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Determinants of Post-Displacement Reemployment Outcomes and Occupation Changes
Vasilios D. Kosteas
Cleveland State University
2121 Euclid Avenue, RT 1719
Cleveland, OH 44115-2214
Tel: 216-687-4526, fax: 216-687-9206
Abstract
This paper estimates the effect of MSA labor market characteristics on employment outcomes for displaced workers. Specifically, we investigate the role played by MSA size and occupational distribution on the probability of being employed, the likelihood of changing occupations for those workers who are employed, and the degree of dissimilarity between the old and new occupations for those workers who do switch occupations. We find strong evidence the share of employment for the occupation of the job from which a worker was displaced has a positive effect on the likelihood of being employed and a negative effect on the likelihood of switching occupations. There is weaker evidence for a positive link between average occupational distance between the occupation of the job from which a worker was displaced and the other jobs in the individual’s MSA of residence and the distance between the old and new job for workers who did change occupations.
JEL Codes: J24, J62, R23
Key Words: Occupation changes, displaced workers, agglomeration effects, market thickness
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Introduction
It has been well established that displaced workers suffer significant wage losses (see for example
Jacobson et al, 1993). Earlier research showed that wage losses after displacement are larger for workers
with longer industry (Neal, 1995) or occupational (Kambourov and Manovskii, 2009) tenure. These
findings have been interpreted as signifying the importance of industry and occupation specific human
capital, respectively, and indicate that changing industry or occupation of employment leads to greater
wage losses after experiencing job displacement. Rather than treating occupational changes as binary
events, recent literature looks at the importance of moving to a job that requires a different skill portfolio
(Poletaev and Robinson, 2008) or the occupational distance between the old and new job where
occupational distance is measured by the difference in the importance of various tasks in performing the
old and the new job (Gathmann and Schonberg, 2010). Given the negative wage effects associated with
occupation changes for displaced workers, uncovering the determinants of occupation changes and
occupational distance is an important part of understanding the economic losses associated with job
displacement. A few papers in the urban economics literature have examined the determinants of
occupational switching, focusing on population size or density in the metropolitan statistical area (MSA)
in which the individual resides (Bleakley and Lin, 2012) or the specificity of the prior occupation (Geel
and Backes-Gellner, 2011).
While urban economists have traditionally focused on measures of total labor market size
(generally proxied by population) or density (proxied by population density) when examining
occupational switching, we argue that the local distribution of occupations may be a more important
determinant of post-displacement employment outcomes. Displaced workers living in a large
metropolitan area may find it difficult to acquire another job in the same occupation if there are relatively
few jobs in that occupation locally. The fact that one MSA is larger than another does not mean there are
more jobs in the larger MSA in a given occupation compared with the smaller one. For example, The
Denver-Aurora-Lakewood MSA had total employment of approximately 1.37 million in May of 2015,
compared with total employment of 1.02 million for the Cleveland-Elyria MSA in Ohio. In terms of
employment, the Denver area was 34 percent larger than Cleveland. However, the Cleveland area had
more workers employed as registered nurses (29,070 versus 25,820) and more individuals employed in
the health care sector overall (75,720 compared to 72,500).1 For a more extreme example, consider the
fact that the greater Boston area had 920 people employed as economists and 550 employed as
postsecondary teachers of economics. The greater Chicago area, which is much more populous than
Boston, had only 310 economists and 200 postsecondary teachers of economics. Of course, local labor
1 All employment figures come from the Bureau of Labor Statistics’ Occupational Employment Statistics database: https://data.bls.gov/oes/#/home.
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markets might have less relevance for economics professors, who tend to search for jobs nationally (or
even internationally) compared with the typical registered nurse who might take a more geographically
focused job search.
The present paper contributes to the literature examining occupation changes by comparing the
importance of population measures (density and population size) against measures of occupational
distribution (occupation employment shares and the degree of dissimilarity between the occupation from
which the worker was displaced and all other jobs in the metropolitan area) in influencing whether
displaced workers take jobs in different occupations and the degree of dissimilarity between the old and
new jobs. Following the standard practice in the literature, we use information on the importance of
various job tasks for disaggregated occupations to construct a measure of occupational dissimilarity
between the occupation from which a worker was displaced and her current occupation. The tasks data
come from the Occupational Information Network (O*NET) which is sponsored by the Employment and
Training Administration which is under the U.S. Department of Labor. We also construct a measure of
the distance between the occupation of displacement and the other jobs in the worker’s MSA of residence
using data from the Bureau of Labor Statistics Occupational Employment Surveys (OES), where distance
is defined in terms of the degree of dissimilarity between the two occupations.
The analysis in the present paper is conducted using the 2004-2012 waves of the Displaced
Worker Survey (DWS) supplement of the Current Population Surveys (CPS). The DWS contains a rich
set of information, including the occupation codes for the job from which the worker was displaced and
the occupation of the current job (if she is currently employed). Neither MSA size, occupational share of
employment or average occupational distance have a significant impact on the probability a displaced
worker will be employed at the time of the survey. Conditional on being employed, results indicate that a
larger MSA population and higher occupational share of employment reduce the probability a displaced
worker will be reemployed in a different occupation, with the latter having a larger impact. Average
occupational distance does not affect the probability a displaced worker will switch to a new occupation.
Conditional on changing occupations, a higher average occupational distance significantly increases the
occupational dissimilarity between the pre and post-displacement occupations of employment for workers
in the DWS than does MSA size (measured in population). By contrast MSA size and occupational share
of employment do not affect the degree of occupational dissimilarity. These results are robust to the
inclusion of both MSA and occupation of displacement fixed effects to the model. The results are also
consistent when restricting the sample to workers whose job loss was the result of plant closure or to
those workers who did not move post-displacement. The results for the change of occupation models are
also robust to the use of alternative measures of occupational dissimilarity.
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Background and Literature Review
Traditionally, labor economists investigating occupational changes focused on simple measures such as
whether an individual changed occupations. This approach treats any two occupation switches as
equivalent. This is unlikely to be the case in practice. It would be reasonable to assume that switching
from operating an excavator to driving a delivery truck likely entails a smaller change in skill
requirements or tasks performed than switching to being a security guard, since the first two occupations
both entail operating large vehicles while the third does not (according to our occupational distance
measures, that assumption is indeed valid). Additionally, the rate of occupation changes is overestimated
due to misreporting and miscoding of occupations (Speer 2016).
A growing literature shows the importance of accounting for job skills or tasks when considering
occupation changes and the resulting wage changes. Occupation changes, particularly those which are
involuntary in nature, are likely to result in wage losses for a variety of reasons, including: 1) specificity
of human capital, 2) lower match quality/skill mismatch, 3) workers with long tenure losing the wage
premium when the employment relationship involved the posting of a bond. Using data from West
Germany, Gathman and Schonberg (2010) find that wage losses increase with the occupational distance
between the previous and the current job while task-specific human capital accounts for roughly half of
individual wage growth. Defining skill switches as a change in the primary or secondary skill of
importance for a worker’s job, Poletaev and Robinson (2008) use the 1984-2000 waves of the DWS to
show that switching skill portfolios after job displacement is more important in determining wages than
switching industry or occupation. Skill mismatch (defined as disconnect between a worker’s skill set and
job requirements) also has a significant impact on post-displacement earnings losses (Nedelkoska et al,
2013) and provides an important source of wage variation in general (Yamaguchi, 2012). When
examining occupation changes and skill matching, distinguishing between the transferability of skills
across occupations and the extent to which “the applied knowledge, skills, and abilities” employed in one
occupation qualifies an individual to work in another occupation is important (Ormiston, 2014).
Given the significant role of occupation changes and occupational distance following an
occupation switch in determining wage outcomes, identifying the determinants of these changes and the
accompanying occupational distance is an important research goal. Labor economists have shown that
greater specificity of an occupation is associated with fewer occupation changes but more changes within
a cluster of occupations (Geel and Backes-Gellner, 2011), that individuals tend to switch to occupations
that have a small occupational distance from their current/previous job (Gathman and Schonberg, 2010),
and that the distance between occupations declines with experience (Gathman and Schonberg, 2010).
Meanwhile, the urban economics literature has focused on the importance of population size and density
in explaining job matches, occupational changes and wage loss after displacement. Using IPUMS data,
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Bleakley and Lin (2012) observe less occupation switching in more densely populated areas (they also
find similar results when replacing population density with the occupation share of area employment).
Abel and Deitz (2015) show that agglomeration effects improve matching for college educated workers;
they are more likely to be employed in jobs requiring a college degree and in jobs related to their major in
more populated areas. Crossley et al (1994) find that wage loss after job displacement is smaller when the
plant is located in a more populated area.
The present paper focuses on the importance of occupational distribution (occupation shares and
average occupational distance) relative to population measures in post-displacement outcomes. In this
sense, we contribute to the urban economics literature which has focused on the importance of population
size and density in determining labor market outcomes, including the likelihood of switching occupations,
while also contributing to the labor economics literature examining the importance of occupational
distance for employment outcomes. The focus on outcomes for displaced workers makes the theoretical
foundations for the analysis less complex since we are not concerned with occupation changes which
result from voluntary job changes. Begin by considering the determinants of occupation switches.
Ignoring for now the case of tied movers, if a worker is making a voluntary job change, it is likely driven
by an attempt to improve the job match. In thicker labor markets, workers should achieve better initial
matches, decreasing the likelihood the worker will voluntarily make an occupation switch. Workers in
thicker markets may also be more likely to invest in specific human capital since involuntary job losses
are less likely to result in significant wage losses (Lazear, 2009). Both mechanisms indicate a negative
effect of labor market size on occupation changes connected to voluntary job changes. They would have
the same effect for involuntary job changes as well. Losing a job that was a very good match for the
worker’s skills increases the probability that switching occupations will result in a lower match quality
and lower wages on the new job. Thus, these displaced workers may be less willing to take job offers in
different occupations, preferring to remain unemployed and continue searching for a new job. Similarly,
greater investment in specific human capital makes the worker’s skills match the current job requirements
more closely. Again, this increases the likelihood that wage offers for jobs in other occupations will be
lower relative to the wage in the previous job.
However, when focusing on displaced workers, we also need to consider the scenario where a
worker loses her job in a metropolitan area which experiences significant job losses clustered in an a
particular industry (and which heavily employs workers in certain occupations). In particular, we might
think of production workers in cities with a traditional manufacturing base. In those cases, we might
expect that having a large fraction of jobs concentrated in these occupations will make it harder for her to
find a new job in the same occupation. However, residing in a larger metropolitan area is still likely to be
positively correlated with the probability of finding and accepting a new job in the same occupation.
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Finally, consider what may happen when there are a lot of jobs in similar occupations. When two
occupations are similar in terms of tasks or skill requirements, the wage losses associated with moving to
the new occupation will be, on average, considerably lower. Thus, workers residing in an MSA where
there are many jobs in closely related occupations are more likely to receive good wage offers for
employment in a different occupation from the one from which they were displaced, increasing the
probability they switch occupations. Thus, while market thickness is associated with fewer occupation
switches, a large number of jobs in proximal occupations should lead to a higher probability of being
employed in a different occupation post-displacement since moving to closely related occupations does
not carry as significant of a wage loss, but a smaller distance between pre and post-displacement
occupations in the event of an occupation change. Stops (2014) develops a model indicating the number
of matches in a given occupation is affected by the number of unemployed and job vacancies in similar
occupations. Generally, labor market size and occupational distribution may have different impacts on
employment outcomes post-displacement.
While theory clearly indicates market thickness should lead to fewer occupation changes, either
voluntary or as a result of job displacement, the question is how, empirically, should we measure market
thickness. From an individual worker’s perspective, what matters is the number of jobs in her current
occupation. In matching models, the probability of receiving a match depends on the ratio of job
vacancies to job seekers. Thus, from a matching perspective, the likelihood of receiving an offer for a job
in the same occupation depends not on the total number of jobs in the labor market, but how tight the
market is for a given occupation. Lacking data on the number of job vacancies and seekers at the
occupation level for different metropolitan areas, we need to proxy for availability of jobs. To that effect,
we use two variables: total population and occupation share of MSA employment. Alternatively, we could
multiply the two variables to obtain a proxy for the number of workers employed in each occupation in
each MSA. While this variable does contain additional information, including it alongside with
occupation share and MSA population does not improve the empirical model.
The present paper is most closely related to Bleakley and Lin (2012) and Gathmann and
Schonberg (2010). However, there are some key differences. While the bulk of the Bleakley and Lin
(2012) study examines occupation switches for all workers, they do provide estimates of occupation and
industry switches using a sample from the 1994-2002 DWS. However, their study does not include a
measure of average occupational distance, nor do they examine what factors impact the degree of
occupational distance between the pre and post-displacement occupations of employment. While
Gathmann and Shonberg (2010) examine which factors determine the occupational distance for job
movers, they do not assess the roles played by agglomeration or labor market thickness.
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Data and key variables
The empirical analysis examines the effect of local labor market size (proxied by MSA population), the
employment share for the occupation of displacement at the MSA level, and the average distance of the
occupation of displacement from other jobs in the MSA on three outcomes: 1) being employed at the time
of the survey; 2) conditional on being employed, whether the individual took a job in a different
occupation compared with the job from which she was displaced; and 3) conditional on being employed
and changing occupations, the distance between the occupation from which the individual was displaced
and the current occupation of employment. Conducting the empirical exercises requires combining data
from several sources. Individual level data on employment outcomes and worker characteristics come
from the displaced worker surveys (DWS) which are a part of the current population surveys (CPS). In
order to construct the occupational distance measures, we use information on job tasks and skills from the
Occupational Information Network (O*NET) which is sponsored by the Employment and Training
Administration in the U.S. Department of Labor, combined with data from the Bureau of Labor Statistics’
Occupational Employment Surveys (OES). Data from the Annual Social and Economic Supplement
(ASEC) of the CPS are used to construct MSA level measures for the unemployment rate and educational
attainment. Finally, the population data come from the U.S. Census.
Displaced worker survey
The displaced worker survey is a supplement to the current population surveys (CPS) conducted in
January (starting in 2002) in even numbered years. The survey collects information on the pre-
displacement occupation along with reason for displacement and whether the individual moved as a result
of the job loss, among other key information. The analyses performed in the present study use data from
the 2004-2012 DWS. The surveys include individuals twenty years and older who had experienced a job
displacement in the previous three years (i.e. the 2012 survey includes individuals who experienced a job
displacement between January 2009 and December 2011). In addition to key demographic information
(age, gender, race, education) the DWS contains information on the job from which the individual was
displaced, including occupation, tenure at the previous job, reason for displacement (plant/firm closure,
shift abolished, insufficient work), and how long ago the individual lost her job (one, two, or three years
ago). The DWS also contains information on the current job and geographic information on the
individual’s current location. The geographic information is crucial for incorporating the MSA level
variables. By focusing on individuals who live in metropolitan areas, and for whom we can identify the
MSA of residence, we lose approximately twenty-three percent of the workers in the DWS, with 17.5% of
workers not living in an MSA. Both the DWS and CPS also include the county of residence, which could
be used to include these individuals in the estimation sample. However, at this level, the populations
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become too small to construct a meaningful average distance variable. As it stands, the smallest MSA
included in the sample had a population of approximately 94,000. Due to this limitation, all results are
interpreted as applying only to individuals living in MSAs. The relatively small sample loss due to
missing geographic information does not raise any concerns about the sample.
Annual Social and Economic Supplement (ASEC) of the CPS
The ASEC is an annual, national survey of more than 75,000 households. We use two years of data to
map into each DWS wave. Restricting the samples to individuals between the ages of 24 and 65, we
construct MSA level variables for the area unemployment rate, and shares of the population whose
highest degree earned is a HS diploma, associate’s degree, bachelor’s degree, and graduate degree.2 Data
from the 2003 and 2004 ASEC are used to construct these variables for the 2004 DWS, the 2005 and
2006 ASEC are used to create the variables for the 2006 wave of the DWS, and so on.
O*NET Data- Tasks and Skills
I gather data on the importance of a variety of tasks and skills for each occupation from O*NET.
Measures of the Importance and Level of each task and skill are provided for each occupation, where
level measures how frequently this task is needed or the degree of the skill that is required. The
occupational skills requirements are generated through ratings by occupational analysts while the
occupational tasks data come from worker surveys. Thus, the skills and tasks data vary not just in terms
of what they measure, but the source of the information as well. For these reasons, occupational distance
measures based on the tasks data may be quite different from those based on the skills data.
O*NET employs the Standard Occupational Classification (SOC) system, while the DWS uses
census occupation codes (COC). The SOC lists approximately 900 occupations while the COC contains
over 400 occupations. As an additional complication, the COC change at various times. Over the sample
period examined in this study, the COC underwent minor changes between 2010 and 2011. Creation of a
consistent set of occupation codes between the two data sources and across the sample period proceeds in
two stages. First, I map the 2003-2010 COC codes into the 2012 and later COC codes. Next, I construct a
bridge between the SOC and COC classification schemes, which results in several instances where
multiple SOC occupations are mapped into a single census occupation. In these cases, I take the simple
average of the score for that task across all SOC occupations included in the COC category. The common
2 Following Bleakley and Lin (2012) we also created a variable for the share of workers in the MSA employed in the manufacturing sector. However this variable was not significant in any of the models and subsequently dropped form the estimation routines.
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coding scheme contains 433 occupations. Changes in the COC between 2002 and 2003 were a significant
factor in limiting the sample period to start with the 2004 wave of the DWS.
O*NET contains information regarding work activities for each occupation. There are a total of
41 activities grouped into 4 categories: information input (estimating the quantifiable characteristics of
products, events, or information; getting information; identifying objects, actions and events; inspecting
equipment, structures, or material; monitor processes, materials, or surroundings), interacting with others
(assisting and caring for others; coaching and developing others; communicating with persons outside
organization; communicating with supervisors, peers, or subordinates; coordinating the work activities of
others; developing and building teams; establishing and maintaining interpersonal relationships; guiding,
directing, and motivating subordinates; interpreting the meaning of information for others; monitoring
and controlling resources; performing administrative activities; performing for or working directly with
the public; provide consultation and advice to others; resolving conflicts and negotiating with others;
selling or influencing others; staffing organizational units; training and teaching others), mental process
(analyzing data or information; developing objectives and strategies; evaluating information to determine
compliance with standards; judging the qualities of things, services, or people; making decisions and
solving problems; organizing, planning, and prioritizing work; processing information; scheduling work
and activities; thinking creatively; updating and using relevant knowledge), work output (controlling
machines and processes; documenting/recording information; drafting, laying out, and specifying
technical devices, parts, and equipment; handling and moving objects; interacting with computers;
operating vehicles, mechanized devices, or equipment; performing general physical activities; repairing
and maintaining electronic equipment, repairing and maintaining mechanical equipment).
O*NET also contains information on the skills employed different occupations, grouped into the
following categories: basic skills (active learning, active listening, critical thinking, learning strategies,
mathematics, monitoring, reading comprehension, science, speaking, and writing), social skills
(coordination, instruction, negotiation, persuasion, service orientation, and social perceptiveness),
complex problem solving, technical skills (equipment maintenance, equipment selection, installation,
operation and control, operation monitoring, operations analysis, programming, quality control analysis,
repairing, technology design, and troubleshooting), systems skills (judgement and decision making,
systems analysis, and systems evaluation), and resource management skills (management of financial
resources, management of material resources, management of personnel resources, and time
management). As a robustness check, I construct measures of occupational distance using skills
requirements instead of job tasks.
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Bureau of Labor Statistics Occupational Employment Statistics (OES)
We use the Bureau of Labor Statistics’ OES survey to construct the MSA-level occupation shares which
are included in the empirical model and are also used to create a measure of the average distance between
the individual’s occupation at the time of displacement and all jobs in the individual’s local labor market.
The OES is a semiannual establishment survey where the sample is selected so that data are available for
all industries and all metropolitan and nonmetropolitan areas. The occupation coding scheme used by the
OES contains over 800 occupations, which are condensed into the 433 occupations of the coding scheme
common to the DWS and O*NET data described above. Occupation share is defined as the fraction of
workers in the CPS sample in the respondent’s MSA who are employed in the occupation. We use data
from the previous year’s OES to construct occupation shares for a given year’s DWS. That is, we use the
2011 OES to create the occupation shares for use with the 2012 DWS data, the 2009 OES for use with the
2010 DWS survey, and so on. Employment data is not available for all occupation-MSA cells. In these
cases, we set the employment share to zero. This affects approximately 3.8% of the observations in the
DWS sample.3
Monthly CPS surveys
We use the outgoing rotations from the monthly CPS surveys in order to generate alternative measures for
the MSA-level occupation shares and hence the average occupational distance variable. In order to
construct occupation shares from this data source, we match two years of CPS data to one year of DWS
data. This is particularly important for the smaller MSAs, for which we have far fewer observations than
there are occupations. For example, the MSA level occupation variables for the 2012 DWS use CPS data
for 2011 and 2012. One could argue these variables should be constructed using additional years of CPS
data since a worker in the 2012 DWS might have been displaced in 2009 or 2010. However, the more
recent years of data represent a better measure of the occupational mix at the time of the follow-up
survey; given the January survey date, the 2011 and 2012 monthly CPS surveys represent a twenty-four
month window around the DWS. In spite of our best efforts, the CPS data do not have any observations
for many MSA-by-occupation cells. In those cases, the employment share for that occupation in that
MSA is set to zero. In the DWS sample, roughly seven percent of observations are affected.
Population Data
The MSA population data come from the US Census Bureau. The US Census redefines MSA definitions
with each decennial census. Thus, while the MSA definitions are consistent from 2000-2009, they change
3 As a robustness check, we excluded these observations from the estimation sample. The results are highly consistent with the main results.
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with the 2010 census. Generally, the most significant changes involved the splitting of an MSA into two
separate MSAs. We are able to generate a fairly consistent set of MSA codes to span the entire sample
period by recombining the newly separated areas back into their original MSA definition. In some cases,
the MSA boundaries were redefined in 2010. In general, this did not pose a significant issue. A check of
the population did not show any significant jumps or drops in MSA population from year to year, outside
of the trends prior to 2010. As an additional complication, the MSA coding scheme used by the CPS and
the DWS is slightly different from the one used by the Census. We were able to map the two coding
schemes with minimal loss of observations. The final result is a set of 308 MSA codes which are
consistent across the different data sources and over the sample period of 2003-2012. The CPS MSA
codes include micropolitan as well as metropolitan statistical areas, allowing us to include smaller
population centers in the final estimation sample.
Measuring Occupational Distance
There is no singular definition of occupational distance. Rather, there are several ways to operationalize
this concept and multiple sources of data, resulting in a variety of alternative measures. The first choice
one must make when constructing the occupational distance variable is to decide whether that distance
should be measured in terms of occupational tasks or skills, or a combination of the two. Gathman and
Schonberg (2010) focus on occupational skills and use only the importance, not the level. This approach
treats two occupations as similar if they both use the same skills, but require different ability levels for
those skills (Nedelkoska et al, 2013). In light of this criticism, it would make sense to include the
task/skill levels in any measure of occupational dissimilarity. Our primary measures of occupational
distance are task based, and the level for a task is missing any time 75% or more of respondents indicate
that task is not at all important for their occupation. As a result, many occupations are missing
information on the level for one or more tasks. One approach is to set the value for the level to zero in
those cases. Alternatively, we follow Gathman and Schonberg (2010) and only use the importance
measure for each task.
After deciding whether to use information on skills or tasks to measure occupational distance, we
must then decide on the functional form for the distance variable. Gathmann and Schonberg (2010) use
the angle of separation for the vector of tasks while Speer (2016) employs the Euclidean distance between
the task measures for the two occupations. Following Speer, our basic measure of occupational distance
between the current occupation (i) and the occupation from which the individual was displaced (j) is
measured as follows:
(1) 𝐷𝐷𝑖𝑖𝑖𝑖 = �∑ �𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖 − 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖�2𝑁𝑁
𝑖𝑖=1 �1/2
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where N is the number of tasks, and task is the level of importance for each task n in occupation j. The
average distance between the occupation from which the individual was displaced (j) and all other jobs in
the MSA is defined as follows
(2) 𝐷𝐷𝑖𝑖𝑗𝑗𝑗𝑗 = ∑ 𝑡𝑡𝑖𝑖𝑗𝑗𝑗𝑗𝐽𝐽𝑖𝑖=1 𝐷𝐷𝑖𝑖𝑖𝑖
for occupation i, in MSA m, in year t, where sjmt is the employment share for occupation i in MSA m at
time t. The average distance variable is a weighted average of the distance between occupation j and all
other occupations, where the weights are given by the employment shares. We construct an average
distance variable for of the 433 occupations in each of the 308 MSAs, giving a total of 133,364 average
distance values for each survey year.
Methodology
We estimate the effect of MSA size, occupation shares, and occupational distance on three outcomes:
whether a displaced worker is employed at the time of the interview, whether a currently employed
displaced worker is employed in a different occupation compared with the occupation from which he was
displaced, and the distance between the previous and current occupation for those occupation switchers.
Since the first two variables are binary, those models are fitted via probit estimation with marginal effects
calculated at the mean values of the explanatory variables. The occupational distance models are fitted via
ordinary least squares. For each model, a benchmark specification includes controls for the log of the
population in the MSA where the individual resides, the occupation share, the average distance variable,
the MSA level unemployment rate, degree attainment shares at the MSA level, and the following
individual-level variables: gender and race indicators, degree attainment indicators, age and age squared,
marital status, tenure in the job from which the individual was displaced, and indicators for whether the
individual was displaced from her job two or three years ago (with one year ago serving as the excluded
category). All models also include indicator variables for year, state of residence, occupation of the job
from which the individual was displaced, and industry of the job from which the individual was
displaced. All standard errors are clustered by occupation of displacement.
In order to more readily compare coefficient estimates and marginal effects for our three key
explanatory variables, we standardize the occupational distance variables and the MSA population
variable. Occupation share is not standardized since it possesses a standard deviation equal to one in the
sample. Thus, all marginal effects are interpreted as the effect of a one standard deviation increase in the
explanatory variable in the outcome variable.
There is potentially significant measurement error in the occupation shares and occupational
distance variables. While it is also likely that measurement error in the MSA population variable exists,
the extent of that measurement error is probably not as significant. Measurement error in the population
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variable comes from the fact the MSA population numbers outside of the decennial census years are
projections based on estimates of birth and death rates and migration flows into and out of the MSA.
Actual population may differ from estimated population if the assumed values for those parameters differ
from the observed values. However, these errors are likely to play a minimal role in estimates where we
do not control for MSA fixed effects due to the fact that the within-MSA variance of the population
variable is equal to less than 3.6 percent of the between MSA variance. The change in MSA definitions
starting with the 2010 census does not pose an issue for the estimates. Only 16 out of 308 MSAs showed
population growth in excess of 15 percent between 2008 and 2010, affecting 0.67 percent of the
observations in the DWS sample. Removing those observations from the estimation samples does not
significantly alter any of the estimates.4 Overall, the population measures should be measured accurately
enough so as not to pose significant issues for our estimation routines.
The occupation share and average occupation distance variables, on the other hand, are likely to
suffer from significant measurement error for the following reasons: mistakes in the occupation codes
(both variables), small sample sizes for each MSA-by-occupation cell (both variables), and
misspecification of the functional form for occupational distance (occupation distance variable).
Occupation coding is subject to substantial measurement error in survey based datasets, leading to
overestimates of the frequency of occupation changes (Speer, 2016). Gathman and Schonberg (2010) tout
the fact that their study makes use of data taken from social security records, which they assert is less
prone to measurement error in wages and occupations. This miscoding of occupations is likely to generate
random measurement error in both the occupation share and average distance variables, resulting in an
attenuation bias for those coefficients. The small number of observations in the monthly CPS data for
some of the MSAs results in many occupation-by-MSA cells with zero observations. In those cases, we
assign an occupation share equal to zero, likely underestimating the true occupation share. More
generally, the small number of observations in each occupation-by-MSA cell likely results in significant
measurement error for the occupation share. Unless this mismeasurement is correlated with unobserved
individual characteristics (and we do not see any reason why it should), then this source of measurement
error will compound the attenuation bias resulting from the occupation coding errors. Finally, the
occupation coding variable is subject to measurement error based on the method used to construct the
variable from the underlying data. Our measure ascribes equal weight to each task when constructing the
average distance measure.
4 We chose 15 percent as the cutoff due to the fact that growth in excess of this rate was only observed between 2008 and 2010. Population growth in excess of 10 percent was observed in other years. At any rate, we obtain similar estimates when using the 10 percent cutoff.
14
An additional complicating factor for the occupation share and average distance variables is that
they are based on the MSA of residence at the time of the interview, not the MSA where the individual
resided when she was displaced from her previous job. Nearly 12.6 percent of the individuals in the DWS
moved since being displaced (with roughly 7.3 out of the 12.6 percent having moved due to the job loss).
In order to address any bias introduced from this issue, we estimate each model only for those workers
who did not move since being displaced. The results are generally highly similar to those for the full
sample.
Errors in the occupation codes also affect two of our dependent variables: occupation changes and
occupational distance. We have no reason to believe the measurement error in the occupation change
indicator or the occupational distance variables is correlated with the residual or the explanatory
variables. As is well known, random measurement error in the dependent variables does not bias the
coefficient estimates, but leads to inflated estimates for the standard errors. This may cause us to
incorrectly fail to reject the null hypothesis of no relationship between the key explanatory variables and
the poorly measured dependent variables. To address this issue, we estimate the models using alternative
measures of occupational distance. Additionally, we take advantage of having multiple measures for
occupation share and average distance and use the alternative measures to instrument for our preferred
measures. In order to minimize the attenuation bias due to measurement error, we fit each model via
instrumental variables estimation using alternative measures for our MSA population, occupation share,
and average occupational distance variables as instruments for the primary measure, while still including
fixed effects for year, state, and occupation.
While the various sources of measurement error create the potential to underestimate the
relationships between the occupation based variables and the outcome variables, sorting into occupations
or industries based on unobserved worker characteristics may have the opposite effect. For example, in
some occupations, agglomeration effects may draw workers who are disinclined to change occupations in
the future into certain metro areas. Within occupation, the variation in 𝐷𝐷𝑖𝑖𝑗𝑗𝑗𝑗 comes from differences across
MSAs. This allows us to include occupation fixed effects for the occupation from which the individual
was displaced into our empirical specifications. Controlling for occupation specific fixed effects
eliminates any bias arising from the sorting of individuals into occupations along unobservable individual
characteristics which may also affect their ability or willingness to change occupations and possibly take
a new job in an occupation that is very different from the occupation of the job from which the individual
was displaced.
Table 1 provides summary statistics for the key variables for the full DWS sample, the MSA
sample used in the main estimation, and the non-MSA sample which includes all individuals who either
live outside of an MSA or for whom we could not identify the MSA of residence. These statistics clearly
15
show the two samples are different in terms of key demographic variables. The individuals in the MSA
sample are more highly educated and a larger fraction of them are married and black compared with
individuals in the non-MSA sample. Additionally, we observe that displaced workers living in an MSA
are less likely to switch occupations upon reemployment. The fact that the individuals in these sample
differ in terms of some key demographic factors does not necessarily mean that the models estimated
using the MSA sample will yield very different estimates than they would for a full sample. Table 2
estimates models for the three outcome variables excluding the population, occupation share, and average
distance variables, first for the full sample, and then for the MSA sample. Qualitatively, the results are
very similar. Nonetheless, we err on the side of caution and interpret all of our main results as applying to
individuals residing with an MSA.
Results
Employment
Table 3 presents the results for the employment models, with marginal effects reported in brackets. All
models include the MSA and individual level controls described in the methodology section. However,
for the sake of brevity, the coefficient estimates and marginal effects for those variables are not presented.
The baseline model (model 1) includes year and state fixed effects in addition to the control variables,
model 2 adds fixed effects for both the occupation and the industry of the job from which the individual
was displaced to control for worker sorting into occupations and industries based on unobservable
characteristics, and model 3 adds MSA fixed effects in place of the state fixed effects to control for
potential worker sorting into MSAs. According to the baseline model, a one standard deviation increase in
MSA population is associated with a 1.4 percentage point decline in the probability of being employed at
the time of the follow up survey, while a one standard deviation increase in the average occupational
distance is associated with a 1.1 percentage point decrease in the probability of being employed. The
negative relationship between MSA population and is contrary to our expectations. However, as we will
see, this finding is not robust to all specifications or samples. Occupation share of MSA employment does
not impact the probability of being employed. Adding occupation and industry fixed effects does not alter
the coefficient or marginal effect for MSA population, but does have a significant impact on the
occupation distribution variables. A one percentage point increase in the occupation share is associated
with a 2 percentage point increase in the probability of being employed, while average occupational
distance no longer affects employment likelihood. Finally, adding MSA fixed effects in place of the state
fixed effects indicates that only occupation share has any impact on the probability of being employed.
These results indicate that sorting on the basis of occupation or industry may have caused the model to
underestimate the effect of occupation share on employment.
16
In a series of robustness checks, we replicate model 2 for subsamples of the data. First, we restrict
the sample to individuals who lost their jobs due to plant closure. This restriction serves to minimize any
sorting into the sample based on unobserved characteristics; when a firm or plant has layoffs but
continues to operate the firm may eliminate the least productive workers first, but when the plant closes
all workers lose their jobs. For the plant closure sample, we see that none of the agglomeration or
occupation distribution variables are correlated with the probability of being employed. We also estimated
model 2 restricting the sample to individuals who did not move out of the MSA after being displaced and
to those who were displaced from a full-time job. In both cases, the results are very similar to those for
the full sample for model 2. By restricting the sample to people who did not move, we eliminate any
measurement error arising from the fact that our MSA level variables are constructed for the MSA where
the individual currently resides, not the MSA where the individual lived when suffering the job loss.
Thus, these results indicate that measurement error from this source is not a significant problem in our
model. We might expect that individuals who lost a full-time job might have had more human capital that
was specific to the old job, therefore their employment prospects would be more significantly affected by
market thickness. This does not appear to be the case.
Occupation Changes
Next we present estimates for the relationship between our MSA-level agglomeration and occupation
distribution variables on occupation changes (table 4). As before, all models contain the full set of
covariates. According to the basic model, only the occupation share of employment has a statistically
significant correlation with occupation switching. Conditional on being employed, a one point increase in
the share of MSA employment for the occupation from which the individual was displaced results in a 3.2
percentage point decrease in the probability a displaced worker will be employed in a different
occupation. Given that roughly 70 percent of re-employed workers report an occupation change, this
represents a modest decrease in the likelihood of switching occupations.
Adding fixed effects for the occupation and industry from which the individual was displaced to
the model yields similar results, but with a stronger estimated relationship between occupation share and
occupation switching, indicating that worker sorting into occupations does not significantly bias our
estimates. If anything, the bias associated with occupational sorting tended to understate the link between
market thickness and occupation switching. Neither MSA population nor average occupational distance
exhibits a strong correlation with occupation switching. This is in sharp contrast with Bleakley and Lin
(2012) who found a statistically significant, negative relationship between population density and
occupation and industry switches in their sample of displaced workers. This difference is driven by the
lack of controls for state fixed effects in their models; when we exclude the state fixed effects, we
17
estimate a negative and statistically significant effect of MSA population on the probability of changing
occupations post-displacement.
When adding MSA fixed effects in place of state fixed effects (model 3) we see the estimated
relationship between occupation share of employment and occupation switches grow even stronger and
observe a strong predicted impact of MSA size on occupational switches. The model estimates that a one
standard deviation increase in MSA population decreases the probability of switching occupations by 41
percentage points. This result is clearly out of line with the results from models 1 and 2 and caution must
be exerted when interpreting the coefficient. Since this model controls for MSA fixed effects, all variation
in the population variable is within MSA. Given the relatively short time frame (10 years) most of the
variation in the population variable is between MSAs. Furthermore, a large fraction of the within MSA
variation in population will be driven by the fastest growing MSAs. The coefficient and marginal effect of
the population variable are more accurately interpreted as the effect of population growth on occupation
changes. The same is not true for the occupation share of employment and average occupational distance
variables since they are constructed at the MSA-occupation-year level. Given these caveats, and the fact
that the results for model 3 are so dramatically different from the other models, we continue with model 2
as our preferred model. With respect to the occupation share variable, it appears that sorting by
occupation, industry, or MSA again causes the model to underestimate the importance of occupation
share for the outcome variable.
When restricting the sample to individuals who lost their job due to plant closure we observe an
even stronger effect of occupation share on occupation switching; a one point increase in the occupation
share of employment for the occupation from which the individual was displaced decreases the
probability the individual will be employed in a different occupation by 7.7 percentage points; this
represents a roughly ten percent decline from the rate of reported occupation switches. Thus, it appears
our results are not being driven by unobserved worker characteristics which affected the firm’s layoff
decisions. Finally, estimating model 2 on the sample restricted to workers who did not move or the
sample restricted to those who lost full-time job does not significantly change the coefficient estimates or
marginal effects. In all three sub-samples, only the occupation share of employment shows a significant
link with the probability of switching occupations.
Occupational distance between old and new job
Table 5 presents the estimates for the occupational distance models fitted on samples of occupation
switchers. Only average occupational distance (or dissimilarity) is consistently estimated to have a
significant effect on the distance between the occupation from which the individual was displaced and the
occupation in which the individual is currently employed. This result is robust to the inclusion of fixed
18
effects for occupation, industry, and MSA. According to model 2, a one standard deviation increase in the
average degree of dissimilarity between the previous occupation and all other occupations in the MSA
increases the distance between the old and the current occupation by 0.138 standard deviations. Only
when the model is estimated for the sample of workers who lost their job due to plant closure do we not
obtain a statistically significant coefficient. However, much of this is due to the reduction in sample size
and attending increase in standard errors. Neither the occupation share of employment nor MSA
population has a statistically significant impact on the occupational distance between the old and current
job.
Heckman correction models
Next, we estimate the baseline models using the Heckman selection model to determine whether the
models should be estimated simultaneously (Table 6). First, we simultaneously estimate the employment
and change of occupation models (Model 1) then we simultaneously estimate the change of occupation
and occupational distance models (Model 2). The estimated coefficients and marginal effects obtained
from these procedures are highly similar to those obtained when the models are estimated separately.
Wald tests for the independence of the equations also fail to reject the null hypothesis that the equations
are independent. Finally, the coefficient and marginal effects estimates for the change in occupation
equations in Model 1 and Model 2 are nearly identical, providing further evidence it is appropriate to
estimate these equations separately.
Alternative measures for occupation shares and occupational distance
As a next set of robustness checks, we use skills based occupational distance measures in place of the
tasks based measure. The results are presented in Table 7. The first three columns recreate the analogous
results presented in tables 3-5. Here, we continue to find a negative relationship between employment and
MSA population and between occupation changes and the occupational share of employment for the
occupation of displacement. Columns 4-6 present results for the models which use the skills requirements
data to create the occupation distance measures. These models show that employment status is positively
affected by the occupation share of employment and negatively affected by MSA population and average
occupational distance between the occupation of displacement and all other jobs in the MSA. Consistent
with the results presented in table 4, we continue to find a negative relationship between occupation
changes and occupation shares. However, we no longer observe a positive effect of average occupational
distance on occupational distance after occupation switches. Overall, this set of robustness checks
indicates the negative effect of occupation share on occupation switching is robust to using skills based
19
measures of occupational distance. The positive relationship between average occupational distance and
occupational distance observed in the main results is not robust to these changes.
Sample split by educational attainment
Next, we split the sample according to whether the individual has any degrees beyond high school
(associates, bachelors, or graduate degree). The two groups are likely to differ in two important aspects.
First, less educated workers have not spent significant time investing in occupation specific human capital
through formal education (although they may do so through on the job training) while workers with
degrees are more likely to have invested in occupation specific human capital through their education.
This would make more educated workers’ occupation switching more sensitive to the local distribution of
occupations. Conversely, more educated workers may be more likely to search for jobs outside of their
current MSA of residence, making their occupation switches less sensitive to local labor market
characteristics. The results (Table 8) show some key differences for the two samples. For both types of
workers, a larger MSA population results in a lower probability of being employed while a higher
occupation share of employment for the occupation from which they were displaced results in a greater
likelihood of being employed and a lower probability of switching occupations. For more educated
workers, average occupational distance also increases the probability of being employed and decreases
the likelihood of switching occupations. Thus, it appears that more educated workers are more sensitive to
the mix of occupations in the MSA.
Instrumental variables estimation
Finally, in order to deal with potential attenuation bias that results from measurement error in our primary
explanatory variables, we instrument for our three primary variables of interest using alternative measures
for each. For MSA population, we include as an instrument total employment in the MSA reported in the
OES files. For the occupation share variable, we include occupation shares estimated via the outgoing
rotation files of the CPS. For each occupational dissimilarity index, we include three instruments. When
using the task (skills) based index, we instrument with the skills (task) based index and skilled and task
based indexes created using the CPS occupation shares. Thus, for each model we have five instruments
for three endogenous regressors, allowing us to calculate the J-statistic for the model. All models continue
to include the full list of control variables, state dummy variables, occupation, and industry dummy
variables.
The results from the IV regressions a largely consistent with the OLS based estimates with a few
notable exceptions. Focusing on the task based occupation distance measures, we observe that average
occupational distance is now correlated with all three outcomes, decreasing the probability of being
20
employed, leading to a lower probability of changing occupations when employed, and resulting in a
higher distance between the pre and post-displacement occupations in cases where the worker did change
occupations. Occupation shows an even stronger, negative impact on the probability of changing
occupations while MSA population also shows a small, negative impact on occupation switching. The
models estimated using the skills based occupation distance measures yield similar results; however the
negative relationships between average occupational distance and occupational distance between the old
and new occupations and between MSA population and occupation switching are no longer statistically
significant. These results are consistent with the expectations outlined in the background section.
Conclusions
This paper estimates the effect of MSA labor market characteristics on employment outcomes for
displaced workers. Specifically, we investigate the role played by MSA size and occupational distribution
on the probability of being employed, the likelihood of changing occupations for those workers who are
employed, and the degree of dissimilarity between the old and new occupations for those workers who do
switch occupations. We find strong evidence the share of employment for the occupation of the job from
which a worker was displaced has a positive effect on the likelihood of being employed and a negative
effect on the likelihood of switching occupations. There is weaker evidence for a positive link between
average occupational distance between the occupation of the job from which a worker was displaced and
the other jobs in the individual’s MSA of residence and the distance between the old and new job for
workers who did change occupations.
While the theoretical literature on job matching provides some useful insights into what
determines occupation switching behavior, more work is needed to incorporate the importance of the
occupation distribution on these behaviors. Specifically, while existing models address how market
thickness in a worker’s given occupation and similar occupations affect occupation switching, these
models should be extended to address not just the switching itself, but the distance of the occupation
change. Also, future research should address how geographic mobility and decisions to look for jobs in
other metro areas interact with the distribution of jobs in the current area of residence to determine
whether an individual takes a job in a new occupation.
On the empirical side, future research should focus on developing better measures of occupational
distance in order to provide a better understanding of exactly what types of occupation changes are taking
place. Research should also focus on testing the links between occupation distribution and other worker
decisions, such as investment in occupation specific human capital, since this is posited to be one of the
key mechanisms through which occupation distribution impacts occupational mobility.
21
References
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22
Table 1: Summary Statistics
Full
MSA
non-MSA
Variable Sample
Sample
Sample
T-test Employed indicator 0.617
0.615
0.625
1.37
(.486)
(0.487)
(0.484)
Changed occupation indicator 0.7
0.69
0.732
5.68**
(0.458)
(0.462)
(0.443)
Displaced 2 years ago indicator 0.315
0.316
0.313
0.343
(0.465)
(0.465)
(0.464)
Displaced 3 years ago indicator 0.275
0.277
0.273
0.42
(0.447)
(0.447)
(0.446)
Log years in displaced job 0.871
0.877
0.848
1.34
(1.336)
(1.321)
(1.385)
HS degree indicator 0.533
0.507
0.619
13.92**
(0.499)
(0.50)
(0.486)
Bachelors degree indicator 0.19
0.21
0.121
14.06**
(0.392)
(0.408)
(0.326)
Graduate degree indicator 0.07
0.08
0.038
9.96**
(0.255)
(0.271)
(0.192)
Female indicator 0.437
0.437
0.44
0.44
(0.496)
(0.496)
(0.496)
Black indicator 0.103
0.119
0.051
13.7**
(0.304)
(0.323)
-0.221
Age 41.89
41.89
41.9
0.07
(12.89)
(12.8)
(13.19)
Married indicator 0.539
0.534
0.556
2.71**
(0.498)
(0.499)
(0.497)
Observations 21174
16239
4935
23
Table 2: Basic model estimates for full and MSA-only samples
Employment
Occupation Change
Occupational Dissimilarity
Full Sample
MSA Sample
Full Sample
MSA Sample
Full Sample
MSA Sample
Displaced 2 years ago indicator 0.166** 0.175**
0.032** 0.038**
0.005 -0.002
(0.007) (0.008)
(0.01) (0.011)
(0.025) (0.028)
Displaced 3 years ago indicator 0.216** 0.228**
0.062** 0.061**
0.027 0.018
(0.009) (0.009)
(0.011) (0.013)
(0.029) (0.034)
Log years in displaced job 0.007* 0.006*
0.002 0.006
0.003 0.008
(0.003) (0.003)
(0.004) (0.004)
(0.012) (0.013)
HS degree indicator 0.093** 0.091**
0.067** 0.092**
0.359** 0.369**
(0.012) (0.013)
(0.016) (0.019)
(0.039) (0.049)
Associates degree indicator 0.169** 0.169**
0.034 0.068*
0.383** 0.361**
(0.015) (0.015)
(0.029) (0.031)
(0.058) (0.069)
Bachelors degree indicator 0.193** 0.169**
0.052* 0.075**
0.291** 0.284**
(0.014) (0.015)
(0.026) (0.029)
(0.066) (0.072)
Graduate degree indicator 0.233** 0.232**
0.007 0.039
0.111+ 0.111+
(0.019) (0.02)
(0.036) (0.04)
(0.067) (0.067)
Female indicator -0.048** -0.049**
0.038+ 0.03
0.093* 0.063
(0.007) (0.008)
(0.021) (0.021)
(0.039) (0.039)
Black indicator -0.084** -0.093**
0.047** 0.047*
-0.007 0.008
(0.01) (0.01)
(0.018) (0.02)
(0.039) (0.042)
Age 0.02** 0.02**
-0.012** -0.013**
-0.011+ -0.017*
(0.002) (0.002)
(0.003) (0.004)
(0.007) (0.007)
Age squared -0.0003** -0.0003**
0.0001** 0.0001**
0.000+ 0.000*
(0.0000) (0.0000)
(0.00004) (0.0000)
(0.000) (0.000)
Married indicator 0.025** 0.02*
-0.002 -0.017
0.019 0.029
(0.008) (0.009)
(0.008) (0.011)
(0.023) (0.026)
Observations 21174 16239
13065 9979
9111 6851
24
Table 3: Employment status determinants
Sample:
Full Sample
Plant Did Not Full Time
1 2 3
Closure Move Job Lost
Log of MSA population -0.040* -0.048* 0.171
-0.013 -0.047* -0.053*
(0.019) (0.020) (0.407)
(0.034) (0.021) (0.023)
[-0.014] [-0.016] [0.0055]
[-0.004] [-0.015] [-0.017]
Occupation share of 0.001 0.062* 0.067*
0.004 0.051+ 0.067* employment in MSA (0.014) (0.028) (0.026)
(0.062) (0.029) (0.034)
[-0.004] [0.02] [0.021]
[0.001] [0.017] [0.021]
Occupational dissimilarity -0.032* -0.006 0.006
0.045 -0.010 -0.010 index at the MSA level (0.015) (0.046) (0.047)
(0.096) (0.049) (0.047)
[-0.011] [-0.002] [0.002]
[0.013] [-0.003] [-0.003]
Year Fixed Effects Yes Yes Yes
Yes Yes Yes State Fixed Effects Yes Yes No
Yes Yes Yes
Occupation Fixed Effects No Yes Yes
Yes Yes Yes Industry Fixed Effects No Yes Yes
Yes Yes Yes
MSA Fixed Effects No No Yes
No No No
Observations 16026 15840 15800
4824 13914 13360 R-squared 0.1137 0.1457 0.1541
0.1988 0.1479 0.1558
Standard errors clustered by occupation from which the individual was displaced in parentheses. Marginal effects presented in brackets. +, *, * denote significance at the 10%, 5%, and 1* level, respectively. All models contain the full set of covariates.
25
Table 4: Occupation change determinants
Sample:
Full Sample
Plant Did Not Full Time
1 2 3
Closure Move Job Lost
Log of MSA population -0.024 -0.019 -1.362**
0.032 -0.016 -0.026
(0.022) (0.025) (0.518)
(0.043) (0.029) (0.029)
[-0.008] [-0.006] [-0.41]
[0.01] [-0.009] [-0.008]
Occupation share of -0.093* -0.157** -0.192**
-0.254** -0.152** -0.177** employment in MSA (0.044) (0.045) (0.042)
(0.079) (0.051) (0.061)
[-0.032] [-0.048] [-0.058]
[-0.077] [-0.045] [-0.054]
Occupational dissimilarity 0.021 -0.020 -0.030
-0.053 -0.006 -0.018 index at the MSA level (0.036) (0.069) (0.068)
(0.134) (0.076) (0.074)
[0.007] [-0.006] [-0.009]
[-0.016] [-0.002] [-0.005]
Year Fixed Effects Yes Yes Yes
Yes Yes Yes State Fixed Effects Yes Yes No
Yes Yes Yes
Occupation Fixed Effects No Yes Yes
Yes Yes Yes Industry Fixed Effects No Yes Yes
Yes Yes Yes
MSA Fixed Effects No No Yes
No No No
Observations 9830 9284 9182
2853 7971 7869 Pseudo R-squared 0.0224 0.1372 0.1524
0.1731 0.1461 0.1424
Standard errors clustered by occupation from which the individual was displaced in parentheses. Marginal effects presented in brackets. +, *, * denote significance at the 10%, 5%, and 1* level, respectively. All models contain the full set of covariates.
26
Table 5: Occupational Dissimilarity
Sample:
Full Sample
Plant Did Not Full Time
1 2 3 Closure Move Job Lost
Log of MSA population -0.000 -0.003 0.098 0.006 -0.000 0.005
(0.010) (0.011) (0.222) (0.021) (0.011) (0.012)
Occupation share of 0.035** 0.022 0.021 0.002 0.027+ 0.004 employment in MSA (0.012) (0.016) (0.015) (0.031) (0.016) (0.021)
Occupational dissimilarity 0.155** 0.144** 0.138** 0.089 0.147** 0.112** index at the MSA level (0.017) (0.036) (0.036) (0.061) (0.041) (0.037)
Year Fixed Effects Yes Yes Yes Yes Yes Yes State Fixed Effects Yes Yes No Yes Yes Yes Occupation Fixed Effects No Yes Yes Yes Yes Yes Industry Fixed Effects No Yes Yes Yes Yes Yes MSA Fixed Effects No No Yes No No No
Observations 6843 6843 6843 2284 5861 5875 R2/Pseudo R2 0.0887 0.2536 0.2781 0.4027 0.2576 0.2739
Standard errors clustered by occupation from which the individual was displaced in parentheses. +, *, * denote significance at the 10%, 5%, and 1* level, respectively. All models contain the full set of covariates.
27
Table 6: Heckman selection model estimates
Heckman correction models: Model 1
Model 2
Employed Change Occ
Change Occ Occ Dist
Log of MSA population -0.048* -0.021
-0.019 -0.004
(0.02) (0.027)
(0.024) (0.01)
[-0.015] [-0.006]
[-0.006]
Occupation share of 0.061* -0.156**
-0.157** 0.014
employment in MSA (0.029) (0.05)
(0.034) (0.018)
[0.019] [-0.046]
[-0.046]
Occupational dissimilarity -0.008 -0.02
-0.02 0.141**
index at the MSA level (0.047) (0.07)
(0.065) (0.029)
[-0.003] [-0.006]
[-0.006]
Wald test of indep eqns (p-value) 0.01 (0.92)
0.11 (0.88)
Observations 15964
9830
Standard errors clustered by occupation from which the individual was displaced in parentheses. Marginal effects presented in brackets. +, *, * denote significance at the 10%, 5%, and 1* level, respectively. All equations control for year, state, occupation, and industry fixed effects. All equations contain the full set of covariates.
28
Table 7: Tasks versus skills based measures of occupational distance
Distance Measure
Tasks
Skills
Outcome: Employed Change Occ Occ Dist
Employed Change Occ Occ Dist
Log of MSA population -0.048* -0.019 0.098
-0.048* -0.019 -0.008
(0.020) (0.025) (0.222)
(0.020) (0.025) (0.013)
[-0.016] [-0.006]
[-0.016] [-0.006]
Occupation share of 0.062* -0.157** 0.021
0.051* -0.154** -0.021
employment in MSA (0.028) (0.045) (0.015)
(0.025) (0.042) (0.017)
[0.02] [-0.048]
[0.016] [-0.047]
Occupational dissimilarity -0.006 -0.020 0.138**
-0.101* -0.007 -0.003
index at the MSA level (0.046) (0.069) (0.036)
(0.050) (0.060) (0.046)
[-0.002] [-0.006]
[-0.033] [-0.002]
Year Fixed Effects Yes Yes Yes
Yes Yes Yes
State Fixed Effects Yes Yes Yes
Yes Yes Yes Occupation Fixed Effects Yes Yes Yes
Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Yes Yes Yes MSA Fixed Effects No No No
No No No
Observations 15840 9284 6843
15846 9291 6847 R2/Pseudo R2 0.1457 0.1372 0.2536
0.1459 0.1374 0.2451
Standard errors clustered by occupation from which the individual was displaced in parentheses. Marginal effects presented in brackets. +, *, * denote significance at the 10%, 5%, and 1* level, respectively. All equations control for year, state, occupation, and industry fixed effects. All equations contain the full set of covariates.
29
Table 8: Results by educational attainment
Employed
Changed Occupation
Occupational Dissimilarity
No Degree Degree
No Degree Degree
No Degree Degree
Log of MSA population -0.039+ -0.071*
0.009 -0.033
0.014 0.002
(0.023) (0.031)
(0.034) (0.040)
(0.016) (0.019)
[-0.012] [-0.021]
[0.002] [-0.01]
Occupation share of 0.081* 0.059+
-0.158** -0.179*
-0.020 0.064+
employment in MSA (0.039) (0.035)
(0.044) (0.073)
(0.040) (0.038)
[0.027] [0.017]
[-0.048] [-0.055]
Occupational dissimilarity -0.019 0.156+
0.070 -0.250*
-0.012 0.039+
index at the MSA level (0.060) (0.092)
(0.107) (0.124)
(0.024) (0.023)
[-0.006] [0.046]
[0.021] [-0.076]
Observations 9598 5917
4857 3851
3898 2945
R2/Pseudo R2 0.1546 0.1658
0.1566 0.1578
0.3358 0.3303
Standard errors clustered by occupation from which the individual was displaced in parentheses. Marginal effects presented in brackets. +, *, * denote significance at the 10%, 5%, and 1* level, respectively. All equations control for year, state, occupation, and industry fixed effects. All equations contain the full set of covariates.
30
Table 9: Instrumental variables estimates
Distance measure base:
Tasks
Skills
Outcome: Employed Change
Occ Average Occ Dist
Employed
Change Occ
Average Occ Dist
Log of MSA population -0.014* -0.016* -0.002
-0.012+ -0.011 -0.009
(0.007) (0.008) (0.011)
(0.007) (0.008) (0.012)
Occupation share of -0.005 -0.288** 0.035
0.070 -0.160* -0.257* employment in MSA (0.019) (0.058) (0.051)
(0.058) (0.072) (0.129)
Occupational dissimilarity -0.036+ -0.154** 0.166**
0.044 -0.139+ -0.075
index at the MSA level (0.019) (0.046) (0.042)
(0.061) (0.078) (0.114)
Under-identification test (p-value)
10.0 (.02)
15.9 (.00)
15.5 (0.00)
16.3 (.00)
16.6 (.00)
18.3 (.00)
Weak ID Test 454.5 220.5 90.3
109.4 60.2 38.9
J-statistic (p-value)
1.89 (.39)
0.35 (.84)
2.97 (0.23)
0.22 (.63)
0.36 (.55)
2.24 (.13)
Observations 16026 9864 6843
16026 9864 6843 R2/Pseudo R2 0.1847 0.1385 0.2535
0.1831 0.1753 0.2178
Standard errors clustered by occupation from which the individual was displaced in parentheses. +, *, * denote significance at the 10%, 5%, and 1* level, respectively. All equations control for year, state, occupation, and industry fixed effects. All equations contain the full set of covariates.
