BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR

Bureau of Labor Statistics

 

OFFICE OF EMPLOYMENT RESEARCH

AND PROGRAM DEVELOPMENT

Did Job Security Decline in the 1990s?

Jay Stewart, U.S. Bureau of Labor Statistics

Working Paper 330

August 2000

 

The views expressed are those of the author and do not necessarily reflect the policies of the U.S. Bureau of Labor

Statistics or the views of other staff members. This paper has benefited from conversations with Harley Frazis, Mark

Loewenstein, Marilyn Manser, Jim Spletzer, and Rob Valletta. I thank two anonymous referees from their suggestions. I also

thank Hank Farber, Larry Katz, David Neumark, and other participants in the Russell Sage Foundation's "Conference on

Changes in Job Stability and Job Security" for their comments on an earlier version of the paper. Hector Rodriguez assisted in

putting together the tables and figures.
July 6, 1999

Did Job Security Decline in the 1990s?

Jay Stewart*

Office of Employment Research

and Program Development

Bureau of Labor Statistics

2 Massachusetts Avenue, NE

Room 4945

Voice (202) 691-7376

FAX (202) 691-7425

E-Mail Stewart_J@BLS.GOV

* This paper has benefited from conversations with Harley Frazis, Mark Loewenstein, Marilyn

Manser, Jim Spletzer, and Rob Valletta. I thank two anonymous referees for their suggestions. I

also thank Hank Farber, Larry Katz, David Neumark, and other participants in the Russell Sage

Foundation’s "Conference on Changes in Job Stability and Job Security" for their comments on

an earlier version of the paper. Hector Rodriguez assisted in putting together the tables and

figures. Any opinions expressed here are mine, and do not necessarily reflect those of the

Bureau of Labor Statistics.
Did Job Security Decline in the 1990s?

Jay Stewart

Office of Employment Research

and Program Development

Bureau of Labor Statistics

2 Massachusetts Avenue, NE

Room 4945

Voice (202) 606-7376

FAX (202) 606-4602

E-Mail Stewart_J@BLS.GOV

Abstract

This paper examines long term trends in job security by looking at employment-tounemployment transitions in the March CPS. Unlike other datasets used to examine this

issue, these data provide a consistently defined measure for the period covering 1967 through

1997, are available every year, and cover all workers. I find that job security appears to have

declined during the early 1970s, but from the mid 1970s through the mid-to-late 1990s, there

has been no change. These findings are consistent with the popular perception that jobs were

less secure in the 1980s than in the 1970s, but they are not consistent with the perception that

job security continued to decline in the 1990s.
I. Introduction

There has been a growing interest, both among the popular press and among researchers,

in whether job stability and security have declined in recent years.1

 The popular perception is

that jobs were less stable and less secure in the 1980s than in the 1970s and that this decline in

stability and security continued into the 1990s.2

 So far, the evidence from academic studies

indicates that job stability did not decline between the mid 1970s and the early 1990s, but that

there have been changes for some groups. Job stability declined among men, primarily due to

declining stability among high school dropouts.3 Stability decreased slightly among high tenure

workers.4 For women with at least a high school diploma, job stability has increased

significantly.5

However, the absence of a decline in job stability does not preclude declines in job

security. Studies conducted so far have found that job loss appears to have been more common

in the late 1980s and early 1990s than in the 1970s, and that the consequences may have become

more severe. But as in the job stability debate, data quality has become an important issue.6

Because the primary measure of job security is the job-loss rate, most researchers have

used either the Current Population Survey (CPS) Displaced Worker Supplements (DWS), the

Panel Study of Income Dynamics (PSID), or the National Longitudinal Surveys (NLS). These

datasets have, in principle, the correct measure, but they all have breaks in series that make it

difficult to generate a consistent series over a long period (and sometimes even a short period) of

time. Although researchers have devised clever ways of addressing these breaks in series, one

can never be sure whether observed changes are real or due to changes in the survey instruments.

I take a different approach and use the March CPS. Although the March CPS does not

have a direct measure of involuntary separations, it does contain an indirect measure that is
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consistently defined over a long period of time. It is well known that job losers are more likely

to become unemployed and take longer to find employment than job leavers.7 These facts

suggest that I can shed additional light on the job security issue by examining trends in

employment-to-unemployment (EU) transitions.

The EU transition rate is an imperfect measure of job security. Not everybody who

makes an EU transition is a job loser, and not all job losers show up as having experienced an

EU transition. However, later in the paper I show that the vast majority of people who

experience an EU transition are job losers, and that nearly all of the variation in the EU transition

rate, at least during the 1987-97 period, is due to job loss. So even though the EU transition rate

does not capture all job losses, it does include the job losses policymakers are most concerned

about: those resulting in unemployment. As long as the relationship between job loss that results

in unemployment and the EU transition rate remains fairly constant, the EU transition rate should

be a reasonable indicator of trends in job security.

There are a number of advantages to using the March CPS. First, the series is

consistently defined over a long period of time (31 years), permitting longer coverage than other

commonly used datasets. Since my data cover through March 1998, I can also look at very

recent trends. Second, this dataset is a large nationally representative sample, making it possible

to identify changes for specific demographic groups. Third, because the data are available every

year, turning points can be more precisely dated.

This is not the first paper to use March CPS data to examine trends in separations. In an

earlier paper (Stewart 1998), I used these data to examine trends in job stability between 1975

and 1995.8

 In that paper, I showed that it is possible to generate a reliable measure of job

separations by combining the regular monthly labor force data with the data from the income

supplement. I compared the labor market histories generated from the 1987 March CPS to the
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labor market histories generated from the 1987 January Tenure Supplement and found that the

histories from the two data sources were consistent nearly 90 percent of the time. And because

the questions used to identify a job separation did not change, this measure is consistently

defined over the 1975-95 period. Using these data, I found that overall job stability remained

constant over this period, although there were dramatic changes for some groups. In particular,

job stability fell for men without a high school diploma and increased for women with more than

a high school diploma.

Before continuing, it is useful to describe the findings of other researchers in the context

of the data sources used and discuss some of the advantages and disadvantages of the DWS,

PSID, and NLS.

The DWS

The DWS has been conducted as a supplement to the CPS every two years since 1984. It

asks individuals about displacement from employers in the previous five years (1984-1992) or

three years (1994-1996). The DWS has the advantage of collecting information on job loss from

a large nationally representative sample. But the DWS tends to underestimate the incidence of

job loss for two reasons.

First, responses are subject to recall bias. Evans and Leighton (1995) show that recall

bias is severe, with the DWS underestimating displacement by about one-third. Boisjoly,

Duncan, and Smeeding (1998) show that the DWS understates job loss compared to the PSID,

though part of this difference appears to be a difference in concept. When they look only at job

losses that resemble displacements, they get numbers that are closer to--though still larger than--

numbers from the DWS. However, because the literature on job security has focused on trends
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in the job-loss rate, recall bias should not be a serious problem provided it has not changed over

time.9

The second reason is that the DWS collects information on at most one job loss, which

means that it measures the number of people who experienced a job loss during the period in

question, rather than the total number of job losses. Again, trend should not be affected unless

the fraction of individuals with multiple job losses has changed.

The biggest drawback to using the DWS to look at recent trends in job security is that the

main question changed in 1994 and again in 1996. Hence, one cannot be certain whether

changes in the job-loss rate are real or due to changes in the survey, making it difficult to get a

clear picture of job loss trends in the 1990s. In 1994, the reporting window was reduced from

five years to three years. To illustrate how this affects responses, consider a worker who lost a

job in 1993 and in 1990, with the earlier job being the longest. With a three-year window, this

worker would report the 1993 job loss. If the window had been five years, the respondent could

have reported either the 1990 or the 1993 job loss. If respondents always reported the most

recent job loss, researchers could make the older data comparable to the more recent data simply

by counting only job losses that occurred in the last three years. To the extent that respondents

report the longest job lost rather than the most recent job, counting only job losses that occurred

in the last 3 years would result in an underestimate of the incidence of job loss.

To address this break in series, Farber computed adjustment factors using individual

labor market history data from the PSID and used those factors to adjust for the longer window

in the earlier DWSs. Letting t be the current year, he calculated the fraction of workers who lost

a job in year t-4 and t-5 that subsequently lost a job in years t-3 through t-1. For example, among

workers who lost a job in period t-5, 27.0 percent also lost a job during the t-3 through t-1 period

(the analogous rate for t-4 is 30.17 percent). The 3-year job-loss rates from the earlier data were
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adjusted using the following formula:

3i 4i 5i

a

r3i = r + 0.3017ρ + 0.2705ρ ,

where a

3i r and 3i r are the adjusted and unadjusted 3-year job-loss rates for group i, and ρ 4i and

ρ5i are the job-loss rates in t-4 and t-5 for group 1.

There are several problems with Farber’s adjustments. First, he assumes that all multiple

job losers reported the longest job lost. Bureau of Labor Statistics (BLS) research connected

with the redesign of the DWS has found that about half of respondents report the most recent job

lost. Second, using PSID is problematic, because the job loss concepts are not comparable (see

Abraham 1997) and the PSID sample is not large enough to allow adjustment factors to vary by

demographic group.10

In 1996, a subtle change in question wording appears to have led more quitters to show

up as having lost a job. In 1994 the main question asked "During the last 3 calendar years, that

is, January 1991 through December 1993, did ... lose or leave a job because: a plant or company

closed or moved, (your/his/her) position or shift was abolished, insufficient work, or another

similar reason?", and in 1996 the main question asked "During the last 3 calendar years, that is,

January 1993 through December 1995, did ... lose a job, or leave one because: (your/his/her)

plant or company closed or moved, (your/his/her) position or shift was abolished, insufficient

work, or another similar reason?" Both questions ask whether "Dottie" lost or left a job, but the

1996 question places greater emphasis on leaving a job. This greater emphasis on leaving a job

could have caused more job leavers to answer "Yes" to the main question. If the main question

picked up more job leavers in 1996, they would most likely have shown up in the "Other"

category. Given that the increase in the "Other" category drives Farber's finding of increased job
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loss between the 1991-93 and 1993-95 periods, it is crucial to know the extent to which the

change in wording affected responses.

Research at the BLS sheds some light on this question. Abraham (1997) reports that the

"Other" category is composed mostly of quitters. In response to this research, Farber (1997b)

adjusted the DWS data to account for misreporting in the "Other" category in all years, and

concluded that, although job loss did not increase in the 1993-95 period, it almost certainly did

not decrease. However, because the debriefing questions used to determine this fact were not

asked in 1994, it is impossible to directly determine whether there has been a change in the

composition of the "Other" category. His correction is appropriate only if the 1996 wording

change did not affect the composition of the "Other" category. Later in the paper, I present

evidence that the change in wording did result in more job leavers answering "Yes" to the main

displacement question and that it affected the non-"Other" categories.

The PSID

Several studies have used PSID data (Boisjoly, Duncan and Smeeding 1998, Polsky

1998, and Valletta 1998) to look at trends in job loss. Although the PSID collects data on job

losses, it has a number of disadvantages that are worth noting. The PSID collects employment

data only on household heads, so that analyses are generally restricted to male household heads.

Male household heads may not be representative of all men, and are certainly not representative

of the population as a whole. The sample size is relatively small, resulting in larger standard

errors than in CPS data.11 There are well documented inconsistencies in reported tenure (see

Brown and Light 1992), and the tenure and job change questions have changed over time.

Dieblod, Neumark, and Polsky (1997) examined the questions used to determine turnover

in the PSID and found that the wording changed. For most of the time between 1970 and 1990,
-7-

the PSID asked about job changes that occurred over a 12 month period. However, from 1984

through 1987 the reporting window was closer to a year and a half (see Diebold, Neumark, and

Polsky 1997 and Polsky 1998 for details). As a result, more job separations were observed in the

mid 1980s simply because the window was longer. The longer window appears to have affected

Boisjoly, Duncan, and Smeeding's (1998) results that job-loss rates were higher in the 1980s than

in the 1970s and that job losses increased by more when looking at growth years than when

looking at recession years.

To get around the problem of the longer reporting period, Polsky (1998) looked at

separations using the "months in current position" question. Unfortunately, this question

changed in 1983. Because the earlier question resulted in considerable heaping at 12 months and

separations are detected by comparing employer tenure and the time between interviews,

whether a separation is observed will be sensitive to the exact length of time between interviews.

Taking advantage of the panel nature of the PSID, Polsky performed consistency checks to

identify individuals who rounded up and those who rounded down. He then used the "reason for

new position" question to distinguish between quits and job losses. Comparing the 1976-1981

period to the 1986-1991 period he found that there was virtually no change in job separations,

but there was a large increase in the incidence of job loss conditional on a separation. Further, in

comparing the late 1980s to the late 1970s, he found that job losers had become less likely to

find new jobs and earned less when they did.

Valletta (1998) looked at job security in the context of an implicit contract model. He

found that, for male household heads, job loss increased over the period 1976-1992, but found no

such increase for women. Interestingly, the decline in job security is largest among men with

higher tenure with those in declining industries faring the worst.
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The NLS

A paper by Monks and Pizer (1998) used the National Longitudinal Survey of Youth

(NLSY) and the NLS-YM (Young Men) to examine whether job stability and job security among

young men declined between the 1970s and the 1980s.12 They found that both voluntary and

involuntary separations were more common in the 1980s than in the 1970s.

Apart from the limited sample coverage, the main difficulty with using these two datasets

is that their measures of separations and job loss are not necessarily comparable. The NLS-YM

asks if the individual was working two years ago13 and what happened to that job, whereas the

NLSY collects job information using a calendar approach. The latter approach is likely to detect

more jobs and, hence, more job changes. Also, because the NLS-YM interviews were conducted

two years apart for the years used in their study,14 Monks and Pizer used a 2-year window to

analyze job separations. To make the time period comparable in the NLSY, they used a 2-year

window for these data as well. But because the NLSY interviews were conducted annually

through 1994, they had to use data from two annual interviews to determine whether a separation

occurred during the 2-year window of interest. It is well known that transitions are more likely

to be reported between interviews than within interviews.15 These "seam effects" imply that job

separation rates should be higher in the NLSY than in the NLS-YM, even if there is no actual

difference between the two cohorts. Hence, it is not clear the extent to which the 1970s-to-1980s

increase in job separations found by Monks and Pizer is real and how much is due to differences

in the surveys.

The rest of the paper proceeds as follows: Section II describes the data, Section III looks

at the long term trends in EU transitions and compares my results to those of other studies, and

Section IV concludes.
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II. Data

The data used in this paper are from the 1968-1998 March CPS files. My sample consists

of men and women age 19 and above in March16 who worked at least one week in the previous

year and had 40 or fewer years of potential experience.17 Because I am interested in whether job

security has changed for workers in regular (post-schooling) jobs, I omit individuals with less

than one year of potential experience.18 I also exclude self-employed workers19 and people with

zero earnings in the previous year. To simplify computation of standard errors and keep the

dataset to a manageable size, I used only the first four rotation groups leaving me with a sample

of 831,762 observations.20

As noted in the introduction, I use EU transitions as my measure of job security. It is

relatively straightforward to define EU transitions in the March CPS. An individual is

considered to have made an EU transition if he or she worked during the previous year and was

unemployed during the reference week of March in the current year.

We know from the literature on job displacement that job losers are more likely to

become unemployed and spend more time unemployed than workers who did not lose a job (see

Ruhm 1991 and Farber 1993), suggesting that a large fraction of EU transitions are due to job

loss. However, for the purpose of examining trends in job security, it is more important that

most of the year-to-year variation in the EU transition rate be due to job loss. Fortunately, I can

shed some light on this issue by using more detailed data available in the 1988 through 1998

March CPS files, which contain information on the reason for unemployment.

Over the 1988-1997 period most EU transitions were due to job losses. The EU

transition rate averaged 4.65 percent over this period, with job loss accounting for 3.11
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percentage points, quitters (job leavers) accounting for 0.63 percentage points, and reentrants

accounting for 0.91 percentage points.21

The next step is to determine how much of the variation in the EU transition rate is due to

job loss. Figure 1 shows the overall EU transition rate, the EU job-loss rate (this rate is the

fraction of workers who worked in the previous year and were unemployed in March because of

a job loss), and the EU non-job-loss rate (this rate is the fraction of workers who worked in the

previous year and were unemployed in March for reasons other than job loss) for 1987-1997. It

is clear from this figure that virtually all of the variation in the EU transition rate can be

accounted for by variation in the EU job-loss rate.

This observation is confirmed when the 11 yearly observations are used to regress the

overall EU transition rate on the EU job-loss rate. The estimated coefficient estimate on the EU

job-loss rate is 0.97, which is statistically different from zero at the 1-percent level but not

statistically different from one at any conventional level of significance (the R2

 on this regression

is 0.96). Regressing the EU transition rate EU non-job-loss rate yields a coefficient of 0.40,

which is not statistically different from zero at any conventional level of significance (the R2 for

this regression is 0.01).22 These regressions imply that changes in the EU job-loss rate account

for about 96 percent of the year-to-year variation in the EU transition rate over the 1987-97

period and suggest that the EU transition rate is a reasonable measure of job security.

It is important for my analysis that the relationship between job loss and EU transitions

be fairly constant over time. For example, if workers have become more inclined to quit a job to

become unemployed the EU transition rate will increase even if there has been no decline in job

security. The maintained assumption throughout the rest of the paper is that this relationship was

constant over the 1967-1986 period as well.
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Potential Breaks in Series

There have been two changes in the CPS that could lead to potential breaks in series. The

first occurred in 1989 when the Census Bureau changed the CPS processing system. Beginning

in 1989, the Census changed the criteria by which variables were allocated and began imputing

entire Income Supplement records using a hot deck procedure (these are known as "Type A"

allocations and represent 8-10 percent of the sample). Since the Type A allocated records

generate a large number of spurious transitions, I omitted them from my sample.

The second change occurred in 1994, when the Basic CPS was completely overhauled

and computer assisted interviewing was introduced. The variable that is a potential problem is

Employment Status Recode from the Basic CPS (ESR) [PEMLR in the New CPS]. Research by

Polivka and Miller (1998) provides some guidance on how the redesign affected the ESR. They

compute adjustment factors for various labor force indicators by demographic characteristics.

While I cannot use these factors to adjust my estimates, they do provide useful information about

which groups were most affected. The redesigned CPS detects more unemployment among both

men and women. However, the largest increases in the unemployment rate are for men 65+ and

women 55+. My restriction to workers with 40 or less years of potential experience largely

eliminates these older workers from my analysis. Hence, any effects of the redesign are likely to

be small.

III. Has Job Security Declined?

As noted in the introduction, the popular perception is that jobs were less secure in the

1980s than in the 1970s and that the decline in job security continued into the 1990s. In this

section, I examine whether trends in the EU transition rate are consistent with these perceptions.
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The EU transition rates in Figures 2-8 were estimated using a two step procedure. In the

first step, I estimated a probit equation using only data for 1967 and obtained the predicted EU

transition rate for 1967. I then estimated a probit on the full sample using year dummies (with

1967 being the reference year).23 The EU transition rate for each year (1968 through 1997) was

obtained by adding the marginal effect of the year dummy to the predicted 1967 transition rate

obtained from the first step probit. To derive the upper and lower bounds of the confidence

interval, I took a simple average of the standard errors on the year dummies from the second step

probit and multiplied by two. These bounds were used for all years, including 1967.

Figure 2 shows the EU transition rates for all workers, men, and women. As one would

expect, these series are counter-cyclical, with the EU transition rate for men exhibiting much

wider swings. The EU transition rates for men and women were fairly close to each other in the

late 1960s and early 1970s. The rate for both men and women ratcheted up during the 1970 and

1974 recessions, but the 1974 increase was much larger for men than for women. From the late

1970s through the mid 1990s, the EU transition rates remained roughly constant except for

cyclical variation. The behavior of these EU transition rates is consistent with the popular

perception that jobs were less secure in the 1980s than in the 1970s, but it is not consistent with

the perception that job security declined in the 1990s. However, topside numbers can be

misleading because they mask changes in composition and within group changes. Below, I

present a more detailed look by demographic characteristics with the goal of determining

whether job security has declined for some groups.

EU Transitions By Sex and Education

Figures 3 shows the trends in the EU transition rate for men by education level. As one

might expect, the cyclical fluctuations are greater for high school dropouts and high school
-13-

graduates than for workers with some college and college graduates. However, the EU transition

rate for all education groups ratcheted up during the 1970 and 1974 recessions with the rates for

less educated workers increasing the most. Comparing high school dropouts and high school

graduates, there was a slight difference in the timing of these changes. Most of the increase

among high school dropouts occurred during the 1970 recession, whereas among high school

graduates most of the increase occurred during the 1974 recession. From the late 1970s through

the mid 1990s, the EU transition rate for men of all education levels remained roughly constant

except for cyclical fluctuations.

The corresponding EU transition rates for women are shown in Figure 4. For high school

dropouts and high school graduates the patterns are similar to those for men, except that the

changes are small relative to their standard errors. Like the men in these education categories the

EU transition rates for women ratcheted up during the 1970 and 1974 recessions, but the

increases were not as sharp. After the 1974 recession, the EU transition rate exhibited no upward

or downward trend. For women with some college and college graduates there do not appear to

have been any changes over the entire 1967-97 period. Again, the movements in the series are

small relative to their standard errors.

In light of the debate over whether job security declined in the early 1990s, it is

interesting to compare the 1982 and 1990 recessions. Although the drop in GDP was greater in

the 1982 recession, the EU transition rate for male college graduates--a group that is typically

insulated from cyclical job loss--was about the same in both recessions. In contrast, the EU

transition rates for men in the other education groups were lower in the 1990 recession.

EU Transitions By Sex and Potential Experience

The EU transition rate for men and women by years of potential experience are shown in
-14-

Figures 5 and 6. At all levels of experience, the rates for women exhibit considerably less

cyclical fluctuation than the rates for men. For men and women of all experience levels, the EU

transition rate ratcheted up during the 1970 and 1974 recessions. Again, the increases were

much larger for men than for women. For men with more than 10 years of experience the rates

ratcheted up again during the 1982 recession. For all other groups, the EU transition rate

remained relatively constant from about the mid 1970s through the mid-to-late 1990s.

Again, the 1990 recession was different from the 1982 recession for workers who are

usually insulated from cyclical job loss. For men with 21 or more years of experience, the EU

transition rate was as high during the 1990 recession as it was during the 1982 recession.

EU Transitions By Industry and Occupation

Figure 7 shows the EU transition rates for goods producing and services producing

industries. Not surprisingly, the EU transition rate for goods producing industries exhibits much

wider cyclical swings. Again, the EU transition rate for both industry groups ratcheted up during

the 1970 and 1974 recessions, but remained relatively flat from the mid 1970s through the late

1990s. Comparing the 1982 and 1990 recessions, the later recession was less severe in goods

producing industries, but there was no difference between the two recessions in services

producing industries.

The EU transition rates in Figure 8 reveal some interesting patterns by major occupation

group.

24 Note that these graphs begin in 1970 because occupation codes used in the 1968-1970

March CPS files are not compatible with the 1970 and later Census occupation codes. As

expected, the EU transition rate for blue collar workers exhibits much wider cyclical swings than

the rates for the other two groups. In all occupations, the EU transition rate ratcheted up during

the 1974 recession, with the increase being larger for blue collar workers. A comparison of the
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1982 and 1990 recessions reveals a familiar pattern. For blue collar occupations, the 1990

recession was much less severe than the 1982 recession, but for white collar occupations there

was no difference between the two recessions.

Because business cycle effects can make it difficult to distinguish trends from cyclical

effects in these figures, I compared average EU transition rates for the five expansions and four

recessions covered by my data.

Table 1 summarizes the trends in EU transition rates for the different groups. Each line

contains the results from a separate probit equation and corresponds a graph in Figures 2-8. I

divided the 1967-97 period into 5 expansion periods (1967-69, 1971-73, 1976-80, 1983-89, and

1992-1997) and 4 recession periods (1970, 1974-75, 1981-82, and 1990-91) and then defined

dummy variables corresponding to each of these periods (1967-69 was the omitted period). The

coefficients in the first four columns correspond to expansion years, while the coefficients in the

second four columns correspond to recession years. The coefficients on these dummies are

expressed as marginal effects. As before, all equations include demographic control variables.

The results in Table 1 are consistent with Figures 2-8. These coefficients indicate that the

EU transition rate ratcheted up in the 1970 and 1974 recessions and remained high from the late

1970s through the late 1990s. For nearly every group there were statistically significant

increases in the EU transition rate between the 1967-69 and the 1971-73 periods, and for every

group the increases were statistically significant between the 1971-73 and 1976-80 periods. In

the last three periods (1976-80, 1983-89, and 1992-97), the EU transition rates remained

relatively constant. Even when the EU transition rates are statistically different from each other,

the differences are small in economic terms.

Overall, and for most groups, the 1982 recession was more severe than the 1990
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recession. These differences are statistically significant at the 5-percent level. However, for a

few groups there is no statistically significant difference between the EU transition rates during

the two recessions. As noted above, these are groups that are normally insulated from cyclical

job loss: men with college degrees, men with 21 or more years of experience, and white collar

workers.

Although the behavior of the EU transition rate differs somewhat by demographic

characteristics, a clear picture emerges. Job security declined during the 1970s. However, most

of this decline was concentrated in the early part of the decade. The EU transition rate ratcheted

up during the 1970 and 1974 recessions, but remained fairly constant after that. There is no

evidence that job security continued to decline in the 1990s. These findings are consistent with

the popular perception that jobs were more secure in the 1970s than in the 1980s but not with the

perception that job security continued to decline in the 1990s.

Part of the reason for the inconsistency between the evidence and popular perceptions

may be that the 1990 recession had a differential effect on different groups. Neumark and

Polsky (1998) have suggested that reporters may have written more articles about worker

displacement because a relatively large fraction of their peers--managerial and professional

workers--had lost jobs. The evidence presented here is consistent with that hypothesis. Groups

that are usually insulated from cyclical job loss were relatively harder hit by the 1990 recession.

Even though the decline in GDP was less severe in this recession than in the 1982 recession, the

EU transition rate for these insulated groups was about the same in the two recessions. In

contrast, the 1990 recession was far less severe for groups that are typically less insulated from

cyclical job loss.
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Comparisons to Other Research

In this section I use the March CPS data to replicate, to the extent possible, the results of

three other studies: Boisjoly, Duncan, and Smeeding (1998), Farber (1997a), and Monks and

Pizer (1998). I seek to determine if the results presented above are consistent with these studies

and to reconcile differences wherever possible.

Boisjoly, Duncan, and Smeeding (1998)

The sample used by Boisjoly, Duncan, and Smeeding consisted of male household heads

aged 25-59 who had at least one year of tenure and worked at least 1,000 hours in the previous

year. I was able to impose the demographic restrictions but not the tenure and hours restrictions.

Hence, the March CPS sample is composed of individuals who have a weaker labor force

attachment than those in the PSID sample. As a result, the job-loss rates from the PSID are

lower than the EU transition rate from the March CPS.

Figure 9 shows the EU transition rate from the March CPS data and the job-loss rate from

the PSID. The two series generally follow a very similar pattern, though they differ somewhat

with respect to the timing of changes. Perhaps the most notable of these differences is that the

EU transition rate fell between 1983 and 1987, whereas the job-loss rate from the PSID

increased. This could be, at least in part, due to the longer recall period during those years.25

Even so, their results are generally consistent with those from the March CPS.

Farber (1997a)

In replicating Farber's results with the CPS data, the difficulty was with constructing a

comparable measure of EU transitions, not with replicating the sample. I computed average 3-

year EU transition rates from the March CPS that are roughly comparable to Farber’s 3-year job-
-18-

loss rates from the DWS.26 Because Farber’s job-loss rate measures the fraction of workers that

experienced a job loss during each 3-year period, I computed 3-year EU transition rate by

summing the EU transition rates for the three individual years. To replicate the effect of recall

bias, the rates for each year were weighted using the implied adjustment factors in Evans and

Leighton (1995).27 I also omitted the "Other" category from the DWS job-loss rates because of

the problems with this category that I noted in the introduction. Figure 10 compares the 3-year

EU transition rates from the March CPS to the 3-year job-loss rates from the DWS for all

workers.28

The two series tell qualitatively the same story until the 1993-95 period. But between the

1991-93 period and the 1993-95 period the EU transition rate fell, while the job-loss rate

remained roughly constant. Whether the job-loss rate fell during this period is important for

determining the trend in job security in the 1990s. The natural expectation is for the job-loss rate

to fall as the labor market recovers from the recession. But if, as Farber claims, the job-loss rate

did not fall then that would be evidence of a permanent decline in job security.

The divergence in the 1993-95 period does not necessarily imply that the two data

sources are inconsistent with each other. In particular, Farber found that the consequences of job

loss became less severe between the 1991-93 and 1993-95 periods. Reemployment rates for job

losers increased and their earnings losses fell.

The results from the DWS and the March CPS are consistent with two hypotheses. The

first is that the job-loss rate did not fall, but instead the consequences of job loss--reemployment

rates and earnings losses--were less severe during this period. The second is that the job-loss

rate did fall, but that the wording change in the 1996 DWS caused more job leavers to be

classified as job losers, even among the non-"Other" categories. In that case, the milder

consequences of job loss that Farber found would be due to the higher fraction of job leavers that
-19-

were misclassified as lob losers.

Recall that the main question in the 1996 DWS placed greater emphasis on leaving a job

than did the 1994 question. It has been hypothesized that this wording change resulted in more

job leavers answering "Yes" to the main question and that it accounts for the large increase in the

"Other" category in the 1996 DWS. But it is possible that some job leavers who answered "Yes"

to the main question gave a reason other than "Other."

Although it is not possible to determine what reason these job leavers might have given,

the "Insufficient work" category seems like a likely candidate because it may not be clear what

the phrase means. It was intended to describe a situation in which an employee was terminated

because his employer had experienced a decline in demand. But respondents may have

interpreted the term to include situations in which the worker left a job voluntarily because he

wanted to work more hours or there was not enough work to keep him busy.

Research associated with the redesign of the DWS has shown that a large fraction of

people in the "Insufficient work" category are actually job leavers. The 1996 and 1998 DWSs

included a set of debriefing questions that were designed to determine whether respondents were

answering the questions correctly.29 When they analyzed the data, BLS researchers found that

32 percent of people who gave "Insufficient work" as a reason were job leavers. Of these, only

38 percent left because they expected their job to end. Hence, a relatively large fraction --20

percent--were true job leavers. In contrast, about 14 percent of people who said that they had

lost a job due to "Plant closing" or "Position abolished," which are less ambiguous terms, were

actually job leavers. Approximately two-thirds of these job leavers said that they left because

they expected their job to end, so that less than 5 percent of individuals in these two categories

were true job leavers.

However, the real issue is whether this type of misreporting became more common
-20-

between the 1994 and 1996 DWS. Unfortunately, there were no follow-up questions in the 1994

DWS, so a direct test is not possible. However, it is possible to perform a consistency check by

determining whether the increase in the reemployment rate for job losers in the DWS was large

enough to reconcile Farber's finding of no change in job-loss rates with my finding of a decline

in the EU transition rate.

My strategy is to estimate a lower bound on the amount by which the reemployment rate

must have increased in order for Farber's results to be consistent with mine. If the change in the

reemployment rate from the DWS is less than this lower bound, it would be evidence that the

1996 DWS classified a higher fraction of job leavers as job losers than the 1994 DWS. Because

the reemployment rates from the DWS cover a 3-year period and the EU transition rates from the

March CPS cover approximately a 1-year period, the levels are not comparable. Instead I

compare the percentage changes in the reemployment rates from the two surveys.

Let me begin with the following identity:

(1) PL ≡ PLU + PLN + PLE,

where PL is the fraction of employed workers who lose a job over the course of a year, PLU is the

fraction that loses a job and becomes unemployed, PLN is the fraction that loses a job and leaves

the labor force, and PLE is the fraction that loses a job and becomes reemployed.

Computing the percentage change in PLE from the DWS was straightforward. I used

Farber's estimates of PLE from the DWS to compute the percentage change between the 1991-93

and 1993-95 periods.30 As before, I excluded the "Other" category from my calculations. The

reemployment rate (expressed as a fraction of employment)31 increased from 6.4 percent to 6.7

percent, an increase of 5.3 percent.
-21-

It is more difficult to compute the percentage change in PLE from the March CPS data

because PLE cannot be estimated directly. In fact, of the variables in Equation (1), I can only

estimate PLU (using the reason for unemployment question). For the other variables, it is

necessary to obtain estimates from other sources or make assumptions about their values.

For some of the variables, it is easier to make assumptions about the changes than the

levels. Taking the difference between the 1991-1993 and 1993-95 periods yields

(2) ∆PL ≡ ∆PLU + ∆PLN + ∆PLE.

As noted above, I can estimate ∆PLU directly from the March CPS data. By assumption, ∆PL is

close to zero. Given that ∆PLU < 0 (from the March CPS) and ∆PLE > 0 (from the DWS), the

labor market must have been improving. Hence, it is reasonable to suppose that ∆PLN < 0. Since

smaller (more negative) values of ∆PLN increase the lower bound on the percentage change in

PLE, I take the conservative approach and assume ∆PLN = 0.

For both three-year periods, my estimate of PLU was calculated by taking the number of

people who made EU job-loss transitions and dividing by the number of people who worked at

all during the previous year (2.74 percent in 1991-93 and 2.10 percent in 1993-95). I adjusted

my estimates of PLU to account for recall bias (as described above) and the 1994 redesign of the

CPS. For the latter adjustment, I used adjustment factors from Polivka and Miller (1998) to

account for the fact that the redesigned CPS classifies a smaller fraction of the unemployed as

job losers.32

For my estimates of PL, I used the job-loss rates from the DWS (9.6 percent in 1991-93

and 9.5 percent in 1993-95). This is a conservative approach, because the percentage change in
-22-

the reemployment rate decreases as PL increases33 and the DWS job-loss rate (which is a threeyear rate) is an overestimate of the average annual job-loss rate.34 Because I had no way of

estimating PLN, I performed the calculations using assumed values that ranged from zero to two

percent (which would be about 20 percent of all job losers). My estimate of the initial value of

PLE was calculated by plugging these values into Equation (1).

The results of these calculations are presented in Table 2. The first four columns show

the values of the variables from Equation (1), while the second four columns show the changes

in these variables between 1991-93 and 1993-95 from Equation (2). Using the most conservative

estimate (PLN = 0 in the 1991-93 period), the reemployment rate for job losers would had to have

increased by at least 7.7 percent--compared with the actual increase of 5.3 percent in the DWS--

for the small decline in the job-loss rate observed in the DWS to be consistent with the EU jobloss rate from the March CPS. If one makes the more reasonable assumption that PLN = 1 in

1991-93, (or about 10 percent of job losers), then the implied percentage increase in the

reemployment rate is 9.2 percent, well above the DWS estimate.

Hence, these calculations support the hypothesis that the job-loss rate actually decreased

between the 1991-1993 and 1993-95 periods and that the change in the DWS resulted in more

job leavers being classified as job losers than in previous years.

These calculations also illustrate the potential danger of trying to examine trends when

the underlying data collection instrument has changed. The raw data seem to indicate that job

security fell during the mid 1990s. But further examination shows that this finding was most

likely caused by a change in the questionnaire rather than a true change in job security.

Monks and Pizer (1998)

The sample used by Monks and Pizer was restricted to young men in two NLS cohorts--
-23-

one aged 19-27 in 1971 and one aged 19-27 in 1984--who were working full time (at least 30

hours per week) in the following years: 1971, 1973, 1976, and 1978 (from the NLS-YM) and

1984, 1985, 1989, and 1990 (from the NLSY). I was able to impose the cohort and sample year

restrictions, but not the full-time restriction.35

As noted in the introduction, Monks and Pizer determined whether a job separation

occurred in the 2-year period following each sample year and whether the separation was

involuntary. Because the March CPS data measure EU transitions over a 14.5 month window, I

used data for each of their starting years plus the following year: 1971-74 and 1976-79 for the

NLS-YM cohorts and 1984-86 and 1989-91 for the NLSY cohorts.36

I estimated Monks and Pizer's probit equations for involuntary separations (see their

Table 6) using March CPS data. The main variables of interest are the set of education dummy

variables interacted with a time trend variable.37 My results were qualitatively very similar to

theirs for the control variables, but there were some differences in the coefficients on the main

variables of interest.

Tables 3 and 4 compare the results on the main variables of interest from the two

datasets. Table 3 compares the coefficients from probit equations on the EU transition rate (from

the March CPS) and the job-loss rate (from the NLS) for both whites and nonwhites. Both data

sources indicate that there were large and statistically significant increases in the job-loss rate

(NLS) and the EU transition rate (March CPS) among high school dropouts and high school

graduates. Among men with some college and college graduates, the NLS data show large and

statistically significant increases in the job-loss rates for whites, but not for nonwhites. In

contrast, for both whites and nonwhites the March CPS data show no change in the EU transition

rate among men with some college and college graduates.

To better compare the magnitude of the estimated changes, I compared the 1971-to-1991
-24-

changes implied by the point estimates. These changes, which are expressed as percentage

changes to make them more comparable, are shown in Table 4.38

Among whites, the two data sources tell identical stories for high school dropouts and

high school graduates, but differ significantly when it comes to people with more than a high

school diploma. Both data sources show that the probability of a job loss/EU transition increased

by about 75 percent among white high school dropouts and by about 50 percent among white

high school graduates. In the some college and college graduates categories, the NLS shows a

much larger increase in the probability of a job loss than the March CPS. For the some college

category the job-loss rate increased by 100 percent in the NLS, compared with a 13-percent

increase in the EU transition rate in the March CPS. For college graduates, the increases were 65

percent in the NLS and 11 percent in the March CPS.

The differences between the two data sources are even greater for nonwhites. For high

school dropouts the increase in the March CPS data was twice that in the NLS data, while for

high school graduates the reverse was true. I hesitate to say much about the other two education

categories because none of the changes are statistically significant.

It is not clear why the two surveys tell such different stories, but differences in survey

design between the NLS-YN and the NLSY are probably not the cause. If they were the cause,

then one would expect the NLS to indicate uniformly larger increases in probabilities between

1971 and 1990. The fact that the NLS and the March CPS produced very similar results for

some education groups but not for others suggests that something else is going on.

V. Summary and Conclusions

The evidence presented here suggests that job security, as measured using EU transition
-25-

rates, declined in the early to mid 1970s and, except for cyclical fluctuations, remained constant

through the mid 1990s. This finding is consistent with the popular perception that jobs were less

secure in the 1980s than in the 1970s, but not with the perception that job security continued to

decline in the 1990s. A more detailed look at the EU transition rate for different demographic

groups does not change this conclusion, but it does shed some light on how the perception that

job security declined in the 1990s may have come about.

The 1990 recession was more "white collar" than previous recessions. Neumark and

Polsky (1998) hypothesized that reporters may have written more articles about worker

displacement because a relatively large fraction of their peers had lost jobs. The evidence

presented here is consistent with that hypothesis. Groups that are usually insulated from cyclical

job loss--such as men with a college degree, men with more labor market experience, and white

collar workers--were relatively harder hit by the 1990 recession. The EU transition rates for

these groups were about the same in both the 1982 and 1990 recessions, even though the 1990

recession was far less severe in terms of the decline in GDP. In contrast, the EU transition rates

for less educated men, less experienced men, and blue collar workers were much lower in the

1990 recession than in the 1982 recession.

It is also possible that the slow recovery from the 1990 recession contributed to this

perception. Even though the recession officially ended in March 1991, the labor market did not

begin to recover until much later. The EU transition rate peaked in 1991, but remained high

through 1992. The unemployment rate did not peak until 1992, and job growth was sluggish

through 1992.

The advantage of using March CPS data is that the series extends through 1997, so it is

possible to distinguish between a slower than normal recovery from the 1990 recession and the

start of a secular trend. The EU transition rate had returned to its pre-recession level by 1994
-26-

implying that the higher EU transition rates in the early 1990s were not the start of a secular

trend. Most other studies cannot make this distinction because the data used stop in the early

1990s. Farber's (1997a) data cover through 1995, but it is problematic to make inferences about

trends in the 1990s because of wording changes in the main question in the DWS.

In comparing my results to those of other authors, I found that my results were generally

consistent though there were some differences worth noting.

I found that my results are generally consistent with those of Boisjoly, Duncan, and

Smeeding (1998). Both the job-loss rate in the PSID and the EU transition rate in the March

CPS show jobs were less secure in the 1980s compared with the 1970s. Both data sources

indicate that job security declined throughout the 1970s, and remained constant or decreased

slightly in the 1980s. However, by looking at only men Boisjoly, Duncan, and Smeeding tend to

overstate the overall decrease in job security, because women did not experience as large a

decline.

My results are similar to Farber's (1997a), except for the mid 1990s. The job-loss rate in

the DWS data remained constant while the EU transition rate in the March CPS data fell. I

presented evidence that the DWS job-loss rate for the 1993-95 period may have been affected by

a subtle wording change in the main question. A consistency check indicates that a larger

fraction of job leavers were classified as job losers in the 1996 DWS than in previous years.

Hence, it appears that job-loss rates actually fell in the mid 1990s. This finding illustrates the

danger of trying to examine trends when the underlying data collection instrument has changed.

I was able to replicate Monks and Pizer's results for some groups but not for others.

Results from the NLS and the March CPS were close for white high school dropouts and high

school graduates. In the some college and college graduate categories, Monks and Pizer found a

large increase in the job-loss rate, whereas I found very little change.
-27-

References

Aaronson, Daniel and Daniel Sullivan (1998) "The Decline on Job Security in the 1990s:

Displacement, Anxiety, and their Effect on Wage Growth," Economic Perspectives, First

Quarter 1998, Federal Reserve Bank of Chicago, pp. 17-43.

Abraham, Katharine G. (1997) Comment on Farber "The Changing Face of Job Loss in the

United States: 1981-1995," Brookings Papers on Economic Activity: Microeconomics, in

press.

Bernhardt, Annette, Martina Morris, Mark Handcock, and Marc Scott (1998) "Job Instability and

Wage Inequality Among Young Men: A Comparison of Two NLS Cohorts," mimeo.

Boisjoly, Johanne, Greg J. Duncan, and Timothy Smeeding (1998) "The Shifting Incidence of

Involuntary Job Losses from 1968 to 1992," Industrial Relations 37(2), April 1998, pp.

207-31.

Brown, James N. and Audrey Light (1992) "Interpreting Panel Data on Job Tenure," Journal of

Labor Economics 10(3), July 1992, pp. 219-57.

Diebold, Francis X., David Neumark, and Daniel Polsky (1997) "Job Stability in the United

States," Journal of Labor Economics 15(2), April 1997, pp. 206-33.

Diebold, Francis X., David Neumark, and Daniel Polsky (1996) "Comment on Kenneth A.

Swinnerton and Howard Wial, ‘Is Job Stability Declining in the U.S. Economy?’"
-28-

Industrial and Labor Relations Review 49(2), January 1996, pp. 348-352.

Evans, David S. and Linda S. Leighton (1995) "Retrospective Bias in the Displaced Worker

Surveys," Journal of Human Resources 30(2), Spring 1995, pp.386-196.

Fallick, Bruce C. (1996) "A Review of the Recent Literature on Displaced Workers," Industrial

and Labor Relations Review 50(1), October 1996, pp.5-16.

Farber, Henry S. (1993) "The Incidence and Costs of Job Loss: 1982-1991," Brooking Papers

on Economic Activity: Microeconomics, pp.73-132.

Farber, Henry S. (1997a) "The Changing Face of Job Loss in the United States: 1981-1995,"

Brookings Papers on Economic Activity: Microeconomics, in press.

Farber, Henry S. (1997b) "Has the Rate of Job Loss Increased Over Time?" mimeo, December

1997.

Farber, Henry S. (1998) "Are Lifetime Jobs Disappearing? Job Duration in the United States:

1973-1993," in Labor Statistics Measurement Issues, John Haltiwanger, Marilyn Manser,

and Robert Topel, eds., University of Chicago Press.

Jacobson, Louis, Robert LaLonde, and Daniel Sullivan (1993) "Earnings Losses of Displaced

Workers," American Economic Review 83(4), September 1993, pp. 685-709.
-29-

Jaeger, David (1997) "Reconciling the Old and New Census Bureau Education Questions:

Recommendations for Researchers," Journal of Business and Economic Statistics 15(3),

pp.300-9.

Jaeger, David and Ann Huff Stevens (1997) "Is Job Stability in the United States Falling?

Trends in the Current Population Survey and Panel Study of Income Dynamics," mimeo.

Marcotte, David (1996) "Has Job Stability Declined?: Evidence from the Panel Study of Income

Dynamics," mimeo.

Martini, Alberto and Paul Ryscavage (1991) "The Impact of Survey and Questionnaire Design

on Longitudinal Labor Force Measures," mimeo.

Monks, James, and Steven Pizer (1997) "Trends in Voluntary and Involuntary Job Turnover,"

Industrial Relations 37(4), October 1998, pp. 440-59.

Murphy, Kevin M. and Finis Welch (1992) "The Structure of Wages," Quarterly Journal of

Economics 107(1), February 1992, pp. 285-326.

Neumark, David and Daniel Polsky (1998) "Changes in Job Stability and Job Security:

Anecdotes and Evidence" in the Proceedings of the 50th Annual IRRA Meetings.

Neumark, David, Daniel Polsky, and Daniel Hansen (1998) "Has Job Stability Declined Yet?

New Evidence for the 1990s," mimeo.
-30-

Pierret, Charles (1999) "Event History Data and Survey Recall: An Analysis of the NLSY

Recall Experiment," mimeo.

Polivka, Anne E. and Steven Miller (1995) "The CPS After the Redesign: Refocusing the

Economic Lens," in Labor Statistics Measurement Issues, John Haltiwanger, Marilyn

Manser, and Robert Topel, eds., University of Chicago Press.

Polsky, Daniel (1996) "Changes in the Consequences of Job Separations in the U.S. Economy,"

Mimeograph, University of Pennsylvania.

Rose, Stephen (1995) "Declining Job Security and the Professionalization of Opportunity,"

National Commission for Employment Policy, Research Report 95-4, April 1995.

Ruhm, Christopher (1991) "Are Workers Permanently Scarred by Job Displacements?"

American Economic Review 81(1), March 1991, pp. 319-24.

Stewart, Jay (1998) "Has Job Mobility Increased? Evidence from the Current Population

Survey: 1975-1995," BLS Working Paper #308.

Swinnerton, Kenneth A. and Howard Wial (1995) "Is Job Stability Declining in the U.S.

Economy?" Industrial and Labor Relations Review 48(2) January 1995, pp. 293-304.

Swinnerton, Kenneth A. and Howard Wial (1996) "Is Job Stability Declining in the U.S.
-31-

Economy? Reply to Diebold, Neumark, and Polsky" Industrial and Labor Relations

Review 48(2) January 1996, pp. 352-355.

Valletta, Robert (1996) "Has Job Security in the U.S. Declined?" FRBSF Weekly Letter 96-07,

February 16,

Valletta, Robert (1998) "Declining Job Security" mimeo.
-32-

Endnotes

1 Job stability refers to the duration of jobs, without regard to the reasons for increasing or

decreasing duration. Examples of job stability measures include retention rates (Swinnerton and

Wial 1995,1996, Marcotte 1996, and Diebold, Neumark, and Polsky 1996,1997, and Neumark,

Polsky, and Hansen 1998), job tenure (Farber 1998), the fraction of workers in new jobs (Jaeger

and Stevens 1998), and turnover (Rose 1995, Monks and Pizer 1998, and Stewart 1998). Job

security refers to the extent to which job separations are involuntary. The primary measure of

job security is the rate of job loss (Polsky 1998, Farber 1997a,b, Boisjoly, Duncan, and

Smeeding 1998, Monks and Pizer 1998, and Valletta 1998).

2

 See Neumark and Polsky (1998).

3

 See Marcotte (1996), Farber (1998), and Stewart (1998). Diebold, Neumark, and Polsky

(1997) found that job stability fell among less educated workers but did not present gender

breaks.

4

 See Neumark, Polsky, and Hansen (1998).

5

 See Farber (1998) and Stewart (1998).

6

 See Stewart (1998) for a discussion of the data issues in the job stability literature.

7

 See Ruhm (1991). See also Fallick (1996) for a nice survey of the job displacement literature.

8

 The shorter time period was used because a key variable for identifying job separations was not

available until March 1976 (covering calendar year 1975). This variable is not required to

identify EU transitions.

9

 Recall bias is more of a problem if information on the year of job loss is used to determine

changes in the job-loss rate within the period covered by the survey. Because more recent years
-33-

are "weighted" more heavily, increases in the job-loss rate will be overstated, while decreases

will be understated. However, using three-year job-loss rates (as Farber does) minimizes the

impact of this bias.

10 Aaronson and Sullivan (1998) avoided the problem completely by restricting their analysis to

workers with 5 or more years of tenure and job losses that occurred in the three years prior to the

survey. They found that job-loss rates increased in the 1990s, but their analysis does not account

for the 1996 change in the wording of the main question in the DWS.

11 See Jaeger and Stevens (1998) for a comparison.

12 In a similar analysis, Bernhardt, et al. (1998) look at job stability using NLS data.

13 More specifically, the respondent is asked if they were working during a specific month and

year about two years prior to the interview.

14 Interviews for the NLS-YM were conducted annually from 1966 through 1971 and in the

following years: 1973, 1975, 1976, 1978, 1980, 1981, 1983, and 1990. Monks and Pizer used

data from 1971, 1973, 1976, and 1978 as their base years and determined whether respondents

were still working at their the main (CPS) job two years later.

15 Most of the research looking at this issue looks at SIPP data in the context of generating

monthly gross flows data (for example, see Martini and Ryscavage 1991). Pierret (1999)

analyzes data from an NLSY test that collects data for 1992 in both the 1993 and 1994

interviews. He finds that respondents were more likely to both forget and misremember things

events from 1992 in the 1994 interview than in the 1993 interview.

16 I used 19 rather than 18 as the cutoff because the age refers to the age at the time of the survey.

17 I compute potential experience as Age - Years of Schooling Completed - 6 if Years of
-34-

Schooling is greater than 10 years, and as Age - 16 for those with 10 or fewer Years of Schooling

Completed. This definition is used in Murphy and Welch (1992). Experience is computed for

March of the previous year.

18 I use this restriction because it is not possible to identify students and recent graduates on a

consistent basis over the years covered by my data.

19 I include wage and salary workers who have some self-employment income.

20 Using the full sample would result in a relatively small decline in standard errors. If the

observations were independent there would be a 40 percent reduction in the standard errors. But

because each individual shows up twice (in consecutive years) in the full sample, it would be

necessary to account for the covariance between observations making the actual reduction much

smaller.

21 It is not clear how the reentrants should be classified. These individuals lost or left a job, left

the labor force, but had reentered the labor force by March. They could be job losers who

became discouraged over their prospects of finding a new job, or they could be job leavers who

left the labor force.

Note also that the reentrants group includes the 0.03 percent who were classified as new

entrants. Presumably these new entrants were miscoded because everybody in the sample

worked at some point during the previous year. For that reason (and because there are so few of

them), I grouped the new entrants with the reentrants.

22 The results did not change when I looked at job leavers and reentrants separately.

23 All of the probit equations used for Figures 2-8 include demographic controls (dummy

variables for 3 education levels, 4 experience levels, and dummy variables for race, sex, and
-35-

marital status). In the probits for subgroups, the appropriate control variables are omitted.

24 I thank Dave Macpherson for providing me with the program used to convert the 1970 Census

occupation to 1980 codes.

25 Boisjoly, Duncan, and Smeeding did not adjust their data for the change in recall period noted

in Diebold, Neumark, and Polsky (1997).

26 Farber used three-year intervals because the DWS measures the number of people who lost at

least one job in the previous three years. While the DWS identifies the year in which the job loss

occurred, it is not really designed to count the number of people who lost a job in each year.

27 Evans and Leighton (1995) found that respondents forgot job losses at a rate of about 17

percent per year. The exact adjustment factors are 0.908 for the previous year, 0.748 for the

second year, and 0.616 for the third year. So to adjust the data for the 1981-82 period, the EU

transition rate for 1983 was multiplied by 0.908, the EU transition rate for 1982 was multiplied

by 0.748, and the EU transition rate for 1981 was multiplied by 0.616.

28 I also compared the EU transition rate and the job-loss rates by sex, by sex and education

level, and by sex and age and got qualitatively similar results.

29 The data reported here were provided by Jim Esposito of BLS. They are from the debriefing

questions in the 1998 DWS (the results are very similar to those from the 1996 DWS).

30 The data were taken from Farber’s (1997a) Tables 7 and Appendix Table 1.

31 This measure is more intuitively described as the fraction of workers who lost a job and then

found a new job.

32 These adjustments to not affect the results. In fact, the calculated lower bounds are larger

before making the adjustments for recall bias and the CPS redesign.
-36-

33 Actually, it is a decreasing function of PL in the initial period. But recall that, by assumption,

∆PL is close to zero.

34 There is the issue of recall bias, which would tend to work in the opposite direction. To check

the reasonableness of using the DWS rate, I computed job-loss rates from the Basic CPS using

the reason for unemployment variable. I computed the "monthly" job-loss rate as the number of

permanent job losers that were unemployed for 4 or fewer weeks divided by total employment in

March. To arrive at an annual number, I multiplied by 13. The estimated annual rate of .092 is

an overestimate of the relevant job-loss rate because it counts multiple job losers more than once.

35 The required hours data are not available until the March 1976 CPS.

36 The results do not qualitatively change if only the sample years are used.

37 In addition to the main variables of interest, their equation included three dummy variables for

education level, the average unemployment rate, age at time of interview, marital status, and

dummy variables for industry and occupation. They also included a variable to account for

differences in the time between the initial interview and the interview approximately two years

later. I did not include this variable because there is so little variation in the time between

interviews in the CPS.

38 Steve Pizer kindly provided the probabilities from the NLS.
Figure 1: EU Transition Rates by Reason for Unemployment

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

19871988198919901991199219931994199519961997

Year

All reasons

Job loss

Other reasons
Figure 2: EU Transition Rates for All Workers, Men, and Women

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

Year

All workers

Men

Women
Figure 3: EU Transition Rates for Men by Educational Level

High School Dropouts

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

High School Graduates

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

Some College

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

College Graduates

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

19671988

Employment-to-unemployment transition rate

95% Confidence bounds
Figure 4: EU Transition Rates for Women by Education Level

High School Dropouts

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

High School Graduates

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

Some College

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

College Graduates

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

0

0.1

19671970197319761979198219851988199119941997

Employment-to-unemployment transition rate

95% Confidence bounds
Figure 5: EU Transition Rates for Men by Years of Potential Experience

1 - 10 Years of Potential Experience

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

11 - 20 Years of Potential Experience

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

21 - 30 Years of Potential Experience

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

31 - 40 Years of Potential Experience

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

Employment-to-unemployment transition rate

95% Confidence bounds
Figure 6: EU Transition Rates for Women by Years of Potential Experience

1 - 10 Years of Potential Experience

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

11 - 20 Years of Potential Experience

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

21 - 30 Years of Potential Experience

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

31 - 40 Years of Potential Experience

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

Employment-to-unemployment transition rate

95% Confidence bounds
Figure 7: EU Transitions by Major Industry Group

Goods Producing Industries

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

Services Producing Industries

0

0.05

0.1

0.15

0.2

0.25

1967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

Employment-to-unemployment transition rate

95% Confidence bounds
Figure 8: EU Transitions by Major Occupation Group

White Collar Occupations

0

0.05

0.1

0.15

0.2

0.25

0.3

1970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

Blue Collar Occupations

0

0.05

0.1

0.15

0.2

0.25

0.3

1970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

Service Occupations

0

0.05

0.1

0.15

0.2

0.25

0.3

1970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

Employment-to-unemployment transition rate

95% Confidence bounds
Table 1. Probit Estimates of Changes in EU Transition Rates for Expansion Years and Recession Years

1971 - 73 1976 - 80 1983 - 89 1992 - 97 1970 1974 - 75 1981 - 82 1990 - 91

All Workers 0.0152* 0.0428* 0.0436* 0.0456 0.0279 0.0596* 0.0895* 0.0654* 831,762

(0.0015) (0.0016) (0.0014) (0.0016) (0.0023) (0.0023) (0.0026) (0.0023)

Men 0.0207* 0.0564* 0.0604* 0.0623 0.0360 0.0754* 0.1226* 0.0929* 450,297

(0.0022) (0.0024) (0.0022) (0.0025) (0.0034) (0.0035) (0.0040) (0.0037)

Women 0.0091* 0.0271* 0.0247 0.0265 0.0187 0.0415* 0.0528* 0.0354* 381,465

(0.0020) (0.0020) (0.0018) (0.0019) (0.0030) (0.0030) (0.0030) (0.0027)

Men by Education Level:

 High school dropouts 0.0335* 0.0792* 0.0929* 0.0870* 0.0522 0.1025* 0.1689* 0.1362* 91,969

(0.0050) (0.0051) (0.0052) (0.0062) (0.0073) (0.0072) (0.0085) (0.0091)

 High school grads 0.0243* 0.0716* 0.0769* 0.0793 0.0414 0.1003* 0.1587* 0.1143* 157,633

(0.0045) (0.0048) (0.0044) (0.0050) (0.0068) (0.0070) (0.0078) (0.0071)

 Some college 0.0125* 0.0404* 0.0373 0.0389 0.0374 0.0572* 0.0895* 0.0613* 98,506

(0.0049) (0.0052) (0.0046) (0.0048) (0.0083) (0.0075) (0.0084) (0.0072)

 College grads 0.0131* 0.0306* 0.0316 0.0353 0.0200 0.0331 0.0576* 0.0551 102,189

(0.0045) (0.0051) (0.0043) (0.0050) (0.0069) (0.0068) (0.0082) (0.0080)

Women by Education Level:

 High school dropouts 0.0056 0.0409* 0.0393 0.0456 0.0318 0.0593* 0.0872* 0.0490* 61,124

(0.0047) (0.0049) (0.0049) (0.0060) (0.0073) (0.0071) (0.0082) (0.0081)

 High school grads 0.0124* 0.0289* 0.0307 0.0327 0.0144 0.0488* 0.0614* 0.0411* 158,721

(0.0033) (0.0032) (0.0030) (0.0034) (0.0047) (0.0049) (0.0050) (0.0046)

 Some college 0.0112* 0.0244* 0.0182* 0.0170 0.0287 0.0357 0.0382 0.0307 88,716

(0.0055) (0.0052) (0.0044) (0.0044) (0.0090) (0.0075) (0.0070) (0.0063)

 College grads 0.0102* 0.0191* 0.0107* 0.0137 0.0125 0.0219 0.0289 0.0183* 72,904

(0.0054) (0.0053) (0.0039) (0.0042) (0.0078) (0.0072) (0.0072) (0.0059)

Men by Years of

 Potential Experience:

 1 - 10 years 0.0336* 0.0676* 0.0549* 0.0513* 0.0469 0.1058* 0.1347* 0.0886* 151,281

(0.0047) (0.0044) (0.0040) (0.0045) (0.0070) (0.0069) (0.0069) (0.0065)

 11 - 20 years 0.0133* 0.0724* 0.0779* 0.0786 0.0473 0.0745* 0.1602* 0.1076* 127,307

(0.0042) (0.0054) (0.0046) (0.0051) (0.0073) (0.0072) (0.0092) (0.0076)

 21 - 30 years 0.0199* 0.0541* 0.0737* 0.0775 0.0257 0.0627* 0.1139* 0.1146 97,705

(0.0042) (0.0052) (0.0051) (0.0054) (0.0060) (0.0069) (0.0089) (0.0089)

 31 - 40 years 0.0078* 0.0420* 0.0559* 0.0586 0.0203 0.0515* 0.1007* 0.0960 74,004

(0.0038) (0.0049) (0.0051) (0.0059) (0.0059) (0.0065) (0.0090) (0.0096)

Expansion Years Recession Years

Observations
Table 1 (continued)

Women by Years of 1971 - 73 1976 - 80 1983 - 89 1992 - 97 1970 1974 - 75 1981 - 82 1990 - 91 Observations

 Potential Experience:

 1 - 10 years 0.0150* 0.0342* 0.0265* 0.0242 0.0271 0.0532* 0.0588 0.0350* 132,041

(0.0040) (0.0037) (0.0033) (0.0037) (0.0061) (0.0056) (0.0054) (0.0050)

 11 - 20 years 0.0083* 0.0321* 0.0322 0.0330 0.0260 0.0372 0.0637* 0.0419* 105,272

(0.0041) (0.0044) (0.0037) (0.0041) (0.0067) (0.0061) (0.0067) (0.0056)

 21 - 30 years 0.0013 0.0205* 0.0200 0.0276* 0.0100 0.0288* 0.0512* 0.0382* 83,869

(0.0032) (0.0037) (0.0033) (0.0036) (0.0050) (0.0053) (0.0063) (0.0056)

 31 - 40 years 0.0095* 0.0204* 0.0203 0.0206 0.0087 0.0419* 0.0385 0.0269* 60,283

(0.0039) (0.0040) (0.0038) (0.0042) (0.0054) (0.0064) (0.0064) (0.0062)

All Workers by

 Industry Group:

 Goods producing 0.0200* 0.0631* 0.0699* 0.0722 0.0474 0.0977* 0.1487* 0.1056* 261,885

(0.0029) (0.0031) (0.0030) (0.0034) (0.0046) (0.0046) (0.0052) (0.0050)

 Services producing 0.0135* 0.0342* 0.0335 0.0364 0.0186 0.0409* 0.0629* 0.0509* 569,877

(0.0017) (0.0018) (0.0016) (0.0018) (0.0025) (0.0026) (0.0028) (0.0026)

All Workers by

 Occupation Group:

 White collar - 0.0110* 0.0097 0.0133* 0.0059 0.0159* 0.0257* 0.0222 418,571

 - (0.0013) (0.0012) (0.0013) (0.0020) (0.0019) (0.0021) (0.0020)

 Blue collar - 0.0476* 0.0548* 0.0511* 0.0203 0.0753* 0.1270* 0.0826* 252,949

 - (0.0029) (0.0028) (0.0031) (0.0041) (0.0042) (0.0048) (0.0046)

 Service - 0.0273* 0.0291 0.0247 0.0087 0.0326* 0.0694* 0.0355* 91,078

 - (0.0042) (0.0038) (0.0040) (0.0060) (0.0059) (0.0067) (0.0057)

Note: Each line contains the results from a separate probit equation for the indicated group. The dependent variable equals one if the

individual made an EU transition. All coefficients are expressed as marginal effects (relative to the 1967 period). The regressions include

the following control variables (where appropriate): sex, non white, a set of experience dummies, a set of education dummies, and the 

percentage change in real GDP.

* Significantly different from the previous period of the same type (that is, the coefficient to the left) at the 5% level. Note that the 1970 

recession period is not compared to a previous recession period because the last recession is not covered by the data.

Expansion Years Recession Years
Figure 9: Comparison to Boisjoly, Duncan, and Smeeding(1998): Job Loss Rates from 

the PSID and EU Transition Rates from the March CPS

Note: The PSID data are from Boisjoly, Duncan, and Smeeding (1998)

0

1

2

3

4

5

6

7

8

196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991

EU transition rate - March CPS

Job loss rate - PSID
Figure 10: Comparison to Farber (1997a): Job Loss Rates from the DWS and EU Transition Rates 

from the March CPS

Note: The DWS data are from Farber (1997a).

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1981-83 1983-85 1985-87 1987-89 1989-91 1991-93 1993-95

EU transition rate - March CPS

Job loss rate - DWS
Table 2: Estimated Lower Bounds on the Change in the Reemployment Rate for

 Job Losers Between the 1991-93 and 1993-95 Periods

Initial Values (1991-93 period) Change between the 1991-93 and 1993-95 periods

PL PLU PLN PLE ∆PL ∆PLU ∆PLN ∆PLE ∆PLE/PLE

9.60 2.74 0.00 6.86 -0.10 -0.64 0.00 0.54 7.86

9.60 2.74 0.50 6.36 -0.10 -0.64 0.00 0.54 8.47

9.60 2.74 1.00 5.86 -0.10 -0.64 0.00 0.54 9.20

9.60 2.74 1.50 5.36 -0.10 -0.64 0.00 0.54 10.05

9.60 2.74 2.00 4.86 -0.10 -0.64 0.00 0.54 11.09

Note: The data have been adjusted to account for recall bias and the CPS redesign.
Table 3: Comparison to Monks and Pizer (1998): Probit Equations on Job Loss

 in the NLS and EU Transitions in the March CPS

Time trend

 interacted with:

 High school dropout 0.0141*** 0.0182*** 0.0218*** 0.0135*

(0.0028) (0.0060) (0.0065) (0.0077)

 High school graduate 0.0090*** 0.0126** 0.0094** 0.0206***

(0.0022) (0.0053) (0.0053) (0.0076)

 Some college 0.0019 0.0194*** 0.0080 0.0097

(0.0029) (0.0063) (0.0075) (0.0101)

 College graduate 0.0019 0.0138** -0.0166 0.0217

(0.0040) (0.0063) (0.0115) (0.0167)

Number of

 observations

Note: The NLS results are from Monks and Pizer (1998). The dependent variables equal one

if the individual made an EU transition (March CPS) or involuntarily left a job (NLS). The

regressions include the following control variables: a set of education dummies, the

unemployment rate, age, marital status, and industry and occupation dummies.

* Significant at the 10% level

** Significant at the 5% level

*** Significant at the 1% level

NLS

Nonwhite

March CPS NLS

White

March CPS

53,053 14,551 6,541 5,442
Table 4: Comparison to Monks and Pizer: Implied changes in Job Loss and EU Transition Rates

Education level:

 High school dropout 3.8 2.9 76 9.9 7.4 75

 High school graduate 2.5 1.2 48 7.1 3.8 54

 Some college 2.4 0.3 13 5.4 5.4 100

 College graduate 1.8 0.2 11 5.4 3.5 65

Nonwhite

Education level:

 High school dropout 5.8 6.6 114 14.8 6.7 45

 High school graduate 4.5 2.0 44 10.4 8.9 86

 Some college 3.9 1.5 38 10.1 3.7 37

 College graduate 5.5 -2.7 -49 3.1 4.2 135

Note: These changes were computed using the coefficients fro the probit equations in Table 3. Steve Pizer

kindly provided the percentages from the NLS.

in 1971 change change

Percentage

change in 1971

1971-90

change

1971-90 Probability

March CPS (EU transitions) NLS (job loss) White Probability Percentage