Issue Date

Publication Number

Current Population Survey—America’s Source for Labor Force Data

Design and Methodology

Technical Paper 77

October 2019
Acknowledgments This Current Population Survey Technical Paper (CPS TP77) is an updated 

version of TP66 released in 2006. Previous content has been brought 

up-to-date and reorganized to reflect the 2010 Current Surveys’ Sample 

Redesign, U.S. Census Bureau organizational changes, and revised Section 

508 standards of the Rehabilitation Act of 1973 for electronic document 

accessibility. As future updates in CPS design and methodology occur, they 

will be posted to the Census Bureau’s Internet site. 

Many individuals contributed to the publication of the CPS TP77. Some 

individuals authored chapters, sections, and appendixes, and others 

reviewed the text for technical, procedural, and grammatical accuracy. Some 

individuals served in both roles. While certain individuals are still with their 

respective agency, some have since left the agency. 

This technical paper was written under the coordination of Leslie R. Flores, 

Mei Li, and Aaron J. Gilary of the Census Bureau. Here, we have attempted to 

document all individuals whose combined effort produced this publication. 

Authors and reviewers of this paper at the Census Bureau were David E. 

Adams, James R. Back, John T. Baker II, Candice A. Barnes, Redouane 

Betrouni, Benjamin C. Bolender, Rosemary Byrne, Stephanie ChanYang, Yang Cheng, Dennis Clark, Lisa A. Clement, Jeffrey S. Corteville, 

Annetta Golding DePompa, Christopher Dick, Khoa Dong, James Farber, 

Tremika R. Finney, Leslie R. Flores, Brian A. Fouche, Susan S. Gajewski, 

Susan T. Galeano, Aaron J. Gilary, Jennifer Williams Hill, David V. Hornick, 

John A. Jones, Olga Koly, Chris Kuwik, Mei Li, Kyra M. Linse, Clifford L. 

Loudermilk, Antoinette Lubich, Tim J. Marshall, Alan K. Peterson, KeTrena 

Phipps, Jimmie B. Scott, Franklin D. Silberstein, Larry Douglas Sink, Daniel 

Sommers, Thomas R. Spaulding, Kenisha Staine, Tim Trudell, Katrina L. 

Wengert, and Gregory D. Weyland. 

Authors and reviewers of this paper at the U.S. Bureau of Labor Statistics 

(BLS) were Dorinda Allard, Andrew Blank, Thomas Evans, Steven Haugen, 

Fran Horvath, Karen Kosanovich, Justin McIllece, Stephen M. Miller, Anne 

Polivka, and Ed Robison.

Approving officials from the Census Bureau and BLS were Dorinda Allard,

John T. Baker II, Benjamin C. Bolender, Lisa A. Clement, Glenn A. Eanes, 

Julie Hatch Maxfield, Karen S. Silloway, Jeffrey D. Sisson, and Anthony G. 

Tersine, Jr. 

At the Census Bureau, Musbaw Alawiye, Corey T. Beasley, Faye E. Brock, 

Linda Chen, Christine E. Geter, Amanda J. Perry, and Janet S. Sweeney of 

the Public Information Office provided publication management, graphics 

design and composition, and editorial review for print and electronic media, 

and Linda Vaughn of the Administrative and Customer Services Division 

provided printing management. Lawrence Malakhoff served as accessibility 

consultant, and Cynthia L. Wellons-Hazer provided administrative support. 

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. 
U.S. Department of Commerce

Wilbur Ross,

Secretary

Karen Dunn Kelley, 

Deputy Secretary 

U.S. CENSUS BUREAU

Steven D. Dillingham,

Director

U.S. Department of Labor

Eugene Scalia,

Secretary of Labor 

U.S. Bureau of Labor Statistics

William W. Beach,

Commissioner

Current Population Survey—America’s Source for Labor Force Data

Design and Methodology Issued October 2019
Suggested Citation

 U.S. Census Bureau, 

Current Population Survey 

Design and Methodology 

Technical Paper 77, 

October 2019.

U.S. CENSUS BUREAU

Steven D. Dillingham,

Director

Ron S. Jarmin,

Deputy Director and 

Chief Operating Officer

Victoria A. Velkoff,

Associate Director for 

Demographic Programs

Eloise K. Parker,

Assistant Director for 

Demographic Programs

Anthony G. Tersine, Jr.,

Chief, 

Demographic Statistical Methods Division

U.S. BUREAU OF LABOR 

STATISTICS 

William W. Beach,

Commissioner

William J. Wiatrowski,

Deputy Commissioner

Julie Hatch Maxfield,

Associate Commissioner for 

Employment and Unemployment

Statistics
FOREWORD

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

the United States. It is an important survey, providing information on many of the things that define us 

as a society—our work, our earnings, our education. It is also very complex. In this publication, the staff 

of the U.S. Census Bureau and the U.S. Bureau of Labor Statistics have attempted to provide data users 

with a thorough description of the design and methodology used to administer the CPS and create 

estimates. 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 

since this report was last updated, much has changed. In particular, since CPS Design and Methodology 

Technical Paper 66 was issued in 2006, annual sampling replaced once-a-decade sampling to increase 

flexibility in design and operations. Another major difference from the previous design is the adoption of 

the master address file (MAF) to take advantage of the extensive and regularly-updated address information from the U.S. Postal Service. The MAF is now the primary source of frames and sample units for 

the CPS, as well as for many other demographic surveys the Census Bureau conducts. This change has 

allowed the Census Bureau to abandon costly address listing operations in the field. 

In addition to this technical paper, please visit our CPS Web sites at <www.bls.gov/cps/> and 

<www.census.gov/programs-surveys/cps.html>, where additional information about CPS methodology 

and data is available. Also, we welcome comments from users about the value of this document and 

ways that it could be improved. To provide comments or submit a question, please refer to the CPS 

contact page at <www.bls.gov/cps/contact.htm>.

Steven D. Dillingham 

Director 

U.S. Census Bureau 

October 2019 

William W. Beach

Commissioner

U.S. Bureau of Labor Statistics

October 2019

Current Population Survey TP77 i

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

CONTENTS

1. General Current Population Survey Information .

1-1 Background and History .

1-2 Questionnaire Concepts and Definitions .

1-3 Supplements . 

1-4 Data Products . 

Appendix: History of the Current Population Survey . 

2. Current Population Survey Sample Design and Estimation . 

2-1 Sample Frame . 

2-2 Sample Design . 

2-3 Weighting and Estimation . 

2-4 Variance Estimation . 

2-5 Seasonal Adjustment . 

3. Current Population Survey Operations . .

3-1 Instrument Design . .

3-2 Conducting the Interviews . .

3-3 Transmitting the Interview Results . .

3-4 Data Preparation . .

3-5 Organization and Training of the Data Collection Staff . .

4. Current Population Survey Data Quality . .

4-1 Nonsampling Error . .

4-2 Reinterview Design and Methodology . .

Appendix: Weighting Scheme for Quality Control Reinterview Estimates . .

Acronyms . .

Index . .

LIST OF FIGURES

Figure 1-3.1 Diagram of the Annual Social and Economic Weighting Scheme . 

Figure 3-2.1 Sample Introductory Letter . .

Figure 3-3.1 Overview of Current Population Survey Monthly Operations for a Typical Month . .

Figure 3-5.1 Census Bureau Regional Office Boundaries . .

Figure 4-1.1 Current Population Survey Total Coverage Ratios: December 2014–December 2017, 

National Estimates . .

Figure 4-1.2 Current Population Survey Nonresponse Rates: December 2014–December 2017, 

National Estimates . .

Figure 4-2.1 Quality Control Reinterview Sampling Diagram . .
ii Current Population Survey TP77

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

LIST OF TABLES

Table 1-3.1. Current Population Survey Supplements, January 2004–December 2017 . 

Table 1-3.2 Month-In-Sample Groups Included in the Annual Social and Economic Supplement 

Sample for Years 2001 to 2017 . 

Table 1-3.3 Summary of the Annual Social and Economic Supplement Interview Months . 

Table 1-3.4 Summary of the Annual Social and Economic Supplement Children’s Health Insurance 

Program Adjustment Factor . 

Table 2-1.1 Address Sources for the Current Population Survey Sampling Frames for the 2000 and 

2010 Sample Designs . 

Table 2-2.1 Civilian Noninstitutional Population 16 Years and Over in Sample Areas for 852-

Primary-Sampling-Unit Design by State . 

Table 2-2.2 Current Population Survey/Children’s Health Insurance Program Rotation Chart 

January 2018–March 2020 Month-in-Sample by Sample Designation and Rotation . 

Table 2-3.1 National Coverage Step Cell Definitions . 

Table 2-3.2 State Coverage Adjustment Cell Definition . 

Table 2-3.3 Second-Stage Adjustment Cell by Race, Age, and Sex . 

Table 2-3.4 Second-Stage Adjustment Cell by Ethnicity, Age, and Sex . 

Table 2-3.5 Composite National Ethnicity Cell Definition . 

Table 2-3.6 Composite National Race Cell Definition . 

Table 2-4.1 Generalized Variance Function Parameters for Estimates of Monthly Levels . 

Table 2-4.2 Within-Primary Sampling Unit to Total Variance Ratios, 2016 Annual Averages . 

Table 2-4.3 Relative Standard Errors After Selected Weighting Stages: 2016 Annual Averages . 

Table 2-4.4 Design Effects After Selected Weighting Stages: 2010–2017 Averages . 

Table 3-2.1 Noninterviews: Types A, B, and C . .

Table 3-2.2 Noninterviews: Main Household Items Asked for Types A, B, and C . .

Table 3-2.3 Interviews: Main Household Items Asked in Month-In-Sample 1 and Replacement 

Households . .

Table 3-2.4 Summary Table for Determining Who is to be Included as a Member of the Household . 1

Table 3-2.5 Interviews: Main Demographic Item Asked . .

Table 3-2.6 Demographic Edits in the Current Population Survey Instrument . .

Table 3-2.7 Interviews: Main Household and Demographic Items Asked in Month-In-Sample 5 . .

Table 3-2.8 Interviewing Results for October 2017 . .

Table 3-2.9 Telephone and Personal Field Interviewing for October 2017 . .

Table 3-5.1 Average Monthly Workload by Regional Office: 2017 . .

Table 4-1.1 National Current Population Survey Item Types, Monthly Average Percentage of Invalid 

or Missing Values, July 2017–December 2017 . .

Table 4-1.2 Percentage of Current Population Survey Labor Force Reports by Type of 

Respondents: 2015–2017 . .

Table 4-1.3 Month-in-Sample Bias Indexes for Total Employed and Total Unemployed Average: 

January 2014–December 2015 . .

Table 4-2.1 Reinterview Results for November 2016—Counts . .

Table 4-2.2 Reinterview Results for November 2016—Statistics . .
Unit 1 

General Current Population 

Survey Information
This page is intentionally blank.
Current Population Survey TP77 Chapter 1-1: Background and History 3

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

Chapter 1-1: Background and History

BACKGROUND

The Current Population Survey (CPS) is a monthly 

survey sponsored jointly by the U.S. Census 

Bureau and the U.S. Bureau of Labor Statistics 

(BLS). It is the source of the national unemployment rate, along with a wide range of information 

about employment, unemployment, and people not in the labor force. 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 and geographic areas. 

The labor force concepts and definitions used in 

the CPS have undergone only slight modifications 

since the survey’s inception in 1940. Those concepts and definitions are discussed in Chapter 1-2, 

Questionnaire Concepts and Definitions. Although 

labor market information is central to the CPS, the 

survey provides a wealth of other demographic 

and socioeconomic 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 

focuses on labor force data because the timely 

and accurate collection and publication of these 

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 eligible households. Survey data generally are collected during the calendar week that 

includes the nineteenth of the month. The questions refer to activities during the prior week for 

those who are employed and the prior 4 weeks for 

those who are unemployed. Activities in the prior 

week generally refer to those done in the week 

that includes the twelfth of the month.1 Sampled 

households from all 50 states and the District 

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

1 week earlier to avoid conflicting with the holiday season. 

Additionally, since 2006, the November collection may be moved 

1 week earlier to avoid the holiday and/or to allow adequate 

processing time before the December collection. The reference 

week is then also moved 1 week earlier.

of Columbia are in the survey for 4 consecutive 

months, are out for 8 months, and then return 

for another 4 consecutive months before leaving 

the sample permanently. This design ensures a 

high degree of continuity from one month to the 

next, as well as year to year. This 4-8-4 sampling 

scheme has the added benefit of allowing the 

constant replenishment of the sample without 

excessive burden to respondents. A new group of 

respondents starts its 4-8-4 rotation each calendar month, while at the same time another group 

completes its rotation.

The CPS is collected by Census Bureau field representatives (FR) across the country, through both 

personal and telephone interviews, using laptop 

computers. Additional telephone interviewing is 

conducted from the Census Bureau’s two centralized contact centers in Jeffersonville, Indiana, and 

Tucson, Arizona.

To be eligible to participate in the CPS, individuals must be U.S. residents aged 15 or over who 

are not in the armed forces. People in institutions, such as prisons, long-term care facilities, 

and nursing homes, are ineligible for the CPS. In 

general, the BLS publishes labor force data only 

for people aged 16 and over, since those under 

the age of 16 are limited in their labor market 

activities by compulsory schooling and child labor 

laws. No upper age limit is used and students are 

treated the same as nonstudents. One person 

generally responds for all eligible members of the 

household. 

Usually within 2 weeks of the completion of these 

interviews, the BLS publishes the Employment 

Situation news release. This release highlights 

monthly labor force statistics from the CPS, as 

well as data on employment, hours, and earnings 

from the Current Employment Statistics (CES) 

survey of establishments. This closely watched 

release provides some of the earliest economic 

indicators available each month and represents 

the nation’s most comprehensive measures of 

national employment and unemployment. Dozens 

of data tables and thousands of time series estimates are also made available to the public at the 

time of the release. 
4 Chapter 1-1: Background and History Current Population Survey TP77

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

In addition to the regular labor force questions, 

the CPS often includes special supplementary 

questions on a variety of topics. The best known 

of the CPS supplements is the Annual Social and 

Economic Survey (ASEC), which is the source 

of the national poverty rate and is also used to 

measure income and health insurance coverage. 

Additional supplement topics include veterans, 

school enrollment, worker displacement, and 

job tenure. Because of the large sample size and 

broad population coverage of the CPS, a wide 

range of sponsors use supplements to collect 

data on topics that are not directly related to the 

labor market, such as participation in the arts, 

tobacco use, computer use, and voting patterns. 

The supplements are described in greater detail in 

Chapter 1-3, Supplements. 

HISTORY OF THE CURRENT 

POPULATION SURVEY 

The CPS has its origin in a program established 

to provide direct measurement of national 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 

by the WPA, combined with the experience from 

the Enumerative Check Census, led to a regular 

monthly sample survey of unemployment that 

provided an accurate and timely measurement of 

employment, unemployment, and the size of the 

labor force on a systematic basis. Early tests of 

this first national monthly sample survey were initiated in December 1939 and the survey officially 

began in March 1940 with the collection of data 

for the 1940 Census. 

Over the years, survey questions have been 

expanded to capture additional labor market data. 

Since the survey's inception, there have been 

numerous modifications to the definitions, sample 

design, and data collection methods to enhance 

the reliability of labor force statistics derived from 

the survey data. See the Appendix: History of the 

Current Population Survey for a list of important 

modifications to the CPS since 1940.
Current Population Survey TP77 Chapter 1-2: Questionnaire Concepts and Definitions 5

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

Chapter 1-2: Questionnaire Concepts and Definitions

INTRODUCTION

An important component of the CPS is the questionnaire, also called the survey instrument. The 

survey instrument utilizes automated data collection methods: computer-assisted personal interviewing (CAPI) and computer-assisted telephone 

interviewing (CATI). This chapter explains the 

definitions embedded in the questionnaire and 

key survey questions. For details on data collection procedures and protocols, see Chapter 3-1, 

Instrument Design and Chapter 3-2, Conducting 

the Interviews. 

STRUCTURE OF THE SURVEY 

INSTRUMENT

The CPS interview questionnaire is divided into 

three basic parts: (1) household and demographic questions, (2) labor force questions, and 

(3) supplemental questions. Supplemental questions are added to the CPS nearly every month 

and cover a number of different topics. The order 

in which interviewers attempt to collect information is: (1) housing unit (HU) data, (2) demographic data, (3) labor force data, and (4) supplemental data. Supplemental data may include 

more demographic and household questions.

The definitions underlying the household, demographic, and labor force data are described below. 

For more information about supplements to the 

CPS, see Chapter 1-3, Supplements.

CONCEPTS AND DEFINITIONS

Household and Demographic Information

Upon contacting a household, interviewers 

proceed with the interview unless the case is a 

definite noninterview. (Chapter 3-2, Conducting 

the Interviews, 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 HU and all individuals who usually live at the 

address.

Housing unit information. Upon first contact with 

a HU, interviewers collect information on the HU 

physical address, its mailing address, the year it 

was constructed, the type of living quarters (e.g., 

a house, apartment, or mobile home), whether it 

is renter- or owner-occupied, and whether it has a 

telephone and, if so, the telephone number.

Household roster. After collecting or updating 

the HU data, the interviewer either creates or 

updates a list of all individuals living in the unit 

and determines whether 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 the person is a household member 15 years 

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 HU, 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 

first (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 the military, individuals who 
6 Chapter 1-2: Questionnaire Concepts and Definitions Current Population Survey TP77

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

have usual residences elsewhere, and individuals 

in institutions such as prisons or nursing homes.) 

Once household or nonhousehold membership 

has been established for all people on the roster, 

the interviewer proceeds to collect all other demographic data, but only for household members.

Relationship to reference person. The interviewer will show a flash card with relationship 

categories (i.e., opposite-sex spouse, same-sex 

spouse, opposite-sex unmarried partner, samesex unmarried partner, child, grandchild, parent, 

brother or sister, housemate or roommate) to the 

household respondent and ask them to report 

each household member’s relationship to the reference person (the person listed on line one of the 

household roster). These relationships are used to 

define families, subfamilies, and unrelated individuals. A family is defined as a group of two or more 

individuals residing together who are related by 

birth, marriage, or adoption; all such individuals 

are considered members of one family. Families 

are further classified either as married-couple 

(spouse present) families or as families maintained by women or men without spouses present. Subfamilies are further classified as related 

subfamilies or unrelated subfamilies. A related 

subfamily is a married couple with or without children, or one parent with one or more own, single 

(never-married) children under 18 years old, living 

in a household, and related to, but not including, 

the householder or spouse. An unrelated subfamily is a family that lives in a HU where none of its 

members is related to the reference person. An 

unrelated individual(s) may be part of a household 

containing one or more families (like an unmarried 

partner, with or without children, or a housemate), 

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 or veteran status, level of education, 

race, ethnicity, nativity, and disability status. Total 

family income is also collected. 

The following terms define an individual’s marital 

status at the time of the interview: 

• Married, spouse present: applies to a married 

couple 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. 

• Married, spouse absent: refers to married 

people living apart because a spouse was 

employed and living at a considerable distance from home, was serving away from 

home in the armed forces, had moved to 

another area, or had a different place of residence for any other reason except separation 

as defined below. 

• Separated: includes people with legal separations, those living apart with intentions of 

obtaining a divorce, and other people permanently or temporarily separated because of 

marital discord. 

• Widowed.

• Divorced.

• Never married.

Educational attainment for each person in the 

household aged 15 or older is obtained through a 

question asking about the highest grade or degree 

completed. Beginning in 2015, additional questions about professional certifications and state 

or industry licenses used for getting or keeping a 

job were added to measure credentials granted 

outside of the regular education system. 

Questions on race and Hispanic or Latino ethnicity 

comply with federal standards established by the 

Office of Management and Budget. 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 

of Hispanic, Latino, or Spanish origin, and appears 

before the question on race. Next, all respondents, including those who identify themselves 

as Hispanic, are asked to choose one or more 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. They are reminded that 

Hispanic origin is not a race. Although not indicated to the respondent, responses of “other” are 

accepted and allocated among the race categories. Respondents may choose more than one 

race, so data may be tabulated for many different 

combinations of race and Hispanic ethnicity.
Current Population Survey TP77 Chapter 1-2: Questionnaire Concepts and Definitions 7

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

The CPS uses a set of six questions to identify 

persons with disabilities. People are classified as 

having a disability if there is a response of “yes” 

to any of these questions. Each of the questions asks the respondent whether anyone in the 

household (civilian, aged 15 and older) has the 

condition described, and if the respondent replies 

“yes,” they are then asked to identify everyone 

in the household who has the condition. A brief 

description of the six conditions are: (1) deaf or 

serious difficulty hearing; (2) blind or serious difficulty seeing; (3) serious difficulty concentrating, 

remembering, or making decisions; (4) serious 

difficulty walking or climbing stairs; (5) difficulty 

dressing or bathing; and (6) difficulty doing 

errands.

Labor Force Information

The CPS provides a measure of monthly employment—not jobs. An employed person, as measured in the CPS, may have more than one job. 

Labor force information is obtained after most 

household and demographic information has been 

collected. Besides determining labor force status, 

information is also collected on hours worked, 

occupation, industry, self-employment, earnings, 

duration of job search, and other labor force 

characteristics. 

The primary purpose of the labor force questions 

is to classify all individuals as employed, unemployed, or not in the labor force. 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, no matter how many hours 

they worked, are not asked the questions about 

having looked for work and cannot be classified as 

unemployed. Survey respondents are never asked 

specifically if they are unemployed, nor are they 

given an opportunity to decide their own labor 

force status. Similarly, an individual’s personal 

activities or characteristics, like going to school, 

taking care of a family member, or the presence of 

a disability or long-term illness, do not determine 

how labor force status is measured. Their status 

will be determined based on how they respond to 

a specific set of questions about their recent labor 

market activities. For example, someone who 

“retired” from one job may be working at another 

job and, thus, is classified as employed. 

The current concepts and definitions underlying 

the collection and estimation of labor force data 

are presented below. A more expanded, nontechnical discussion of the various labor force categories is available in the BLS publication “How the 

Government Measures Unemployment,” online at 

<www.bls.gov/cps/cps_htgm.htm>.

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 typically 

includes the twelfth of the month. (For November 

and December, the reference week and survey 

collection period may be moved 1 week earlier to 

avoid the Thanksgiving and Christmas holidays 

and allow enough time for processing between 

data collection.)

Civilian noninstitutional population. In the CPS, 

labor force data are restricted to people who currently reside in one of the 50 states or the District 

of Columbia, who do not reside in institutions 

(such as a correctional institution or a residential nursing or mental health care facility), and 

who are not on active duty in the armed forces. 

Although data are collected from household members 15 years and over, official labor force estimates refer to people 16 years and over.

Employed people. Employed people are those 

who, during the reference week, (1) did any work 

at all (for at least 1 hour) as paid employees; (2) 

worked for profit in their own business, profession, 

or farm; (3) worked 15 hours or more as unpaid 

workers in an enterprise operated by a family 

member they lived with; or (4) were temporarily absent from their jobs because of illness, bad 

weather, vacation, labor dispute, or another reason (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.) 

Citizens of foreign countries who are working 

in the United States, not living on the premises 

of an embassy, and not simply visiting or traveling, are included in the number of employed 

people. People whose only activity consisted of 

work around their own house (painting, repairing, 

cleaning, or other home-related housework) or 
8 Chapter 1-2: Questionnaire Concepts and Definitions Current Population Survey TP77

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

unpaid volunteer work for religious, charitable, or 

other organizations are excluded.

The key questions used to determine whether an 

individual is employed or not are presented at the 

end of this unit.

Multiple jobholders. These are employed people who, during the reference week, had two or 

more jobs as wage and salary workers; were selfemployed 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. 

Self-employed people with multiple unincorporated businesses and people with multiple jobs as 

unpaid family workers are excluded.

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 is collected. Published data on 

hours of work generally 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 Labor Day 

holiday would be reported as working 32 hours, 

even though they were paid for the holiday. For 

people working 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 are also available on usual hours worked by 

all employed people, including those who were 

absent from their jobs during the reference week. 

Usual full- or part-time status. In order to differentiate a person’s normal schedule from his or 

her activity during the reference week, people 

are also classified according to their usual full- or 

part-time status. In this context, full-time workers 

are defined as 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, parttime workers are those who usually work fewer 

than 35 hours per week (at all jobs combined), 

regardless of the number of hours worked in the 

reference week. This may include some individuals who actually worked more than 34 hours in 

the reference week, as well as those who were 

temporarily absent from work. The full-time labor 

force includes all employed people who usually 

work full-time and unemployed people who are 

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.

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. (This includes both those who 

usually work part-time and those who worked 

part-time in the reference week, but usually work 

full-time.) 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.

Occupation, industry, and class of worker. For the 

employed, this information applies to the job held 
Current Population Survey TP77 Chapter 1-2: Questionnaire Concepts and Definitions 9

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

in the reference week. A person with two or more 

jobs is classified according to their main job—the 

job at which they usually worked the greatest 

number of hours. The unemployed are classified 

according to their last job, if any. CPS data use 

the census occupational and industry classification systems, which are based on the Standard 

Occupational Classification (SOC) and the North 

American Industry Classification System (NAICS). 

A list of these census codes can be found in the 

Alphabetical Index of Industries and Occupations 

at <www.census.gov/topics/employment/industry

-occupation/guidance/indexes.html>. 

The class-of-worker designation assigns workers to one of the following categories: wage and 

salary workers, self-employed workers, and unpaid 

family workers. Wage and salary workers are 

those who receive wages, salary, commissions, 

tips, or pay-in-kind from a private employer or 

from a government unit. The class-of-worker question also includes separate response categories for 

government, private for-profit company, and nonprofit organization to further classify wage and 

salary workers. The self-employed are those who 

work for profit or fees in their own businesses, 

professions, trades, or farms. Self-employed 

individuals are identified as those whose businesses are either incorporated or unincorporated. 

Typically, self-employment refers to only the unincorporated self-employed category since those 

whose businesses are incorporated are technically 

wage and salary employees of the corporation. 

However, BLS publishes some data separately for 

the unincorporated self-employed and the incorporated self-employed. 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-ofworker information for individuals’ second jobs is 

collected in order to obtain a more accurate measure of multiple jobholders and to obtain more 

detailed information about their employment 

characteristics. 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 who 

say they are “self-employed, unincorporated” on 

their main jobs, class of worker of the second job 

is collected each month. This is done because, 

according to the official definition, individuals who 

are “self-employed, unincorporated” on both of 

their jobs are not considered multiple jobholders.

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. Employed respondents are 

asked to report their usual earnings before taxes 

and other deductions and to include any overtime 

pay, commissions, or tips usually received. The 

term “usual” is as perceived by the respondent. 

If the respondent asks for a definition of usual, 

interviewers are instructed to define the term as 

more than half the weeks worked during the past 

four or five months. Respondents may report 

earnings in the period of time they prefer—for 

example, hourly, weekly, biweekly, monthly, or 

annually. (Allowing respondents to report in a 

periodicity with which they are 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 whose businesses are 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 are used to analyze the characteristics 

of hourly paid workers such as 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 
10 Chapter 1-2: Questionnaire Concepts and Definitions Current Population Survey TP77

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

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 contacting an employer directly, using 

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 auditioning for a part in a play, bidding on 

a contract, or waiting at a designated labor pickup 

point. Passive methods, which do not qualify as 

job search, include simply reading “help wanted” 

ads, researching companies as places to work, 

and taking a job training course, as opposed to 

actually answering “help wanted” ads or posting a resume online. The response categories for 

active and passive methods are clearly delineated 

in separately labeled columns on the interviewers’ computer screens. Job search methods are 

identified by the following questions: “Have you 

been doing anything to find work during the last 4 

weeks?” and “What are all of the things you have 

done to find work during the last 4 weeks?” To 

ensure that respondents report all of the methods 

of job search used, interviewers ask “Anything 

else?” after the initial or a subsequent job search 

method is reported.

Persons “on layoff” are defined as those who 

have been separated from a job to which they are 

waiting to be recalled (i.e., their layoff status is 

temporary). In order to measure layoffs accurately, 

the questionnaire determines whether people 

reported to be on layoff did in fact have an expectation of recall; that is, whether they had been 

given a specific date to return to work or, at least, 

had been given an indication that they would be 

recalled within the next 6 months. As previously 

mentioned, people on layoff need not be actively 

seeking work to be classified as unemployed.

The key questions used to classify an individual as 

unemployed are presented at the end of this unit. 

Reason for unemployment. Unemployed individuals are categorized according to their status at the 

time they became unemployed. The categories 

are: 

• Job losers and people who completed temporary jobs: a group composed of (1) people 

on temporary layoff from a job to which they 

expect to be recalled, (2) permanent job 

losers, whose employment ended involuntarily 

and who began looking for work, and (3) people who completed temporary jobs—that is, 

individuals who began looking for work after 

their jobs ended. 

• Job leavers: people who quit or otherwise 

terminated their employment voluntarily and 

began looking for work.

• Reentrants: unemployed people who previously worked but were out of the labor force 

prior to beginning their job search. 

• New entrants: unemployed individuals who 

never worked before and who were entering 

the labor force for the first time.

Duration of unemployment. The duration of 

unemployment is typically expressed in weeks, 

although the survey collects information in weeks, 

months, or years. For individuals 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 weeks (through the reference week) they 

have been on layoff.

Not in the labor force. Included in this group are 

all members of the civilian noninstitutional population who are neither employed nor unemployed. 

Information is collected on their desire for and 

availability to take a job at the time of the CPS 

interview, job search activity in the prior year, and 
Current Population Survey TP77 Chapter 1-2: Questionnaire Concepts and Definitions 11

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

reason for not looking in the 4-week period prior 

to the survey week. Responses to the question “Do 

you currently want a job, either full- or part-time?” 

are used in determining desire for work.

The not-in-labor-force group includes a subset 

of individuals marginally attached to the labor 

force, defined as people not working 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). They are not counted as unemployed because they had not actively searched for 

work in the prior 4 weeks. Within the marginally 

attached group are discouraged workers—people 

who are not currently looking for work because 

they believe there are no jobs available or there 

are none for which they would qualify. Reasons 

identified by discouraged workers for not recently 

looking for work are: 

• Belief that no work is available in line of work 

or area. 

• Inability to find any work.

• Lack of necessary schooling, training, skills, or 

experience. 

• Belief that employers have age bias.

• Belief that there are other types of 

discrimination. 

The other persons marginally attached to the 

labor force group includes persons who want a job 

but had not looked for work in the past 4 weeks 

for reasons such as family responsibilities, being in 

school or training, ill health or disability, or transportation problems.

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 aged 16 and older in the civilian noninstitutional population classified as employed 

or unemployed according to the criteria 

described above. In other words, the labor 

force level is the number of people who are 

either working or actively seeking work.

• 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, civilian, noninstitutional population that is in the labor force. For the CPS, 

the age-eligible population consists of people 

aged 16 and older. It represents the proportion of the population that is either working or 

unemployed and actively seeking work or are 

temporarily laid off from a job to which they 

expect to be recalled. 

• Employment-population ratio. The employment population ratio is the number of 

employed people aged 16 and older as a 

percentage of the civilian noninstitutional 

population. It represents the proportion of the 

population that is working. 

KEY QUESTIONS FOR EMPLOYED AND 

UNEMPLOYED

1. Does anyone in this household have a business 

or a farm? 

This question is asked once for each household. A series of questions is then asked for 

each household member. The parentheticals 

below are filled if there is a business or farm in 

the household. 

2. LAST WEEK, did you do ANY work for (either) 

pay (or profit)?

3. LAST WEEK, did you do any unpaid work in 

the family business or farm?

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.

5. LAST WEEK, were you on layoff from a job?

6. Has your employer given you a date to return 

to work?

7. Have you been given any indication that you 

will be recalled to work within the next 6 

months?

8. Have you been doing anything to find work 

during the last 4 weeks?

9. What are all of the things you have done to 

find work during the last 4 weeks? 

Interviewers ask “Anything else?” until all of 

the methods of job search used by the individual are reported.
12 Chapter 1-2: Questionnaire Concepts and Definitions Current Population Survey TP77

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

10. LAST WEEK, could you have started a job if 

one had been offered?

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 or farm), or 4.

Individuals who are available to work (“yes” 

to 10) are classified as unemployed if they 

say “yes” to 5 and either 6 or 7 (on temporary 

layoff), or if they say “yes” to 8 and provide 

an active job search method (one that could 

potentially result in a job offer) in 9.

For a complete version of the questionnaire, see 

the CPS Web site at <www.census.gov/programs

-surveys/cps/technical-documentation

/questionnaires.html>.

REFERENCES

U.S. Department of Commerce, U.S. Census 

Bureau, Alphabetical Index of Industries and 

Occupations, 2014, retrieved from <www.census

.gov/topics/employment/industry-occupation

/guidance/indexes.html>.

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

Statistics, How the Government Measures 

Unemployment, 2015, retrieved from <www.bls

.gov/cps/cps_htgm.htm>.

FURTHER READING

U.S. Bureau of Labor Statistics definitions related 

to Current Population Survey concepts retrieved 

from <www.bls.gov/cps/definitions.htm>.

U.S. Census Bureau definitions related to Current 

Population Survey concepts retrieved from 

<www.census.gov/programs-surveys/cps

/technical-documentation/subject-definitions

.html>.
Current Population Survey TP77 Chapter 1-3: Supplements 13

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

Chapter 1-3: Supplements

INTRODUCTION

In addition to providing data on the labor force 

status of the population, the CPS supports a 

variety of supplemental studies on the entire 

U.S. population and specific population subsets. 

Upon completing the basic CPS interview, some 

respondents are requested to answer a series of 

supplemental questions. These questions provide 

information on the economic and social well-being 

of the nation. Federal agencies and federal independent organizations sponsor these supplemental data collections. Supplemental inquiries take 

advantage of several special features of the CPS 

such as large sample size; 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 are every other year, and still 

other inquiries are 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 the strict criteria discussed in the next 

section.

Producing supplemental data from the CPS 

involves more than including additional questions. Separate data processing is required to 

edit responses for consistency and sometimes 

to impute missing values. Additional weighting 

methods are 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.

With the many different subject matters that 

the supplements address, the level of data (data 

about household versus data about individuals), target populations, contact strategies, and 

weighting procedures, etc., may differ from that of 

the CPS.

CRITERIA FOR CURRENT POPULATION 

SURVEY SUPPLEMENTS

Over the years, the Census Bureau, in consultation 

with BLS, has developed and refined a number of 

criteria to determine the acceptability of undertaking supplements for federal agencies or federal 

independent organizations.

Working with the sponsors, the staff of the Census 

Bureau develop the survey design including 

the methodologies, 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 also apply 

to the supplements.

The Census Bureau considers the following criteria 

before undertaking a supplement:

• The subject matter of the inquiry must be in 

the public interest and must be appropriate 

for inclusion in a government-run survey. The 

questions should be of a factual nature rather 

than gathering opinions.

• The inquiry must not have an adverse effect 

on the CPS or on the image of BLS or the 

Census Bureau. Specifically, the questions 

must not be so far removed from the subject 

and tenor of the basic CPS or from the missions of BLS and the Census Bureau that they 

damage survey or institutional legitimacy. 

They must not cause respondents to question the importance of the survey or result in 

significantly decreased response rates or data 

quality. BLS and the Census Bureau must not 

be affected in terms of congressional acceptance and approval of their programs.

• 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 could 
14 Chapter 1-3: Supplements Current Population Survey TP77

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

inadvertently affect responses to the standard 

labor force items and would not be allowed.

• 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. Religious affiliation and activity 

are examples of subjects currently considered 

sensitive.

• 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 household. Competing 

requirements for the use of BLS and Census 

Bureau staff or facilities that arise in dealing 

with a supplemental inquiry are resolved by 

giving the basic CPS first priority. BLS and the 

Census Bureau will not jeopardize the schedule for completing the basic CPS or other 

work to favor completing a supplemental 

inquiry within a specified time frame.

• It must be possible to meet the objectives of 

the inquiry through survey methods. 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.

• If the Census Bureau is to collect the supplemental information during the CPS interview, 

the inquiry must be suitable for the personal 

visit and telephone procedures used in the 

CPS.

• All supplements must abide by the Census 

Bureau’s enabling legislation, which, in part, 

ensures that the Census Bureau will not 

release information that can identify any 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 the dataset. 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.

• 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 BLS’ 

and the Census Bureau’s pretesting policies. 

These policies encourage questionnaire 

research aimed at improving data quality. 

While BLS and the Census Bureau may reject 

proposed supplement questions and topics, 

they cannot give final approval of questions or 

supplements. The Office of Management and 

Budget (OMB), through its Statistical Policy 

Division, reviews the proposed supplement 

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. They may 

not allow some questions, or even whole supplements, that had been approved by the BLS 

and Census Bureau. The risk of this occurring 

is minimized by consulting with OMB early 

in the development process if there is some 

question about the appropriateness or validity 

of the proposed questions.

RECENT CURRENT POPULATION 

SURVEY SUPPLEMENTS 

The scope and type of CPS supplements vary 

considerably from month to month and from 

year to year. Generally, the interviewers ask the 

selected respondent(s) the additional questions 

that are included in the supplemental survey after 

completing the basic CPS. Table 1-3.1 summarizes 

CPS supplements that were conducted between 

January 2004 and December 2017.
Current Population Survey TP77 Chapter 1-3: Supplements 15

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

Table 1-3.1.

Current Population Survey Supplements, January 2004–December 2017

Title Month and 

year(s) Purpose Recent 

sponsor

Annual Social 

and Economic 

Supplement

February, March, 

April 2004–2017

Provide data concerning family characteristics, household 

composition, marital status, educational attainment, 

health insurance coverage, foreign-born population, prior 

year’s income from all sources, work experience, receipt 

of noncash benefit, poverty, program participation, and 

geographic mobility. 

Census Bureau/

Bureau of Labor 

Statistics (BLS)

Child Support April 2004, 2006, 

2008, 2010, 

2012, 2014, 2016

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. 

Also provide data on why child support was not received 

or awarded.

Office of 

Child Support 

Enforcement

Civic 

Engagement

November 2008, 

2009, 2010, 

2011, 2013

Provide information on the extent to which our nation’s 

communities are places where individuals are civically 

active.

Corporation for 

National and 

Community Service 

(CNCS)

Computer Use/

Internet Use

October 2007, 

July 2011, July 

2013, July 2015, 

November 2017

Provide information about household access to 

computers and the use of the Internet.

National 

Telecommunications 

and Information 

Administration

Contingent 

Workers

February 2005, 

May 2017

Provide information on the type of employment 

arrangement workers have on their current job and 

other characteristics of the current job, e.g., earnings, 

benefits, longevity, along with their satisfaction with and 

expectations for their current jobs. 

Department of 

Labor

Disability May 2012 Provide information on labor market challenges facing 

people with a disability.

Office of Disability 

Employment Policy

Displaced 

Workers 

January 2004, 

2006, 2008, 

2010, 2012, 

2014, 2016

Provide data on workers who lost a job in the last 5 years 

because of plant closing, shift elimination, or other workrelated reason. 

Department of 

Labor

Fertility June 2004, 2006, 

2008, 2010, 

2012, 2014, 2016

Provide data on the number of children that women 

aged 15 through 44 have ever had and the children’s 

characteristics. 

Census Bureau

Food Security December 

2004–2017

Provide data that will measure hunger and food security 

such as food expenditure, access to food, and food 

quality and safety.

Food and Nutrition 

Service

Housing 

Vacancy

Monthly Provide quarterly data on vacancy rates and 

characteristics of vacant units. 

Census Bureau

International 

Migration

August 2008 Provide information on how migration patterns have 

changed, as well as how migrants adapt to living in the 

United States. 

Census Bureau

Participation in 

the Arts (PPA)/

Annual Arts 

Benchmark 

Survey (AABS)

PPA: 

February 2008, 

February 2012

PPA provides data on the type and frequency of 

adult participation in the arts; training and exposure 

(particularly while young); and their musical artistic 

activity preferences.

National 

Endowment for the 

Arts

AABS: 

February 2013, 

2014, 2015, 2016

AABS collects a subset of the same information from PPA.
16 Chapter 1-3: Supplements Current Population Survey TP77

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

Table 1-3.1.

Current Population Survey Supplements, January 2004–December 2017—Con.

Title Month and 

year(s) Purpose Recent 

sponsor

School 

Enrollment

October 2004– 

2017

Provide information on the population 3 years and older on 

school enrollment, junior or regular college attendance, and 

high school graduation. 

BLS/Census 

Bureau/

National 

Center for 

Education 

Statistics

Tobacco Use May 2006, 

August 2006, 

January 2007, 

May 2010, 

August 2010, 

January 2011, 

July 2014, 

January 2015, 

May 2015

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. 

National 

Cancer 

Institute/Food 

and Drug 

Administration

Unbanked and 

Underbanked

June 2009, 2011, 

2013, 2015, 2017

Provide data on unbanked and underbanked households, 

their demographic characteristics, and their reasons for being 

unbanked and underbanked.

Federal 

Deposit 

Insurance 

Corporation

Unemployment 

Insurance 

Nonfiler

January, May, 

July, November 

2005

Provide data on individuals who do apply for unemployment 

insurance and the reasons they do not apply.

Department of 

Labor

Veterans August 2005, 

2007, 2009, 

July 2010, 

August 2011–

2017

Provide data for veterans of the Unites States on Vietnamtheater and Persian Gulf-theater status, service-connected 

income, effect of a service-connected disability on current 

labor force participation, and participation in veterans’ 

programs. 

Veterans’ 

Employment 

and Training 

Service and 

Department 

of Veterans 

Affairs

Volunteers September 

2004–2015

Provide a measurement of participation in volunteer service, 

specifically about the 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.

CNCS

Voting and 

Registration 

November 2004, 

2006, 2008, 

2010, 2012, 

2014, 2016

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. 

Census Bureau
Current Population Survey TP77 Chapter 1-3: Supplements 17

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

A widely used supplement is the ASEC that the 

Census Bureau conducts every February, March, 

and April. This supplement collects data on work 

experience, several sources of income, poverty, 

migration, household composition, health insurance coverage, and receipt of noncash benefits.

The Housing Vacancy Survey (HVS) is unusual in 

that it is conducted every month. The HVS collects 

additional information (e.g., number of rooms, 

plumbing, and rental or sales price) on HUs identified as vacant in the basic CPS. 

The basic CPS weighting is not always appropriate for supplements, since supplements tend to 

have higher response rates. In general, only CPS 

respondents are requested to participate in supplements. So, response to supplements is conditional on CPS response. 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. 

Although it is not a supplement, there is one 

survey that uses the CPS as its sampling frame: 

the American Time Use Survey (ATUS). The ATUS 

draws sample households from households that 

have completed their final CPS interview. The 

ATUS, which is conducted 2 to 5 months after the 

final CPS interview, collects information about 

how people spend their time. More information 

about the ATUS, including sampling and weighting 

procedures, can be found in the American Time 

Use Survey User’s Guide, at <www.bls.gov/tus

/atususersguide.pdf>. 

HOUSING VACANCY SURVEY

Description of supplement

The Housing Vacancy Survey (HVS) is a monthly 

supplement to the CPS. The Census Bureau 

administers this supplement when the CPS interviewers encounter a sample unit 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 HU 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. 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 Census Bureau performs this survey 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 HU 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. In addition, vacant HUs 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 rates are calculated using 

both vacant and occupied HUs.

The rental vacancy rate is the ratio of vacant yearround 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.

The homeowner vacancy rate is the ratio of vacant 

year-round units for sale to the sum of owner-occupied units, vacant year-round units sold but 

awaiting occupancy, and vacant year-round units 

for sale.

Weighting procedure

Since the HVS universe differs from the CPS universe, the HVS records require a different weighting procedure from the CPS records. The HVS 

records are weighted by the CPS basic weight, 

the CPS special weighting factor, two HVS adjustments, and a regional HU adjustment. (Refer to 

Chapter 2-3, Weighting and Estimation, for a 
18 Chapter 1-3: Supplements Current Population Survey TP77

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

description of the CPS weighting adjustments.) 

The two HVS adjustments are referred to as the 

HVS first-stage weighting adjustment factor and 

the HVS second-stage weighting adjustment 

factor.

The HVS first-stage adjustment factor is comparable to the CPS first-stage adjustment factor in 

that it reduces the contribution to variance from 

the sampling of Primary Sampling Units (PSUs). 

The adjustment factors are based on 2010 Census 

data. There are separate first-stage factors for 

year-round and seasonal HUs. For each state, 

they are calculated as the ratio of the state-level 

census count of vacant year-round or seasonal 

HUs in all non-self-representing (NSR) PSUs to the 

corresponding state-level estimate of vacant yearround or seasonal HUs from the sample NSR PSUs. 

The appropriate first-stage adjustment factor is 

applied to every vacant year-round and seasonal 

HU in the NSR PSUs. The following formula is used 

to compute the first-stage adjustment factors for 

each state for the year-round and seasonal HUs:

where

FSS = The first-stage ratio adjustment factor for 

state s. 

CS,i = The number of seasonal or year-round 

vacancies based on 2010 census data for all 

NSR PSU i (sample and nonsample) in state 

s.

CS,k = The number of seasonal or year-round 

vacancies based on 2010 census data for 

only sampled NSR PSU k in state s.

πS,k = The probability of selection for sample PSU 

in state s.

n = The number of NSR PSUs (sample and 

nonsample) in state s.

m = The number of NSR sample PSUs in state s.

The HVS second-stage adjustment, which applies 

to vacant year-round and seasonal HUs in self-representing and NSR PSUs, is calculated as the ratio 

of the weighted CPS interviewed HUs after CPS 

second-stage adjustment to the weighted CPS 

interviewed HUs after CPS first-stage adjustment. 

The cells for the HVS second-stage adjustment are 

calculated within each month-in-sample (MIS) by 

census region and type of area (metropolitan or 

nonmetropolitan, central city or balance of Core 

Based Statistical Area, and urban or 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 

(including occupied and vacant HUs) 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. This 

factor is applied to both occupied and vacant 

HUs.

The final weight for each HVS record is determined by calculating the product of the CPS basic 

weight, the HVS first-stage adjustment, and the 

HVS second-stage adjustment, that is, 

Final weight = (baseweight) 

x (HVS first-stage adjustment)

x (HVS second-stage 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-occupied and owner

-occupied units is the CPS household weight. 

(Refer to Chapter 2-3, Weighting and Estimation, 

for a description of the CPS household weight.)

ANNUAL SOCIAL AND ECONOMIC 

SUPPLEMENT

Description of supplement

The Census Bureau and BLS sponsor the ASEC. 

The Census Bureau has collected ASEC data since 

1947. From 1947 to 1955, the ASEC interviews 

took place in April; from 1956 to 2001, the ASEC 

interviews took place in March; and from 2002 to 

the present, the ASEC interviews take place in 

February, March, and April, with most interviews 

in March.
Current Population Survey TP77 Chapter 1-3: Supplements 19

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

Prior to 2003, the ASEC was known as the Annual 

Demographic Supplement (ADS) or the March 

Supplement. In 2001,2 a sample increase was 

implemented that required more time for data 

collection. Thus, additional ASEC interviews now 

take place in February through April. 

The supplement collects data on health insurance 

coverage, work experience, and income from all 

sources, receipt of noncash benefits, poverty, 

program participation, and geographic mobility. 

A major reason for conducting the ASEC in the 

month of March is to obtain better income data, 

given proximity to tax season. The universe of 

the ASEC is slightly different from that of the 

basic CPS—it includes certain members of the 

armed forces. This difference requires some minor 

changes to the weighting methodology.

Sample

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 1-3.2 shows the months when the 

eligible sample is identified for years 2001 through 

2017. Starting in 2004, the eligible ASEC sample 

households are:

• The entire March CPS sample—8 MIS groups.

• Hispanic households—identified in November 

(from all eight MIS groups)

• Hispanic households—identified in April (from 

MIS 1 and 5 groups)—a total of two additional 

MIS groups.

• Non-Hispanic, non-White households—identified in August (MIS 8), September (MIS 8), 

2 The Census Bureau first used the expanded sample 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 by the Census Bureau using the expanded sample. The 

2001 expanded sample statistics were released and are used for 

comparing the 2001 data to the official 2002 statistics.

October (MIS 8), November (MIS 1 and 5), and 

April (MIS 1 and 5)—a total of seven additional 

MIS groups.

• 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)—a total of seven additional MIS groups.

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 (item 2 above) were 

added to the ASEC. The rest of the households 

(items 3, 4, and 5 above) were added in 2001, 

along with a general sample increase in selected 

states, and are collectively known as the Children’s 

Health Insurance Program (CHIP) sample expansion.3 The added households improve the precision of the ASEC estimates for the Hispanic 

households; non-Hispanic, non-White households; 

and non-Hispanic White households with children 

18 years or younger.

Because of the characteristics of CPS sample 

rotation (see Chapter 2-2, Sample Design), the 

additional cases from the August, September, 

October, November, and April CPS do not overlap with those in the March CPS. The March CPS 

sample alone consists of eight MIS groups. The 

additional sample cases in the ASEC increase its 

effective sample size in comparison. 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.

3 The CHIP was formerly known as the State Children’s Health 

Insurance Program. For additional information about CHIP, see 

Unit 2-2, Sample Design.
20 Chapter 1-3: Supplements Current Population Survey TP77

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

Table 1-3.2 

Month-In-Sample Groups Included in the Annual Social and Economic Supplement Sample 

for Years 2001 to 20171

Current Population Survey 

month/Hispanic status

Month-in-sample

1 2 3 4 5 6 7 8

August Hispanic2 NI

Non-Hispanic3

NI 2004–

20171

September Hispanic2 NI

Non-Hispanic3

NI 2004–

20171

October Hispanic2 NI

Non-Hispanic3

NI 2003–

20171

November Hispanic2 2001–20171

Non-Hispanic3 2001–

20171 NI 2001–20171

2001–

2002

2001–

2003

2001–

2003

March Hispanic2

2001–20171

Non-Hispanic3

April Hispanic2

2001–

20171 NI 2001–20171 NI

Non-Hispanic3

NI Not interviewed for the Annual Social and Economic Supplement.

1

 The 2010 sample design was phased-in in 2014. The month-in-sample groups for 2014 continue in subsequent years.

2 Hispanics may be any race.

3 The non-Hispanic group includes both non-Hispanic, non-Whites, and non-Hispanic Whites with children 18 years or younger.

Prior to 2004, non-Hispanic, non-White and 

non-Hispanic White with children-under-18 households were selected from different months-insample. This table shows the sample selection as 

of 2004.

The March and April ASEC eligible cases are 

given the ASEC questionnaire in those respective 

months (see Table 1-3.3). The November eligible 

Hispanic households are administered the ASEC 

questionnaire in February for November MIS 

groups 1 and 5, during their regular CPS interviewing time, and the remaining MIS groups (MIS 

2 to 4 and 6 to 8) receive the ASEC interview in 

March. November MIS 6 to 8 households have 

already completed all 8 months of interviewing for 

the CPS before March, and the November MIS 2 

to 4 households have an extra contact scheduled 

for the ASEC before the fifth interview of the CPS 

later in the year.

The August, September, October, and November 

eligible non-Hispanic households are given 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 given in February. The August, September, and 

October MIS 8 eligible cases are split between the 

February and April CPS interviewing months. The 

households in other rotation groups in February 

and April receive the corresponding CPS nonASEC supplement for that month.

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 sample receive the ASEC questionnaire.
Current Population Survey TP77 Chapter 1-3: Supplements 21

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

Weighting procedure

Prior to weighting, missing supplement items are 

assigned values based on hot deck imputation, a 

method in which each missing value is replaced 

with an observed response from a “similar” unit. 

Values are imputed even if all of the supplement 

data are missing; thus, there is no separate adjustment for households that respond to the basic 

CPS survey but not to the supplement. The ASEC 

records are weighted by the CPS base weight, the 

CPS special weighting factor,4 the CPS noninterview adjustment, the CPS first-stage adjustment, 

and the CPS second-stage adjustment procedure. 

(Chapter 2-3, Weighting and Estimation, contains a description of these adjustments.) The 

ASEC also receives an additional noninterview 

adjustment for the August, September, October, 

and November ASEC sample, a CHIP Adjustment 

Factor, a family equalization adjustment, and 

weights applied to armed forces members.

The August, September, October, and November 

eligible samples are each weighted through the 

CPS noninterview adjustment and then combined. 

A noninterview adjustment for the combined 

4 For the 2010 Sample Design, the CPS special weighting 

factor applies only to group quarters frame units. 

samples and the CPS first-stage adjustments are 

applied before the CHIP adjustment is applied.

The March eligible sample and the April eligible 

sample are also weighted separately before the 

second-stage weighting adjustment. All of the 

samples are then combined so that one second-stage adjustment procedure is performed. 

The flowchart in Figure 1-3.1 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 

base CPS weight as calculated in the appropriate 

month, modified by the appropriate CPS special 

weighting factor and appropriate CPS noninterview adjustment in the August, September, 

October, or November interviews. Next, a second noninterview adjustment is made for eligible 

households that are still occupied but for which 

an interview could not be obtained to account for 

the nonrespondent households in the February, 

March, or April interviews. Then, the ASEC sample weights for the prior sample are adjusted 

by the CPS first-stage adjustment and the CHIP 

Adjustment Factor.

Table 1-3.3.

Summary of the Annual Social and Economic Supplement Interview Months

Current Population Survey 

month/Hispanic status

Month-in-sample

Mover Nonmover

1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

August Hispanic1 NI NI

Non-Hispanic2 NI NI Feb

September Hispanic1 NI NI

Non-Hispanic2 NI NI Feb

October Hispanic1 NI NI

Non-Hispanic2 NI NI Apr

November Hispanic1 NI Feb March Feb March

Non-Hispanic2 NI Feb NI Feb NI

March Hispanic1

March March

Non-Hispanic2

April Hispanic1 Apr NI Apr NI Apr NI Apr NI

Non-Hispanic2

NI Not interviewed for the Annual Social and Economic Supplement.

1

 Hispanics may be any race.

2 The non-Hispanic group includes both non-Hispanic, non-Whites, and non-Hispanic Whites with children 18 years or younger.
22 Chapter 1-3: Supplements Current Population Survey TP77

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

Figure 1-3.1. 

Diagram of the Annual Social and Economic Supplement Weighting Scheme

September1

Sample

Basic

Weight

Special*

Weighting

Factor

September NI6

Adjustment

ASEC NI6 

Adjustment

CHIP

Adjustment

Mover

Households

Removed

First–Stage

Adjustment

March4

Sample

Basic

Weight

Special*

Weighting

Factor

March NI6 

Adjustment

First–Stage

Adjustment

Non–Mover Hispanic;

Non–Mover Non–Hispanic, Non–White;

Non–Mover Non–Hispanic Whites with

Children Housholds

   

   

    

CHIP

Adjustment

April5

Sample

Basic

Weight

Special*

Weighting

Factor

April NI6 

Adjustment

First–Stage

Adjustment

CHIP

Adjustment

Civilian Persons

Second–Stage (SS) -

Adjustment

Family

Equalization

ASEC Weight

August1

Sample

Base

Weight

Special*

Weighting

Factor

October2

Sample

Basic

Weight

Special*

Weighting

Factor

November3

Sample

Basic

Weight

Special*

Weighting

Factor

August NI6

Adjustment

October NI6 

Adjustment

November NI6 

Adjustment

* For the 2010 Sample Design, the Current Population Survey

Special Weighting Factor applies only to group quarters frame 

units.

Eligible Cases for:

1 From MIS 8 rotations that are non–Hispanic, non–Whites 

and non–Hispanic Whites with children; interviewed in February.

2 From MIS 8 rotations that are non–Hispanic, non–Whites and 

non–Hispanic Whites with children; interviewed in April.

3 Hispanics from MIS 2–4 and 6–8 groups; interviewed in March. 

From MIS 1 and 5, Hispanic; non–Hispanic, non–Whites; and 

non–Hispanic Whites with children; interviewed in February as 

part of their MIS 4 and 8 interviews.

4 All cases interviewed from March.

5 From MIS 1 and 5, all Hispanics; non–Hispanic, non–Whites; and 

non–Hispanic Whites with children; all interviewed in April.

6 NI (Not interviewed for the Annual Social and Economic Supplement.)

Armed Forces 

Weighting

Base

Weight

Base

Weight

Base

Weight

Base

Weight

Base

Weight

Mover Hispanic;

Mover Non–Hispanic, Non–White;

Mover Non–Hispanic Whites with

Children Housholds

and All Others
Current Population Survey TP77 Chapter 1-3: Supplements 23

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

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 

nonresponse of occupied HUs that occur in the 

February, March, or April interviews. If a nonresponding household is actually a mover household, it would not be eligible for interview. Since 

the mover status of nonresponding households 

is not known, we assume that the proportion of 

mover households is the same for interviewed 

and nonresponding households. This is reflected 

in the noninterview adjustment. With this exception, the noninterview adjustment procedure is 

the same as described in Chapter 2-3, Weighting 

and Estimation. At this point, the nonresponding 

households and those mover households receive 

no further ASEC weighting. The weights of nonresponding, occupied, nonmover households from 

August, September, October, and November are 

transferred to the ASEC respondents from August, 

September, October, and November so that the 

ASEC respondents represent all eligible persons. 

The noninterview adjustment factor, Fij, is computed as follows:

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.

Nij = The weighted number of August, 

September, October, and November eligible sample occupied, nonresponding HUs 

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 i refer to the noninterview clusters (NICL), which are groups of PSUs. 

NICL boundaries do not cross the census regions 

(Northeast, Midwest, South, and West). Within 

each of these clusters, the cell j could be (1) 

Central City, (2) Balance of Metropolitan Statistical 

Area, or (3) Nonmetropolitan, depending on the 

type of cluster, with each cluster having either 

cells (1) and (2), or only cell (3).

CHIP adjustment factor for the August, 

September, October, and November eligible 

sample. The CHIP 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 CHIP adjustment factor of 8/18 

and non-Hispanic, non-White households and 

non-Hispanic White households with children 18 

years or younger receive a CHIP adjustment factor 

of 8/15 (see Table 1-3.4). 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 

adjustment.

Eligible households from the March sample: The 

March eligible sample households start with their 

CPS base weight, modified by the CPS special 

weighting factor, the March CPS noninterview 

adjustment, the March CPS first-stage adjustment (as described in Chapter 2-3, Weighting and 

Estimation), and the CHIP adjustment factor. After 

the CHIP adjustment factor is applied, the March 

eligible sample is ready to be combined with the 

August, September, October, November, and April 

eligible samples for the application of the secondstage adjustment.

CHIP adjustment factor for the March eligible sample. The CHIP adjustment factor is applied to the 

March eligible nonmover households that contain 

residents who are Hispanic; non-Hispanic, nonWhite; 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 CHIP adjustment 

factor of 8/18 and non-Hispanic, non-White households and non-Hispanic White resident households with children 18 years or younger receive 

a CHIP adjustment factor of 8/15. Mover households, households with newborns, and all other 
24 Chapter 1-3: Supplements Current Population Survey TP77

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

households receive a CHIP adjustment of one. 

Table 1-3.4 summarizes these weight adjustments.

Eligible households from the April sample: The 

households in the April eligible sample start with 

their CPS base weight as calculated in April, modified by the April CPS special weighting factor, the 

April CPS noninterview adjustment, the April CPS 

first-stage adjustment, and the CHIP adjustment 

factor. After the CHIP adjustment factor is applied, 

the April eligible sample is ready to be combined 

with the August, September, October, November, 

and March eligible samples for the application of 

the second-stage adjustment.

CHIP adjustment factor for the April eligible 

sample. The CHIP 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 CHIP 

adjustment factor of 8/18 and non-Hispanic, nonWhite households and non-Hispanic White households with children 18 years or younger receive 

a CHIP adjustment factor of 8/15. Table 1-3.4 

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 adjustment: The secondstage adjustment adjusts the ASEC estimates, 

so that they agree with independent age, sex, 

race, and Hispanic-origin population controls 

as described in Chapter 2-3, Weighting and 

Estimation. 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, the estimates of men in partnerships would be more than the number of women 

in partnerships. Weights are adjusted to give a 

married partners and unmarried partners the same 

weight, while maintaining the overall age and race 

and sex and Hispanic origin 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 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. Control totals, used in the second-stage 

factor, do not include armed forces members; 

thus, armed forces members do not go through 

the second-stage adjustment. During family equalization, the weight of an armed forces member 

with a spouse or partner is assigned to the weight 

of their spouse or partner. 

Family equalization: The family equalization procedure equalizes the estimates of the number of 

people in partnerships, both married and unmarried, while maintaining the overall civilian age/

race/sex/ethnicity control totals. Seven categories 

of adults (at least 16 years old) based on sex and 

household composition are formed:

• Female partners in female/female married or 

unmarried partner households.

• All other civilian females.

• Married males, spouse present.

• Male partners in male/female unmarried partner households.

• Other civilian male heads of households.

• Male partners in male/male married or unmarried partner households.

• All other civilian males.
Current Population Survey TP77 Chapter 1-3: Supplements 25

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

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 two 

CPS supplements (the HVS and the ASEC), every 

supplement has its own unique objectives. The 

Census Bureau tailors the particular questions, 

edits, and imputations to each supplement’s data 

needs. For many supplements, this also means 

altering the weighting procedure to reflect a 

different universe, account for a modified sample, 

or adjust for a higher rate of nonresponse. The 

weighting revisions discussed here for HVS and 

ASEC indicate only the types of modifications that 

the Census Bureau might use for a supplement. 

Technical documentation for the CPS supplements 

can be found at <www.census.gov/programs

-surveys/cps/technical-documentation.html>.

REFERENCES

U.S. Bureau of Labor Statistics, American Time 

Use Survey User’s Guide retrieved from <www.bls.

gov/tus/atususersguide.pdf>. 

U.S. Census Bureau, Current Population Survey 

Questionnaires retrieved from <www.census.gov

/programs-surveys/cps/technical-documentation

.html>.

Table 1-3.4.

Summary of the Annual Social and Economic Supplement Children's Health Insurance 

Program Adjustment Factor

Current Population Survey 

month/Hispanic status

Month-in-sample

Mover Nonmover

1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

August Hispanic1 02 02

Non-Hispanic3 02 02

September Hispanic1 02 02

Non-Hispanic3 02 02

October Hispanic1 02 02

Non-Hispanic3 02 02

November Hispanic1 02 8/18

Non-Hispanic3 02 8/15 02 8/15 02

March Hispanic1

1

8/18

Non-Hispanic3,4 8/15

April Hispanic1 8/18

02 8/1802 8/1802 8/1802

Non-Hispanic3 8/15 8/15 8/15 8/15

1

 Hispanics may be any race.

2 Zero weight indicates the cases are ineligible for the Annual Social and Economic Supplement.

3 The non-Hispanic group includes both non-Hispanic, non-Whites, and non-Hispanic Whites with children 18 years or younger.

4 Nonmover non-Hispanic Whites without children 18 years or younger get a Children’s Health Insurance Program Adjustment Factor.
26 Chapter 1-4: Data Products Current Population Survey TP77

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

Chapter 1-4: Data Products

INTRODUCTION

Information collected in the CPS is made available 

by both the BLS and the Census Bureau through 

broad publication programs that include news 

releases, reports, charts, data tables, time series, 

and other formats. In addition to tabulated data, 

public-use microdata files are also available. Some 

of the major products are identified below. This 

section is not intended to be an exhaustive reference for all information available from the CPS. 

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 can be 

accessed at <www.bls.gov/news.release/empsit

.toc.htm>.

In addition to the news release, detailed tables 

provide information on the labor force, employment, and unemployment by a number of characteristics such as age, sex, race, Hispanic and 

Latino ethnicity, educational attainment, industry, 

and occupation. Tables may contain monthly, 

quarterly, or annual average data. Annual average 

tables typically show more detail than tables published on a monthly or quarterly basis. See 

<www.bls.gov/cps/tables.htm>. 

The BLS LABSTAT database system contains four 

separate databases with CPS time series data. 

(They can be accessed from <www.bls.gov/cps

/data.htm>). The largest database contains the 

main labor force statistics, and others contain 

earnings, union, and marital and family statistics. About 15,000 labor force series are updated 

each month, including a few hundred time series 

that go back to the 1940s. In total, there are over 

100,000 monthly, quarterly, or annual-only time 

series available from the CPS. 

Some of the tables and time series contain seasonally adjusted monthly or quarterly data. These 

estimates, useful for month-to-month analysis, 

undergo a process that removes predictable seasonal patterns in the data and makes it easier to 

see the underlying trends. However, only a small 

portion of estimates are available on a seasonally 

adjusted basis. The majority of the data are not 

seasonally adjusted. An over-the-year comparison 

of unadjusted data is recommended rather than 

a month-to-month analysis. (Note that annual 

averages do not exhibit seasonality.) A discussion 

of seasonal adjustment is available in Chapter 2-5, 

Seasonal Adjustment.

BLS publishes CPS data in annual news releases 

on a wide variety of subjects including union 

membership, veterans, people with disabilities, 

families, the foreign-born, and summer employment of youth. BLS also publishes several large 

reports on minimum wage workers, labor force 

characteristics by race and ethnicity, women in the 

labor force, and earnings of men and women. All 

of these publications include both analytical content and data tables. They also include a Technical 

Note describing the source of the data and key 

concepts or issues in interpreting the information 

presented. Reports and news releases are available at <www.bls.gov/cps/news.htm>. 

BLS also sponsors (or co-sponsors) many of the 

supplemental surveys described in Chapter 1-3, 

Supplements. BLS news releases and reports from 

the CPS supplements include work experience 

over the calendar year (annual), a profile of the 

working poor (annual), characteristics of veterans 

(annual), college enrollment and work activity of 

recent high school graduates (annual), worker 

displacement (biennial), and employee tenure 

(biennial). Other topics have also periodically 

been addressed, like barriers to employment and 

labor-related issues for people with disabilities, 

workers on flexible and shift schedules, work at 

home, and contingent and alternative employment 

arrangements. 
Current Population Survey TP77 Chapter 1-4: Data Products 27

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

Because the CPS provides information on such 

a wide variety of topics, the best way to explore 

data from the BLS is using the subject matter 

summaries of available data on the labor force, 

employment, unemployment, persons not in the 

labor force, hours of work, earnings, and other 

demographic and labor force characteristics. 

The CPS topics A to Z list is often a good place 

to start: <www.bls.gov/cps/cpsaz.htm>. Each 

subject overview presents relevant news releases, 

tables, database series, charts, and analytical articles authored by BLS staff. 

Information about the CPS itself, including concepts, measurement, and changes to the program 

over time, is available at <www.bls.gov/cps

/documentation.htm>.

Most of the CPS data available from BLS are estimates for the United States as a whole, but some 

information is available for states and other geographic areas. The BLS Local Area Unemployment 

Statistics (LAUS) program publishes monthly 

estimates of employment and unemployment 

for all states, counties, metropolitan areas, cities 

of 25,000 population or more, and New England 

cities and towns of 1,000 population or more, 

by place of residence. Labor force data from the 

LAUS program follow the same CPS concepts 

and definitions used for the national labor force 

data. However, because the CPS sample is insufficient for creating reliable monthly estimates for 

statewide and substate areas, LAUS uses different estimating procedures for different levels of 

geography. In general, monthly estimates for the 

states, such as the official state unemployment 

rates, are developed using statistical models that 

incorporate current and historical data from the 

CPS, the monthly CES establishment survey, and 

regular state unemployment insurance systems. 

The annual subnational demographic labor force 

data published in the Geographical Profile of 

Employment and Unemployment are derived 

solely from the CPS. For more information about 

these estimates, see <www.bls.gov/lau/>. 

CENSUS BUREAU PRODUCTS

The Census Bureau has been analyzing data from 

the CPS and reporting the results to the public for 

over five decades. The reports provide information 

on a recurring basis about a wide variety of social, 

demographic, and economic topics. Most of these 

reports appear in one of the following 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 by the ASEC, which is a CPS 

supplement. However, other reports use data from 

various CPS supplements. Generally, reports are 

announced by news release and are released to 

the public via <www.census.gov>.

Census Bureau Report Series

P-20, Population Characteristics. Regularly 

recurring reports in this series include topics such 

as geographic mobility, educational attainment, 

school enrollment, marital status, households 

and families, Hispanic origin, fertility, voter registration and participation, and the foreign-born 

population.

P-23, Special Studies. Information pertaining 

to methods, concepts, or specialized data is 

furnished in these publications. Also included 

are occasional reports on the Black population, 

metropolitan-nonmetropolitan population, youth, 

women, the older population, and other topics.

P-60, Consumer Income. Information concerning 

families, individuals, and households at various 

income levels is presented in this group of reports. 

Data are also presented on noncash benefits and 

relationship of income to age, sex, race, family 

size, education, occupation, work experience, and 

other characteristics.
28 Chapter 1-4: Data Products Current Population Survey TP77

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

Public-Use Microdata Files

In addition to the regularly tabulated statistics 

described above, special data can be generated 

through the use of the CPS individual record 

(microdata) files. These files contain records of 

the responses to the survey questionnaire for all 

respondents 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 users outside of the Census Bureau. 

Access to CPS raw microdata, that is, the untabulated data from survey items, is available through 

an FTP site <https://thedataweb.rm.census.gov

/ftp/cps_ftp.html?#>. This site provides the 

microdata files for the basic monthly labor force 

survey from 1994 forward, along with record 

layouts (data dictionaries). It also includes files for 

selected CPS supplements. 
Current Population Survey TP77 Appendix: History of the Current Population Survey 29

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

Appendix: History of the Current Population Survey

• March 1940. WPA compiles the first official 

monthly figures on unemployment; estimates 

are released in an internal memorandum in 

April 1940 with the title “Monthly Report of 

Unemployment.” The reference week for the 

survey was March 24–30; data were collected 

the following week. (This is generally thought 

of as the survey's first collection date.) March 

was chosen since it closely matched the reference period for the 1940 decennial census. 

• August 1942. The Census Bureau assumes 

responsibility for the survey, and the program 

is renamed the Monthly Report on the Labor 

Force.

• October 1943. Following the 1940 decennial 

census, the sample for the survey is revised to 

make full use of probability sampling principles. PSUs of one or more counties were 

defined covering the entire United States. 

Sixty-eight PSUs were selected, and about 

26,000 HUs were designated for the sample, of 

which about 22,500 were eligible for interview. 

• March 1945. The monthly sample is reduced to 

about 25,000 designated households (21,500 

eligible for interview) without affecting the 

reliability of the estimates. 

• July 1945. The CPS questionnaire is 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 such as students, housewives, and unpaid family workers on farms. 

These groups were erroneously reported as 

not active in the labor force.

• August 1947. The sample selection method 

is 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 

estimation procedures.

• July 1949. Previously excluded dwelling places 

are 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 are 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 document-sensing 

equipment.

• January 1953. Ratio estimates begin to use 

data from the 1950 population census. Prior 

to January 1953, 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. 

• July 1953. The 4-8-4 rotation system is 

introduced. This sample rotation system was 

adopted to improve measurement of changes 

over time. In this system, households are 

interviewed for 4 consecutive months, leave 

the sample for 8 months, and then return for 

the same period of 4 months the following 

year. In the previous system, households were 

interviewed for 6 consecutive months and 

then replaced. The 4-8-4 system provides 

some year-to-year overlap (50 percent), thus 

improving estimates of change on a year-toyear basis (while largely preserving the degree 

of month-to-month overlap).

• September 1953. In second-stage ratio 

estimation, information on “color” (the race 

terminology used at that time) is added and 

information on “veteran status” is omitted. 

This change made it feasible to publish separate, absolute numbers for individuals by race, 

whereas previously only the percent distributions had been published. 

High-speed electronic equipment is introduced for tabulations. The introduction of 

electronic calculation greatly increased data 

timeliness and led to other improvements in 
30 Appendix: History of the Current Population Survey Current Population Survey TP77

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

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.) 

• February 1954. For the redesign following 

the 1950 Census, a new sample is phased in 

from February 1954 to May 1955. The number 

of PSUs was increased from 68 to 230 while 

retaining the overall sample size of 25,000 

designated HUs (21,500 eligible for interview). 

At the same time, a substantially improved 

estimation procedure is introduced, referred 

to as composite estimation. This procedure 

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. 

Major reliance on telephone interviewing 

begins, though personal interviews are preferred for MIS 1, 2, and 5. A regular reinterview program was initiated as a quality control 

measure.

• May 1955. Monthly questions exploring the 

reasons for part-time work were added to the 

standard set of employment status questions. 

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. The survey reference week is 

moved to the calendar week containing the 

twelfth day of the month to align the CPS time 

reference with that of other employment statistics. Previously, the survey reference week 

had been the calendar week containing the 

eighth day of the month. 

• May 1956. The number of PSUs is expanded 

from 230 to 330. All of the former 230 PSUs 

were also included in the expanded sample. 

The overall sample size also increased by 

roughly two-thirds to a total of about 40,000 

households (about 35,000 eligible units). The 

expansion improved the reliability of the major 

statistics by around 20 percent and made it 

possible to publish more detailed statistics.

• January 1957. In response to recommendations from an interagency committee tasked 

with reviewing conceptual and methodological issues in the measurement of employment and unemployment, two relatively small 

groups of people, both formerly classified as 

employed ‘‘with a job but not at work,’’ are 

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 labor force questions or the enumeration procedures.

• June 1957. A seasonally adjusted unemployment rate is introduced reflecting ongoing 

advances in electronic computers. A further 

extension of the data—using more refined 

seasonal adjustment methods—was introduced in July 1957. (Some seasonally adjusted 

unemployment data were introduced early in 

1955 in index form.) The new data included a 

seasonally adjusted rate of unemployment and 

trends in seasonally adjusted levels for total 

employment and unemployment. Significant 

improvements in methodology emerged 

from research conducted at the BLS and the 

Census Bureau in the following years.

• July 1959. Responsibility for the planning, 

analysis, seasonal adjustment, and publication of the labor force statistics from the 

CPS is transferred to BLS as part of a large 

exchange of statistical functions between the 

Department of Commerce and Department of 

Labor. 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 Bureau 

of the Budget (now the Statistical Policy 

Division, OMB).
Current Population Survey TP77 Appendix: History of the Current Population Survey 31

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

• January 1960. Upon achieving statehood, 

Alaska and Hawaii are 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. There was an increase of about 500,000 

in the noninstitutional population aged 16 and 

over 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. The CPS questionnaire is 

converted to the Film Optical Sensing Device 

for Input to the Computer system (the system used in the 1960 Census). Entries were 

made by filling in small circles with an ordinary 

lead pencil. The questionnaires were photographed to microfilm. The microfilms were 

then scanned by a reading device that transferred the information directly to computer 

tape. This system permitted a larger form and 

a more flexible arrangement of items than the 

previous document-sensing procedure and did 

not require the preparation of punch cards. 

This data entry system was used through 

December 1993.

• December 1961. For the redesign following the 1960 decennial census, new sample 

is phased in from December 1961 to March 

1963. There were 357 PSUs; 35,000 households were eligible for interview. In a major 

improvement, most of the sample is drawn 

from household lists prepared during the 

decennial census. Starting February 1962, 

population controls from the decennial census 

were used in weighting. 

• January 1963. In response to recommendations from the President’s Committee to 

Appraise Employment and Unemployment 

Statistics (also referred to as the “Gordon 

Committee”), two new items are added to the 

monthly questionnaire. The first was a question, formerly asked 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 

relationships of unemployed people.

• January 1967. The CPS sample is 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 HUs (50,000 eligible units). This 

expansion improved the reliability of the major 

statistics by about 20 percent and made it 

possible to publish more detailed statistics. 

The definitions of employment and unemployment are modified. In line with the basic recommendations of the President’s Committee 

to Appraise Employment and Unemployment 

Statistics (U.S. Department of Commerce, 

1962), 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 lower age 

limit in defining the labor force (from age 14 

to age 16) 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. A 4-week job search period was 

specified, and new questions on job search 

methods and current availability for work were 

added. The refined definition of unemployment led to small differences in the estimates of levels and month-to-month change. 

Collection of additional information on those 

not in the labor force began.

• March 1968. Separate age and sex ratio 

estimation cells are introduced for Negro and 

Other races. (Negro was the race terminology 

used at that time. Other includes American 

Indian, Eskimo, Aleut, Asian, and Pacific 

Islander.) Previously, the second-stage ratio 

estimation used White and non-White race 

categories by age groups and sex. The revised 

procedures allowed separate ratio estimates 

for Negro and Other race categories. This 

change increased the number of ratio estimation cells from 68 to 116.

• January 1970. The detailed not in labor force 

questions are shifted from the incoming 

rotation groups (MIS 1 and 5) to the outgoing 

rotation groups (MIS 4 and 8). 

• January 1971. The 1970 Census occupational 

classification system is introduced. 
32 Appendix: History of the Current Population Survey Current Population Survey TP77

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

• December 1971. For the redesign following the 1970 decennial census, new sample 

is phased in from December 1971 to March 

1973. There were 461 PSUs; 47,000 HUs were 

eligible for interview. Also, the cluster design 

was changed from six nearby households 

(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. Also, 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.

• January 1972. The population estimates used 

in second-stage ratio estimation are updated 

to the 1970 Census base. Data tabulated using 

the 1970 Census occupation classification 

system were produced.

• January 1973. Seasonal adjustment converts 

to X-11 software, replacing the BLS Factor 

Method (which had been introduced in 1960). 

The X-11 software included several options 

for handling both additive and multiplicative 

series, diagnostics were improved, and variable trends and seasonals were allowed. 

• January 1974. The inflation-deflation method 

is introduced for deriving independent estimates of the civilian noninstitutional population by age, race, and sex used in second-stage ratio estimation. 

• July 1975. As a result of the large inflow of 

Vietnamese refugees to the United States, the 

total and Black-and-Other independent population controls for those 16 years and over are 

adjusted upward.

• September 1975. In order to obtain state 

estimates from the CPS, state supplementary 

samples are introduced; these samples were 

not used for national estimation. 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. 

• January 1978. State supplemental sample 

for 24 states and the District of Columbia are 

incorporated into national estimation. Also, 

a supplemental sample was introduced to 

improve coverage in “address list” enumeration districts. 

• October 1978. Procedures for determining 

demographic characteristics are modified. At 

this time, changes were made in the collection 

methods for household relationship, race, and 

ethnicity data. Race is now determined by the 

respondent rather than by the interviewer. 

Other modifications included the introduction 

of earnings questions for the two outgoing 

rotations in the monthly survey (with regular 

collection of these data beginning in January 

1979). New questions focused on usual hours 

worked, hourly wage rate, and usual weekly 

earnings. Earnings questions were asked of 

currently employed wage and salary workers. 

Previously, earnings data were collected in 

supplements.

• January 1979. A new two-level, first-stage 

ratio estimation procedure is introduced. 

This procedure was designed to improve the 

reliability of metropolitan and nonmetropolitan estimates. Also introduced were monthly 

tabulations of children’s demographic data 

including relationship, age, sex, race, and 

origin.

• September 1979. The final report of the 

National Commission on Employment 

and Unemployment Statistics (“Levitan 

Commission”) is issued (Executive Office of 

the President, 1979). This report shaped many 

of the future changes to the CPS. 

• January 1980. To improve coverage, about 

450 households are added to the sample, 

increasing the number of total PSUs to 629. 

Also, X-11 Auto-Regressive Integrated Moving 

Average (ARIMA) software was introduced for 

seasonal adjustment.

• May 1981. The sample is reduced by approximately 6,000 assigned households, bringing 

the total sample size to approximately 72,000 

assigned households (with about 60,000 

households eligible for interview).
Current Population Survey TP77 Appendix: History of the Current Population Survey 33

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

• January 1982. The race categories in secondstage ratio estimation adjustment are changed 

from White and non-White to Black and nonBlack. 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 CPS 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, current population estimates used in 

the second-stage estimation procedure were 

based on findings from the 1980 Census. The 

use of the 1980 Census-based population 

estimates, in conjunction with the revised 

second-stage adjustment, resulted in about 

a 2 percent increase in the estimates for total 

civilian noninstitutional population aged 16 

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, BLS revised approximately 30,000 

series going back to 1970.

• January 1983. The occupational and industrial 

data are coded using the 1980 classification 

systems. While the effect on industry-related 

data was minor, the conversion was viewed 

as a major break in occupation-related 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. 

Among the “Black and Other” race category, 

data for Blacks are broken out and tabulated 

separately going back to 1972. Data continued to be published for a number of “Black 

and Other” data series until 2003. Two questions on union membership and union coverage for the two outgoing rotations in the 

monthly survey are introduced. The questions 

were asked of currently employed wage and 

salary workers. Previously, union membership 

data were collected in supplements.

Reflecting a recommendation from the Levitan 

Commission, selected series that included the 

resident armed forces among the employed 

are introduced. This included an overall unemployment rate for the nation, which tended to 

be about 0.1 percentage point lower than the 

civilian unemployment rate.

• October 1984. School enrollment questions 

are added to the basic CPS for people 16 to 

24 years of age.

• April 1984. The 1970 Census-based sample is 

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.

• January 1985. Estimation procedures are 

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 was added as a 

control to the Hispanic population. (Hispanics 

may be of any race.) 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 to the 

major Hispanic data series were made back to 

January 1980 to the extent possible.

• June 1985. The CPS CATI contact center is 

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 official CPS estimates.

• July 1985. Response categories that obtain 

information on period of service for female 

veterans are added to the monthly CPS questionnaire. Estimates of the number of female 
34 Appendix: History of the Current Population Survey Current Population Survey TP77

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

veterans based on these questions were first 

published in a 1986 Monthly Labor Review 

article.

• January 1986. For the first time, the population controls used in the second-stage ratio 

adjustment method are revised to reflect an 

explicit estimate of the number of undocumented immigrants (largely Hispanic) since 

1980. In addition, the population controls 

included an improved estimate of emigration, or movement out of the United States, 

by legal residents. Taken together, the two 

changes had a comparatively small effect on 

the total population figure, but their effect 

on the Hispanic population was more pronounced. Because of the magnitude of the 

adjustments for Hispanics, many Hispanic data 

series were revised back to January 1980.

• January 1989. First CATI cases are used on a 

large scale in CPS monthly estimates. Initially, 

CATI started with several hundred cases each 

month. As operational issues were resolved 

and new contact centers were opened—

Tucson, Arizona (May 1992) and Jeffersonville, 

Indiana (September 1994)—the CATI workload 

was gradually increased to about 10,000 cases 

a month. 

• June 1990. The first of a series of experiments 

to test alternative labor force questionnaires is 

started at the Hagerstown Telephone Center. 

These tests used random-digit dialing and 

were conducted in 1990 and 1991.

• January 1992. Industry and occupation (I&O) 

codes from the 1990 Census are introduced. 

Population estimates were converted to the 

1990 Census base for use in ratio estimation 

procedures.

• July 1992. The CATI and CAPI Overlap (CCO) 

experiments begins. 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 CCO’s main purpose was to gauge the 

combined effect of the new questionnaire and 

computer-assisted data collection. The initial 

CCO ran parallel to the official CPS from July 

1992 to December 1993. An extended parallel 

survey with modifications ran from January 

1994 to May 1994. Research indicated that 

the initial CCO parallel survey results provided misleading indications of the impact 

of the redesign on major CPS indicators. 

Additional research, using data collected from 

both parallel surveys, along with data collected from the official CPS both prior to and 

after January 1994 redesign, enabled a more 

accurate interpretation of the effects of the 

redesigned survey on CPS estimates. 

• January 1994. A new questionnaire designed 

solely for use in computer-assisted interviewing is introduced in the official CPS. This 

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 1 

month (I&O, retired and disabled statuses, and 

duration of unemployment) was confirmed or 

updated in subsequent months. In developing 

the automated questionnaire, extensive use 

was made of cognitive testing techniques. 

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 I&O distribution of the 

unemployed with previous work experience. 

It is also estimated that the redesign significantly increased the employment-population 

ratio and the labor force participation rate 

for women, but significantly decreased the 

employment-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 

the I&O distribution of the employed.

The redesigned questionnaire collects some 

new data that sharpens labor force concepts and definitions and incorporates some 

of the recommendations from the Levitan 

Commission. For example, the definition of 

discouraged workers is tightened by requiring some evidence of attachment to the job 
Current Population Survey TP77 Appendix: History of the Current Population Survey 35

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

market, and it improves the measurement of 

involuntary part-time employment by explicitly asking about current availability for fulltime work. Questions were added on citizenship, country of birth of a person’s parents, 

and whether they were immigrants. These 

questions were phased into the sample. 

CPS data used by BLS are adjusted to reflect 

an undercount in the 1990 decennial census. 

Quantitative measures of this undercount 

are derived from a post-enumeration survey. 

Because of reliability issues associated with 

the post-enumeration survey for small areas 

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

less than 1,000,000), 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 aged 

16 and over civilian noninstitutional population. 

Most interviews are conducted by FRs in 

person or by telephone, but the transfer of 

workload to CATI contact centers continued.

• April 1994. For the redesign following the 

1990 decennial census, new sample is phased 

in from April 1994 to July 1995. This resulted 

in a monthly overall sample size of 68,000, 

with about 58,000 eligible HUs in 792 PSUs. 

• December 1994. Starting in December 1994, 

a new set of response categories are phased 

in for the relationship to reference person 

question. This modification was directed at 

individuals not formally related to the reference person to determine whether there were 

unmarried partners in a household. The old 

partner or roommate category was deleted 

and replaced with the following categories: 

unmarried partner, housemate or roommate, 

and roomer or 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 is 

changed because of a funding reduction. The 

original reliability requirements of the sample were relaxed, allowing a reduction in the 

national sample size. The overall sample size 

decreased from 68,000 to 59,500 HUs, with 

a reduction of 56,000 eligible HUs to about 

50,000 eligible HUs. The reduced CPS national 

sample contained 754 PSUs. As a result of the 

sample reduction, it was decided to use time 

series models to produce monthly employment and unemployment estimates for all 

states. 

• January 1998. A new composite weighting 

methodology is implemented. The procedure 

starts with computed composite estimates for 

the main labor force categories, classified by 

key demographic characteristics. Then, the 

procedure adjusts person weights, through a 

series of ratio adjustments, to agree with the 

composite estimates—thus incorporating the 

effect of composite estimation into the person 

weights. 

• July 2001. Effective with the release of July 

2001 data, official labor force estimates 

from the CPS and the LAUS program reflect 

the expansion of the monthly CPS sample. 

The overall sample size increased to 72,000 

assigned HUs, with 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, more recently referred to as CHIP) 

legislation. This 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 (now known as the 

ASEC). 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 regular CPS design was 

unchanged, and the SCHIP sample was added 

using its own design criteria. The additional 

10,000 households were added to the sample over a 3-month period. 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. 
36 Appendix: History of the Current Population Survey Current Population Survey TP77

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

• January 2003. The 2002 Census Bureau occupational and industrial classification systems, 

which are derived from the 2000 SOC and 

the 2002 NAICS, are 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 are 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 asked directly if they are 

Spanish, Hispanic, or Latino. With respect 

to race, the response category of Asian and 

Pacific Islander was split into two categories: 

(1) Asian and (2) Native Hawaiian or Other 

Pacific Islander. 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 their race. These changes had 

little or no impact on the overall civilian noninstitutional 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 0.5 percentage points in 

their unemployment rate.

New population controls reflecting the results 

of the 2000 Census substantially increase the 

size of the civilian noninstitutional 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 begins 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 

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. Most of these series were 

related to I&O. 

Improvements are introduced to both the 

second-stage and composite weighting procedures. These changes adapted the weighting 

procedures to the new race and ethnicity classification system and enhanced the stability 

over time of national, state, and substate labor 

force estimates for demographic groups.

• January 2004. Population controls are 

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 noninstitutional 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. 

Beginning with the publication of December 

2003 estimates in January 2004, the practice of concurrent seasonal adjustment is 

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. 
Current Population Survey TP77 Appendix: History of the Current Population Survey 37

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

With the introduction of concurrent seasonal 

adjustment, BLS no longer published projected seasonal factors for CPS data. 

In addition to introducing population controls 

that reflected revised estimates of net international migration for 2000 through 2003 in 

January 2004, the LAUS program introduces 

a linear wedge adjustment to CPS statewide 

estimates of the civilian noninstitutional population aged 16 and over, labor force, employment, unemployment, and unemployment 

rate. This adjustment linked the 1990 decennial census-based 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 the LAUS program’s 

econometric models used to produce the 

official labor force estimates for states and 

selected substate areas. These models use 

CPS employment and unemployment estimates as dependent variables.

• April 2004. For the redesign following the 

2000 decennial census, new sample is phased 

in from April 2004 to July 2005. There were 

824 PSUs; the overall sample size decreased 

slightly, to 71,000 assigned HUs, with about 

60,000 households eligible for interview. 

• September 2005. Hurricane Katrina made 

landfall on the Gulf Coast on August 29, 

2005, 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 this unusually catastrophic 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 

people 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. 

• November 2006. For the first time, the 

November survey is conducted 1 week earlier 

in the month than usual. Interviewing is done 

during the calendar week that includes the 

twelfth of the month, and the reference week 

is the week containing the fifth of the month. 

(This has often been done for December.) 

Policy now allows moving November collection earlier in the month to (1) avoid conflicting with the Thanksgiving holiday, and 

(2) to allow adequate processing time before 

December data collection begins.

• January 2007. The system running the data 

collection instrument changes from a DOSbased system to BLAISE©, a windows-based 

system. BLAISE© increases the interviewer’s 

awareness of their position in the survey flow 

during the interview and enables them to better navigate backward and forward. 

• January 2008. A new feature is added to 

the questionnaire to comply with the new 

Respondent Identification Policy. The policy 

requires surveys to protect certain sensitive 

information when using dependent interviewing techniques. 

• June 2008. Six questions are added to the 

CPS to identify people with disabilities aged 

15 and over. They are typically asked in new 

and returning households (MIS 1 and 5), in 

replacement households, and of new household members.

• January 2009. CPS switches to the 2007 

Census industry classification system (used 

through 2013). The codes are based on the 

2007 NAICS. Changes were relatively minor 

and historical data were not revised.

• January 2011. CPS switches to the 2010 

Census occupational classification system. 

This classification system is based on the 2010 

SOC and replaced an earlier version based on 

the 2000 SOC. The names of broad- and 

intermediate-level occupational groups 

remained the same, but some detailed occupations were reclassified between them. This 
38 Appendix: History of the Current Population Survey Current Population Survey TP77

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

affected comparability with earlier data, but 

historical data were not revised. 

The questionnaire is modified to allow 

reported durations of unemployment of up to 

5 years. The previous limit was 2 years. The 

change was phased in two panels (MIS rotation groups) at a time from January 2011 to 

April 2011. The change increased estimates of 

mean duration of unemployment, but not the 

medians. 

The Census Bureau incorporates additional 

safeguards for CPS public-use microdata files 

to ensure that respondent identifying information is not disclosed; this includes perturbing 

respondent ages. One result of the measures 

taken to enhance data confidentiality is that 

most estimates from public-use microdata 

files will no longer exactly match the comparable estimates published by BLS. Although 

certain topside labor force estimates will continue to match published data—such as the 

overall levels of employed, unemployed, and 

not in the labor force—estimates below the 

topside level all have the chance of differing 

slightly from the published data (but will be 

well within the sampling variability associated 

with the CPS). 

• January 2012. New benchmark population 

controls reflecting the results of the 2010 

Census are used in the weighting process. The 

civilian noninstitutional population aged 16 

and over increased by 1,510,000, but those 

not in the labor force were disproportionately 

affected and increased by 1,252,000. There 

were increases of 258,000 in the civilian labor 

force, 216,000 in employment, and 42,000 in 

unemployment. The labor force participation 

rate and the employment-population ratio 

were each reduced by 0.3 percentage points. 

Historical data were not revised. 

• January 2013. The Census Bureau regional 

office (RO) structure is revised after remaining 

substantially unchanged for 50 years. Staff in 

the 12 ROs began transitioning in phases to a 

6-RO structure in January 2012, and the new 

structure took effect in January 2013. CPS 

operations began reporting results based on 

the six ROs immediately.

• January 2014. CPS switches to the 2012 

Census industry classification system. The 

codes are based on the 2012 NAICS. The differences between the 2012 and 2007 industry 

classification systems were minor and did not 

involve reclassification of industries between 

the broader industry sectors. Historical data 

were not revised. 

• April 2014. For the sample redesign following the 2010 decennial census, new sample 

is phased in from April 2014 to July 2015. 

There were 852 PSUs; the overall sample size 

increased to 74,000 assigned HUs, with about 

61,000 households eligible for interview. There 

was no attempt to maximize PSU overlap with 

the previous design. 

• January 2015. BLS begins using X-13ARIMASEATS (Signal Extraction in ARIMA Time 

Series) software to seasonally adjust data. 

Questions are added on professional certifications and state or industry licenses, and 

whether they were required for a person’s current or most recent job. To limit lengthening 

the survey and increasing respondent burden, 

three little-used questions on graduate and 

professional coursework were removed from 

the CPS when the three certification and 

licensing questions were added. 

• May 2015. Changes to response categories 

take effect for the relationship-to-reference-person question. The two response 

categories “spouse (husband or wife)” and 

“unmarried partner” were replaced with four 

response categories: “opposite-sex spouse 

(husband or wife),” “opposite-sex unmarried partner,” “same-sex spouse (husband or 

wife),” and “same-sex unmarried partner.”

• December 2017: Due to the reduction in 

contact center workloads and more effective 

data collection modes the centralized CATI 

contact center in Hagerstown, Maryland, 

is closed. The caseload previously handled 

by the Hagerstown center is reassigned 

to the remaining two contact centers in 

Jeffersonville, Indiana, and Tucson, Arizona. 

(Less than 10 percent of CPS interviews were 

conducted from the centralized contact centers in 2017.) 
Current Population Survey TP77 Appendix: History of the Current Population Survey 39

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

FURTHER READING

Bregger, J., “The Current Population Survey: a 

Historical Perspective and BLS’ Role,” Monthly 

Labor Review, June 1984, pp. 8–14.

Dunn, M., S. E. Haugen, and J. Kang, “The Current 

Population Survey – Tracking Unemployment 

in the United States for over 75 years,” Monthly 

Labor Review, January 2018, pp. 1–23.

Executive Office of the President, Office of 

Management and Budget, Statistical Policy 

Division, Federal Statistics: Coordination, 

Standards, Guidelines: 1976, Washington, DC: 

Government Printing Office.

Frankel, L. R., and J. Stevens Stock, “On the 

Sample Survey of Unemployment,” Journal of the 

American Statistical Association, March 1942.

U.S. Department of Commerce, U.S. Census 

Bureau, “Sampling Procedures and Method of 

Operation of the Monthly Report on the Labor 

Force,” November 1942. 
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Unit 2 

Current Population Survey Sample 

Design and Estimation
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Current Population Survey TP77 Chapter 2-1: Sample Frame 43

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

Chapter 2-1: Sample Frame

INTRODUCTION

For the 2010-based sample design, the sampling 

frames and sampling methodology for Current 

Population Survey (CPS) have undergone important changes. CPS staff now selects the CPS sample from two dynamic sampling frames, one for 

housing units (HUs) and one for group quarters 

(GQs). Both frames are based upon the Master 

Address File (MAF). The MAF is a national inventory of addresses that is continually updated by 

the U.S. Census Bureau to support its decennial 

programs and demographic surveys. The MAF, 

which is maintained by Geography Division (GEO), 

is described in greater detail in later sections, 

while the CPS sampling methodology is described 

in Chapter 2-2, Sample Design.

The MAF replaces a variety of address sources 

used in the past to construct sampling frames for 

CPS. For the sample design based upon the 2000 

and earlier censuses, CPS sample was selected 

from a coordinated set of four sampling frames: 

the unit frame, the area frame, the GQ frame, and 

the new construction permit frame. The address 

sources for these frames included the official 

address list from the most recent census, block 

listings, and addresses from building permits 

(Table 2-1.1).

As a comprehensive source for all HU and GQ 

addresses in the nation, the MAF eliminates the 

need for costly field visits to conduct area block 

listings or to collect building permit information for new addresses. Instead, new growth is 

captured through semiannual updates to the 

MAF from a variety of address sources, the most 

important of which is the Delivery Sequence File 

(DSF). The DSF is the master file of mail delivery 

points maintained and regularly updated by the 

U.S. Postal Service (USPS). The DSF and other 

MAF update sources are discussed in more detail 

in following sections.

The American Community Survey (ACS), the largest demographic survey conducted by the Census 

Bureau, has been using the MAF as the sole 

source for its HU sampling frame since its earliest 

testing phases in the late 1990s. ACS paired its 

adoption of a MAF-based sampling frame with an 

annual sampling methodology that was designed 

to take full advantage of the dynamic qualities 

of the MAF. Likewise, during its 2010 Sample 

Redesign, the Demographic Statistical Methods 

Division (DSMD) paired its proposal to switch to 

MAF-based sampling frames with a recommendation to convert to annual sampling for CPS and the 

other current household surveys. Annual sampling, 

in which only a year’s worth of sample is selected 

at a time, replaced the previous model in which 

enough sample to last a decade or more was 

selected in a one-time operation at the beginning 

of each sample design. Going forward, annual 

sampling from a continuously updated MAF-based 

sampling frame allows CPS the flexibility to adjust 

sample sizes, reorder the HU universe (including 

new growth) using updated sort variables, or even 

change the universe sort variables altogether. 

Table 2-1.1.

Address Sources for the Current Population Survey Sampling Frames for the 2000 and 

2010 Sample Designs

Sample

design

Sampling

frame

Address

source

Old construction 

housing units

New growth 

housing units

Group

 quarters

2000

Housing units 2000 Census √

Area Block listings √ √ √

Group quarters 2000 Census and 

area block listings √

Permit Building permits √

2010 Housing units Master Address File √ √

Group quarters Master Address File √
44 Chapter 2-1: Sample Frame Current Population Survey TP77

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

THE MASTER ADDRESS FILE

A Short History of the MAF

In preparation for the 2000 Census, the Census 

Bureau created a preliminary version of the MAF 

that would be used as the comprehensive address 

source for decennial operations. The core of this 

early MAF consisted of addresses collected during 

the 1990 Census operations (the 1990 Address 

Control File) merged with DSFs provided by the 

USPS. This proto-MAF was then supplemented 

and updated by a series of decennial operations. 

These included the Local Update of Census 

Addresses (LUCA), a partnership program by 

which local governments could provide their own 

address lists, and field listing operations like 2000 

Address Canvassing. At the conclusion of the 

2000 Census, the MAF was considered a virtually 

complete inventory of all known HU addresses in 

the nation.1

While previous census address lists were not maintained after the census was complete, the vision 

for the MAF after 2000 was very different—the 

MAF would be continually updated with the DSF 

and other sources so it could serve as the official 

inventory of HUs and GQs for all future censuses. 

The MAF would also support the demographic 

surveys and other statistical programs conducted 

by the Census Bureau.

Consistent with this vision, the MAF has evolved 

into a critical corporate resource for the Census 

Bureau: 

• The MAF has been the sole source of 

addresses for the HU sampling frame for ACS 

since its implementation as a national survey 

in 2005.

• The MAF was the major address source for the 

2010 Census and will serve the same role for 

future censuses.

• The MAF is a critical input to the Population 

Estimates Program.

• The MAF is the sole source of addresses for 

the HU and GQ sampling frames for CPS and 

other demographic household surveys, including the American Housing Survey (AHS), the 

1 The MAF did not include GQ addresses at this point in time. 

GQs were maintained in a separate file called the Special Place/

GQ Inventory. GQs would not be merged into the MAF until the 

2010 Census.

National Crime Victimization Survey (NCVS), 

the Consumer Expenditure (CE) Surveys, 

and the Survey of Income and Program 

Participation (SIPP). 

To further enhance its value, the MAF was integrated in 2007 with the Census Bureau’s geospatial database, the Topologically Integrated 

Geographic Encoding and Referencing (TIGER) 

System. The TIGER database contains digital 

representations of all map features and related 

attributes required by the census. An important 

function of TIGER is to assign geocodes (i.e., 

state, county, tract, and block) to the addresses 

on the MAF. For a short history of TIGER, see 

Thompson (2014).

The integrated database created by merging 

the MAF with TIGER is called the MAF/TIGER 

Database. While the MAF is actually the address 

portion of the MAF/TIGER Database, it is the 

better-known acronym and we will use it throughout this chapter to refer to the database maintained by GEO.

The Content of the MAF

The major purpose of the MAF is to store address 

and geographic information about the HUs and 

GQs (as well as some nonresidential addresses) 

in the United States.2 Accurate, complete location address information is critical to CPS and the 

other demographic surveys that conduct personal 

visit interviews. Most MAF records have complete 

city-style location addresses, which consist of 

a house number, street name, and ZIP code; an 

example of a complete city-style address is “3227 

Holt Lane, Anytown, PA, 29999.”3 The importance 

of a city-style location address is that it is usually 

sufficient in itself to locate a sample case in the 

field.

Incomplete location addresses, conversely, are 

missing house number, street name, or both. 

For the 2010 Census, the percentage of location addresses that were incomplete dropped to 

1.9 percent. For incomplete addresses, the MAF 

usually has other information, including a location 

description (for example, “SECOND HOUSE ON 

2 The MAF includes addresses for Puerto Rico, which are outof-scope for CPS but in scope for ACS. 3 The MAF does not contain locality or post office names 

(“Anytown” in this example) or the two-letter postal abbreviation 

for the state. The ZIP code must be matched to a separate file to 

pick up post office name.
Current Population Survey TP77 Chapter 2-1: Sample Frame 45

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

LEFT DOWN UNNAMED DIRT ROAD OFF HOLT 

LANE”) or latitude and longitude coordinates 

that can help a field representative (FR) locate 

the HU in the field. One of the imperatives of the 

2010 Address Canvassing operation was to collect 

Geographic Positioning System (GPS) coordinates 

for as many listed HUs as possible. As a result, 

only 8.2 percent of the incomplete city-style 

addresses in the 2010 Census were also missing 

coordinates.

Every MAF record contains a MAFID, which is a 

unique identifier for that record across the entire 

MAF. Other fields that are always filled are the 

HU/GQ indicator, unit status (one purpose of 

which is to flag known duplicates), and residential status. The MAF also contains action codes 

(“add,” “delete,” “verify,” etc.) and dates from 

each source that has provided information for the 

MAF record.4

A MAF record may, but does not always, include 

entries for these fields: mailing address, location 

description, latitude and longitude coordinates, 

census tract, and census block. MAF records may 

be linked as duplicates through the Surviving 

MAFID. A record with a nonblank Surviving MAFID 

is a “retired” record. The Surviving MAFID denotes 

a different record that replaces or “survives” the 

original MAFID. 

The MAF contains several variables relating to the 

DSF, including the historical series of DSF flags. 

Each DSF flag indicates whether the MAFID was 

on that version of the DSF and whether it was residential or nonresidential. A more detailed discussion of the DSF later in this section provides more 

information about the DSF content on the MAF. 

For GQ records, the MAF contains GQ names, GQ 

type, location address information, contact information, and information about the size of the GQ.

The geographic information on the MAF is mainly 

restricted to tract and block codes and latitude 

and longitude coordinates, but more information 

can be obtained by matching by block to other 

geographic files maintained by GEO. This leads 

4 The action codes and dates for MAF sources are actually 

provided in a separate product from the MAF extracts. These 

files, the MAF Operations (MAFOP) data sets, are delivered in 

conjunction with the MAF extracts, and data from the two files 

are merged for each county during MAF processing.

to an important concept for MAF-based sampling 

frames, the ungeocoded HU. MAF records that 

have been assigned census tract and block codes 

are said to be geocoded, while records without 

tract and block codes are ungeocoded. 

All MAF HUs that were included on the final census address list for 2010 were geocoded; it was a 

census requirement that all valid living quarters 

be assigned block codes for tabulation purposes. 

Also, MAF HUs that originate from a block listing 

operation like the Community Address Updating 

System (CAUS) operation will have geocodes. 

Ungeocoded HUs result from adding addresses to 

the MAF from a source that does not provide its 

own block codes. The primary MAF source without block codes, and therefore for ungeocoded 

MAF addresses, is the DSF.

Block codes are important to the CPS HU Frame 

for several reasons:

• Block information can be important in locating 

an address in the field for personal interview.

• The block code is the “gateway” to other 

census geography; if you know the block for 

an address, then you can determine its place, 

county subdivision, urban or rural status, 

urban area, principal city status, etc. None of 

this information is available for ungeocoded 

HUs (unless imputed).

• Block summary data from the census or ACS 

is linked to geocoded HUs in the CPS HU 

Frame for efficient sorting of the universe for 

sampling.

Keeping the MAF Up-to-Date

The Census Bureau keeps the MAF as current 

as possible by continuously updating it using a 

diverse array of address sources, imagery, and 

field interview and listing operations. While geographic updates to the TIGER are very important, 

it is the address updates to the MAF portion that 

are most critical to the mission of maintaining 

address-based sampling frames for CPS and other 

surveys. Demographic surveys depend upon frequent and accurate address updates to the MAF 

to maintain survey coverage and quality. 
46 Chapter 2-1: Sample Frame Current Population Survey TP77

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

The MAF updates take two forms:

• Augmenting and improving the information for existing MAF addresses. Examples 

include (1) adding or correcting location 

address components like street names, unit 

designations, and ZIP codes; (2) establishing 

a link between two MAF records because new 

information shows they actually represent the 

same HU; and (3) adding a block code to a 

previously ungeocoded address.

• Adding new HU or GQ addresses to the MAF 

from other address sources or field operations. Sources of new addresses include the 

DSF, local partnership files, ACS interviews, 

field listing operations, and decennial field 

activities. 

The Delivery Sequence File

The DSF is a national inventory of mail delivery points maintained by the USPS. Since the 

inception of the MAF, the DSF has been its major 

source of new growth addresses on the MAF. 

As discussed previously, the DSF was first used 

to update the MAF in the late 1990s in preparation for the 2000 Census. Since 2000, the Census 

Bureau has “refreshed” the MAF with new versions 

of the DSF twice a year. By 2009, after 9 years of 

these DSF updates, 15.5 percent of the eligible 

HUs in the ACS HU frame were DSF addresses 

added to the MAF since the 2000 Census. This figure far surpassed all other sources of new growth 

addresses between the 2000 and 2010 censuses. 

The semiannual DSF refreshes involve a complex 

address matching operation. The USPS does not 

specifically identify the new records on the DSF 

since the last time the file was delivered, so the 

Census Bureau determines which records represent new addresses not already on the MAF by 

matching the entire DSF to the MAF by address.5

DSF addresses that match existing MAF addresses 

are used to update those records, while new MAF 

records are created for DSF addresses not found 

on the MAF. For further detail on the DSF update 

process, see U.S. Census Bureau (2017). 

An important feature of the DSF refresh is that 

non-city-style addresses from the DSF are 

5 Other USPS products, like the Locatable Address 

Conversion System (LACS) file and the ZIP+4 house number 

range files, are also used in this process. The LACS file will be 

discussed in more detail later in this section.

discarded and not used to update the MAF. 

Examples of non-city-style addresses are “RR 4, 

Box 16, Anytown, PA 29999” and “P.O. Box 11896, 

Anytown, PA 29999.” These addresses are not 

used in the DSF match to the MAF because of the 

low match rates and the impermanent relationship 

between such addresses and HUs on the ground. 

To the extent that these non-city-style addresses 

represent new growth in certain areas, MAF coverage is lost. Another limitation of the DSF is that 

GQ addresses are not flagged as such, so DSF 

address updates are classified by default as HUs. 

The DSF is therefore not a source of new GQs on 

the MAF, while inadvertently contributing some 

GQ addresses that are misclassified as HUs and 

included in the HU Frame.

As discussed above, most ungeocoded HUs on 

the MAF originate from the DSF. This is because 

the DSF contains no block codes or GPS coordinates. The primary means by which GEO assigns 

geocodes to DSF additions, then, is by finding a 

match to an address segment already in TIGER. 

Because many new DSF addresses are located 

on newly built streets or street segments not 

yet included in TIGER, a large share of the new 

addresses added to the MAF in each year cannot be assigned a geocode through TIGER. As 

an example, 56.1 percent of the DSF addresses 

added to the MAF in 2018 were ungeocoded.6

The DSF refresh of the MAF is a complicated 

process, but there are corresponding challenges 

for MAF users in determining how to use these 

DSF updates. To consider just one example among 

many, the USPS classifies each DSF address 

as either an Include in Delivery Statistics (IDS) 

address or an Exclude from Delivery Statistics 

(EDS) address. The nominal definition of an 

IDS address is that it represents a current mail 

delivery point, while an EDS address does not. 

Research has suggested that some portion of EDS 

addresses represent planned new construction, 

so (subject to other criteria) such addresses are 

included in the CPS HU Frame (Loudermilk, 2010; 

Ying, 2012). There are many other such choices 

that must be made in determining which DSF 

addresses should be included and which should 

not. This process, referred to as MAF filtering, is a 

critical component of frame construction for CPS 

6 Computed using data from the MAF extracts delivered to 

DSMD in January 2018 and July 2018. 
Current Population Survey TP77 Chapter 2-1: Sample Frame 47

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

and all other surveys building a sampling frame 

from the MAF.

Local Partnership Files

In 2013, the Census Bureau began receiving 

address and Geographic Information System (GIS) 

files through a partnership program involving 

local governments. This program is modeled upon 

the decennial partnership program, the LUCA 

program, and is intended to extend the benefits 

of such a program to the intercensal period. The 

files submitted by the local government partners (“local partnership files”) can cover places, 

counties, county subdivisions, American Indian 

Areas, and even entire states. The files undergo a 

review for quality and, if acceptable, are used to 

update existing records and add new addresses 

to the MAF. The data sources for the partnership 

files can include building permits, tax assessment 

data, GIS databases, real estate records, and other 

types of administrative data. Since many local 

governments now have very complete and accurate GIS information, the partnership file updates 

can be an important source of geocodes for 

previously ungeocoded postcensus additions to 

the MAF. For more information on the partnership 

file program, see Trainor (2014). See U.S. Census 

Bureau (2015) for details on the partnership 

updates.

Field Listing Operations

Several post-2010 block-listing operations have 

provided updates to the MAF, including:

• CAUS and MAF Coverage Study (MAFCS). 

CAUS was implemented in 2003 specifically to 

address ACS coverage concerns with the MAF. 

CAUS targeted predominantly rural blocks 

where DSF coverage of postcensus growth 

was most problematic due to a combination 

of suspected high growth and a prevalence 

of non-city-style addresses. Through block 

canvassing based on a dependent list from 

the MAF, CAUS supplemented MAF coverage 

by adding new addresses and changing or 

deleting existing MAF addresses in up to 1,500 

blocks per year through 2016. 

Starting in 2016, CAUS blocks were combined 

with sample blocks from the MAF Coverage 

Study to create a national canvassing workload of 20,000 blocks. MAFCS was designed 

to produce yearly MAF coverage estimates for 

the United States and to provide continuous 

updates to the MAF for current surveys and 

the 2020 Census. For 2017, MAFCS selected 

20,000 sample blocks; at that point, CAUS 

was essentially absorbed into MAFCS and no 

longer existed as a separate program. Though 

originally planned to continue annually 

through 2019, the MAFCS program was discontinued in April 2017 (U.S. Census Bureau, 

2018).

• Demographic Area Address Listings (DAAL).

7

Block listings for the 2000-based area frame 

for the current household surveys continued 

to be conducted until 2014, when the DAAL 

program was phased out and replaced by the 

MAF as the sole source of sample for the 2010 

sample design. The 2000-based block listings 

were conducted in blocks that were screened 

into the area frame for the current surveys; 

these were in primarily rural areas with high 

concentrations of non-city-style addresses or 

in areas with no building permit coverage. The 

listing results were used to update the MAF. 

Starting in 2012, the Coverage Improvement 

(CI) Frame began sending blocks out for 

listing under the DAAL banner. These listings 

were very limited in scope and were discontinued after 2 years when CPS and AHS, the 

only participants in the frame, decided to drop 

out. The results from these listings were used 

to update the MAF and will do so again in the 

future should the program be revived. The CI 

Frame is discussed in more detail later.

• Census tests and dress rehearsals. Prior to the 

2010 Census, block listings were conducted in 

a very limited set of counties to support various census tests and dress rehearsals. These 

listings updated the MAF, as will any future 

listings conducted in support of 2020 decennial efforts.

ACS provides other MAF updates through its 

time-of-interview (TOI) updates and the ACS 

GQ program, which includes both a field listing 

component and headquarters research. ACS-TOI 

7 For operational reasons, the term DAAL came over time to 

refer to both the 2000-based area frame block listings for the 

current surveys and the CAUS listings. Here, we use DAAL in its 

original sense to refer to the block listings conducted for the 

current surveys—the 2000-based area frame listings and their 

successors, the 2010-based CI Frame listings. 
48 Chapter 2-1: Sample Frame Current Population Survey TP77

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

provides only updates to existing MAF records 

that are in the ACS sample, such as adding block 

codes or modifying address information. The ACS 

GQ program is one of the only sources of GQ 

updates to the MAF in the years between censuses; these updates include both new GQ records 

and HU-to-GQ conversions. The partnership files 

provide a very limited number of GQ updates, as 

did the DAAL program before it was phased out 

for the current surveys.

Another USPS product, the LACS file, is used in 

conjunction with the DSF to help mitigate duplication on the MAF. The LACS file is a national 

dataset of address conversions, often from E-911 

readdressing operations in rural areas.8 If both the 

“old” and “new” address on a LACS record are 

found in the MAF, they are linked; the old address 

is the retired record and the new address is the 

surviving record. This allows surveys that use the 

MAF to avoid including both records in their sampling frames, thereby avoiding duplication. 

CREATING AND UPDATING THE 

CURRENT POPULATION SURVEY 

SAMPLING FRAMES 

While both CPS sampling frames for the 2010 

sample design are based upon the MAF, they are 

created in different ways and on different cycles. 

The HU Frame was created for the first time in 

2013 and is updated every 6 months with the 

latest MAF data. The GQ Frame, conversely, is 

created every 3 years. The frame construction and 

updating processes will be described separately 

for each frame. 

Current Population Survey Housing Unit 

Frame 

GEO delivers MAF extracts to DSMD twice each 

year, in January and July.9 A MAF extract is a 

“snapshot” of the MAF for a given county that 

8 E-911 readdressing is the process whereby local or state 

governments mandate new city-style addresses in areas (typically rural) where non-city-style addresses are common. The 

purpose of the new addresses, which are usually assigned in a 

systematic manner (for example, 1550 Fogerty Lane represents 

the house that is 1550 feet from the start of Fogerty Lane), is to 

provide better location information to allow faster response by 

emergency vehicles. 9 The July MAF delivery also includes two sets of Geographic 

Reference Files (GRFs), one set with block-level records with 

corresponding geographic attributes (county subdivision, place, 

urban or rural, etc.) and the other set with the names for all the 

geographic entities. The GRFs contain geographic codes that 

are not included on the MAF extracts, so are needed in MAF 

processing to attach these geographic codes to the Edited MAF 

Extract files.

reflects 6 months of DSF and other updates. The 

MAF extracts do not contain all information from 

the MAF. The MAF often contains, for example, 

multiple location addresses and multiple mailing 

addresses for a given HU; the MAF extract contains only one instance of each address type (the 

“preferred address”). 

The delivery of the MAF extracts from GEO to 

Demographic Systems Division (DSD) kicks off 

the semiannual HU Frame update cycles.10 Details 

on the production of MAF extracts can be found 

in U.S. Census Bureau (2016). The MAF extracts 

are reviewed by DSMD for quality, with a focus on 

the most recent MAF updates. If errors are discovered, DSMD may request a fix and redelivery 

of the extracts. Once the MAF extracts have all 

been delivered by GEO and formally accepted by 

DSMD, the files are submitted to the MAF processing systems within DSD. These systems perform 

various quality edits, assign codes, and apply the 

MAF filtering rules to create a set of Edited MAF 

Extract files.

The MAF filtering is a critical feature of the frame 

creation process; its outcomes can have an 

important effect on frame coverage. The MAF 

extracts contain all records from the MAF for a 

given county, including many that should not 

be eligible for the CPS HU Frame. The filtering 

rules designate each MAF record as either “valid” 

(passed the filter and eligible for the HU frame) or 

“invalid” (failed the filter, ineligible for the frame). 

While some filtering decisions are easy (for example, any record denoted as “nonresidential” or 

as a “duplicate” is invalid), others are much less 

obvious. Should ungeocoded additions from the 

DSF be valid? How about addresses deleted by 

the 2010 Census that continue to show up on the 

DSF? 

As a result of research conducted by DSMD as 

part of the 2010 Sample Redesign, DSMD decided 

to adopt the ACS filtering rules for CPS and 

the other current household surveys when they 

switched to a MAF-based HU frame. DSMD plans 

to continually assess the filter rules and work with 

ACS to identify possible enhancements. Most 

filtering questions revolve around this question, 

10 DSD is responsible for the programming systems and production files for all the current surveys, while DSMD is responsible for sample design, survey quality, and operational systems. 

DSMD and DSD work very closely together on frame construction issues.
Current Population Survey TP77 Chapter 2-1: Sample Frame 49

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“When does a new DSF address represent a new 

growth HU rather than an existing HU already 

on the MAF from the census or another source?” 

Failing to provide the correct answer for any 

subclass of DSF addresses can create either 

overcoverage (a new DSF address is included in 

the frame, but the HU is already on the MAF) or 

undercoverage (a new DSF address is excluded, 

but it represents a new growth HU not otherwise 

represented on the MAF).

The CPS HU Frame actually takes the form of 

separate HU universes by county, just as the 

MAF extracts are separate by county. The HU 

Frame files are called the Unit Frame Universe 

Files (UFUFs). The original UFUFs for CPS and 

the other current surveys were created in 2013 

and consisted of all the valid and invalid HUs 

from the MAF at that time. If the invalid HUs 

are MAF records that we consider ineligible for 

the HU Frame, though, why are they included 

on the UFUFs? A decision was made to include 

all MAFIDs on the UFUFs and make the valid or 

invalid status the first sort key for sampling. All 

MAFIDs would thereby be given a chance of selection, but any invalid HUs selected for sample will 

be suppressed from interview unless the MAFID 

changes to valid status by the time of the first 

interview.

Starting with those initial 2013 universe files, the 

UFUFs are updated every 6 months with MAF data 

in two ways:

• Each existing UFUF record is updated with 

the most recent MAF data (addresses, block 

codes, etc.) and its latest filtering status.

• New growth records are added to the UFUF.

The UFUFs are also updated with sort information 

from ACS and decennial block-level data as part 

of the annual sampling process, which takes place 

once each year as part of the January MAF processing cycle. Each survey participating in annual 

sampling can sort the frame units in its own way. 

The CPS sort keys for the UFUFs are discussed in 

Chapter 2-2, Sample Design.

If the UFUFs that are updated from the January 

MAF extracts each year are used for annual sampling, what is the purpose of the July updates? In 

addition to refreshing the MAF data for sample 

cases that have not yet gone out to interview, 

the new growth cases added to the UFUF in the 

July phase can “activate” CPS sample that was 

selected in a prior annual sampling phase, thereby 

increasing the CPS sample size. In fact, every MAF 

update, whether January or July, has this ability 

to activate sample cases by adding new growth to 

the UFUF.11

To understand how this new growth works, consider that each UFUF at time of annual sampling 

consists of two portions: the actual universe, 

which consists of actual MAFIDs, versus the “skeleton” universe, made up of units not yet linked up 

with MAFIDs. The skeleton portion of the UFUF is 

essentially an empty framework that is to be filled 

in with new growth over time. During annual sampling, CPS selects its sample across both portions 

of the UFUF. Many lines in the skeleton universe 

are thereby selected for sample, but will not be 

activated unless associated with MAFIDs at some 

later point. This feature of the HU Frame allows 

for constant augmentation of the CPS sample with 

new growth up until the final scheduled interview 

for a given sample designation. 

Consider this example:

In the 2013 annual sampling phase, CPS 

selected sample cases that were all designated A03. Many of these cases were 

assigned to UFUF units associated with 

MAFIDs, while others were assigned to empty 

records in the skeleton portion of the universe. As the skeleton universe was filled in 

with actual MAF data by subsequent MAF 

updates (July 2013, January 2014, July 2014, 

etc.), additional A03 cases were activated and 

added to the CPS sample. The first interviews 

for A03 cases began in August 2014, but 

some A03 cases started interviews as late as 

March 2016. Therefore, the A03 sample that 

was originally selected from the HU Frame in 

2013 could have been augmented with new 

cases as late as July 2015.

11 Note that while every MAF processing cycle can add new 

growth to the UFUFs, not all do. An example is the January 2015 

cycle, when problems with the MAF delivery could not be fixed 

in time, so MAF extracts from the previous delivery (July 2014) 

were substituted. No new growth was added to the UFUFs for 

that cycle. In July 2015, though, the UFUFs “caught up” when 

updated with MAF extracts that by then contained an entire year 

of new growth. 
50 Chapter 2-1: Sample Frame Current Population Survey TP77

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Group Quarters Frame

The GQ Frame for CPS and the other current surveys is created from MAF extracts only once every 

3 years. CPS and the other current surveys participating in the frame each select a 3-year sample 

of GQs. The first GQ Frame was created from the 

July 2012 MAF extracts; the second GQ Frame 

was created from the January 2016 MAF extracts. 

The GQs on the frame all come from the MAF, and 

nearly all GQs on the MAF were collected during 

the 2010 Census through operations like 2010 

Address Canvassing and GQ Validation. Unlike the 

DSF on the HU side, there is not a dependable 

source of new GQ addresses for the MAF. ACS 

GQ operations add some new GQs and HU-to-GQ 

conversions, the local partnership files can contribute some GQs, and block listing operations 

like CAUS capture a relatively small number of 

GQ addresses. For the most part, though, the GQ 

Frame remains relatively static from one version 

to the next. There is some possibility that future 

GQ Frames may include new college housing GQs 

collected through an independent study by DSMD.

Note that, while the HU Frame contains information down to the HU level, including individual 

units within apartment complexes, the GQ Frame 

does not include “unit” information for each GQ. 

Instead, the MAF provides an expected GQ size; 

any GQ selected for sample must then be sent to 

the field for a listing of the “units” (which can be 

rooms, beds, or people) at the GQ before individual sample units can be identified and interviewed.

The GQ Frame consists only of noninstitutional GQs and excludes institutional GQs. 

Noninstitutional GQs are facilities for people who 

are not under formally authorized and supervised 

care and custody such as college housing, group 

homes for adults, workers’ living quarters, and 

convents. Institutional GQs include facilities such 

as prisons, skilled nursing facilities, and residential 

treatment centers for juveniles.

Coverage Improvement Frame

In the period immediately following the 2010 

Census, the MAF had a very high level of coverage due to 2010 Address Canvassing and other 

decennial operations that systematically captured address information. That coverage may 

start to degrade over time in areas where there 

is no reliable source of new growth (postcensus) 

addresses. The DSF, as the major source of HU 

updates to the MAF between censuses, is the primary driver of new growth coverage on the MAF. 

In most urban and suburban areas, the DSF should 

be a thorough source of new growth. In more 

rural areas with more non-city-style addresses 

or lack of home mail delivery, though, the lack of 

DSF coverage may lead to MAF undercoverage 

concerns.12

Research conducted as part of the 2010 Sample 

Redesign (Liu, 2009) suggested a risk of future 

bias in the CPS state-level, labor force participation estimates due to potential MAF undercoverage. DSMD proposed a coverage improvement 

operation in the 13 states identified as at-risk in 

the study: Alabama, Alaska, Arkansas, Kentucky, 

Maine, Mississippi, Montana, New Hampshire, 

New Mexico, Oklahoma, Vermont, West Virginia, 

and Wyoming. Within these 13 states, a universe 

of blocks with suspect DSF coverage would be 

identified each year. The blocks would be sampled 

and the selected blocks would be sent to the field 

to be canvassed. Because the HU Frame already 

provides coverage of any HUs in these blocks that 

were on the MAF, the CI Frame was concerned 

only with the HUs that were added by the field 

listers. Any added HUs within the blocks selected 

by a survey would automatically be in sample for 

the survey; in effect, these CI Frame additions 

would supplement and be indistinguishable from 

the HU Frame sample cases for the survey. 

CPS was a participant in the CI Frame from its 

first sampling and listing phases in 2012, with AHS 

joining a year later. By late 2014, though, CPS 

and AHS decided to end their participation in the 

frame due to cost. Therefore, the CI Frame has 

been suspended and will not be reinstated unless 

CPS or other surveys decide that MAF supplementation is needed.

12 As stated earlier, non-city-style addresses from the DSF are 

not used to refresh the MAF, so the MAF may be deficient in coverage in areas with such addresses (even if covered by the DSF).
Current Population Survey TP77 Chapter 2-1: Sample Frame 51

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REFERENCES

Liu, X., “A Supplemental Report of the Frame 

Assessment for Current Household Surveys: 

Effects of MAF-Based Frame Coverage on Survey 

Estimates (Doc. #2010-4.0-G-7),” U.S. Census 

Bureau, February 20, 2009.

Loudermilk, C., and J. Martin, “Frame Assessment 

for Current Household Surveys (FACHS) Filter 

Rules Research: 2009 Analytical Report,” 

U.S. Census Bureau, May 24, 2010.

Thompson, J. H., “Happy 25th Anniversary, 

TIGER,” U.S. Census Bureau, November 20, 2014, 

retrieved from <www.census.gov/newsroom

/blogs/director/2014/11/happy-25th

-anniversary-tiger.html>.

Trainor, T. F., “Geography Division Address 

Canvassing Recommendation,” U.S. Census 

Bureau, November 15, 2014.

U.S. Census Bureau, Decennial Census 

Management Division, “2020 Census Detailed 

Operational Plan for 8. Address Canvassing 

Operation,” U.S. Census Bureau, May 9, 2018.

U.S. Census Bureau, Geography Division, 

“Delivery Sequence File (DSF) Refresh Software 

Requirements Specification,” U.S. Census Bureau, 

July 3, 2017.

U.S. Census Bureau, Decennial Census 

Management Division, “2020 Census Detailed 

Operational Plan for 6. Geographic Programs 

Operation (GEOP) – 6-3. Geographic Data 

Processing Component (GEOP/GDP),” 

U.S. Census Bureau, September 13, 2016.

U.S. Census Bureau, Decennial Census 

Management Division, “Geographic Support 

System Initiative Master Address File Updates 

using Partnership Data Software Requirements 

Specification,” U.S. Census Bureau, March 31, 

2015.

Ying, S., “Identifying Excluded from Delivery 

Statistics Records that Elude the American 

Community Survey Housing Unit Frame Filters,” 

U.S. Census Bureau, February 27, 2012.
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Chapter 2-2: Sample Design

INTRODUCTION

For more than seven decades, the 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 is evaluated periodically. The 

design has often been under close scrutiny in 

response to demand for new data and the need to 

improve the reliability of the estimates by applying 

research findings and new types of information 

(especially decennial 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 reevaluated after 

each decennial census. Based on these reevaluations, the design of the survey is modified and 

systems are put in place to provide sample for the 

following decade. The most recent decennial revision incorporated new information from the 2010 

Census and was fully implemented as of July 2015.

This chapter describes the CPS sample design as 

of July 2015. 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. 

SURVEY REQUIREMENTS AND DESIGN

Survey Requirements

The following bulleted items briefly describe the 

major characteristics of the CPS sample as of July 

2015:

• The CPS sample is a probability sample.

• The sample is designed primarily to produce 

national and state estimates of labor force 

characteristics of the civilian noninstitutional 

population aged 16 and older (CNP16+).

• The CPS sample consists of independent 

samples from 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.13 Since the CPS 

design consists of independent samples for 

the states and substate areas, it is said to be 

state-based.

• Sample sizes are determined by reliability 

requirements that are expressed in terms of 

the coefficient of variation (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 6.0 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 unemployment rate between 2 

consecutive months be statistically significant 

at the 0.10 level.

• The required CV on the annual average unemployment level for each state, substate area, 

and the District of Columbia, given a 6.0 percent unemployment rate, is 8.0 percent.

Overview of Survey Design

The CPS sample is a multistage stratified sample 

of approximately 72,000 assigned HUs from 852 

sample areas. It is designed to measure demographic and labor force characteristics of the civilian noninstitutional population aged 16 and older. 

Approximately 12,000 of these assigned HUs are 

sampled under the Children’s Health Insurance 

Program (CHIP) expansion that has been part of 

the official CPS sample since July 2001. CPS samples HUs from the MAF HU and GQ sections that 

include all the official 2010 Census addresses and 

postcensus additions from the USPS, local jurisdictions, and field listings. As of July 2015, sample 

is drawn annually to allow newly constructed HUs 

a chance of selection before the transition to a 

new sample. 

The first stage of sampling involves dividing the 

United States into primary sampling units (PSUs)—

most of which comprise a metropolitan area, a 

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

and Richmond Counties.
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large county, or a group of smaller counties. Every 

PSU is nested within the boundary of a state. 

The PSUs are then grouped into strata based on 

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 

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 the 2010 Census. 

A second stage of sampling is conducted annually; a sample of HUs within the sample PSUs is 

drawn. Ultimate sampling units (USUs) are small 

groups of HUs. The bulk of the USUs sampled 

in the second stage consist of sets of addresses 

that are systematically drawn from sorted lists 

of blocks. HUs 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 twostage sample because PSUs and groups of HUs 

are each selected. PSUs are selected from strata, 

and HUs are selected from these PSUs.

Each month, interviewers collect data from the 

sample HUs. A HU 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 HU is interviewed 8 times; 

this is known as the 4-8-4 design.

Households are rotated in and out of the sample 

in a way that improves the accuracy of the monthto-month and year-to-year change estimates. 

The rotation scheme ensures that in any single 

month, approximately one-eighth of the HUs are 

interviewed for the first time, another eighth are 

interviewed for the second time, and so on. That 

is, after the first month, six of the eight rotation 

groups will have been in the survey for the previous month—there will always be a 75 percent 

month-to-month overlap. Thus, four of the eight 

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 an unbiased representation of the target 

population. The rotation system makes it possible 

to reduce sampling error by using a composite 

estimation procedure (Chapter 2-3, Weighting 

and Estimation) and, at slight additional cost, by 

increasing the representation in the sample of 

USUs with unusually large numbers of HUs.

Each state’s sample design ensures that most HUs 

within a state have the same overall probability 

of selection. Because of the state-based nature 

of the design, sample HUs in different states have 

different overall probabilities of selection. The 

system of state-based designs ensures that both 

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 

FRs needed to conduct the survey and reducing 

the travel cost incurred in visiting the sample HUs. 

Two features of 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 

FR is allotted a manageable workload in his or her 

sample area.

The first-stage sample selection is carried out in 

three major steps:

• Definition of the PSUs.

• Stratification of the PSUs within each state.

• Selection of the sample PSUs in each state.

These steps are implemented soon after the 

decennial census.

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 an interviewer can traverse it 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 

between-PSU variance. Between-PSU variance 
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is a component of total variance that arises from 

selecting a sample of PSUs rather than selecting 

all PSUs. In each stratum, one PSU is selected to 

represent the other PSUs in the same stratum. 

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 restrict the distance an FR must travel.

Rules for Defining Primary Sampling Units

• Each PSU is contained within the boundary of 

a single state.

• Metropolitan Statistical Areas (MSAs) are 

defined as separate PSUs using projected 

2013 Core-Based Statistical Area (CBSA) 

definitions. CBSAs are defined as 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.

• 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.

• The area of the PSU should not exceed 3,000 

square miles except in cases where a single 

county exceeds the maximum area.

• The population of the PSU is at least 7,500 

except where this would require exceeding 

the maximum area specified as 3,000 square 

miles.

• 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 PSUs consistent with 

other Census Bureau demographic surveys.14 The 

following are steps for combining counties, county 

equivalents, and independent cities into PSUs for 

the 2010 design:

• The 2010 PSUs are revised by incorporating 

new or redefined metropolitan areas into the 

PSU definitions.

• Any single county is classified as a separate 

PSU if it exceeds the maximum area limitation 

deemed practical for FR travel (regardless of 

its 2010 population).

• 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.

• Contiguous counties with natural geographic 

barriers between them are placed in separate 

PSUs to reduce the cost of travel within PSUs.

These steps created 1,987 PSUs in the United 

States from which to draw the sample for the CPS 

when it was redesigned after the 2010 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 (i.e., uses the same sampling fraction in all 

strata) and one FR is assigned to each sample 

PSU, equal stratum sizes have the advantage of 

providing equal FR workloads (before population 

growth and migration significantly affect the PSU 

population sizes).

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). Each 

14 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, 2013).
Current Population Survey TP77 Chapter 2-2: Sample Design 55

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SR PSU is treated as a separate stratum and is 

included in the sample.

The following describes the steps for stratifying 

PSUs for the 2010 redesign:

1. CPS used several criteria to determine which 

PSUs would be SR. First, all counties that 

existed in the 150 most populous CBSAs were 

set as SR after determining that a natural 

break in population existed between CBSAs 

ranked 150 and 151. Then, a formula was used 

to determine which of the remaining PSUs 

would become SR. If the calculated field 

workload (#HU selected) for a PSU is greater 

than or equal to 55, the PSU is classified as 

SR.

Where

PSU MOS = total number of HUs in PSU

p(selection) = probability of selection

SI = state sampling interval

2. The remaining PSUs were grouped into 

non-self-representing (NSR) strata within 

state boundaries. In each NSR stratum, one 

PSU was 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 FR workloads of roughly 35 to 55 

HUs in an NSR PSU. The number of NSR 

strata in a state is a function of the 2010 

population, civilian labor force, state CV, 

and between-PSU variance on the unemployment level. (Workloads in NSR PSUs 

are constrained because one FR 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 betweenPSU variance. 

Key variables used for stratification 

include:

• Number of males unemployed.

• Number of females unemployed.

• Number of families with female head of 

household.

• Number of households with three or 

more people.

In addition to these, a number of other 

variables were used for stratification in 

certain states, such as industry and wage 

variables obtained from Quarterly Census 

of Employment and Wages program at the 

BLS. The number of stratification variables 

in a state ranged from three to six except 

in Alaska, where the only variable used is 

the number of males unemployed.

d. Starting with the 2010 sample redesign, 

stratifications and PSUs in sample for CPS 

and CHIP are exactly the same in states 

that contain CHIP sample.

e. Table 2-2.1 summarizes the percentage of 

the targeted population in SR and sampled NSR areas by state. SR percentages 

for a given state were computed as the 

ratio of the sum of MOS (measure of size) 

of all SR PSUs and the total MOS for that 

state. NSR percentages for a given state 

were computed as the ratio of the sum of 

MOS of only the selected NSR PSUs and 

the total MOS for that state.

The CPS used the PSU Stratification 

Program (PSP), created by the 

Demographic Statistical Methods Division 

of the Census Bureau, to perform the PSU 

stratification. CPS strata in all states are 

formed by the PSP. The PSP randomly 

places NSR PSUs into strata for each state 

or area by adhering to the NSR stratum 

size tolerance as the initial stratification. 

The criterion score is how the stratifications were compared. It is an estimate of 

variance that would result from the given 

stratification. The criterion score (using 

between or total variance) is computed. 

PSUs are next moved from strata to strata 

(maintaining sample size criterion) in an 

attempt to improve (lower) the criterion 

def
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Table 2-2.1. 

Civilian Noninstitutional Population 16 Years and Over in Sample Areas for 852-PrimarySampling-Unit Design by State

State

Total Self-representing Non-self-representing

Population1 Percent Population1 Percent Population1 Percent

   Total. . . . . . . . . . . . . . .  185,883,503 85.8 1

Alabama. . . . . . . . . . . . . . . . . .  2,201,

Alaska. . . . . . . . . . . . . . . . . . . 

Arizona. . . . . . . . . . . . . . . . . . .  3,9

Arkansas. . . . . . . . . . . . . . . . . .  1,30

California. . . . . . . . . . . . . . . . . .  25,685,

 Los Angeles. . . . . . . . . . . . .  7,286,643

 Remainder of California. . .  18,399,287 95.2 17,588,592 91.0 810,6

Colorado. . . . . . . . . . . . . . . . . .  2,853

Connecticut. . . . . . . . . . . . . . .  2,568

Delaware. . . . . . . . . . . . . . . . 

District of Columbia. . . . . . . .  472,453 100.0 4

Florida. . . . . . . . . . . . . . . . . . . .  12,

Georgia. . . . . . . . . . . . . . . . . . .  5,17

Hawaii. . . . . . . . . . . . . . . . 

Idaho. . . . . . . . . . . . . . . . . . . .

Illinois. . . . . . . . . . . . . . . . . . . . . 

Indiana. . . . . . . . . . . . . . . . . . . .  3,

Iowa. . . . . . . . . . . . . . . . . . . . .

Kansas. . . . . . . . . . . . . . . . . . . .  1,

Kentucky. . . . . . . . . . . . . . . . . .  1,903,

Louisiana. . . . . . . . . . . . . . . . . .  2,572,

Maine. . . . . . . . . . . . . . . . . . . .

Maryland. . . . . . . . . . . . . . . . . .  3,864

Massachusetts. . . . . . . . . . . . .  4,811,359

Michigan. . . . . . . . . . . . . . . . . .  6,062,

Minnesota. . . . . . . . . . . . . . . . .  2,597,4

Mississippi. . . . . . . . . . . . . . . . .  1,183,

Missouri. . . . . . . . . . . . . . . . . . .  2,8

Montana. . . . . . . . . . . . . . . . . .  5

Nebraska. . . . . . . . . . . . . . . . . .  87

Nevada. . . . . . . . . . . . . . . . . . .  1,

New Hampshire. . . . . . . . . . . .  933,310

New Jersey. . . . . . . . . . . . . . . .  6,3

New Mexico. . . . . . . . . . . . . . .  1,133,369 

New York. . . . . . . . . . . . . . . . . .  13,219,

 New York City. . . . . . . . . . .  6,316,113 100

 Remainder of New York. . .  6,903,646 86.0 6,423,009 80.0 480,6

North Carolina. . . . . . . . . . . . .  5,144,855 77.8 4

North Dakota. . . . . . . . . . . . . .  365,709 7

Ohio. . . . . . . . . . . . . . . . . . . . . 

Oklahoma. . . . . . . . . . . . . . . . .  1,701,7

Oregon. . . . . . . . . . . . . . . . . . .  2,29

Pennsylvania. . . . . . . . . . . . . .  7,883,979 87.

Rhode Island. . . . . . . . . . . . . .  764

South Carolina. . . . . . . . . . . . .  2,757,560 86.3 2

South Dakota. . . . . . . . . . . . . .  321,254 5

Tennessee. . . . . . . . . . . . . . . . .  3,488,33

Texas. . . . . . . . . . . . . . . . . . . . .  15,

Utah. . . . . . . . . . . . . . . . . . . . . 

Vermont. . . . . . . . . . . . . . . . 

Virginia. . . . . . . . . . . . . . . . . . .  4,49

Washington. . . . . . . . . . . . . . .  3,532,297 7

West Virginia. . . . . . . . . . . . . .  764,529 58

Wisconsin  . . . . . . . . . . . . . . .  2,785,300 

Wyoming. . . . . . . . . . . . . . . . . . 284,725 74.6 225,093 59.0 59,632 15.6

Z Represents or rounds to zero.

1

 Civilian noninstitutional population from sample areas 16 years of age and over based on the 2010 Census.

Source: U.S. Census Bureau, 2010 Census.
Current Population Survey TP77 Chapter 2-2: Sample Design 57

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score. All possible swaps of PSUs from 

one stratum to another are then evaluated. 

A list of the best stratifications based on 

criterion score was created and given to 

the analyst for each state. A national file 

is then produced containing the chosen 

stratification for each state.

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, and Vermont. 

Additionally, the District of Columbia and 

the New York City and Los Angeles substate areas are entirely SR.

Selection of Primary Sampling Units

Each SR PSU is in the sample by definition. There 

are currently 506 SR PSUs. In each of the remaining 346 NSR strata, one PSU is selected for the 

sample following the guidelines described next.

At each sample redesign of the CPS, it is important to minimize the cost of introducing a new set 

of PSUs. Substantial investment has been made 

in hiring and training FRs 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 FR must be 

accepted. Furthermore, there is a temporary loss 

in accuracy of the results produced by new and 

relatively inexperienced FRs. Concern for these 

factors is reflected in the procedure used for 

selecting PSUs.

Objectives of the Non-Self-Representing 

Selection Procedure

The selection of the NSR PSUs was carried out 

within the strata using the 2010 Census population. The selection procedure selected one PSU 

from each stratum with probability proportional to 

the 2010 population.

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 HU 

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 for growth 

of the CPS sample because of HUs built after the 

2010 Census. (See below for information about 

how the desired CPS 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.

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, is expressed as

 (2-2.1)

where 

�b

2 = between-PSU variance contribution to 

the variance of the state unemployment level 

estimator

�w

2 = within-PSU variance contribution to 

the variance of the state unemployment level 

estimator

E(X) = x, the expected value of unemployment 

level for the state

The term �w

2

 can be written as the variance 

assuming a binomial distribution from a simple 

random sample multiplied by a design effect

where 

N = the civilian noninstitutional population, 16 

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

state

p = proportion of unemployed in the CNP16+ for 

the state, or 

n = the state sample size

q = 1 – p

def f = 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.
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Substituting Np= x, this formula can be rewritten as 

 (2-2.2)

where SI = the state sampling interval, or 

Substituting Formula 2-2.2 into Formula 2-2.1 and 

rewriting in terms of the state sampling interval 

gives 

where 

CV2 X2= is the variance. Generally, the 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 FR workloads.) When 

computing the sampling interval for the current 

CPS sample, a six percent state unemployment 

rate is assumed. Table 2-2.1 provides information 

on the proportion of the population in sample 

areas for each state.

The CHIP sample is allocated among the states 

after the CPS sample is allocated. A sampling 

interval accounting for both the CPS and CHIP 

samples can be computed as:

The between-PSU variance for the combined CPS/

CHIP sample can be estimated using:

SECOND STAGE OF THE SAMPLE 

DESIGN

The second stage of the CPS sample design is the 

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

• Select a probability sample that is representative of the civilian noninstitutional population.

• Give each HU in the population one chance 

of selection, with virtually all HUs in a state or 

substate area having the same overall chance 

of selection.

• For the sample size used, keep the withinPSU variance of the labor force statistics (in 

particular, unemployment) at as low a level as 

possible, subject to respondent burden, cost, 

and other constraints.

• Select within-PSU sample units annually.

• 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. Most 

USUs consist of a geographically compact cluster 

of approximately four addresses, corresponding to 

four HUs at the time of the census. Use of HU clusters lowers travel costs for FRs. 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 2010 

Census. The 2010 Census collected information on 

all living quarters existing as of April 1, 2010, 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 was obtained from the American Community 

Survey. 

These sources provide sampling information for 

numerous demographic surveys conducted by 

the Census Bureau.15 In consideration of respondents, sampling methodologies are coordinated 

among these surveys to ensure that a sampled HU 

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 

15 CPS sample selection is coordinated with the following 

demographic surveys in the 2010 redesign: the AHS-Metropolitan 

sample, the AHS-National sample, the CE Survey-Diary sample, the CE Survey-Quarterly sample, the Telephone Point of 

Purchase Survey, the NCVS, the National Health Interview 

Survey, the Rent and Property Tax Survey, and the SIPP.
Current Population Survey TP77 Chapter 2-2: Sample Design 59

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interview for each survey. This coordinated sampling approach is computer intensive and started 

with the 2000 sample redesign.

Type of Living Quarters

Two types of living quarters were defined for the 

census. The first type is a HU. A HU is a group of 

rooms or a single room occupied as a separate 

living quarter or intended for occupancy as a 

separate living quarter.16 A HU may be occupied 

by a family, one person, or two or more unrelated 

people who share the living quarter. About 99 percent of the population counted in the 2010 Census 

resided in HUs.

The second type of living quarter is a GQ. A GQ 

is a living quarter where residents share common facilities or receive formally authorized care. 

Examples include college dormitories, retirement 

homes, and communes. Some GQs, such as fraternity and sorority houses and certain types of 

group houses, are distinguished from HUs if they 

house ten or more unrelated people. The GQ population is classified as institutional or noninstitutional and as military or civilian. CPS targets only 

the civilian noninstitutional population residing in 

GQs. As a cost-savings measure, student dormitories are not sampled, since the vast majority 

of students in dormitories either have a usual 

residence elsewhere, are not in the labor force, 

or both. A subset of institutional GQs is included 

in the GQ frame and given a chance of selection 

in case of conversion to civilian noninstitutional 

housing by the time it is scheduled for interview. 

Less than 1 percent of the population counted in 

the 2010 Census resided in GQs.

Development of Sampling Frames

The primary sampling frame used by the CPS 

in 2010 was the MAF. This file is used by many 

demographic surveys and comprises 2010 decennial census addresses with updates from the USPS 

and local governments. Refer back to Chapter 

2-1, CPS Frame for more about the MAF. Separate 

16 Separate living quarters are living quarters in which one or 

more occupants live separately from any other individual(s) in 

the building and have direct access to the living quarters without 

going through another living quarters, such as from outside the 

building or through a common hall. For vacant units, the criteria 

of separateness and direct access are applied to the intended 

occupants (U.S. Census Bureau, Decennial Management Division 

Glossary [2014]).

HU and GQ frames are created from the MAF. 

The skeleton frame, described below, is also used 

as a placeholder for future sample from new 

construction.

Housing Units on the MAF

The HU portion of the MAF includes all the official 

2010 census addresses and postcensus additions 

from the USPS, local jurisdictions, and field listings. About 99 percent of CPS sample comes from 

the HU portion of the MAF. The unit frame consists 

of HUs in census blocks that contain a very high 

proportion of complete addresses. 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 HUs. However, over time 

some buildings may be demolished or converted 

to nonresidential use, and others may be split up 

into several HUs. These addresses remain sample 

units, resulting in a small variability in cluster size.

Group Quarters on the MAF

About 1 percent of sample is also selected from 

the GQs portion of the MAF. The GQs on the MAF 

consist of noninstitutional facilities such as college 

dorms, adult group homes, Job Corps centers, 

and religious GQs. 

The GQ frame covers a small proportion of the 

population. A CPS USU in the GQ frame consists 

of two HU equivalents. The GQ frame is converted 

into HU equivalents because the 2010 Census 

addresses of individual GQs or people within a 

GQ are not used in the sampling. The number of 

HU equivalents is computed by dividing the 2010 

Census GQs population by the average number of 

people per household (calculated from the 2010 

Census as 2.61).

Skeleton Frame

The skeleton frame in the 2010 redesign is different from the skeleton frame created for the 

2000 redesign. The 2000 skeleton frame, most 

commonly referred to as the permit frame, was 

filled in with HUs listed in the permit address 

listing/new construction operations. The 2010 

skeleton frame provides placeholders to be filled 
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in by new growth identified in the MAF extracts 

every 6 months. The purpose of the 2000 skeleton frame was to allocate new growth sample in 

selected areas to a 15-year sampling period. The 

primary purpose of the skeleton frame for the 

2010 redesign is to select sample units from new 

growth updates as part of annual sampling, and 

its secondary purpose is to provide representative 

new growth sample between main sample selection periods.

Group Quarters Details

An integer number of GQ units is calculated at 

the census block level. The number of GQ units is 

referred to as the GQ block MOS and is calculated 

as follows

where

NI= tabulation block noninstitutional GQ population excluding college housing and military 

GQs

CH= tabulation block college housing population

IN= number of institutional GQs in the tabulation 

block

α = estimated average household size in the 

United States (2.61 for the 2010 redesign)

A quarterly listing of GQs with closeouts occurs 

every 3 months. Sampling of GQ units is done 

after units are sampled in the within-PSU sampling 

stage and after GQ listings have been loaded onto 

the database. Units are selected monthly by interview date, meaning only the current upcoming 

sample designation, rotation, and panel are sampled. If more units are selected than the cut-off 

per segment for the survey, subsampling occurs.

Only the civilian noninstitutional population is 

interviewed for CPS. An institutional GQ is equivalent to one measure, regardless of the number of 

people counted there in the 2010 Census.

Unduplication will occur to allow at least 2 years 

between interviews for a unit within a GQ. For 

instance, a unit could be selected for CPS in the 

first sample period with an initial interview date 

of January 2016. This unit is next eligible to be 

selected and interviewed with initial interview 

date after January 2018. This would give that unit 

at least 9 months off between the two 16-month 

interview cycles.

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. 

This interval 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.”)

CPS sample is selected separately for the unit and 

GQ frames. Since sample is selected at a constant 

overall rate, the percentage of sample selected 

from each frame is proportional to population size. 

Within-Primary Sampling Unit Sampling 

Procedure

Units are arranged within sampling frames based 

on characteristics of the 2010 Census and geography. The characteristics of the 2010 Census used 

are percentage of households with female householder, the percentage that are owner occupied, 

percentage Black, and percentage aged 65 and 

over. Sorting minimizes within-PSU variance of 

estimates by grouping together units with similar 

characteristics. The 2010 Census data and geography are used to sort blocks and units. (Sorting 

is done within block and state since sampling is 

performed within block and state.) The MAF HU 

frame is sorted on block level characteristics, 

keeping HUs in each block together, and then 

by a HU identification number to sort the HUs 

geographically.

CPS selects HUs from the HU frame every year, 

and selects group quarters from the GQ frame 

every 3 years. This is different from selecting samples for the entire decade like the past designs. 
Current Population Survey TP77 Chapter 2-2: Sample Design 61

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After frame files are ready, the sampling intervals 

are computed and adjustments are made, random starts are calculated, and then the sample is 

selected. A sampling interval is an integral representation of a percentage; i.e., a sampling interval 

of 20 means 1 out of 20, or 5 percent. A random 

start is just the initial position in the list where 

sampling is started.

Example:

Final sampling interval = 5.75

String length = 4

Random start = 3.76

Number of Records = 25

Then the following sequence will be created:

3.76

3.76 + (1 * 5.75) = 3.76 + 5.75 = 9.51

3.76 + (2 * 5.75) = 3.76 + 11.5 = 15.26

3.76 + (3 * 5.75) = 3.76 + 17.25 = 21.01

The rounded up sequence would then be 4, 10, 16, 

and 22.

The sample cases would be records 4-7, 10-13, 

16-19, and 22-25.

Assignment of Postsampling Codes

Two types of postsampling 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 and control number.

Final hit number—The final hit number identifies 

the original within-PSU order of selection. All 

USUs in a hit string are assigned the same final 

hit number. For each PSU, this code is assigned 

sequentially, starting with one. The final hit number is used in the application of the CPS variance 

estimation method discussed in Chapter 2-4, 

Variance Estimation.

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. Rotation groups are assigned after sorting 

hits by state, MSA or non-MSA status (old construction only), SR or 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. 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 FR 

assignments among demographic surveys.

ROTATION OF THE SAMPLE

The CPS sample rotation scheme is a balance 

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:

• Variance of estimates of month-to-month 

change: three-fourths of the sample units are 

the same in consecutive months.

• Variance of estimates of year-to-year change: 

one-half of the sample units are the same in 

the same month of consecutive years.

• Variance of other estimates of change: outgoing sample is replaced by sample likely to 

have similar characteristics.

• Response burden: eight interviews are dispersed across 16 months.
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The rotation scheme follows a 4-8-4 pattern. A 

HU or GQ is interviewed 4 consecutive months, 

removed from sample for the next 8 months, interviewed the next 4 months, and then retired from 

sample. The rotation scheme is designed so outgoing HUs are replaced by HUs from the same hit 

string, which tend to have similar characteristics.

Rotation Chart

The CPS rotation chart illustrates the rotation pattern of CPS sample over time. Table 2.2-2 presents 

the rotation chart beginning in January 2018. The 

following statements provide guidance in interpreting the chart:

• The chart covers the interview period from 

January 2018 through March 2020 for the 

CPS and for CHIP. For each month, the chart 

shows the sample designation and rotation 

(or rotation group) for the units interviewed. 

A sample designation is represented by the 

combination of the letter A or B with a twodigit number. The letter A represents CPS and 

the letter B represents CHIP, so that the designation A06 is a CPS sample and B06 is a CHIP 

sample. Each sample designation consists 

of rotations numbered 1 through 8. Sample 

designations and rotations appear as column 

headings and the numbers within the chart 

refer to the month-in-sample (MIS). For example, the 5 under the column heading A08/B08 

(rotation 2) for January 2019 indicates that 

rotation 2 of sample designation A08 (or B08) 

is being interviewed for the fifth time (MIS 5).

• The sample units in a particular rotation are 

interviewed for 4 consecutive months; then, 

after a lapse of 8 months, they are interviewed 

for another 4 months. For example, A08/B08 

(rotation 4) is interviewed from March 2018 to 

June 2018 and again from March 2019 to June 

2019. In a given month, two or three sample 

designations are in operation. For example, in 

January 2019, units with sample designation 

A07/B07 (rotations 7 and 8), A08/B08 (rotations 1 and 2), and A09/B09 (rotations 3, 4, 5, 

and 6) are being interviewed.

• Each month, a new rotation comes into sample for the first time, and another returns to 

sample after an 8-month rest. The remaining 

sample designation/rotations were interviewed during the preceding month. For 

example, in January 2019, A09/B09 (rotation 

6) units come into sample for the first time; 

A08/B08 (rotation 2) units return after an 

8-month lapse; A07/B07 (rotations 7 and 8), 

A08/B08 (rotation 1), and A09/B09 (rotations 3, 4 and 5) sample units are interviewed 

again after being interviewed in the preceding 

month, December 2018.

• The chart differentiates the annual samples 

using light and dark gray shading, with each 

sample consisting of one-and-a-half sample 

designations. Each annual sample begins its 

interviews in April.

• 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 and Cahoon, 1982).
Current Population Survey TP77 Chapter 2-2: Sample Design 63

U.S. Bureau of Labor Statistics and U.S. Census Bureau Table 2-2.2. Current Population Survey/Children’s Health Insurance Program Rotation Chart January 2018–March 2020 Month-in-Sample by Sample Designation and Rotation Sample design A06/B06 A07/B07 A08/B08 A09/B09 A10/B10 A11/B11 Rotation: 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 2018 JAN. . . . . . 8 7 6 5 . . . . . . . . 4 3 2 1 FEB. . . .  . . . 8 7 6 5 . . . . . . . . 4 3 2 1 MAR. . .  . . . . 8 7 6 5 . . . . . . . . 4 3 2 1 APR. . .  . . . . . 8 7 6 5 . . . . . . . . 4 3 2 1 MAY. . .  . . . . . . 8 7 6 5 . . . . . . . . 4 3 2 1 JUN. . .  . . . . . . . 8 7 6 5 . . . . . . . . 4 3 2 1 JUL. . . .  . . . . . . . . 8 7 6 5 . . . . . . . . 4 3 2 1 AUG. . .  . . . . . . . . . 8 7 6 5 . . . . . . . . 4 3 2 1 SEP. . . .  . . . . . . . . . . 8 7 6 5 . . . . . . . . 4 3 2 1 OCT. . .  . . . . . . . . . . . 8 7 6 5 . . . . . . . . 4 3 2 1 NOV. . .  . . . . . . . . . . . . 8 7 6 5 . . . . . . . . 4 3

2019 JAN. . . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . . 4 3 2 1

FEB. . . .  . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . .

MAR. . .  . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . . 4

APR. . .  . . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . . 4

MAY. . .  . . . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . . 4JUN. . .  . . . . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . . 4JUL. . . .  . . . . . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . .AUG. . .  . . . . . . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . . 4

SEP. . . .  . . . . . . . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . .

OCT. . .  . . . . . . . . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . . 4NOV. . .  . . . . . . . . . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . . 4

DEC. . .  . . . . . . . . . . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . . 4

2020 JAN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . . 4 3 2 1

FEB. . . .  . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . .

MAR. . .  . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 7 6 5 . . . . . . . . 4

2016 Annual sample 2017 Annual sample 2018 Annual sample 2019 Annual sample
64 Chapter 2-2: Sample Design Current Population Survey TP77

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Overlap of the Sample

Table 2-2.3 shows the approximate 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.

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 1 month and replace it with a completely 

redesigned sample the next month. Since redesigned sample contains different sample areas, 

new FRs 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 2014, the 2010 Census-based 

design was phased in through a series of changes 

completed in July 2015 (BLS, 2014).

MAINTAINING THE DESIRED SAMPLE 

SIZE 

The CPS sample is continually updated to include 

recently built HUs. If the same sampling rates were 

used throughout the decade, the growth of the 

U.S. housing inventory would lead to increases 

in the CPS sample size and, consequently, to 

increases in cost. To avoid exceeding the budget, 

the sampling rate is periodically reduced to maintain the desired sample size. Referred to as maintenance reductions, these changes in the sampling 

rate are implemented in a way that retains the 

desired set of reliability requirements. 

These maintenance reductions are different from 

changes to the base CPS sample size resulting 

from modifications 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 BLS. For example, the sample reduction implemented in January 

1996 was due to a reduction in CPS funding; new 

design requirements were specified at that time.

Developing the Reduction Plan

The CPS sample size for the United States is 

projected forward for about 1 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 postreduction 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 PSUs to 

maintain the self-weighting nature of the statelevel design.

Reduction Groups

The CPS sample size is reduced by deleting one 

or more subsamples of USUs from each sampling 

frame.

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 

Table 2-2.3. 

Approximate Proportion of Sample in 

Common for 4-8-4 Rotation System

Interval (in months)

Percentage of 

sample in common 

between 2 months

1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75.0

2, 12. . .

3, 10, 14. . .

4–8, 16 and greater. . . . . . . . . . . . . . . 0.0

9, 15. . .

11, 13. . .
Current Population Survey TP77 Chapter 2-2: Sample Design 65

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not have enough sample to have all reduction 

groups represented.

All USUs in a hit string have the same reduction 

group number. For all 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. MSA or non-MSA status.

3. SR or NSR status.

4. Stratification PSU.

5. Final hit number, which defines the original 

order of selection.

The state or national sample can be reduced by 

deleting USUs from the frame 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 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 2010 

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

This makes the resulting overall selection probability in the state approximately 1 in 526. 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.

REFERENCES

Executive Office of the President, Office of 

Management and Budget, Metropolitan Area 

Changes Effective with the Office of Management 

and Budget’s Bulletin 13-01, February 28, 2013.

Tegels, R., and L. Cahoon, “The Redesign of the 

Current Population Survey: The Investigation 

into Alternate Rotation Plans,” paper presented 

at the 1982 Joint Statistical Meetings, American 

Statistical Association, 1982.

U.S. Bureau of Labor Statistics, “Redesign 

of the Sample for the Current Population 

Survey,” Current Population Survey, Technical 

Documentation, April 2014, retrieved from 

<www.bls.gov/cps/sample_redesign_2014.pdf>.
66 Chapter 2-2: Sample Design Current Population Survey TP77

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FURTHER READING

Farber, J., “2010 Sample Redesign 

Requirements for Creating the Skeleton Frame,” 

Doc. #2010-3.8-Q-7, Version 1.5, U.S. Census 

Bureau, 2011.

Farber, J., and M. Li, “2010 Sample Redesign 

Requirements for Within-PSU Unit Frame 

Sample Selection,” Doc. #2010-3.8-Q-9, Version 

1.2, U.S. Census Bureau, 2014.

Farber, J., and M. Neiman, “Sample Code 

Assignments for the Current Population Survey 

and the State Children’s Health Insurance 

Program Survey for the 2010 Redesign on the 

Unit Frame,” Doc. #2010-3-8-Q-12, Version 1.2, 

U.S. Census Bureau, 2012.

Farber, J., and M. Sundukchi, “PSU Stratification 

Program Requirements for the 2010 Sample 

Redesign,” Doc. #2010-3-2-Q-2, Version 1.4, 

U.S. Census Bureau, 2012.

Kostanich, D., D. Judkins, R. Singh, and 

M. Schautz, “Modification of Friedman-Rubin’s 

Clustering Algorithm for Use in Stratified PPS 

Sampling,” paper presented at the 1981 Joint 

Statistical Meetings, American Statistical 

Association, 1981.

Kuwik, C., “Group Quarters Overview 

Document,” Doc. #2010-3.4-G-2, Version 0.7, 

U.S. Census Bureau, 2012.

Ludington, P. W., “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, 

1992.

Rawlings, A., “Annual Sampling 

Recommendation for the 2010 Sample 

Redesign,” Doc. #2010-3.8-R-3, Version 1.0, 

U.S. Census Bureau, 2010.

Statt, R., E. A. Vacca, C. Wolters, and 

R. Hernandez, “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, 

1981.

U.S. Bureau of Labor Statistics, “Redesign of 

the Sample for the Current Population Survey,” 

Employment and Earnings, Government Printing 

Office, Washington, DC, December 2004.
Current Population Survey TP77 Chapter 2-3: Weighting and Estimation 67

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Chapter 2-3: Weighting and Estimation

INTRODUCTION

The CPS is a multistage probability sample of HUs 

in the United States. It produces monthly labor 

force and related estimates for the total U.S. civilian 

noninstitutional population (CNP) and provides 

details by age, sex, race, and Hispanic ethnicity. In 

addition, the CPS produces estimates for a number of other population subgroups (e.g., families, 

veterans, and earnings of employed people) on 

either a monthly, quarterly, or annual basis. Each 

month a sample of eight panels, or rotation groups, 

is interviewed, with demographic data collected for 

all occupants of the sample HUs. Labor force data 

are collected from people aged 15 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. In addition, the weighting at 

each step is replicated in order to derive variances 

for the labor force estimates (see Chapter 2-4, 

Variance Estimation for details).

The weighting procedures of the CPS supplements 

are discussed in Chapter 1-3, Supplements. 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.

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 described in this chapter:

1. Base weighting produces simple, unbiased 

estimates for the basic CPS universe under 

ideal survey conditions, such as 100 percent 

response rate, zero frame error, and zero 

reporting error. Most sample units within a 

state have the same probability of selection 

and therefore have the same base weight.

2. Nonresponse adjustment reduces bias that 

would arise from ignoring HUs that do not 

respond.

3. First-stage weighting reduces variances due 

to the sampling of NSR PSUs.

4. State and national coverage steps and second-stage weighting reduce variances by 

controlling, or benchmarking, CPS estimates 

of the population to independent estimates of 

the current population.

5. Composite weighting uses estimates from previous months to reduce the variances, particularly for certain estimates of change.

In addition to estimates of basic labor force characteristics, several other types of estimates can be 

produced on a monthly, quarterly, or annual basis. 

Each of these involves additional weighting steps 

to produce the final estimate. These additional 

estimation procedures provide the estimates for 

particular subgroups of the CNP. The types of 

characteristics include:

• Household-level estimates and estimates of 

families (such as married-couple families living 

in the same household) using family weights.

• Estimates of earnings, union affiliation, and 

industry and occupation (I&O) of second jobs 

collected from respondents in the two outgoing rotation groups (about one-fourth of the 

sample) using outgoing rotation weights.

• Estimates of labor force status for veterans 

and nonveterans using veterans’ weights.

The independent population controls used in second-stage weighting are produced by the Census 

Bureau’s Population Estimates Program. Monthly 

population figures are estimated using the 2010 

Census as the basis and information from a variety 

of primarily administrative 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 CNP. These population controls are updated annually. CPS demographic weighted estimates are benchmarked to 

the independent monthly controls. The Derivation 

of Independent Population Controls section 

later in the chapter provides a reference to the 

methodology. 

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 
68 Chapter 2-3: Weighting and Estimation Current Population Survey TP77

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the procedures from time to time. For example, 

in January 1998, a new compositing procedure 

was introduced. In January 2003, new race cells 

were introduced for first-stage weighting and 

second-stage weighting; also, national and state 

coverage steps were added. In January 2005, the 

number of cells used in the national coverage 

adjustment and in second-stage weighting was 

expanded to improve the estimates of children.

BASE WEIGHTING

The CPS selects a sample for every state based on 

state-specific survey requirements. A sample unit’s 

base weight is equal to the inverse of its probability of selection. 

where

�s = base weight for a unit in state s,

πs = probability of selection for a unit in state s.

Almost all sample people within the same state 

have the same probability of selection. As the first 

step in estimation, the base weights from eligible 

individuals in eligible HUs are summed.

Effect of Annual Sampling 

As described in Chapter 2-2, Sample Design, the 

CPS selects samples annually, which allows for 

a stabilized sample size at both the state and 

national levels. CPS annual sampling also includes 

a selection of samples for newly constructed HUs, 

and the probabilities of selection for every state 

are adjusted accordingly to approximately maintain a constant sample size from year to year. Base 

weights also adjust, as these are inversely related. 

Construction growth on the sampling frame from 

year to year is small enough that the adjustments 

to national base weights are typically only marginal. However, state-specific growth does have 

the potential to introduce a more noticeable 

change in a state’s base weight.

In addition to the growth associated with the 

annual sampling, midyear growth also introduces 

a small amount of additional sample to the CPS. 

These sample cases are selected with the same 

probability of selection as other cases in the state 

and therefore have the same base weight.

WEIGHTING CONTROL FACTOR 

The weighting control factor (WCF) adjusts the 

base weight to account for any subsampling 

required in the field or within the Census Bureau’s 

Demographic Statistical Methods Division after a 

survey selects its sample. As a means to control 

sample size overrun, field subsampling reduces 

the number of interviews in a segment to a manageable number when there are more than 15 designated interviews. In the 2010 design, it is no longer necessary to create a separate WCF file. For 

each month, WCFs are provided in the Universe 

Control File before FRs go out for interviews. 

NONRESPONSE ADJUSTMENT 

WEIGHTING

Nonresponse arises when HUs 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 units selected can 

arise from several different sources, depending 

upon the survey situation. There are two major 

types of nonresponse: item nonresponse and unit 

nonresponse. Item nonresponse occurs when a 

cooperating HU fails or refuses to provide some 

specific items of information. 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 HU 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. 

Unit nonresponse in the CPS is also called Type 

A nonresponse. The nonresponse adjustment is 

limited to eligible or in-scope HUs. Some HUs are 

permanently out-of-scope, such as those that are 

demolished (Type C nonresponse). Some HUs are 

temporarily out-of-scope, such as those that are 

vacant or those without any people in the CNP 

(Type B nonresponse). Eligible HUs that do not 

respond are Type A nonresponses.

In the CPS estimation process, the weights for 

all eligible interviewed households are adjusted 

to account for occupied sample households for 

which no information was obtained because of 

unit nonresponse. This nonresponse adjustment is 
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made separately for similar sample areas that are 

usually, but not necessarily, contained within the 

same state. Increasing the weights of responding 

sample units to account for eligible sample units 

that have not responded is valid if the responding 

units are similar to the nonresponding units with 

regard to their demographic and socioeconomic 

characteristics. Nonresponse bias is present in 

CPS estimates when the nonresponding units differ in relevant aspects from those that respond to 

the survey. For more information, see Chapter 4-1, 

Nonsampling Error.

Nonresponse Clusters and Nonresponse 

Adjustment Cells

Nonresponse adjustment is performed on groups 

of sample PSUs that have similar metropolitan 

status and population size in order to reduce bias 

due to nonresponse. These groups of PSUs are 

called nonresponse clusters. In general, PSUs with 

a metropolitan status of similar size in the same 

state belong to the same nonresponse cluster. 

PSUs classified as MSAs are assigned to metropolitan clusters of similar size, and nonmetropolitan 

PSUs are assigned to nonmetropolitan clusters. 

Within each metropolitan cluster, there is a further 

breakdown into two nonresponse adjustment 

cells: “principal city” and “not principal city.” The 

nonmetropolitan clusters are not divided further. 

In the 2010 redesign, there are 125 clusters (82 

metropolitan and 43 nonmetropolitan).

Computing Nonresponse Adjustment 

Factors

Weighted counts of responding and nonresponding households are tabulated separately for each 

nonresponse adjustment cell. The base weight is 

used as the weight for this purpose. The nonresponse adjustment factor NRAFij is computed as:

where

Zij = weighted count of eligible responding HUs in 

cell j of cluster i,

Nij = weighted count of eligible nonresponding 

HUs in cell j of cluster i.

These factors are applied to data for each 

responding person except in cells where any of 

the following situations occur:

• The computed factor is greater than 2.0.

• There are fewer than 50 unweighted responding HUs in the cell.

• The cell contains only nonresponding HUs. 

If one of these situations occurs, the weighted 

counts are combined for the nonresponse adjustment cells within the nonresponse cluster. A common adjustment factor is computed and applied 

to weights for responding 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. This allows the extreme cells to be tracked 

over time, so adjustments can be made to the cell 

definitions if needed. 

Nonresponse Adjustment Weights

At the completion of the nonresponse adjustment 

procedure, the weight for each interviewed person 

is the product

 (base weight) x (nonresponse 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.

BENCHMARKING

Distributions of demographic characteristics 

derived from the CPS sample in any month will 

be somewhat different from the true distributions 

even for such basic characteristics as age, race, 

sex, and Hispanic ethnicity.17 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 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 adjusting the weights 

through a series of benchmarking adjustments. 

17 Hispanics may be of any race. 
70 Chapter 2-3: Weighting and Estimation Current Population Survey TP77

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There are five of these primary weighting adjustments in the CPS estimation process: 

• First-stage weighting. 

• National coverage step.

• State coverage step.

• Second-stage weighting.

• Composite estimation.

In first-stage weighting, weights are adjusted 

to reduce the variance caused by the PSU sampling using the race distribution. In the national 

and state coverage steps, weights are adjusted 

in preparation for second-stage weighting. In 

second-stage weighting, weights are iteratively 

adjusted so that aggregated CPS sample estimates match independent estimates of population 

controls in various age/sex/race and age/sex/ethnicity cells at the national level. Adjustments are 

also made so that the estimated state populations 

from CPS match independent state population 

estimates by age and sex. In first-stage weighting, 

the population distribution comes from the 2010 

Census. For the other steps, the population distribution comes from estimated monthly population 

controls.

FIRST-STAGE WEIGHTING

In first-stage weighting, weights are adjusted so 

that the Black alone/non-Black alone population 

distribution from the sample NSR PSUs in a state 

corresponds to the Black alone/non-Black alone 

population distribution from the 2010 Census for 

all PSUs in the state.

The purpose is to reduce the contribution to 

the variance of state estimates arising from the 

sampling of NSR PSUs. This is called betweenPSU variance. For some states, the between-PSU 

variance makes up a relatively large proportion of 

the total variance, while the overall 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 first-stage 

weighting. The information must be available for 

each PSU, correlated with as many of the critical 

statistics published from the CPS as possible, and 

reasonably stable over time so that the accuracy 

gained from the weighting adjustment procedure does not deteriorate. The distribution of the 

population by race (Black alone/non-Black alone) 

crossed with age groups 0 to 15 and 16 and over 

satisfies all three criteria.

By using the four race/age categories, first-stage 

weighting compensates for the possibility that 

the racial composition of the sampled NSR PSUs 

in a state could differ substantially from the racial 

composition of all NSR PSUs in the state. The 

adjustment is not necessary for SR PSUs. The 

weight adjustment factors are computed once and 

are unchanged until a new sample of NSR PSUs is 

selected in a state.

Computing First-Stage Adjustment Factors

The first-stage adjustment factors are based on 

the 2010 Census data and are applied only to sample data for the NSR PSUs. Factors are computed 

in four race/age cells (Black alone/non-Black 

alone crossed with 0 to 15 and 16 and over age 

groups) for each state containing NSR PSUs. The 

following formula is used to compute the firststage adjustment factors for each state:

where

FSAFs j = first-stage adjustment factor for state s 

 and age/race cell j (j = 1, 2, 3, or 4),

Csi j = 2010 Census civilian noninstitutional 

 population for NSR PSU i (sample or 

 nonsample) in state s, race/age cell j,

n = number of total (sampled and nonsampled) 

 NSR PSUs in state s,

Csk j = Census 2010 civilian noninstitutional 

 population for NSR sample PSU k in 

 state s, race/age cell j,

m = number of sampled NSR PSUs,

πsk = 2010 probability of selection for sample 

 NSR PSU k in state s.

The estimate in the denominator of each of the 

factors is obtained by multiplying the 2010 Census 

CNP in the appropriate race/age cell for each NSR 

sample PSU by the inverse of the probability of 
Current Population Survey TP77 Chapter 2-3: Weighting and Estimation 71

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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 (FSs,j) is greater than 1.5.

• The factor is less than 0.5. 

• There are fewer than four sampled NSR PSUs 

in the state.

• There are fewer than ten expected interviews 

in an age/race cell in the state.

First-Stage Weights

At the completion of first-stage weighting, the 

weight for each responding person is the product

(base weight) x (nonresponse adjustment factor) 

x (first-stage adjustment factor) 

The weight after the first-stage adjustment is 

called the first-stage weight. As in nonresponse 

adjustment weighting, records for all individuals 

in the same household have the same first-stage 

weight, since the adjustments discussed so far 

depend only on household and PSU characteristics, not on any respondent characteristics.

PAIRING ROTATION GROUPS

Rotation groups, or months-in-sample (MIS), are 

paired for the national coverage step, state coverage step, and second-stage weighting. Prior to 

2003, second-stage benchmarking to population 

controls was done separately for each of the eight 

rotation groups in a given month, labeled MIS 1–

MIS 8. There were no coverage steps at that time.

Pairing the rotation groups enables the creation 

of more cell detail in estimation steps. The particular pairing was motivated by the structure 

of the composite estimation formula, detailed 

in the Composite Estimation section below, and 

the observed patterns of MIS bias through 2012 

(Erkens, 2012). The pairings are: 

• MIS 1 and MIS 5.

• MIS 2 and MIS 6.

• MIS 3 and MIS 7.

• MIS 4 and MIS 8.

NATIONAL COVERAGE STEP

The national coverage step is an adjustment 

by race/ethnicity/age/sex groups that ensures 

weighted CPS estimates match independent 

national population controls. This coverage step 

helps correct for interactions between race and 

Hispanic ethnicity that are not addressed in 

second-stage weighting. Research has shown 

that the undercoverage of certain race/ethnicity 

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

be corrected with second-stage weighting alone. 

The national coverage step also helps to speed 

the convergence of the second-stage benchmarking process (Robison, Duff, Schneider, and 

Shoemaker, 2002).

Computing National Coverage Adjustment 

Factors

In the national coverage step, adjustment factors 

are calculated that are based on independently 

derived estimates of the population. People 

records are grouped into four pairs (MIS 1 and 5, 

MIS 2 and 6, MIS 3 and 7, and MIS 4 and 8). Each 

MIS pair is then adjusted to age/sex/race/ethnicity population controls (see Table 2-3.1) using the 

following formula:

where

NCAFjk = national coverage adjustment factor for 

 cell j and MIS pair k,

Cj = national coverage adjustment control for cell j,

Ejk = weighted tally (using first-stage weights) for 

 cell j and MIS pair k.

The age ranges in Table 2-3.1 are used to maximize demographic detail while limiting extreme 

cells with 1) fewer than 20 persons responding 

each month, and 2) adjustments outside the range 

0.6 to 2.0.
72 Chapter 2-3: Weighting and Estimation Current Population Survey TP77

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

Table 2-3.1. National Coverage Step Cell Definitions

White alone, 

non-Hispanic

White alone, 

Hispanic

Black alone,

non-Hispanic

Asian alone, 

non-Hispanic

 Residual race, 

non-Hispanic

Non-White alone, 

Hispanic

Age

M F Age

M F Age

M F Age

M F Age

M F Age

M F

0. . . . . . . . . 0

1. 

2. . . 

3. 

4. 

5. . . . . . . 

6. 

7. 

8. . . 

9. 

10–11. . . . . 10–11. . . . .

12–13. . . . . 12

14. . . 

15. . . 

16–19. . . . . 16–19. . . . .

16 and 

older

20–24. . . . . 20

25–29. . . . . 25–29. . . . .

30–34. . . . . 30

35–39. . . . . 35–39. . . . .

40–44. . . . . 40

45–49. . . . . 45–49. . . . .

50–54. . . . . 50

55–59. . . . . 55–64. . . . .

60–62

63–64

65–69

65 and 

older

65 and 

older

65 and 

older

65 and 

older

70–74

75 and 

older

Note: Taken from Weighting Specifications for the Current Population Survey, memorandum October 23, 2018.
Current Population Survey TP77 Chapter 2-3: Weighting and Estimation 73

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National Coverage Weights

After the completion of the national coverage 

step, the weight for each person is the product

(base weight) x (nonresponse adjustment 

factor) x (first-stage adjustment factor) 

x (national coverage adjustment factor).

This weight will usually vary for people in the 

same household due to household members having different demographic characteristics.

STATE COVERAGE STEP

In the state coverage step, weights are adjusted so 

that CPS estimates by race/sex/age groups match 

each month’s independent state population controls.18 This coverage steps compensates for some 

differences in race/sex/age coverage by state. 

Computing State Coverage Adjustment 

Factors

Table 2-3.2 shows the maximum number of adjustment cells in each state:

• Maximum of six age/sex cells for Black alone, 

with no pairing of rotation groups.

• Maximum of 24 cells for non-Black alone—the 

same six age/sex groups further split by MIS 

pair.

Some collapsing is needed to avoid extreme cells 

with: (1) fewer than 20 people responding each 

month, and (2) adjustments outside of the range 

0.6 to 2.0.

18 In the state coverage step, California is split into two parts 

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

the rest 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. The District of Columbia is generally 

treated as a state.

In the District of Columbia, all MIS are combined 

for the non-Black alone group, resulting in six 

cells. For all other states (including Los Angeles 

County, balance of California, New York City, and 

balance of New York State), no further collapsing 

is done in the non-Black alone cells.

For the Black alone cells, the collapsing varies by 

state:

• No further collapsing: AL, AR, CA (Los 

Angeles County and balance of CA), CT, DC, 

DE, FL, GA, IL, LA, MA, MD, MI, MO, MS, NC, 

NJ, NY (New York City and balance of NY), 

OH, PA, SC, TN, TX, VA.

• Collapse to two-sex cells (males of all ages, 

females of all ages): AK, AZ, CO, IN, KS, KY, 

MN, NE, NV, OK, RI, WA, WI, WV.

• Collapse to one cell (males and females of all 

ages): HI, ID, IA, ME, MT, ND, NH, NM, OR, SD, 

UT, VT, WY.

Each cell is then adjusted to age/sex/race population controls in each state using the following 

formula:

where

SCAFjk = state coverage adjustment factor for 

cell j and MIS pair k,

Cj = state coverage adjustment control for cell j,

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

The independent population controls used for the 

state coverage step are from the same source as 

those for second-stage weighting. 

State Coverage Weights

After the completion of the state coverage adjustment, the weight for each person is the product

(base weight) x (nonresponse adjustment 

factor) x (first-stage adjustment factor) 

x (national coverage adjustment factor) 

x (state coverage adjustment factor).

Table 2-3.2. 

State Coverage Adjustment Cell Definition

Age

Black alone,

all months-in-sample 

combined (6 cells)

Non-Black alone,

by months-insample pair 

(24 cells)

Male Female Male Female

0–15

16–44

45 and 

older
74 Chapter 2-3: Weighting and Estimation Current Population Survey TP77

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

This weight will vary for people in the same household due to household members having different 

demographic characteristics.

SECOND-STAGE WEIGHTING

Second-stage weighting decreases the error in 

the great majority of sample estimates. Chapter 

2-4, Variance Estimation, illustrates the amount 

of reduction in relative standard errors for key 

labor force estimates. The benchmark procedure 

is also a method used to reduce the bias due to 

coverage errors (see Chapter 4-1, Nonsampling 

Error). The benchmark procedure adjusts the 

weights within each MIS pair such that the sample 

estimates for geographic and demographic subgroups are matched to independent population 

controls. These independent population controls 

are updated each month. Three sets of controls 

are used:

• State/sex/age: the CNP for the states (see 

footnote 2 for state coverage) by sex and age 

(0-15, 16-44, 45 and older).

• Ethnicity/sex/age: total national CNP for 36 

Hispanic and non-Hispanic age/sex cells (see 

Table 2-3.3).

• Race/sex/age: total national CNP for 56 

White, 36 Black, and 26 “residual race” age/

sex cells (see Table 2-3.4).

The specified demographic detail avoids extreme 

cells with: (1) fewer than 20 people responding 

monthly and (2) overall adjustments outside the 

range 0.6 to 2.0.

Table 2-3.3.

Second-Stage Adjustment Cell by Ethnicity, Age, and Sex

Age Hispanic Non-Hispanic

Male Female Male Female

0–1

2–4

5–7

8–9

10–11

12–13

14

15

16–19

20–24

25–29

30–34

35–39

40–44

45–49

50–54

55–64

65 and older

Note: Taken from Weighting Specifications for the Current Population Survey, memorandum October 23, 2018.
Current Population Survey TP77 Chapter 2-3: Weighting and Estimation 75

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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 near consistency between the 

sample estimates and population controls. The 

three-dimensional (state/sex/age, ethnicity/sex/

age, race/sex/age) weighting adjustment is also 

known as iterative proportional fitting, raking ratio 

estimation, or raking. 

In addition to reducing the error in many CPS estimates and converging to the population controls 

within ten iterations for most items, this estimator 

minimizes the statistic

where

W2, i = weight for the i th sample record after the 

second-stage adjustment,

W1, i = weight for the i th record after the firststage 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. 

Ireland and Kullback (1968) provide more details 

on the properties of raking ratio estimation. 

Computing Second-Stage Adjustment 

Factors

As mentioned before, second-stage weighting 

involves a three-step weighting adjustment, or 

rake:

Table 2-3.4. 

Second-Stage Adjustment Cell by Race, Age, and Sex

White Alone Black Alone Residual Race

Age Male Female Age Male Female Age Male Female

0. . . . . . . . . . 0–1. . 

1. . . . . . . . . . 

2. . . . . . . . . . 2–4. . 

3. . . . . . . . . .

4. . . . . . . . . .

5. . . . . . . . . . 5–7. . 

6. . . . . . . . . .

7. . . . . . . . . .

8. . . . . . . . . . 8–9. . 

9. . . . . . . . . .

10–11. . . . . . 

12–13. . . . . . 

14. . . . . . . . . 14. . . 

15. . . . . . . . . 15

16–19. . . . . . 

20–24. . . . . . 

25–29. . . . . . 

30–34. . . . . . 

35–39. . . . . . 

40–44. . . . . . 

45–49. . . . . . 

50–54. . . . . . 

55–59. . . . . . 

60–62

63–64 

65–69

65 and 

older. . . . . .

65 and 

older. . . . . .

70–74

75 and 

older. . . . . .

Note: Taken from Weighting Specifications for the Current Population Survey, memorandum October 23, 2018
76 Chapter 2-3: Weighting and Estimation Current Population Survey TP77

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• State step: state/sex/age.

• Ethnicity step: ethnicity/sex/age.

• Race step: race/sex/age.

Second-stage adjustment factors are successively 

calculated for each raking step, using estimates 

based on the most recently iterated weights as the 

basis for each successive adjustment. Since every 

iterated weighting adjustment is incorporated at 

every step of the three-step raking procedure, the 

successive estimates gradually converge to the 

population controls in all three dimensions.

where

SSAFijk = second-stage adjustment factor for cell 

 j and MIS pair k after iteration i,

C j = second-stage adjustment control for cell j,

Ei–1, jk = weighted tally for cell j and MIS pair 

k at iteration i–1 tabulated using the 

intermediate second-stage weights 

after iteration i–1 (when i–1 = 0, this is 

equal to the estimate tabulated using 

state-coverage weights),

m j = number of split MIS pairs in cell j (1 if all MIS 

 combined, 4 if split into pairs).

Each of three raking steps is iterated ten times, 

enough for most estimates to fully converge.

Note that the matching of estimates to controls 

for one dimension causes the cells to differ slightly 

from the controls from the previous dimension. 

With each successive iteration, these differences 

decrease. For most cells, after ten iterations, the 

estimates for each cell have converged to the population controls for each cell. Thus, the weight for 

each record after second-stage weighting can be 

thought of as the weight for the record after the 

state coverage adjustment multiplied by a series 

of 30 adjustment factors (ten iterations, each with 

three raking steps). 

Second-Stage Weights

At the completion of second-stage (SS) weighting, the record for each person has a weight 

reflecting the product of:

(base weight) x (nonresponse adjustment 

factor) x (first-stage adjustment factor) x 

(national coverage adjustment factor) x 

(state coverage adjustment factor) x 

(second-stage adjustment factor).

The estimates produced after second-stage 

weighting are referred to as second-stage estimates. Once each record has an second-stage 

weight, an estimate for any given set of characteristics identifiable in the CPS can be computed 

by summing the second-stage weights for all 

respondents that have that set of characteristics. 

The process for producing this type of estimate is 

referred to as a Horvitz-Thompson estimator or a 

simple weighted estimator.

COMPOSITE ESTIMATION

In general, a composite estimate is a weighted 

average of several estimates. Most official CPS 

labor force estimates are derived using a composite estimator. Historically, the CPS composite estimate consisted only of the second-stage estimate, 

the composite from the preceding month, and 

an estimate of change from preceding to current 

month. Over time, the CPS refined the composite, updating the weights used in the weighted 

average as well as adding a component that 

captures the net difference between the incoming 

and continuing parts of the current month’s sample. In 1998, the BLS introduced a compositing 

method that allows more operational simplicity 

for microdata users as well as determining better 

compositing coefficients for different labor force 

categories.

Breaking down the CPS composite estimator into 

more detail and keeping in mind the underlying 

4-8-4 rotation pattern, the estimator and its separate components are understood in terms of the 

following expression:

where
Current Population Survey TP77 Chapter 2-3: Weighting and Estimation 77

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i = MIS 1,2,...,8, 

xti = sum of second-stage weights of respondents in month t and MIS i with characteristic of 

interest,

K = 0.4 for unemployed, 0.7 for employed,

A = 0.3 for unemployed, 0.4 for employed.

K determines the weight to get the weighted 

average of two estimators for the current month: 

(1) the current month’s second-stage estimate 

and (2) the sum of the previous month’s composite 

estimator , and an estimator of the change 

since the previous month. The estimate of change 

is based on data from sample households in the 

six rotation groups common to months t and t-1 

(about 75 percent). Higher correlation in the estimates coming in this part of the sample tends to 

reduce variance of the estimated month-to-month 

change. A determines the weight of βt , 

a term adjusting for the difference between the 

continuing months in sample and the new months 

in sample. It is intended to reduce both the variance of the composite estimator and the bias 

associated with time in sample (Breau and Ernst, 

1983; and Bailar, 1975). The values given above for 

the constant coefficients A and K were selected 

after extensive review of many series for minimum 

variance for month-to-month change estimates of 

unemployment and employment.

The composite estimator for the CPS provides, on 

average, an overall improvement in the variance 

for estimates of month-to-month change. The 

composite also provides additional improvements 

for estimates of change over longer intervals of 

time, for a given month (Breau and Ernst, 1983).

Computing Composite Weights

Weights are derived for each record that, when 

aggregated, produce estimates consistent with 

those produced by the composite estimator. 

Composite estimation is performed at the macro 

level. The composite weights allow one month 

of microdata to be used to produce estimates 

consistent with the composite estimators for any 

given month.

The 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 weighting 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 people 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. This composite 

weighting approach also improves the accuracy of labor force estimates by using different 

compositing coefficients for different labor force 

categories. The weighting adjustment method 

assures additivity while allowing this variation in 

compositing coefficients.

Composite weights are produced only for sample 

persons aged 16 or older. The method of computing composite weights for the CPS imitates 

second-stage weighting. Sample person weights 

are raked to force their sums to equal control 

totals. Composite labor force estimates are used 

as controls in place of independent population 

controls. The composite raking process is performed separately within each of the three major 

labor force categories: employed (E), unemployed 

(UE), and not in the labor force (NILF). These 

three labor force totals sum to the CNP. Since CNP 

is a population control, and E and UE are directly 

estimated, the NILF is indirectly computed as the 

residual:

NILF = CNP — (E + UE)

The composite weighting process is similar to 

the raking process to compute the second-stage 

weights. Within each labor category, a threedimensional rake is applied, using the second-stage estimate at the state level as one step 

and national second-stage estimates for age/sex/

race (Table 2-3.5) and age/sex/ethnicity (Table 

2-3.6) as the second and third steps. 
78 Chapter 2-3: Weighting and Estimation Current Population Survey TP77

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Data from all eight rotation groups are combined 

for the purpose of computing composite weights. 

As with second-stage weighting, each iteration of 

the composite weight calculation uses the most 

recently adjusted weights as the basis. The formulas are analogous. Like second-stage weighting, 

ten iterations are performed in each of the three 

dimensions for each of the three composited labor 

force groups (E, UE, and NILF).

While E and UE are estimated directly in composite estimation, NILF is indirectly calculated as 

the difference between the relevant population 

control and the sum of E and UE, which is the estimate of the civilian labor force. 

The specified demographic detail avoids extreme 

cells with: (1) fewer than 10 people responding 

monthly, and (2) overall adjustments outside the 

range 0.7 to 1.3.

Final Weights

The composite weights are the final CPS weights 

and the weights used to compute most BLS published estimates. Within each rotation group or 

paired MIS, summation of the final weights do not 

match independent population controls, because 

data from all eight rotation groups are combined 

to form the composite weights. However, summation of the final weights for the entire sample will 

match the independent population controls.

PRODUCING OTHER LABOR FORCE 

ESTIMATES

In addition to base weighting to produce estimates for people, several special-purpose weighting procedures are performed each month. These 

include:

• Weighting to produce estimates for household 

and families.

• Weighting to produce estimates from data 

based on only two of eight (outgoing) rotation groups.

• Weighting to produce labor force estimates 

for veterans and nonveterans (veterans’ 

weighting).

Most special weights are based on second-stage 

weights. Some also make use of composited 

Table 2-3.5. 

Composite National Race Cell Definition

White alone Black alone Residual race

Age Male Female Age Male Female Age Male Female

16–19. . . . . . 

20–24. . . . . . 

25–29. . . . . . 

30–34. . . 

35–39. . . . . . 

40–44. . . 

45–49

45 and 

older. . . . . .

45 and 

older. . . . . .

50–54

55–59

60–64

65 and 

older. . . . . .

Note: Taken from Weighting Specifications for the Current Population Survey, memorandum October 23, 2018.

Table 2-3.6. 

Composite National Ethnicity Cell Definition

Ages Hispanic Non-Hispanic

Male Female Male Female

16–19

20–24

25–34

35–44

45+

Note: Taken from Weighting Specifications for the Current Population Survey, memorandum October 23, 2018.
Current Population Survey TP77 Chapter 2-3: Weighting and Estimation 79

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estimates. In addition, consecutive months of data 

are often used 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. 

More than one family may be identified in a HU. 

For families maintained by women or men with no 

spouse present, the family weight is equivalent to 

the second-stage weight of the reference person. 

For married-couple families, the family weight is 

equivalent to the wife’s second-stage weight (in 

same-sex marriages, the family weight is the second-stage weight of the reference person). 

Weighted tabulations of CPS data by sex and 

marital status show the same number of married 

women and married men with their spouses present. The wife’s weight is used as the family weight, 

because 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 HU 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. This varies from 

family weight in the situations where more than 

one family is living in the same household.

Outgoing Rotation Weights 

Some items in the CPS questionnaire are asked 

only in HUs due to rotate out of the sample temporarily (MIS 4) or permanently (MIS 8) after the 

current month. These are referred to as the outgoing rotation groups. Items asked in the outgoing 

rotations include those on earnings (since 1979), 

union affiliation (since 1983), and I&O of second 

jobs of multiple jobholders (beginning in 1994). 

Since the data are collected from only one-fourth 

of the sample each month, data from 3 or 12 

months are used to improve their reliability, and 

published as quarterly or annual averages. 

Since 1979, most CPS files have included separate 

weights for the outgoing rotations. An individual’s 

outgoing rotation weight is approximately four 

times that of his or her final weight. However, 

these outgoing rotation weights are benchmarked 

to controls for employment status, which are 

based on the composited estimates of E, UE, and 

NILF each month from the full sample. The cells 

are classified by age, race, sex, and employment 

status: employed wage and salary workers, other 

employed, UE, and NILF. The outgoing adjustment 

factor for each cell is computed by taking the ratio 

of the total composite weights in the control to 

the total second-stage weights for the outgoing 

rotation groups.

The outgoing rotation weights are obtained by 

multiplying the outgoing adjustment factors by 

the second-stage weights. For consistency, an 

outgoing rotation group weight of 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 people aged 15 and younger). 

Production of monthly, quarterly, and annual 

estimates using the outgoing rotation weights is 

completely parallel to production of second-stage 

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. 

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 second-stage weights.

Veterans’ Weights

Since 1986, CPS interviewers have collected 

data on veteran status from all household members. Veterans’ weights are calculated for all CPS 

household members based on their veteran status. 

This information is used to produce tabulations of 

employment status for veterans and nonveterans.

Each individual is classified as a veteran or a nonveteran. Veterans are currently classified into cells 

based on age, sex, and veteran status (Gulf War, 

Other War, or Non-War). The composite weights 
80 Chapter 2-3: Weighting and Estimation Current Population Survey TP77

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

for CPS veterans are tallied into type-of-veteran/

sex/age cells. Separate adjustment factors are 

computed for each cell, using independently 

established monthly estimates of veterans provided by the Department of Veterans Affairs. The 

adjustment factor is the ratio of the independent 

control total to the sample estimate total for each 

cell. This adjustment factor is multiplied by the 

composite weight for each veteran to produce the 

veterans’ weight.

To compute veterans’ weights for nonveterans, a 

table of composited estimates is produced from 

the CPS data by race (White alone/non-White 

alone), sex, age, and labor force status (E, UE, 

and NILF). The veterans’ weights produced in the 

previous step are tallied into the same cells. The 

estimated number of veterans is then subtracted 

from the corresponding cell entry for the composited table to produce nonveterans control totals. 

The composite weights for CPS nonveterans are 

tallied into the same race/sex/age/labor force 

status cells. The adjustment factor for each cell 

is the ratio of the nonveteran control total to the 

sample estimate total in each cell. The composite 

weight for each nonveteran is multiplied by the 

adjustment factor to produce the veterans’ weight 

for nonveterans. 

Estimates of Averages

CPS frequently produces estimates of averages, 

using multiple months of data. 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, percentages, means, and medians, 

are computed from the averages for the component levels, not by averaging the monthly values 

(for example, 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 increases the number of 

interviews contributing to the resulting estimates 

by a factor of approximately 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 mean that people respond in several 

months and estimates from the individual months 

are not independent. 

DERIVATION OF INDEPENDENT 

POPULATION CONTROLS

The Census Bureau’s Population Estimates 

Program (PEP) produces and publishes estimates 

of the population for the nation, states, counties, 

state/county equivalents, cities, towns, and for 

Puerto Rico and its municipios. The PEP estimates 

the population for each year following the most 

recent decennial census using measures of population change. These population estimates are 

used for federal funding allocations, as controls 

for major surveys including the CPS and the ACS, 

for community development, to aid business planning, and as denominators for statistical rates.

The PEP produces estimates of the resident 

population, which includes all people currently 

residing in the United States as well as estimates 

of selected subpopulations. A more complete 

description of PEP methodology can be found at 

<www.census.gov/programs-surveys/popest

/technical-documentation/methodology.html>.

REFERENCES

Bailar, B. A., “The Effects of Rotation Group Bias 

on Estimates from Panel Surveys,” Journal of the 

American Statistical Association, Vol. 70, 1975, 

pp. 23−30.

Breau, P., and L. Ernst, “Alternative Estimators to 

the Current Composite Estimator,” Proceedings 

of the Section on Survey Research Methods, 

American Statistical Association, 1983, 

pp. 397−402. 

Erkens, G., “Changes in Panel Bias in the 

U.S. Current Population Survey and its Effects 

on Labor Force Estimates,” Proceedings of the 

Section on Survey Research Methods, American 

Statistical Association, 2012, pp. 4220−4232.
Current Population Survey TP77 Chapter 2-3: Weighting and Estimation 81

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

Ireland, C. T., and S. Kullback, “Contingency Tables 

with Given Marginals,” Biometrika, 55, 1968, 

pp. 179−187.

Robison, E., M. Duff, B. Schneider, and 

H. Shoemaker, “Redesign of Current Population 

Survey Raking to Control Totals,” Proceedings of 

the Survey Research Methods Section, American 

Statistical Association, 2002.

FURTHER READING

Bell, W. R., and S. C. Hillmer, “The Time Series 

Approach to Estimation for Repeated Surveys,” 

Survey Methodology, 16, 1990, pp. 195−215. 

Copeland, K. R., F. K. Peitzmeier, and C. E. Hoy, 

“An Alternative Method of Controlling Current 

Population Survey Estimates to Population 

Counts,” Proceedings of the Survey Research 

Methods Section, American Statistical Association, 

1986, pp. 332−339.

Evans, T. D., R. B. Tiller, and T. S. Zimmerman, 

“Time Series Models for State Labor Force 

Estimates,” Proceedings of the Survey Research 

Methods Section, American Statistical Association, 

1993, pp. 358−363.

Hansen, M. H., W. N. Hurwitz, and W. G. Madow, 

Sample Survey Methods and Theory, Vol. II, John 

Wiley and Sons, New York, 1953.

Harvey, A. C., Forecasting, Structural Time 

Series Models and the Kalman Filter, Cambridge 

University Press, Cambridge, 1989.

Kostanich, D., and P. Bettin, “Choosing a 

Composite Estimator for CPS,” presented at 

the International Symposium on Panel Surveys, 

Washington, DC, 1986.

Lent, J., S. Miller, and P. Cantwell, “Composite 

Weights for the Current Population Survey,” 

Proceedings of the Survey Research Methods 

Section, American Statistical Association, 1994, 

pp. 867−872.

Lent, J., S. Miller, P. Cantwell, and M. Duff, “Effect 

of Composite Weights on Some Estimates from 

the Current Population Survey,” Journal of Official 

Statistics, Vol. 15, No. 3, 1999, pp. 431−438.

Oh, H. L., and F. Scheuren, “Some Unresolved 

Application Issues in Raking Estimation,” 

Proceedings of the Section on Survey Research 

Methods, American Statistical Association, 1978, 

pp. 723−728.

Scott, J. J., and T. M. F. Smith, “Analysis of 

Repeated Surveys Using Time Series Methods,” 

Journal of the American Statistical Association, 69, 

1974, pp. 674−678.

Thompson, J. H. , “Convergence Properties of the 

Iterative 1980 Census Estimator,” Proceedings 

of the American Statistical Association on 

Survey Research Methods, American Statistical 

Association, 1981, pp. 182−185.

Tiller, R. B., “A Kalman Filter Approach to Labor 

Force Estimation Using Survey Data,” Proceedings 

of the Survey Research Methods Section, 

American Statistical Association, 1989, pp. 16−25.

Tiller, R. B., “Time Series Modeling of Sample 

Survey Data from the U.S. Current Population 

Survey,” Journal of Official Statistics, 8, 1992, pp. 

149−166.

Zimmerman, T. S., T. D. Evans, and R. B. Tiller, 

“State Unemployment Rate Time Series,” 

Proceedings of the Survey Research Methods 

Section, American Statistical Association, 1994, 

pp. 1077−1082.
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Chapter 2-4: Variance Estimation

INTRODUCTION

Variance estimation of major CPS statistics serves 

the following two objectives:

• Estimate the variance of the survey estimates 

for use in various statistical analyses.

• Evaluate 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 

nonresponse and coverage error. Chapter 4.1, 

Nonsampling Error, provides additional information on these topics. Certain aspects of the CPS 

sample design, such as the use of one sample PSU 

per NSR stratum and the use of cluster subsampling 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 that show the effects of estimation steps on 

variances, are presented at the end of this chapter.

CURRENT POPULATION SURVEY 

REPLICATION METHODS

Replication methods are able to provide satisfactory estimates of variance for a wide variety of 

designs that use probability sampling, even when 

complex estimation procedures are used (Dippo et 

al., 1984). This method presumes that the sample 

selection, the collection of data, and the estimation procedures are independently carried out 

(replicated) several times. The dispersion of the 

resulting estimates can be used to measure the 

variance of the full sample.

It would not be feasible to repeat the entire 

CPS several times each month simply to obtain 

variance estimates. A practical alternative is to 

perturb the weights of the full sample many different times to create new artificial samples, and 

to apply the regular CPS estimation procedures to 

these subsamples, or replicates. While CPS uses 

a fixed number of replicates, the optimal number 

of replicates can vary by estimate series, meaning 

that replication variances for any particular estimate may be suboptimal.

Replication Methods for 1970 and 1980 

Designs

Historically, computational burden was balanced 

with optimality considerations to determine the 

number of replicates to use. Prior to the 1970 CPS 

design, variance estimates were computed using 

40 replicates. The replicates were subjected to 

only second-stage weighting for the same age/

sex/race categories used for the full sample at 

the time. Nonresponse and first-stage weighting 

adjustments were not replicated. (See Chapter 

2-3, Weighting and Estimation, for more context 

on CPS weighting.) 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 (Keyfitz, 

1957). 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. 

Starting with the 1980 design, variances were 

computed using a modified balanced half-sample 

approach. The sample was divided in half 48 times 

to form replicates that retained all the features 

of the sample design, such as stratification and 

within-PSU sample selection. For total variance, 

a pseudo first-stage design was imposed on the 

CPS by dividing large SR PSUs into smaller pieces 

called Standard Error Computation Units (SECUs) 

and combining small 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 
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estimator. For SR 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 the other panel 

in the SR SECU or NSR pseudostratum (Dippo et 

al., 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 (Ŷr) and the full sample 

estimate (Ŷo). The complete formula19 is

Due to costs and computer limitations, variance 

estimates were calculated for only 13 months 

(January 1987 through January 1988) and for 

only 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). 

Generalized sampling errors (explained below) 

were calculated based on the 13 months of variance estimates. (See Wolter, 1985; Fay, 1984; or 

Fay, 1989 for more details on half-sample replication for variance estimation.)

Replication Methods for 1990, 2000, and 

2010 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 sample size and the continuous addition 

of new construction to the sample complicated 

the strategy 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 

19 The basic formula for balanced half-sample replcation 

uses replicate factors of 2 and 0 with the formula: 

where k is the number of replicates. The 

factor of 4 in our variance estimator is the result of using a modified formula with replicate factors of 1.5 and 0.5. See Dippo et al. 

(1984) for more details.

the prereduction 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 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 

USUs are paired in the order of their selection to 

take advantage of the systematic nature of the 

CPS within-PSU sampling scheme.20 Each USU 

usually occurs in two consecutive pairs: (USU1, 

USU2), (USU2, USU3), (USU3, USU4), etc. A pair 

is therefore 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. In addition, a 

large increase in the number of SECUs and in the 

number of replicates (160 vs. 48) starting with the 

1990 design increases the precision of the variance estimator.

The 2010 sample design introduced two major 

changes in methodology. First, the samples are 

now selected on an annual rather than decadal 

basis. Second, a new variance estimator was 

introduced that includes rotation group among 

the sort criteria for successive difference replication. This estimator allows estimation of variances 

and covariances at the rotation group level. See 

Chapter 2.2, Sample Design, for more information 

about CPS sampling procedures and 2010 design 

changes.

Replicate Factors for Total Variance

Total variance is composed of two types of variance, the variance due to sampling of HUs within 

PSUs during second-stage sampling (within-PSU 

variance) and the variance due to the selection of 

PSUs during first-stage sampling (between-PSU 

20 An ultimate sampling unit is usually a group of four HUs.
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variance). Due to the selection of one PSU within 

NSR stratum in the CPS Design, between-PSU 

variance cannot be estimated directly using this 

methodology, but it can be estimated as the difference between the estimates of total variance 

and within-PSU variance.

To produce estimates of total variance, replicates 

are formed differently for SR and NSR samples. 

For NSR PSUs, the original strata within a state 

are collapsed into pseudostrata of pairs and at 

most one triplet for states with an odd number 

of NSR PSUs. After that collapsing, a replication 

method of the Collapsed Stratum Estimator is 

used to assign replicate factors to units in these 

PSUs (Wolter, 1985). In pseudostrata containing 

a pair of NSR PSUs, replicate factors of 1.5 or 0.5 

adjust the weights.21 These factors are assigned 

based on a single row from the Hadamard matrix 

and are further adjusted to account for unequal 

sizes of the original strata within the pseudostratum (Wolter, 1985). In pseudostrata containing 

a triplet, for the 1990 design, two rows from the 

Hadamard matrix were assigned to the pseudostratum, resulting in replicate factors of approximately 0.5, 1.7, and 0.8; or 1.5, 0.3, and 1.2 for the 

three PSUs assuming equal sizes of the original 

strata. However, for the 2000 design, these factors 

were further adjusted to account for unequal sizes 

of the original strata within the pseudostratum. All 

USUs in a pseudostratum are assigned the same 

row number(s).

In SR strata, a single PSU is picked with probability 1; therefore, there is no contribution from 

between-PSU variance, and the total variance is 

the within-PSU variance. Successive difference 

replication is used in these PSUs to create replicate factors. For an SR sample, two rows of the 

Hadamard matrix are assigned to each pair of USUs 

creating replicate factors f

ir for r = 1, ... , 160:

where 

ai,r = a number in the Hadamard matrix (+1 or 

−1) for the i

th USU in the systematic sample. This formula yields replicate factors of 

approximately 1.7, 1.0, or 0.3. 

21 Replicate factors are calculated using a 160-by-160 

Hadamard matrix.

As in the 1980 methodology, the unbiased weights 

(base weight x special weighting factor) are multiplied by the replicate factors to produce unbiased replicate weights. These unbiased replicate 

weights are further adjusted through the weighting steps applied to the full sample, as described 

in Chapter 2-3, Weighting and Estimation. A 

variance estimator for the characteristic of interest 

is a sum of squared differences between each replicate estimate (Ŷr ) and the full sample estimate 

(Ŷo). The formula is 

The replicate factors 1.7, 1.0, and 0.3 for the 

SR portion of the sample were specifically constructed to yield a numerator of 4 in the above 

formula, such that the formula remains consistent 

between SR and NSR areas (Fay and Train, 1995).

Replicate Factors for Within-Primary 

Sampling Unit Variance

All PSUs (NSR or SR) undergo systematic sampling during second-stage sampling; therefore, 

successive difference replication can be used to 

estimate within-PSU variance. For SR PSUs, the 

same replicate factors from total variance estimation are assigned to the within-PSU estimator. This 

is because the PSUs were picked with probability 

1 during first-stage sampling, so only within-PSU 

variance contributes to the total variance. For 

sample in an NSR PSU, successive difference 

replication is also used in order to estimate the 

contribution to variance from second-stage 

sampling (within-PSU variance). 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 Gunlicks 

(1996).

Variances for State and Local Areas

For estimates at the national level, total variances are estimated from the sample data by the 
Current Population Survey TP77 Chapter 2-4: Variance Estimation 85

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successive difference replication method previously described. For local areas that are coextensive with one or more sample PSUs, variance 

estimators can be derived from the same variance 

estimation methods used for the SR portion of the 

national sample. However, variance estimation for 

states and areas that have substantial contributions from NSR sample areas can be problematic.

Most states contain a small number of NSR sample 

PSUs, so between-PSU variances at the state level 

are based on relatively small sample sizes. Pairing 

these PSUs into pseudostrata further reduces the 

number of NSR SECUs and increases reliability 

problems. In addition, 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.

GENERALIZING VARIANCES

With some exceptions, the standard errors for 

CPS estimates 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 approximate standard errors for numerous characteristics.

One could not possibly predict all of the combinations of results that may be of interest to data 

users. Therefore, a presentation of the individual 

standard errors based on survey data, while technically possible to compute, would be of limited 

use. In addition, 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. The estimates of variance 

for the same characteristic may vary considerably 

from month to month or for related characteristics 

(that might have similar levels of true precision) in 

a given month. Therefore, some method of stabilizing these estimates of variance, such as by generalization or by averaging over time, is needed to 

improve both their reliability and usability.

Generalization Method

The GVF that is used to estimate the variance of 

an estimated monthly level, x, given a population 

total N (CNP, 16 years and over) is of the form

 (2-4.1)

where α and β are two parameters estimated 

using least squares regression. The rationale for 

this form of the GVF model is the assumption that 

the variance of χ 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 (δ) 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 some characteristic, 

where n is the size of the sample, the variance of 

the estimated monthly level x is

 (2-4.2)

Letting , then 

 (2-4.3)

The design effect component d (inclusive of the 

sampling interval, which fluctuates monthly and 

tends to increase over time) is estimated by the 

regression term (α + βN), yielding the functional 

form given in formula 2-4.1. Since N is a known 

population total, α and β are the two modeled 

GVF parameters for a particular series.

The generalized variance models used from 1947 

to 2016 were based on the model form:

Var (x) = ax2 + bx (2-4.4)
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The only difference between these model forms 

is that the historical model (2-4.4) assumed a 

fixed population level N, therefore producing 

GVF parameters that were useful for time periods 

in which the true population level was approximately equal to the fixed value. The current model 

(2-4.2), implemented in 2017, allows N to vary 

from month to month, resulting in GVF parameters 

that are useful over the entire reference period of 

the model (typically 10 or more years). Since N

is a population control readily available for every 

month, the current model was adopted to increase 

the generalizability of the GVF parameters. 

The α and β parameters are designed to produce 

variance estimates for monthly CPS estimates. 

For estimates of changes or averages over time, 

adjustment factors are calculated based on historical correlations and an assumption of equal 

monthly variances, which simplifies the calculation. While monthly variances do change over 

time, empirical review suggests the equal-variance 

assumption has negligible impact on the quality of 

variance estimates for most series. 

The α and β parameters are updated annually 

to incorporate recent information into the GVF 

models. Since the overlap from one model period 

to the next is very high, the resulting parameters 

usually do not change much from one year to 

the next, allowing for smooth comparisons over 

time. In the event of a major sample reduction, the 

GVF models would be adjusted to account for the 

changing sampling intervals and design effects. 

Calculation of Generalized Variance 

Function Parameters

In 2015, the CPS introduced a new method for 

computing GVF parameters (McIllece, 2016). The 

new process models each series individually rather 

than clustering series with similar design effects. 

Under the previous method, the model used 

relative variance as a dependent quantity. Under 

the new method, the dependent quantity is the 

design effect times the sampling interval, both of 

which are stable over time for most series, presuming stability of the CPS sample size. Notably, 

the fluctuation of the replication process, which 

is implicitly included in d since direct replicate 

variances are used (see formulations below), is 

relatively unstable. By fitting each model on a 

monthly series history of approximately 10 years 

or more, the long-term stability of the sampling 

interval and the design effect are leveraged, while 

the relative volatility of the replication procedure 

is smoothed, resulting in a GVF model fit that 

accounts for binomial variance and seasonality. 

Unlike direct replication variances, estimates of 

variances from this GVF method are not typically 

volatile, excepting some sparse subgroups. 

The GVF model subsequently detailed is only 

applied to level (i.e., count) and rate series that 

may be considered binomial, such as number of 

employed people and the unemployment rate, 

respectively. Most CPS series fit this criterion. The 

variances of nonbinomial series, such as means 

and medians, are not estimated using this specific 

method.

To estimate the variance of some level estimate x, 

such as total employed or total unemployed, recall 

the formula 2-4.3:

While this formula represents the theoretical form, 

binomial variances are not directly computed. 

Direct CPS variances, as referred to in this chapter, 

are calculated by the successive difference replication method described in earlier sections. These 

replicate variances are used in the construction of 

the GVF model.

Define V* (x; N) as the replicate estimate of the 

variance of x, given the population total N. Then 

calculate the design effect component d as

By computing d as a ratio based on replicate 

variance, d implicitly includes the volatility of the 

replication procedure. Since the quantity is large 

relative to the size of the estimate x, the direct 

replicate variance estimates are impractical for 

use. The GVF model, therefore, is constructed to 

retain the form of binomial variance and any associated seasonal effects, while smoothing through 

the replication volatility that otherwise destabilizes the variance estimation series.

Let

d = the average value of over the model period

N = the average value of over the model period
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rd*,N* = the correlation between d* and N* over

the model period,

sd* = the standard deviation of d* over the model

period,

sN* = the standard deviation of N* over the

model period,

The GVF model is constructed as a least squares 

regression model:

Then expanded to estimate by the modeled 

value d ˆ :

Therefore, the α and β GVF parameters from 2-4.1

are calculated as:

Modeled variances for rate estimates, such as 

the unemployment rate or the labor force participation rate, are developed analogously to level 

estimates, but with the base y (the denominator

of the rate) replacing N. Though y is often an

estimate, it is treated as a population value for 

GVF parameter estimation, as empirical results 

have indicated that treating the base as a random 

variable typically has little impact on the variance 

estimates. 

By treating y as a population value, the variance

of rate estimates can be approximated by a slight 

modification to 2-4.1:

where .

The design effect component d for rate series has 

a slightly different form, which accounts for the 

difference in the form of the variance estimators 

between levels and rates:

Once d is computed over the modeling period, the 

α and β GVF parameters for rate series are developed using the same modeling method that is 

used for level series (see above), but with the base 

quantities y and y replacing N and N, respectively, 

in the least squares regression model.

Computing Standard Errors

After the parameters α and β of formula 2-4.1 are 

determined, it is a simple matter to construct a 

table of standard errors of estimates. 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. The Bureau of Labor 

Statistics publishes tables of GVF parameters, 

which are available in the “Reliability of estimates 

from the CPS” section of its website 

https://www.bls.gov/cps/documentation.htm. 

Table 2-4.1 provides example GVF parameters for 

the labor force characteristics of several demographic subgroups for the purpose of demonstrating standard error computation. Refer to the 

source listed previously for current parameters.

Since the standard error of an estimate is the 

square root of its variance, the approximate standard error, se(x; N), of an estimated monthly total 

can be obtained with α and β from the above table 

and square root of formula 2-4.1:

(2-4.5)

In January 2007, there were an estimated 

4,406,000 unemployed men from a population N

of 230,650,000 people. Obtaining the appropriate 

α and β parameters from Table 2-4.1, an approximate standard error can be calculated for this 

estimate using equation 2-4.5:

se (4,406,00O; 230,650,000)

In January 2017, there were an estimated 

4,514,000 unemployed men from a population N of 
88 Chapter 2-4: Variance Estimation Current Population Survey TP77

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254,082,000 persons. Using the same α and β

parameters from Table 2-4.1, an approximate 

standard error can be calculated for this estimate 

using equation 2-4.5: 

se (4,514,000; 254,082,000)

In this example, since the values of x are similar, 

the increase in the standard error estimate of 

unemployed men is primarily driven by the 23.4 

million increase in N population over that decade. 

To form 90 percent confidence intervals (rounded 

to the nearest thousand) around these estimates, 

1.645 times the standard error is added and 

subtracted from the estimate x. For the January 

2007 estimate of unemployed men, the 90 percent 

confidence interval is calculated as:

4,406,00O � 1.645 * 115,501,86 � (4,216,000; 4,596,000)

As a practical interpretation, there is about 

a 90 percent chance that the true number of 

unemployed men in January 2007 was between 

4.216 million and 4.596 million. Using the same 

approach, the 90 percent confidence interval for 

unemployed men in January 2017 was approximately 4.315 million to 4.713 million.

TOTAL VARIANCE COMPONENTS

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 2010 

sample design and averaged across all twelve 

months of 2016. This period was used for estimation because the sample design was essentially 

unchanged throughout the year, and the variances 

tend to be more stable than in earlier postrecessionary years.

Within-Primary Sampling Unit Variance 

Ratios by Weighting Stage

The CPS employs a complex sample design that 

selects both SR and NSR PSUs within states (see 

Chapter 2-2, Sample Design). An NSR stratum 

consists of a group of PSUs, typically including 

Table 2-4.1. 

Generalized Variance Function Parameters for Estimates of Monthly Levels

Characteristic

Parameters

� �

Total:

Civilian labor force. . . . . . . . . . .

Employed. . . . . . . . . . .

Unemployed. . . . . . . . . 

Not in labor force. . . . . . . . . . .

Men:

Civilian labor force. . . . . . . . . . .

Employed. . . . . . . . . . .

Unemployed. . . . . . . . . . 

Not in labor force. . . . . . . . . . .

Women:

Civilian labor force. . . . . . . . . . .

Employed. . . . . . . . . . .

Unemployed. . . . . . . . . . .

Not in labor force. . . . . . . . . . .

Both sexes, 16 to 19 years:

Civilian labor force. . . . . . . . . . .

Employed. . . . . . . . . . .

Unemployed. . . . . . . . . . .

Not in labor force. . . . . . . . . . .

Note: These values are based on standard-error models that were updated with the release of July 2018 data.

Source: Bureau of Labor Statistics, “Reliability of estimates from the CPS” section of <www.bls.gov/cps/documentation.htm>. 
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rural areas, from which only one PSU is selected 

to be included in the CPS sample. Since this PSU 

represents the entire stratum, between-PSU 

variability is introduced, which is a measure of 

the variability that results from having selected 

a subset of NSR PSUs throughout the nation. An 

SR stratum includes only one PSU; therefore, the 

contribution of between-PSU variance is zero by 

definition from these sample areas. Both SR and 

NSR PSUs contribute within-PSU variance, which 

is the variability introduced by sampling a subset 

of HUs within each selected PSU. 

Total variance is the sum of between-PSU and 

within-PSU variance. Table 2-4.2 presents ratios of 

within-PSU to total variance for the primary labor 

force estimates of selected demographic groups 

after various stages in weighting. For national 

estimates, within-PSU variance is the dominant 

contributor to total variance, which is clearly 

demonstrated in the table and is consistent across 

weighting stages. Thus, the effect of selecting 

NSR PSUs in some strata has a small effect on the 

total variance of most national estimates. 

Different replicate weights are assigned to sample units to reflect either total variance or within-PSU variance. Since the assignment of replicate 

weights cannot be perfectly optimized for these 

variance levels—and since the variances calculated using those replicates are only estimates 

themselves, with their own errors—in some cases, 

Table 2-4.2. 

Within-Primary Sampling Unit to Total Variance Ratios: 2016 Annual Averages

(In percent)

Characteristic Nonresponse adjustment Second stage Compositing

Civilian labor force. . . . . . . .

Men. . . . . . . .

Women. . . . . . . .

White. . . . . . . .

Black. . . . . . . .

Hispanic. . . . . . . .

Asian. . . . . . . .

Employed. . . . . . . .

Men. . . . . . . .

Women. . . . . . . .

White. . . . . . . .

Black. . . . . . . .

Hispanic. . . . . . . .

Asian. . . . . . . .

Unemployed. . . . . . . .

Men. . . . . . . .

Women. . . . . . . .

White. . . . . . . .

Black. . . . . . . .

Hispanic. . . . . . . .

Asian. . . . . . . .

Unemployment rate. . . . . . .  

Men. . . . . . . .

Women. . . . . . . .

White. . . . . . . .

Black. . . . . . . .

Hispanic. . . . . . . .

Asian. . . . . . . .

Not in labor force. . . . . . . .

Men. . . . . . . .

Women. . . . . . . .

White. . . . . . . .

Black. . . . . . . .

Hispanic. . . . . . . .

Asian. . . . . . . .

Source: U.S. Census Bureau, Tabulation of 2016 Current Population Survey microdata.
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the ratio of within-PSU to total variance is slightly 

greater than one. While this implies a negative 

between-PSU variance (since it can be derived 

as total variance minus within-PSU variance), it 

should not be interpreted that way. By definition, 

variances cannot be less than zero. Practically 

speaking, a ratio slightly greater than one can be 

interpreted to mean that the true between-PSU 

variance contribution is close to zero, but not 

actually negative.

Relative Standard Errors by Weighting 

Stage

Table 2-4.3 shows how the separate estimation 

steps affect a survey estimate’s relative standard 

errors (RSEs), calculated as the replicate standard 

error of an estimate divided by the estimate itself. 

As stated before, the standard error of an estimate is simply the square root of its variance. It is 

more instructive to compare RSEs than the standard errors themselves, since the various stages 

of estimation can affect both the level of a survey 

estimate and its variance (Hanson, 1978; Train, 

Cahoon, and Makens, 1978). The nonresponseadjusted estimate includes base weights, special 

weighting factors, and nonresponse adjustment. 

The second-stage estimate includes all weighting 

steps applied to the full sample except compositing (see Chapter 2-3, Weighting and Estimation).

Table 2-4.3.

Relative Standard Errors After Selected Weighting Stages: 2016 Annual Averages

(In percent)

Labor force status Nonresponse adjustment Second stage Compositing

Civilian labor force. . . . . . .  

Men. . . . . . . .

Women. . . . . . . .

White. . . . . . . .

Black. . . . . . . .

Hispanic. . . . . . . .

Asian. . . . . . . .

Employed. . . . . . . .

Men. . . . . . . .

Women. . . . . . . .

White. . . . . . . .

Black. . . . . . . .

Hispanic. . . . . . . .

Asian. . . . . . . .

Unemployed. . . . . . . .

Men. . . . . . . .

Women. . . . . . . .

White. . . . . . . .

Black. . . . . . . .

Hispanic. . . . . . . .

Asian. . . . . . . . . 

Unemployment rate. . . . . .  2.

Men. . . . . . . .

Women. . . . . . . .

White. . . . . . . .

Black. . . . . . . .

Hispanic. . . . . . . .

Asian. . . . . . . . . 

Not in labor force. . . . . . . .

Men. . . . . . . .

Women. . . . . . . .

White. . . . . . . .

Black. . . . . . . .

Hispanic. . . . . . . .

Asian. . . . . . . .

Source: U.S. Census Bureau, Tabulation of 2016 Current Population Survey microdata.
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In Table 2-4.3, the figures across demographic 

groups for the civilian labor force, employed, and 

not in labor force show, for example, that the second-stage weighting process substantially reduces 

RSEs, while compositing further decreases the 

RSEs but more modestly. The figures for unemployed and unemployment rate demonstrate little 

difference between RSEs of nonresponse-adjusted 

and second-stage estimates, but a more consistent, minor decrease in RSEs from second-stage 

to composite estimates. The RSEs of the unemployment statistics tend to be larger because 

unemployment is a relatively rare characteristic, 

typically less than ten percent for most groups in 

the primary estimation tables, depending on the 

state of the economy. 

DESIGN EFFECTS

Table 2-4.4 shows the second-stage and composite design effects for the total variances of major 

labor force characteristics for selected demographic groups. A design effect (δ) is the ratio of 

the variance from a complex sample design to the 

variance of a simple random sample (SRS) design. 

The design effects in this table were computed 

by solving equation 2-4.3 for δ for a theoretical 

response rate of 100 percent and also for realized 

response rates of approximately 90 percent over 

the reference period, 2010 to 2017. The design 

effects for the realized response rates are smaller 

because the estimated SRS variances increase as 

the number of respondents decrease. 

For the unemployed, assuming about a 90 percent 

response rate, the design effect for total variance is approximately 1.59 for the second-stage 

estimate and 1.49 for the composite estimate. 

Table 2-4.4.

Design Effects After Selected Weighting Stages: 2010–2017 Averages

(In percent)

Labor force status

100 percent 

response rates

2010–2017 

response rates

Second stage Compositing Second stage Compositing

Civilian labor force. . .  1.39 1.09 1.2

Men. . . . . . . . . . 

Women. . . . . . . . . . 

White. . . . . . . . . . 

Black. . . . . . . . . . 

Hispanic. . . . . . . . . . 

Asian. . . . . . . . . . 

Employed. . . . . . . . . . 

Men. . . . . . . . . . 

Women. . . . . . . . . . 

White. . . . . . . . . . 

Black. . . . . . . . . . 

Hispanic. . . . . . . . . . 

Asian. . . . . . . . . . 

Unemployed. . . . . . . .  1.7

Men. . . . . . . . . . 

Women. . . . . . . . . . 

White. . . . . . . . . . 

Black. . . . . . . . . . 

Hispanic. . . . . . . . . . 

Asian. . . . . . . . . . 

Not in labor force. . . .  1.39 1.09 1

Men. . . . . . . . . . 

Women. . . . . . . . . . 

White. . . . . . . . . . 

Black. . . . . . . . . . 

Hispanic. . . . . . . . . . 

Asian. . . . . . . . . . 

Source: U.S. Census Bureau, Tabulation of 2010–2017 Current Population Survey microdata.
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This means that, for the same number of sample 

cases, the design of the CPS (including sample 

selection and weighting) increases total variance 

by about 60 percent for second-stage estimates 

and about 50 percent for composite estimates, 

relative to an SRS design. The design effects for 

the civilian labor force, employed, and not in labor 

force for all groups tend to be smaller than those 

for unemployed. The design effects for composite 

estimates tend to be smaller than those for SS, 

which indicates that composite weighting generally increases the precision of estimates.

The design effects in Table 2-4.4 tend to be larger 

than the analogous design effects reported in 

past technical papers, such as Table 14-4 of 

CPS Technical Paper 66, due to a change in 

the accounting of the sample size. Table 2-4.4 

assumes an average of slightly over 1.9 responding adults per eligible sampled household (excluding out-of-scope HUs selected in the sample), 

which was derived from average respondent data 

from 2010 to 2017. Past technical papers used 

different denominators that are not reflective of 

recent years of data, resulting in smaller design 

effects than are reported in Table 2-4.4.

REFERENCES

Corteville, J., “State Between-PSU Variances 

and Other Useful Information for the CPS 1990 

Sample Design (VAR90−16),” Memorandum for 

Documentation, Demographic Statistical Methods 

Division, U.S. Census Bureau, March 6, 1996.

Dippo, C., R. Fay, and D. Morganstein, “Computing 

Variances from Complex Samples with Replicate 

Weights,” Proceedings of the Section on 

Survey Research Methods, American Statistical 

Association, 1984, pp. 489−494.

Fay, R. E., “Some Properties of Estimates of 

Variance Based on Replication Methods,” 

Proceedings of the Section on Survey Research 

Methods, American Statistical Association, 1984, 

pp. 495−500.

Fay, R. E., “Theory and Application of Replicate 

Weighting for Variance Calculations,” Proceedings 

of the Section on Survey Research Methods, 

American Statistical Association, 1989, 

pp. 212−217.

Fay, R., and G. Train, “Aspects of Survey and 

Model-Based Postcensal Estimation of Income and 

Poverty Characteristics for States and Counties,” 

Proceedings of the Section on Government 

Statistics, American Statistical Association, 1995, 

pp. 154−159.

Gunlicks, C., “Overview of 1990 CPS PSU 

Stratification (S−S90−DC−11),” Internal 

Memorandum for Documentation, Demographic 

Statistical Methods Division, U.S. Census Bureau, 

February 24, 1993.

Gunlicks, C., “1990 Replicate Variance System 

(VAR90−20),” Internal Memorandum for 

Documentation, Demographic Statistical Methods 

Division, U.S. Census Bureau, June 4, 1996.

Hanson, R. H., “The Current Population Survey: 

Design and Methodology,” Technical Paper 40, 

Government Printing Office, Washington, DC, 

1978.

Keyfitz, N.,“Estimates of Sampling Variance Where 

Two Units are Selected from Each Stratum,” 

Journal of the American Statistical Association, 

52(280), 1957, pp. 503–510.

Kostanich, D.,“Proposal for Assigning Variance 

Codes for the 1990 CPS Design (VAR90−22),” 

Internal Memorandum to BLS/Census Variance 

Estimation Subcommittee, Demographic 

Statistical Methods Division, U.S. Census Bureau, 

June 17, 1996.

Lent, J., “Variance Estimation for Current 

Population Survey Small Area Estimates,” 

Proceedings of the Section on Survey Research 

Methods, American Statistical Association, 1991, 

pp. 11−20.

McIllece, J., “Calculating Generalized Variance 

Functions with a Single-Series Model in the 

Current Population Survey,” paper presented at 

the 2016 Joint Statistical Meetings, Proceedings 

of the Section on Survey Research Methods, 

American Statistical Association, 2016, 

pp. 2176–2187. 

Train, G., L. Cahoon, and P. Makens, “The Current 

Population Survey Variances, Inter-Relationships, 

and Design Effects,” Proceedings of the Section 

on Survey Research Methods, American Statistical 

Association, 1978, pp. 443−448.
Current Population Survey TP77 Chapter 2-4: Variance Estimation 93

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

Wolter, K., “An Investigation of Some Estimators 

of Variance for Systematic Sampling,” Journal of 

the American Statistical Association, 79, 1984, 

pp. 781−790.

Wolter, K., Introduction to Variance Estimation, 

Springer-Verlag, New York, 1985.

FURTHER READING

Kostanich, D., “Revised Standard Error 

Parameters and Tables for Labor Force Estimates: 

1994−1995 (VAR80−6),” Internal Memorandum for 

Documentation, Demographic Statistical Methods 

Division, U.S. Census Bureau, February 23, 1996.

U.S. Bureau of Labor Statistics, Current Population 

Survey online documentation, 2018, retrieved 

from <www.bls.gov/cps/documentation.htm>. 

U.S. Census Bureau, “Current Population Survey: 

Design and Methodology, Technical Paper 66,” 

Washington, DC, October 2006. 

Valliant, R., “Generalized Variance Functions in 

Stratified Two-Stage Sampling,” Journal of the 

American Statistical Association, 82, 1987, 

pp. 499−508.
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Chapter 2-5: Seasonal Adjustment

INTRODUCTION

Short-run movements in labor force time series 

are strongly influenced by seasonality—periodic 

fluctuations associated with recurring calendarrelated events such as weather, holidays, and the 

opening and closing of schools. Seasonal adjustment removes the influence of these fluctuations 

and makes it easier for users to observe fundamental changes in the level of the series, particularly changes associated with general economic 

expansions and contractions.

While seasonal adjustment is feasible only if 

the seasonal effects are reasonably stable with 

respect to timing, direction, and magnitude, these 

effects are not necessarily fixed and often evolve 

over time. The evolving patterns are estimated 

by the X-13ARIMA-SEATS (X-13) program, with 

procedures based on “filters” that successively 

average a shifting timespan of data, thereby providing estimates of seasonal factors that change in 

a smooth fashion from year to year.

For observations in the middle of a series, a set 

of symmetric moving averages with fixed weights 

produces final seasonally adjusted estimates. A 

filter is symmetric if it is centered around the time 

point being adjusted, with an equal amount of 

data preceding and following that point. Standard 

seasonal adjustment options imply a symmetric 

filter that uses from 6 to 10 years of original data 

to produce a final seasonally adjusted estimate. 

Obviously, this final adjustment can be made only 

where there are enough data beyond the time 

point in question to adjust with the symmetric 

filter.

To seasonally adjust recent data, shorter filters 

with less desirable properties must be used. These 

filters are referred to as asymmetric because they 

use fewer observations after the reference point 

than preceding it. The weights for such filters vary 

with the number of observations that are available 

beyond the time point for which estimates are 

to be adjusted. Seasonally adjusted data for the 

current year are produced with a technique known 

as concurrent adjustment. Under this practice, the 

current month’s seasonally adjusted estimate is 

computed with the use of all relevant original data 

up to and including data for the current month. 

Every time an observation is added, previous 

estimates will be revised. The number of estimates 

that are revised depends on the filter. Revisions 

to a seasonally adjusted estimate for a given time 

point continue until enough future observations 

become available to use the symmetric weights. 

This effectively means waiting up to 5 years for a 

final adjustment when standard options are used.

At the end of each calendar year, the BLS recalculates the seasonal factors for CPS national 

series by including another full year of data in the 

estimation process. On the basis of this annual 

recalculation, BLS revises the historical seasonally adjusted data for the previous 5 years. As a 

result, each year’s data are generally subject to 

five revisions before the values are considered 

final. The fifth and final revisions to data for the 

earliest of the 5 years are usually quite small, 

while the first-time revisions to data for the most 

recent years are generally much larger. For the 

major aggregate labor force series, however, the 

first-time revisions rarely alter the essential trends 

observed in the initial estimates. (For information about seasonal adjustment of the Local Area 

Unemployment Statistics program’s state and 

local area estimates, see the BLS Handbook of 

Methods [2018])). 

ADJUSTMENT METHODS AND 

PROCEDURES

Beginning in 2003, BLS adopted the use of X-12-

ARIMA (X-12) as the official seasonal adjustment 

program for CPS labor force series, replacing the 

X-11-ARIMA (X-11) program that had been used 

since 1980. Both X-12-ARIMA and X-11-ARIMA 

incorporate the widely used X-11 method developed at the Census Bureau in the 1960s. Statistics 

Canada added to the X-11 method the ability to 

extend the time series with forward and backward 

extrapolations from Auto-Regressive Integrated 

Moving Average (ARIMA) models prior to seasonal adjustment. The X-11 algorithm for seasonal 

adjustment is then applied to the extended series. 

When adjusted data are revised after future data 

become available, the use of forward extensions 

results in initial seasonal adjustments that are subject to smaller revisions on average.
Current Population Survey TP77 Chapter 2-5: Seasonal Adjustment 95

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The enhancements in the X-12 program fall into 

three basic categories: (1) enhanced ARIMA 

model selection and estimation, (2) detection 

and estimation of outlier, trading day, and holiday 

effects, and (3) new postadjustment diagnostics.

Starting in 2015, BLS moved to the X-13 program, 

which was developed by the Census Bureau and 

the Bank of Spain. The X-13 program includes 

all of the capabilities of the X-12 program, while 

adding the signal extraction for ARIMA (SEATS) 

seasonal adjustment methodology. (See the 

subsequent section titled “X-11 and SEATS 

decompositions.”)

The X-11 and SEATS methods have strong similarities. The ARIMA model of the observed series is 

the starting point for both. They both also use the 

same basic estimator, which is a weighted moving 

average of the series, to produce the seasonally 

adjusted output. The methods differ in the derivation of moving average weights. 

The X-11 method is empirically based. It directly 

selects the seasonal moving averages from a 

prespecified set. The weights are not designed 

for any specific series, but fit a wide variety of 

series. SEATS is model-based and derives a moving average from the ARIMA model of the series. 

The moving averages are tailored to the specific 

modeled properties of the series. The SEATS 

methodology is flexible in that it can adjust some 

series that the X-11 method finds too variable. It 

also facilitates analysis with a variety of error measures produced within the system. See Shiskin et 

al., (1967), Dagum (1980), Findley et al., (1998), 

Gomez and Maravall (2001), and Census Bureau 

(2015) for details on these methods.

For almost all of the national labor force series 

that are seasonally adjusted by BLS, the main 

steps of the seasonal adjustment process proceed 

in the following order:

1. Time series modeling. A REGARIMA model 

(a combined regression and ARIMA model) is 

developed to account for the normal evolutionary behavior of the time series and to 

control for outliers and other special external 

effects that may exist in the series.

2. Prior adjustments. Given an adequate 

REGARIMA model, the series is modified 

by prior adjustments for external effects 

estimated from the regression part of the 

model and extrapolated forward 24 months or 

more by the ARIMA part of the model.

3. X-11 or SEATS decomposition. The modified 

and extrapolated series is decomposed into 

trend-cycle, seasonal, and irregular components by means of a series of moving averages 

to produce seasonal factors for implementing 

seasonal adjustment.

4. Evaluation. A battery of diagnostic tests is 

produced to evaluate the quality of the final 

seasonal adjustment.

Time series modeling

Time series models play an important role in 

seasonal adjustment. They are used to identify 

and correct the series for atypical observations 

and other external effects, as well as to extend 

the original series with backcasts and forecasts so 

that fewer asymmetric filters can be used at the 

beginning and end of the series.

ARIMA models (see Box and Jenkins, 1970 or 

Kendall and Ord, 1990) are designed to make 

forecasts of a time series based on only its past 

values. While these models can represent a wide 

class of evolving time series patterns, they do not 

account for the presence of occasional outliers 

and other special external effects. An outlier represents a sudden break in the normal evolutionary 

behavior of a time series. Ignoring the existence 

of outliers may lead to serious distortions in the 

seasonally adjusted series.

A common form of outlier that presents a special 

problem for seasonal adjustment is an abrupt shift 

in the level of the data that may be either transitory or permanent. Three types of outliers are 

usually distinguished: (1) An additive change that 

affects only a single observation, (2) a temporary change having an effect that diminishes to 

zero over several periods, and (3) a level shift or 

a break in the trend of the data, which represents 

a permanent increase or decrease in the underlying level of the series. These three main types of 

outliers, as well as other types of external effects, 

may be handled by the time series modeling 

component of the X-13 program. This is done by 

adding to the ARIMA model appropriately defined 

regression variables based on intervention analysis 

originally proposed by Box and Tiao (1975).
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The combined regression and ARIMA model is 

referred to as a REGARIMA model and is represented by

Yt t t = βX + Z ,

where Y is the original series or a logarithmic t

transformation of it; X is a set of fixed regres- t

sion variables; β represents the regression coeffcients; and Zt is a standard seasonal ARIMA model 

described by the notation (p,d,q)(P,D,Q), where 

p is the number of regular (nonseasonal) autoregressive parameters, d is the number of regular 

differences, q is the number of regular moving 

average parameters, P is the number of seasonal 

autoregressive parameters, D is the number of 

seasonal differences, and Q is the number of seasonal moving average parameters.

While the ARIMA model can be very complicated 

theoretically, in practice it takes a parsimonious 

form involving only a few estimated parameters. 

There are well-developed methods for determining the number and types of parameters and the 

degree of differencing appropriate for a given 

series.

With respect to specifying the regression component to control for outliers, the X-13 program 

offers two approaches. First, major external 

events, such as breaks in trend, are usually associated with known events. In such cases, the user 

has sufficient prior information to specify special 

regression variables to estimate and control for 

these effects.

Second, because it is rare that there is sufficient 

prior information to locate and identify all of the 

atypical observations that may exist in a time 

series, as a second approach to specifying the 

regression component, REGARIMA offers automatic outlier detection based on work by Chang, 

Tiao, and Chen (1988). This approach is especially 

useful when a large number of series must be 

processed. Of course, both approaches may be 

combined so that readily available prior information can be used directly, while unknown substantial outliers may still be discovered.

Model adequacy and length of series. The preference is to use relatively long series in fitting 

time series models, but with some qualifications. 

Sometimes, the relevance of data in the distant 

past to seasonal adjustment is questionable, which 

may lead to using a shorter series.

Even though the filters have limited memory, 

there are reasons for using longer series. First, for 

homogenous time series, the more data used to 

identify and estimate a model, the more likely it 

is that the model will represent the structure of 

the data well and the more accurate the parameter estimates will be. The exact amount of data 

needed for time series modeling depends on 

the properties of the series involved. Arbitrarily 

truncating the series, however, may lead to more 

frequent changes in model identification and to 

large changes in estimated parameters, which in 

turn may lead to larger-than-necessary revisions in 

forecasts.

Second, although level shifts and other types of 

outliers tend to occur more often in longer series, 

the X-13 program has the capability of automatically controlling for these effects. Third, some 

very useful diagnostics available in X-13 typically 

require a minimum of 11 years of data and, in 

some cases, as much as 14 years of data. Fourth, 

attempting to fit longer series often provides 

useful insights into the properties of the series, 

including their overall quality and the effects of 

major changes in survey design.

Extensive use is made of intervention analysis 

to estimate the magnitude of known breaks in 

CPS series and of automatic outlier detection to 

identify and correct for the presence of additional 

atypical observations. Once a model is estimated, 

it is evaluated in terms of its adequacy for seasonal adjustment purposes. The criteria essentially 

require a model to fit the series well (there should 

be no systematic patterns in the residuals) and to 

have low average forecasting errors for the last 3 

years of observed data. When there is a tradeoff 

between the length of the series and the adequacy of the model, a shorter series is selected. 

In this case, the identification of the model is not 

changed with the addition of new data unless the 

model fails diagnostic testing.

Acceptable REGARIMA models have been developed for all of the labor force series that are 

directly adjusted. (For information about directly 

and indirectly adjusted series, see subsequent section titled “Aggregation procedures.”)
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Prior adjustments

Prior adjustments are adjustments made to the 

original data prior to seasonal adjustment. Their 

purpose is to correct the original series for atypical observations and other external effects that 

otherwise would seriously distort the estimates of 

the seasonal factors. Prior adjustment factors are 

subtracted from or used as divisors for the original 

series, depending on whether the seasonal adjustment is additive or multiplicative.

Prior adjustment factors for CPS series may be 

based on special user-defined adjustments or 

handled more formally with REGARIMA modeling. Most of the prior adjustment factors for the 

labor force series are estimated directly from 

REGARIMA.

Level shifts. The most common type of outlier that 

occurs in CPS series is the permanent level shift. 

Most such shifts have been due to noneconomic 

methodological changes related to revisions in 

population controls and to major modifications to 

the CPS design. One notable economic level shift 

was due to the 2001 terrorist attacks.

Population estimates extrapolated from the latest 

decennial census are used in the second-stage 

estimation procedure to control CPS sample 

estimates to more accurate levels. These intercensal population estimates are regularly revised 

to reflect the latest information on population 

change.

During the 1990s, three major breaks occurred in 

the intercensal population estimates. Population 

controls based on the 1990 Census, adjusted 

for the estimated undercount, were introduced 

into the CPS series in 1994 and, in 1996, were 

extended back to 1990. In January 1997 and again 

in January 1999, the population controls were 

revised to reflect updated information on net 

international migration.

Population revisions that reflected the results of 

the 2000 Census were introduced with the release 

of data for January 2003 and were extended back 

to data beginning in January 2000. Since 2003, 

population controls also are updated in January 

to reflect new estimates of net international 

migration in the postcensus period, updated vital 

statistics and other information, and any methodological changes in the population estimation 

process. The population revisions introduced in 

January 2012 incorporated the results of the 2010 

Census; those revisions were not extended back to 

January 2010. Further information on CPS population controls for 1996 to the present is available 

on the BLS Web site at <www.bls.gov/cps 

/documentation.htm>. 

In 1994, major changes to the CPS were introduced, including a redesigned and automated 

questionnaire and revisions to some of the labor 

force concepts and definitions. In January 2003, 

new I&O classifications also were introduced and 

were extended back to data beginning in 2000.

To test for the possibility that revisions to the 

population controls or significant survey changes 

have important effects on those CPS series with 

large numerical revisions, each REGARIMA model 

is modified to include intervention variables for 

those years. The coefficients for these variables 

provide estimates of the direction and magnitude 

of the intervention effects.

Intervention effects for 2000 were necessary for 

selected employment series related primarily 

to Hispanic, adult, and agricultural categories. 

These effects mainly reflect increases in adult and 

Hispanic employment due to the introduction of 

the 2000 Census-based population controls and a 

decline in agricultural employment caused by the 

change in the industry classification system (see 

Bowler et al., 2003).

Due to an unusual revision in the population controls in January 2000, the unadjusted employment 

level of Black or African-American men 20 years or 

over had a strong upward shift in the first quarter of 2000. This temporary effect is permanently 

removed from the seasonally adjusted series with 

the use of the REGARIMA model. 

At the end of 2001, unemployed job losers were 

identified as having had substantial upward level 

shifts 1 month after the September 11, 2001, 

terrorist attacks on the World Trade Center in 

New York City. For more details, see McIntire et 

al., (2002). Also, four additional series related to 

workers employed part time for economic reasons 

were identified as having substantial upward shifts 

at the time of the terrorist attacks.

Calendar effects. Calendar effects are transitory 

level shifts in a series that result from calendar 
98 Chapter 2-5: Seasonal Adjustment Current Population Survey TP77

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events such as moving holidays or the differing 

composition of weekdays in a month between 

years. These effects have different influences on 

the same month across years, thereby distorting 

the normal seasonal patterns for the given month.

X-11 and Signal Extraction in ARIMA Time 

Series Decompositions

The X-11 and SEATS methods of seasonal adjustment contained within the X-13 program assume 

that the original series is composed of three 

components: trend-cycle, seasonal, and irregular. 

Depending on the relationship between the original series and each of the components, the mode 

of seasonal adjustment may be additive or multiplicative. Formal tests are conducted to determine 

the appropriate mode of adjustment.

The multiplicative mode assumes that the absolute magnitudes of the components of the series 

are dependent on each other, which implies that 

the size of the seasonal component increases and 

decreases with the level of the series. With this 

mode, the monthly seasonal factors are ratios 

with all positive values centered around unity. The 

seasonally adjusted series values are computed by 

dividing each month’s original value by the corresponding seasonal factor.

In contrast, the additive mode assumes that 

the absolute magnitudes of the components of 

the series are independent of each other, which 

implies that the size of the seasonal component 

is independent of the level of the series. In this 

case, the seasonal factors represent positive or 

negative deviations from the original series and 

are centered around zero. The seasonally adjusted 

series values are computed by subtracting the 

corresponding seasonal factor from each month’s 

original value.

Most seasonally adjusted CPS series are seasonally adjusted using the X-11 component of 

the X-13 program. Given an appropriate choice 

for the mode of adjustment, the prior-adjusted, 

forecasted series is seasonally adjusted by the 

X-11 component of the X-13 program. The X-11 

method applies a sequence of moving average and smoothing calculations to estimate the 

trend-cycle, seasonal, and irregular components. 

The method takes either a ratio-to- or difference-from-moving-average approach, depending 

on whether the multiplicative or additive model is 

used. For observations in the middle of the series, 

a set of fixed symmetric moving averages (filters) 

is used to produce final estimates. The implied 

length of the final filter under standard options 

is 72 time points for the 3 by 5 seasonal moving 

average or 120 time points for the 3 by 9 moving average. That is, to obtain a final seasonally 

adjusted estimate for a single time point requires 

up to 5 years of monthly data preceding and 

following that time point. For recent data, asymmetric filters with less desirable properties than 

symmetric filters must be used.

Some seasonally adjusted CPS series are adjusted 

using the SEATS component of the X-13 program rather than the X-11 component. Like the 

X-11 method, SEATS decomposes the observed 

series into trend-cycle, seasonal, and irregular 

components, and the relationship between the 

components may be additive or multiplicative 

(via a log transformation). Both the X-11 and 

SEATS methods use moving averages to decompose the series. The major difference between 

the two procedures is how the moving averages 

are constructed. The X-11 method selects from a 

set of predefined filters, while the SEATS method 

derives its filters from a decomposition of the 

ARIMA model fit to the observed series into 

models for the trend-cycle, seasonal, and irregular 

components. The SEATS filters therefore are more 

tailored to the specific properties of the series 

as reflected in the ARIMA model fit, while X-11 

filters are nonparametric in that they can fit a wide 

variety of series without depending on a specific 

model.

Evaluation

A series should be seasonally adjusted if three 

conditions are satisfied: the series is seasonal, the 

seasonal effects can be estimated reliably, and no 

residual seasonality is left in the adjusted series. A 

variety of diagnostic tools is available for the X-11 

method to test for these conditions, including 

frequency-spectrum estimates, revision-history 

statistics, and various seasonal tests. The X-13 

program provides some of the above diagnostics for SEATS analysis but also provides its own 

battery of model-based diagnostics. If diagnostic 

testing shows that any of the three conditions 

listed fails to hold for a given series, that series is 

deemed not suitable for seasonal adjustment.
Current Population Survey TP77 Chapter 2-5: Seasonal Adjustment 99

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

CONCURRENT SEASONAL 

ADJUSTMENT

Concurrent seasonal adjustment of national labor 

force data began for CPS with the release of estimates for December 2003 in January 2004. This 

practice replaced the projected-factor method, 

which updated seasonal factors twice a year. 

Under the latter procedure, projected seasonal 

factors were used to seasonally adjust the new 

original data as they were collected. At midyear, 

the historical series were updated with data for 

January through June, and the seasonal adjustment program was rerun to produce projected 

seasonal factors for July through December.

With concurrent seasonal adjustment, the seasonal adjustment program is rerun each month 

as the latest CPS data become available. The 

seasonal factors for the most recent month are 

produced by applying a set of moving averages 

to the entire data set, extended by extrapolations, 

including data for the current month. While all 

previous-month seasonally adjusted estimates 

are recalculated in this process, BLS policy is 

not to revise previous months’ official seasonally adjusted CPS estimates as new data become 

available during the year. Instead, revisions are 

introduced for the most recent 5 years of data at 

the end of each year.

Numerous studies, including that discussed in 

Methee and McIntire (1987) on the CPS laborforce series, have indicated that concurrent 

adjustment generally produces initial seasonally 

adjusted estimates requiring smaller revisions than 

do estimates produced with the projected-factor 

method. Revisions to data for previous months 

also may produce gains in accuracy, especially 

when the original data are themselves regularly 

revised on a monthly basis. Publishing numerous 

revisions during the year, however, can confuse 

data users.

The case for revisions to previous-month seasonally adjusted estimates is less compelling for CPS 

series because the original sample data normally 

are not revised. Moreover, an empirical investigation indicated that there were no substantial 

gains in estimating month-to-month changes by 

introducing revisions to the data for the previous 

month. For example, it was found that if previous-month revisions were made to the labor force 

series, the overall unemployment rate would be 

different in only 2 months between January 2001 

and November 2002, in each case by only onetenth of a percentage point.

AGGREGATION PROCEDURES

BLS directly seasonally adjusts national series 

on the basis of age, sex, industry, education, and 

other characteristics. BLS also provides seasonally 

adjusted totals, subtotals, and ratios of selected 

series. It is possible to seasonally adjust an aggregate series either directly or indirectly by seasonally adjusting its components and adding the 

results, or dividing in the case of ratios. Indirect 

and direct adjustments usually will not give identical results because, (1) seasonal patterns vary 

across series, (2) there are inherent nonlinearities 

in the X-13 program, (3) many series are multiplicatively adjusted, and (4) some series are ratios.

BLS uses indirect seasonal adjustment for most of 

the major labor force aggregates. Besides retaining, so far as possible, the essential accounting 

relationships, the indirect approach is needed 

because many of the aggregates include components having different seasonal and trend characteristics that sometimes require different modes of 

adjustment.

Examples of indirectly seasonally adjusted series 

are the levels of total unemployment, employment, and the civilian labor force, as well as the 

unemployment rate for all civilian workers. These 

series are produced by the aggregation of some 

or all of the seasonally adjusted series for the 

eight major civilian labor force components. The 

seasonally adjusted level of total unemployment 

is the sum of the seasonally adjusted levels of 

unemployment for four age/sex groups: men 16 

to 19 years, women 16 to 19 years, men 20 years 

and over, and women 20 years and over. Likewise, 

seasonally adjusted civilian employment is the 

sum of employment in all industries for the same 

four age/sex groups. The seasonally adjusted civilian labor force is the sum of all eight components. 

The seasonally adjusted civilian unemployment 

rate is computed as the ratio of the total seasonally adjusted unemployment level to the total 

seasonally adjusted civilian labor force (expressed 

as a percentage).

A problem with producing seasonally adjusted 

estimates for a series by aggregation is that 

seasonal adjustment factors cannot be directly 
100 Chapter 2-5: Seasonal Adjustment Current Population Survey TP77

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

computed for that series. Implicit seasonal adjustment factors, however, can be calculated after the 

fact by taking the ratio of the unadjusted aggregate to the seasonally adjusted aggregate or, for 

additive implicit factors, the difference between 

those two aggregates.

REFERENCES

Bowler, M., R. E. Ilg, S. Miller, E. Robison, and 

A. Polivka, “Revisions to the Current Population 

Survey Effective in January 2003,” 2003, retrieved 

from <www.bls.gov/cps/rvcps03.pdf>.

Box, G. E. P., and G. M. Jenkins, Time Series 

Analysis: Forecasting and Control, Holden Day, 

San Francisco, 1970.

Box, G. E. P., and G. C. Tiao, “Intervention Analysis 

with Applications to Economic and Environmental 

Problems,” Journal of the American Statistical 

Association, 70:349, 1975, pp. 70–79.

Chang, I., G. C. Tiao, and C. Chen, “Estimation 

of Time Series Parameters in the Presence of 

Outliers,” Techonometrics, 1988, pp. 193–204.

Dagum, E. B., “The X-11-ARIMA Seasonal 

Adjustment Method,” Statistics Canada, Catalog 

No. 12-564E, 1980, retrieved from <www.census 

.gov/ts/papers/1980X11ARIMAManual.pdf>. 

Findley, D. F., B. C. Monsell, W. R. Bell, M. C. Otto, 

and B. Chen, “New Capabilities and Methods of 

the X-12-ARIMA Seasonal Adjustment Program,” 

Journal of Business and Economic Statistics, April 

1998, pp. 127–152, retrieved from <www.census 

.gov/ts/papers/jbes98.pdf>.

Gomez, V., and A. Maravall, “Seasonal Adjustment 

and Signal Extraction in Economic Time Series,” 

in A Course in Time Series Analysis, eds. D. Peña, 

G.C. Tiao, and R.S. Tsay, pp. 202–247, J. Wiley and 

Sons, New York, 2001, retrieved from <www.bde 

.es/f/webbde/SES/servicio/software/tramo 

/sasex.pdf>.

Kendall, M., and J. K. Ord, Time Series, University 

Press, New York, 1990.

McIntire, R. J., R. B. Tiller, and T. D. Evans, 

“Revision of Seasonally Adjusted Labor Force 

Series,” 2002, retrieved from <www.bls.gov/cps 

/cpsrs2002.pdf>.

Methee, G. R., and R. J. McIntire, “An Evaluation 

of Concurrent Seasonal Adjustment for the Major 

Labor Force Series,” Proceedings of the Business 

and Economic Statistics Section, American 

Statistical Association, pp. 358–363, 1987, 

retrieved from <www.bls.gov/cps/methee 

_mcintire_asa_1987.pdf>.

Shiskin, J., A. Young, and J. Musgrave, “The 

X-11 Variant of the Census Method II Seasonal 

Adjustment Program,” Technical Paper 15, 

U.S. Census Bureau, 1967, retrieved from 

<www.census.gov/ts/papers 

/ShiskinYoungMusgrave1967.pdf>.

U.S. Bureau of Labor Statistics, “Local Area 

Unemployment Statistics: Estimation,” Handbook 

of Methods, 2018, retrieved from <www.bls.gov 

/opub/hom/lau/calculation.htm>. 

U.S. Census Bureau, X-13ARIMA-SEATS Reference 

Manual (Version 1.1), 2017, retrieved from 

<www.census.gov/ts/x13as/docX13AS.pdf>.
Unit 3 

Current Population Survey 

Operations
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Current Population Survey TP77 Chapter 3-1: Instrument Design 103

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

Chapter 3-1: Instrument Design

BASIC DESIGN

Chapter 1-2, Questionnaire Concepts and 

Definitions, describes the current concepts and 

definitions underpinning the Current Population 

Survey (CPS) data collection instrument. This 

chapter offers a general description of how the 

data collection is designed and how it operates. 

The current data collection instrument is designed 

to be administered by live interviewers using a 

laptop computer or calling from a centralized telephone facility. The CPS is designed to use computer-assisted interviewing (CAI). CAI consists of 

either a computer-assisted telephone interviewing 

(CATI) or a computer-assisted personal interviewing (CAPI), which can be conducted in person 

or by telephone. Interviewers ask the survey 

questions as they appear on the screen of the 

laptop (or desktop) computer and then type the 

responses directly into the computer. A portion of 

sample households—currently about 10 percent—

is interviewed via CATI at contact centers located 

in Tucson, Arizona, and Jeffersonville, Indiana.

CAI methods allow greater flexibility in questionnaire design than paper-and-pencil data collection 

methods. Complicated skips, respondent-specific 

question wording, and carryover of data from one 

interview to the next are all possible in an automated environment. 

CAI allows capabilities such as: 

• The use of dependent interviewing, i.e., 

carrying over information from the previous 

month for industry, occupation, and duration 

of unemployment data.

• 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, CAI 

reduces the probability that an interviewer will 

ask the wrong set of questions. 

• CAI 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.

The CPS instrument is created using Blaise®, a 

CAI system and survey processing tool for the 

Windows operating system and the Internet. 

Developed by Statistics Netherlands and licensed 

and supported in the United States by Westat, 

Blaise® includes advanced capabilities that meet 

the CAI needs of the CPS. The instrument consists 

of complicated skip patterns and automated question text fills. Other features include help screens 

and pop-up check boxes to guide and assist 

accurate data entry. The instrument also collects 

paradata, which is respondent contact history 

information recorded by interviewers to help better evaluate the survey experience and willingness 

to respond.

Typically, households’ first and fifth monthly 

interviews are conducted in person. The remaining monthly interviews are generally done by 

telephone. The survey instrument questions differ 

slightly, depending on where households are in 

their schedule of monthly interviews. The first and 

fifth interviews collect the vast majority of the 

demographic information, which is carried forward 

to subsequent months. The fourth and eighth 

interviews collect information on earnings and 

union membership for wage and salary workers. 

This information is often referred to as outgoing 

rotation data. Earnings data are collected only 

in the outgoing rotations in order to reduce the 

potential for higher nonresponse in other monthly 

interviews.

At the end of each day’s interviewing, the data 

collected are transmitted to the U.S. Census 

Bureau’s central database. Once files are transmitted, they are deleted from the laptops.

A copy of the questionnaire can be obtained from 

the Internet at <www.census.gov/programs

-surveys/cps/technical-documentation

/questionnaires.html>. 
104 Chapter 3-1: Instrument Design Current Population Survey TP77

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

CURRENT POPULATION SURVEY 

QUESTIONNAIRE

The CPS has been conducted for more than 

three-quarters of a century and 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 has 

experienced major changes since the survey was 

first conducted in 1940, the concepts used in the 

measurement of unemployment and the unemployment rate have remained largely unchanged. 

During the history of the survey, there have been 

several wholesale reviews of CPS concepts, most 

notably two presidentially appointed evaluations—the Gordon Committee in the 1960s and the 

Levitan Commission in the 1970s. While both of 

these reviews found the main labor force definitions to be sound, they suggested tightening the 

definition of unemployment at the margins. Many 

recommendations from the Gordon Committee 

were implemented into a major redesign of the 

CPS in 1967. From 1967 to 1993, the survey questionnaire remained virtually unchanged. A major 

redesign of the survey in 1994 implemented many 

of the Levitan Commission recommendations. In 

addition, the CPS questionnaire has expanded 

to collect more detail about a variety of demographic and labor market characteristics over the 

years. For more information about the history of 

the CPS, see Dunn, Haugen, and Kang (2018). 

The remainder of this chapter describes the work 

performed on the data collection instrument and 

changes implemented in 1994 because of that 

work. A description of more recent improvements and additions to the survey instrument also 

appears in this chapter. 

1994 REDESIGN

A major redesign of the CPS was implemented 

in January 1994. The 1994 revisions to the survey were designed to take advantage of major 

advances in survey research methods and data 

collection technology, as well as to account 

for recommendations made by the Levitan 

Commission. This section describes in detail the 

work performed on the data collection instrument 

and changes implemented in 1994.

Objectives of the Redesign

There were five main objectives in redesigning the 

CPS questionnaire: 

• Better operationalize existing definitions and 

reduce reliance on volunteered responses. 

• Reduce the potential for response error in the 

questionnaire-respondent-interviewer interaction and, hence, improve measurement of CPS 

concepts. 

• Implement minor definitional changes within 

the labor force classifications. 

• Expand the labor force data available and 

improve longitudinal measures. 

• Exploit the capabilities of CAI 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 Bureau 

of Labor Statistics (BLS) and the Census Bureau 

developed questions to lessen the potential for 

response error. Among the approaches used were: 

• Shorter, clearer question wording.

• Splitting complex questions into two or more 

separate questions. 

• Building concept definitions into question 

wording.

• Reducing reliance on volunteered information.

• Explicit and implicit strategies for the respondent to provide numeric data such as hours 

and earnings.

• 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, i.e., people outside the labor 
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force who are not looking for work because they 

believe that there are no jobs available for them. 

As noted in Chapter 1-2, Questionnaire Concepts 

and Definitions, 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 previous measurement of discouraged 

workers was criticized by the Levitan Commission 

as being 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 

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 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 conceptual and questionnaire 

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 became 

available each month on the number of employed 

people who have more than one job and the hours 

multiple jobholders work on their main job and 

all of their other jobs combined. By separately 

collecting information on the number of hours 

multiple jobholders work on their main job and 

secondary jobs, it became possible to derive estimates of the number of workers who combined 

two or more part-time jobs into a full-time workweek and the number of full- and part-time jobs 

in the economy. The inclusion of the multiple job 

questions 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 (CES), the survey of nonfarm business 

establishments. (For a discussion of the CES, see 

the "BLS Handbook of Methods," referenced at 

the end of the chapter.) In addition, beginning 

in 1994, monthly data on the number of hours 

usually worked per week and data on the number 

of discouraged workers became available from 

the entire CPS sample rather than just from those 

respondents who were in their fourth or eighth 

monthly interviews.

Evaluation and Selection of Revised 

Questions

Planning for the CPS questionnaire revisions 

implemented in 1994 began 8 years beforehand, 

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).

The revised questionnaire developed jointly by 

Census Bureau and BLS staff used information 

collected in a large two-phase test of question 

wording in addition to qualitative research. 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 contact centers of the Census Bureau (Polivka 

and Rothgeb, 1993).

Both quantitative and qualitative information 

was used in the two phases to select questions, 
106 Chapter 3-1: Instrument Design Current Population Survey TP77

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identify problems, and suggest solutions. Analyses 

were based on information from item response 

distributions, respondent and interviewer debriefing data, and behavior coding of interviewer and 

respondent interactions. For more on the evaluation methods used for redesigning the questions, see Esposito et al. (1992) and Esposito and 

Rothgeb (1997).

Item Response Analysis

The primary use of item response analysis was 

to determine whether different questionnaires 

produce different response patterns, which may 

affect the labor force estimates. Unedited data 

were used for this analysis. Statistical tests were 

conducted to ascertain whether differences 

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 based on 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?” The classification of vignette debriefings 

was designed to determine if alternative question 

wording altered respondents’ understanding of 

the question and changed their perception of the 

concepts underlying them. 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. For example, “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 (for example, minor 

or 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; or 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 
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or interviewer procedures that were problematic 

for interviewers or respondents. The information collected was used to identify questions 

that needed revision, and to modify 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 contact centers and in geographically dispersed 

regional offices (ROs). 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 1994 

QUESTIONNAIRE REVISION

General

Definition of reference week. In the interviewer 

debriefings that were conducted in 13 different geographic areas during 1988, interviewers 

reported that the 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 major activity 

question was deleted from the questionnaire. In 

the past, the question had served as a preamble to 

the labor force questions, although many people’s 

labor force status was found to be determined by 

responses to it. 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 pre-1994 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. For 

a fuller discussion of the need for a direct question on presence of a business, see Rothgeb et al. 

(1992), Copeland and Rothgeb (1990), and Martin 

(1987).

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 in an earlier question. 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 pre-1994 questionnaire, the “at 

work” question read, “LAST WEEK, did you do any 
108 Chapter 3-1: Instrument Design Current Population Survey TP77

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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 displayed 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 measurement of “work.”

Direct question on multiple jobholding. In the 

pre-1994 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 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 pre-1994 question on “hours 

worked” read: “How many hours did you work last 

week at all jobs?” If a person reported 35 to 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 fulltime 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, separate data on hours worked at a main job and other 

jobs are also collected. 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. Finally, a question is 

asked on the number of actual hours worked last 

week. The new hours series improves the accuracy 

of the data and allows monthly estimates of usual 

hours worked to be produced for all employed 

people (Polivka and Rothgeb, 1993). In the pre1994 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. The revised questionnaire 

also permits estimates of usual and actual hours 

on multiple jobholders’ main jobs and their other 

jobs. This information is used to derive estimates 

of the number of multiple jobholders who work 

full-time on their main jobs and part-time on their 

other jobs, those who work full-time on both their 

main jobs and their other jobs, and those who 

work part-time on all of their jobs.

Industry and occupation (Dependent interviewing). Prior to the 1994 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, i.e., 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 (MIS 2–4) households and 

MIS 6−8 households. Different variations of dependent interviewing were evaluated during testing. 

See Rothgeb et al. (1992) for more detail.

In the revised CPS, respondents are provided the 

name of their employer from the previous month 

and asked if they still work for that employer. If 

they answer “no,” respondents are asked the independent questions on I&O.

If they answer “yes,” respondents are asked, “Have 

the usual activities and duties of your job changed 
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since last month?” If individuals say “yes,” they are 

then asked the independent questions on occupation, usual activities or duties, and class of worker. 

If their usual 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, 

usual activities or 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 

pre-1994 questionnaire. In the pre-1994 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 pre-1994 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) of non-hourly-wage 

workers (n = 853) were paid at a weekly rate, and 

less than (25 percent) of non-hourly-wage workers 

(n = 1623) found it easiest to report earnings as a 

weekly amount (Polivka and Rothgeb, 1993).

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 statement 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.

Individuals for whom the easiest way to report is 

hourly are assumed to be paid hourly. An additional question is asked of people who do not 

report that it is easiest to report their earnings 

hourly. The question is displayed below. Studies of 

the number of minimum-wage workers are based 

upon those individuals identified as 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

People on layoff (direct question). Previous 

research (Rothgeb, 1982; Palmisano, 1989) 

demonstrated that the pre-1994 question on layoff status—“Did you have a job or business from 

which you were temporarily absent or on layoff 

LAST WEEK?”—was long, awkwardly worded, and 

frequently misunderstood by respondents. Some 

respondents heard only part of the question, 

while others thought that they were being asked 

whether they had a business.

In an effort to reduce response error, the revised 

questionnaire includes two separate direct questions about layoff and temporary absences. The 

layoff question is, “LAST WEEK, were you on 

layoff from a job?” Questions asked later screen 
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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 

pre-1994 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 pre1994 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, i.e., a job search method 

that could not directly lead to a job offer, 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 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.

Research conducted in the 2000s indicated a need 

for instructions on how to code job search methods such as “online advertisements” or “placed 

resume online.” The result of this research was 

the introduction in 2014 of clearer descriptions 

on how to code such job search methods into 

the interviewer instructions and the interviewer 

manual.

Duration of job search and layoff. The duration 

of unemployment is an important labor market 

indicator published monthly by BLS. In the pre1994 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. In addition, 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 
Current Population Survey TP77 Chapter 3-1: Instrument Design 111

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themselves and interviewers are able to record the 

duration in weeks, months, or years.

To avoid the 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 pre-1994 

questionnaire.

As mentioned earlier, the CATI and CAPI technology makes it possible to automatically update 

duration of job search and layoff for people who 

are unemployed in consecutive 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 pre-1994 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 pre-1994 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, these individuals are not asked the remainder of the labor 

force questions 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 I&O 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 the person can do any 

gainful work during the next 6 months. Different 

versions of the follow-up probe are used depending on whether the person identifies as disabled 

or as unable to work. Individuals who indicated 

that their disability prevents them from working 

in the next 6 months also are skipped around the 

remaining labor force questions. 

Dependent interviewing for people reported as 

retired, disabled, or unable to work. The revised 

questionnaire also was designed to use dependent interviewing across months 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.
112 Chapter 3-1: Instrument Design Current Population Survey TP77

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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 (NCEUS, 

1979). The Levitan Commission criticized the pre1994 definition because it was based on a subjective desire for work and questionable inferences 

about an individual’s availability to take a job. 

Because 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 have been published 

monthly since the 1994 redesign. 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 as was done prior to the redesign, 

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

quarterly.

Findings. Tests of the revised questionnaire 

showed that the quality of labor force data 

improved because 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 

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 yearand-a-half parallel survey prior to implementation 

as the official survey in January 1994. In addition, 

the unrevised procedures were used with the 

parallel survey sample from January 1994 through 

May 1994. 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 (1998).

RECENT IMPROVEMENTS IN THE 

QUESTIONNAIRE

Changes to Race and Ethnicity Questions

Starting in 2003, race and ethnicity questions 

were modified to allow individuals to report being 

of more than one race. Hispanics were identified 

through a direct question; previously, individuals’ 

ethnicity was determined through their country 

of origin or that of their ancestors (Bowler et al., 

2003). These changes were made so CPS data 

would be in accord with Office of Management 

and Budget (OMB) guidelines on collection of race 

and ethnicity. The changes were the result of a 

multiyear research effort that started in 1996 and 

included two supplements to the CPS that tested 

alternative question wordings.

Disability Questions

Development of disability questions for the CPS 

was a long process that started in the late 1990s. 

The Presidential Task Force on Employment of 

Adults with Disabilities formed the Employment 

Rate Measurement Work Group, which included 

members from 17 different agencies, to develop 

a measure of the employment rate of people 
Current Population Survey TP77 Chapter 3-1: Instrument Design 113

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with disabilities. Questions proposed for the CPS 

were tested in the National Comorbidity Survey 

(NCS) and again in a 2006 supplement to the CPS. 

However, the disability rate obtained from the 

CPS was much lower than that from the NCS. The 

Census Bureau had independently developed and 

tested six questions on disability for the American 

Community Survey (ACS). Because the NCS and 

CPS supplement testing had uncovered problems 

and because OMB was encouraging federal surveys to use the same questions, the ACS questions were modified slightly so that they could be 

incorporated into the CPS questionnaire. The six 

disability questions were added to the monthly 

CPS in June 2008 (McMenamin and Hipple, 2014). 

Certification and Licensing Questions

The federal Interagency Working Group on 

Expanded Measures of Enrollment and Attainment 

(GEMEnA) was formed in 2009 to develop questions to measure the prevalence and key characteristics of nondegree credentials, such as 

certifications and licenses, for existing federal 

surveys. The question development process took 

several years and included cognitive testing, focus 

groups, consultation with experts, and a number 

of pilot studies. After additional cognitive testing and stakeholder outreach, BLS made minor 

changes to three general GEMEnA questions 

on certifications and licenses so they could be 

incorporated into the CPS questionnaire. Fielding 

of the three new questions began in January 2015 

(Allard, 2016). 

Introduction of New Relationship Categories 

to Capture Same-Sex Marriages

In 2010, the Interagency Working Group on 

Measuring Relationships in Federal Household 

Surveys was established, convened, and chaired 

by the Statistical and Science Policy Branch of 

OMB. At the behest of this group, the Census 

Bureau conducted focus groups and cognitive 

testing to develop revised relationship categories. The actual question did not change. Because 

of these focus group findings, two alternative 

relationship category groupings were developed. 

Both groupings underwent further cognitive testing. The version implemented in the CPS contains 

the response categories that were recommended 

for use following this cognitive testing (Kreider, 

2017). The Census Bureau began phasing in the 

new relationship question categories in CPS in 

May 2015. In addition to the change to response 

categories for the relationship question, the questions that ask about the presence of parents in the 

household have also changed. Instead of asking 

whether the person has a mother present (who is 

always female) and then whether the person has a 

father present (who is always male), the questions 

now ask whether the person has a parent present, 

and then whether they have another parent present. This allows for the identification of children 

who live with same-sex parents. 

CONTINUOUS TESTING AND 

IMPROVEMENTS OF THE CURRENT 

POPULATION SURVEY AND ITS 

SUPPLEMENTS

Experience gained during the 1994 redesign of the 

CPS demonstrates the importance of testing questions and monitoring data quality. The experience, 

along with contemporaneous advances in research 

on questionnaire design, informs the development 

of methods for testing new or improved questions 

for the basic CPS and its periodic supplements 

(Martin and Polivka, 1995; Oksenberg et al., 1991; 

Bischoping et al., 1989; Campanelli et al., 1991; 

Esposito et al., 1992; Forsyth and Lessler, 1991). 

Methods to continuously test questions and 

assess data quality are discussed in Chapter 4-1, 

Nonsampling Error. Changes to the CPS are made 

carefully and their effects are consistently monitored. A major strength of the CPS is the ability 

to compare estimates over time, and even minor 

changes to the questionnaire can affect data 

comparability. Proposed revisions to the CPS are 

evaluated within the context of this comparability. Furthermore, methods to bridge differences 

caused by changes and techniques to avoid the 

disruption of historical series are included in the 

testing of new or revised questions.

REFERENCES

Allard, M. D., “Adding Questions on Certifications 

and Licenses to the Current Population Survey,” 

Monthly Labor Review, U.S. Bureau of Labor 

Statistics, November 2016, retrieved from 

<https://doi.org/10.21916/mlr.2016.52>.
114 Chapter 3-1: Instrument Design Current Population Survey TP77

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

Bischoping, K., C. Cannell, F. Fowler, G. Kalton, 

and L. Oksenberg, “New Techniques for Pretesting 

Survey Questions,” Survey Research Center, 

University of Michigan, 1989, Chapter 2. 

Bowler, M., R. Ilg, S. Miller, E. Robinson, and 

A. Polivka, “Revisions to the Current Population 

Survey Effective in January 2003,” 2003, retrieved 

from <www.bls.gov/cps/rvcps03.pdf>.

Campanelli, P. C., E. A. Martin, and J. M. Rothgeb, 

“The Use of Interviewer Debriefing Studies as a 

Way to Study Response Error in Survey Data,” The 

Statistician, vol. 40, 1991, pp. 253−264.

Cohany, S. R., A. E. Polivka, and J. M. Rothgeb, 

“Revisions in the Current Population Survey 

Effective January 1994,” Employment and 

Earnings, vol. 41, no. 2, 1994, pp. 1337.

Copeland, K., and J. M. Rothgeb, “Testing 

Alternative Questionnaires for the Current 

Population Survey,” Proceedings of the Section 

on Survey Research Methods, American Statistical 

Association, 1990, pp. 63−71.

Dunn, M., S. E. Haugen, and J. Kang, “The Current 

Population Survey – Tracking Unemployment 

in the United States for over 75 years,” Monthly 

Labor Review, January 2018, pp. 1–23.

Edwards, S. W., R. Levine, and S. R. Cohany, 

“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, 1989.

Esposito, J. L., and J. Hess, “The Use of 

Interviewer 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, 1992.

Esposito, J. L., J. M. Rothgeb, A. E. Polivka, 

J. Hess, and P. C. Campanelli, “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, 1992.

Esposito, J. L., and J. M. Rothgeb, “Evaluating 

Survey Data: Making the Transition From 

Pretesting to Quality Assessment,” Survey 

Measurement and Process Quality, Wiley, New 

York, 1997, pp. 541−571.

Forsyth, B. H., and J. T. Lessler, “Cognitive 

Laboratory Methods: A Taxonomy,” Measurement 

Errors in Surveys, Wiley, New York, Ch. 20, 1991, 

pp. 393−418.

Fracasso, M. P., “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, 1989, pp. 481−485.

Gaertner, G., D. Cantor, and N. Gay, “Tests of 

Alternative Questions for Measuring Industry 

and Occupation in the CPS,” Proceedings of the 

Section on Survey Research Methods, American 

Statistical Association, 1989.

Kreider, R., “Changes to the Household 

Relationship Data in the Current Population 

Survey,” Paper presented at the Joint Statistical 

Meetings, Baltimore, MD, July 31, 2017.

Martin, E. A., “Some Conceptual Problems in the 

Current Population Survey,” Proceedings of the 

Section on Survey Research Methods, American 

Statistical Association, 1987, pp. 420−424.

Martin, E. A., and A. E. Polivka, “Diagnostics for 

Redesigning Survey Questionnaires: Measuring 

Work in the Current Population Survey,” Public 

Opinion Quarterly, Winter 1995, vol. 59, no.4, 

pp. 547−567.

McMenamin, T. M., and S. F. Hipple, “The 

Development of Questions on Disability for 

the Current Population Survey,” Monthly Labor 

Review, U.S. Bureau of Labor Statistics, April 

2014, retrieved from <https://doi.org/10.21916

/mlr.2014.15>.

National Commission on Employment and 

Unemployment Statistics (NCEUS), Counting the 

Labor Force, 1979. (Also known as the Levitan 

Commission.) 
Current Population Survey TP77 Chapter 3-1: Instrument Design 115

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

Oksenberg, L., C. Cannell, and G. Kalton, “New 

Strategies for Pretesting Survey Questions,” 

Journal of Official Statistics, 1991, vol. 7, no. 3, 

pp. 349−365.

Palmisano, M., “Respondents’ Understanding 

of Key Labor Force Concepts Used in the CPS,” 

Proceedings of the Section on Survey Research 

Methods, American Statistical Association, 1989.

Polivka, A. E., and S. M. Miller, “The CPS after the 

Redesign: Refocusing the Economic Lens,” Labor 

Statistics Measurement Issues, 1998, pp. 249−286.

Polivka, A. E., and J. M. Rothgeb, “Overhauling 

the Current Population Survey: Redesigning the 

Questionnaire,” Monthly Labor Review, 1993, vol. 

116, no. 9, pp. 10−28.

Rothgeb, J. M., “Summary Report of July 

Followup of the Unemployed,” U.S. Bureau of 

Labor Statistics, December 20, 1982.

Rothgeb, J. M., A. E. Polivka, K. P. Creighton, 

and S. R. Cohany, “Development of the Proposed 

Revised Current Population Survey Questionnaire,” 

Proceedings of the Section on Survey Research 

Methods, American Statistical Association, 1992, 

pp. 56−65.

U.S. Bureau of Labor Statistics, "Handbook of 

Methods," retrieved from <www.bls.gov/opub

/hom/pdf/ces-20110307.pdf>.

U.S. Bureau of Labor Statistics, “Response Errors 

on Labor Force Questions Based on Consultations 

with Current Population Survey Interviewers 

in the United States,” Paper prepared for the 

Organisation for Economic Co-Operation and 

Development Working Party on Employment and 

Unemployment Statistics, 1988.
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Chapter 3-2: Conducting the Interviews

INTRODUCTION

Each month during the interview week for CPS, 

field representatives (FRs) and computer-assisted 

telephone interviewers attempt to contact and 

interview a knowledgeable adult household member living in each sample household to complete 

an interview. Generally, the week containing 

the twelfth is the reference week (i.e., the week 

about which the labor force questions are asked). 

Interviewing typically begins the week containing the nineteenth of the month. As outlined in 

Chapter 2-2, Sample Design, a sample household 

is in sample for 8 months total. It is in sample for 4 

consecutive months, not in the sample for the next 

8 consecutive months, and in the sample again 

for the next 4 consecutive months. Each month, 

one-eighth of the households are in sample for 

the first time (referred to as MIS 1), one-eighth for 

the second time (MIS 2), and so on, through the 

eighth time (MIS 8). Because of this schedule, each 

interviewer conducts different types of interviews 

within their weekly assignment (due to differing 

MIS cases). 

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, provides potential 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 3-2.1 shows the front side of the introductory letter sent to sample units in the area administered by the Atlanta Regional Office. Ideally, a 

personal visit interview is required for all MIS 1 

households because the CPS sample is strictly a 

sample of addresses. The Census Bureau has no 

way of knowing who the occupants of the sample household are or whether the household is 

occupied or eligible for interview. A personal visit 

is attempted for its MIS 5 interview. For some MIS 

1 and MIS 5 households, telephone interviews 

are conducted if, for example, the respondent 

requests a telephone interview during the initial 

personal visit.

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 condemned, demolished, or converted to 

a permanent business. Regardless of the reason, 

such sample addresses are classified as Type C 

noninterviews. 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 

lists of sample addresses and are not visited again 

in subsequent months. All households classified 

as Type C undergo a supervisory review of the 

circumstances surrounding the case before the 

determination becomes 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) or a unit occupied entirely by 

individuals who are not eligible for a CPS labor 

force interview, such as individuals with a usual 

residence elsewhere (URE), or who were in the 

armed forces. These Type B noninterview units 

have a chance of becoming eligible for interview in future months because the condition is 

considered temporary (e.g., a vacant unit could 

become occupied). Therefore, Type B units are 

reassigned to FRs in subsequent months. These 

sample addresses remain in sample for the entire 

8 months that households are eligible for interview. An FR visits the unit each succeeding month 

to determine whether it has changed status and 

either continues the Type B classification, revises 

the noninterview classification, or conducts an 

interview as applicable. All Type B cases are 

subject to supervisory review. Some of these 

Type B households are eligible for the Housing 

Vacancy Survey, which is described in Chapter 

1-3, Supplements.
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Figure 3-2.1. 

Sample Introductory Letter

CPS-263(MIS-1)(L) ATLANTA (3-2018) UNITED STATES DEPARTMENT OF COMMERCE

Economics and Statistics Administration

U.S. Census Bureau

Washington, DC 20233-0001 OFFICE OF THE DIRECTOR

A Message from the Director of the U.S. Census Bureau:

Dear Resident,

Your address has been selected to participate in the Current Population Survey. This 

monthly survey is the source of the Nation’s unemployment rate that you may hear about in 

the news. The U.S. Census Bureau conducts this survey in partnership with the U.S. Bureau 

of Labor Statistics.

The Current Population Survey collects information on employment and earnings of people

living in the United States. The results help determine federal funding for veteran’s 

programs, youth activities, food assistance and more. The survey results are also used by 

the Federal Reserve to set interest rates.

The success of this survey depends on your participation. We cannot substitute another 

address for yours. Your address is part of a scientifically selected sample of addresses 

chosen throughout the country. Your answers represent hundreds of other U.S. households.

Answers to frequently asked questions are on the back of this letter. If you have 

other questions, please visit census.gov/cps, or call your Census Bureau Regional 

Office at 1-800-424-6974, #53939.

You do not need to take any action at this time. A Census Bureau representative will 

contact you soon to ask your household to complete the survey.

Thank you in advance for participating in this important survey.

census.gov
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One final set of households not interviewed for 

CPS are Type A households. These are households 

that the FR determined were 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 (i.e., an individual who is a civilian, at least 

15 years of age, and does not have a URE). Even 

though these households were eligible, they were 

not interviewed because the household members 

refused, were absent during the interviewing 

period, or were unavailable for other reasons. All 

Type A cases are subject to 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. 

Table 3-2.1 shows how the three types of noninterviews are classified and various reasons that 

define each category. Table 3-2.2 lists the main 

household information collected for each noninterview category and briefly summarizes each 

item. 

INITIAL INTERVIEW

If the unit is not classified as a noninterview, 

then 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 Table 

3-2.3 shows some of the household items asked at 

the beginning of the interview. Once eligibility is 

established, the interview moves into the demographic portion of the instrument. The primary 

purpose of this portion is to establish the household’s roster, which is the listing of all household 

residents at the time of the interview. The main 

concern here is to establish an individual’s usual 

place of residence. (These rules are summarized in 

Table 3-2.4.) 

For all individuals residing in the household who 

do not have a URE, a number of personal and family demographic characteristics are collected. Part 

1 of Table 3-2.5 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 or cohabitation partner 

pointers (if applicable), age, sex, marital status, 

educational attainment, veteran status, current 

armed forces status, race, and Hispanic ethnicity. 

As shown in Table 3-2.6, 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 

Table 3-2.1. 

Noninterviews: Types A, B, and C

Type A

1 No one home

2 Temporarily absent

3 Refusal

4 Language barrier

5 Unable to locate

6 Other occupied

Type B

1 Entire household in the armed forces

2 Entire household under 15 years

3 Vacant regular

4 Temporarily occupied by persons with usual residence elsewhere

5 Vacant—storage of household furniture

6 Unfit or to be demolished

7 Under construction, not ready

8 Converted to temporary 

business or storage

9 Unoccupied tent site or trailer site

10 Permit granted, construction not started

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 Unused line of listing sheet

8 Unlocatable sample address

9 Unit does not exist or unit is out-of-scope

10 Other Type C—Specify
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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. 

The FR then collects labor force data about all 

civilian adult individuals (at least 15 years of age) 

who do not have a URE. In the interest of timeliness and efficiency, a household respondent 

(preferably, the most knowledgeable household 

member, though any household member aged 15 

or over can be a household respondent) generally provides the data for each eligible individual. 

Since household respondents provide data about 

themselves, just over one-half of the CPS labor 

force data is collected by self-response. The 

remainder is collected by proxy from the household respondent. In certain limited situations, 

collection of the data from a nonhousehold member is allowed; all such cases receive supervisory 

review before the data are accepted into the CPS 

processing system.

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 

Table 3-2.2.

Noninterviews: Main Household Items Asked for Types A, B, and C

Note: This list is not complete and covers only the main data items. It does not include related items used to identify 

the final response such as probes and verification screens. See the Current Population Survey Interviewing Manual 

for illustrations of the actual instrument screens for all Current Population Survey items.

TYPE A CASES

Item name Item generally asks

1 TYPA Which specific kind of Type A is the case?

2 ABMAIL What is the property’s mailing address?

3 ACCESS Is access to the household 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/apartment, mobile home/trailer, etc.)? This item 

is answered by the interviewer based on observation.

5 INOTES-1 The interviewer may want to make notes about the case that might help with the next 

interview.

TYPE B CASES

Item name Item generally asks

1 TYPB Which specific kind of Type B is the case?

2 ABMAIL What is the property’s mailing address?

3 ACCESS Is access to the household 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/apartment, mobile home/trailer, etc.)? This item 

is answered by the interviewer based on observation.

5 SEASON Is the unit intended for occupancy year-round, by migrant workers, or seasonally?

6 BCINFO What is the name, title, and phone number of the contact who provided the Type B information or was the information obtained by interviewer observation?

7 INOTES-1 The interviewer may want to make notes about the case that might help with the next 

interview.

TYPE C CASES

Item name Item generally asks

1 TYPC Which specific kind of Type C is the case?

2 ACCESS Is access to the household direct or through another unit? This item is answered by the 

interviewer based on observation.

3 LIVQRT What is the type of housing unit (house/apartment, mobile home/trailer, etc.)? This item 

is answered by the interviewer based on observation.

4 BCINFO What is the name, title, and phone number of the contact who provided the Type C information or was the information obtained by interviewer observation?

5 INOTES-1 The interviewer may want to make notes about the case that might help with the next 

interview.
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Table 3-2.3.

Interviews: Main Household Items Asked in Month-In-Sample 1 and Replacement 

Households

Note: This list is not complete and covers only the main data items. It does not include related items used to identify 

the final response such as probes and verification screens. See the Current Population Survey Interviewing Manual 

for illustrations of the actual instrument screens for all Current Population Survey items.

PART 1. Items Asked at the Beginning of the Interview

Item name Item generally asks

1 INTROb Does the interviewer want to classify the case as a noninterview?

2 NONTYP What type of noninterview is the case (A, B, or C)? It is asked or not depending on the 

answer to INTROb. 

3 VERADD What is the street address (as verification)?

4 MAILAD What is the mailing address (as verification)?

5 TENUR Is the unit owned, rented, or occupied without paid rent?

6 ACCESS Is access to the household direct or through another unit? This item is answered by the 

interviewer (not read to the respondent).

7 LIVQRT What is the type of housing unit (house/apartment, mobile home/trailer, etc.)? This item 

is answered by the interviewer (not read to the respondent).

PART 2. Items Asked at the End of the Interview

Item name Item generally asks

1 TELHH Is there a telephone in the unit?

2 TELAV Is there a telephone elsewhere on which people in this household can be contacted? This 

is asked or not depending on the answer to TELHH.

3 TELWHR If there is a telephone elsewhere, where is the phone located? This is asked or not 

depending on the answer to TELAV.

4 TELIN Is a telephone interview acceptable?

5 TELPHN What is the phone number and is it a home or office phone?

6 BSTTM When is the best time to contact the respondent?

7 NOSUN Is a Sunday interview acceptable?

8 THANKYOU Is there any reason why the interviewer will not be able to interview the household next 

month?

9 INOTES-1 The interviewer may want to make any notes about the case that might help with the next 

interview. The interviewer is also asked to list the names/ages of ALL additional persons 

if there are more than 16 household members.
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Table 3-2.4. 

Summary Table for Determining Who Is to Be Included as a Member of the Household

Include as a 

member of the 

household

A. PERSONS STAYING IN SAMPLE UNIT AT TIME OF INTERVIEW

Person is member of family, lodger, servant, visitor, etc.

1. Ordinarily stays here all the time (sleeps here). . 

2. Here temporarily—no living quarters held for person elsewhere. . 

3. Here temporarily—living quarters held for person elsewhere. .

Person is in armed forces

1. Stationed in this locality, usually sleeps here. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes

2. Temporarily here on leave—stationed elsewhere. .

Person is a student—here temporarily attending school—living quarters held for person

elsewhere

1. Not married or not living with immediate family. .

2. Married and living with immediate family. . 

3. Student nurse living at school. . 

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. .

Person is temporarily absent on vacation, in general hospital, etc. (including veterans’ facilities that are general hospitals)—living quarters held here for person. . 

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). . 

2. Living quarters held here and elsewhere for person but comes here infrequently 

(e.g., construction engineer). .

3. Living quarters held here at home for unmarried college student working away from 

home during summer school vacation  . 

Person is in armed forces—was member of this household at time of induction but currently 

stationed. .

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. . 

2. Married and living with immediate family. .

3. Attending school overseas. .

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. .

2. Has slept greater part of that time in sample unit. . 

Citizen of foreign country temporarily in the United States

1. Living on premises of an Embassy, Ministry, Legation, Chancellery, Consulate. .

2. Not living on premises of an Embassy, Ministry, etc.

a. Living here and no usual place of residence elsewhere in the United States. . 

b. Visiting or traveling in the United States. .
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Table 3-2.5.

Interviews: Main Demographic Item Asked1

Note: This list is not complete and covers only the main data items. It does not include related items used to identify 

the final response such as probes and verification screens. See the Current Population Survey Interviewing Manual 

for illustrations of the actual instrument screens for all Current Population Survey items.

PART 1. Items Asked at the Beginning of the Interview

Item name Item generally asks

1 HHRESP What is the line number of the household respondent?

2 REFPER 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 URE Is the sample unit the person’s usual place of residence?

5 HHMEM Does the person have his/her usual place of residence elsewhere? (This is asked only 

when the sample unit is not the person’s usual place of residence.)

6 SEX What is the person’s gender? This item is answered by the interviewer (not read to the 

respondent).

7 MCHILD Is the household roster (displayed on the screen) missing any babies or small children?

8 MLODGE Is the household roster (displayed on the screen) missing any lodgers, boarders, or live-in 

employees?

9 MELSE Is the household roster (displayed on the screen) missing anyone else staying there?

10 RRP How is the person related to the reference person? The interviewer shows a flashcard 

from which the respondent chooses the appropriate relationship category.

11 SUBFAM Is the person related to anyone else in the household? This is asked only when the person 

is not related to the reference person.

12 SBFAM_WHO Who on the household roster (displayed on the screen) is the person related to? This is 

asked/not asked depending on answer to VR-NONREL.

13 PAR1/PAR2 What is the parent’s line number?

14 BIRTHM What is the month of birth?

15 BIRTHD What is the day of birth?

16 BIRTHY What is the year of birth?

17 AGE How many years of age is the person (as verification)?

18 MARITL What is the person’s marital status? This is asked only of people 15+ years of age.

19 SPOUSE What is the spouse’s line number? This is asked only of people 15+ years of age.

20 COHAB Does the person have a boyfriend, girlfriend, or partner in the household?

21 AFEVER Has the person ever served on active duty in the U.S. armed forces? This is asked only of 

people 17+ years of age.

22 AFWHEN When did the person serve? This is asked only of people 17+ years of age who have served 

in the U.S. armed forces.

23 AFNOW Is the person in the U.S. armed forces now? This is asked only of people 17+ years of age 

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.’’

24 EDUCA What is the highest level of school completed or highest degree received? This is asked 

only of people 15+ years of age. It is asked for the first time in Months-in-Sample 1, and 

then verified in Months-in-Sample 5 and in specific months (i.e., February, July, and 

October).

25 CERT1 Does the person have a currently active professional certification or a state or industry 

license? Do not include business licenses such as liquor or vending.

26 CERT2 Were any of the certifications/licenses issued by the federal, state, or local government?

See note at end of table.
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Table 3-2.5.

Interviews: Main Demographic Item Asked1—Con.

PART 2. Items Asked at the End of the Labor Force Interview

Item name Item generally asks

27 DS1 Does anyone in this household have serious difficulty hearing? If the response is yes, then 

the instrument asks “Who is that?” and “Anyone else?”

28 DS2 Does anyone in this household have serious difficulty seeing, even when wearing glasses? 

If the response is yes, then the instrument asks “Who is that?” and “Anyone else?”

29 DS3 Because of a physical, mental, or emotional condition, does anyone in this household have 

serious difficulty concentrating, remembering, or making decisions? If the response is 

yes, then the instrument asks “Who is that?” and “Anyone else?”

30 DS4 Does anyone in this household have serious difficulty walking or climbing stairs? If the 

response is yes, then the instrument asks “Who is that?” and “Anyone else?”

31 DS5 Does anyone in this household have serious difficulty dressing or bathing? If the response 

is yes, then the instrument asks “Who is that?” and “Anyone else?”

32 DS6 Because of a physical, mental, or emotional condition, does anyone in this household 

have difficulty doing errands alone such as visiting a doctor’s office or shopping? If the 

response is yes, then the instrument asks “Who is that?” and “Anyone else?”

33 NTVT What is the person’s country of birth?

34 MNTVT What is his/her mother’s country of birth?

35 FNTVT What is his/her father’s country of birth?

36 CITIZN Is the person is a U.S. citizen? This is asked only when neither the person nor both of his/

her parents were born in the United States or U.S. territory.

37 CITYPA Was the person born a U.S. citizen? This is asked when the answer to CITZN-scrn is yes.

38 CITYPB Did the person become a U.S. citizen through naturalization? This is asked when the 

answer to CITYA-scrn is no.

39 INUSYR When did the person come to live in the United States? This is 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.

40 FAMINC 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.

1

 There are two occasions when demographic items are asked: (1) all month-in-sample 1 households and (2) any month-insample 2 through 8 household where additional members are added.
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Table 3-2.6.

Demographic Edits in the Current Population Survey Instrument

Note: The following list of edits is not complete; only the major edits are described. The demographic edits in the 

Current Population Survey 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 such as adding or deleting persons or changing variables.

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 of age, then the person should have an education level below that of a master’s 

degree (EDUCA-scrn < 44).

• If 16–18 years of age, then the person should have an education level below that of a bachelor’s 

degree (EDUCA-scrn < 43).

• If 15 years or younger , the person should have an education level below college (EDUCA-scrn 

< 40).

2 The instrument will force the interviewer to probe before it allows lowering an education level reported in a 

previous month-in-sample.

VETERANS EDITS

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-yearold veteran (e.g., World War I, World War II, Korean War), but it will display them for a 99-year-old veteran. 

NATIVITY EDITS

The instrument will force the interviewer to probe if the person’s year of entry into the United States 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, their SPOUSE line number will 

be set to zero. It will also omit the first answer category (i.e., married spouse present) when it asks for the 

spouse; likewise for the reference person.

2 The instrument will not ask the SPOUSE line number for both spouses in a married couple. Once it obtains 

the first SPOUSE line number on the roster, it will set the second spouse’s marital status equal to that of his/

her spouse.

3 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 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 was reported on the roster as the reference person’s parent 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 (S_RRP=22), 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 an individual is reported to be a brother or sister of the reference person, and the reference person’s 

PARENT is recorded, the instrument will: (a) first verify that the parent to which both siblings are pointing 

to is indeed the line number belonging to the reference person’s PARENT; and if so, (b) set the PARENT line 

number equal to the reference person’s PARENT line number.

5 For each household member, the instrument will ensure that the 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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questions on family income, disabilities, country of 

birth of each household member, country of birth 

of each member’s father and mother, and for the 

foreign-born, year of entry into the United States 

and citizenship status. See Part 2 of Table 3-2.5 for 

a list of these items.

SUBSEQUENT MONTHS’ INTERVIEWS

For households in sample for the second, third, 

and fourth months, the FR has the option to 

conduct the interview over the telephone. The 

respondent must approve the use of this interviewing mode. 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. Approximately 85 percent 

of interviews in these three MIS are obtained by 

telephone. See Part 2 of Table 3-2.3 for the questions asked to 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 fifth-month 

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 households to be a 

replacement household—i.e., a 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 in MIS 6, 7, and 8 

are also eligible for telephone interviewing. Once 

again, approximately 85 percent of the interviews 

of these cases are obtained by telephone.

The first thing the FR does in subsequent interviews is update the 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 establish the 

household’s current roster and to update some 

characteristics. See Table 3-2.7 for a list of the 

major items asked in MIS 5 interviews. If there are 

any changes, the interviewer goes through the 

necessary steps to add or delete individuals and 

update relationship items (e.g., relationship to reference person, marital status, and parent, spouse, 

or unmarried partner pointers) that may be 

subject to change. After making the appropriate 

corrections, the instrument takes 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. These interviews include additional 

questions for employed wage and salary workers 

about their usual weekly earnings at their sole or 

main job, as well as union membership for that 

job. In addition, for all individuals who are multiple 

jobholders, information is collected on the I&O 

of their second job. For individuals who are not 

in the labor force, additional information on their 

previous labor force attachment is obtained. 

After an initial interview, dependent interviewing is 

used in the collection of several items. Information 

collected in the previous month’s interview is 

carried forward into the current interview to ease 

response burden and improve the quality of the 

labor force data. This is most noticeable in the 

collection of I&O of the main job; job description 

data from the previous month are imported into 

the current month’s interview, and interviewers 

verifying whether an individual still has the same 

job. Other information collected using dependent interviewing includes items on duration of 

unemployment and data on the not in labor force 

subgroups of the retired and people who are not 

working due to a disability. Dependent interviewing is not used in the MIS 5 interviews or for any of 

the data collected solely in MIS 4 and 8 interviews.

The CPS uses centralized contact centers in 

Jeffersonville, Indiana, and Tucson, Arizona, for 

CATI. These have been used since 1983. The first 
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Table 3-2.7.

Interviews: Main Household and Demographic Items Asked in Month-in-Sample 5

Note: This list is not complete and covers only the main data items. It does not include related items used to identify 

the final response such as probes and verification screens. See the Current Population Survey Interviewing Manual 

for illustrations of the actual instrument screens for all Current Population Survey items.

HOUSEHOLD ITEMS

Item name Item generally asks

1 HHNUM Is the household a replacement household?

2 VERADD What is the street address (as verification)?

3 CHNGPH Does the current phone number need updating?

4 MAILAD What is the mailing address (as verification)?

5 TENUR Is the unit owned, rented, or occupied without paid rent?

6 TELHH Is there a telephone in the unit?

7 TELIN Is a telephone interview acceptable?

8 TELPHN What is the phone number and is it a home or office phone?

9 BESTTI When is the best time to contact the respondent?

10 NOSUN Is a Sunday interview acceptable?

11 THANKYOU Is there any reason why the interviewer will not be able to interview the household next 

month?

12 INOTES-1 The interviewer may want to make any notes about the case that might help with the next 

interview. The interviewer is also asked to list the names/ages of ALL additional persons 

if there are more than 16 household members.

DEMOGRAPHIC ITEMS

Item name Item generally asks

13 RESP1 Is the respondent different from the previous interview?

14 STLLIV Are all persons listed still living in the unit?

15 NEWLIV Is anyone else staying in the unit now?

16 MCHILD Is the roster (displayed on the screen) missing any babies or small children?

17 MAWAY Is the roster (displayed on the screen) missing usual residents temporarily away from the 

unit (e.g., traveling, at school, in the hospital)?

18 MLODGE Is the roster (displayed on the screen) missing any lodgers, boarders, or live-in employees?

19 MELSE Is the roster (displayed on the screen) missing anyone else staying in the unit?

20 EDUCA What is the highest level of school completed or highest degree received? This is asked 

for the first time in Month-in-Sample 1, and then verified in Month-in-Sample 5 and in 

specific months (i.e., February, July, and October).

21 CHANGE Since last month, has there been any change in the roster (displayed with full demographics), particularly in the marital status?
Current Population Survey TP77 Chapter 3-2: Conducting the Interviews 127

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Table 3-2.8.

Interviewing Results for October 2017

Description October 2017

TOTAL HOUSEHOLDS. . . .

 Eligible households. . . .

 Interviewed households. . . .

  Response rate (unweighted). . . 

  Response rate (weighted). . . 

 Noninterviews. . . .

  Rate. . . 

  Type A. . . 

   Rate (unweighted). . . 

   Rate (weighted). . . 

     No one home. . . 

     Temporarily absent. . 

     Refused. . . 

     Language barrier. .

     Unable to locate. .

     Other—specify. . 

     Callback needed—no progress. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0

  Type B. . . .

   Rate (unweighted). . . 

   Rate (weighted). . . 

    Entire household armed forces. . 

    Entire household under the age of 15. 

    Temporarily occupied with people with URE. . 

    Vacant regular. . . 

    Vacant household furniture storage. . 

    Unfit, to be demolished. . 

    Under construction, not ready. . 

    Converted to temporary business or storage. . 

    Unoccupied tent or trailer site. . 

    Permit granted, construction not started. .

    Other Type B. .

  Type C. . 

   Rate (unweighted). . .

   Rate (weighted). . .

    Demolished. .

    House or trailer moved. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

    Outside segment. 

    Converted to permanent business or storage. .

    Merged. .

    Condemned. .

    Unused serial number/listing sheet line. 

    Unlocatable sample address. 

    Unit does not exist or unit is out-of-scope. . 

    Other Type C. .

Source: Derived from the Current Population Survey Summary Report, October 2017.
128 Chapter 3-2: Conducting the Interviews Current Population Survey TP77

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Table 3-2.9.

Telephone and Personal Field Interviewing for October 2017 

Months-in-sample 

(MIS)

Total Field telephone Field personal visit CATI center 

Number Percent Number Percent Number Percent Number Percent

MIS 1. . . . . . . . . . . . . . . .

MIS 2. . . . . . . . . . . . . . . . . . .

MIS 3. . . . . . . . . . . . . . . . . . 

MIS 4. . . . . . . . . . . . . . . . . . 

MIS 5. . . . . . . . . . . . . . . .

MIS 6. . . . . . . . . . . . . . . . . . 

MIS 7. . . . . . . . . . . . . . . . . . 

MIS 8. . . . . . . . . . . . . . . . . . 

Total (MIS 1–8). . . . . . . . . 52,638 25,435 100.0 22,546 

MIS 1 and MIS 5. . . . . . . . .  12,904 24.5 3,641 2

MIS 2–4 and MIS 6–8. . . . . . 39,734 75.5 21,794 54.9 13,285 33.4

Z Represents or rounds to zero.

Note: This table for October 2017 is consistent with usual monthly results.

Source: Derived from the Current Population Survey Summary Report, October 2017.

use of CATI in production was the Tri-Cities Test, 

which began in April 1987. Each month about 

10,000 cases are sent to these telephone centers, 

but no MIS 1 or MIS 5 cases are sent. Most of 

the cases sent to CATI are in metropolitan areas, 

which eases recruiting and hiring efforts in those 

areas. Selection is made by RO supervisors based 

on the FR’s analysis of a household’s probable 

acceptance of a telephone center interview and 

the need to balance workloads and meet specific 

goals on the number of cases sent to the centers. 

About 65 percent are interviewed and the remainder is recycled to FRs. The net result is that about 

8 percent of all CPS interviews are completed at 

a CATI center. The centers generally cease labor 

force collection on the Wednesday of interview 

week (which is generally the week containing the 

nineteenth of the month), allowing field staff 3 or 

4 days for follow-up activities. Generally, about 80 

percent of the recycled cases are interviewed. 

Tables 3-2.8 and 3-2.9 show the results of a typical 

month’s CPS interviewing, October 2017. Table 

3-2.8 lists the outcomes of all the households in 

the CPS sample. For monthly interviewing, Type 

A rates average around 15 percent with an overall 

noninterview rate in the range of 28 to 30 percent. In October 2017, the Type A rate was 13.76 

percent. The overall noninterview rate for October 

2017 was 27.04 percent. 

REFERENCES

U.S. Census Bureau, Current Population Survey 

Interviewing Manual, 2015, retrieved from 

<www.census.gov/programs-surveys/cps

/technical-documentation/complete.html>.
Current Population Survey TP77 Chapter 3-3: Transmitting the Interview Results 129

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

Chapter 3-3: Transmitting the Interview Results

INTRODUCTION

FRs send completed CPS work through electronic 

data transmissions. Secure lines are used for all 

data transmissions between the Census Bureau 

headquarters, the Jeffersonville, Indiana, National 

Processing Center (NPC), two CATI contact centers, ROs, field supervisors, and FRs. This chapter 

provides a summary of these procedures and how 

the data are prepared for production processing. 

THE SYSTEM

The system for the transmission of data is centralized at the Census Bureau headquarters. All 

data transfers must pass through headquarters 

even if headquarters 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 as 

well as between different surveys. The transmission system was designed to satisfy the following 

requirements:

• Provide minimal user intervention.

• Upload or download in one transmission.

• Transmit interview responses in one 

transmission.

• Transmit software upgrades with data.

• Maintain integrity of the software and the 

assignment.

• Prevent unauthorized access.

The central database system at headquarters 

cannot initiate transmissions. Either the FRs or 

the ROs must initiate any transmissions. Devices 

in the field are not continuously connected to the 

headquarters’ computers; rather, the field devices 

contact the headquarters’ computers to exchange 

data using a secure Census Virtual Private 

Network (VPN). The central database system servers store messages and case information required 

by the FRs or the ROs. When an FR connects, the 

transfer of data from the FR’s device to headquarters’ database is completed first and then any 

outgoing data are transferred to the FR’s device.

A major concern is the need for security. Both the 

Census Bureau and the BLS are required to honor 

the pledge of confidentiality given to CPS respondents. The system is designed to safeguard this 

pledge. All transmissions between the headquarters’ central database and the FRs’ devices are 

encrypted and occur over the secure Census VPN. 

All transmissions between headquarters, the NPC, 

the CATI centers, and the ROs are over secure 

telecommunications lines that are leased by the 

Census Bureau and are accessible only by Census 

Bureau employees.

TRANSMISSION OF INTERVIEW DATA

Each FR is expected to make a telecommunications transmission at the end of every workday 

during the interview period. Additional transmissions may be made at any time as needed. 

Each transmission is a batch process in which all 

relevant files are automatically transmitted and 

received. The device connects to the headquarters’ central database and transmits the completed cases. At the same time, the central database returns any other data to complete the FR’s 

assignment and any software or security updates. 

The RO or field supervisory staff performs a data 

transmission when releasing or reassigning work 

and after completing check-in and supervisory 

review activities.

CATI Contact Center Transmissions

All cases are initially sent to the ROs and assigned 

to FRs. A subset of the cases are selected for 

CATI. These cases are then sent back to the 

Master Control System (MCS) and subsequently 

to a WebCATI System. The WebCATI system has a 

central database where cases are shared between 

the two CATI contact centers. Data are moved 

between MCS and WebCATI using secure database links. Data are moved between WebCATI and 

the two CATI contact centers using encryption 

and secure transmission lines. 

All cases sent to CATI are ultimately sent back to 

the MCS. Both response data and operational data 

are provided. 

• Cases with an interview disposition of complete, sufficient partial, out-of-scope, or 

“Congressional refusal” are sent to the MCS 

and headquarters. 

• All other cases are recycled to the field. These 

include noncontacts, refusals, and unavailable. 
130 Chapter 3-3: Transmitting the Interview Results Current Population Survey TP77

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Each recycled case is transmitted directly to the 

assigned FR. Case notes that include the reason 

for the recycle are also transmitted to the FR to 

assist in follow-up. These transmissions are similar 

to the transmission of the initial assignment except 

for the timing (i.e., they occur after the start of 

data collection). The ROs monitor the progress of 

the CATI recycled cases. 

Transmission of Interviewed Data From the 

Centralized Database

Each day during the production cycle, field staff 

send files to headquarters with the results of the 

previous day’s interviewing. (See Figure 3-3.1 for 

an overview of the daily processing cycle.) The 

headquarters’ production processing system usually receives three files per day: one combined file 

from the CATI contact centers and two files from 

the field. At this time, cases requiring I&O coding 

are identified and a file of these cases is created. 

This file is then used by NPC coders to assign 

the appropriate I&O codes. The I&O data are not 

transmitted to NPC; rather, the NPC coding staff 

remotely accesses the data on headquarters’ 

servers. Chapter 3-4, Data Preparation, provides 

an overview of the I&O coding and processing 

system.

The CATI contact centers finish sending files to 

headquarters by the middle of the week including 

the nineteenth of the month (the week that interviewing generally begins). All data are received by 

headquarters usually on Tuesday or Wednesday of 

the week after interviewing begins. 
Current Population Survey TP77 Chapter 3-3: Transmitting the Interview Results 131

U.S. Bureau of Labor Statistics and U.S. Census Bureau Figure 3-3.1. Overview of Current Population Survey Monthly Operations for a Typical Month Week 1 (week containing the 5th) Week 2 (week containing the 12th) Week 3 (week containing the ) 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 Sat thru Thu Fri Field represen- tatives (FRs) pick up computerized questionnaire via transmission. FRs and computer-assisted telephone interviewing (CATI) interviewers complete practice interviews and home studies. FRs pick up assign- ments via transmission. CATI Interviewing:

• CATI contact centers 

transmit completed work to 

headquarters nightly.

• CATI supervisors monitor 

interviewing progress.

Computer-assisted-personal interviewing:

• FRs transmit completed work to headquarters nightly.• Regional offices (ROs) monitor interviewing 

assignments and progress.

All assignments completed except 

for telephone holds.

All 

interviews completed.

ROs 

complete final field closeout.

Processing begins:

• Files received overnight are checked in by ROs and headquarters daily.• Headquarters performs initial processing and sends eligible cases to 

the National Processing Center (NPC) for Industry and Occupancy (I&O)coding.

 NPC sends back completed I&O cases.

Headquarters performs final computer 

processing edits, recodes, weighting. Census Bureau delivers data to Burea of Labor Statistics (BLS).

BLS analyzes 

data.

Results 

released to public at 8:30 a.m. by BLS.
132 Chapter 3-4: Data Preparation Current Population Survey TP77

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Chapter 3-4: Data Preparation 

INTRODUCTION

For the CPS, postdata collection activities transform raw data 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 I&O 

responses must be coded. Even though some 

editing takes place in the instrument at the time 

of the interview (see Chapter 3-2, Conducting 

the Interviews), further editing is required once 

all 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 2-3, Weighting and 

Estimation.

DAILY PROCESSING

For a typical month, CATI starts on Sunday of the 

week containing the nineteenth 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. CAPI 

also begins on the same Sunday and continues 

through Tuesday of the following week. The CAPI 

answer files are again sent to headquarters daily

until all the interviewers and ROs have transmitted 

the workload for the month. This phase is generally completed by Wednesday of the week containing the 26th of the month.

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 I&O codes are electronically sent to the NPC for assignment of these

codes. Once the NPC 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 

NPC’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 CODING

The I&O code assignment operation requires 10 

coders to assign approximately 30,000 codes 

for 1 week. The volume of codes has decreased 

significantly with the introduction of dependent

interviewing for I&O codes (see Chapter 3-1, 

Instrument Design). Both new monthly CPS cases 

and people whose I&O have changed since the 

previous interview are sent to NPC for coding. For 

those whose I&O information has not changed, 

the four-digit codes are brought forward from the 

previous month of interviewing and require no 

further coding.

A computer-assisted I&O coding system is used 

by the NPC I&O coders. Files of all eligible I&O 

cases are sent to this system each day. Each coder 

works at a computer where the screen displays 

the I&O descriptions that were captured by the 

FRs at the time of the interview. The coder then 

enters a four-digit numeric code for industry 

based on the Census Industry Classification. The 

coder also assigns a four-digit numeric code for 

occupation based on the Census Occupational 

Classification. 

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 most frequent source is nonresponding 

households (unit nonresponse). To compensate 
Current Population Survey TP77 Chapter 3-4: Data Preparation 133

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for unit nonresponse, the weights of nonresponse 

households are distributed among interviewed 

households as explained in Chapter 2-3, Weighting 

and Estimation. The second source of data loss 

is from item nonresponse, which occurs when a 

respondent either does not know the answer to 

a question or refuses to provide the answer. Item 

nonresponse in the CPS is modest (see Chapter 

4-1, Nonsampling Error, Table 4-1.1).

Before the edits are applied, the daily data files 

are merged and the combined file is sorted by 

state and primary sampling unit (PSU). This is 

important because many labor force and I&O 

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 

that 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 

one of the three imputation techniques described 

below.

The edits are run in a deliberate and logical 

sequence. Household and demographic variables 

are edited first because several of those variables 

are used to allocate missing values in later edits. 

The labor force variables are edited next since 

labor force status and related items are used to

impute missing values for I&O 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 are missing, 

it is assigned by looking at the age and sex

of the person in conjunction with the known 

relationships 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. The exception to filling missing values from the previous 

month longitudinally is when handling blank 

labor force items for MIS 5 interviews. In 

this specific instance, the longitudinal link is 

broken for MIS 5 interviews. If the item cannot 

be assigned using this technique, the item is

assigned a value using the appropriate hot 

deck as described next.

3. The ‘‘hot deck’’ imputation method assigns a 

missing value from a record with similar characteristics. 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 and older. 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, 
134 Chapter 3-4: Data Preparation Current Population Survey TP77

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that record’s value for question X replaces the 

value in the hot deck reserved for non-Black, 

male, and age 25 and older. Comparably, if the 

record was missing a value for question X, it 

would be assigned the value in the hot deck 

designated for non-Black, male, and age 25 

and older.

As stated above, the various edits are logically 

sequenced, in accordance with the needs of subsequent edits. The edits and recodes, in order of 

sequence, are:

1. Household edits and recodes. This processing 

step performs edits and creates recodes for 

items pertaining to the household. It classifies 

households as interviews or nonresponse and 

edits items appropriately. Hot deck imputations defined by geography and other related 

variables are used in this edit.

2. Demographic edits and recodes. 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 relational imputation, longitudinal editing, and hot deck imputation defined 

by related demographic characteristics. 

Demographic-related recodes are created for 

both individual and family characteristics.

3. Labor force edits and recodes. This processing step first establishes an edited Major Labor 

Force Recode (MLR), which classifies adults 

as 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 imputation 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 recodes. This processing step 

assigns four-digit I&O 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, 

and hot deck imputation. The hot decks are 

defined by such variables as age, sex, race, 

and educational attainment. 

5. Earnings edits and recodes. 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 imputation 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 recodes. School 

enrollment items are edited for individuals 16 

to 54 years old. Longitudinal edits or hot deck 

imputation based on age and other related 

variables are used as necessary.

7. Disability edits and codes. The disability items are asked of all interviewed adult 

records in MIS 1 and 5. Valid responses from 

MIS 1 and 5 are longitudinally filled into MIS 

2−4 and 6−8 as appropriate. Any missing data, 

regardless of MIS, are filled using longitudinal 

or hot deck imputation.

8. Certification edits and recodes. The certification items are edited in their own separate 

edit after completion of the I&O edit since the 

occupation and labor force data are used in 

the imputation process for these items. Any 

missing data are filled using longitudinal or 

hot deck imputation. 
Current Population Survey TP77 Chapter 3-5: Organization and Training of the Data Collection Staff 135

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

Chapter 3-5: Organization and Training of the Data 

Collection Staff

INTRODUCTION

The data collection staff for all Census Bureau 

programs is directed through six ROs and two

contact centers. The ROs collect data using two 

modes: CAPI and CATI. The six ROs report to the 

Chief of the Field Division, whose headquarters is 

located in Washington, DC. The two CATI facility

managers report to the Chief of the NPC located 

in Jeffersonville, Indiana.

ORGANIZATION OF REGIONAL 

OFFICES/CATI FACILITIES

The staffs of the ROs and CATI contact centers 

carry out the Census Bureau’s field data collection 

programs for both sample surveys and censuses. 

Currently, the ROs supervise over 7,000 parttime and intermittent FRs who work on continuing current programs and one-time surveys. 

Approximately 2,700 of these FRs work on the 

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 3-5.1. 

RO areas were originally defined to evenly distribute the office workloads for all programs. Table 

3-5.1 shows the average number of CPS units 

assigned for interview per month in each RO.

Each RO is headed by a Regional Director (RD) 

and has two Assistant Regional Directors (ARDs). 

The CPS is the responsibility of the program and 

management analysis coordinator who reports 

to the RD through the ARD. The program coordinator has two or three CPS Regional Survey 

Managers-Expert (RSM-E) on staff. The RSM-Es 

work with Regional Survey Managers-Geography 

(RSM-G) in the RO. Each RO has eight RSM-Gs. 

The typical RO employs about 360 to 580 FRs who 

are assigned to the CPS. Most FRs also work on 

other surveys. ROs have a total of 408 full-time 

Field Supervisors (FSs) who supervise teams 

of FRs. Each FS is assigned around 17 FRs. The 

primary function of the FS is to assist the RSM-Es 

and RSM-Gs with training and supervising the field 

interviewing staff. In addition, the FSs conduct 

nonresponse reinterview follow-up with eligible

households. Like FRs, the FSs work out of their 

home. However, the FRs are part-time or intermittent employees.

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. 

The ROs, RSMs, and FSs also communicate with 

the FRs using e-mail. FRs mostly communicate 

with their FS and sometimes their RSM-G. The 

RSM-Gs and FSs communicate with the RSM-Es. In

addition to communications relating to the work 

content, there is a regular system for reporting 

progress and costs.

The CATI contact centers are staffed with one 

facility manager per each site who directs the 

work of two to four Supervisory Operations 

Specialists or Ops Specialists. Each Ops Specialist 

is in charge of about five to ten supervisors or 

Supervisory Statistical Assistants (Sups). Sups are 

in charge of about 10 to 20 interviewers. In addition to conducting data collection for CPS, the 

CATI contact centers also conduct quality control 

reinterview follow-up with eligible households.

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 FS 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 Basic Skills Test on reading, 
136 Chapter 3-5: Organization and Training of the Data Collection Staff Current Population Survey TP77

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

arithmetic, and map reading. Applicants who pass 

the Basic Skills Test must then pass the scored 

Mock and Structured Job Interviews. Additionally, 

the FR is required to live in or near the 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 level 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 

When appointed, each FR undergoes an initial 

training program prior to starting his/her assignment. The initial training program consists of up 

to 20 hours of preclassroom home study, 3.5 to 

4.5 days of classroom training (dependent upon 

the trainee’s interviewing experience) conducted 

by an RSM or coordinator. The first day of training 

(not required for experienced staff) is dedicated 

to Generic Path to Success training, where FRs 

learn generic soft interviewing skills and administrative tasks. An on-the-job field observation by 

the FS during the FR’s first 2 days of interviewing 

is also required. 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. A 12-hour 

post-classroom training consists of home-study 

exercise and test.

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. Before beginning the 

first month observation and assignment, each FR 

also completes a practice phone interview with an 

experienced FS. 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 

FS who gives supplementary training if 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 RSM 

or FSs. These group sessions cover regular CPS 

and supplemental survey procedures. 

Training for Interviewers at the CATI Centers 

Candidates selected to be CATI interviewers 

receive classroom training. The classroom training 

consists of 3 days (15 hours) of new hire orientation training and 2 days (10 hours) of computer 

training in the CATI case management system, 

WebCATI. Additionally, CATI interviewers selected 

for CPS complete a 5-hour CPS subject matter 

self-study and receive a 3-day classroom training 

(15 hours) specifically about the CPS. New CPS 

interviewers are then closely monitored by having coaches observe recorded interviews. Prior to 

data collection each month, all CATI interviewers 

are required to come into the contact center to 

complete a self-study that provides a review and 

clarification of specific CPS concepts.

FIELD REPRESENTATIVES 

PERFORMANCE GUIDELINES

Performance guidelines have been developed 

for CPS CAPI FRs for response, nonresponse, 

and production. Response and 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. 
Current Population Survey TP77 Chapter 3-5: Organization and Training of the Data Collection Staff 137

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Each FS is responsible for developing each 

employee to his or her fullest potential. Employee 

development requires meaningful feedback on a 

continuous basis. By acknowledging strong points 

and highlighting areas for improvement, the FS 

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 FS, 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, known as CARMN systems. 

This report provides the supervisor information 

on:

• Workload and number of interviews.

• Response rate and numerical rating.

• Noninterview results (counts).

• Production rate, numerical rating, and 

mileage. 

• Meeting transmission goals.

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 production guidelines, modifying them, or establishing a completely different 

set of standards for their FRs. ROs must notify the 

FRs of the standards.

Response Rate Guidelines

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 eligible households that were not interviewed 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. 

The measure is calculated by dividing total time 

reported on payroll documents, which includes 

interview time and travel time, by total workload. 

When looking at an FR’s production, FSs 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 the nonresponse 

rate.

• Other substantial changes in normal assignment conditions.

• Personal visits/phones.

The supplement response rate is another measure 

that FSs 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 Fieldwork 

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:
138 Chapter 3-5: Organization and Training of the Data Collection Staff Current Population Survey TP77

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

• Initial observations (N1, N2, N3).

• General performance review.

• 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.

SN observations are made when an FR has problems or poor performance. The need for a SN 

observation is usually detected by other checks on

the FR’s work. For example, SN 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.

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 the 

requirement to ask questions as worded and in the

order presented on the CAPI screen. The observer 

also covers 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 or she may be placed in a trial period for 90 

days. Depending on the circumstances, and with

guidance from the Human Resources Division, the 

FR will be issued a letter stating that he or she 

is being placed in a Performance Improvement 

Plan (PIP). These administrative actions warn 

the FR that his or 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 in 

improving his or her performance, and notify the 

FR that he or she is subject to separation if the 

work does not show improvement in the allotted 

time. Once placed on a PIP, the RO provides performance feedback during a specified time period 

(usually 30 days, 60 days, and 90 days).

EVALUATING INTERVIEWER 

PERFORMANCE AND QUALITY 

GUIDELINES AT THE CATI CONTACT 

CENTERS

Each CPS supervisor is responsible for ensuring 

their assigned interviewers perform at satisfactory levels. Interviewer performance is measured 

at both an overall level and a survey-specific 

level. Interviewers’ performance evaluations take 

into account their attendance and administrative 

responsibilities such as completing their payrolls 

correctly, completing required self-studies, and 

completing required training classes. Additionally, 

interviewer performance is measured for each 

survey they are assigned to, and performance is 

measured qualitatively and quantitatively.

Each CATI interviewer’s work on CPS is measured 

qualitatively through the review of recorded interviewing sessions. Calls are digitally recorded and 

reviewed using the NICE Interaction Management 

(NIM) software. The recordings capture the 
Current Population Survey TP77 Chapter 3-5: Organization and Training of the Data Collection Staff 139

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content of each call, and the content is stored in 

NIM. Informed consent is received from respondents in order for the calls to be recorded, and 

all calls that receive consent are recorded. The 

NIM software randomly assigns recent recordings 

to coaches or monitors at the contact centers. 

Coaches are then required to review and evaluate 

these sessions in a timely manner as the coach/

monitor assignments expire in the NIM software 

after 5 days. Coaches evaluate these sessions 

using an evaluation scorecard that has numerous 

categories relating to survey specific guidelines 

and procedures. Each category has a set point 

value and a “yes/no” option. Coaches are also 

required to note a specific timeframe on the 

recording when an issue occurred and describe 

the reason an element was marked down. 

The overall score can range from zero to 100. 

Scores below 81 are considered unsatisfactory. 

Interviewers are given feedback on all of their 

monitoring sessions below the score of 100; 

however, only those sessions below 81 are given 

immediate feedback. Feedback can be conducted 

by either a coach or a supervisor, and the interviewer has the opportunity to observe their own 

recording. This gives an interviewer an opportunity to see any errors made and ensure the evaluation is reflective of their performance. If the interviewer is in agreement with the evaluation, they 

digitally sign the evaluation; however, if they are in 

disagreement, they have the option to dispute the 

evaluation and have it reviewed by a supervisor.

Interviewers who receive three consecutive 

unsatisfactory evaluations or demonstrate severe 

misconduct on CPS are placed on an Individual 

Coaching Assistance plan. Under this plan, a 

minimum of three to five inbound and three to five 

outbound calls are randomly assigned to coaches 

Figure 3-5.1. 

U.S. Census Bureau Regional Office Boundaries 
140 Chapter 3-5: Organization and Training of the Data Collection Staff Current Population Survey TP77

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

to evaluate each month. Interviewers remain in 

the Individual Coaching Assistance plan until they 

receive three consecutive satisfactory evaluations. 

Once this occurs, the interviewer is placed onto 

a Systematic plan where one inbound and one 

outbound call is assigned to coaches to evaluate 

each week. Interviewers who are newly trained on 

CPS are placed on an Initial plan. This plan follows 

the same guidelines as the Individual Coaching 

Assistance plan; however, it is considered a continuation of training and poor scores under this 

program do not affect an employee’s performance 

evaluation.

Interviewers’ work on CPS is measured quantitatively using WebCATI statistics. Supervisors 

meet with interviewers on an individual basis 

each month to review their statistics on each 

survey. Supervisors are able to use statistics from 

WebCATI to track interviewer completes, efficiency (measured by completes per hour), call 

attempts per hour, refusal conversion rates, and 

numerous other categories. Supervisors can then 

compare these statistics to prior months’ performance levels and the contact center averages.

Table 3-5.1.

Average Monthly Workload by Regional Office: 2017

Regional office Total Percent

Base 

workload

CATI 

workload

    Total. . . . . . . .  73,382 100

New York. . . . . . . . . . . . .

Philadelphia. . . . . . . . . . 12,982

Chicago. . . . . . . . . . . 

Atlanta. . . . . . . . . . . . .

Denver. . . . . . . . . . . . . 

Los Angeles. . . . . . . . . . 11,236

Note: Derived from Current Population Survey Monthly Summary Report, January 2017–December 2017.
Unit 4 

Current Population Survey

Data Quality
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Current Population Survey TP77 Chapter 4-1: Nonsampling Error 143

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Chapter 4-1: Nonsampling Error

INTRODUCTION

This chapter discusses sources of nonsampling 

error that can affect the Current Population 

Survey (CPS) estimates. The effect of nonsampling error is difficult to measure, and the full 

impact on estimates is generally unknown. The 

CPS strategy is to examine potential sources of 

nonsampling error and to take steps to minimize 

their impact. Some sources of nonsampling error 

are external; CPS measures cannot control those 

(e.g., Master Address File [MAF] errors or errors in 

benchmark population controls). 

Total survey error comprises sampling error and 

nonsampling error. The sampling error of an 

estimate is commonly measured by its standard 

error (square root of the variance). Nonsampling 

errors can arise at any stage of a survey, and they 

can affect both bias and variance, but the principal concern is usually with introducing bias into a 

survey. 

Nonsampling error can be attributed to many 

sources and many types could affect the results of 

a census as well as a survey. The following categories of nonsampling errors are discussed in this 

chapter:

• Processing error.

• Coverage error.

• Nonresponse error.

• Measurement error.

• Other sources of nonsampling error.

The sources, controls, and quality indicators of 

nonsampling errors presented in this chapter do 

not necessarily constitute a complete list. 

PROCESSING ERROR

Computer Processing Error 

All survey operations, from sampling to field processing to tabulation, involve computer processing. At any stage of survey operations, it is conceptually possible for computer processing errors 

to be introduced. Best practices in development 

and testing prevent major computer processing 

problems. Quality assurance procedures also track 

data processing and detect errors.

Computer systems at the U.S. Census Bureau and 

the Bureau of Labor Statistics (BLS) are compliant with federal requirements. This applies to 

hardware, platforms, operating systems, software, 

backups, and security procedures that control 

access to computers. There is tight security on 

data files and the production systems for CPS processing. There are contingency plans for continuing operations under weather-related and other 

emergencies. Both agencies have offsite backups 

that allow processing to continue should primary 

systems fail. 

Production system development is rigorous. 

Programs have written specifications and data 

files have detailed documentation. Production 

programs are written with standard programming languages that have commercial support. 

Testing procedures ensure files can be read and 

that programs conform to specifications. Changes 

to the production system are tested using prior 

data, simulating associated changes in data files 

as needed. 

Quality assurance procedures during monthly processing include the following:

• Embedded checks in programs identify faulty 

data.

• Record counts and file sizes are tracked at various stages of production.

• Person weights are summed to make sure they 

add to the benchmark population controls.

• The Census Bureau and the BLS independently 

produce a set of tables and compare them cell 

by cell.

• Data are compared to the previous month 

and to the same month of the previous year 

by geographic, demographic, and labor force 

characteristics.

Sample Processing Error

The CPS sample is coordinated with the samples 

of several other demographic surveys conducted 

by the Census Bureau. The sample selection 

process accounts for needed sample sizes, determines necessary sampling intervals, and has a 

protocol to ensure that a housing unit (HU) is 
144 Chapter 4-1: Nonsampling Error Current Population Survey TP77

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selected for at most one survey in a 5-year period. 

Sampling verification has a testing phase and a 

production phase. Verification in both phases is 

done independently by an information technology 

specialist and a subject-matter expert.

The testing phase includes systems testing and 

unit testing. In systems testing, programs are 

tested individually and collectively per specifications. In unit testing, trial data sets are run 

through the programs. In particular, simulated 

data sets with unusual observations are used to 

test the performance of the system in extreme 

situations. 

The goal of the production phase is to verify 

that samples were selected as specified and that 

selected HUs have appropriate information, such 

as correct sampling probabilities. The number of 

HUs selected is compared to a computed target 

for each state. Outputs at several stages of sampling are compared to results from the previous 

annual sample to monitor consistency. The sample 

files are checked for incomplete or inconsistent 

HU information.

Field Processing Error

Many field processing errors are prevented by 

computer systems. Particular attention is given to 

the transmission of sample assignments and data.

Monthly sample processing operations send out 

assignments through the regional offices (ROs) 

to field representatives (FRs) and to telephone 

interviewers in the contact centers. That information is shared with supervisory and RO staff. ROs 

verify that each interviewer obtained their entire 

assignment. The system ensures each sampled 

HU that is to be interviewed is assigned once and 

only once. During data collection, daily summary 

information about each interviewer is available. 

The ROs can make modifications to interviewer 

assignments, if necessary. 

Quality assurance procedures are implemented 

on data transmissions from FRs and the contact 

centers. During data collection, daily summary 

information on transmissions is tracked. ROs verify 

that all FR assignments are returned and resolve 

any discrepancies before certifying completion. 

System checks verify total counts and ensure that 

one final report—either a response or a reason for 

nonresponse—is obtained for every HU and only 

one response is retained per HU. 

New interviewers are thoroughly trained on 

retrieving assignments and transmitting data. 

They are periodically assigned refresher training. 

See Chapter 3-5, Organization and Training of 

Staff, for more information. 

COVERAGE ERROR

Coverage error exists when a survey does not 

completely represent the population of interest. 

The population of interest in the CPS is the civilian 

noninstitutional population aged 16 and over 

(CNP16+). Ideally, every person in this population 

would have a known, nonzero probability of being 

sampled or “covered” by the CPS. 

Sampling Frame Omission

The CPS reaches persons in the CNP16+ through 

a sample of HUs selected from the MAF, which 

serves as its sampling frame. The MAF is used by 

several demographic surveys conducted by the 

Census Bureau. For more information about the 

MAF, see Chapter 2-1, CPS Frame.

The MAF is a list of living quarters nationwide, 

updated semiannually, which may be incomplete 

or contain units that cannot be located. Delays 

in identifying new construction and adding those 

HUs to the MAF is the primary cause of frame 

omission. Other examples of omission include 

units in multiunit structures missed during canvassing, mobile homes that move into an area 

after canvassing, and new group quarters that 

have not been identified.

Housing Unit Misclassification

CPS operations classify some sampled HUs, 

including some group quarters (Chapter 2-1, 

CPS Frame), as temporarily (Type B) or permanently (Type C) out-of-scope. The remaining 

HUs are treated as in-scope, although some 

do not respond (Type A). Misclassification can 

result by identifying too few (overcoverage) or 

too many (undercoverage) HUs as out-of-scope. 

Misclassification reduces the efficiency of weighting adjustments made to compensate for HU 

nonresponse. 
Current Population Survey TP77 Chapter 4-1: Nonsampling Error 145

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Type B HUs remain in interviewer assignments and 

are rechecked in later months. There are many 

reasons for a Type B classification. There may be 

no in-scope persons living in the HU. For example, 

the unit may be vacant, the adults may all be in 

the armed forces, the unit may only be inhabited 

by persons under the age of 16, or all persons in 

the unit may have a usual residence elsewhere. 

Examples of other Type B HUs include those 

under construction but not ready for habitation, 

conversions to temporary business or storage, and 

to-be-demolished structures. Type C HUs do not 

remain in interviewer assignments. Type C examples include demolished structures, mobile residences that have moved, and structures converted 

to permanent business or storage.

Misclassification errors are minimized with uniform 

procedures and guidelines for identification of 

Type B and Type C HUs. However, some structures 

may not be easily classified by the interviewer. 

Type B HUs may be especially difficult to identify 

in some situations as there is a wide range of reasons for that classification. Errors made in classifying out-of-scope HUs affect CPS coverage.

Housing Unit Roster Error 

Creating the HU roster is a step in determining 

CPS coverage. During the first interview of a HU, 

the HU roster is established. It can be changed 

during subsequent interviews. 

The automated questionnaire guides the rostering 

process, minimizing potential errors, and interviewers are trained to handle difficult situations. 

Some errors may still arise, affecting CPS coverage. Examples include omission of residents, 

erroneous inclusion of persons, misclassification 

of persons as armed forces or usual residence 

elsewhere, and misclassification of demographic 

information of residents. Demographic information, such as age, sex, race, and ethnicity, is used 

to benchmark CPS data to corresponding population controls. Misclassification of respondents 

affects the efficiency of CPS weighting. 

Another potential source of coverage error is 

changing HU composition. A HU is included in the 

CPS sample for 8 months. It is interviewed for 4 

consecutive months, excluded from interview the 

next 8 months, and interviewed again for the next 

4 months. The characteristics of the HU roster 

are subject to change over this 16-month period 

(Grieves and Robison, 2013).

Coverage Ratios

Coverage ratios are measures of CPS coverage 

monitored at various stages of the weighting 

process, calculated by dividing weighted respondent totals by corresponding population controls. 

The weighting and benchmarking steps reduce 

CPS coverage error by calibrating respondent 

weights to the population controls for certain 

demographic subgroups. However, since both the 

weighting process and the population controls 

themselves are imperfect, some coverage error 

remains after the weighting process is complete. 

For example, the iterative second-stage weighting procedure does not eliminate all nonsampling 

errors associated with undercoverage or overcoverage. Instead, it eliminates the relative undercoverage or overcoverage compared to benchmark 

CNP16+ controls for particular subgroups. See 

Chapter 2-3, Weighting and Estimation, for details 

about the weighting process and population 

controls, including specific weighting terminology 

that is utilized in the tables of this section. 

A coverage ratio less than 1.00 represents undercoverage, while a coverage ratio greater than 1.00 

represents overcoverage. As displayed in Figure 

4-1.1, using first-stage weights, the national coverage ratio for total CNP16+ ranged from 0.872 

to 0.900 between December 2014 and December 

2017. Coverage ratios remained generally the 

same over this 3-year time period. Coverage for 

Whites (0.896 to 0.927) is higher than for total 

CNP16+, while coverage is poorer for Blacks 

(0.771 to 0.820), Hispanics (0.787 to 0.842), and 

other race and ethnicity groups (0.765 to 0.835).

NONRESPONSE ERROR

After identifying out-of-scope (Type B and Type 

C) HUs, the remaining sampled HUs are identified 

as eligible to be interviewed. In any given month 

of inclusion in the CPS sample, the HU’s residents 

may not provide usable or complete data. This 

section discusses nonresponse errors in the CPS 
146 Chapter 4-1: Nonsampling Error Current Population Survey TP77

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and the methods used to minimize their impact on 

the quality of CPS estimates. 

The CPS is affected primarily by unit nonresponse 

and item nonresponse. Unit nonresponse occurs 

when the residents of eligible HUs are not interviewed. Item nonresponse occurs when the residents of HUs eligible for interviewing provide 

incomplete responses, answering some but not all 

questions in the interview. Both of these types of 

nonresponse are discussed in more detail in the 

following sections.

Person nonresponse occurs when some individuals within an eligible HU are not interviewed. 

Person nonresponse has not been much of a problem in the CPS because any responsible person 

aged 15 or over in the HU is able to respond for 

others in the HU as a proxy.

Unit (Type A) Nonresponse

In CPS, unit nonresponses—eligible HUs that do 

not yield any interviews—are called Type A nonresponses. There are many reasons why a Type 

A nonresponse may occur. A majority of Type 

A nonresponses are due to refusals, which can 

result from privacy concerns, attitudes toward 

the government, or other reasons. Other potential reasons, such as language barriers or inability 

to locate HUs, cause few Type A nonresponses, 

since alternative language speaking interviewers 

(from the contact centers) and Global Positioning 

Systems are utilized to avoid these problems.

The Type A nonresponse rate is calculated by 

dividing total Type A nonresponses by total eligible HUs. This can be computed using raw counts 

or by weighting each HU by its base weight. 

National unweighted Type A nonresponse and 

refusal rates from December 2014 to December 

2017 are shown in Figure 4-1.2. The overall Type 

A rate ranged from 11.7 percent to 15.1 percent 

during this 37-month timespan, mostly resulting 

from refusals, which varied between 8.0 percent 

and 11.9 percent. About two-thirds of Type A 

nonresponses were due to refusals. 

As discussed in Chapter 2-3, Weighting and 

Estimation, the weights of responding HUs are 

increased to account for nonresponding units. 

These nonresponding HUs may systematically 

differ from responding HUs, introducing bias 

into the weighting and estimation process. This 

bias is a source of nonsampling error. Weightingcell nonresponse adjustment compensates for 

0.70

0.75

0.80

0.85

0.90

0.95

1.00

Total White Black Other Hispanic

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

Overall Type A Rate Refusal Rate

Source: U.S. Census Bureau, Current Population Survey Monthly Summary Report, December 2014–December 2017.

Source: U.S. Census Bureau, Current Population Survey Monthly Summary Report, December 2014–December 2017.

Figure 4-1.1.

Current Population Survey Total Coverage Ratios: December 2014–December 2017, 

National Estimates
Current Population Survey TP77 Chapter 4-1: Nonsampling Error 147

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

nonresponse rates that differ by geographic area 

and metropolitan status, which reduces bias due 

to HU nonresponse but does not eliminate it. How 

closely responding HUs resemble nonresponding 

HUs varies by weighting cell.

Dissimilarities between respondents and nonrespondents can arise for many reasons. Mode of 

interview can affect responses. Households in 

the CPS sample in the first or fifth month tend 

to have different response propensities than in 

other months. Respondents refusing to answer 

items related to income data are more likely to 

refuse to participate in subsequent months and 

are less likely to be unemployed (Dixon, 2012). 

Demographic information may be related to 

response propensity in some areas, but it is not 

explicitly included in weighting cell construction. 

The best way to limit nonresponse error is to have 

uniform procedures in place to limit nonresponse 

and increase response rates. For example:

• An interviewer must report a possible refusal 

to the RO as soon as possible so conversion 

attempts can be made. 

• Monthly feedback is provided to the interviewers for converting reluctant respondents.

• All ROs send tailored letters to not-interviewed HUs that remain in the survey for 

additional months. 

Unit nonresponse is also subject to misclassification error. Some Type A nonresponses may be 

misclassified as Type B (temporarily out-of-scope) 

or Type C (permanently out-of-scope) HUs. For 

example, a difficult-to-reach HU may be misclassified as “vacant” when it should be classified as a 

Type A nonresponse. 

By its very nature, unit nonresponse error can 

be difficult to quantify or assess since it occurs 

when sample units do not respond to the survey. 

Research findings by Tucker and Harris-Kojetin 

(1997) and Dixon (2004, 2012), show little bias 

arising from unit nonresponse, particularly for 

major CPS estimates like the unemployment rate.

Item Nonresponse

Item nonresponse occurs when answers are not 

provided for all questions (or items) of the CPS 

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

Overall Type A Rate Refusal Rate

Source: U.S. Census Bureau, Current Population Survey Monthly Summary Report, December 2014December 2017.

Source: U.S. Census Bureau, Current Population Survey Monthly Summary Report, December 2014–December 2017.

Figure 4-1.2. 

Current Population Survey Nonresponse Rates: December 2014–December 2017,

National Estimates 

(Percent)
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interview. Item nonresponse rates, which include 

invalid data as well as missing values, vary by 

question, as shown in Table 4-1.1. Item nonresponse is quite small for demographic and labor 

force questions, but is larger for industry, occupation, and earnings data. 

To minimize item nonresponse, navigation is built 

into the automated CPS questionnaire so that 

questions are not inadvertently skipped, and only 

appropriate responses for a person’s path through 

the questionnaire can be recorded. However, the 

automated questionnaire does not include extensive editing, and some items can be left blank. For 

example, a proxy respondent may be unsure of 

another HU member’s hours of work, or a person 

may refuse to answer a sensitive question.

Ignoring item nonresponse runs the risk of incurring bias. Item nonresponses generally are not 

randomly distributed. For example, it is common 

for item nonresponse rates to differ by geography and demographics. The CPS uses item 

imputation to fill in item nonresponses. Three 

imputation methods, detailed in Chapter 3-4, Data 

Preparation, are used:

• Relational. Likely values are logically inferred 

by analyzing monthly answers for other data 

items provided by the respondent.

• Longitudinal. A previous month’s value provided by the respondent may be used.

• Hot deck. A value is assigned from a similar 

HU or person.

The CPS imputation procedures are imperfect and 

can introduce bias. A nonsampling error occurs 

when an imputed value is incorrect. However, 

there is no definitive way of knowing which 

imputed values are incorrect. What is important is 

the net effect of all imputed values. Imputations 

are consistent with what is known about the 

distributions of responses. Biased estimates 

result when the characteristics of nonrespondents 

differ from the characteristics of respondents. 

The magnitude of nonsampling error arising from 

item nonresponse is difficult to quantify, but its 

impact is likely very small for demographic and 

labor force questions since their item nonresponse 

rates are very low. Depending on the net effect of 

imputed values, bias may be (but is not necessarily) larger for industry, occupation, and earnings 

data since their item nonresponse rates are higher.

MEASUREMENT ERROR

Survey measurement may be incorrect for a 

variety of reasons. Vague concepts, imperfectly 

designed questions, interviewer errors, and 

respondent errors all can contribute to measurement error. The mode of interview, personal visit 

or telephone, is known to affect measurement. 

Errors may arise when a respondent provides 

answers for another member of the household. 

A respondent may not know the true answer to 

a question or may provide an estimate such as 

for earnings or the dates of a job search. Despite 

extensive training for interviewers and coders, 

industry and occupation (I&O) data based on 

open-ended descriptions given by respondents 

may be coded incorrectly. 

Automated Questionnaire

CPS concepts are well defined; particularly those 

for labor force classification (see Chapter 1-2, 

Questionnaire Concepts and Definitions). When 

the first automated questionnaire was designed, 

it was thoroughly tested for agreement with the 

concepts. 

The Census Bureau and the BLS are cautious 

about adding or changing questions. New questions are reviewed to make sure they will not 

interfere with the collection of basic labor force 

information. The testing protocol avoids unintended disruptions to historical series and detects 

problems with question wording. Refer to Chapter 

1-2, Questionnaire Concepts and Definitions, for 

more information on the CPS questionnaire and 

the protocol for testing questions. 

Table 4-1.1. 

National Current Population Survey Item Types, Monthly Average Percentage of Invalid or 

Missing Values: July 2017–December 2017

Household Demographic Labor force Industry and occupation Earnings1

5.5 2.9 3.2 15.0 24.4

1

 Certain items of the earnings series of questions had invalid/missing rates around 40 percent.

Source: U.S. Census Bureau, Current Population Survey Summary Report, July 2017 through December 2017.
Current Population Survey TP77 Chapter 4-1: Nonsampling Error 149

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The automated questionnaire contains several 

features that help control measurement error as 

listed below.

• Names, pronouns, and reference dates are 

inserted into questions.

• Question wording is standard and uses plain 

language.

• Complex questions are split into several 

shorter questions.

• Navigation of complicated skip patterns is 

built into the questionnaire, so that a respondent is not asked questions that can be 

skipped based on answers to earlier questions.

• Human error is reduced by keying responses 

into predefined categories, so that only valid 

responses may be accepted for a question.

• On-screen help is available to assist matching 

open-ended responses to predefined response 

categories.

• Basic real-time edits check responses for 

internal consistency, allowing many potential 

errors to be corrected prior to transmission.

Another helpful feature in controlling measurement error is that HU and person data are carried forward to the next interview. Prior months’ 

responses can be accepted as still current under 

the proper circumstances such as a person’s occupation when there is no change of job. In addition 

to reducing respondent and interviewer burden, the automated questionnaire avoids erratic 

variation in I&O codes for people who have not 

changed jobs but describe their I&O differently in 

successive months. The automated questionnaire 

provides the interviewer opportunities to review 

and correct information before continuing to the 

next series of questions. 

Mode of Interview

A panel of HUs is included in the CPS sample for 8 

months. Ideally, interviews are conducted in person by a FR during a HU’s first and fifth monthsin-sample (MIS 1 and 5). Interviews during the 

other months (MIS 2–4 and MIS 6–8) are usually 

conducted over telephone by a FR or an interviewer from the contact centers. In some cases, a 

combination of in-person and telephone may be 

needed to complete an interview. 

The mode of interview can systematically affect 

responses. Mode effects cannot be easily estimated using current CPS sample since there are 

confounding influences—e.g., HUs are not randomly assigned to interview mode. 

A parallel survey was conducted in 1992 to 1993 

before the implementation of the automated questionnaire. A study found only modest mode-ofinterview effects (Thompson, 1994). It is unknown 

if similar effects would be realized in the full CPS 

survey conducted by staff fully acclimated to the 

automated questionnaire. 

Proxy Reporting

The CPS seeks information about all eligible people in a sample HU, whether or not they are available for an interview. Since CPS data collection 

occurs in a brief time schedule, interviewers can 

accept reports from any knowledgeable person in 

the household aged 15 or older to provide information about all household members. 

Respondents who provide answers about other 

household members are called proxy reporters. 

Measurement error occurs when proxy reporters 

are unable to provide accurate information about 

the demographic and labor force characteristics 

of other household members. Self-reporting is 

typically more accurate for CPS data (Kojetin and 

Mullin, 1995; Tanur, 1994). The CPS instrument is 

designed to reduce measurement error if a proxy 

respondent cannot provide labor force or earnings 

information for someone. The instrument allows 

the interviewer to skip over the questions for that 

person and go to the next eligible person. At the 

end of the interview, the interviewer can ask if 

anyone else can provide the needed information 

for the person, and if not, set up a callback for a 

later time.

Table 4-1.2. 

Percentage of Current Population Survey 

Labor Force Reports by Type of 

Respondents: 2015–2017

Type Percent

Self reports. . .

Proxy reports. . .

Both self and proxy. . 

Source: U.S. Census Bureau, Tabulation of 2015–2017 

Current Population Survey Microdata.
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Table 4-1.2 shows overall proxy reporting levels for 

2015 through 2017. The level of proxy reporting in 

the CPS has historically been around 50 percent. 

Proxy reporters are more likely to be found at 

home when the interviewer calls or visits. For this 

reason, the incidence of proxy reporting tends 

to be systematically related to important labor 

force characteristics and demographics. If proxy 

answers contain systematic reporting errors, then 

bias is introduced into related estimates. As it 

is difficult to measure how much response error 

occurs due to proxy reporting, it is difficult to 

assess the resulting bias in CPS estimates. 

OTHER SOURCES OF NONSAMPLING 

ERROR

Industry and Occupation Coding Verification

Files containing records needing I&O codes 

are transmitted electronically to the National 

Processing Center (NPC). For each record on the 

file, the NPC coder enters four-digit numeric I&O 

codes that best represent the job description 

recorded during the CPS interview. Nonsampling 

error can arise during the coding process due 

to unclear descriptions or incorrect coding 

assignment. 

A substantial effort is directed at the supervision 

and quality control of the I&O coding operation. 

If the initial coder cannot determine the proper 

code, the case is assigned a referral code and will 

later be coded by a referral coder. During coding, 

a supervisor may turn on dependent verification 

in which the work is verified by a second coder. 

Additionally, a sample of each month’s cases is 

selected to go through a quality assurance system 

to evaluate each coder’s work. See Chapter 3-4, 

Data Preparation, for more information on I&O 

coding.

Current Population Survey Population 

Controls

Benchmark controls for the monthly CNP16+ 

are used for the weighting steps that produce 

second-stage weights and composite weights. 

National and state CPS population controls are 

developed by the Census Bureau. Starting with 

data from the most recent decennial census, the 

population controls are estimates that are annually updated using administrative records and 

projection techniques. This is done independently 

from the collection and processing of CPS data. 

See Chapter 2-3, Weighting and Estimation, for 

more detail on weighting and population controls.

As a means of controlling nonsampling error 

throughout the process of creating population 

controls, numerous internal consistency checks 

in the programming are performed. For example, input files containing age and sex details are 

compared to external counts and internal redundancy is intentionally built into the system. The 

BLS reviews a time series of CNP16+ population 

controls for unexpected changes. 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.

The population controls have no sampling error, 

but nonsampling error is present, specifically 

arising from statistical modeling. The modeled changes over time tend to be smooth, and 

although some subnational CNP16+ populations 

may fluctuate seasonally, there is no seasonal 

component in the models. There is also nonsampling error in the input data for the models. The 

inputs are examined and frequently updated in 

order to limit nonsampling error. CPS implements 

changes in the CNP16+ controls each January, 

resulting in level shifts for some series that affect 

CPS weighting and estimation. 

While the CNP16+ controls contain nonsampling 

error, there are many benefits to using them in 

CPS weighting. The decennial censuses exercise 

extreme care in ensuring good population coverage. Changes over time are relatively smooth, 

whereas CPS estimates of the CNP16+ would 

appear erratic, primarily due to sampling error, if 

not benchmarked to these controls. The models 

used to update the population controls have performed well historically.

Month-in-Sample Bias

The CPS is a panel survey with a 4-8-4 rotation 

scheme (Chapter 2-2, Sample Design). Each 

panel, or rotation group, is included in the survey 

4 consecutive months-in-sample (MIS 1–MIS 4), is 

rotated out for 8 months, and is rotated back in 

for another 4 consecutive months (MIS 5–MIS 8). 

Collected data systematically differs across the 8 
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months in sample, and this is referred to as MIS 

bias. Several possible sources contribute to MIS 

bias. Each MIS has a different mix of data collection modes, persons listed on housing rosters 

(Grieves and Robison, 2013), and different housing-unit nonresponse rates. Although MIS bias 

has changed over the decades (Erkens, 2012), 

one continuing characteristic is that the estimated 

unemployment rate is usually highest in MIS 1.

The usual MIS bias measure used by the CPS is 

a simple ratio index. This index is the ratio of the 

estimate based on a particular MIS group to the 

average estimate from all eight MIS groups combined, multiplied by 100. This calculation treats 

the average of all eight MIS groups as an unbiased 

estimate. Table 4-1.3 shows 2014 to 2015 2-year 

average MIS bias indexes for total employed and 

total unemployed. 

While the MIS bias indexes are close to one for all 

months for total employed, for total unemployment they are much more variable. Relatively, the 

estimate of unemployed persons is higher in MIS 

1 than in other months, and there is a decreasing 

trend across the MIS, such that MIS 7 and MIS 8 

tend to have the lowest estimates of unemployed 

persons. 

Composite Estimation

Composite estimates, described in Chapter 2-3, 

Weighting and Estimation, systematically differ 

from second-stage estimates. Presuming that second-stage estimates are closer to unbiased than 

composite estimates, the composite estimation 

process introduces a bias into estimates that is 

more noticeable for estimates of levels than rates. 

This bias is a form of nonsampling error.

In 2014 and 2015, average monthly composite 

estimates of total employment fell below second-stage estimates by more than 400,000. 

Analogously for unemployment, composite 

estimates fell below second-stage estimates by 

more than 100,000. Over that same time frame, 

average monthly composite estimates of the labor 

force participation rate and employment-population ratio were about 0.2 percent lower, while 

the composited unemployment rate fell below 

second-stage estimates by an average of 0.05 

percent. 

Seasonal Adjustment

Seasonal adjustment models, described in Chapter 

2-5, Seasonal Adjustment, are applied to CPS 

composite estimates. Seasonal adjustment models 

are generated using X-13ARIMA-SEATS software. 

Relating to nonsampling errors, two perspectives 

may be considered:

• In the strict sense of classical finite sampling 

theory, seasonal adjustment is a linear or 

near-linear transformation of the survey data, 

which does not introduce any new nonsampling errors into the estimation process. Any 

nonsampling error present in the seasonal 

adjustment process already existed in the 

survey.

• Viewed in a broader context, seasonal adjustment may introduce some model specification 

error and error arising from estimating the 

unobserved seasonal component, but because 

it performs smoothing of the estimates it 

likely reduces total error (inclusive of sampling 

error). To minimize the potential impact of 

specification error, seasonally adjusted series 

are thoroughly reviewed to confirm that the 

ARIMA models fit sufficiently well.

In either perspective, seasonal adjustment is not 

considered to be a major source of nonsampling 

error in the CPS, and sampling error tends to be 

reduced as a result of the seasonal adjustment 

Table 4-1.3. 

Month-in-Sample Bias Indexes for Total Employed and Total Unemployed Average: 

January 2014–December 2015

Employment Month-in-sample

1 2 3 4 5 6 7 8

Employed. . . . . . . . . . . 1.02 1.02 1.01 1.0

Unemployed. . . . . . . . . 1.14 1.06 1.04 0.98 0.

Source: U.S. Census Bureau, Tabulation of 2014–2015 Current Population Survey microdata.
152 Chapter 4-1: Nonsampling Error Current Population Survey TP77

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process at the state or local level (Tiller, 1992) and 

the national level (Evans et al., 2015).

METHODS TO REDUCE NONSAMPLING 

ERROR

Interviewer Training and Performance 

Monitoring 

Comprehensive interviewer training can reduce 

nonsampling error. Training to convert reluctant 

respondents can improve CPS response rates, and 

higher response rates are associated with lower 

nonresponse error. In addition, interviewer training 

lessens response error by improving the accuracy 

of survey data.

Interviewers are trained on concepts, on what 

answers are acceptable, on how to interpret 

verbal responses, on how to choose the correct 

responses from menus, and on how to probe to 

obtain sufficient information from a respondent. 

Detailed manuals are provided to interviewers. 

Monitoring and observing interviewers can also 

reduce nonsampling error. Interviews are observed 

immediately after interviewers complete initial 

training. Additionally, observations are made 

as part of general performance review and for 

interviewers who are identified as needing additional help. Interviews in the contact centers are 

regularly monitored by supervisors with feedback 

provided to the interviewer. The emphasis is on 

reinforcing good habits and correcting habits that 

interfere with the collection of reliable statistics. 

Refer to Chapter 3-5, Organization and Training of 

the Data Collection Staff, for more information.

Behavior that is examined during observation or 

monitoring includes whether interviewers:

• Ask questions as worded and in the order presented on the questionnaire.

• Adhere to instructions on the instrument and 

in the manuals.

• Probe for answers that are consistent with 

CPS concepts.

• Avoid follow-up questions or probes that suggest a particular answer to the respondent.

• Record answers in the correct manner and in 

adequate detail.

• Develop and maintain good rapport with the 

respondent conducive to obtaining accurate 

information.

• Determine the most appropriate time and 

place for the interview.

• Are competent in using the laptop or 

computer.

In addition, continuing efforts are made to 

improve interviewer performance. Periodic 

refresher training and training on special topics 

is given to all interviewers. Quality assurance 

procedures (such as the reinterview program 

described in Chapter 4-2, Reinterview Design and 

Methodology), administrative procedures, and the 

collection of performance measures all contribute 

to evaluating the performance of interviewers. 

When needed, tailored training is assigned to an 

interviewer. 

Performance measures are gathered on each 

interviewer’s monthly assignment, including Type 

A nonresponse rate, item nonresponse rates, reasons for nonresponse, number of HUs classified as 

Type B and Type C, and length of time taken per 

interviewed HU. Many of these measures are gathered and examined by automated methods.

Acceptable ranges are set for some performance 

measures. An interviewer may have poor response 

rates, too many noninterviews, an unusually high 

rate of blank items, or an unusually high or low 

minutes-per-interview measure. If so, the interviewer is considered to be in need of additional 

training or other remedial action. 

FR performance measures are summarized for 

the nation and each RO. Each RO can make a 

determination about any general remediation that 

is needed to improve interviewer performance. 

National “don’t know” and refusal rates are monitored for individual questions; a high item nonresponse rate may be attributable to the question 

itself and not to poor interviewer performance. 

Changes to historical response patterns can be 

detected.
Current Population Survey TP77 Chapter 4-1: Nonsampling Error 153

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Finally, additional monitoring and evaluation is 

done by experts in questionnaire design and 

interviewing (behavioral scientists). Observation 

by behavioral scientists provides a platform for 

assessing interviewers’ attitudes toward the job 

and is an attempt to evaluate individual interviewers. In conjunction with other techniques, such 

as interviewer debriefing, the aim is to improve 

overall data collection. Behavioral scientists’ studies can recommend changes to training, instructions, the automated questionnaire, or survey 

procedures.

Edit Modules

After transmission to Census Bureau headquarters, there are eight edit modules applied to the 

data (Chapter 3-4, Data Preparation): 

• Household.

• Demographic.

• Labor force.

• I&O.

• Earnings.

• School enrollment.

• Disability.

• Certification.

Each module establishes consistency between 

logically related monthly items. Longitudinal 

checks are made for consistency with prior 

responses. Inappropriate entries are deleted and 

become item nonresponses. If the labor force edit 

finds that insufficient data has been collected to 

determine a person’s labor force status, then data 

for that person is dropped. 

REFERENCES

Dixon, J., “Using Census Match Data to Evaluate 

Models of Survey Nonresponse,” Proceedings 

of the Section on Survey Research Methods, 

American Statistical Association, 2004, pp. 

3421−3428.

Dixon, J., “Using Contact History Information 

to Adjust for Nonresponse in the Current 

Population Survey,” Proceedings of the Section 

on Government Statistics, American Statistical 

Association, 2012, pp. 3421−3428.

Erkens, G., “Changes in Panel Bias in the 

U.S. Current Population Survey and its Effects 

on Labor Force Estimates,” Proceedings of the 

Section on Survey Research Methods, American 

Statistical Association, 2012, pp. 4220−4232.

Evans, T., J. McIllece, and S. Miller, “Variance 

Estimation by Replication for National CPS 

Seasonally Adjusted Series,” Proceedings of the 

Section on Business and Economic Statistics, 

American Statistical Association, 2015, pp. 

884–895.

Grieves, C., and E. Robison, “Panel Analysis of 

Household Nonresponse and Person Coverage 

in the Current Population Survey,” Proceedings 

of the Section on Survey Research Methods, 

American Statistical Association, 2013, pp. 

1339–1349.

Kojetin, B. A., and P. Mullin, “The Quality of Proxy 

Reports on the Current Population Survey (CPS),” 

Proceedings of the Section on Survey Research 

Methods, American Statistical Association, 1995, 

pp. 1110−1115.

Tanur, J. M., “Conceptualizations of Job Search: 

Further Evidence from Verbatim Responses,” 

Proceedings of the Section on Survey Research 

Methods, American Statistical Association, 1994,

pp. 512−516.

Thompson, T., “Mode Effects Analysis of Labor 

Force Estimates,” Current Population Survey 

Overlap Analysis Team Technical Report 3, 

U.S. Census Bureau, April 14, 1994. 

Tiller, R. B., “Time Series Modeling of Sample 

Survey Data from the U.S. Current Population 

Survey,” Journal of Official Statistics, Statistics 

Sweden, 1992, pp. 149–166.

Tucker, C., and B. A. Harris-Kojetin, “The Impact 

of Nonresponse on the Unemployment Rate in 

the Current Population Survey,” paper presented 

at the 8th International Workshop on Household 

Survey Nonresponse, 1997, Mannheim, Germany.

FURTHER READING

Groves, R. M., Survey Errors and Survey Costs, 

John Wiley & Sons, 1989. 
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Chapter 4-2: Reinterview Design and Methodology

INTRODUCTION

The CPS Reinterview Program, which has been 

in place since 1954, conducts a second interview on about 3 percent of sample households 

each month. This quality control (QC) reinterview program provides a measure of control or 

feedback about the quality of data received from 

the respondent and the original interviewer. The 

QC reinterview is used to deter data falsification 

and to monitor the FRs adherence to established 

procedures.

REINTERVIEW DATA SELECTION AND 

COLLECTION

The sample for the QC reinterview is a subsample 

of the CPS. For this subsample, HUs are selected 

by a specific method and specific field procedures 

are followed. To minimize respondent burden, a 

subsampled household is reinterviewed only once. 

Sample selection for reinterview is performed 

immediately after the monthly interview assignments to FRs. This sample does not include the 

contact center’s Computer-Assisted Telephone 

Interviewing (CATI) interviewers because the contact centers have their own QC program in place. 

With the CATI monitoring program, interviews are 

recorded (with the permission of the respondent) 

and reviewed by coaches and monitoring staff. 

These staff members review and evaluate the 

interviewers’ performance and provide feedback 

to the interviewers.

The QC reinterview is used to measure some 

nonsampling errors (as discussed in Chapter 

4-1, Nonsampling Error) attributable to FRs. The 

QC reinterview is used to make decisions as to 

whether the FRs adhered to procedures and to 

check for any possible falsification. QC reinterview 

checks if the FR:

• Falsified responses.

• Did not ask questions as worded.

• Did not ask all questions. 

• Provided auxiliary information that could 

influence or lead a respondent to answer 

differently.

• Did not use the proper equipment to capture 

data. 

• Did not maintain the standards established by 

the Census Bureau and BLS.

Sampling Methodology

Previous research has indicated that inexperienced 

FRs (less than 5 years of work with the Census 

Bureau) are more likely to have significant procedural errors than experienced FRs, so more of 

them are selected for QC reinterview each month 

(U.S. Census Bureau, 1993 and 1997). Research 

has also indicated that inexperienced FRs falsify 

a greater percentage of households than experienced FRs, so fewer of their cases (households) 

are needed to detect falsification. The QC reinterview sampling selection system is set up so that a 

selected FR is in reinterview at least once but no 

more than four times within a 15-month cycle.

The QC reinterview sample is selected in two 

stages: 

First Stage (assignment of FRs for QC reinterview)

• The QC reinterview uses a 15-month cycle. 

FRs are randomly assigned to one of the 15 

different groups. Throughout the cycle, new 

FRs are assigned to the groups.

• The system is set up so that an FR is in reinterview at least once but no more than four times 

within the 15-month cycle.

Second Stage (subsample of households for QC 

reinterview)

• Each month, one of the 15 groups of interviewers is included for QC reinterview.

• A subsample of households is selected from 

each FR’s interview assignment. 

• The number of households subsampled per FR 

assignment is based on the length of tenure 

of the FR (fewer for inexperienced FRs since 

fewer cases are needed to detect falsification). 

• Typically, 3 percent of CPS households are 

assigned for QC reinterview each month. 

In addition to the two-stage QC reinterview sample, any FR with an original interview assignment 

can be put into supplemental reinterview. These 

are cases available to the ROs to add to reinterview at their discretion. The FR can be flagged for 

supplemental reinterview for the next assignment 
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period or the FR’s inactive cases can be activated 

during the current assignment period. Inactive 

cases are those cases that were not selected for 

the prior two-stage or the supplemental reinterview. FRs flagged for review based on various 

data quality indicators may be put into supplemental reinterview. 

All households are eligible for QC reinterview, 

except noninterviews for occupied households. 

Eligible households include completed and partial 

interviews as well as noninterviews due to the HU 

being unoccupied such as vacant units. In addition, FRs are evaluated on their rate of noninterviews for occupied households with penalties for 

a high noninterview rate. Therefore, FRs have no 

incentive to misclassify a case as a noninterview 

for an occupied household. See Figure 4-2.1 for an 

overview of determining QC reinterview household eligibility and data collection methods.

Data Collection Methods

Contact centers attempt all QC reinterviews with 

a valid telephone number, and if not completed, 

the cases are then transferred to the ROs for 

assignment to FRs. Cases without a valid telephone number are sent directly to the ROs for 

assignment to FRs. These FRs must not fall under 

the same supervisory chain as the original FR who 

conducted the interview. This minimizes bias when 

reviewing the work of the FR. Most reinterviews 

are conducted by telephone, but in some cases by 

personal visit. They are conducted on a flow basis 

extending through the week following the interview week. 

For QC reinterview, the reinterviewers are 

instructed to try to reach the original household 

respondent, but are allowed to conduct the reinterview with a proxy, another eligible household 

respondent. 

The QC reinterview asks questions to determine 

if the FR conducted the original interview and 

followed interviewing procedures. The labor 

force questions are not asked. The QC reinterview instrument allows reinterviewers to indicate 

whether any noninterview misclassifications 

occurred or any falsification is suspected.

Quality Control Reinterview Evaluation 

Cases are assigned outcome codes and disposition codes. These codes are based upon the judgment of the reinterviewer and the response of the 

reinterview respondent. The outcome codes are 

used as an indicator of the reinterview result (e.g., 

confirming correct or incorrect information, the 

Figure 4-2.1.

Quality Control Reinterview Sampling Diagram

Interview 

Not Eligible for

 Quality Control 

Reinterview

Does Interview Case 

Have a Valid 

Phone Number

Reinterview Cases 

Sent to 

Regional Oces

Complete?

Reinterview Cases 

Sent to 

Contact Centers

Current

 Population

 Survey

Production

Interviews 

No Yes

Yes

No

No

Interviews 

Transmitted From

the Contact

Centers 

Interviews 

Transmitted From 

the Regional 

Oces

Was Interview Case 

Selected and Eligible for 

Quality Control 

Reinterview
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type of noninterview, or any discrepancies found). 

The reinterview outcome codes are similar to the 

ones used for the original interviews (see Table 

3-2.1 of Chapter 3-2, Conducting the Interviews).

Disposition codes are used as an indicator for 

cases suspected of falsification, which triggers 

a falsification investigation. The most common 

cause for a disposition of suspected falsification 

is a finding that FR training procedures were not 

followed. A data falsification investigation is triggered by the case being flagged as having at least 

one of three discrepancy types: household was 

not contacted; the interviewer classified the unit 

as a Type B or C noninterview when it should have 

been an interview or Type A; or the respondent 

said the interviewer did not use a laptop during a 

personal visit interview. Any of these discrepancies would be considered a major departure from 

FR training procedures. 

Reinterview outcome and disposition codes are 

summarized in a manner similar to those for the 

original interview (refer to Table 3-2.8 of Chapter 

3-2, Conducting the Interviews). Examples of reinterview results are shown in Table 4-2.1 and Table 

4-2.2.

Adherence to Training Procedures

Reinterview is a quality check for FRs, to check 

their adherence to training procedures. Most of 

the suspected cases stem from a deviation from 

training procedures. Findings such as those below 

typically result from these deviations: 

• According to the respondent, the FR did not 

contact the household.

• The FR was not polite.

• The FR did not use a laptop during an in-person 

interview.

• According to the respondent, there were errors 

made on the household roster. 

Falsification

Reinterview can help provide measures of “curbstoning” or survey data falsification (Kennickell, 

2015) and serves as a deterrent. 

Falsification can be defined as a manipulation 

of the data collected. It can be intentional or 

unintentional. Examples of unintentional falsification can range from a simple typo to a household 

member omission or deviations from procedure. 

Examples of intentional falsification can range 

from making up one or more responses to having 

fictional interviews. FRs suspected of falsification 

may undergo an investigation; results of these 

investigations can vary from an oral or written 

warning, recommendations for additional training, 

or termination. 

Periodic reports on the QC reinterview program 

are issued. These reports identify: (1) a preliminary count of cases and FRs suspected of falsification, (2) the results of the investigation with 

the confirmed falsification counts for cases and 

FRs, and (3) unweighted (see formula below) and 

weighted falsification rates. (See Appendix A, 

Weighting Scheme for Quality Control Reinterview 

Estimates). 

Since FRs are made aware of the QC reinterview 

program and are informed of the results following reinterview, falsification has not been a major 

problem in the CPS. Only about 0.5 percent of 

CPS FRs are found to have falsified data. FRs 

found to have falsified data typically resign or 

have their employment terminated. 

REFERENCES

Kennickell, A., “Curbstoning and culture,” 

Statistical Journal of the International Association 

for Official Statistics (IAOS), 2015, (31), 237–240, 

retrieved from <http://content.iospress.com

/download/statistical-journal-of-the-iaos

/sji900?id=statistical-journal-of-the-iaos%2Fsji900>.

U.S. Census Bureau, “Falsification by Field 

Representatives 1982−1992,” memorandum from 

Preston Jay Waite to Paula Schneider, May 10, 

1993.

U.S. Census Bureau, “Falsification Study Results 

for 1990−1997,” memorandum from Preston Jay 

Waite to Richard L. Bitzer, May 8, 1997.
Current Population Survey TP77 Chapter 4-2: Reinterview Design and Methodology 157

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

Table 4-2.1. 

Reinterview Results for November 2016—Counts

Description November 2016

ORIGINAL INTERVIEW CASES SELECTED FOR RANDOM REINTERVIEW. . . 

ORIGINAL INTERVIEW CASES ELIGIBLE FOR RANDOM REINTERVIEW. . . 

 Complete or sufficient partial reinterviews. . . 

 Reinterview noninterviews. . 

  Noninterviews. . 

  Headquarters discretion. .

  Regional office discretion. 

NONINTERVIEWS EXCLUDING HEADQUARTERS AND REGIONAL OFFICE DISCRETION 

CASES

 Type A. . 

  Language problem. 

  Unable to locate. 

  Unable to complete, bad telephone number. .

  Insufficient partial. 

  No one home. . 

  Temporarily absent. 

  Respondent can’t remember or refused. .

  Other Type A. . 

 Type B. 

  Entire household under or over age limit. 

  Temporarily occupied by persons with URE. 

  Vacant, regular. 

  Vacant, storage of household furniture. 

  Converted to temporary business or storage. 

  Unoccupied mobile home, trailer, or tent site. 

  Household institutionalized or temporarily 

ineligible. 

  Unfit, to be demolished. 

  Other Type B. 

 Type C. 

  Demolished. 

  House or trailer moved. 

  Converted to permanent business or storage. 

  Condemned. 

  Moved out of country. 

  Deceased. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

  Other Type C. 

 Type D. 

  Household replaced by new household. 

Source: U.S. Census Bureau, Demographic Statistical Methods Division, November 2016 Current Population Survey Reinterview.
158 Chapter 4-2: Reinterview Design and Methodology Current Population Survey TP77

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Table 4-2.2.

Reinterview Results for November 2016—Statistics

Description November 2016

Number of interviewers in reinterview. . 

Number of reinterview cases conducted. . . 

Reinterview completion rate. . . .. . . . . . .

Telephone reinterview rate. . . . . . . . .

Proxy reinterview rate. . . . . . . . .

Household accuracy rate. . . . . . . . .

Households with missing members (adds). .

Households with additional members (drops). 

Households with both adds and drops. 

 Original noninterview misclassification rate. . . . . . . .

Total discrepancies. . 

Household not contacted. .

Status incorrect. .

Roster incorrect. .

Not all questions asked. .

Laptop usage rate. . . . . . . . .

No laptop used. 

Bad phone number collected. .

Suspected falsification rate by reinterview cases 

conducted. . . . . . . . .

Suspected falsification rate by interviewer. . . . . . . . 

Number of interviewers with falsification confirmed. 

Source: U.S. Census Bureau, Demographic Statistical Methods Division, November 2016 Current Population Survey Reinterview.
Current Population Survey TP77 Appendix: Weighting Scheme for Quality Control Reinterview Estimates 159

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

Appendix: Weighting Scheme for Quality Control 

Reinterview Estimates

To produce reinterview weights, data are stratified by the reinterview period and the experience level of 

the FRs. For the CPS, FRs are considered experienced after 5 years of work at the Census Bureau. Each 

month of the year is an interview period for the CPS. The data related to the quality of the production 

survey (falsification, household coverage/roster accuracy, politeness, noninterview misclassification, discrepancies) are weighted for the QC reinterview reports, provided there are sufficient experience-level 

data available for the CPS FRs. The data related to the quality of the reinterview survey (reinterview 

completion rate, lag time average, reinterview telephone rate) are not weighted.

At the beginning of each 15-month CPS cycle, CPS FRs are randomly assigned to one of 15 groups. New 

CPS FRs throughout the cycle are added to the groups. Generally, FRs are not regrouped throughout 

the cycle. A sample for random reinterview is selected each month. The sample size will depend on the 

experience level of the FR originally assigned to the case. Each case preselected for reinterview remains 

in the reinterview sample even if the case is reassigned to another FR in the same RO. 

The reinterview weight is the reciprocal of the probability of selection into the reinterview sample. The 

probability of selection in an interview period is dependent upon:

• The numbers of experienced and inexperienced FRs selected for random reinterview for the interview period.

• The total numbers of experienced and inexperienced FRs for the interview period.

• The number of cases preselected for random reinterview in the interview period for each originally 

assigned FR.

• The originally assigned FR’s assignment size in the interview period.

To determine the reinterview weight for cases originally assigned to a specific FR during an interview 

period:

1. Assess the FR’s experience level, j, for that interview period (month).

2. Compute P1j as follows: 

P = # of cases preselected for random reinterview for that FR for interview period

1j # of production cases originally assigned to that FR for interview period

3. Compute P2j as follows: 

P # of FRs of experience level j preselected for reinterview for interview period

2j = # of FRs of experience level j in interview period

4. The probability of selection of production cases originally assigned to that FR into reinterview for 

that interview period is pj = P1j * P2j .

5. The reinterview weight for all random reinterview cases originally assigned to that CPS FR for that 

interview period is wj = 1/Pj .
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Current Population Survey TP77 Acronyms 161

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

Acronyms 

ACS American Community Survey

ADS Annual Demographic Supplement

AHS American Housing Survey

ARD Assistant Regional Director

ARIMA Auto-Regressive Integrated Moving Average 

ASEC Annual Social and Economic Supplement

ATUS American Time Use Survey

BLS Bureau of Labor Statistics

CAI Computer-Assisted Interviewing

CAPI Computer-Assisted Personal Interviewing

CARMN Cost and Response Management Network

CATI Computer-Assisted Telephone Interviewing

CAUS Community Address Updating System

CBSA Core-Based Statistical Area

CCM Civilian U.S. Citizen’s Migration 

CCO CATI and CAPI overlap

CE Consumer Expenditure

CES Current Employment Statistics

CHIP Children’s Health Insurance Program

CI Coverage Improvement

CNP Civilian Noninstitutional Population

CNP16+ Civilian Noninstitutional Population age 16 and over

CNCS Corporation of National and Community Service

CPS Current Population Survey

CV Coefficient of Variation

DAAL Demographic Area Address Listings

DSD Demographic Systems Division

DSF Delivery Sequence File

DSMD Demographic Statistical Methods Division

EDS Exclude from Delivery Statistics

EDMAFX Edited MAF Extract

FNS Food and Nutrition Service

FR Field Representative

FS Field Supervisor

GEMEnA Group on Expanded Measures of Enrollment and Attainment

GEO Geography Division

GEOP Geographic Programs Operation

GDP Geographic Data Processing

GIS Geographic Information System

GPS Global Positioning System
162 Acronyms Current Population Survey TP77

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GQ Group Quarters

GRF Geographic Reference File

GVF Generalized Variance Function

HU Housing Unit

HVS Housing Vacancy Survey

I&O Industry and Occupation

IDS Include in Delivery Statistics

LACS Locatable Address Conversion System

LAUS Local Area Unemployment Statistics

LUCA Local Update of Census Addresses

MAF Master Address File

MAFCS MAF Coverage Study

MAFID MAF Identifier

MAFOP MAF Operations

MCS Master Control System

MIS Month-In-Sample

MIS1 Month-In-Sample 1

MLR Major Labor Force Recode

MOS Measure of Size

MSA Metropolitan Statistical Area

N1, N2, N3 Initial Observations 1, 2, and 3

NAICS North American Industry Classification System

NCEUS National Commission on Employment and Unemployment Statistics

NCS National Comorbidity Survey

NCVS National Crime Victimization Survey

NICL noninterview clusters

NILF Not in Labor Force

NIM NICE Interaction Management

NPC National Processing Center

NRAF Net Recruits to the Armed Forces

NSR Non-Self-Representing

OMB Office of Management and Budget

PIP Performance Improvement Plan

PSU Primary Sampling Unit

PWBA Pension and Welfare Benefits Administration

QC Quality Control

RD Regional Director

RDD Random-Digit Dialing

REGARIMA Combined regression and ARIMA time series model

RO Regional Office

ROSCO Regional Office Survey Control System

RSE Relative Standard Error
Current Population Survey TP77 Acronyms 163

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

RSM-E Regional Survey Managers - Expert

RSM-G Regional Survey Managers - Geography

SCHIP State Children’s Health Insurance Program

SEATS Signal Extraction in ARIMA Time Series

SECU Standard Error Computation Unit

SIPP Survey of Income and Program Participation

SN Special Needs

SOC Standard Occupational Classification

SR Self-Representing

SRS Simple Random Sample

SS Second-Stage

Sups Supervisory Statistical Assistants

TIGER Topologically Integrated Geographic Encoding and Referencing System

TOI Time of Interview

UE Unemployed

UFUF Unit Frame Universe File

URE Usual Residence Elsewhere

USPS United States Postal Service

USU Ultimate Sampling Unit

VPN Virtual Private Network

WPA Work Projects Administration
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Current Population Survey TP77 Index 165

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

Index

4-8-4 rotation 3, 29, 76, 150

A

Address sources 43, 45–46

American Time Use Survey (ATUS) 17

Annual Demographic Supplement 19, 35

Annual Social and Economic Supplement (ASEC)

18–25

Area frame 43, 47 

Auto Regressive Integrated Moving Average 

(ARIMA) 32, 36, 38, 94–96, 150–151

Armed Forces 

adjustment 24

members 19, 21, 24

B

Base (basic) weight 17, 18, 21–24, 65, 67–69, 71, 

73, 76, 78, 84, 90, 146

Behavior coding 106

Births

births and deaths 29, 67

Building permits 43, 47, 66

Business presence 107

C

CATI recycled 130

Census Bureau Report Series 27

Citizenship 35, 125

Civilian noninstitutional population (CNP) 7, 10, 

11, 32, 33, 35–38, 52, 56–60, 67, 68, 70, 74, 

77, 85, 144, 145, 150 

Civilian noninstitutional housing 59

Class-of-worker 8, 9, 109

Coefficient of variation (CV) 52, 55, 57, 58

Collapsing 69, 73, 84

Composite estimation 30, 35, 53, 70, 71, 76–78, 

151

Composite weight 35, 36, 67, 77–81, 92, 150

Computer-Assisted Personal Interviewing (CAPI) 

5, 103

Computer-Assisted Telephone Interviewing (CATI) 

5, 103

CATI and CAPI Overlap (CCO) 34

Core-Based Statistical Area (CBSA)

Definition 54

Criteria to determine SR PSUs 55

Counties 27, 29, 47, 53–55, 80

Coverage errors 74, 82, 143–145 

Coverage Improvement (CI) Frame 47, 50

Coverage ratio 79, 145, 146

Current Employment Statistics 3, 105 

Current Population Survey (CPS)

Background and history 3, 4 

concepts 5–11

data products 26–28

key questions 11, 12

sample frame 43–50

sample rotation 61–63

supplements 13–25

variance estimation 82–92

weighting and estimation 67–80 

D

Data collection staff 135–140

Data preparation 132–134

Data quality 13–14, 113, 154–155, 159

Demographic Area Address Listings (DAAL) 47–48

Dependent interviewing 34, 37, 103, 108, 111, 

125, 132

Design effects 86, 91, 92

Disabled 111, 112

Discouraged workers 11, 34, 104, 105, 112

Dormitories 59

E

Earnings

earnings edits 134

 instrument design 9, 103

 revision 104, 109

 weighting 67, 79

Edits, demographic 124, 134

Educational attainment 6

Employment status 7, 8

Employment-population ratio 11, 34, 38, 151 

Enumerative Check Census 4

Estimators 77, 80, 85, 87, 93 

Expected value 52, 57, 85 
166 Index Current Population Survey TP77

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

F 

Families 6 

Family business 11, 107

Family weights 67, 79

Field representatives (FRs) (see also Interviewers)

 conducting interviews 3, 116, 136

 evaluating performance 137, 138

 training 135, 136

 transmitting interview results 129, 130, 131

Field subsampling 68 

Final hit number 61, 65 

Final weight 18, 78, 79 

First-stage ratio adjustment 18, 

First-stage weighting 18, 67–68, 70, 71 

Full-time workers, definition 8 

G 

Generalized variance functions (GVF) 85–88, 92, 

93 

H 

Hadamard matrix 84 

Hit string 61, 62, 65 

Home Ownership Rates 17

Homeowner Vacancy Rate 17

Horvitz-Thompson estimators 76 

Hot deck 21, 133, 134

Hours of work 8

Household

 definition 5–6

Housing Unit (HU) Frame 45, 46, 48–51, 60 

Housing unit (HU) 5, 143

Housing Vacancy Survey (HVS) 17–18

I

Imputation method 133, 148

Independent population controls 24, 32, 67, 71, 

73, 74, 77, 78, 80, 

Industry and occupation data (I & O) 

 coding of 130–132, 148–150

 edits and codes 133, 134 

 questionnaire revision 34

Interview week 116, 128, 132, 155

Interviewers (see also Field representatives) 

116, 125, 132, 135–136, 138–140, 144–149, 

152–155

Item nonresponse 68, 133, 146–148, 152, 153 

Iterative proportional fitting 75 

J

Job leavers 10

Job losers 10

Job seekers 10

L

Labor force

edits and recodes 134

 information 7–11, 105

Labor force participation rate, definition 11

LABSTAT 26

Layoff 10–12, 109–111

Levitan Commission (see also National 

Commission on Employment and 

Unemployment Statistics) 32–34, 104–105, 

112, 114

Listing operation 44–47

Longitudinal edits 133–134

M

Maintenance reductions 64, 65, 83 

Major Labor Force Recode 134

Marital status categories 6

Master Address File (MAF) 43–51, 143 

Measure of size (MOS) 55, 60 

Measurement error 112, 143, 148, 149

Metropolitan Statistical Areas (MSAs) 54, 61, 65

Microdata files 26, 28, 38

Month-in-sample (MIS) 18, 62, 

N

National Commission on Employment and 

Unemployment Statistics (see also Levitan 

Commission) 32, 114

National Processing Center (NPC) 129–132, 135

New entrants 10

Noninstitutional GQ 50, 60 

Noninterviews 116, 118–119

Nonresponse adjustment 67–69, 89, 90

Nonresponse clusters 69 

Nonresponse error 143, 145–147 

Nonresponse rate 67, 136, 146–148, 152 

Nonsampling error 82, 143, 145, 146, 148, 

150–152, 154 

Non-self-representing primary sampling units 

(NSR PSUs) 18, 55, 57

North American Industry Classification System

(NAICS) 9
Current Population Survey TP77 Index 167

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

O

Office of Management and Budget (OMB) 14

Old construction 43, 61

Outgoing rotation weight 67, 79

Overlap of the sample 64

P

Part-time status 8

Performance Improvement Period (PIP) 138

Permit Address List 59

Permit frame 43, 59 

Phase-in of a new design 64

Population Control adjustments 36

Primary Sampling Unit (PSU) 18, 52–61, 64–66, 

69–71, 82–85, 88–89, 133, 135–137

Probability sample 52, 58, 67

Processing error 143, 144

Professional Certifications 6, 38

Proxy reporting 149–150

Publications 26–27

Public Use Microdata 26, 28, 38

Q

Quality control (QC) 

 reinterview program 30, 154, 156

Quality measures of statistical process 95, 145

Questionnaires (see also interviews) 

 1994 redesign 104–112

 household and demographic 

 information 5–7

 labor force information 7–11

 recent improvements 112–113

 reinterview program 154–158

 structure of the survey 5

R

Race 

 determination of 32

racial categories 6

Raking 75–77, 81 

Random digit dialing sample (CATI/RDD) 34, 105

Random group 61

Random start 61

Ratio adjustment 70–71, 73, 82

Reduction group 64–65

Reduction plan 64

Reentrants 10

Reference person 5–6, 118, 122, 124, 126

Reference week 3, 7–12, 107, 116

Referral code 150

Refusal rate 146–147, 152

REGARIMA 95–97

Regional offices (ROs) 

 organization and training of data collection 

 staff 131, 135

 operations of 135–138, 144, 147, 154

 transmitting interview results 129, 132

Reinterview Program 30, 154–156,

Relational imputation 133, 134

Relationship categories 6, 113

Relative variance 57, 86

Rental vacancy rate 17

Replicate factors 83–84

Replication methods 82–83, 88

Respondent Identification Policy 37

Response distribution 106

Rotation chart 62–63

Rotation system 29, 53, 64

S

Sample characteristics 52, 60–62

Sample design 

 annual sampling 43, 49, 68, 

 definition of the primary sampling units 

 (PSUs) 52–54

 field subsampling 68

 permit frame 43, 59 

 sampling frames 43–50, 58–60, 144 

 sampling procedure 58, 60

 sampling sources 58–59

 selection of the sample primary sampling 

 units (PSUs) 53–57

stratification of the PSUs 53–57, 61

within PSU sampling 58, 60, 83

Sample preparation 

 MAF updates 45–49

Sample redesign 38, 43, 48, 50, 57, 66

Sample size 

 determination of 52 

 maintenance reduction 64–65, 83

Sampling 

 error 52–53, 82–83, 143, 150–151

frame 43–50, 58–60, 144

 interval 57–58, 60–61, 65, 85–86, 143

School enrollment 

 edits and codes 134 

 general 16, 27, 33

Seasonal adjustment of employment 94, 97, 99 

Second jobs 9, 67

Second-stage weighting 67–68, 70–71, 74–78

Security 

 transmitting interview results 129

Self-employed definition 9
168 Index Current Population Survey TP77

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

Self-representing primary sampling units 

(SR PSUs) 54–57, 70, 82, 84, 88–89

Self-weighting 54, 60, 64

Simple weighted estimator 76

Skeleton frame 59–60, 65

Standard Error Computation Units (SECUs) 

82–83, 85

Standard Occupational Classification (SOC) 9 

State sampling interval 55, 57–58, 60, 66

State supplementary samples 32

State-based design 53, 62 

State 

 coverage step 68, 70, 73

Statistical Policy Division 14, 30

Subfamilies 6

Subsampling 60, 68, 82 

Successive difference replication 83–86

Supplemental inquiries

Annual Social and Economic Supplement

 (ASEC) 15, 18–25

 criteria for 13

Survey week 5, 11, 30

Systematic sampling 83–84, 93

T

The Employment Situation 3, 26

Time series modeling 81, 95–96, 153

Topologically Integrated Geographic Encoding 

and Referencing (TIGER) System 44

Type A nonresponse 146–147, 152

Type B noninterviews 68, 116–119, 127, 137, 

144–147, 152, 157

Type C noninterviews 68, 116–119, 127, 144–147, 

152, 157

U

Ultimate sampling units (USUs) 53, 58, 61, 64–65, 

83–84

Unable to work 111–112

Unemployment 

 classification of 9, 134

 definition 9

 duration of 10

 early estimates of 4

 questionnaire information 104–105

 reason for 10

 seasonal adjustment 94, 99

 variance on estimate of 89–91

Union membership 26, 33, 103

Unit frame 43–49, 59

Unit nonresponse (see also nonresponse) 68, 

132–133, 146–147, 151

Usual residence elsewhere (URE) 116, 118, 122

V

Vacancy rate 15, 17

Variance estimation 82–92

Veterans, data for females 33–34

Veterans’ weights 67, 79–80

W

Weighting Procedure

Annual Social and Economic Supplement

 (ASEC) 21

 Current Population Survey (CPS) 67–80

 Housing Vacancy Survey (HVS) 17–18

Work Projects Administration (WPA) 4

X

X-11, X-12 and X-13 ARIMA programs 32, 94–95, 

98