this glossary.

Emigration is the departure of a person from a country of

residence, in this case the United States. In the present

context, it refers to people legally resident in the United

States but is not confined to legally permanent residents

(immigrants) who make their usual place of residence outside of the United States.

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 data).

Population estimates discussed in this appendix stipulate

an enumerated base population, coupled with estimates of

population change from the enumeration date of the base

population to the date of the estimate.

The institutional population refers to a population universe consisting of inmates or residents 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 population 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

people intending to live or make their usual residence

abroad, in Puerto Rico, or the outlying areas.

Legal permanent residents are people 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 race describes the census population after the

definition of race has been aligned with definitions from

other administrative sources.

The natural increase of a population over a time interval

is the number of births minus the number of deaths during the interval.

Population projections are population figures relying on

modeled or assumed values for some or all of their components, and 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 applications to the CPS series beginning in 2001

include some count resolution corrections to Census 2000

not included in original census publications.

A population universe is the population represented in a

data collection, in this case, the CPS. It is determined by a

set of rules or criteria defining inclusion and exclusion of

people 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 as the civilian noninstitutional population residing in the United States.

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 close to the 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 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 could be 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

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
year prior to the current year. Hence, we refer to CPS controls as projections, despite their frequent description as

estimates in the literature.

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-race-Hispanic 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 useful.

POPULATION UNIVERSE FOR CPS CONTROLS

In the concept of ‘‘population universe,’’ as used in this

appendix, are found not only the rules specifying who is

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. This section considers three population universes: the resident population universe defined by Census 2000, 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, the extensive detail

that it provides on the geographic distribution and characteristics of the U.S. population and the legitimacy

accorded it by the U.S. Constitution.

The universe defined by Census 2000 is the U.S. resident

population, including people 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, the Virgin Islands of the

United States, and the 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. People 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

counted as U.S. residents. Exceptions may occur in the

case of Armed 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 for 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 latest census (2000) if this census had been held on

the reference date of the estimates. However, subgroups

within this universe are distinct from those in the census

universe due to race recoding, which does not affect the

total population.

The estimates differ from the census in their definition of

race, with categories redefined to be consistent with other

administrative-data sources. Specifically, people enumerated in Census 2000 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 people (18.5 million

nationally) were Hispanics who gave a Hispanic-origin

response to the race question. Publications from Census

2000 do not incorporate this modification, while all official population estimates do incorporate it.

The universe underlying the CPS sample is confined to the

civilian noninstitutionalized population. Thus, independent population controls are sought for this population to

define the CPS control universe. The previously mentioned

alternative definitions of race, associated with the universe for official estimates, is carried over to the CPS control universe. Three additional changes are incorporated,

which distinguish the CPS control universe (civilian noninstitutional population) from the universe for official Census Bureau estimates (resident population).

1. The CPS control universe excludes active-duty Armed

Forces personnel, including those stationed within the

United States, while these individuals 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.

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2. The CPS control universe excludes people 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 institutionalized 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 difference

affects estimation procedures at the state level, but

not at the national level.

CALCULATION OF POPULATION PROJECTIONS FOR

THE CPS UNIVERSE

This section explains 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, age, sex, and

race.

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 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 that the

population at some reference date is 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

PRESt1 = PRESt0 + B − D + NM (1)

where:

PRESt1 = Resident population at time t1

(the reference date),

PRESt0 = 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, deaths in the civilian noninstitutional

population 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:

PCNt1 = PCNt0 + B − CND +(IM − NRAF − NEI) (2)

where:

PCNt1 = civilian noninstitutionalized population,

time t1,

PCNt0 = civilian noninstitutionalized population,

time t0,

B = births to the U.S. resident population,

time t0 to time t1,

CND = deaths of the civilian noninstitutionalized

population, time t0 to time t1,

I M = 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 institutionalized 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 = PCIt1 − PCIt0 + DI (5)

where PCIt1 and PCIt0 represent the civilian institutionalized 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

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
applied to equation (2). This fact obviates measuring institutionalized inmate deaths or deaths of the Armed Forces

members residing in the United States.

where IM = net international migration in the time interval

(t0,t1).

At this point, we restate equation (2) to incorporate equations (3), (4), and (5), and simplify. The resulting equation,

which follows, is the operational equation for population

change in the civilian noninstitutionalized population universe.

PCNt1 = PCNt0 + B − D + IM −

(WWAFt1 − WWAFt0 + DAFO − NRO)−(PCIt1 − PCIt0) (6)

Equation (6) identifies all the components that must be

separately measured or projected to update the total civilian noninstitutionalized 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 (6) 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

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 (6) 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 and residential definition, and this requirement is a

major aspect of the way they are measured. A description

of the individual inputs to equation (6), which applies to

the CPS control universe, and their data sources follows.

The census base population (PCNt0). While any base

population (PCNt0, in equation [6]), whether a count or an

estimate, can be the basis of 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. For the survey controls in

this decade, Census 2000 results were not adjusted for

undercount. The census base population includes minor

revisions arising from count resolution corrections. These

corrections arise from the tabulation of data from the enumerated population. As of May 21, 2004, count resolution

corrections incorporated in the estimates base population

amounted to a gain of 2,697 people from the originally

published count. In order to apply equation (6), which is

derived from the balancing equation for the civilian noninstitutionalized 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:

PCN0 = R0 − RAF0 − PCI0 (7)

where R0 = the enumerated resident population, RAF0 =

the Armed Forces resident in the United States on the census date, and PCI0 = the civilian institutionalized 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 (6). In practice, this is what occurs when

the monthly CPS population controls are produced.

Births and deaths (B, D). In estimating total births and

deaths of the resident population (B and D, in equation

[6]), we assume the population universe for vital statistics

matches 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 this 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 2004 CPS control series were for calendar

year 2001. Final birth and death data are summarized in

NCHS publications (see Martin et al., 2002, and Arias et

al., 2003). Monthly births and deaths for calendar year(s)

up to 2 years before the CPS reference dates (e.g., 2002

for 2004 controls) are based on provisional estimates by

NCHS (see Hamilton et al. and Kochanek et al., 2004). 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.

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A concern exists relative to the consistency of the NCHS

vital statistics universe with the census resident universe

and the CPS control universe derived from it.

Birth and death statistics obtained from NCHS exclude

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.

This source of bias, which is assumed to be minor, affects

the accounting of total population. Other 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.

Net International Movement. The net international

component combines three parts: (1) net migration of the

foreign-born, (2) emigration of natives, and (3) net movement from Puerto Rico to the United States. Data from the

American Community Survey (ACS) is the basis for estimating the level of net migration of the foreign-born

between 2000 to 2001 and 2001 to 2002 because it provided annually updated data based on a larger sample

than the CPS. After determining the level of migration for

the foreign-born (the net difference between two time

periods for the foreign-born population), we accounted for

deaths to the entire foreign-born population during the

periods of interest to arrive at the final estimate of net

migration of the foreign-born. We applied the Census

2000 age-sex-race-Hispanic-origin county-level distribution of the non-citizen foreign-born population who

entered in 1995 or later to the national-level estimate of

net migration of the foreign-born. The remaining two parts

of the net international migration component, the net

movement from Puerto Rico to the United States and the

emigration of natives, were produced in similar ways. For

both parts, we do not have current annually updated information. Therefore, we used the levels of movement produced from change in the decennial censuses.2 For both

parts, we applied the age-sex-race-Hispanic origin-county

distributions from Census 2000 that were most similar to

the population of interest. For the net movement from

Puerto Rico, the underlying distribution was based on

those who indicated that their place of birth was Puerto

Rico and who had entered the United States in 1995 or

later. We assumed that natives who emigrated were likely

to have the same distributions as natives who currently

reside in the United States. Therefore, the characteristics

of natives who emigrated were assumed to be the same

age-sex-race-Hispanic-origin county-level distribution as

natives residing in the 50 states and the District of Columbia in Census 2000.

Once the net migration of the foreign-born, net movement

from Puerto Rico, and emigration of natives were estimated, all three parts were combined to estimate a final

net international migration component. Adding up the

parts of net international migration, the net migration of

the foreign-born (1,300,000) plus the net migration from

Puerto Rico (+11,133) minus the net emigration of natives

(−18,012) yielded 1,293,121 as the annual estimate for

net international migration for the 2000−2001 year and

the 2001−2002 year.

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 one 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

on active duty.

The last three components of net recruits to the Armed

Forces from the U.S. civilian population, deaths in the resident Armed Forces (DRAF), deaths in 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

2 For more information on the estimate of 11,133 for the net

movement from Puerto Rico, see Christenson. For information on

estimates of native emigration, see Gibbs, et al.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
is available, these are assigned to domestic or overseas

populations, 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 individuals 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 people with home

of record outside the U.S. yield the series for net recruits

to the Armed Forces from overseas.

Change in the institutionalized population (CIPt1 -

CIPt0). The change in the civilian population residing in

institutions is currently held constant, equal to the civilian

institutionalized group quarters population obtained from

Census 2000.

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. Thirty-one categories of race and

two classes of ethnic origin (Hispanic, non-Hispanic) are

required, with no cross-classifications of race and Hispanic

origin. The population projection program now uses the

full cross-classification of age by sex and by race and by

Hispanic origin in its monthly series, with 101 single-year

age categories (single years from 0 to 99, and 100 and

over), two sex categories (male, female), 31 race categories (White; Black; American Indian, Eskimo, and Aleut;

Asian; Native Hawaiian and Pacific Islander; and all

multiple-race groupings), and two Hispanic-origin categories (not Hispanic, Hispanic).3 The resulting matrix has

12,524 cells (101 x 2 x 31 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 124 cells, defined by variables not naturally

changing over time (2 values of sex by 31 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 (6). Similarly, the derivation of the civilian noninstitutionalized

base population from the census resident population follows equation (7).

Distribution of Census Base and Components of

Change by Age, Sex, Race, and Hispanic Origin

The 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.

Census Base Population by Age, Sex, Race and

Hispanic Origin: Modification of the Census Race

Distribution

The race modification conforms to the Office of Management and Budget’s (OMB) 1997 revised standards for collecting and presenting data on race and ethnicity. The

revised OMB standards identified five race categories:

White, Black or African American, American Indian and

Alaska Native, Asian, and Native Hawaiian and Other

Pacific Islander. Additionally, the OMB recommended that

respondents be given the option of marking or selecting

one or more races to indicate their racial identity. For

respondents unable to identify with any of the five race

categories, the OMB approved including a sixth category,

‘‘Some other race,’’ on the Census 2000 questionnaire.

No modification was necessary for responses indicating

only an OMB race alone or in combination with another

race. However, about 18.5 million people checked ‘‘Some

other race’’ alone or in combination with another race.

These people were primarily Hispanic and many wrote in

their place of origin or Hispanic-origin type (such as Mexican or Puerto Rican) as their race. For purposes of estimates production, responses of ‘‘Some other race’’ alone

were modified by blanking the ‘‘Some other race’’ response

and imputing an OMB race alone or in combination with

another race response. The responses were imputed from

a donor who matched on response to the question on Hispanic origin. Responses of both ‘‘Some other race’’ and an

OMB race were modified by blanking the ‘‘Some other race’’response and keeping the OMB race response.

3 Throughout this appendix, ‘‘American Indian’’ refers to the

aggregate of American Indian, Eskimo, and Aleut; ‘‘API’’ refers to

Asian and Pacific Islander.

Current Population Survey TP66 Derivation of Independent Population Controls C–7

U.S. Bureau of Labor Statistics and U.S. Census Bureau
The resulting race categories (White, Black, American

Indian and Alaska Native, Asian, and Native Hawaiian and

Other Pacific Islander) conform with OMB’s 1997 revised

standards for the collection of data on race and ethnicity

and are more consistent with the race categories in other

administrative sources, such as vital statistics.

Distribution of births by sex, race, and Hispanic

origin. Data on registered births to U.S. residents by birth

month, sex of child, and race and Hispanic origin of

mother and father were supplied by the National Center

for Health Statistics (NCHS). For the 2004 CPS controls,

final data were available through 2001 and preliminary

data were available for 2002.

Registered births to U.S. resident women were estimated

from data supplied by NCHS. At present, NCHS continues

to collect birth certificate data using the 1977 race standards of White, Black, American Indian, Eskimo or Aleut,

and Asian or Pacific Islander, under the ‘‘mark one race’’

scenario. To produce post-2000 population estimates, it

was necessary to develop birth data that coincided with

the new race categories: White, Black, American Indian and

Alaska Native, Asian, and Native Hawaiian and other

Pacific Islander, under the ‘‘mark one or more races’’ scenario. Because of this inconsistency in data on race, it was

necessry to model the full 31 possible single- and

multiple-race combinations. For a detailed description of

this modeling process, see Smith and Jones, 2003.

Data on births by birth month, sex, and race and Hispanic

origin of the mother and father are based on final microdata files for calendar year 2001 from the NCHS registration system. The model was based on information from

Census 2000 on race and Hispanic origin reporting within

households for the age zero (under 1 year of age) population and their parent(s). First, the NCHS births were tabulated for each of the combinations of parents’ race and

Hispanic origin. These births by parents’ race and Hispanic

origin were then distributed according to the Census 2000

race and Hispanic origin distribution for the age zero

population for the matching combination of parents’ race

and Hispanic origin. Race and Hispanic origin modeling

was done separately for mother-only and two-parent

households.

To estimate the distribution of births for calendar year

2002, data on preliminary 2002 births received from

NCHS from their 90-percent sample of final births were

distributed according to the 2001 births by birth month,

sex and modeled race and Hispanic origin.

To estimate the distribution of births by race and Hispanic

origin of the mother for the first half of 2003, age-specific

birth rates centered on July 1, 2002, for women in each

race group and for women of Hispanic origin were applied

to preliminary estimates of the number of resident women

in the specified age groups.

Births through the remaining period through the end of

2004 were then projected from the births estimated

above.

Deaths by age, sex, race, and Hispanic origin. Data

on registered deaths of U.S. residents by death month,

age, sex, race, and Hispanic origin were supplied by

NCHS. Final data were available through 2001 and preliminary data were available for 2002.

It was again necessary to model the race distribution

because death certificates ask for the race of the deceased

using only four race categories (1977 race categories for

White, Black, American Indian, Eskimo or Aleut, Asian or

Pacific Islander). Separate death rates were calculated for

the 1977 race categories by age, sex, and Hispanic origin.

Rates were constructed using the 1998 mortality4 and

1998 population estimates.5 These rates were then

applied to the July 2001 population in the 31 modified

race categories from the Vintage 2002 estimates. Death

rates for the White, the Black, the American Indian, Eskimo

or Aleut, and the Asian and Pacific Islander groups were

applied to the corresponding White alone, Black alone,

American Indian and Alaska Native alone, Asian alone, and

Native Hawaiian and Other Pacific Islander alone populations. The Asian and Pacific Islander death rate was

applied to both the Asian alone population and the Native

Hawaiian and Other Pacific Islander alone population.

Multiple-race deaths were estimated as the difference

between total 2001 deaths as reported by NCHS and the

sum of deaths estimated for the single-race groups. Consequently, a constant death rate was applied to each of

the 26 multiple- race groups.

The 2002 deaths were distributed using 2001 deaths by

modeled race, death month, age, and sex and were controlled to the 2002 preliminary deaths from NCHS by Hispanic origin.

To estimate the distribution of deaths by race and Hispanic origin for the first half of 2003, projected agespecific mortality rates for July 1, 2002, were applied to

preliminary 2003 estimates of the population by single

year of age, sex, race, and Hispanic origin.

Deaths by race and Hispanic origin for the period beyond

the first half of 2003 through the end of 2004 are projected from the deaths produced above.

4 The race distribution for the 1998 deaths as set in the processing of the national estimate is used here because it adjusts

for NCHS/Census race inconsistencies. In the production of

national population estimates in the 1990s, preliminary deaths to

the American Indian and Asian and Pacific Islander populations by

sex were projected using life tables, with proportional adjustment

to sum to the other races total. Hispanic origin deaths by sex and

race were estimated for all years using life tables applied to a distribution of the Hispanic population by age, sex, and race. 5 The 1998 population estimate from the vintage 2000 population estimates.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
International migration by age, sex, race, and

Hispanic origin. For the purposes of the estimates program, the net international migration component consists

of three parts: (1) net migration of the foreign-born, (2)

emigration of natives, and (3) net movement from Puerto

Rico to the United States.

Net migration of the foreign-born component.

Although a component basis was used throughout most of

the 1990s, the findings from an extensive evaluation

showed that the use of administrative sources for developing migration components by migrant status for the

foreign-born was problematic.6 Therefore, we assumed

that the net difference between two time periods for the

foreign-born population using the same data source would

be attributable to net migration. To maximize the use of

available data, the American Community Survey (ACS) data

for 2000, 2001, and 2002 was the basis for the net migration between 2000 to 2001, and 2001 to 2002.7 The calculation of the change in the size of the foreign-born

population, must be adjusted for deaths in the population

during the period of interest.

Characteristics for net migrants were imputed by taking

the estimated net migration of the foreign-born and then

redistributing that total to the Census 2000 distribution

by single year of age (0−115+), sex-race (31 groups), Hispanic origin and county distribution of the noncitizen,

foreign-born who reported that they entered in 1995 or

later. The rationale was that the most recent immigrants in

Census 2000 would most closely parallel the distribution

of the foreign-born in the ACS, by age-sex-race-Hispanic

origin and county.

Net migration from Puerto Rico component. Since no

data set would easily provide updated information on

movement from Puerto Rico, there was no way to update

the estimate that was based on the average movement of

the 1990s.8

Characteristics were imputed for the net migration from

Puerto Rico using the age-sex-race-Hispanic origin-county

distribution of those who indicated that their place of

birth was Puerto Rico and who had entered the United

States in 1995 or later. The underlying assumption was

that the characteristics of those who indicated that they

were born in Puerto Rico and had migrated since 1995

would parallel the distribution of recent migrants from

Puerto Rico.

Net native emigration component. Since no current

data set would easily provide updated information on

native emigration, there was no systematic way to update

the estimate.9 We assumed that natives who emigrated

were likely to have the same distributions as natives who

currently reside in the United States. Therefore, the level

was distributed to the same age-sex-race-Hispanic origincounty proportions as those of the native population (born

in the 50 states and DC) living in the 50 states and the

District of Columbia in Census 2000.

Combined net international migration component.

The estimates of net migration of the foreign-born, net

movement from Puerto Rico, and net emigration of natives

were combined to create the estimate of the total net

international migration component.

The net international migration component was kept constant for years 2000 to 2003. The change in the foreignborn population from the 2000 to 2001 period and from

the 2001 to 2002 period was not statistically significant.

This finding, in conjunction with limited time for additional research, led to the decision to keep the estimate

constant for the projected years 2004 and 2005. The additional parts of the net international migration component

are kept constant because they are currently measured

only once per decade.

Migration of Armed Forces by age, sex, race, and

Hispanic origin. Estimation of demographic details for

the remaining components of change requires estimation

of the details for the Armed Forces residing overseas and

in the United States. These data are 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/Hispanic origin, and location inside or outside the United States are provided by the Department of

Defense, Defense Manpower Data Center. The location and

service-specific 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 people residing on board ships to

conform to census definitions, which is accomplished

through a special tabulation (also provided by Defense

Manpower Data Center) of people assigned to sea duty.

These data 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-Hispanic-origin distribution

must also be adapted. The Armed Forces ‘‘race-ethnic’’ categories supplied by the Defense Manpower Data Center

6 An overview of the Demographic Analysis-Population Estimates (DAPE) project along with a summary of findings is in Deardorff, K. E. and L. Blumerman. Mulder, T., et.al., gives an overview

of the methods used throughout the 1990s’ estimates series. 7 Throughout, ‘‘ACS’’ will refer to the ACS combined with the

Supplementary Surveys for the corresponding years. 8 See Christenson, M., for an explanation of how the annual

level of 11,133 was derived.

9 See Gibbs, et.al., for an explanation of how the estimated

annual level of 18.000 native emigrants was derived. Because of

rounding, the sum of the detailed age, sex, race, and Hispanic origin estimates used in the actual estimates process is 18,012.

Current Population Survey TP66 Derivation of Independent Population Controls C–9

U.S. Bureau of Labor Statistics and U.S. Census Bureau
treat race and Hispanic origin as a single variable, with the

Hispanic component of Blacks and Whites included under

‘‘Hispanic,’’ and the Hispanic components of the American

Indian and the API populations assumed to be nonexistent. A residual ‘‘other race’’ category is also caculated. The

method of converting this distribution to be consistent

with Census-modified race categories employs, for each

age-sex category, the Census 2000 race distribution of the

total population (military and civilian) to supply all race

information missing in the Armed Forces race-ethnic categories. Hispanic origin is thus prorated to the White and

Black populations according to the total race-modified

population of each age-sex category in 2000; American

Indian and API are similarly prorated to Hispanic and nonHispanic categories. 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. This

adaptation is robust; it requires imputing the distributions

of only small race and Hispanic-origin categories.

The two 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 Hispanic origin based on data from an aggregate of overseas censuses, in which Puerto Rico dominates numerically. The

age and sex distribution follows the worldwide Armed

Forces.

The civilian institutionalized population by age,

sex, race, and Hispanic origin. The fundamental source

for the distribution of the civilian institutionalized population by age, sex, race, and Hispanic origin is a Census

2000 tabulation of institutional population by age, sex,

race (modified), Hispanic origin, and type of institution.

The last variable has four categories: nursing home, correctional facility, juvenile facility, and a small residual.

Data for the resident armed forces are also obtained from

Census 2000 and updated with data from the Department

of Defense. The size of the civilian population is obtained

by subtracting the resident armed forces from the resident

population. The civilian noninstitutionalized population is

obtained by subtracting the institutionalized population

from the civilian population.

Population Controls for States

State coverage adjustment for the CPS requires a distribution of the national civilian noninstitutionalized population

by age, sex, and race to the states. The second-stage

weighting procedure requires a disaggregation only by

age and sex by state. This distribution is determined by a

linear extrapolation through two population estimates for

each characteristic group 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 for

each age, sex, race and Hispanic-origin group by proportional adjustment. For example, the state distribution for

June 1, 2003, was determined by extrapolation along a

straight line determined by two data points (July 1, 2001,

and July 1, 2002) for each state. The resulting distribution

for June 1, 2003, was then proportionately adjusted to the

national total for each age, sex and race group, 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

forms the basis for the extrapolation, differs from the

national-level procedure in a number of ways. The primary

reason for the differences is the importance of interstate

migration for 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 noninstitutionalized population but to the

household population under 65 years of age and 65 years

of age and over. Estimates procedures for the population

under 65 and the population 65 and older are similar, but

differ in that they employ a different data source for developing mea- sures of internal migration. Furthermore, while

national-level population estimates are produced for the

first of each month, state estimates are produced at oneyear intervals, with July 1 reference dates.

The household population under 65 years of age.

The procedure for estimating the nongroup quarters population under 65 is analogous to the national-level procedures for the total population, with the following major

differences.

1. Because the population is restricted to individuals

under 65 years of age, the number of people advancing to age 65 must be subtracted, along with deaths

of those 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 people under 65 and people 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

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
state representatives. These data, after appropriate

review, are adopted in place of NCHS data for those

states. The immigration total and the emigration of

native-born residents are distributed on the basis of

the various segments of the population enumerated in

Census 2000.

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

net 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 ZIP-CRS program

(Sater, 1994) 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.

A detailed explanation of the use of IRS data and other facets of the subnational estimates can be found at <http://

www.census.gov/popest/estimates.php>.

The population 65 years of age and over. For the

population ages 65 and over, the approach is similar to

that used for the population under age 65. The population

ages 65 and over in Census 2000, is added to the population estimated to be turning age 65. This quantity, as discussed in the previous section, is subtracted from the

base under-age-65 population. The Census Bureau subtracts deaths and adds net immigration, occurring for this

same age group. Migration is estimated using data on

Medicare enrollees, which are available by county of residence from the U.S. Centers for Medicare and Medicaid

Services. Because there is a direct incentive for eligible

individuals to enroll in the Medicare program, the coverage rate for this source is high. To adapt this estimate to

the Census 2000 level, the Medicare enrollment is

adjusted to reflect the difference in coverage of the population 65 years of age and over.

Exclusion of the Armed Forces population and

inmates of civilian institutions. Once the resident

population by the required age, sex, and race groups has

been estimated for each state, the population must be

restricted to the civilian noninstitutionalized 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).

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.

PROCEDURAL REVISIONS

The process of producing estimates and projections, like

most 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, reflecting the introduction of data from a

new decennial census, which can be associated with new

policies regarding population universe or estimating methods. However, revisions may occur at any time, depending

on new information obtained regarding the components of

population change, or the availability of resources to complete methodological research.

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 Census 2000 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

Current Population Survey TP66 Derivation of Independent Population Controls C–11

U.S. Bureau of Labor Statistics and U.S. Census Bureau
REFERENCES

Ahmed, B. and J. G. Robinson, (1994), Estimates of Emigration of the Foreign-Born Population: 1980−1990,

U.S. Census Bureau, Population Division Technical Working

Paper No. 9, December, 1994, <http://www.census.gov

/population/www/documentation/twps0009

/twps0009.html>.

Arias, Elizabeth, Robert Anderson, Hsiang-Ching Kung,

Sherry Murphy, and Kenneth Kochaneck,‘‘Deaths: Final

Data for 2001,’’ National Vital Statistics Reports, Volume 52, Number 3, Division of Vital Statistics, National

Center for Health Statistics, <http://www.cdc.gov/nchs

/data/nvsr/nvsr52/nvsr52_03.pdf>.

Christenson, M., ‘‘Evaluating Components of International

Migration: Migration Between Puerto Rico and the United

States,’’ Population Division Technical Working Paper No.

64, January 2002, <http://www.census.gov/population

/www/documentation/twps0064.html>.

Deardoff, K.E. and L. Blumerman, ‘‘Evaluating Components

of International Migration: Estimates of the Foreign- Born

Population by Migrant Status in 2000,’’ Population Division

Technical Working Paper No. 58, December, 2001, <http://

www.census.gov/population/www/documentation

/twps0058.html>.

Gibbs, J. G. Harper, M. Rubin, and H. Shin, ‘‘Evaluating

Components of International Migration: Native-Born Emigrants,’’ Population Division Technical Working Paper No.

63, January, 2003, <http://www.census.gov/population

/http://www/documentation/twps0063.html>.

Hamilton, Brady E., Joyce A. Martin, and Paul D. Sutton

(2003), ‘‘Births: Preliminary Data for 2002,’’ National Vital

Statistics Reports, Volume 51, Number 11, Division of

Vital Statistics, National Center for Health Statistics,

<http://www.cdc.gov/nchs/data/nvsr/nvsr51

/nvsr51_11.pdf>.

Kochaneck, Kenneth; and Betty Smith (2004), ‘‘Deaths: Preliminary Data for 2002,’’ National Vital Statistics

Reports, Volume 52, Number 13, Division of Vital Statistics, National Center for Health Statistics, <http://www.cdc

.gov/nchs/data/nvsr/nvsr52/nvsr52_13.pdf>.

Martin, Joyce A., Brady E. Hamilton, Stephanie Ventura, Fay

Menacker, Melissa M. Park, and Paul D. Sutton (2002),

‘‘Births: Final Data for 2001,’’ National Vital Statistics

Reports, Volume 51, Number 2, Division of Vital Statistics, National Center for Health Statistics, <http://www

.cdc.gov/nchs/data/nvsr/nvsr51/nvsr51/nvsr51_02.pdf>.

Mulder, T., Frederick Hollmann, Lisa Lollock, Rachel

Cassidy, Joseph Costanzo, and Josephine Baker, ‘‘U.S. Census Bureau Measurement of Net International Migration to

the United States: 1990 to 2000,’’ Population Division

Technical Working Paper No. 51, February, 2002, <http://

www.census.gov/population/www/documentation

/twps0051.html>.

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,

<http://www.census.gov/population/www/documentation

/twps0007/twps0007.html>.

Smith, Amy Symens, and Nicholas A. Jones (2003), Dealing With the Changing U.S. Racial Definitions: Producing Population Estimates Using Data With Limited Race Detail. Paper presented at the 2003 meetings

of the Population Association of America, Minneapolis,

Minnesota, May, 2003.

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. Immigration and Naturalization Service (1997), Statistical Yearbook of the Immigration and Naturalization Service, 1996, Washington, DC: U.S. Government

Printing Office.

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Appendix D.

Organization and Training of the Data Collection Staff

INTRODUCTION

The data collection staff for all U.S. Census Bureau programs is directed through 12 regional offices (ROs) and 3

telephone centers. The ROs collect data using two modes:

CAPI (computer-assisted personal interviewing) and CATI

(computer-assisted telephone 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), located in Jeffersonville, Indiana.

ORGANIZATION OF REGIONAL OFFICES/CATI

FACILITIES

The staffs of the ROs and CATI facilities carry out the Census Bureau’s field data collection programs for both

sample surveys and censuses. Currently, the ROs supervise over 6,500 part-time and intermittent field representatives (FRs) who work on continuing current programs

and one-time surveys. Approximately 1,900 of these FRs

work on the Current Population Survey (CPS). When a census is being taken, the field staff increases dramatically.

The location of the ROs and the boundaries of their

responsibilities are displayed in Figure D–1. RO areas were

originally defined to evenly distribute the office workloads

for all programs. Table D–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 two or three

CPS program supervisors on staff. The program supervisors have a staff of two to four office clerks working

essentially full-time. Most of the clerks are full-time federal

employees 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 20−35

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 nonresponse

and quality assurance reinterview 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, 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 leadership responsibilities, 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) is 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 or near 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 or fewer hours 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

Current Population Survey TP66 Organization and Training of the Data Collection Staff D–1

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Figure D–1. Map

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U.S. Bureau of Labor Statistics and U.S. Census Bureau
Table D–1. Average Monthly Workload by Regional Office: 2004

Regional office Base workload CATI workload Total workload Percent

Total................. 67,383 6,839 74,222 100.00

Boston.......................... 8,813 844 9,657 13.01

New York . . . . . . . . . . . . . . . . . . . . . . . 2,934 275 3,209 4.32

Philadelphia .................... 6,274 620 6,894 9.29

Detroit ......................... 4,848 495 5,343 7.20

Chicago ........................ 4,486 507 4,993 6.73

Kansas City ..................... 6,519 657 7,176 9.67

Seattle.......................... 5,251 676 5,927 7.99

Charlotte ....................... 5,401 541 5,942 8.01

Atlanta ......................... 4,927 455 5,382 7.25

Dallas .......................... 4,418 391 4,809 6.48

Denver ......................... 9,763 1,029 10,792 14.54

Los Angeles .................... 3,749 349 4,098 5.52

to starting his/her assignment. The initial training program consists of up to 24 hours of pre-classroom home

study, 3.5 to 4.5 days of classroom training (dependent

upon the trainee’s interviewing 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 FR’s 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.

Trainees receive extensive training on interviewing skills,

such as how to handle noninterview situations, how to

probe for information, ask questions as worded, and

implement face-to-face and telephone techniques. 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, compared with 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 their performance.

Each CPS supervisor is responsible for developing each

employee to his/her fullest potential. Employee development requires 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, accurate payrolls submitted on time, deadlines met, reports to

team leaders, training sessions, and administrative

responsibilities.

The most useful tool to help supervisors evaluate the FRs

performance is the CPS 11−39 form. This report is generated monthly and is produced using data from the

Regional Office Survey Control System (ROSCO) and Cost

and Response Management Network (CARMN) systems.

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This report provides the supervisor 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 that NPC could

not code.

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 ROs establish their own standards, they 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 is actually interviewed. It is calculated by dividing the

total number of interviewed households by the sum of the

number of interviewed households, the number of refusals, noncontacts, and noninterviewed households for

other reasons, including temporary refusals. (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

include interview time and travel time. The measure 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 FR’s 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 assignments that cover multiple PSUs.

• Large number of inherited or confirmed refusals.

• Working part of another FR’s assignment.

• Inordinate number of temporarily absent cases.

• High percentage of Type B/C noninterviews that

decrease the base for nonresponse rate.

• Other substantial changes in normal assignment conditions.

• Personal visits/phones.

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 FR’s attitude toward the job, use of the

computer, and the extent to which FRs apply CPS concepts

and procedures during actual work situations. There are

three types of observations:

1. Initial observations (N1, N2, N3)

2. General performance review (GPR)

3. Special needs (SN)

Initial observations are an extension of the initial classroom training for new hires and provide 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 are

an integral part of the initial training given to all FRs. A

2-day initial observation (N1) is scheduled during the FR’s

first CPS CAPI assignment. A second 1-day initial observation (N2) is scheduled during the FR’s second CPS CAPI

assignment. A third 1-day initial observation (N3) is scheduled during the FR’s 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 FRs. Each FR is regularly observed at least once a year.

Special-needs observations are made when an FR has

problems or poor performance. The need for a specialneeds observation is usually detected by other checks on

the FR’s work. For example, special-needs observations

are conducted if an FR has a high Type A noninterview

rate, a high minutes-per-case rate, a failure on reinterview,

an unsatisfactory evaluation on a previous observation,

made a request for help, or for other reasons related to

the FR’s performance.

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An observer accompanies the FR for a minimum of 6

hours during an actual work assignment. The observer

notes 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 also stresses vehicular and personal safety practices.

The observer reviews the FR’s 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 fully understood

and to seek the observer’s advice on solving other problems encountered.

Unsatisfactory performance. When the performance of

an 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, and with

guidance from the Human Resources Division, 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, provide

specific suggestions on ways to improve performance,

alert the FR to actions that will be taken by the survey

supervisor to assist the FR to improve his/her performance, and notify the FR that he/she is subject to separation if the work does not show improvement in the allotted time. Once placed on a PIP, the regional office provides

performance feedback during a specified time period (usually 30 days, 60 days, and 90 days).

Current Population Survey TP66 Organization and Training of the Data Collection Staff D–5

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Appendix E.

Reinterview: Design and Methodology

INTRODUCTION

A continuing program of reinterviews on subsamples of

Current Population Survey (CPS) households is carried out

every month. The reinterview is a second, independent

interview of a household conducted by a different interviewer. The CPS reinterview program has been in place

since 1954. Reinterviewing for CPS serves two main quality control (QC) purposes: to monitor the work of the field

representatives (FRs) and to evaluate data quality via the

measurement of response error (RE) where all the labor

force questions are repeated. The reinterview program is a

major tool for limiting the occurrence of nonsampling

error and is a critical part of the CPS program.

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 samples, 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 the 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. It provides a means of limiting nonsampling

error, as described in Chapter 15. The RE reinterview currently estimates simple response variance, a measure of

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 previous

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, 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

the sample. This rule applies to both the RE and QC

samples. Any household contacted for reinterview is ineligible for reinterview selection during its remaining

months in the 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 (ROs). Only

households that 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 uses 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. A

falsification study determined that a relationship exists

between tenure and both frequency of falsification and the

percentage of assignments 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).

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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. 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. 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.1 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 the reional 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.2 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 were

asked in the original interview.3 Currently, no reconciliation is conducted.4

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 resign or are terminated.

Results from the RE reinterview 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., J. A. Dewey, and G. Weller, (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 Prior to October 2001, QC reinterviews asked the full set of

labor force questions for all eligible household members.

2 Beginning in February 1998, the RE reinterview respondent

rule was made the same as the QC reinterview respondent rule. 3 Prior to October 2001, RE reinterviews asked the industry

and occupation questions independently. 4 Reconciled reinterview, which was used to estimate reponse

bias and provide feedback for FR performance, was discontinued

in January 1994.

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Acronyms

ADS Annual Demographic Supplement

API Asian and Pacific Islander

ARMA Autoregressing Moving Average

ASEC Annual Social and Economic Supplement

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

FNS Food and Nutrition Service

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

HUD Housing and Urban Development

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

NCES National Center for Education Statistics

NCEUS National Commission on Employment and

Unemployment Statistics

NCHS National Center for Health Statistics

NCI National Cancer Institute

NEA National Endowment for the Arts

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 Non-Self-Representing

NTIA National Telecommunications and

Information Administration

OB Number of Births

OCSE Office of Child Support Enforcement

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

PWBA Pension and Welfare Benefits

Administration

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

SS Second-Stage

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

Current Population Survey TP66 Acronyms–1

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Index

4-8-4 rotation system 2-1−2-2, 3-13−3-15

A

Active duty

components of population change C-2

net recruits to the armed forces from the civilian

population (NRAF) C-6−C-7

population universe for CPS controls C-3

summary list of sources for CPS population controls

C-12

terminology used in the appendix: civilian population

C-1

Address identification 4-1−4-2

Adjusted

census base population C-5

population 65 years of age and over C-11

population controls for states C-10

Age

age definition C-7

births and deaths C-5

census base population C-7

civilian institutional population C-10

death by C-8

distribution of census base and components of change

C-7

exclusion of armed forces population and inmates of

civilian institutions C-11

household population under 65 years of age

C-10−C-11

international migration C-6, C-9

introduction, demographic characteristics C-1

migration of armed forces C-9−C-10

net migration movement C-6, C-9

population 65 years of age and over C-11

population by age, sex, race, and Hispanic origin C-7

population controls C-10

population universe for CPS controls C-3

Alaska addition to population estimates 2-2

American Indian Reservations 3-9

American Time Use Survey (ATUS) 11-4−11-5

Annual Demographic Supplement 11-5−11-10

Annual Social and Economic Supplement (ASEC)

11-5−11-10

Area frame

address identification 4-1−4-2

description 3-8

listing in 4-3−4-5

Armed Forces/Military Personnel

adjustment 11-8

census base population C-5

civilian institutional population by age, sex, race, and

Hispanic origin C-10

components of population change C-1−C-2

exclusion of armed forces population and inmates of

civilian institutions C-11

members 11-7−11-8, 11-10

migration of armed forces by age, sex, race, and

Hispanic origin C-9−C-10

net recruits to the armed forces from the civilian

population C-6−C-7

population universe for CPS controls C-3

summary list of sources for CPS population controls

C-12

terminology used in the appendix, civilian population

C-1

total population C-4−C-5

B

Base population

census base population C-5

census base population by age, sex, race, and Hispanic

origin: modification of the census race distribution

C-7

definition C-1

measuring the components of the balancing equation

C-5

population estimation C-2

population projection C-2

total population C-4−C-5

update of the population by sex, race, and Hispanic

origin C-7

Baseweights (see also basic weights) 10-2

Basic primary sampling unit components (BPCs) 3-9−3-10

Behavior coding 6-3

Between primary sampling unit (PSU) variances 10-3−10-4

Births

births and deaths C-5−C-6

components of population change C-1−C-2

distribution of births by sex, race, and Hispanic origin

C-8

household population under 65 years of age

C-10−C-11

natural increase C-2

summary list of sources for CPS population controls

C-12

Current Population Survey TP66 Index−1

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Births—Con.

total population C-4−C-5

Building permits

offices 3-8−3-9

permit frame 3-9

sampling 3-2

survey 3-7, 3-9, 4-2

Business presence 6-4

C

California substate areas 3-1

CATI recycles 4-6, 8-1−8-2

Census base population

census base population C-5

census base population by age, sex, race, and Hispanic

origin: modification of the census race distribution

C-7

Census Bureau report series 12-2

Census-level population, terminology used in the

appendix C-1

Check-in of sample 15-4

Citizens/Citizenship/Non-Citizen

international migration C-2

permanent residents C-2

Civilian institutional population by age, sex, race, and

Hispanic origin C-10

Civilian noninstitutional population (CNP)

census base population C-5

general 3-1, 3-6−3-7, 3-9−3-10, 5-2−5-3, 10-4,

10-7−10-8

population universe C-2−C-3

total population C-4

Civilian noninstitutional housing 3-7

Civilian population

change in the institutional population C-7

civilian institutional population by age, sex, race, and

Hispanic origin C-10

migration of armed forces C-9

net recruits to the armed forces from the civilian

population C-6−C-7

terminology used in the appendix: civilian population

C-1

terminology used in the appendix: universe C-2

total population C-4

Class-of-worker classification 5-4

Cluster design, changes in 2-3

Clustering, geographic 4-2−4-3

Coding data 9-1

Coefficients of variation (CV), calculating 3-1

Collapsing cells 10-3−10-6, 10-8

Complete nonresponse 10-2

Components of population change

definition C-1−C-2

household population under 65 years of age C-10

measuring the components of the balancing equation

C-5

Components of population change—Con.

population projection C-2

procedural revisions C-11

total population C-4−C-5

Composite estimation

estimators 10-9−10-11

introduction of 2-6

Computer-Assisted Interviewing System 2-5, 4-5

Computer-Assisted Personal Interviewing (CAPI)

daily processing 9-1

description of 4-1, 4-5, 6-2

effects on labor force characteristics 16-7

implementation of 2-5

nonsampling errors 16-3−16-4, 16-7

Computer-Assisted Telephone Interviewing (CATI)

daily processing 9-1

description of 4-5, 6-2, 7-6−7-9

effects on labor force characteristics 16-7

implementation of 2-4−2-5

nonsampling errors 16-3−16-4, 16-7

training of staff D-1−D-3

transmission of 8-1−8-3

Computer-Assisted Telephone Interviewing and ComputerAssisted Personal Interviewing (CATI/CAPI) Overlap 2-5

Computerization

electronic calculation system 2-2

questionnaire development 2-5

Consumer income report 12-2

Control card information 5-1

Control universe

introduction C-1−C-2

population controls for states C-10

population universe for CPS controls C-3−C-4

total population C-4

Core-Based Statistical Area (CBSA) 3-2

Counties

combining into primary sampling units 3-2−3-3

selection of 3-2

Coverage errors

controlling 15-2−15-3

coverage ratios 16-1−16-2

sources of 15-1−15-2

CPS control universe

births and deaths C-5−C-6

calculation of population projections for the CPS

universe C-4

components of population change C-1−C-2

CPS control universe C-2

distribution of census base and components of change

by age, sex, race and Hispanic origin C-7

measuring the components of the balancing equation

C-5

net recruits to the armed forces from the civilian

population C-6−C-7

organization of this appendix C-1

population universe for CPS controls C-3

Index−2 Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
CPS control universe—Con.

universe C-2

CPS reports 12-2−12-4

Current Employment Statistics 1-1

Current Population Survey (CPS)

data products from 12-1−12-4

decennial and survey boundaries D-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 3-1

sample design 3-2−3-15

sample rotation 3-13−3-15

supplemental inquiries 11-1−11-8

D

Data collection staff, organization and training D-1−D-5

Data preparation 9-1−9-3

Data quality 13-1−13-3

Deaths

births and deaths C-5−C-6

components of population change C-1−C-2

deaths by age, sex, race, and Hispanic origin C-8

household population under 65 years of age

C-10−C-11

migration of armed forces by age, sex, race, and

Hispanic origin C-9−C-10

natural increase C-2

net international movement C-6

net migration of the foreign-born component C-9

net recruits to the armed forces from the civilian

population C-6−C-7

population 65 years of age and over C-11

summary list of sources for CPS population controls

C-12

total population C-4−C-5

Demographic components of population change C1−C2

introduction C-1

migration of armed forces by age, sex, race, and

Hispanic origin C-9−C-10

organization of this appendix C-1

population controls for states C-10

population universe C-2

summary list of sources for CPS population controls

C-12

Demographic data

edits 7-8

edits and codes 9-2−9-3

for children 2-4

interview information 7-3, 7-7, 7-9

procedures for determining characteristics 2-4

questionnaire information 5-1−5-2

Department of Defense

civilian institutional population by age, sex, race, and

Hispanic origin C-10

migration of armed forces by age, sex, race, and

Hispanic origin C-9−C-10

net recruits to the armed forces from the civilian

population C-6−C-7

summary list of sources for CPS population controls

C-12

Dependent interviewing 7-5−7-6

Design effects 14-7

Disabled persons, questionnaire information 6-7

Discouraged Workers

definition 5-5, 6-1−6-2

questionnaire information 6-7−6-8

Document sensing procedures 2-1

Dormitory, population universe for CPS controls C-3−C-4

Dwelling places 2-1

E

Earnings

edits and codes 9-3

information 5-4, 6-5

weights 10-13−10-14

Edits 9-1−9-3, 15-8−15-9

Educational attainment categories 5-2

Electronic calculation system 2-2

Emigration

combined net international migration component C-9

definition 5-3, C-2

household population under 65 years of age C-9−C-10

international migration by age, sex, race, and Hispanic

origin C-9

net international movement C-6

net native emigration component C-9

Employment and Earnings (E & E) 12-1, 12-3, 14-5

Employment status (see also Unemployment)

full-time workers 5-3

layoffs 2-2, 5-5, 6-5−6-7

monthly estimates 10-15

not in the labor force 5-2, 5-5−5-6

occupational classification additions 2-3

part-time workers 2-2, 5-3

seasonal adjustment 2-2, 10-15−10-16

Employment-population ratio definition 5-5

Estimates, population

after the second-stage ratio adjustment 14-5−14-7

census base population C-5

CPS population controls: estimates or projections

C-2−C-3

deaths by age, sex, race, and Hispanic origin C-8

definition C-2

distribution of births by sex, race, and Hispanic origin

C-8

introduction C-1

organization of this appendix C-1

Current Population Survey TP66 Index−3

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Estimates, population—Con.

population for states C-10

population universe for CPS controls C-3−C-4

total population C-4−C-5

Estimators 10-1−10-2, 10-10−10-11, 13-1

Expected values 13-1

F

Families, definition 5-1−5-2

Family businesses, presence of 6-4

Family equalization 11-10

Family weights 10-13

Federal State Cooperative Program for Population

Estimates (FSCPE), household population under 65 years

of age C-10−C-11

Field primary sampling units (PSUs) 3-11, 3-13

Field representatives (FRs) (see also Interviewers)

conducting interviews 7-1, 7-3−7-6, 7-10

controlling response error 15-7

evaluating performance D-1− D-5

interaction with respondents 15-7−15-8

performance guidelines D-3−D-4

training procedures D-1−D-3

transmitting interview results 8-1−8-3

Field subsampling 3-13

Film Optical Sensing Device for Input to the Computer

(FOSDIC) 2-2

Final hit numbers 3-11−3-15

Final weight 10-12−10-13

First-stage ratio adjustment

Annual Social and Economic Supplement (ASEC) 11-6

Housing Vacancy Survey (HVS) 11-4

First-stage weights 10-4

Full-time workers, definition 5-3

G

Generalized variance functions (GVF) 14-3−14-5, B-2

Geographic clustering 4-2−4-3

Geographic Profile of Employment and Unemployment

12-1, 12-3

Group Quarters (GQ) frame

address identification 4-2, 4-4

change in the institutional population C-7

description 3-8−3-9

general 4-1−4-4

group quarters (GQ) listing 4-4

household population under 65 years of age

C-10−C-11

listing in 4-5

population universe for CPS controls C-3

summary list of sources for CPS population controls

C-12

H

Hadamard matrix 14-1−14-3

Hawaii addition to population estimates 2-2

Hispanic origin

births and deaths C-5−C-6

calculation of population projections for the CPS

universe C-3−C-4

census base population by age, sex, race, and Hispanic

origin: modification of the census race distribution

C-7−C-8

deaths by age, sex, race, and Hispanic origin C-8

distribution of births by sex, race, and Hispanic origin

C-8

international migration by age, sex, race, and Hispanic

origin C-9

migration of armed forces by age, sex, race, and

Hispanic origin C-9−C-10

mover and nonmover households 11-5−11-10

net international movement C-6

net migration from Puerto Rico component C-9

net migration of the foreign-born component C-9

net native emigration component C-9

population by age, sex, race, and Hispanic origin C-7

population controls for states C-10

population universe for CPS controls C-4

summary list of sources for CPS population controls

C-12

update of the population by sex, race, and Hispanic

origin C-7

Hit strings 3-10−3-11

Home Ownership Rates 11-2

Homeowner Vacancy Rates 11-2

Horvitz-Thompson estimators 10-10

Hot deck

allocations 9-2−9-3

imputation 11-6

Hours of work

definition 5-3

questionnaire information 6-4

Households

definition 5-1

distribution of births by sex, race, and Hispanic origin

C-8

edits and codes 9-2

eligibility 7-1, 7-3−7-4

household population under 65 years of age

C-10−C-11

mover and nonmover households size factor

11-5−11-10

population controls for states C-10

questionnaire information 5-1−5-2

respondents 5-1

rosters 5-1, 7-3, 7-7

summary list of sources for CPS population controls

C-12

weights 10-13

Housing units (HUs)

definition 3-7

frame omission 15-2

Index−4 Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Housing units (HUs)—Con.

measures 3-8

misclassification of 15-2

questionnaire information 5-1

selection of sample units 3-6−3-7

ultimate sampling units (USUs) 3-2

vacancy rates 11-2−11-4

Housing Vacancy Survey (HVS) 11-2−11-4, 11-10

I

Immigration

household population under 65 years of age

C-10−C-11

permanent residents C-2

population 65 years of age and over C-11

Imputation techniques 9-2−9-3, 15-8−15-9

Incomplete Address Locator Action Forms A-1

Industry and occupation data (I & O)

coding of 2-3−2-4, 8-2−8-3

data coding of 15-8

data questionnaire information 6-5

edits and codes 9-2−9-3

questionnaire information 5-3−5-4

verification 15-8

Inflation-deflation method 2-3−2-4

Initial second-stage adjustment factors 10-8

Institutional housing 3-7, 3-9

Institutional population

census base population C-5

change in the institutional population C-6

civilian institutional population by age, sex, race, and

Hispanic origin C-10

control universe C-2

definition C-2

population universe for CPS controls C-3−C-4

total population C-4

Internal consistency of a population

definition C-2

total population C-4−C-5

International migration

combined net international migration component C-9

definition C-2

international migration by age, sex, race, and Hispanic

origin C-9

net international movement C-6

total population C-4−C-5

Internet

CPS reports 12-1−12-2

questionnaire revision 6-3

Interview week 7-1

Interviewers (see also Field representatives)

assignments 4-1, 4-5−4-6

controlling response error 15-7

debriefings 6-3

evaluating performance D-1−D-5

interaction with respondents 15-7−15-8

Interviewers (see also Field representatives)—Con.

performance guidelines D-3−D-4

Spanish-speaking 7-6

training procedures D-1−D-3

Interviews

beginning questions 7-5, 7-7

controlling response error 15-6

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 15-8, E-1−E-2

results 7-10

revision of 6-1−6-8

telephone interviews 7-4−7-10

transmitting results 8-1−8-3

Introductory letters 7-1−7-2

Item nonresponse 9-1−9-2, 10-2, 16-5

Item response analysis 6-3

Iterative proportional fitting 10-7

J

Job leavers 5-5

Job losers 5-5

Job seekers

composite estimators 10-10−10-11

definition 5-4, 5-5

duration of search 6-6−6-7

edits and codes 9-3

effect of Type A noninterviews on classification

16-4−16-5

family weights 10-13

first-stage ratio adjustment 10-3−10-4

gross flow statistics 10-14−10-15

household weights 10-13

interview information 7-3

longitudinal weights 10-14−10-15

methods of search 6-6

monthly estimates 10-13, 10-15

outgoing rotation weights 10-13−10-14

questionnaire information 5-2−5-6, 6-2

ratio estimation 10-3

seasonal adjustment 10-15−10-16

second-stage ratio adjustment 10-7−10-10

unbiased estimation procedure 10-1−10-2

L

Labor force participation rate, definition 5-5

LABSTAT 12-1,12-4

Layoffs 2-2, 5-5, 6-5−6-7

Legal permanent residents, definition C-2

Levitan Commission 2-4

Levitan Commission (see also National Commission on

Employment and Unemployment Statistics) 2-4, 6-1, 6-7

Listing activities 4-3

Current Population Survey TP66 Index−5

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Listing checks 15-4

Listing Sheets

reviews 15-3−15-4

Unit/Permit A-1−A-2

Longitudinal edits 9-2

Longitudinal weights 10-14−10-15

M

Maintenance reductions B-1−B-2

Major Labor Force Recode 9-3

March Supplement (see also Annual Social and Economic

Supplement or Annual Demographic Supplement)

11-5−11-10

Marital status categories 5-2

Maximum overlap procedure 3-4

Mean squared error 13-1−13-2

Measure of size (MOS) 3-8

Metropolitan Statistical Areas (MSAs) 3-2, 3-4

Microdata files 12-1−12-2, 12-4

Military housing/barracks 3-7, 3-9

Modeling errors 15-9

Modified age, race, and sex

census base population by age, sex, race, and Hispanic

origin modification of the census race distribution

C-7−C-8

civilian institutional population by age, sex, race, and

Hispanic origin C-10

deaths by age, sex, race, and Hispanic origin C-8

definition C-2

migration of armed forces by age, sex, race, and

Hispanic origin C-9−C-10

Month-in-sample (MIS) 7-1, 7-4−7-6, 11-5−11-10,

16-7−16-9

Monthly Labor Review 12-1, 12-3

Mover households 11-5−11-10

Multiple jobholders

definition 5-3

questionnaire information 6-2, 6-4

Multiunit structures

Multiunit Listing Aids A-1

Unit/Permit Listing Sheets A-1−A-2

Multiunits 4-3, 4-5

N

National Center for Health Statistics (NCHS)

births and deaths C-5−C-6

deaths by age, sex, race, and Hispanic origin C-8

distribution of births by sex, race, and Hispanic origin

C-8

household population under 65 years of age C-10

introduction C-1

National Commission on Employment and Unemployment

Statistics (see also Levitan Commission) 6-1, 6-7

National Park blocks 3-9

National Processing Center (NPC) 8-1−8-3, D-1

Net recruits to the armed forces

migration of armed forces by age, sex, race, and

Hispanic origin C-9−C-10

net recruits to the armed forces from the civilian

population C-6−C-7

total population C-4−C-5

New entrants 5-5

New York substate areas 3-1

Noninstitutional housing 3-7−3-8

Noninterview

adjustments 10-3

American Time Use Survey (ATUS) 11-5

Annual Social and Economic Supplement (ASEC)

11-6−11-8

clusters 10-3

factors 10-3

Noninterviews 7-1, 7-3−7-4, 9-1, 16-2−16-5, 16-8

Nonmover households 11-5−11-8, 11-10

Nonresponse 9-1−9-2, 10-2, 15-4−15-5

Nonresponse errors

item nonresponse 16-5

mode of interview 16-7

proxy reporting 16-9

response variance 16-5−16-6

time in sample 16-7−16-9

type A noninterviews 16-2−16-5

Nonresponse rates 11-2

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

response errors 15-6

Non-self-representing primary sampling units (NSR PSUs)

3-3−3-5, 10-4, 14-1−14-3

North American Industry Classification System 2-6

O

Occupational data

coding of 2-4 , 8-2−8-3, 9-1

edits and codes 9-3

questionnaire information 5-3−5-4, 6-5

Office of Management and Budget (OMB) 2-2, 2-7

Old construction frames 3-7−3-12

Outgoing rotation weights 10-13−10-14

P

Part-time workers

definition 5-3

economic reasons 5-3

monthly questions 2-2

noneconomic reasons 5-3

Performance Improvement Period (PIP) D-5

Performance Opportunity Period (POP) D-5

Index−6 Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Permit Address List (PAL) 4-2, 4-4−4-5

forms 4-6

operation 4-2, 4-4−4-5

Permit frames materials A-1−A-2

address identification 4-1−4-2

description of 3-9−3-10

listing in 4-6

Permit Sketch Map A-2

segment folder A-1

Unit/Permit Listing Sheets A-1−A-2

Permit Sketch Maps A-1

Persons on layoff, definition 5-5, 6-5−6-7

Population base

CPS control universe C-4

definition C-1

measuring the components of the balancing equation

C-5

Population Characteristics report 12-2

Population Control adjustments

American Time Use Survey (ATUS) 11-5

CPS population controls: estimates or projections

C-2−C-3

measuring the components of the balancing equation

C-5

nonsampling error 15-9

organization of this appendix C-1

population by age, sex, race, and Hispanic origin C-7

population controls for states C-10

population universe for CPS controls C-3

sources 10-8

summary list of sources for CPS population controls

C-12

total population C-4−C-5

Population data revisions 2-3

Population universe

births and deaths C-5−C-6

definition C-2

institutional population C-2

measuring the components of the balancing equation

C-5

population universe for CPS controls C-3−C-4

procedural revisions C-11

total population C-4−C-5

Post-sampling codes 3-9−3-10

President’s Committee to Appraise Employment and

Unemployment Statistics 2-3

Primary Sampling Units (PSUs)

basic components 3-10

definition 3-2

field 3-4

increase in number of 2-3

maximum overlap procedure 3-4

noninterview clusters 10-3

non-self-representing (NSR) 3-3−3-5, 10-4, 14-1−14-3

rules for defining 3-2−3-3

selection of 3-4−3-6

Primary Sampling Units (PSUs)—Con.

self-representing (SR) 3-3−3-5, 10-4, 14-1−14-3

stratification of 3-3−3-4

Probability samples 10-1−10-2

Projections, population

births and deaths C-5−C-6

calculation of population projections for the CPS

universe C-4−C-5

CPS population controls: estimates or projections

C-2−C-3

definition C-2

measuring the components of the balancing equation

C-5

organization of this appendix C-1

procedural revisions C-11

total population C-4−C-5

Proxy reporting 16-9

Publications 12-1−12-4

Q

Quality control (QC)

reinterview program 15-8, E-1−E-2

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

reinterviews 15-8

revision of 2-1−2-3, 2-5, 6-1−6-8

structure of 5-1

R

Race

category modifications 2-4, 2-6

CPS population controls: estimates or projections

C-2−C-3

determination of 2-4

distribution of births by sex, race, and Hispanic origin

C-8

introduction C-1

modified race C-2

net international movement C-6

population by age, sex, race, and Hispanic origin C-7

population universe for CPS controls C-3−C-4

racial categories 5-2

total population C-4−C-5

update of the population by sex, race, and Hispanic

origin C-7

Raking ratio 10-7, 10-9

Random digit dialing sample (CATI/RDD) 6-2

Random groups 3-11

Random starts 3-10

Ratio adjustments

first-stage 10-3−10-4

general 10-3

Current Population Survey TP66 Index−7

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Ratio adjustments—Con.

second-stage 10-7−10-10

Ratio estimates

revisions to 2-1−2-4

two-level, first-stage procedure 2-4

Recycle reports 8-1−8-2

Reduction groups B-1−B-2

Reduction plans B-1−B-2

Reentrants 5-5

Reference persons 1-1, 2-5, 5-1−5-2

Reference week 5-2−5-3, 6-3−6-4, 7-1

Referral codes 15-8

Refusal rates 16-2−16-3

Regional offices (ROs)

organization and training of data collection staff

D-1−D-5

operations of 4-1, 4-6

transmitting interview results 8-1−8-3

Reinterviews 15-8, E-1−E-2

Relational imputation 9-2

Relationship information 5-1−5-2

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

Respondents

controlling response error 15-6−15-8

debriefings 6-3

interaction with interviewers 15-7−15-8

Response distribution analysis 6-3

Response error (RE) 6-1, 15-6−15-8, E-1

Response variance 13-1−13-2, 16-5−16-6

Retired persons, questionnaire information 6-7

Rotation chart 3-11

Rotation groups 3-11, 10-1,10-11, 10-13−10-14

Rotation system 2-1, 3-2, 3-13−3-14

S

Sample characteristics 3-1

Sample data revisions 2-3

Sample design

controlling coverage error 15-2−15-3

county selection 3-2−3-3

definition of the primary sampling units (PSUs) 3-2

field subsampling 3-13, 4-4−4-5

old construction frames 3-7−3-9

permit frame 3-9

permit frame materials A-1−A-2

post-sampling code assignment 3-11−3-13

sample housing unit selection 3-6−3-13

sampling frames 3-7−3-10

sampling procedure 3-10−3-11

sampling sources 3-7

Sample design—Con.

selection of the sample primary sampling units (PSUs)

3-4−3-6

stratification of the PSUs 3-3−3-5

unit frame materials A-1

variance estimates 14-5−14-6

within primary sampling unit (PSU) sort 3-10

Sample preparation

address identification 4-1−4-2

components of 4-1

controlling coverage error 15-3−15-4

goals of 4-1

interviewer assignments 4-5

listing activities 4-3

Sample redesign 2-5

Sample size

determination of 3-1

maintenance reductions B-1−B-2

reduction in 2-3−2-5, B-1−B-2

Sample Survey of Unemployment 2-1

Sampling

bias 13-2

error 13-1−13-2

frames 3-7−3-10

intervals 3-6, 3-10

variance 13-1−13-2

School enrollment

edits and codes 9-3

general 2-4

Seasonal adjustment of employment 2-2, 2-7,

10-15−10-16

Second jobs 5-4

Second-stage ratio adjustment

armed forces 11-10

Annual Social and Economic Supplement (ASEC),

general 11-8

Housing Vacancy Survey (HVS) 11-14

Security

transmitting interview results 8-1

Segment folders

permit frame A-1

unit frame A-1

Segment number suffixes 3-11

Selection method revision 2-1

Self-employed definition 5-4

Self-representing primary sampling units (SR PSUs)

3-3−3-5, 10-4 , 14-1−14-3

Self-weighting samples 10-2

Senior field representatives (SFRs) D-1

Sex

calculation of population projections for the CPS

universe C-4

census base population by age, sex, race, and Hispanic

origin: modification of the census race distribution

C-7−C-8

Index−8 Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau
Sex—Con.

CPS population controls: estimates or projections

C-2−C-3

deaths by age, sex, race, and Hispanic origin C-8

distribution of births by sex, race, and Hispanic origin

C-8

international migration by age, sex, race, and Hispanic

origin C-9

introduction C-1

net international movement C-6

population by age, sex, race, and Hispanic origin C-7

population controls for states C-10

population universe for CPS controls C-3−C-4

update of the population by sex, race, and Hispanic

origin C-7

Simple response variance 16-5−16-6

Simple weighted estimators 10-10, 10-12−10-13

Skeleton sampling 4-2

Sketch maps A-2

Spanish-speaking interviewers 7-6

Special studies report 12-2

Standard Error Computation Units (SECUs) 14-1−14-2

Standard Metropolitan Statistical Areas (SMSAs) 2-3

Standard Occupational Classification 2-6

Standards of Quality 11-1

Start-with procedure (SW) 3-13, 4-2

State Children’s Health Insurance Program (SCHIP)

adjustment 11-7−11-10

general 2-6, 3-1, 3-4, 3-6, 3-11, 11-7−11-10

State sampling intervals 3-6, 3-10

State supplementary samples 2-4

State-based design 2-4

States

coverage adjustment 10-6

exclusion of the armed forces population and inmates of

civilian institutions C-11

household population under 65 years of age

C-10−C-11

population controls for states C-10

two-stage ratio estimators 10-10

Statistical Policy Division 2-2

Statistics

quality measures 13-1−13-3

Stratification Search Program (SSP) 3-4

Subfamilies

definition 5-2

Subsampling 3-4, 4-4−4-5

Successive difference replication 14-2−14-4

Supplemental inquiries

American Time Use Survey (ATUS) 11-4−11-5

Annual Social and Economic Supplment (ASEC)

11-5−11-10

criteria for 11-1−11-2

Survey of Construction (SOC) 4-2

Survey week 2-2

Systematic sampling 4-2

T

Take-every procedure (TE) 3-13

Telephone interviews 7-4−7-10, 16-7

Temporary jobs 5-5

The Employment Situation 12-1, 12-3

Type A noninterviews (see also Unit nonresponse) 7-1,

7-3−7-4, 10-2−10-3, 16-2−16-5

Type B noninterviews 7-1, 7-3−7-4

Type C noninterviews 7-1, 7-3−7-4

U

U.S. Bureau of Labor Statistics (BLS) 1-1, 12-1−12-2, 12-4

U.S. Census Bureau 1-1, 4-2, 12-1−12-2

Ultimate sampling units (USUs)

area frames 3-8, 3-10

description 3-2

group quarters frame 3-8−3-10

hit strings 3-11

permit frame 3-9−3-10

reduction groups B-1−B-2

selection of 3-10−3-11

special weighting adjustments 10-2

unit frame 3-8, 3-10

variance estimates 14-1−14-4

Unable to work persons, questionnaire information 6-7

Unbiased estimation procedure 10-1−10-2

Unbiased estimators 13-1−13-2

Unemployment

classification of 2-3

definition 5-5

duration of 5-5

early estimates of 2-1

household relationships and 2-3

monthly estimates 10-10−10-11

questionnaire information 6-5−6-7

reason for 5-5

seasonal adjustment 2-2, 10-15−10-16

variance on estimate of 3-6−3-8, 3-13

Union membership 2-4

Unit frame

address identification 4-1

description of 3-8

Incomplete Address Locator Action Forms A-1

listing in 4-4−4-5

materials A-1

Multiunit Listing Aid A-1

segment folders A-1

Unit/Permit Listing Sheets A-1

Unit nonresponse (see also Type A noninterviews) 10-2,

16-2−16-5

Unit/Permit 4-3−4-5

Unit/Permit Listing Sheets

general 4-5

permit frame A-1−A-2

unit frame A-1

Usual residence elsewhere (URE) 7-1

Current Population Survey TP66 Index−9

U.S. Bureau of Labor Statistics and U.S. Census Bureau
V

Vacancy rates 11-2, 11-4

Variance estimation

definitions 13-1−13-2

design effects 14-7−14-9

determining optimum survey design 14-5−14-6

generalizing 14-3−14-5

objectives of 14-1

replication methods 14-1−14-2

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 10-14

W

Web sites

CPS reports 12-2

employment statistics 12-1

questionnaire revision 6-3

Weighting Procedure

American Time Use Survey (ATUS) 11-5

Annual Social and Economic Supplement (SEC)

11-6−11-10

Housing Vacancy Survey (HVS) 11-4

Weighting samples 15-8−15-9

Work Projects Administration (WPA) 2-1

X

X-11 and X-12 ARIMA program 2-6, 10-15−10-16

Index−10 Current Population Survey TP66

U.S. Bureau of Labor Statistics and U.S. Census Bureau