THE BURDENS OF JOB HISTORY: OCCUPATIONAL TENURE, RACE AND
UNEMPLOYMENT DURATION
Clayton Otis Anderson
B.A., University of California, Davis, 2003
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
ECONOMICS
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
FALL
2010
THE BURDENS OF JOB HISTORY: OCCUPATIONAL TENURE, RACE AND
UNEMPLOYMENT DURATION
A Thesis
by
Clayton Otis Anderson
Approved by:
__________________________________, Committee Chair
Jessica Howell, Ph.D.
__________________________________, Second Reader
Suzanne O’Keefe, Ph.D.
____________________________
Date
ii
Student: Clayton Otis Anderson
I certify that this student has met the requirements for format contained in the University format
manual, and that this thesis is suitable for shelving in the Library and credit is to be awarded for
the thesis.
__________________________, Graduate Coordinator
Jonathan Kaplan, Ph.D.
Department of Economics
iii
___________________
Date
Abstract
of
THE BURDENS OF JOB HISTORY: OCCUPATIONAL TENURE, RACE AND
UNEMPLOYMENT DURATION
by
Clayton Otis Anderson
There is a persistent gap in the durations of unemployment between races in the
U.S. labor market. This paper examines one possible explanation for that gap, differences
in job history and human capital acquired on the job. Specific human capital can lead to a
mismatch between offers and expectations that can lead to a longer unemployment spell.
It is found that the number of jobs reduces the length of unemployment, while the length
of the most recent tenure at an occupation is found to increase it. However, neither of
these factors explain much of the differences in unemployment durations between race
and ethnic groups.
_______________________, Committee Chair
Jessica Howell, Ph.D.
_______________________
Date
iv
ACKNOWLEDGMENTS
This thesis would not have been completed without the hard work and patience of
many people. I am indebted to my advisor, Jessica Howell for her advice, and for helping
me to manage the timeline. My second reader, Suzanne O’Keefe not only made several
key suggestions, but should also be credited for introducing me to the article that inspired
this topic. I am also grateful to Ta-Chen Wang for his support, and the rest of the
Sacramento State Economics department for helping me to develop the analytical tools
that were necessary to complete this project.
Another person without whom this thesis would not be complete is Dan Kuang,
who has been a tireless teacher when it comes to statistics and data analysis. I would also
like to extend my gratitude to Jason Rowell and Brian Marentette, who first suggested
that I seek a Master’s degree to begin with.
I would also like to thank my wife, Elizabeth Fein, for putting up with my
continual distraction while completing this. To my mother, father and sister: thank you
for your love and support.
v
TABLE OF CONTENTS
Page
Acknowledgments..................................................................................................................... v
List of Tables ......................................................................................................................... vii
Chapter
1. INTRODUCTION
………………………………………………………… 1
2. ECONOMIC MODEL AND LITERATURE REVIEW .................................................... 6
3. DATA
......................................................................................................... 16
4. EMPIRICAL STRATEGY ............................................................................................... 25
5. PRELIMINARY ANALYSIS .......................................................................................... 28
6. ANALYSIS ....................................................................................................................... 36
7. CONCLUSION ................................................................................................................. 47
Appendix A.
Marginal Effects for Models 1-4 .................................................................... 51
Appendix B. Marginal Effects for Models 5-7 ..................................................................... 52
Works Cited ........................................................................................................................... 54
vi
LIST OF TABLES
Page
1.
Race/Ethnicity by Occupation in U.S. as of 2008…………………………… ….. 4
2.
Descriptive Statistics for Primary Variables, 2002-2008
3.
Correlations ……………………………….………………………………….. 23
4.
Median Weeks Unemployment Spells, by Race/Ethnicity
5.
Median Weeks Unemployment, by Occupation
6.
Mean Weeks Unemployed, By Occupation and Tenure Length………………….. 30
7.
Survival Analysis Results: Estimated Effects on Unemployment Duration
2.
Estimated Effects on Unemployment Duration, Interaction Models
3.
Computing Group AFTs from Model
…………………...... 21
………………….. 28
.…………………… ……...... 28
…….. 33
…………...... 43
….………………………………….. 46
vii
1
Chapter 1
INTRODUCTION
Unemployment, it has been long argued, is endemic to modern capitalism. For a
seemingly inexhaustible set of reasons there are always people seeking jobs and there is
an uncertain amount of time that it takes to find one. For some, unemployment is a brief
splash in between jobs, for others, it is a long swim through uncertain waters.
The duration of unemployment spells matters greatly to the health of an economy
and to the income and well-being of the unemployed person. Economists have argued
that long spells of unemployment deplete an individual’s human capital and reduce their
long-term earning potential. Beyond that, there is also an issue of fairness in
unemployment durations. In the US, different groups experience different lengths of
unemployment, exasperating already troubling between-group inequalities. This paper
will examine unemployment duration with an eye on differences by race and ethnicity.
This should not be taken as a minimization of the problems associated with other forms
of between-group inequalities, but merely an acknowledgement that the different contexts
would require background data that would dilute research focus.
Recently, research has focused on geography as a contributing factor to
unemployment duration and to the racial and ethnic gaps in unemployment durations.
Other factors, such as education, reservation wages, family attachments, health and
standardized test scores have been given as explanations for these gaps. This paper
focuses on an explanation for racial and ethnic gaps in unemployment duration that is
often included, but not given much attention; job history.
3
When a labor market participant becomes unemployed, they are often not just
looking for any job, but a job that is similar to one that they had before. Similarly,
employers are often looking to hire workers that have had recent (or merely some) job
experience in a similar job title. The employers are looking to match the human capital of
the job-seeker with the human capital required to do that job. As laid out in (Becker
1975), types of human capital can have varying degrees of application to different jobs.
General human capital is usable in any setting, but some types of human capital can be
thought of as either industry, occupational or firm-specific. Presumably, many jobs
require some degree of specific human capital. There are things learned on the job that do
not necessarily apply to other jobs, and that other jobs do not adequately prepare the jobseeker for. Unemployed persons wishing to take advantage of their accumulated specific
human capital must match that capital to the capital requirements of a vacant job. Exactly
how long this process will take depends (other things equal) on how specific the capital
they have accumulated at previous jobs is (can it be applied at other jobs, either grossly
or perfectly) and how much human capital they have.
This paper seeks to test out a theory of specific human capital’s effect on
unemployment duration by taking job history as a record of human capital. Different
types of job history are controlled for, representing the diversity and depth of an
unemployed person’s job skills, while adjusting for other aspects of worker quality. The
tenure of an employee is taken to represent the accumulation of human capital, both
general and specific and is shown to have a lengthening effect on unemployment duration
in general, but particularly for office workers. The number of jobs that an unemployed
4
person has held is taken to represent the diversity of job skills. A greater diversity of job
skills is shown to reduce the time spent unemployed, other things equal.
Whether type of human capital influences unemployment duration has
implications for unemployment of different groups. This is for largely the same reason
that differences in education and tenure have implications for between-group wage
differences. As shown in the following table, condensed from the Current Population
Survey (CPS), the labor force in America differs greatly by ethnic group with regard to
the distribution of occupations.
Table 1
Race/Ethnicity by Occupation in U.S. as of 2008, from Current Population
Survey
Black or
White
AfricanAmerican
Hispanic or
Latino
Total, 16 years and over (thousands)
119,126
15,953
20,346
Percent
100.0
100.0
100.0
Occupation
Management, business, and financial operations
occupations
15.9
10
8.1
Professional and related occupations
21.1
17.4
10.2
Service occupations
15.7
24.4
24.2
Sales and related occupations
11.4
9.9
9.3
Office and administrative support occupations
13.1
15.7
12.1
Farming, fishing, and forestry occupations
0.7
0.3
1.9
Construction and extraction occupations
6.5
3.4
12.6
Installation, maintenance, and repair occupations
3.7
2.7
3.7
Production occupations
6.1
6.9
9.3
Transportation and material moving occupations
5.8
9.3
8.5
source: Bureau of Labor Statistics, U.S. Department of Labor, Labor Force Statistics from the
Current Population Survey, Bureau of Labor Statistics, Jan. 2010, Web, Table 3.
The degree of difference for between-group unemployment durations could be either
exaggerated or underestimated by analyses that do not account for job-history
5
differences. Further, if occupational differences are leading to further labor market
inequality of outcomes, then policy solutions for these differences may differ.
6
Chapter 2
ECONOMIC MODEL AND LITERATURE REVIEW
When economists seek to model unemployment duration, the closest tool at hand
is job search theory. This family of theories is summarized in Mortenson (1986). In job
search theory, a job-seeker shops for the best offer amongst groups of job offers. The jobseeker is thought to balance current income versus discounted future income. Each jobseeker has a given reservation wage, below which they will not accept a job. This
reservation wage is at least equal to the value of leisure to the job-seeker. The
employment offers that the job-seeker receives will have a probability distribution of
some kind, but will be affected by the job-seeker’s search method and barriers to entry
into the labor market. In each period, the job-seeker weighs the marginal return of
continued search versus the best offer over the reservation wage.
The family of search theory models has much to say about what could prolong an
unemployment spell. Several of these models allow reservation wages and the rate of
escape from unemployment to also vary over the length of the spell. The latter feature is
referred to as duration dependence. One such model, described by Mortensen (1986), is
general enough to help illustrate what economists are looking at when they decompose
unemployment duration. The rate of escape from unemployment (the hazard rate, in this
particular duration model) is given as
7
ϕ = 𝛌[𝟏 − 𝐅(𝐰∗ )]
Where w* is the reservation wage and [1-F(w*)] is the probability that random offer
acceptable, such F(w*) increases when w* increases. The instantaneous job offer arrival
rate is represented by 𝛌. This can be used to model the probability P(t) that a subject
stays unemployed for t periods:
𝑡
𝑃(𝑡) = 1 − exp⁡(− ∫0 ϕ(τ)⁡dτ),
where 𝜏 is the number of remaining time periods before the subject runs out of money.
Also, both the escape rate and the reservation wage can vary over time:
ϕ(t) = λ[1 − F(w(𝑡))].
Mortensen goes on to show that this kind of model can be used to distinguish
indirect effects on unemployment duration from direct effects. Direct effects are those
effects that change the escape rate irrespective of what effect they have on the reservation
wage. Indirect effects are the change in the escape rate induced by a change in the
reservation wage. Much of the literature on unemployment duration could be classified as
attempting to distinguish between direct and indirect effects. It is possible for a variable
to have positive direct effects and negative indirect effects on unemployment duration,
and vice-versa. For example, a college degree could cause employers to value a job
seeker and result in an increase in the probability of receiving an offer as a direct effect.
For an indirect effect, this degree would cause the worker to hold out for a higher paying
job. Thus, the net effect depends on the relative strength of direct and indirect effects. In
most labor market decision models, the reservation wage is predicted to have a positive
8
effect on unemployment spell length. The length of unemployment is equal to the inverse
of the probability of attaining employment in each cumulative time period. The
probability of attaining employment is equal to the area in between the upper bound of
the wage offer curve, and the function that represents the reservation wage. It is important
to note that in this model, it is not the absolute value of the reservation wage that
prolongs unemployment, but the portion of the offer curve that falls below the reservation
wage.
In the simplest version of the search model, the job-seeker receives a job offer in
each time period, and accepts the first one that satisfies the reservation wage requirement.
This is called a sequential job search model. This type of model describes labor market
change as part of a Markov chain of decisions, between school, work and leisure time. In
sequential job search models the wage offer is viewed as a draw from a random
distribution that does not vary over time, and only one job offer is considered per period.
Another type of search model is the simultaneous search model, an example of which can
be found in Stern (1989). In simultaneous search models, the job-seeker considers
multiple job offers at once, and can also affect not just the rate of offer, but also the
distribution of wage offers. In Stern’s model, for instance, the job-seeker chooses a
number of job-applications as well as a reservation wage in each period, and then chooses
the highest wage offer, as long as it satisfies the reservation condition.
Increases in job related skills should have two effects within the standard search
model. First, they should raise the earning potential of the job-seeker, and therefore, other
things equal the reservation wage. Second, the increase in skills should increase the
9
probability of a job offer. Job-skills are a form of human capital, and can be said to be
either portable to another job (general human capital) or not portable (specific human
capital). Valletta (1991) notes that in the standard search model, the amount of firmspecific human capital possessed by the job-seeker should not make an impact on reemployment. If the human capital in question is truly specific and not portable to any
other job, the job-seeker will adjust their reservation wages to reflect the lack of payoff
from their previous job experience. Valletta, however argues that empirically, workers do
not necessarily make such an adjustment, and instead set their wage expectations based
upon what they had earned previously, regardless of the specificity of their human capital
accumulation. If a newly unemployed worker possesses more specific human capital,
they may have expectations of future earnings that exceed their marginal revenue product
at a new firm. This would cause the market not to clear. If the unemployed worker’s
human capital were either extremely specific to their previous job, or merely roughly
applicable to other jobs the wait spent on unemployment would be longer either way. If
longer tenure at the previous occupation were a good measure for the amount of specific
human capital accumulated, we would expect to see that workers with longer tenures
would experience longer unemployment times. Valetta makes use of data provided by the
Displaced Worker’s Survey (DWS) to test this hypothesis. The DWS tracks workers who
have lost their jobs, including through plant closing or mass layoffs. For this analysis the
unemployed are split into blue collar and white collar occupation groups and further
divided into male and female. Extremely long tenure (15+ years) is found to lengthen
unemployment duration by a significant amount for men in both white collar, and blue
10
collar occupations, but has little effect for women. Men with little or no tenure had the
shortest unemployment spells in both the white collar and blue collar analyses. Overall,
tenure can be seen to have a roughly linear impact on unemployment duration,
lengthening the unemployment spell more as tenure increases.
While Valetta’s regressions include occupational dummy variables, little attention
is given to their results. Valetta also does not investigate whether tenure has different
impact at different job titles, beyond the stratification of the whole analysis into blue
collar and white collar. It could very well be that some job titles are more apt to the
accumulation of specific human capital and others are not. Consider the case where a
clerk performs work for a company using a proprietary software program. Compared
with a computer programmer who becomes practiced in a widely used programming
language, our clerk will find that his tenure at the company will be of less help finding a
job than the computer programmer. If specific human capital is really what prevents these
markets from clearing, then we should expect to see that the effects of tenure is not
evenly distributed by occupation, as some occupations are going to be more apt to the
accumulation of general human capital over long tenures. If in fact the effect of tenure
found by Valetta is simply due to the atrophy of job-seeking skills amongst long tenured
workers, we would expect to see a more or less uniform distribution of its impact on
unemployment duration.
While not many articles focus on occupational differences in unemployment
duration, Kletzer (1992) does examine unemployment duration by industry. This paper
seeks to test a theory of “wait unemployment” presented in Summers (1986). Wait
11
unemployment is characterized by workers displaced from high-wage industries
bypassing lower wage work in order to keep searching for much rarer higher wage jobs
within their previous industry. To test this theory, Kletzer examines unemployment spell
lengths in industries that pay higher wages for comparable work. While the results are
ambiguous with regard to industries that exhibit high wage differentials, Kletzer does
find that different industries do exhibit very different unemployment durations.
The literature regarding race an unemployment duration is much more robust.
Holzer (1987) is the seminal examination of unemployment durations across different
racial groups. This paper compares groups of black and white youths to analyze whether
differing reservation wages help to explain the differing unemployment durations of the
two groups. Using the National Longitudinal Survey of Youth (NLSY), Holzer’s data set
consists of non-student young males, both black and white, from the 1979 and 1980
NLSY. All the survey information is taken from a 1979 interview, while the length of the
unemployment spell is taken from a work history given in 1980. Amongst the
information taken from the 1979 survey is the reservation wage of the respondent. Other
variables include years of labor market experience, schooling and variables that control
for various aspects of job and personal history. The only geographic controls included in
the model are region (north or south) and a dummy variable indicating whether the
respondent lives in an urban area or not. Only the spell duration subsequent to the
interview is used, since reservation wages are potentially endogenous with respect to
prior durations.
12
The primary analysis of Holzer’s article uses 1979 reports of the respondent’s
reservation wage to explain the lengths of 1980’s unemployment spells. This information
is used as part of a sequential job search model. This type of model describes labor
market change as part of a Markov chain of decisions, between school work and leisure
time. As with most labor market decision models, the reservation wage is predicted to
have a positive effect on unemployment length, when education and other worker quality
aspects are controlled for. The length of unemployment is equal to the inverse of the
probability of attaining employment in each cumulative time period. The probability of
attaining employment is equal to the area in between the upper bound of the wage offer
curve, and the function that represents the reservation wage.
When it comes to actually predicting unemployment durations, reservation wages
appear to predict unemployment durations for blacks but not for whites. The coefficient
on the reservation wage is significant and positive (as theory predicts) for blacks, but
negative and non-significant for whites. This holds for both OLS and for Weighted Least
Squares (presented because of the possible non-randomness of missing values in the
NLSY). This, combined with the finding that there is a lower demand for labor for black
youth, indicates that for whatever reason black youth do not revise their reservation wage
downward in the face of barriers to labor market entry. Holzer performs a similar
regression for the non-employed (those who are not employed but are not actively
seeking work), and finds that reservation wages have little predictive ability for the
duration of non-employment.
13
Petterson (1998) looks back at Holzer’s data with a critical eye. Petterson uses the
NLSY, but allows for a longer time frame, from 1979 to 1986, while also replicating
Holzer’s 1979-1980 results. Petterson finds that while Holzer’s main findings concerning
1979 and 1980 hold, these years are anomalous. Reservation wages of blacks are higher
than whites when adjusted for demand, but when the expanded time period is used it is
found that the reservation wages of neither group has significant predictive ability for
unemployment duration.
Much of the recent research on racial differences in unemployment duration has
focused on geography. Rogers (1997), Stoll (2005) and finally Dawkins, Shen and
Sanchez (2005) all examine geography’s effect on race and unemployment. The theory
behind the geographic effects on unemployment does have some implications for the idea
that specific human capital can have an effect on unemployment. These authors are all
concerned with the topic of spatial mismatch, a theory which is meant to explain
differences in between group unemployment. Spatial mismatch can be thought of as skill
mismatch with a geographic dimension. The theory goes that lower skilled workers tend
to live in cities, where the greatest share of jobs is actually more high skilled. Coupled
with a lack of transportation access, low skilled workers have a harder time finding a job
in their local labor market. This means that spatial mismatch is as much about having the
wrong skills as being in the wrong space.
All three of the papers above find that spatial mismatch explains a great deal of
variation in unemployment durations, and coupled with American residential segregation,
some of the unemployment duration gaps between races. Stoll lays out the case that
14
residential segregation contributes significantly to inequality of labor market
outcomes. He argues that less educated minorities tend to search close to their
residence. This disadvantages minorities in job searching because they tend to reside in
areas that have a higher concentration of high-skilled jobs that demand higher education
attainment. This paper attributes 18-36 percent of the Black-White employment gap and
18 percent of Latino-White gap to this geography to skill mismatch. Dawkins, Shen and
Sanchez use models both with and without location controls, and find that the dummy
variable indicating that the job seeker is black is significant in every model except when
location characteristics are controlled for.
Combined with search theory, these models can inform an appropriate way to
build an unemployment duration analysis both from their findings of interest and from
their additional controls. Search theory tells us that anything that will affect the offer rate
or the reservation wage is a candidate for using as a covariate to predict unemployment
spell length. Even if there is some ambiguity about the empirical effects of some
reservation wage measures, those measures seem to at least be a candidate for measuring
worker quality, which would theoretically affect the offer rate. In the absence of direct
questionnaire answers about the reservation wage, Valetta (1991) uses prior income
which is thought to be used as the basis for forming a worker’s reservation wage.
Geography is clearly important as a control of labor market demand, though detailed
geographic data is difficult to come by in publicly available data. Education is often
included as control for general human capital and will be used here for that purpose as
well. Family attachments can change the utility of leisure in search theory, which would
15
also change the reservation wage. Marital status and dummy variables that control for the
presence of young children are often thought appropriate. Age is also controlled for in
every one of the papers mentioned above, and is especially appropriate to control for in
any model that includes the effect of tenure, as age and tenure are likely to be highly
correlated.
16
Chapter 3
DATA
The data used in this paper are drawn from the National Longitudinal Study of
Youth (NLSY). The NLSY is commonly used to study unemployment duration because,
unlike many sources of unemployment data, the longitudinal nature of the NLSY can be
used to construct whole unemployment spells. Other sources of unemployment duration
data are often taken from interviews of unemployed persons in the midst of an
unemployment spell. This technique can result in both bias from censoring of
uncompleted spells, and from selection bias in the sense that the longer a spell goes on,
the more likely it is to be ongoing as of the interview date. Because the censoring bias is
positive and the selection bias is negative, which direction the ultimate bias runs is
indeterminate. The NLSY can be used to avoid these problems because it tracks
employment status on a weekly basis, so that a sample of completed spells can be
constructed, and that even very short spells are also counted.
The NLSY also contains a wealth of individual survey responses on subjects
ranging from schooling to health to recreational activities. The survey questions are readministered every two years to the same cohort, though the items often change over
time. This breadth of information allows a researcher to investigate many different causes
of unemployment, and to test for multiple sources of omitted variable bias. While the
NLSY’s sampling methods are designed to give a representative sample of the U.S.
population, it also contains purposeful oversampling of racial minorities in order to
17
facilitate more reliable statistical analysis pertaining to these groups. What the NLSY
lacks is a robust range of ages. There are only two groups of NLSY respondents; a 1979
cohort and a 1997 cohort. Since both cohorts start out as teenagers within the sample, this
means that only two clusters of age groups are represented when using the NLSY to
perform analysis. Another limitation is that the public-use NLSY lacks detailed
geographic information except for of the most limited kind, such as dummy variables
indicating whether the respondent lives in an urban or rural area.
The final NLSY dataset I employ is extracted from the 1979 interview. The final
sample contains 1,235 completed unemployment spells and secondary information from
2002 to 2008 across 803 unique individuals. In order to simplify the universe of study,
only males are included in the final data file. The unemployment spells are constructed
from the weekly job status variables provided in the NLSY. While right-censoring is not
problematic for the statistical methods employed herein, this does allow the removal of
spells that are left censored. Left-censored are removed by including only those which
started in 2002 (using 2001 data to verify). Another element of the unemployment spell
data worth remarking upon is that, due to the weekly nature of the NLSY’s job status
logs, we are able to differentiate between long term and temporary exits from
unemployment. In this analysis, if a respondent is no longer unemployed, but is then
forced back into unemployment shortly (6 weeks or less) thereafter, all of the time spent
unemployed is counted as one spell instead of two. This means that unconventional ends
to unemployment such as week-long temp jobs and temporary drops from the labor force
will not be counted as exits from unemployment. The independent variables are pulled
18
from the survey data from 2000-2008. Since the survey questions are re-asked every two
years, unemployment spell data is matched to survey data from an earlier date. Thus, if a
respondent became unemployed in 2005, his spell data would be matched to survey
questions that he answered in 2004 (the most recent year of resurveying). The
independent variables represented here in both datasets include education (as the highest
year of education completed), age, race, total family income from the previous year,
cognitive ability, occupational code of previous job, as well as length of tenure for that
job, training variables and total number of jobs held.
The main variables of interest in this paper are meant to track, in various ways, the
accumulation of human capital, especially as it relates to job history. The three main
aspects to a respondent’s job history will be represented by the total number of jobs held,
the length of tenure at the respondent’s most recent job, and training experience. Recent
tenure is computed from the answer to the question “When did you begin performing the
duties at your current/most recent job”. The year of the answer to this question is then
subtracted from the year that the unemployment spell began. Total number of jobs is
tracked as of the interview date on the most recent NLSY survey. Training is a more or
less straightforward matter to track. Presumably, the effects of training diminish with the
passage of time, so two alternate measures are used. One measure is a dummy variable
representing the presence of vocational training within the last four years. Another is a
dummy variable, which tracks the presence of vocational training for any time before the
unemployment date. Because it is possible that employer-sponsored training would be
19
designed to enhance specific human capital, an alternate specification that only tracks
employer-sponsored or on-the-job training is also computed.
Race and ethnicity are tracked in the NLSY, but only three categories are given.
Respondents are either categorized as Black, Hispanic, or Non-Black, Non-Hispanic.
Non-Black, Non-Hispanic will be used as a control group for any analysis which includes
race and ethnicity. Occupation is coded in the NLSY using the the 3-digit 2000 census
code. This occupational coding system was expanded in 2004, to add more specific
occupational titles. For the sake of not over specifying the model and also to keep 2002
and 2003 data comparable to 2004 through 2008, the pre-2004 coding is used. These
occupational codes are represented by a series of 23 dummy variables, one for each
occupational title. The occupational classifications are taken from the answers to NLSY
questions in survey years before that of the unemployment spell. Not all of these
occupational titles are robustly represented in the final dataset, ranging from hundreds
(construction) to only a handful (scientists, legal).
The Armed Forces Qualification Test (AFQT) is a cognitive ability test score taken
from a test issued to all NLSY respondents in the initial 1979 survey. It is used, for
example in Peterson (1998) as an additional control of worker quality. It is presented here
in a normalized form, so that 1 represents an AFQT that is 1 standard deviation above the
mean of the sample population. Multiple Spell is a dummy variable representing whether
an individual returned to unemployment more than once during the period of analysis.
On-the-Job Training is another dummy variable, which takes on a value of one when the
respondent has received on-the-job or employer-sponsored training within the previous
20
four years. Vocational Training works similarly, but represents any sort of training that
would increase job skills. Age is measured as age above 37, which is the youngest age in
the dataset. Income is present in the dataset in the form of Total Net Family Income
(TNFI). TNFI is taken from the previous year’s tax return and is represented in thousands
of dollars. There are other measures of income in the NLSY, but most are based on
survey responses and contain many more non-responses than TNFI does.
The descriptive statistics for the primary variables are given in Table 2 below.
21
Table 2.
Descriptive Statistics for Primary Variables, 2002-2008
Full Sample
Hispanic
(N=1510)
Std.
Mean
Dev.
(N=334)
21.21
Multiple
Spell
Variable
Non-Black,
Non-Hispanic
Black
(N=555)
Mean
Std.
Dev.
26.06
19.87
0.07
0.25
Urban
Residence
0.71
Southern
Residence
(N=621)
Mean
Std.
Dev.
Mean
Std.
Dev.
24.97
25.84
30.51
17.79
21.31
0.08
0.28
0.05
0.23
0.07
0.25
0.45
0.81
0.40
0.76
0.43
0.61
0.49
0.39
0.49
0.30
0.46
0.54
0.50
0.31
0.46
Married
0.39
0.49
0.40
0.49
0.27
0.44
0.50
0.50
Training
(On the Job)
0.15
0.36
0.13
0.34
0.10
0.30
0.22
0.41
50.62
52.06
47.65
37.27
40.14
47.93
61.57
59.79
Education:
Less than
High School
0.02
0.12
0.03
0.16
0.01
0.09
0.01
0.12
Education:
Attended
College
0.29
0.46
0.25
0.43
0.26
0.44
0.35
0.48
Age Above
37
5.83
2.89
5.71
2.79
5.85
2.91
5.88
2.92
AFQT
(Normed)
0.06
1.01
-0.09
0.89
-0.38
0.76
0.53
1.07
Recent
Tenure (In
Years)
4.78
5.25
4.53
5.18
4.61
5.20
5.07
5.32
Weeks
Unemployed
TNFI (in
1000s),
2008
Dollars
22
# of Jobs
Held
15.99
8.00
16.84
8.69
15.31
7.00
16.13
8.39
There are several important differences between the three groups. Blacks and Hispanics
on average both less likely to have attended college and are less likely to have received
on the job training. Hispanics seem to have held a higher average number of jobs than
other ethnicities, while both groups have lower recent tenure than that of non-Hispanic
non-blacks. Mean real income is lower for blacks than for Hispanics, which is in turn
much lower than the mean real income for the rest of the sample. Blacks and Hispanic are
also both much more concentrated in urban areas. The correlations between variables are
represented in Table 3
23
TNFI
Less
than
High
School
Age
AFQT
Recent
Tenure
# of
Jobs
Held
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.008
1.000
-
-
-
-
-
-
-
-
0.085
-0.193
-0.406
1.000
-
-
-
-
-
-
-
-0.056
0.042
0.098
-0.036
-0.118
1.000
-
-
-
-
-
-
TNFI
-0.075
0.007
0.365
-0.030
-0.153
0.168
1.000
-
-
-
-
-
Less than
High
School
0.033
0.068
0.0104
0.051
-0.0387
0.0223
0.0124
1.000
-
-
-
-
Age
-0.047
0.005
0.004
-0.023
0.005
-0.034
-0.021
-0.002
1.000
-
-
-
AFQT
-0.093
0.005
0.174
-0.078
-0.330
0.252
0.351
0.313
0.058
1.000
-
-
Recent
Tenure
0.023
-0.091
0.048
-0.025
-0.026
-0.048
0.086
-0.030
0.114
-0.009
1.000
-
# of Jobs
Held
-0.067
0.003
-0.139
0.057
-0.065
-0.022
-0.134
-0.095
0.014
-0.028
-0.194
1.000
Weeks
Unemp
Urban
Res
Hispanic
Training
(OTJ)
Married
Black
Weeks
Unemp
1.000
-
-
-
-
Urban
Residence
0.084
1.000
-
-
Married
-0.070
-0.048
1.000
Black
-0.027
0.114
Hispanic
0.135
Training
(OTJ)
Variable
24
The dummy variable representing black has the single strongest correlation with
unemployment duration. Urban residence, AFQT and previous number of jobs held also
hold negative correlations with length of unemployment. College attendance and the
dummy variable representing the failure to complete high school both have surprisingly
low correlations with the number of weeks unemployed. As far as correlation between
independent variables is concerned, AFQT shows some correlation with education,
income and training experience. Recent tenure is also negatively correlated with the total
number of jobs held. The source of this is fairly clear, as the length of the most recent job
would tend to limit the time frame available to hold more jobs.
25
Chapter 4
EMPIRICAL STRATEGY
The type of analysis that is used here is survival analysis. Duration time (t) of
unemployment is the dependent variable, and the independent variables are analyzed in
terms of their contribution to the probability of exiting unemployment at any given time.
When time is a dependent variable, it is an option to use regular Ordinary Least
Squares (OLS) as a method of analysis. This would be unusual because one of the
assumptions of parametric regression is that the dependent variable is normally
distributed, conditional on the values of the independent variables. The assumption of
normality is unlikely in most time series, but even more so with unemployment spells.
The probability of leaving unemployment is often thought to increase early in the spell
and then decline over time, leaving a normal distribution of durations unlikely. Another
issue is that time is a limited dependent variable, having only the ability to take on
positive real numbers. OLS assumes the possibility of negative occurrences of the
dependent variable, which is impossible when a period of time is the dependent variable.
While OLS can often be robust to deviations from normality, some of the deviations that
can be expected from time duration data can include bimodal or asymmetric distributions
that OLS is not robust to (Cleves 2010, pp 10).
Survival analysis uses assumptions that are more suitable for modeling time.
There are three basic types of survival analysis models; parametric, semi-parametric and
non-parametric. Parametric models make assumptions about the shape of the error term,
26
that it is distributed exponentially, for example. Semi-parametric models do away with
assumptions about the distribution of errors, but still allow that the covariates in a model
can take on a certain functional form. An example of semi-parametric survival analysis is
the Cox regression, which uses each failure time as the dependent variable in an
individual binary regression, calculating coefficients that maximize overall likelihood of
the binary model for each outcome. Non-parametric models let the data speak for itself,
without assumptions about the forms of covariates or of the error term. Non-parametric
analysis is typically used when there are either no covariates or when the only covariates
of interest are categorical variables.
In this particular analysis, there is a fairly well developed theory of what affects
unemployment duration, and about what the distribution of unemployment spells should
look like. This makes nonparametric analysis less interesting. Semi-parametric analysis
can still be useful, but there are strong reasons to think that unemployment durations will
follow a certain distribution, particularly that they should exhibit some form of duration
dependence. The amount of time spent on unemployment in and of itself can be seen as
degrading job skills, or as a signal of poor productivity to potential employers. That
leaves parametric survival analysis as the most attractive option left for this analysis, as it
allows us to keep assumptions about both covariates and distribution of errors to explore
new information added to old models.
Survival analysis works by estimating a set of weights (β) that maximize a loglikelihood function (log(L)). In this case, z=1 indicates that that a spell is not finished, xi
represents an array of independent variables, 𝑦𝑖 is the observed duration of the
27
unemployment spell and λ(t) is the baseline hazard rate. The baseline hazard rate is the
hazard rate for individual i when all xi are equal to zero (Cleves 2010).
𝑛
log(𝐿) = ∑
𝑖,⁡𝑧𝑖 =1
𝑥𝑖′
′
𝑦𝑖
+ log⁡(λ(𝑦𝑖 )) − ∑ (𝑒 𝑥𝑖 𝛽 ∫ λ(t)dt)
𝑖=1
0
There are several distributions of λ that are thought to work well with unemployment
data. The best distributions are thought to allow for some measure of duration
dependence. The exponential distribution, the Weibull distribution and the lognormal
distribution are all possibly appropriate for unemployment duration data.
28
Chapter 5
PRELIMINARY ANALYSIS
Table 4 presents the basic observation that we seek to explain, median
unemployment differs by ethnicity.
Table 4.
Median Weeks Unemployment Spells, by Race/Ethnicity
Race/Ethnicity
Obs
Median
Std. Err.
Hispanic
Black
334
555
621
1510
11
17
13
14
0.824
1.112
0.563
0.668
Non-Black, Non-Hispanic
All
[95% Conf. Interval]
9
14
11
13
17
21
14
15
Blacks have considerably higher median unemployment spells than non-blacks, while
Hispanics have slightly lower unemployment durations. There is some slight overlap
between the confidence interval associated with being black and that of the whole
sample. Unlike for Hispanics, there is only a remote possibility that the true median is the
same as that of the whole sample. While non-normal distributions and some censored
data mean that a simple t-test must be interpreted with caution, comparisons of mean
unemployment durations tell a slightly different story.
Table 5.
Mean Weeks Unemployed, By Race/Ethnicity
Race/Ethnicity
Obs Mean Std. Err.
Black
555
25.836
1.295
Non-Black, Non-Hispanic
621
17.792
0.855
Hispanic
334
19.874
1.366
29
A t-test between Black and Non-Black, Non-Hispanic would be significant at the 1
percent level, while a t-test between Hispanic and Non-Hispanic would not be significant
at any standard level (p-value of 0.1434).
As shown in the descriptive statistics in Table 2, many variables thought to be key
to unemployment duration vary by race. Additionally, occupation is not distributed
equally by race either in the data set or in the U.S. labor market. Hispanics are
overrepresented in the Construction and Extraction occupation codes as well as Building
and Grounds Cleaning and Maintenance amongst occupations with a large sample size.
Blacks are also overrepresented in Building and Grounds Cleaning, as well as
Maintenance Transportation and Material Moving, Food Preparation and Serving
Related and Office and Administrative Support. Both groups are well below their overall
utilization rates in Management. It is possible that these differences in utilization result in
different allocations of human capital (both general and specific) which in turn affect the
duration of unemployment.
Breaking out median unemployment by occupational code, we see a few titles
seem to have distinctly different unemployment patterns from the rest of the sample.
30
Table 6.
Median Weeks Unemployment, by Occupation
Std.
Occupation
Obs Median
Err.
[95% Conf.
Interval]
Management
121
13
0.425
11
19
Business and
Financial Operations
53
15
0.532
8
20
Computer and
Mathematical
32
14
0.487
9
28
Architecture and
Engineering
19
19
0.429
9
53
Life, Physical, and
Social Services
3
15
0.140
4
.
Community and
Social Services
8
6
0.048
1
.
Legal
4
35
0.445
23
.
Education, Training,
and Library
14
14
0.652
2
17
9
21
0.474
2
34
3
47
1.613
1
.
Healthcare Support
6
4
0.368
1
.
Protective Service
38
16
0.794
9
24
Food Preparation and
Serving Related
61
20
0.500
17
38
Building and
Grounds Cleaning
and Maintenance
93
18
0.382
15
26
Arts, Design,
Entertainment,
Sports, and Media
Healthcare
Practitioners and
Technical
31
Table 6 continued
Personal Care and
Service
13
10
0.525
1
18
Sales and Related
75
14
0.419
11
25
Office and
Administrative
Support
72
14
0.601
9
22
Farming, Forestry,
and Fishing
10
18
0.333
3
21
Construction and
Extraction
264
10
0.587
7
13
Installation, Repair,
and Maintenance
119
12
0.408
9
16
Production
226
16
1.181
11
22
Transportation and
Material Moving
265
15
0.639
12
18
Military
2
2
.
2
.
All Occupations
1510
14
0.668
13
15
The confidence intervals presented do adjust for the fact that some of the unemployment
spells in the dataset are truncated. Several occupations have larger median unemployment
spells than the total population’s median unemployment spell of 12 weeks, though the
median for the architectural and education related occupations are suspect due to small
sample size. Only Food Preparation related occupations, as well as Building and
Grounds Cleaning and Maintenance fall outside the 95 percent confidence interval.
32
These results really only show that median unemployment differs by job title.
They say nothing what causes these differences. Employment choice is probably
endogenous with regard to worker quality, education level, race and other factors. Even a
preliminary investigation casts some doubt on the idea that unemployment differences are
simply caused by being in different occupation. Those working in scientific, managerial
and financial occupations have higher mean educations than the Building and Grounds
Cleaning and Maintenance occupational code, for example. It is also notable that blacks
are overrepresented in Food Preparation, and both blacks and Hispanics are
overrepresented in Building and Grounds Cleaning and Maintenance, so it is not certain
if the higher unemployment durations in those occupations are caused by racial
discrimination, the skill levels associated with those types of jobs or some unknown
factor.
Before accounting for worker quality there is one more preliminary element to
investigate. It is possible that the skills picked up while doing these jobs are not easy to
transfer to other jobs, that they lend themselves to the accumulation of specific, rather
than general human capital. In fact, even those occupations which exhibit lower median
unemployment rates overall may display longer unemployment rates for those who spend
more time in those occupations. If this were so, we could expect to see that on the job
tenure could have different effects on unemployment durations at different occupations.
In the final dataset of NLSY respondents used in this analysis, the mean tenure at
the most recent job is about 4.5 years. Table 7 shows the results of comparing those with
above average tenure lengths with those with below average tenure lengths, by
33
occupation. It is important to note that there is nothing special about the average tenure
length. It is just a convenient form for showing that unemployment durations differ by
tenure and occupation.
Table 7.
Mean Weeks Unemployed, By Occupation and Tenure Length
Occupation
Management
Recent Tenure
Group
Obs
Mean
Std.
Err.
Short (< 4.5 Yr)
70
21.186
3.048
Long (> 4.5 Yr)
51
20.333
2.898
0.852
4.206
Difference (short long)
Business and
Financial
Operations
Computer and
Mathematical
Short (< 4.5 Yr)
38
16.500
2.234
Long (> 4.5 Yr)
15
16.867
5.576
-0.367
6.007
Difference (short long)
Short (< 4.5 Yr)
24
16.333
2.988
Long (> 4.5 Yr)
8
38.625
13.476
22.292
13.803
Difference (short long)
Architecture and
Engineering
Short (< 4.5 Yr)
13
19.462
4.147
Long (> 4.5 Yr)
6
29.167
10.008
-9.705
10.833
Difference (short long)
Education, Training,
and Library
Short (< 4.5 Yr)
9
17.667
11.851
Long (> 4.5 Yr)
5
17.600
4.823
0.067
12.795
Difference (short long)
Protective Service
Short (< 4.5 Yr)
33
24.303
4.761
Long (> 4.5 Yr)
5
31.400
15.712
-7.097
16.417
Difference (short long)
Food Preparation
and Serving Related
Short (< 4.5 Yr)
42
28.857
4.763
Long (> 4.5 Yr)
19
25.105
6.121
3.752
7.756
Difference (short long)
tvalues
pvalue
0.20
0.420
-0.06
0.476
-1.61
0.073
-0.90
0.201
0.01
0.498
-0.43
0.342
0.48
0.316
34
Table 7 continued
Building and
Grounds Cleaning
and Maintenance
Personal Care and
Service
Short (< 4.5 Yr)
53
25.585
4.830
Long (> 4.5 Yr)
40
24.100
5.124
1.485
7.042
Difference (short long)
Short (< 4.5 Yr)
10
13.500
5.402
Long (> 4.5 Yr)
3
4.000
3.000
9.500
6.179
Difference (short long)
Sales and Related
Short (< 4.5 Yr)
53
20.736
3.542
Long (> 4.5 Yr)
22
34.818
9.316
14.082
9.967
Difference (short long)
Office and
Administrative
Support
Construction and
Extraction
Short (< 4.5 Yr)
52
16.250
2.416
Long (> 4.5 Yr)
20
20.550
3.930
-4.300
4.613
Difference (short long)
Short (< 4.5 Yr)
167
20.838
2.392
Long (> 4.5 Yr)
97
17.423
2.244
3.416
3.280
Difference (short long)
Installation, Repair,
and Maintenance
Short (< 4.5 Yr)
80
19.738
2.978
Long (> 4.5 Yr)
39
20.795
5.182
-1.057
5.977
Difference (short long)
Production
Short (< 4.5 Yr)
129
21.426
2.191
Long (> 4.5 Yr)
97
22.392
2.539
-0.965
3.354
Difference (short long)
Transportation and
Material Moving
Short (< 4.5 Yr)
180
18.150
1.526
Long (> 4.5 Yr)
85
26.282
2.868
-8.132
3.249
Difference (short long)
0.21
0.417
1.54
0.077
-1.41
0.085
-0.93
0.179
1.04
0.149
-0.18
0.430
-0.29
0.387
-2.50
0.007
35
Table 7 only displays means of occupations where the sample size is large enough (n>10,
more than one member of each of the tenure groups) to make a meaningful comparison
between tenure groups. While the non-normal distributions of unemployment time within
the dataset should caution against over-interpreting t-values, some patterns can be
discerned from Table 7. Bearing in mind that the longer tenured group is typically
unemployed for about two weeks less than the shorter-tenured group, three occupational
groups appear to show different influences from recent tenure. Computer and
Mathematical, Sales and Related and Transportation and Material Moving all seem to
show much longer unemployment durations for those with lengthy tenure at the most
recent job than other occupations. These three occupations represent varying degrees of
educational attainment and skill. The average member of the Computer and
Mathematical occupational code has a bachelor’s degree (16.33 years of education),
Sales and Related occupations have an average of 12.87 years schooling, and
Transportation and Material Moving average 12.04 years.
36
Chapter 6
ANALYSIS
There are many variables that could be causing the patterns in unemployment
occurring in occupation and tenure groups. Using survival analysis and a simple model of
unemployment duration, we can control for other possible causes to see if the differences
in unemployment durations by occupation and tenure are explained by other causes.
Survival analysis is sensitive to the choice of error distribution, so a few different
distributions were tested on the basic model before the full analysis was attempted. The
Akaike Information Criterion (AIC) is used for comparing the Weibull, exponential and
lognormal distributions. The AIC is calculated based on log likelihood and contains a
penalty for extra parameters. It is calculated with the following equation, -2 * log
likelihood + (p*k), where p is the number of parameters and k is a constant left to the
user’s discretion. The parameter k is set to a default of 2, however, calculating AIC with
k=3 did not change the ranking of any of the results that follow. The model with the
lowest AIC is the lognormal distribution, followed by the Weibull. The exponential
distribution had the highest AIC in the basic model. Because a lower AIC indicates better
fit, the lognormal distribution was chosen. The results of the AIC would seem to indicate
little more than the fact that the hazard of escaping unemployment does not change
monotonically in this data set.
The lognormal distribution is compatible with an unemployment distribution
where the chance of leaving unemployment increases rapidly in the first few weeks, then
37
decreases as time goes on. When using a lognormal distribution for survival analysis, the
coefficients represent Accelerated Failure Time (AFT). AFT coefficients are interpreted
similarly to the log-linear model in OLS. They can be interpreted as the percentage
increase (or decrease) in duration for a one-unit increase in the covariate. For additional
ease of interpretation, the marginal effects of each model are also given in appendix A.
These are the predicted effect of a one-unit change of the independent variable on the
predicted median of weeks unemployed. Using AFT to interpret Model 1 (other things
equal) in Table 8 below, living in an urban area is likely to increase the expected median
unemployment duration by about 19.7 percent.
38
Table 8.
Survival Analysis Results: Estimated Effects on Unemployment Duration
VARIABLE
Urban Residence
Married
Southern
Residence
TNFI (in 1000s),
2008 Dollars
Education: Less
than High School
Education: Attend
College
Age Above 37
AFQT (Normed)
Black
Hispanic
Multiple Spell
Coef
Model (1)
AF (%Δ)
0.197**
Model (2)
AFT(%Δ)
0.220***
Model (3)
AFT(%Δ)
0.221***
Model (4)
AFT(%Δ)
0.202**
SE
Coef
-0.083
-0.172**
-0.083
-0.172**
-0.083
-0.197**
-0.083
-0.169**
STAT
SE
-0.081
-0.081
-0.081
-0.081
Coef
-0.138*
-0.130*
-0.125
-0.133*
SE
Coef
-0.078
0
-0.078
-0.001
-0.078
-0.001
-0.077
-0.001*
SE
-0.001
-0.001
-0.001
-0.001
Coef
0.423
0.413
0.391
0.512*
SE
Coef
-0.3
0.043
-0.299
0.056
-0.298
0.056
-0.304
-0.011
SE
-0.097
-0.096
-0.096
-0.101
Coef
0.025
0.025
0.021
0.02
SE
-0.016
-0.016
-0.016
-0.016
Coef
-0.075
-0.055
-0.057
-0.055
SE
-0.048
-0.048
-0.048
-0.049
Coef
0.318***
0.316***
0.292***
0.302***
SE
-0.095
-0.095
-0.095
-0.096
Coef
-0.109
-0.106
-0.104
-0.105
SE
-0.101
-0.101
-0.1
-0.1
Coef
-0.153
-0.122
-0.088
-0.065
SE
-0.145
-0.145
-0.145
-0.144
Training (On the
Job)
Coef
-0.223**
-0.220**
-0.207**
SE
-0.104
-0.104
-0.104
Recent Tenure (In
Years)
Coef
0.020***
0.016**
0.018**
SE
-0.007
-0.007
-0.007
-0.014***
-0.014***
# of Jobs Held
Constant
ln(sig)
Coef
-0.005
-0.005
Coef
SE
2.384***
2.344***
2.605***
2.600***
SE
-0.139
-0.142
-0.168
-0.185
Coef
0.306***
0.302***
0.299***
0.285***
SE
-0.021
-0.021
-0.021
-0.021
No
No
No
Yes
1510
1510
1510
1510
Occupational Controls (Y/N)
Observations
39
Table 8 continued
Failures
1221
1221
1221
1221
Log likelihood
-2306.64
-2299.96
-2295.81
-2276.31
LL (Constant Only)
-2343.86
-2343.86
-2343.86
-2343.86
19
21
22
44
4651.28
4641.93
4635.63
4640.61
Df
AIC
*** p<0.01, ** p<0.05, * p<0.1
Education: High School and Occupation:Transportation and Material Moving are suppressed
dummy variables. Year of unemployment spell is controlled for in all models, but not shown.
Several covariates have significant impacts on unemployment duration. Urban
residence is significantly likely to increase unemployment duration by about 20 percent
in all models. Southern residence has the opposite effect, reducing unemployment
duration by anywhere from 12.5 percent to 13.8 percent and is either significant or close
to significant in every model. This can probably be attributed to the south lower
unionization rate. Compared with the control year of 2002, beginning an unemployment
spell in 2004 significantly lowered the amount of time spent unemployed. Those who
failed to complete high school have 50 percent longer unemployment durations than the
control group of high school graduates, but this effect is only significant when job history
is accounted for. The previous year’s income (TNFI) does not have a significant effect in
the basic model, but becomes significant and negative once number of previous jobs held
and occupation are controlled for. This runs counter to the conclusion of Petterson
(1998) that reservation wages have no predictive power, but still is an awkward fit with
standard job search theory. It would seem that high reservation wages (if the previous
40
year’s TNFI is a good proxy for them) are a signal of high worker quality, and thus raise
the chance of receiving an acceptable job offer.
Job-seekers who failed to graduate high school have unambiguously longer
unemployment spells than their equivalents who did graduate. Having attended college
does not appear to have an effect that is distinguishable from zero in any model. Several
other specifications and educational attainments are also analyzed, but are not displayed
here for brevity. The effect of education beyond high school was not significant in any
formulation. Respondents that experience multiple spells of unemployment during the
time period tend to experience shorter spells, though not significantly so. It is possible
that those who tend to experience multiple unemployment spells are in seasonal
occupations, but a separate regression that controls for leaving a seasonal job shows that
such temporary work does not affect this coefficient.
There are other notable findings in this set of results. Blacks have 29 to 31
percent longer unemployment spells than those classified as Non-Black, Non-Hispanic,
even after the inclusion of this full set of controls. Hispanics appear to have shorter
(though not significantly shorter) unemployment spells once everything is controlled for.
Adding the total number of jobs held accounts for the single largest change in coefficients
for blacks, indicating that this variable accounts for roughly 2 percent of the variance in
unemployment durations for blacks in Model (2). Number of jobs in job history did not
change the AFT for Hispanics by much; despite the fact Hispanics typically had many
more occupations in their job history (blacks tended to have fewer). Overall, respondents
with more occupations in their jobs history spent significantly less time unemployed, by
41
about 1.5 percent per job. This would seem to indicate one of either two things: either a
varied job history gives a job-seeker a diverse portfolio of skills to match with needs in
the labor market, or that job search is a skill and that those who practice it more become
better at it.
The job history variables all appear to have some impact, though with some
qualification. The length of tenure at the most recent job is significant in Model (2) at the
1 percent level, and in Model (3) and (4) at the 5 percent level. Each year of recent tenure
appears to increase duration by about 2 percent per year. When the number of jobs held is
controlled for, recent tenure has less effect, reflecting the correlation between those two
variables. Having experienced on-the-job training at the respondent’s previous
occupation is significant at the 5 percent level. An alternate model that used vocational
training is not found to be significant in any specification. This runs counter to theory that
employers are more likely to pay for training that is not transportable to other jobs.
Controlling for prior occupation is somewhat messier than controlling for other
job history characteristics. While adding recent tenure, on-the-job training and total
number of jobs previously held each make for a better model (by AIC), adding the
occupational controls result in a higher AIC. The individual coefficients for occupation
are not shown, but only a few of them differ significantly from the control group
(transportation occupations). However, we can see that controlling for occupation does
seem to have an effect on the interpretation of the other covariates. Once occupation is
controlled for, dropping out of high school is shown to significantly lengthen
unemployment, indicating that drop out are significantly disadvantaged not just because
42
of their occupations, but within them as well. This change is the single largest change in
AFT coefficient that comes from adding occupational controls. TNFI is also only
significant after occupation is controlled for as well, though it would appear to shorten
unemployment duration, other things equal. Race, does not seem particularly impacted by
controlling for occupation. The AFT associated with being black increases by 0.01. This
means that omitting occupational controls would bias that effect on unemployment
downward by 1 percent.
While adding full occupational controls probably does give a less biased estimate
of education’s coefficient, it does not lead to an appreciably better model, on account of
there being a large number of occupations which do not appear to add anything to the
model. In order to scale back the number of controls, an alternate occupational coding
scheme is also used. Using broad educational patterns, three occupational super-groups
were constructed. Technical, management, educational and scientific occupations were
collapsed into one group, while Sales and Related and Office Administrative occupational
codes were collapsed into another. All other occupations were grouped together to form a
third group. These three groups can be thought of as Professional and Management,
Office, and Less Skilled occupational super-groups. Taking advantage of the smaller
number of covariates, we can also explore interaction terms to see if recent tenure varies
in its effects by occupational super-group.
43
Table 9.
More Survival Analysis Results: Estimated Effects on Unemployment Duration,
Interaction Models
VARIABLE
Urban Residence
Married
Southern Residence
TNFI (in 1000s), 2008
Dollars
Education: Less than
High School
Education: Attended
College
Age Above 37
AFQT (Normed)
Black
Hispanic
Multiple Spell
Training (On the Job)
Recent Tenure (In
Years)
# of Jobs Held
Model (5)
Model (6)
Model (7)
AFT(%Δ)
AFT(%Δ)
AFT(%Δ)
Coef
0.214***
0.202**
0.203**
SE
Coef
-0.083
-0.198**
-0.083
-0.193**
-0.083
-0.177**
SE
Coef
-0.081
-0.130*
-0.081
-0.129*
-0.081
-0.144*
SE
Coef
-0.078
-0.001
-0.077
-0.001
-0.078
-0.001
SE
Coef
-0.001
0.375
-0.001
0.383
-0.001
0.398
SE
Coef
-0.297
0.012
-0.297
0.014
-0.297
0.03
SE
Coef
-0.1
0.021
-0.1
0.021
-0.1
0.019
SE
Coef
-0.016
-0.07
-0.016
-0.07
-0.016
-0.084*
SE
Coef
-0.048
0.295***
-0.048
0.290***
-0.048
0.447***
SE
Coef
-0.095
-0.107
-0.095
-0.107
-0.107
0.039
SE
Coef
-0.1
-0.079
-0.1
-0.083
-0.117
-0.057
SE
Coef
-0.145
-0.231**
-0.145
-0.240**
-0.145
-0.233**
SE
Coef
-0.104
0.016**
-0.104
0.009
-0.103
0.017**
SE
Coef
-0.007
-0.013***
-0.008
-0.013***
-0.007
-0.014***
Stat.
SE
-0.005
-0.005
-0.005
Occ Group:
Management and
Technical
Coef
0.18
0.185
0.435***
SE
-0.113
-0.15
-0.146
Occ Group: Sales and
Office Admin
Coef
0.075
-0.25
0.425**
SE
-0.127
-0.163
-0.203
Interaction:
Tenure*Management
Coef
-0.002
SE
-0.019
44
Table 9 continued
Interaction: Tenure *
Sales & Office
Coef
0.079***
SE
-0.025
Interaction:
Black*Management
Coef
-0.506**
SE
Coef
-0.231
-0.644**
Interaction: Black*
Sales & Office
Interaction:
Hisp*Management
Interaction: Hisp*
Sales & Office
Constant
ln(sig)
Observations
Failures
Log likelihood
LL (Constant Only)
Df
AIC
SE
-0.284
Coef
-0.524**
SE
Coef
-0.245
-0.436
SE
-0.323
Coef
2.594***
2.629***
2.511***
SE
Coef
-0.168
0.298***
-0.169
0.295***
-0.169
0.294***
SE
-0.021
-0.021
-0.021
1510
1221
-2294.53
-2343.86
24
4634.238
1510
1221
-2289.3
-2343.86
26
4630.593
1510
1221
-2289.12
-2343.86
28
4637.059
*** p<0.01, ** p<0.05, * p<0.1
Education:High School Grad and Occ Group: Less Skilled are suppressed dummy variables.
Year of unemployment spell is controlled for in all models, but not shown.
The results here show that the Management occupational group has higher
unemployment durations than the less skilled occupations, and this difference is close to
significant. The effect on the total model of controlling for occupation is only a small
improvement in AIC, over the model without any occupational controls (Model 3 in the
first output). However, this model is preferred to the uncollapsed occupational titles in
Model 4, by the same measure. Adding an interaction between recent tenure and
occupation shows that in actuality most of the variance being explained by recent tenure
in the other models was occurring among the Sales and Office workers. Perhaps long
45
tenure in these kinds of jobs leads to an accumulation of skills that is particular to the
specific workplaces, such as familiarity with specialized computer programs or contacts
with clients in a particular industry. Recent tenure does not seem to matter as much for
the higher skilled or lower skilled positions. It is possible that the development of on the
job skill at the higher skill positions gives a better indication of the ability to pick up new
skills rapidly, while the skills acquired at low-skill jobs are broadly transferable between
low skill jobs.
The addition of the interaction controls between broad occupational group and the
job history variables give us the model with the most explanatory power. However, once
again, though know that occupation and tenure are not equally distributed between race
and ethnic groups, these additional controls do not seem to affect the coefficients for
minority groups. We can conclude that controlling for industry and job history does very
little to explain the difference in unemployment durations between race and ethnic
groups. Adding interactions between race and occupation can tell us what happens to
each ethnic group within the occupational group. Since the coefficients in such an
interaction model can be confusing to keep track of, the coefficients for each group in
model seven are calculated in Table 10. Coefficients that are not significant in Model 7
are assumed to be zero.
46
Table 10.
Computing Group AFTs from Model 7
Occupational Group
Sales and
Management
Less-Skilled
Office
& Technical
Admin
Race
Black
0.447
0.376
0.228
Hispanic
--
-0.089
0.425
Non-Black,
Non-Hispanic
--
0.435
0.425
Using Model 7, we see that the only occupational group that blacks spend less
time unemployed in is the less skilled occupational group. Non-black, non-Hispanic
white collar workers have 43.5 percent and 42.5 percent longer unemployment durations
than the less skilled control group. This seems to indicate that there are only certain kinds
of occupations for which either minority group has a harder time finding employment. In
managerial or office occupational groups, neither minority is more apt to spend more
time unemployed than similarly skilled non-minorities. These conclusions should be read
with caution, though. While the model with race and job interactions does give several
interesting and significant results, it does not have as much explanatory power as the
model without race and occupational interactions.
47
Chapter 7
CONCLUSION
It is fair to conclude that job history has an effect on unemployment. The total
number of jobs held by a job seeker seems to have a role in reducing the length of
unemployment at about 1-2 percent per job. The length of recent tenure at a job seems to
have some effect on unemployment duration, but this may only be restricted to the labor
market experience of office workers. This effect is to make unemployment longer by
somewhere between 1.3 and 2.0 percent per job. Occupational title has less certain effects
on unemployment, and indeed only seems to matter when interacted with race. Even
though all job history variables are not distributed evenly by race either within this
dataset or within the U.S. labor market, it can’t be said that the effects on racial
differences in unemployment outcomes of these findings are particularly large. Indeed,
they only account for at most 2 percent of the variance in unemployment outcomes
between ethnic groups.
The fact the number of jobs and the length of recent tenure are important
predictors of unemployment duration seems to show that having a diverse number of
skills is an easier way to find a job-match than having one well developed skill. This is
compatible with the notion that specific human capital has an effect on unemployment
duration, but it does not prove it. There is also the possibility that job search is simply the
type of skill that works better with practice, and that job-hoppers get more practice than
those workers who spend a long time in one job. The result that tenure lengthens
48
unemployment mainly for sales and office workers, and not for management or less
skilled workers does seem to hint that specific human capital incompatibilities are indeed
a culprit, as there is no intuitive reason why job-search abilities should atrophy at
different rates for different occupations. But it must be admitted that this is not much
more than a hint.
The results from race-occupation interactions imply that much of the
unemployment differences between blacks and non-blacks are due to barriers to
employment for blacks within occupations, and not due to segregation into occupations
with high degrees of non-transferable skills. The race and ethnic groups studied here to
vary in job history and this variation does explain some of the between groupemployment gaps. However the amount of this gap explained by job history is very faint
when compared with the effect of race (and presumably discrimination) itself.
It is difficult to tell the difference between the effects of occupation on race from
the composition of races with occupations. The NLSY is also not exactly representative
of the U.S. labor market and purposefully oversamples minorities. This may mean that
some the results here may have limited external validity when extrapolating to the actual
labor market, especially with regard to race and occupation. A larger sample size is
always better, but in this case the inability to really examine the employment differences
for scarcer occupations (such as legal and scientific) is an important limitation.
This study mostly focuses on aspects of the job-seeker. A deeper investigation
could include aspects of either the local or national labor market beyond the simple ones
used here to control for demand side variables. Geography is undoubtedly important to
49
barriers to entry within a job market, and a variation on the geographical skills mismatch
story could be examined using job history as well. A more detailed analysis of human
capital specificity’s role in the whole labor market could also be achieved by coupling
this duration analysis with occupational quit rates as well as wage losses from
occupational switching. More could also be done to investigate the changes in demand
for particular job types and the effects that these shifts could have on the unemployed.
The problem of unemployment and job history could also be set more within context of
the whole labor market. The effect of gender on job type and job type in turn on
unemployment duration could be performed in a similar sort of analysis to this one, but
would also require a more detailed analysis of the intersection of the household and the
labor market.
This paper has shown that those with long tenure in a job will have the hardest
time finding employment, and has found some implications that the type of job lost has
an impact on just how much damage is done to employment prospects. With further study
this could be used to better target employment policies to the types of workers who need
them most. The results suggest that high tenured blacks or office workers are the most
vulnerable to prolonged unemployment spells.
50
APPENDICES
51
APPENDIX A
Marginal Effects for Models 1-4
Marginal Effects of Covariates Upon Predicted Median Weeks
Unemployed
VARIABLE
Model 1
Model 2
Model 3
Model 4
Urban Residence
2.52**
2.81***
2.82***
2.58**
Married
-2.24**
-2.25**
-2.58**
-2.21**
-1.81*
-1.71*
-1.64
-1.75*
-0.01
-0.01
-0.01
-0.02*
6.95
6.77
6.34
8.83
0.58
0.76
0.76
-0.15
Age Above 37
0.33
0.33
0.28
0.26
AFQT (Normed)
-1
-0.72
-0.76
-0.72
Black
4.43***
4.41***
4.06***
4.20***
Hispanic
-1.41
-1.37
-1.34
-1.35
Multiple Spell
-1.9
-1.53
-1.13
-0.83
-2.75**
-2.72**
-2.57**
0.27***
0.217**
0.24**
-0.184***
-0.18***
13.318
13.309
Southern
Residence
TNFI (in 1000s),
2008 Dollars
Education: Less
than High School
Education: Attend
College
Training (On the
Job)
Recent Tenure (In
Years)
# of Jobs Held
Predicted
median(t)
13.295
13.31
52
APPENDIX B
Marginal Effects for Models 5-7
Marginal Effects of Covariates Upon Predicted Median Weeks
Unemployed
VARIABLE
Model 5
Model 6
Model 7
Urban Residence
2.73***
2.58**
2.57**
Married
-2.59**
-2.52**
-2.36**
Southern Residence
-1.71*
-1.70*
-1.83*
-0.013
-0.012
-0.016
6.01
6.17
0.081
0.16
0.19
2.56
Age Above 37
0.28
0.27
0.26
AFQT (Normed)
-0.93
-0.93
-1.17*
Black
4.10***
4.03***
6.23***
Hispanic
-1.39
-1.39
0.57
Multiple Spell
-1.02
-1.07
-0.7
Training (On the Job)
-2.84**
-2.95**
-2.92**
Recent Tenure (In Years)
0.22**
0.13
0.23**
# of Jobs Held
-0.18***
-0.18***
-0.19***
2.54
2.62
5.91**
1.04
-3.01*
6.58*
TNFI (in 1000s), 2008
Dollars
Education: Less than
High School
Education: Attended
College
Occ Group: Management
and Technical
Occ Group: Sales and
Office Admin
Interaction:
Tenure*Management
Interaction: Tenure *
Sales & Office
Interaction:
Black*Management
Interaction: Black* Sales
& Office
Interaction:
Hisp*Management
-0.03
1.05***
-5.40***
-6.47***
-5.54***
53
Apendix B continued
Interaction: Hisp* Sales
& Office
Predicted median(t)
-4.76*
13.32
13.31
13.32
54
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