The CPS After the Redesign: Refocusing the Economic Lens

Anne E. Polivka

Bureau of Labor Statistics

and

Stephen M. Miller

Bureau of Labor Statistics

December, 1994

Revised March, 1995

The views in this paper are those of the authors,

 and do not represent official BLS policy
2

The CPS After the Redesign: Refocusing the Economic Lens

The Current Population Survey (CPS) is a major source of information about the American

labor market. In addition to providing monthly estimates of unemployment and

employment, economists, sociologists and policy analysts use data from the CPS to examine

broad societal and cyclical changes in economic activity over time. For example, data from

the CPS have been used to investigate the declining rate of employment among men,

especially older men (e.g., Peracchi and Welch, 1994); the rising labor force participation

rate of women since the 1960's (Ward and Smith, 1984; Michael, 1985); the changing

demographic composition and number of the self employed (Devine, 1994a, Devine 1994b,

Aronson, 1991); the fluctuations in the number of involuntary part-time workers over the

business cycle (Blank, 1990); the increase in wage inequality over time (Levy and Murnane,

1992; Bound and Johnson, 1992); and the relationship between unemployment and inflation

(Tobin 1972; Murphy and Topel, 1987).

In January 1994, the CPS underwent a major redesign both in the wording of the

questionnaire and the methodology used to collect the data. The objective of the redesign

was to improve the quality and expand the quantity of available data. However, the

redesign also caused changes in the measurement of many of the estimates derived from the

CPS. The major purpose of this paper is to estimate adjustment factors for various

aggregate measures derived from the CPS in order to permit comparisons of estimates

before and after the redesign. In addition, these adjustment factors will be analyzed to

assess the impact of the redesign on some of the key conclusions drawn from the CPS.1

The remainder of the paper is structured as follows. The first section briefly summarizes the

reasons for the CPS redesign. The second section contains a description of the data and a

discussion of the motivation for the methodology used in the empirical analysis. The third

section presents the empirical model. The estimated adjustment factors, along with a

discussion of possible causes and implications of the estimated changes, are presented in the

fourth section. The paper concludes with a brief summary of the results and implications of

the redesign.

 

1 The estimates presented in this paper are being provided only to aid individuals who use the CPS

historically. The Bureau of Labor Statistics will not revise previously published official estimates.
3

Reasons for Redesigning the CPS

The redesigned CPS was the culmination of a massive 8 year collaborative effort between

the Bureau of Labor Statistics (BLS) and the Bureau of the Census (BC). The impetus for

changing the CPS was fourfold. First, there were indications that some of the concepts and

wording in the CPS were becoming dated. The last major revision to the wording of the

CPS occurred in 1967. Since that time there have been many changes in the United States

labor market. Women's labor force activity has increased dramatically. Service sector

employment has grown. The proportion of the employed working in factory jobs has

declined. Two-income households have become the norm in husband-wife households.

Single-parent households have become more prevalent. The population has grown older,

and minorities constitute a larger proportion of the labor force than previously. Given these

societal changes, some of the wording of the CPS questions were dated and new situations

had arisen that were not adequately covered by the survey.

For example, in the unrevised CPS, interviewers were instructed to tailor the wording of the

first labor force question based on the gender and age of the respondent. Specifically, if the

respondent "appears to be a homemaker," the manual instructed interviewers to ask, "What

were you doing most of last week -- keeping house or something else?" If the respondent

appeared to be relatively young, interviewers were supposed to ask, "What were you doing

most of last week -- going to school or something else?" For all other respondents,

interviewers were instructed to ask, "What were you doing most of last week -- working or

something else?" The next question about work activities in the unrevised questionnaire

included the phrase "not counting work around the house." Given the increased labor

market activity of women and the rising prevalence of home offices or other work

arrangements that involve individuals working from their homes, the wording of these

questions could be both offensive and confusing (Polivka and Rothgeb, 1993; Rothgeb,

1994; and Polivka, 1994).

Other examples of the datedness of the CPS occurred with respect to the recording of the

reasons why individuals were working part time or were absent from work. The unrevised

CPS did not include the response categories of "child care problems" or

"maternity/paternity leave." In the mid 1960's, when the last redesign was implemented,

dual income households and women working outside of the home were not as prevalent.

However, with the tremendous increase over the last quarter century of women in the labor

market, the lack of these response categories raised the probability of answers being

inaccurately classified and reduced the usefulness of the data (Fracasso, 1989).
4

Investigation also revealed that the meaning of several phrases and words in the CPS have

changed over time. An important example of shifting meanings involves the measurement

of individuals "on layoff." In the past, most people defined a layoff as a temporary spell of

unemployment from which an individual expected to be recalled as soon as business

conditions improved or retooling was completed. Research showed, however, that in the

late 1980's and early 1990's, the majority of individuals used the word layoff to refer to

permanent separations from which they did not expect to be recalled ( Rothgeb, 1982;

Palmisano, 1989).

A second motivation for the revisions is that changes previously recommended in the 1970's

-- most notably those from the National Commission on Employment and Unemployment

Statistics -- had not been fully implemented. Several recommendations were tested in the

1980's through the Methods Development Survey jointly developed by BLS and the Bureau

of the Census, but the lack of funding for a large overlap sample to assess the effect of the

changes precluded the implementation of these recommendations.

The changes that were occurring in survey methodology provided a third impetus for

redesigning the CPS. In the early 1980's, the introduction of cognitive psychological theory

and research methods provided a means for understanding and reducing measurement error

in the survey process (Jabine, Straf, Tanur, and Tourangeau, 1984). Two important

aspects of the application of cognitive methodology were used in the redesign of the CPS.

One was the development of a psychological model to relate psychological theory to how

the questionnaire affects responses, and the other was the incorporation of laboratory

techniques into the questionnaire design and testing process (Dippo, Polivka, Creighton,

Kostanich, and Rothgeb, 1994).

A fourth reason for the revision was the advent of the ability to conduct surveys using

laptop computers. The use of laptop computers made it possible to develop a completely

computerized survey instrument. In turn, a computerized instrument permitted the methods

and procedures used to conduct interviews to be altered. For example, use of a computer

allows information from a previous interview to be inserted into the current interview and

permits internal data consistency checks to be built into the survey.2

Given these reasons, an effort to redesign the CPS was begun in 1986. From 1988 through

1991, a series of research projects were conducted to guide the development of the revised

 

2 For a more complete description of the general motivation for the CPS revision see Bregger and Dippo

(1993). For a discussion of the motivation of specific questionnaire changes see Polivka and Rothgeb

(1993) For a description of the use of computers in redesigning and administering of the CPS see Dippo,

Polivka, Creighton, Kostanich, and Rothgeb (1994).
5

CPS. Included in this research were two large-scale tests of alternative versions of the

questionnaire collected using centralized computer-assisted telephone interviewing with

samples of households selected through random digit dialing. The result of these tests was

a completely revised questionnaire designed to be collected with an entirely automated

survey instrument.

The Data

As mentioned in the introduction, the major goal of this paper is to provide a set of

adjustment factors which account for the redesign for application to aggregate estimates

derived from the CPS. Initially, to assess the effect of the redesign a parallel survey was

conducted using the new automated collection procedure from July 1992 through

December 1993. This parallel survey provided BLS its first estimates of the expected effect

of the redesign. For example, based on the parallel survey it was estimated that the redesign

would increase the overall unemployment rate 0.5 percentage point and that the increase

would be relatively larger for women than for men. The initial parallel survey estimates also

indicated that the employment-to-population ratio would be 0.7 percentage point higher for

women and 0.6 percentage points lower for men after implementation of the redesign.3

As an additional tool to assess the impact of the redesign, households in the sample used for

the parallel survey were interviewed with the unrevised procedures from January 1994

through May 1994. The primary purpose of extending the parallel survey, while switching

households to the old procedures, was to obtain an estimate of what the unemployment rate

would have been under the old procedures. However, examination of the data from the

extended parallel survey called into question the interpretation of some of the results of the

initial parallel survey. Specifically, rather than the unemployment rate being lower than in

the CPS when the parallel survey was conducted with the old methodology, the

unemployment rate continued to remain higher in the extended parallel survey. As can be

seen in graph 1, plots of the parallel survey and CPS estimates of the monthly

unemployment rates did not cross as would be expected if the new methodology were

increasing the rate.

 

3 More detailed estimates of the effect of the redesign from the initial parallel survey can be found in

Cohany, Polivka and Rothgeb (1994).
6

GRAPH 1

Total National Unemployment Rates 

Not Seasonally Adjusted

Percent

5

5.5

6

6.5

7

7.5

8

8.5

9

Oct92Nov92Dec92Jan93Feb93Mar93Apr93May93Jun93Jul93Aug93Sep93Oct93Nov93Dec93Jan94Feb94Mar94Apr94May94

CPS

Parallel Survey

The failure of the two plots to cross suggests that there may have been something specific

to or distinct about the parallel survey. In other words, there may have been a "parallel

survey effect." This parallel survey effect could exist even for estimates that appeared to

perform as predicted by the initial parallel survey. For example, graph 2 indicates that the

monthly estimated employment-to-population ratios for women were, by and large, higher

in the parallel survey than in the CPS prior to January 1994 and were lower in the parallel

survey than in the CPS after January 1994. This crossing of the plots is consistent with

there being a new method effect on women's estimated employment-to-population ratio.

However, if there is a parallel survey effect in addition to a new methodology effect, the

effect of the redesign on women's employment-to-population ratio would be different from

what was observed prior to January 1994, since prior to January 1994 the two effects

would be confounded.

GRAPH 2

Employment-to-Population Ratios for Women 

Not Seasonally Adjusted

Percent

52

53

54

55

56

57

Oct92Nov92Dec92Jan93Feb93Mar93Apr93May93Jun93Jul93Aug93Sep93Oct93Nov93Dec93Jan94Feb94Mar94Apr94May94

CPS

Parallel Survey
7

A parallel survey effect could occur for a variety of reasons. For example, a parallel survey

effect could exist due to differences in the CPS sample versus the sample used for the

parallel surveys, differences in supervision of the interviewer staff between the CPS and the

parallel surveys, or differences that arise just because respondents and interviewers know

that they were part of an experiment. The latter effect is sometimes referred to as a

"Hawthorn effect."

Given the graphical results and the possibility of a parallel survey effect existing, it is

important to construct adjustment factors that control for a parallel survey effect.

Consequently, data collected with the parallel surveys using the new procedures prior to

January 1994 and the unrevised procedures from January through May, along with data

collected from the unrevised CPS prior to January 1994 and revised CPS beginning in

January 1994, will all be used in the estimation of adjustment factors. Throughout the

remainder of this paper, estimates and data pertaining to the portion of the parallel survey

test conducted prior to January 1994 will be referred to as "Parallel Survey prior to

January" estimates or data (PSpj). Estimates or data pertaining to the portion of the parallel

survey conducted since January 1994 using the unrevised procedures will be referred to as

"Parallel Survey since January " estimates or data (PSsj). Estimates or data derived from

the unrevised CPS will be referred to as "unrevised CPS, " "old method" or "unrevised

methodology" estimates or data. Estimates or data derived from the revised CPS after

January 1994 will be referred to as "revised CPS" or "revised methodology estimates or

data."

To aid in subsequent discussion of the statistical modeling and to provide additional insight

into why a "parallel survey effect" may exist, the sample design and procedures used in each

of the surveys are described below.

The Unrevised CPS (Old Method before January 1994)

The Current Population Survey includes 60,000 households monthly that are selected to

represent the population in the Nation and each state. The probability sample of housing

units is drawn using a multistage stratification procedure. The largest metropolitan areas

within each state are always included; the remaining areas of a state are sampled on a

probability basis, with the probability of selection being proportionate to the population of

the area. In an effort to balance respondent burden with improved estimates of change,

households are interviewed for 4 consecutive months, not interviewed for the next 8
8

consecutive months, and then interviewed for another 4 consecutive months. Each month,

a new household panel of approximately one-eighth of the total monthly sample size is

initiated and the panel which received its eighth interview the previous month is dropped.

Given this rotating panel structure, in any month one-eighth of the households will be

receiving their first interview, one-eighth will be receiving their second interview, oneeighth will be receiving their third interview, etc. This rotating panel structure means that

three-quarters of the sample in a given month is retained in the sample the next month. This

improves estimates of month-to-month change, but it also means that there is a great deal of

correlation in the data month to month. The first interview in each of the four consecutive

interview months is conducted through a personal visit. In subsequent months, the majority

of interviews are conducted over the phone, either from interviewers' homes or from one of

two centralized computer-assisted telephone interviewing (CATI) facilities. The majority of

the unrevised CPS data were collected with a paper survey instrument, although

approximately nine percent of the data were collected by interviewers working in the two

centralized (CATI) facilities.

The Revised CPS (New Method since January 1994)

Starting in January 1994, the 60,000 household CPS sample was switched to the revised

questionnaire and computerized collection procedure. The rotation pattern established prior

to January 1994 was maintained; therefore, 88 percent of the households that received the

revised CPS questionnaire and procedures in January 1994 had previously received the CPS

using the unrevised questionnaire and procedures, with 75 percent of the households having

experienced the unrevised CPS in December 1993. Except for staff turnover, all of the CPS

interviewers in January 1994 had previous experience with the unrevised CPS. The revised

CPS data were collected entirely with the new computerized instrument. Again, the

majority of the households were interviewed in a decentralized manner, either through

personal visits or by telephone from interviewers' homes. In January 1994, a little less than

13 percent of the data were collected from the centralized CATI facilities. By May 1994,

the percentage of interviews conducted by CATI in the revised CPS had increased to 14.5

percent.

The Parallel Survey Prior to January (Parallel Survey Using the New Method before

January 1994, PSpj)

The Parallel Survey prior to January included 12,000 households that were interviewed

monthly starting in July 1992. The sample design for the Parallel Survey prior to January

was that used by the National Crime Victimization Survey which is conducted by the

Bureau of the Census for the Bureau of Justice Statistics. Like the CPS, the PSpj sample
9

was drawn using a multistage stratified design. Unlike the CPS's state based design,

geographic areas for the PSpj were selected only to be nationally representative. The

Parallel Survey prior to January had the same 4-8-4 interview rotation pattern as the CPS.

However, since the PSpj was initiated in 1992, and it takes 16 months to phase in this type

of rotation scheme, September 1993 was the first month in which the rotation scheme was

fully in place. 4 As in the CPS, the first and fifth month-in-sample households in the PSpj

were interviewed through personal visits. In subsequent months, the majority of interviews

were conducted by telephone. In the Parallel Survey prior to January, 82 percent of the

data were collected by field representatives using laptop computers, either during personal

visits or by telephone from their own homes. The remaining 18 percent of the data were

collected using CATI by a separate staff of interviewers working in the same two

centralized facilities used for the CPS. The interviewer staff for the PSpj was drawn to

reflect the experience of CPS interviewers in a given year. Fifty percent had experience on

the unrevised CPS, 25 percent had experience on other Census surveys, and 25 percent

were new hires. While the PSpj was being conducted, none of the PSpj interviewers

conducted the unrevised CPS. The PSpj had a supervisory staff which was separate and

independent from the unrevised CPS. For each supplement conducted in the CPS from July

1992 through December 1993, a computerized version was also administered in the PSpj.

Due to factors related to the initialization of the new procedures and implementation of the

revised questionnaire, only data from October 1992 to December 1993 will be used for

analysis.

The Parallel Survey Since January (Parallel Survey Using the Old Method since January

1994, PSsj)

Starting in January 1994, the sample used for the Parallel Survey prior to January was

switched to the unrevised CPS paper questionnaire. Given the rotation structure of the

Parallel Survey sample, this meant that in January 1994, 88 percent of the respondents had

 

4 The rotation scheme was such that from October 1992 forward all of the households in month in sample

one through four actually were interviewed for their first through fourth time. In October 1992, the month

in sample one through four households constituted 50 percent of the sample, At the same time half of the

survey households were designated as month in sample five through eight. The majority of these

households actually were having their first through fourth monthly interviews, where households designated

as month in sample five were really month in sample one, households designated as month in sample six

were really month in sample two etc. Historically, changes in estimates from month in sample five through

eight have shown the same pattern as changes in estimates for month in sample one through four. A small

percentage of the MIS 5 households in October 1992 had been previously interviewed in January 1992 as

part of a large scale operations test of the new instrument and collection procedures. Starting in January

1993, 30 percent of the households designated as MIS 5 actually were being interviewed for the fifth time

after having not been interviewed for eight months. The percentage of "true" MIS 5 households increased

to 63 percent in April 1993 and 100 percent in May 1993. The percentage of true MIS 6 through MIS 8

households followed a similar pattern as the MIS 5 households lagged by one calendar month.
10

previous experience with the revised computerized questionnaire, and, for 75 percent of the

sample, this experience had been in December 1993. By May 1994, the percentage of

respondents who had previous experience with the revised CPS had decreased to 50

percent, with none of this experience having occurred in a contiguous month. In January

1994, approximately 16 percent of the PSsj households were eligible to be interviewed

through CATI. By May 1994, the percentage of interviews eligible to be conducted by

CATI had been reduced to 9 percent. Twenty-six percent of the field interviewers for the

Parallel Survey since January had conducted interviews with the unrevised CPS using the

paper instrument. The majority of the remaining field interviewers were newly hired to

work on the PSsj. Approximately 6 percent of these new hires had received training on the

new questionnaire and methodology. None of the CATI interviewers for the PSsj had

experience with the old questionnaire. The Parallel Survey since January had the same

supervisory staff as did the Parallel Survey prior to January . The PSsj did not have any of

the supplements that were administered with the revised CPS in 1994. It is important to

note that the switching of the same households from the Parallel Survey prior to January to

the Parallel Survey since January permitted an estimate of the parallel survey effect.

Description of Statistical Modeling

Introduction

Let Yit be a non seasonally adjusted estimate for a particular labor force measure (e.g., Total

National Unemployment Rate) for the i-th survey in month t. Here i = 1 refers to the CPS

while i = 2 refers to the Parallel Survey. In addition, t ranges from 1 to 20, denoting the

months October 1992 to May 1994 respectively.

We will consider two models, an additive factor model and a multiplicative factor model.
11

The additive factor model is given by

(1) Y me it t i itj j

j

=++ + it

=

μλ δ ∑

1

4

where

μt = True mean for month t

λ1 = Effect due to Current Population Survey

λ2 = Effect due to Parallel Survey

δitj = 1 if mj occurs in Month t and Survey i (zero otherwise)

m1 = Effect due to Old Method before January 1994

m2 = Effect due to New Method before January 1994

m3 = Effect due to Old Method since January 1994

m4 = Effect due to New Method since January 1994

eit = Sampling error for Survey i and Month t

We make the assumption that the sampling errors are Normally distributed with mean zero.

In addition, the sampling errors are uncorrelated between the two surveys, but are

correlated within survey. This within survey correlation is mainly caused by the rotating

panel structure of the CPS, and mimicked in the parallel survey, which creates a 75 percent

overlap between sampled units one month apart and 50 percent overlap between units

twelve months apart.

The multiplicative model is given by

(2) log log log log Y mu it t i itj j

j

=++ + it

∗∗ ∗

=

μ λδ ∑

1

4

where the parameters are defined analogously to those in (1), and the sampling error uit is

Normally distributed with mean zero.

Our goal with the additive model is to estimate the effect of the new methodology, m$ 4 say,

in order to create a revised estimate $Y Ym$ t

A

t

a f = + 1 4 for months before January 1994, which

are comparable to the data from the CPS since January 1994. Under the multiplicative

model we estimate a new methodology effect m$ 4

∗

, in order to create the revised estimate

$

Y Ym$ t

M

1 14 t

a f = ∗

. Unfortunately, the parameters of the models in (1) and (2) are not fully

identified, even though some linear combinations are identified. For example, if we look at

months prior to January 1994 for the additive factor model we get:
12

(3) EY Y m m l q 21 21 2 1 t t − = −+ − λ λ

while since January 1994 for the additive factor model we get:

(4) EY Y m m l q 21 21 3 4 t t − = −+ − λ λ .

The linear combinations in (3) and (4) are estimable, even though the individual parameters

are not estimable. In order to make progress with respect to individual parameters,

additional restrictions need to be imposed.

Basic Assumption

The basic assumption we used is to make everything relative to the CPS prior to January

1994.

(5) Additive Factor : λ1 1 = = 0 0 ,m

Multiplicative Factor : λ1 1 1 1 ∗ ∗ = = ,m

This brings us down to 4 free parameters plus 20 monthly mean parameters. Unfortunately

all of the parameters of the model still are not identified. There are several ways to further

restrict the parameters, and we list three reasonable ones next.

Restriction 1

In addition to the basic restriction, we could assume that the new method had the same

effect before January 1994 as from January 1994 on and that there was no parallel survey

effect

(6) Additive Factor : λ2 24 = = 0,m m

Multiplicative Factor : λ2 24 1 ∗ ∗∗ = = ,m m

This would allow us to estimate a new method effect and an effect due to the way in which

the old methodology was applied from January 1994 forward.

Restriction 2

In addition to the basic restriction, we could assume that the old methodology had the same

effect from January 1994 on as it did previously and that there is no parallel survey effect.

(7) Additive Factor : λ2 3 = = 0 0 ,m
13

Multiplicative Factor : λ2 13 1 1 ∗ ∗∗ = == ,m m

This would allow us to estimate a new method effect before January 1994 and a new

method effect from January 1994 forward.

Restriction 3

In addition to the basic restriction, we could assume that the new methodology had the

same effect before January 1994 as it has had since January 1994, and that the old

methodology has had the same effect since January 1994 as it had previously.

(8) Additive Factor : m mm 2 43 = = , 0

Multiplicative Factor : m mm m 2 41 3 1 ∗ ∗∗ ∗ = == ,

This would allow us to estimate a new methodology effect and an effect due to the parallel

survey.

Specification Used in Our Analysis

For the purposes of our analysis we used the additive and multiplicative models in (1) and

(2) along with the Basic Assumption (5) and Restriction 3 (8). We chose this specification

because it most closely fits our understanding of the data. Specifically, everything possible

was done to ensure that the new methodology was applied in the same way in the Parallel

Survey before January 1994 and in the CPS since January 1994. In addition, all possible

measures were undertaken to ensure that the old method was implemented in the Parallel

Survey since January 1994, in the same way it was in the CPS prior to January 1994. The

measures taken to insure that the old and new methods were implemented in 1994 as they

had been previously, means that we can estimate one parameter for the new method effect.

The addition of a parallel survey effect parameter allows us to use data from 1994 to

disentangle the confounding effects of the parallel survey and new method effect which are

present if one analyzes only data prior to January 1994.

A variety of evidence both empirical and qualitative also supports the specification of a

single new method and a parallel survey effect. Empirically, as will be discussed below, for

the unemployment rate our additive model specification yields an insignificant point estimate

of 0.079 for the new methodology and a significant point estimate of 0.41 for the parallel

survey effect. Modeling done with employment data from the monthly Current

Employment Statistics (CES) and Unemployment Insurance Claims data estimating what

the national unemployment rate would have been with the unrevised CPS methodology

during 1994 also indicates that there was approximately a 0.08 percentage point change in
14

the unemployment rate due to a change in methodology and weights (Tiller and Welch,

1994).

A qualitative explanation of why a parallel survey effect might exist, independent of sample

design differences, was provided by CPS and Parallel Survey supervisors in focus groups

where they discussed their recent experiences. In these focus groups, some supervisors

volunteered that CPS interviewers had larger case loads than those working on the Parallel

Surveys. The larger CPS case loads reduced the amount of time interviewers could followup on households that did not initially respond. Furthermore, members of the focus groups

noted, the smaller case loads of the Parallel Survey supervisors provided them more time to

monitor the survey process and pursue field problems (Tucker, 1994). Differences in

following up on non responders and monitoring of potential problems between the CPS and

the two Parallel Surveys might have contributed to a “parallel survey effect.”

There could be some concern that respondents who switched from the revised to the

unrevised procedure and vice versa were contaminated by their previous experience. It

should be noted, however, that on average the difference in the unemployment rates

between surveys from January through May of 1994 did not diminish or change signs as

would be expected if contamination were effecting the estimates. In addition, in order to

maintain sample size and to capture any effect that was peculiar to the households actually

selected for the Parallel Survey prior to January 1994, a decision was made to use the entire

sample for January through May of 1994, rather than restricting the analysis to households

with no previous experience with another methodology.

Finally, we would like to make two other points about the specification we chose. First,

even though we are modeling non seasonally adjusted data, the parameter estimates for the

parallel survey effect and new method effect can also be applied to seasonally adjusted data

in the following sense. For those data series which are additively seasonally adjusted, we

would get the same parameter estimates, with the additive model, for parallel survey effect

and new method effect if we had used seasonally adjusted or non seasonally adjusted data

(assuming the same variances and covariances were used in the general least squares

estimation). This is because the true monthly mean in (1) for seasonally adjusted data is just

the true mean for the non seasonally adjusted data plus a unique additive monthly seasonal

adjustment factor which can be absorbed into the definition of the mean. A similar situation

occurs for series which are multiplicatively seasonally adjusted, and we use the

multiplicative model. Again, this occurs because the seasonal adjustment is additive on the

scale in which we are modeling (that is, the seasonal adjustment is additive in the

logarithmic scale). The second point we want to make about the selected specification has

to do with why we did not model the underlying true monthly means with some method

other than just monthly dummies. For example it would be possible to specify a polynomial
15

time trend model for the underlying monthly means with splines in time. The specification

of such a model would allow us to, in general, identify an additional parameter such as

freeing up the parameter m3 and m3

∗ in Restriction 3. We actually attempted to estimate

such models but found the models were still “close” to being not identified in the sense that

while we were able to obtain parameter estimates, their standard errors were large, and

multicollinearity inflated the variance estimates of the parallel survey and new method

effects. Thus, we chose to continue modeling the monthly means as main effects in the

linear model for all of our analyses, and gave up trying to identify an additional parameter.

In addition, it was felt that using one specification for all of the analyses would help our

analysis seem more objective, since we would not have to be engaged in fitting different

models for the monthly means, which may have involved the use of additional explanatory

variables apart from the CPS and the Parallel Surveys such as employment data from the

monthly establishment survey (CES) to model nonagricultural employment.

Estimation

For the model specified above we estimated the remaining parameters by generalized least

squares. We illustrate in detail the estimation for the additive model. The estimation for the

multiplicative model is analogous. Let Ya f 1 be the vector of size 20 1 × which contains the

consecutive months of data from the CPS from October 1992 to May 1994, let Ya f 2 be the

data from the Parallel Surveys, and let Y YY ′= ′ ′ af af 1 2 d i , . Let X be the 40 22 × model matrix

associated with the model specified model, and let β be the 22 1 × vector of free

parameters. The 22 free parameters consist of 20 monthly means, a parallel survey effect,

and a new method effect. Then we can write

(9) YX e = β +

where e N 0V ~ , 40 a f, and V VV = Block 1 2 b g , where V1 is the 20 20 × covariance matrix of

the CPS data, and V2 is the 20 20 × covariance matrix of the Parallel Survey data. The

matrices V1 and V2 are estimated by the method of generalized variances along with

correlation estimates obtained from previous CPS research. We will condition on the

covariance matrix V and treat it as known.

The estimated parameters are given by

(10) $β = ′ ′ − − − XV X XV Y 1 1 1 c h
16

and the estimated covariance matrix of the estimates is given by

V XV X $ ${} c β = ′ − h

− 1 1

All of the estimations are done with uncomposited data using 1990 population weights.

The 1990 population weights are used to obtain a pure estimate of a method effect. 5

The Results

General

All of the adjustment factors presented in the tables below were estimated using the linear

model specified above, which included a new method effect and a parallel survey effect.

Standard errors are provided in parentheses below the adjustment factors. Adjustment

factors that were significantly different from 1 for the multiplicative model or 0 for the

additive model at the 5 percent level are indicated with asterisks. Point estimates for

adjustment factors that were not significant are also provided, although when adjustment

factors are not significant, depending on the sensitivity of the analysis, one could historically

compare data before and after January 1994 without adjustment. 1993 annual averages are

also included in the tables, when available, as a point of reference.

The effect of using the adjustment factors is illustrated for several of the characteristics

graphically. The data in these graphs were adjusted multiplicatively. For comparisons

over long time periods, multiplicative factors are recommended, since adjustments using

multiplicative factors will account for differences in the level of the characteristic at

different points in time. It should be noted, however, that, for multiplicatively adjusted

data, changes over time will not be the same as the changes measured by the unadjusted

series. In contrast, the additively adjusted series will change the level of the series, while

leaving the estimates of change unaffected.

 

5 At the time of writing it was unclear whether BLS would adjust previously published data to gradually

incorporate changes in the population as reflected by the difference in the 1980 and 1990 decennial weights.

(The 1980 weights with modifications for projected growth were used from 1985 through 1993.) Appendix

B presents the effects of using 1980 versus 1990 weights for selected 1993 annual average estimates.
17

Unemployment and Related Unemployment Estimates

Unemployment Rate

Table 1 presents adjustment factors for the unemployment rates for detailed demographic

groups. Examination of the adjustment factors in table 1 indicate that, unlike what was

expected from the Parallel Survey conducted prior to January 1994, the new methodology

did not have a significant effect on the overall unemployment rate, although the point

estimate for the additive factor was 0.079 and the point estimate for the multiplicative factor

was 1.009 which would be equivalent to an approximately 1 percent increase in the

unemployment rate. As could be anticipated by graph 1, the parallel survey effect in the

linear model for the overall unemployment rate was estimated to be .41 which was

statistically significant at the 1 percent level.
18

TABLE 1

UNEMPLOYMENT RATE

ADJUSTMENT FACTORS FOR '94 METHODOLOGICAL CHANGE

Multiplicative

Factor

Additive Factor '93 Annual

Average

 Total 16+ 1.009

(0.011)

0.079

(0.076)

6.8

 Men 16+ 1.012

(0.015)

0.10

(0.11)

7.1

 Women 16+ 1.007

(0.016)

0.07

(0.11)

6.5

 White Men 16+ 1.029

(0.018)

0.19

(0.11)

6.2

 White Women 16+ 1.025

(0.021)

0.15

(0.11)

5.7

 Black Men 16+ 0.971

(0.032)

-0.38

(0.49)

13.8

 Black Women 16+ 0.965

(0.031)

-0.48

(0.43)

12.0

 Teenagers (16-19) 1.035

(0.027)

0.65

(0.51)

19.0

 20-24 Year Olds 1.007

(0.026)

0.03

(0.28)

10.5

 25-54 Year Olds 0.985

(0.014)

-0.075

(0.084)

5.8

 55-64 Year Olds 1.121*

(0.053)

0.50*

(0.21)

4.7

 65 Years and Older 1.52*

(0.16)

1.52*

(0.31)

3.2

 Men 16 -19 Years Old 1.029

(0.033)

0.71

(0.66)

20.4

 Men 20-24 Years Old 1.024

(0.035)

0.16

(0.40)

11.3

 Men 25-54 Years Old 0.985

(0.019)

-0.07

(0.12)

5.9

 Men 55-64 Years Old 1.06

(0.06)

0.29

(0.30)

5.2

 Men 65 Years and Older 1.69*

(0.25)

1.93*

(0.42)

3.2

 Women 16-19 Years Old 1.029

(0.040)

0.58

(0.69)

17.4

 Women 20-24 Years Old 0.980

(0.036)

-0.23

(0.38)

9.6

 Women 25-54 Years Old 0.990

(0.020)

-0.05

(0.12)

5.6

 Women 55-64 Years Old 1.232*

(0.096)

0.76*

(0.26)

4.0

 Women 65 Years and Older 1.33*

(0.19)

0.85*

(0.44)

3.1

 Adult Men (20+) 1.005

(0.016)

0.04

(0.11)

6.4

 Adult Women (20+) 1.001

(0.017)

0.016

(0.10)

5.9
19

Further examination of the adjustment factors for the unemployment rates for various

demographic groups reveals that the new methodology did not cause a significantly higher

unemployment rate for any demographic group except older Americans. Specifically, the

adjustment factors for all individuals age 55 to 64, all individuals age 65 and older, men age

65 and older, women age 55-64 and women age 65 and older are each statistically

significant.

The higher unemployment rates for older Americans are probably due to a combination of

the automation and rewording of the questionnaire. One of the most frequently heard

complaints from respondents about the unrevised CPS was that it was burdensome and

irritating for retired workers who had no attachment to the labor force. To alleviate this

burden, the response category of "retired" was added to each question about labor force

activity. If individuals 50 years of age or older volunteer that they are retired in answer to

any of these questions, they are skipped directly to a specific question asking whether they

currently want a job, either full or part time. Individuals who indicate that they want a job

are asked the job search questions to establish if they have been looking for work in order

to potentially be classified as unemployed. It could be that by reducing respondent irritation

with the survey, directly asking older respondents if they currently want a job after they

have said they are retired, and the "part-time" reference could prompt some older

individuals to report that they have looked for work. In addition, reducing the level of

respondents' irritation in combination with the automation of the survey could reduce the

propensity for interviewers to make a personal assessment of older respondents and

inappropriately lead them through the questionnaire.

The effect of applying the multiplicative adjustment factor for men age 65 and older can be

seen in Graph 3.

GRAPH 3

Unemployment Rate of Men 65 and Older 

Multiplicatively Adjusted versus Published Data

Percent

1.5

3

4.5

6

7.5

9

'68 '69 '70 '71 '72 '73 '74 '75 '76 '77 '78 '79 '80 '81 '82 '83 '84 '85 '86 '87 '88 '89 '90 '91 '92 '93 '94

Published

Adjusted
20

In addition to noting the dramatic shift in the graph for men age 65 and older -- the

multiplicative factor increases the unemployment rate for older men as measured by the

unrevised CPS 69 percent-- it also is interesting to note that the redesign brings older men's

unemployment rate closer to the unemployment rate for prime age males. Consequently, as

the population ages, the redesign could have an effect on the overall level of the

unemployment rate independent of what other societal and economic changes may occur.

Reasons for Unemployment

In addition to the unemployment rate, analysts frequently are interested in the reasons why

individuals are unemployed. The CPS allows unemployed individuals to be classified into

one of five reasons for unemployment. Individuals could be unemployed because they were

laid off from their jobs, lost their jobs for some other reasons, voluntarily left their jobs,

were reentrants into the job market, or were new entrants in the job market. Table 2

provides adjustment factors for these five reasons for being unemployed for all unemployed,

unemployed men, and unemployed women respectively. Again, asterisks indicate point

estimates that are statistically significant at a 5 percent level.
21

TABLE 2

REASONS FOR UNEMPLOYMENT

ADJUSTMENT FACTORS FOR '94 METHODOLOGICAL CHANGE

(As a Percentage of Unemployed)

Multiplicative

Factor

Additive Factor '93 Annual

Average

TOTAL

 Laid Off 0.975

(0.027)

-0.51

(.40)

12.6

 Other Job Losers 0.952*

(0.014)

-1.89*

(0.56)

42.0

 Job Leavers 0.866*

(0.027)

-1.39*

(0.31)

10.8

 Reentrants to the Job Market 1.308*

(0.022)

7.79*

(0.47)

24.6

 New Entrants to the Job Market 0.622*

(0.021)

-4.01*

(0.30)

10.0

MEN

 Laid Off 0.932*

(0.031)

-1.30*

(0.57)

15.0

 Other Job Losers 0.974

(0.017)

-1.02

(0.76)

47.7

 Job Leavers 0.910*

(0.041)

-0.88*

(0.40)

9.9

 Reentrants to the Job Market 1.354*

(0.035)

6.80*

(0.58)

18.5

 New Entrants to the Job Market 0.592*

(0.029)

-3.74*

(0.39)

8.9

WOMEN

 Laid Off 1.068

(0.053)

0.43

(0.53)

9.6

 Other Job Losers 0.914*

(0.024)

-2.84*

(0.81)

34.5

 Job Leavers 0.822*

(0.037)

-2.03*

(0.49)

12.0

 Reentrants to the Job Market 1.266*

(0.027)

8.85*

(0.76)

32.4

 New Entrants to the Job Market 0.649*

(0.030)

-4.28*

(0.48)

11.5

Although the new methodology does not seem to have affected the overall unemployment

rate, the adjustment factors in Table 2 suggest that the new methodology did affect the

overall composition of individuals' reasons for unemployment. For all unemployed, the

adjustment factors indicate that the new methodology significantly increased the proportion
22

of unemployed classified as reentrants and decreased the proportion of unemployed in the

other four reasons categories, with the proportions classified as "other job losers," "job

leavers," and "new entrants" decreasing significantly. For men, the estimated adjustment

factors also indicate that the new methodology significantly decreased the proportion

classified as "on layoff."

The estimated effect of the new methodology on reentrants is probably related to a

combination of questionnaire wording and minor definitional changes. First, the wording of

the question where the majority of unemployed provide their reasons for unemployment was

changed from "At the time you started looking for work, was it because you lost or quit a

job or was there some other reason?" in the unrevised CPS to "BEFORE you started

looking for work, what were you doing: working, going to school, or something else?" with

the follow-up for those who said they were working, "Did you lose or quit that job, or was

it a temporary job that ended?" in the revised CPS.

Second, part of the new methodology effect on the estimate of reentrants can be attributed

to a seemingly innocuous definitional change. In the unrevised CPS individuals were asked

when they had last worked full time for two weeks or longer. With this question only

individuals who had worked full time were considered to have previous work experience

and thus were classified as reentrants. Individuals whose entire work experience was part

time or had lasted less than two weeks were classified as new entrants. The wording in the

revised CPS was broadened to take into account any type of previous work experience,

which should serve to reduce the proportion of unemployed classified as new entrants and

increase the proportion classified as reentrants.

Finally, the proportion classified as reentrants could be affected by a change in the

implementation of the on layoff concept. According to the official CPS definition,

individuals must expect to be recalled to be classified as on layoff. However, the unrevised

CPS did not verify whether individuals who said they were on layoff expected to be

recalled. After asking a direct question about whether an individual is on layoff, the revised

CPS verifies whether individuals expect to be recalled through a series of two questions.

Respondents are first asked if they were given a date to be recalled. If they say "no,"

respondents are then asked if they expect to be recalled in the next six months. Only

individuals who indicate either that they were given a recall date or that they expect to be

recalled in the next six months are classified as on layoff in the revised CPS. Those who do

not meet the layoff criteria continue to the job search questions. Consequently, even those

who do not expect to be recalled have an opportunity to be classified as unemployed in the

revised CPS. For men, the adjustment factors indicate that the expectation of recall

questions did screen respondents from being classified as on layoff. However, the lack of
23

significant adjustment factors for men's unemployment rates seem to indicate that the recall

expectation questions did not have an effect on men's overall rate of unemployment.

Graph 4 plots adjusted and unadjusted series for new entrants.

GRAPH 4

New Entrants as a Percentage of Unemployed 

Multiplicatively Adjusted versus Published Data

Percent

5

6.5

8

9.5

11

12.5

14

15.5

'68 '69 '70 '71 '72 '73 '74 '75 '76 '77 '78 '79 '80 '81 '82 '83 '84 '85 '86 '87 '88 '89 '90 '91 '92 '93 '94

Published

Adjusted

Duration of Unemployment

The distribution of the length of time individuals have been unemployed is one indicator of

the economy's relative position in a business cycle. In addition, economists examine the

duration of unemployment spells to obtain a measure of economic hardship, and to test

alternative theories about the effects of unemployment insurance or alternative wages.

Table 3 presents adjustment factors for the proportion of the unemployed who have been

unemployed less than 5 weeks, 5 to 14 weeks and 15 weeks or more.

TABLE 3

DURATION OF UNEMPLOYMENT

ADJUSTMENT FACTORS FOR '94 METHODOLOGICAL CHANGE

(As a Percentage of Unemployed)

Multiplicative

Factor

Additive Factor '93 Annual

Average

Less than 5 weeks 0.830*

(0.011)

-6.32*

(0.46)

36.2

5 to 14 weeks 1.014

(0.016)

0.36

(0.49)

28.9

15 weeks and over 1.169*

(0.019)

5.58*

(0.54)

34.9
24

Examination of the adjustment factors in Table 3 reveals that the new methodology

significantly increased the proportion of unemployed who had long spells of unemployment,

and significantly decreased the proportion of unemployed with spells of unemployment less

than 5 weeks. The 17 percent change between the revised and unrevised questionnaire in

the proportion of the unemployed reported to be without work 15 weeks or longer probably

can be attributed to two methodological changes.

The first change involved the use of dependent interviewing. Previous research indicated

that the duration of unemployment was not reported consistently for individuals who had

been unemployed in consecutive months. (Polivka and Rothgeb). Results collected using

the unrevised CPS from November 1992 through December 1993 verified this previous

research. Specifically, when unemployment durations were collected independently using

the unrevised procedures each month, only 26.1 percent of those unemployed in

consecutive months increased their reported durations by four weeks plus or minus a week.

Only 15.3 percent increased their length of unemployment by exactly four weeks.

Approximately 46 percent of those unemployed in consecutive months reported a duration

in the subsequent month that was less than three weeks greater than the duration reported in

the previous month, and 28.5 percent reported a duration that was more than five weeks

greater than the length of unemployment reported in the previous month.

In the revised CPS, these reporting inconsistencies were eliminated through the use of

dependent interviewing and automatic updating. Rather than asking everyone each month

how long they had been unemployed, individuals who were looking for work or on layoff in

consecutive months had their initially reported durations automatically increased by 4 or 5

weeks in the subsequent months. The choice of adding 4 or 5 weeks was based on the

number of weeks between surveys.

The second methodological change that probably influenced the reported duration of

unemployment involved the reduction of response burden for the longer term unemployed.

In the unrevised CPS, respondents were forced to report how long they had been looking

for work or on layoff in weeks. Research by Bowers and Horvath found that forcing

respondents to report in weeks resulted in the under reporting of durations for those with

spells of unemployment lasting 26 weeks or longer. In the revised CPS respondents are

permitted to report their durations of joblessness in weeks, months, or years as they prefer.

To incorporate this change the question wording was changed from "How many weeks

have you been looking for work?" ("How many weeks ago were you laid off?") to "As of

the end of LAST WEEK, how long had you been looking for work?" ("As of the end of

LAST WEEK, how long had you been on layoff?")
25

There is evidence that the choice of reporting periodicity and alternative wording in the

revised questionnaire increased the reported durations of unemployment independent of

the effect of dependent interviewing. Specifically, the average durations of unemployment

from November 1992 through December 1993 for those who were either in their first or

fifth monthly interviews or not unemployed in consecutive months were 14.96 weeks for

those who received the unrevised CPS and 17.19 weeks for those who received the revised

procedures. In addition, in January 1994 when there was no dependent interviewing in the

revised CPS, the proportion of unemployed whose durations were 15 weeks or longer was

34.23 percent for those who received the revised procedures compared to 29.3 percent for

those who received the unrevised procedures.

Industry and Occupation of the Unemployed

In addition to variations in the measurement of unemployment in the aggregate and for

various demographic groups, analysts are also interested in the cyclical behavior of

unemployment within various industries and occupations. To facilitate comparisons after

the redesign, Table 4 presents adjustment factors for the proportion of unemployed with

previous work experience in nine broad industry categories, and Table 5 provides

adjustment factors for the proportion of unemployed in six broad occupation groups. 6

 

6 Unemployed individuals who were classified as new entrants to the labor market or whose immediate

work experience was in the military were excluded from the analysis.
26

TABLE 4

INDUSTRY OF THE UNEMPLOYED

ADJUSTMENT FACTORS FOR '94 METHODOLOGICAL CHANGE

(As a Percentage of Unemployed Who Had Previous Work Experience)

Multiplicative

Factor

Additive Factor '93 Annual

Average

 Agriculture 1.264*

(0.088)

0.69*

(0.19)

3.0

 Mining 0.79

(0.13)

-0.105

(0.081)

0.7

 Construction 0.981

(0.029)

-0.26

(0.37)

12.3

 Manufacturing 0.910*

(0.023)

-1.57*

(0.46)

19.1

 Transportation and Public Utilities 0.979

(0.051)

-0.10

(0.26)

5.2

 Wholesale and Retail Trade 0.980

(0.020)

-0.43

(0.53)

25.4

 Finance, Insurance and Real Estate 0.941

(0.057)

-0.19

(0.21)

4.1

 Services 1.089*

(0.020)

2.50*

(0.54)

27.9

 Public Administration 0.848*

(0.062)

-0.30

(0.19)

2.4
27

TABLE 5

OCCUPATION OF THE UNEMPLOYED

ADJUSTMENT FACTORS FOR '94 METHODOLOGICAL CHANGE

(As a Percentage of Unemployed Who Had Previous Work Experience)

Multiplicative

Factor

Additive Factor '93 Annual

Average

Managerial and Professional Specialty 1.009

(0.033)

0.23

(0.39)

12.7

Technical, Sales and

Administrative support

0.986

(0.019)

-0.39

(0.53)

26.8

Service Occupations 1.049

(0.026)

0.87

(0.46)

17.6

Precision Production, Craft and Repair 0.952

(0.028)

-0.72

(0.42)

14.8

Operators, Fabricators and Laborers 0.973

(0.020)

-0.65

(0.51)

24.3

Farming, forestry and Fishing 1.190*

(0.071)

0.71*

(0.22)

3.8

The adjustment factors in Table 5 indicate that the new methodology significantly increased

the proportion of unemployed with previous work experience who had worked in the

Agriculture and Service industries, and significantly decreased the proportion who had

worked in the Manufacturing sector. Graph 5 plots adjusted and unadjusted series for the

proportion of unemployed with previous work experience in the Manufacturing sector.

GRAPH 5

Unemployed Who Had Worked in Manufacturing 

as a Percentage of the Unemployed Who Had Previously Worked

Multiplicatively Adjusted versus Published Data

Percent

12

14

16

18

20

22

24

26

28

30

'76 '77 '78 '79 '80 '81 '82 '83 '84 '85 '86 '87 '88 '89 '90 '91 '92 '93 '94

Published

Adjusted
28

Graph 5, along with the indication provided by the adjustment factors that the almost 9

percent decrease in the proportion of unemployed who had worked in the manufacturing

sector corresponded to an almost 9 percent increase in the proportion of unemployed

classified as having worked in the Service sector, suggest that not accounting for the

redesign could distort comparisons over time of slack demand within industries.

The adjustment factors for the occupations of the unemployed with previous work

experience indicate that the new methodology increased the proportion classified as having

worked in the Farming, Forestry and Fishing occupation by 19 percent. None of the other

occupational adjustment factors were significant at the 5 percent level.

The changes between the new and old methodologies in the industry and occupation

distributions of the unemployed with previous work experience are probably due to a

combination of factors. As was previously noted, the new methodology was estimated to

cause a smaller proportion of the unemployed to be classified as new entrants.

A decline in the proportion of unemployed classified as new entrants would cause an

increase in the proportion of the unemployed classified as having previous work experience

which in turn could influence the industry and occupation distribution of the unemployed

with previous work experience.

Other changes in the revised questionnaire such as an explicit probe about the existence of a

family business or farm and the reordering of the questions asking unemployed individuals

about the industry and occupation of their previous employment also could have affected

the industry and occupation distribution of the unemployed.

Employment and Related Employment Estimates

Employment-to-Population Ratios

Table 6 presents adjustment factors for employment-to-population ratios for various

demographic groups.
29

TABLE 6

EMPLOYMENT-TO-POPULATION RATIO

ADJUSTMENT FACTORS FOR '94 METHODOLOGICAL CHANGE

Multiplicative

Factor

Additive Factor '93 Annual

Average

 Total 16+ 1.0053*

(0.0017)

0.33*

(0.10)

61.6

 Men 16+ 0.9964*

(0.0020)

-0.25*

(0.14)

69.9

 Women 16+ 1.0156*

((0.0025)

0.84*

(0.13)

54.1

 White Men 16+ 0.9967

(0.0025)

-0.23

(0.18)

71.3

 White Women 16+ 1.0169*

(0.0030)

0.92*

(0.16)

54.7

 Black Men 16+ 0.9831*

(0.0089)

-1.02*

(0.53)

59.1

 Black Women 16+ 1.0093

(0.0089)

0.48

(0.45)

50.5

 Teenagers (16-19) 1.005

(0.011)

0.21

(0.45)

41.7

 20-24 Year Olds 0.9920

(0.0056)

-0.55

(0.38)

69.0

 25-54 Year Olds 1.0035*

(0.0018)

0.27

(0.14)

78.7

 55-64 Year Olds 1.0124

(0.0075)

0.65

(0.39)

53.8

 65 Years and Older 1.078*

(0.019)

0.84*

(0.20)

10.9

 Men 16 -19 Years Old 0.988

(0.014)

-0.41

(0.60)

42.2

 Men 20-24 Years Old 0.9815*

(0.0068)

-1.38*

(0.51)

73.8

 Men 25-54 Years Old 0.9969

(0.0019)

-0.27

(0.16)

87.1

 Men 55-64 Years Old 0.9927

(0.0089)

-0.44

(0.55)

63.1

 Men 65 Years and Older 1.062*

(0.025)

0.88*

(0.36)

15.1

 Women 16-19 Years Old 1.025

(0.017)

0.97

(0.68)

41.2

 Women 20-24 Years Old 1.0047

(0.0079)

0.30

(0.50)

64.4

 Women 25-54 Years Old 1.0110*

(0.0027)

0.77*

(0.19)

70.5

 Women 55-64 Years Old 1.032*

(0.011)

1.47*

(0.47)

45.4

 Women 65 Years and Older 1.098*

(0.027)

0.77*

(0.20)

7.92

 Adult Men (20+) 0.9970

(0.0024)

-0.21

(0.18)

72.0

 Adult Women (20+) 1.0150*

(0.0029)

0.83*

(0.16)

55.0
30

Examination of the adjustment factors indicates that the new methodology significantly

raised the overall employment-to-population ratio; however, the estimated adjustment

factors also indicate that the overall effect masked differences by gender. Specifically, the

multiplicative adjustment factors for all men, black men, and men 20 to 24 years old were

significantly less than 1 at a 5 percent significant level, and the additive factors were

negative and statistically different from 0 at a 5 percent level. These results suggest that the

new methodology significantly lowered the employment-to-population ratio for these

groups. In contrast, the estimated adjustment factors indicate that the new method would

significantly raise the employment-to-population ratio for women, white women, women

age 55 to 64, and women 65 and older. The only group of men who did not follow this

pattern was men who were 65 years and older. Using the estimated adjustment factor to

account for the new methodology would significantly raise the employment-to-population

ratio for these men.

The effect of the new methodology on women's and older worker's employment-topopulation ratios probably is at least partially attributable to changes in wording of the

questionnaire. These changes include the elimination of the opening labor force question

inquiring about major activities last week which may have caused some respondents to think

that the CPS was not interested in more casual or intermittent work activity; the rephrasing

of the questions asking about work activities last week to specifically refer to any work for

pay and to remove the phrase "not counting work around the house"; and explicitly asking

about employment in family businesses.7

Graph 6 plots both adjusted and unadjusted employment-to-population ratios for women.

 

7 See Cohany, Polivka, and Rothgeb (1994), Polivka (1994) and Rothgeb (1994) for a more detailed

discussion of why women's employment-to-population ratio may be larger with the new methodology.
31

GRAPH 6

Women's Employment-to-Population Ratio 

Multiplicatively Adjusted versus Published Data

Percent

39.5

41

42.5

44

45.5

47

48.5

50

51.5

53

54.5

'68 '69 '70 '71 '72 '73 '74 '75 '76 '77 '78 '79 '80 '81 '82 '83 '84 '85 '86 '87 '88 '89 '90 '91 '92 '93 '94

Published

Adjusted

The increase in women's employment-to-population ratio may not seem large; however, the

increase implied by the multiplicative factor is equivalent to approximately 750,000 women.

Part-time Workers and Workers Who are Part-time for Economic Reasons

In addition to the proportion of the population employed, economists, sociologists and

policy analysts are also interested in the percentage of employed who are working part-time

and the percent of the employed who are part-time for economic reasons such as poor

business conditions or the inability to find full-time work. Table 7 provides adjustment

factors to account for the effect of the new methodology on the number of part-time

workers and workers who are part time for economic reasons.
32

TABLE 7

PART-TIME WORKERS AND ECONOMIC PART-TIME WORKERS

ADJUSTMENT FACTORS FOR '94 METHODOLOGICAL CHANGE

(As a Percentage of Employed)

Multiplicative

Factor

Additive Factor '93 Annual

Average

Part-time Workers

 Total 1.0983*

(0.0080)

1.73*

(0.13)

17.5

 Adult Men 1.074*

(0.016)

0.65*

(0.13)

8.5

 Adult Women 1.1246*

(0.0094)

2.81*

(0.20)

22.8

 Teenagers 1.0329*

(0.0092)

2.35*

(0.64)

67.7

Part-time Workers for Economic

Reasons

0.806*

(0.011)

-1.003*

(0.062)

5.3

The adjustment factors for part-time workers imply that the unrevised CPS either was not

completely enumerating individuals who were working part time or was misclassifying

them. Specifically, the multiplicative adjustment factors indicate that the unrevised CPS

underestimated the percentage of employed who were working part time by 9.8 percent.

The adjustment factors further indicate that this incomplete enumeration or misclassification

occurred across various age and gender groups since both the multiplicative and additive

factors for adult men, adult women and teenagers all are significantly different from 1 or 0

respectively at a 5 percent level.

Part of the estimated effect of the new methodology on the proportion of employed

classified as part-time workers could be due to the elimination of a misclassification caused

by the structure of the unrevised CPS. In the unrevised CPS only individuals who actually

worked less than 35 hours in the reference week were asked how many hours they usually

worked. All individuals who were at work 35 hours or more were automatically classified

as full-time, regardless of the number of hours they usually work. In the revised CPS all

respondents are first asked how many hours they usually work, and are then asked in

subsequent questions about their actual hours . The new methodology could also increase
33

the proportion of employed workers classified as part time if the additional workers

measured in the revised CPS, as evidenced by the revised CPS's higher employment-topopulation ratios, were disproportionately part-time workers.

At the same time that the adjustment factors imply that the new methodology increases the

percentage of the employed working part time, they also indicate that the new methodology

would decrease the proportion of the employed classified as part time for economic reasons

by approximately 20 percent. The reduction in the proportion of the employed classified as

part time for economic reasons most likely occurred because the unrevised CPS did not

directly ask people if they wanted to and were available to work full time. Rather,

individuals' desire and availability to work full time were assumed from the answers

provided for why they were working part time. In the revised CPS individuals are asked

directly if they want to and are available to work full time. In addition, part of the decrease

in the proportion of employed working part time for economic reasons with the new

methodology could be attributable to the more complete measurement of part time workers

in the revised CPS.

Graphs 7 and 8 plot multiplicatively adjusted versus published data for part-time workers

and workers who are part-time for economic reasons respectively. The sharp jumps in the

unadjusted data highlight the importance of adjusting the CPS data for the redesign when

making comparisons over time. Failure to adjust the data could cause analysts to reach

improper policy conclusions about societal or economic changes which may or may not

have occurred between the early 1990's and later years.

 GRAPH 7

Part-time Workers as a Percentage of the Employed 

Multiplicatively Adjusted versus Published Data

Percent

13.5

14.2

14.9

15.6

16.3

17

17.7

18.4

19.1

19.8

20.5

'68 '69 '70 '71 '72 '73 '74 '75 '76 '77 '78 '79 '80 '81 '82 '83 '84 '85 '86 '87 '88 '89 '90 '91 '92 '93 '94

Published

Adjusted
34

GRAPH 8

Workers who are Part time for Economic Reasons 

as a Percentage of the Employed 

Multiplicatively Adjusted versus Published Data

Percent

2.5

3

3.5

4

4.5

5

5.5

6

'76 '77 '78 '79 '80 '81 '82 '83 '84 '85 '86 '87 '88 '89 '90 '91 '92 '93 '94

Published

Adjusted

Class of Worker

Using the CPS data, employed individuals can be classified as wage and salary workers who

work in the private sector, wage and salary workers who work for the government, selfemployed incorporated, self-employed unincorporated, and unpaid family workers. Table 8

contains adjustment factors for these class-of-worker categories, along with factors for selfemployed incorporated and self employed unincorporated combined, and all wage and

salary workers. In addition, since BLS publishes estimates that classify the self-employed

incorporated as wage and salary workers, adjustment factors for wage and salary workers

and the self-employed incorporated combined are also provided.
35

TABLE 8

CLASS OF WORKER

ADJUSTMENT FACTORS FOR '94 METHODOLOGICAL CHANGE

(As a Percentage of Employed)

Multiplicative

Factor

Additive Factor '93 Annual

Average

TOTAL

 Wage and Salary, Private 0.9925*

(0.0018)

-0.55*

(0.14)

72.6

 Wage and Salary, Government 0.9783*

(0.0070)

-0.34*

(0.11)

15.5

 Self-employed Incorporated 1.160*

(0.022)

0.462*

(0.058)

3.0

 Self-employed Unincorporated 1.062*

(0.012)

0.486*

(0.091)

8.7

 Self-employed Uni and Inc. 1.088*

(0.011)

0.95*

(0.11)

11.7

 Unpaid Family Workers 0.750*

(0.062)

-0.057*

(0.015)

0.3

 Wage and Salary and Self -emp Inc. 0.99535*

(0.00089)

-0.429*

(0.083)

91.1

MEN

 Wage and Salary, Private 0.9965

(0.0025)

-0.26

(0.18)

71.7

 Wage and Salary, Government 0.986

(0.011)

-0.18

(0.14)

13.0

 Self-employed Incorporated 1.099*

(0.023)

0.401*

(0.087)

4.3

 Self-employed Unincorporated 1.004

(0.013)

0.03

(0.13)

10.9

 Self-employed Uni and Inc. 1.031*

(0.011)

0.44*

(0.15)

15.2

 Unpaid Family Workers 0.93

(0.13)

-0.007

(0.013)

0.1

 Wage and Salary and Self -emp Inc. 0.9996

(0.0014)

-0.04

(0.12)

89.0

WOMEN

 Wage and Salary, Private 0.9881*

(0.0025)

-0.88*

(0.19)

73.6

 Wage and Salary, Government 0.9677*

(0.0086)

-0.61*

(0.17)

18.5

 Self-employed Incorporated 1.368*

(0.049)

0.547*

(0.060)

1.4

 Self-employed Unincorporated 1.184*

(0.022)

1.02*

(0.11)

6.0

 Self-employed Uni and Inc. 1.22*

(0.02)

1.58*

(0.12)

7.5

 Unpaid Family Workers 0.673*

(0.058)

-0.120*

(0.024)

0.4

 Wage and Salary and Self -emp Inc. 0.9902*

(0.0010)

-0.925*

(0.098)

93.5
36

The adjustment factors in table 5 indicate that under the new methodology a significantly

higher proportion of the total employed and employed women were classified as selfemployed incorporated and self-employed unincorporated. At the same time a significantly

smaller proportion were classified as wage and salary workers --either government or

private-- and unpaid family workers. The larger proportion of employed classified as selfemployed incorporated and unincorporated with the new methodology is probably due to a

combination of changes incorporated into the revised questionnaire. These include a direct

question about household businesses at the beginning of the labor force questions, the

reordering of the class of worker and industry/occupation questions to prevent interviewers

from entering responses without asking all the appropriate questions, and the general

changes in the measurement of employment embodied in the revised CPS.

Industry and Occupation of the Employed

In addition to determining whether individuals are wage and salary workers or selfemployed, the CPS also collects information about the industry and occupation in which

people work. Table 9 contains adjustment factors for the proportion of employed who

were classified as working in one of six broad industry categories. Table 10 presents

adjustment factors for the proportion of employed who were reported as working in one of

nine major occupation groups.
37

TABLE 9

INDUSTRY OF THE EMPLOYED

ADJUSTMENT FACTORS FOR '94 METHODOLOGICAL CHANGE

(As a Percentage of Employed)

Multiplicative

Factor

Additive Factor '93 Annual

Average

 Agriculture 1.088*

(0.024)

0.195*

(0.051)

2.6

 Mining 1.078

(0.056)

0.028

(0.019)

0.6

 Construction 0.960*

(0.013)

-0.247*

(0.081)

6.1

 Manufacturing 1.0197*

(0.0069)

0.33*

(0.11)

16.4

 Transportation and Public Utilities 0.976*

(0.011)

-0.177*

(0.079)

7.1

 Wholesale and Retail Trade 0.9925

(0.0059)

-0.16

(0.12)

20.8

 Finance, Insurance and Real Estate 1.015

(0.012)

0.099

(0.075)

6.7

 Services 0.9987

(0.0041)

-0.05

(0.15)

35.1

 Public Administration 0.991

(0.014)

-0.042

(0.064)

4.8
38

TABLE 10

OCCUPATION OF THE EMPLOYED

ADJUSTMENT FACTORS FOR '94 METHODOLOGICAL CHANGE

(As a Percentage of Employed)

Multiplicative

Factor

Additive Factor '93 Annual

Average

Managerial and Professional Specialty 1.0155*

(0.0050)

0.42*

(0.14)

27.1

Technical, Sales and

Administrative Support

0.9947

(0.0048)

-0.17

(0.15)

30.9

Service Occupations 0.9983

(0.0078)

-0.02

(0.11)

13.8

Precision Production, Craft and Repair 0.9837

(0.0089)

-0.18

(0.10)

11.2

Operators, Fabricators and Laborers 0.9805*

(0.0076)

-0.28*

(0.11)

14.3

Farming, forestry and Fishing 1.082*

(0.026)

0.196*

(0.058)

2.8

Examination of the adjustment factors in Table 9 indicate that the new methodology

significantly increased the proportion of employed classified as working in Agriculture,

Manufacturing and Finance, Insurance and Real Estate industries. The adjustment factors

also indicate that at the five percent level, the new methodology significantly decreased the

proportion of employed classified as working in Construction, and Transportation and

Public Utilities industry.

The adjustment factors for the proportion of employed working in various occupations

indicate that the new methodology significantly increased the proportion classified in the

Managerial and Professional Specialty and significantly decreased the proportion of

employed classified as working as an Operator, Fabricator or Laborer.

Shifts between the revised and the unrevised CPS in the industry and occupation

distribution of the employed are probably attributable to a combination of methodological

differences. Again as with the class of worker distribution, the industry and occupation

distributions could be influenced by the different ordering of the class of work and

industry/occupation questions, and the inclusion of a direct probe about the existence of a

household business or farm in the revised questionnaire. In addition, the industry and
39

occupation distributions of the employed could be affected by the increase in work activity,

particularly among women, measured in the revised questionnaire.

Labor Force Participation and Discouraged Workers

Labor Force Participation Rates

Table 11 presents multiplicative and additive adjustment factors for the labor force

participation rate, the proportion of the population that is either employed or unemployed,

for various demographic groups.
40

TABLE 11

LABOR FORCE PARTICIPATION RATE

ADJUSTMENT FACTORS FOR '94 METHODOLOGICAL CHANGE

Multiplicative

Factor

Additive Factor '93 Annual

Average

 Total 16+ 1.0064*

(0.0014)

0.423*

(0.093)

66.2

 Men 16+ 0.9979

(0.0022)

-0.16

(0.16)

75.2

 Women 16+ 1.016*

(0.0027)

0.95*

(0.15)

57.9

 White Men 16+ 0.9988

(0.0022)

-0.08

(0.17)

76.1

 White Women 16+ 1.0194*

(0.0031)

1.12*

(0.17)

58.0

 Black Men 16+ 0.9885

(0.0076)

-0.83

(0.52)

68.6

 Black Women 16+ 0.9990

(0.0076)

-0.04

(0.45)

57.4

 Teenagers (16-19) 1.0173*

(0.0090)

0.90*

(0.44)

51.5

 20-24 Year Olds 0.9941

(0.0044)

-0.46

(0.34)

77.1

 25-54 Year Olds 1.0024

(0.0015)

0.20

(0.12)

83.5

 55-64 Year Olds 1.0190*

(0.0071)

1.04*

(0.38)

56.4

 65 Years and Older 1.094*

(0.019)

1.03*

(0.20)

11.3

 Men 16 -19 Years Old 1.004

(0.012)

0.24

(0.60)

53.1

 Men 20-24 Years Old 0.9847*

(0.0053)

-1.30*

(0.45)

83.1

 Men 25-54 Years Old 0.9960*

(0.0015)

-0.37*

(0.14)

92.6

 Men 55-64 Years Old 0.9961

(0.0087)

-0.25

(0.56)

66.5

 Men 65 Years and Older 1.084*

(0.026)

1.25*

(0.38)

15.6

 Women 16-19 Years Old 1.033*

(0.014)

1.67*

(0.65)

49.9

 Women 20-24 Years Old 1.0049

(0.0066)

0.35

(0.46)

71.3

 Women 25-54 Years Old 1.0099*

(0.0024)

0.74*

(0.18)

74.7

 Women 55-64 Years Old 1.043*

(0.01)

2.03*

(0.47)

47.3

 Women 65 Years and Older 1.106*

(0.026)

0.85*

(0.20)

8.2

 Adult Men (20+) 0.9975

(0.0022)

-0.20

(0.17)

76.9

 Adult Women (20+) 1.0153*

(0.0027)

0.90*

(0.16)

58.4
41

In general, the adjustment factors for the labor force participation rates follow the pattern

one would expect after examining the unemployment rate and employment-to-population

ratio adjustment factors. The estimated factors indicated that the new methodology would

significantly raise the labor force participation rate of all women, white women and women

in every age category except 20 to 24. In contrast, the adjustment factors suggest that the

new methodology would significantly lower the labor force participation rates of men 20 to

24 years old and 25 to 54 years old.

Discouraged Workers

In the unrevised CPS, individuals who are not in the labor force who wanted jobs, but had

not looked for work in the prior month because they believe no jobs are available, were

defined as discouraged workers. Discouraged workers have been the focus of attention in

the past as one indicator of the economy's health and as a group of individuals who may be

suffering particular economic hardship. Nevertheless, the definition of discouraged workers

in the unrevised CPS frequently has been criticized. The National Commission on

Employment and Unemployment Statistics faulted the definition as being too subjective

because it was based primarily on individuals' desire for work rather than on more objective

criteria such as recent job search. The definition in the unrevised CPS also has been

criticized because individuals' information about availability for work was inferred from

their reasons for not looking. To address the Commission's concerns, two new

requirements were added to the definition in the revised CPS questionnaire. To be

classified as discouraged under the new methodology, individuals have to have engaged in

some job search within the past year (or since they last worked if they have worked within

the last year) and currently be available to take a job, in addition to the old criteria of

currently wanting a job, and having given up looking for reasons related to the economy 8

The adjustment factors for discouraged workers, contained in Table 12, indicate that the

two additional criteria in the revised CPS decreased the proportion of those not in the labor

force classified as discouraged workers by fifty percent.

 

8 Also starting in January 1994, the series of questions that potentially classifies those not in the labor force

as discouraged are asked of the entire CPS sample, rather than being limited to individuals in their fourth

and eighth monthly interviews as was done in the past.
42

TABLE 12

DISCOURAGED WORKERS

ADJUSTMENT FACTORS FOR '94 METHODOLOGICAL CHANGE

(As a Percentage of those Not in the Labor Force)

Multiplicative

Factor

Additive Factor '93 Annual

Average

 Total 0.500*

(0.011)

-0.782*

(0.025)

1.72

Note on Aggregation

The adjustment factors presented in the tables in the preceding sections were estimated to

be optimal for the statistic specified. In order to obtain a set of estimates that are consistent

for both an overall statistic and mutually exclusive subgroups beneath the overall statistic

(for example, the total unemployment rate, adult men's unemployment rate, adult women's

unemployment rate, and teenagers unemployment rate), it would be necessary to subdivide

the population into the lowest level of mutually exclusive subgroups for whom consistent

estimates are desired, and then adjust the component levels that are used for calculating the

statistics for each of the subgroups (for example, employment levels, unemployment levels

and, by subtraction from the population estimates, not in labor force levels). Once the

adjusted levels for the subcategories have been obtained, consistent estimates for the

statistics of interest for the subgroups and the aggregate could be derived. It is important to

note, however, that enforcing consistency would not necessarily result in the same adjusted

aggregate statistics as would be obtained if the aggregate statistic had been adjusted

directly. The issues of aggregation surrounding the adjustment factors for the redesign are

similar to those surrounding the aggregation and estimation of seasonally adjusted statistics.

For statistics that BLS seasonally adjust, consistent estimates are obtained by first

seasonally adjusting levels for subgroups and then aggregating. Adjustment factors for the

12 basic labor force series which are seasonally adjusted to obtain the seasonally adjusted

Total National unemployment rate are provided in Appendix A. A comparison of what the

annual unemployment rate would have been in 1992 if the multiplicative adjustment factor

for the redesign had been applied directly as opposed to adjusting the subgroups first is also

provided in Appendix A. It should be noted that the estimates are only different by .03

percentage point.
43

Conclusion

The purpose of this paper was to provide adjustment factors in order that individuals could

continue to use CPS data historically after the redesign.9 In addition, the adjustment factors

were examined to provide insight into how the unrevised CPS might have been providing a

distorted picture of the American economy. Overall, the adjustment factors suggest that the

unrevised CPS was not mismeasuring individuals who were working full time in steady jobs

or the vast majority of individuals looking for work, those in the center of the lens so to

speak. Rather, the adjustment factors indicate that the unrevised CPS was less in focus for

those on the periphery of the labor market -- those involved in more casual, intermittent or

marginal work activities, individuals who might have tentatively tested the labor market, and

older workers. The adjustment factors also imply that the unrevised CPS was not

measuring as accurately as possible some of the characteristics of the employed and the

unemployed. Specifically, the adjustment factors suggest that the unrevised CPS

underestimated the proportion of employed who were part-time workers, overestimated the

proportion of employed who were part-time for economic reasons, and mismeasured the

individuals' reasons for being unemployed. By providing adjustment factors, it is hoped that

a clearer picture of the economy through a redesigned CPS can be obtained without

precluding the comparisons of CPS estimates over time.

 

9 Note on Micro data Weighting

Our primary concern has been to develop adjustment factors, both additive and multiplicative, for aggregate

series. Undoubtedly, some researchers will want “adjusted micro data weights” so that they can do analyses

which are comparable before and after the redesign. A tempting way to do this is next described by

example. First partition the population into Men aged 16-19, Men aged 20+, Women aged 16-19, Women

aged 20+, and cross these classifications with the labor force categories Unemployed, Employed in

Agriculture, Employed in Nonagricultural Industries, and Not in Labor Force. Multiplicative adjustment

factors for twelve of these are given in Appendix A. Implied adjustment factors for the remaining four Notin-Labor Force categories could be obtained by taking the adjusted Not in Labor Force total (obtained by

subtracting the adjusted labor force total from the unadjusted population count for each group) and dividing

that by the unadjusted Not in Labor Force total. This ratio would be different for any given month (unlike

the direct adjustment factors for unemployment and employment) because Not in labor force is obtained

indirectly by subtraction from the population total which is assumed fixed and not subject to adjustment.

Once the sixteen multiplicative adjustment factors are obtained, they could be applied to the sampling

weights for each of the respondents within each of the sixteen groups, producing “adjusted micro data

weights.” Then, the sum of all of the respondents within each of the sixteen groups, using the “adjusted

micro data weights,” would equal the multiplicatively adjusted aggregate total by the distributive law.

While this may be tempting, we do not recommend using these "micro data weights" for any analyses other

than constructing totals for each of those sixteen groups, since there is no guarantee that these weights

would have any meaning if used in more complicated analyses.
44

APPENDIX A

The table below contains adjustment factors for the 12 series that are seasonally adjusted

and then aggregated together to obtain a seasonally adjusted total national unemployment

rate. Adjusted levels of those not in the labor force could be obtained by subtraction from

the population estimates for the given characteristic.

UNEMPLOYMENT AND EMPLOYMENT LEVELS

ADJUSTMENT FACTORS FOR '94 METHODOLOGY

Characteristic

Multiplicative Factor Additive Factor

Unemployed Teenage Men 1.030

(0.036)

20963

(26192)

Unemployed Teenage Women 1.063

(0.044)

44656

(25338)

Unemployed Adult Men 1.0024

(0.016)

12765

(70595)

Unemployed Adult Women 1.018

(0.017)

62617

(58405)

Teenage Men Employed in Agriculture 1.076

(0.094)

10340

(13515)

Teenage Women Employed in Agriculture 1.034

(0.18)

-1799

(6861)

Adult Men Employed in Agriculture 1.042

(0.024)

80156

(47058)

Adult Women Employed in Agriculture 1.326*

(0.057)

175713*

(24904)

Teenage Men Employed in Nonagriculture 0.986

(0.017)

-32305

(48911)

Teenage Women Employed in Nonagriculture 1.022

(0.020)

56280

(53558)

Adult Men Employed in Nonagriculture 0.9956*

(0.0023)

-263973*

(138281)

Adult Women Employed in Nonagriculture 1.012*

(0.0026)

627993*

(135314)

The 1992 annual average unemployment rate obtained when the levels for the subgroups

were adjusted and the unemployment rate was then calculated was 7.45 percent. The 1992

annual average unemployment rate when the multiplicative adjustment factors in Table 1

were applied directly was 7.47 percent.
45

APPENDIX B

The table below contains differences in 1993 annual average CPS labor force estimates

when 1990 versus 1980 census-based population controls were used. The differences are

defined as the 1990 estimates minus the 1980 estimates. 10

DIFFERENCE IN 1993 ANNUAL AVERAGE LABOR FORCE ESTIMATES

USING 1990 VS 1980 POPULATION WEIGHTS

(Difference =1990-1980)

Unemployment

Rate

Employment-toPopulation RatioLabor Force

Participation

Rate

 Total 16+ 0.10 0.08 0.16

 Men 16+ 0.10 0.17 0.26

 Women 16+ 0.10 -0.02 0.04

 White Men 16+ 0.09 0.07 0.15

 White Women 16+ 0.06 -0.09 -0.06

 Black Men 16+ 0.03 0.85 1.01

 Black Women 16+ 0.13 0.38 0.52

 Teenagers (16-19) 0.03 -0.04 -0.03

 20-24 Year Olds 0.06 -0.13 -0.09

 25-54 Year Olds 0.04 -0.10 -0.07

 55-64 Year Olds -0.02 -0.02 -0.03

 65 Years and Older -0.01 -0.07 -0.08

 Men 16 -19 Years Old -0.04 0.08 0.08

 Men 20-24 Years Old 0.00 0.07 0.08

 Men 25-54 Years Old 0.04 -0.08 -0.05

 Men 55-64 Years Old -0.03 0.04 0.02

 Men 65 Years and Older 0.00 -0.05 -0.05

 Women 16-19 Years Old 0.09 -0.16 -0.14

 Women 20-24 Years Old 0.11 -0.42 -0.38

 Women 25-54 Years Old 0.05 -0.14 -0.12

 Women 55-64 Years Old 0.00 -0.09 -0.09

 Women 65 Years and Older -0.02 -0.03 -0.04

 Adult Men (20+) 0.07 0.28 0.36

 Adult Women (20+) 0.07 0.03 0.08

 

10 The proportion of the population within any subgroup may not remain constant when 1980 versus 1990

population weights are used. For example, the percentage of women age 25 to 54 when 1980 population

weights were used was 55.8 percent. When 1990 population weights were used the percentage was 71.7

percent. Since the proportion of the population within subgroups may not remain constant when different

weights are used, the difference between estimates for an aggregate group (e.g., the labor force participation

rate for all women) does not have to be bound by the differences for various subgroups (e.g., the labor force

participation rates for women age 16-19, 20-24, 25,-54 55-64 and 65 years and older).
46

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48

The CPS After the Redesign: Refocusing the Economic Lens

Anne E. Polivka

Bureau of Labor Statistics

and

Stephen M. Miller

Bureau of Labor Statistics

December, 1994

Revised March, 1995

The views in this paper are those of the authors,

 and do not represent official BLS policy

This paper was prepared for the CRIW Labor Statistics Measurement Issues Conference

held December 15-16, 1994 in Washington, DC.