The effects of macroeconomic conditions at graduation on overeducation.
Summerfield, Fraser ; Theodossiou, Ioannis
The effects of macroeconomic conditions at graduation on overeducation.
I. INTRODUCTION
Research has identified the importance that initial career
conditions, such as graduation during recessions, have on workers'
long-term earning capability (Kahn 2010; Oreopoulos, von Wachter, and
Heisz 2012). Yet, "the literature on the career effects of entry
conditions is still sparse on underlying mechanisms" despite a
growing body of evidence across many countries (Altonji, Kahn, and Speer
2016). One potential underlying mechanism through which entry conditions
affect the worker's subsequent career appears to be related to the
quality of the match between the worker and the job performed. Measures
of vertical mismatch such as overeducation are likely to increase in
slack labor markets where competition for jobs is fierce. The literature
also shows that job match quality varies with the business cycle and
that poor job matches (overeducation in particular) are linked to low
pay. The above literature implies that initial conditions have lasting
effects on job match quality. Yet, an explicit link to overeducation has
not been made. Indeed, Hagedorn and Manovskii (2013) show that past
aggregate labor market states explain current wages only because the
latter are correlated with workers' job match quality and Liu, Sal
vanes, and Sorensen (2016) show that initial labor market conditions
affect the likelihood of workers finding a job in an industry
well-matched to their field of study.
This article adds to this literature by assessing the effects of
past labor market conditions on the quality of the worker-to-job
education match, more specifically on overeducation. Using data from the
German Socio-Economic Panel (GSOEP) for the years 1994-2012, workers
with post-secondary education are linked to the regional (Bundesland)
unemployment rates they faced when graduating from their highest level
of education. Regional unemployment rates are deemed to best reflect the
labor market conditions facing graduates in view of the fact that
regional labor mobility in Germany is relatively low. (1) Overeducation
is measured by the difference between the individual's years of
education and the median years of education observed in their occupation
or industry. An instrumental variables (IV) estimation strategy, similar
to Kahn (2010), is used to overcome the potential endogeneity of
graduation timing. Findings show that a 1 percentage point increase in
the regional unemployment rate facing new graduates causes a 1.0-1.4
percentage point increase in the probability of overeducation. This
impact is strongest among university graduates where occupation and
industry-based overeducation measures increase by 1.6 and 1.7 percentage
points, respectively. When impacts are measured at 3-year intervals over
the career, initial conditions appear to have scarring effects in terms
of increased overeducation for up to 9 years.
Overeducation is an important labor market indicator since it
implies the underutilization of workers' skills and subsequent
earnings losses. The economic significance of these costs depends on
whether or not overeducation is temporary. Chassamboulli (2011) suggests
that workers may accept a bad match rather than unemployment. Yet, the
duration analysis of Baert, Cockx, and Verhaest (2013) shows that
workers temporarily accepting poor quality matches may prolong the wait
for a more appropriate match. Furthermore, Rubb (2003a) shows that in
the 1990s only one in five overeducated workers moved to a better match
within a year and Liu, Salvanes, and Sorensen (2016) show significant
persistence in wage losses from mismatch between field of study and
industry. Both latter studies imply that overeducation is a longer-term
phenomenon. As such, understanding the mechanisms driving persistent
career losses is essential to the design of government employment
programs targeting the employability of young workers. Given the
persistently high youth unemployment rates, which ranged from 16% in the
United States to over 50% in Spain and Greece during 2012 (OECD 2013),
it is important for policy makers to gain knowledge on the mechanisms
through which overeducation causes further disadvantage among young
labor market entrants. Furthermore, in the aggregate, overeducation
affects productivity and economic output. The extent of foregone
productivity may be substantial in Germany and other developed
countries. Using a measure that compares individual years of education
to the median for their occupation (industry), this article finds that
24% (35%) of German workers with higher education were overeducated
during the years 1994-2012. This is in line with the overall incidence
among European countries of 29%, found in the meta-analysis of Leuven
and Oosterbeek (2011). To the extent that initial conditions have
lasting effects, the recovery phase should also be affected by
overeducation. Workers that graduate during a recession may find
themselves overeducated and embarking on career paths with limited scope
for the patterns of cyclical occupational upgrading as described by
Devereux (2002).
Germany has a particularly well-developed apprenticeship system,
which causes it to differ from other countries like the United States or
the United Kingdom where apprenticeships are not well integrated into
the educational system and where the majority of higher education is at
the university level. In this context a greater share of the documented
overeducation may be attributed to labor market conditions. Furthermore,
German university graduates tend to be older than U.S. or U.K.
graduates: the modal graduation age is 27 in Germany compared to 22 in
the United States (Kahn 2010). Hence, although scarring effects due to
graduating in recession among the youth have been demonstrated in the
past (Burgess et al. 2003; Ellwood 1982) the current results provide
evidence that scarring effects are also observed among individuals with
more life experience. These findings may help to distinguish labor
market effects from age effects.
The rest of the article proceeds as follows. Section II outlines
the related literature. In Section III the GSOEP data and measures of
overeducation are described. Section IV.A outlines the identification
strategy and discusses the instrumental variables. Baseline ordinary
least squares (OLS) results in Section V.A that link graduation
conditions to overeducation are complemented with causal IV estimates in
Section V.B. Results are provided separately by education type in
Section V.C, demonstrating that effects are strongest for university
graduates. Section V.D examines the persistence of scarring effects at
3-year intervals beyond initial graduation. Section VI concludes.
II. LITERATURE REVIEW
This article builds on the literature linking labor market entry
conditions to wage outcomes. Raaum and Roed (2006) found that past labor
market conditions affect future employment. Beaudry and DiNardo (1991)
and McDonald and Worswick (1999) have also shown that initial labor
market conditions affect within-job earnings growth because of imperfect
mobility. More recently, it has been shown that individuals who graduate
or enter the labor market when conditions are adverse experience large
and persistent negative effects during their subsequent careers. The
influence of labor market entry conditions on worker wages has been
documented for the United States (Altonji, Kahn, and Speer 2016; Bowlus
and Liu 2003; Hershbein 2012; Kahn 2010; Oyer 2006), Canada (Oreopoulos,
von Wachter, and Heisz 2012), Austria (Brunner and Kuhn 2014), Japan
(Genda. Kondo, and Ohta 2010; Kondo 2007), and Germany (Stevens 2007).
Finally, Fruhwirth-Schnatter et al. (2012) has explicitly linked more
favorable labor market entry conditions to better long-run wage
trajectories.
Furthermore, a separate literature including Barlevy (2002), Bowlus
(1995), and Mustredel-Rio (2014) shows that matches formed in a downturn
tend to be of lower quality compared to these formed during an upswing.
This is an outcome of job search in a slack labor market. With a larger
pool of applicants, all job offers including good matches are less
common (Albrecht and Vroman 2002; Chariot, Decreuse, and Granier 2005;
Moscarini 2001; Wong 2003). As a result, job search is more costly and
workers are more willing to accept jobs for which they are
overqualified. Whereas the above literature examines the effect of labor
market conditions on the outcome of the worker's current job search
process, this study investigates the effects of the labor market
conditions at the start of the worker's career on his or her
subsequent job matches. In addition, whereas the above studies use
responses about the desire to switch jobs or recorded job durations as
measures of mismatch, this article uses overeducation as a measure of
mismatch.
Studies that use overeducation as a measure of job match quality
have produced contradictory results. For instance, Rubb (2014) finds
that unemployment increases overeducation when controlling for
self-selection. Yet, Buchel and Van Ham (2003) who also control for
self-selection and the meta-analysis study of Groot and Maassen van den
Brink (2000), report an insignificant relationship between unemployment
and overeducation. Notwithstanding the importance of unemployment on the
search process, which motivates this article, the current study focuses
on the effect of labor market conditions at the time of graduation on
subsequent job matches during a worker's career. This is a distinct
approach from articles that examine the contribution of contemporaneous
unemployment rates to overeducation (Croce and Ghignoni 2012;
McGuinness, Bergin, and Whelan 2015).
Although the literature has suggested that wage penalties may be
partly due to an increased propensity to accept jobs in low paying firms
or occupations (Kahn 2010; Speer 2016), there has been far less
attention paid to the quality of the match between a worker and their
job. The exceptions are Verhaest and Van der Velden (2013) who
demonstrate a cross-country correlation between the output gap and
overeducation among workers in their first job and Liu, Salvanes, and
Sorensen (2016) who show that initial labor market conditions affect
Norwegian workers' chances of finding a job in an industry that
best suits their field of study. Interestingly, Liu, Salvanes, and
Sorensen (2016) find that mismatch between field of study and industry
can explain most of the long-term wage penalties associated with
graduating during a recession.
The current article differs from the articles above in some crucial
respects. First, Verhaest and Van der Velden (2013) utilize a single
cohort of graduates facing only cross-sectional differences in labor
market conditions, whereas this study includes graduates entering the
labor market during troughs and peaks of several business cycles in
Germany from 1994 to 2012. Second. the current analysis utilizes IV
techniques in order to deal with the endogeneity of graduation timing
and thus provide estimates with a causal interpretation. This is
important because workers of higher ability may purposefully delay their
graduation in order to avoid entering the labor market during a
recession. Wage differentials between overeducated and well-matched
workers have been linked to unobserved factors including workers'
literacy or specific components of skill (Boothby 2002; Sohn 2010) and
unobserved innate ability (Iriondo and Perez-Amal 2013, for the EU and
Tsai 2010, for the United States), suggesting that overeducated
individuals are of lower ability than their well-matched counterparts
with similar qualifications.
The current article also differs substantially from Liu, Salvanes,
and Sorensen (2016) because of the measure of mismatch. Their study uses
a so-called "horizontal mismatch" measure, which compares the
field of study of a worker to the most common fields of study among
other workers in a given industry. This type of measure is informative
regarding whether workers possess the industry-specific skills needed
for their jobs. By contrast, the current article uses overeducation,
which is considered a "vertical mismatch" measure because it
compares the quantity of schooling among workers within an occupation or
industry. The current analysis is more likely to reflect mismatches in
general human capital. This distinction is also important because
horizontal measures of mismatch have generally not been linked to wage
penalties to the same extent as measures of "vertical
mismatch" (Eymann and Schweri 2015; McGuinness and Sloane 2011;
Verhaest, Sellami, and Van der Velden 2015). A notable exception to this
is Liu. Salvanes, and Sorensen (2016). Instead, the vertical measures
used in the current analysis have been shown to have important
consequences for worker wages in a separate literature surveyed by
Leuven and Oosterbeek (2011), McGuinness (2006), Rubb (2003b), and Groot
and Maassen van den Brink (2000).
There is a particular need for clarification on the relationship
between initial labor market conditions and measures of mismatch because
existing studies provide somewhat contradictory evidence. For example,
Bowlus (1995) uses long job tenure as a measure of a good job match.
Yet, the findings of Kahn (2010) suggest that workers graduating during
a recession and experiencing scarring effects tend to have longer job
tenure. Similarly, Altonji, Kahn, and Speer (2016) find that labor
market entry conditions affect wages but not horizontal measures of
match quality, which would seem to contradict the findings of Liu,
Salvanes, and S0rensen (2016).
III. DATA
This article uses data from the GSOEP for the years 1994-2012. The
GSOEP is a nationally representative dataset with a wealth of detail on
workers and their job characteristics. Using information on the timing
and location of graduation, as well as detailed histories of schooling
spells, indicators are constructed to identify the labor markets into
which workers graduate. This initial labor market information is matched
with the region-level unemployment rates for the civilian population
(excluding entrepreneurs) provided by the German statistical agency
Statistisches Bundesamt. (2) The analysis is restricted to workers
graduating from post-secondary education after 1994 because this is the
extent of the availability of regional-level unemployment rates. (3)
Indicators are available for three streams of post-secondary education:
university, "other" tertiary education (which encompasses
technical training such as teacher education and some medical fields),
and apprenticeship. Since some workers graduate from more than one level
of education. the date of graduation from the highest level of education
is the assigned graduation date in this study. (4) Summary statistics
are presented in Table 1. The average worker observed in the data is age
28 with just under 14 years of education and about 5 years of work
experience. (5) The main estimation sample has 13,563 observations
representing 2,421 workers.
The overeducation measures in this article use information from the
education distribution of employed workers in the sample to define the
"required" or appropriate level of education. These
overeducation measures therefore reflect the current position of workers
in the education distribution within occupation or industry in the year
of observation. Thus these measures implicitly account for time trends
in various occupational assignments. The impact from initial labor
market conditions on the worker's relative job match can be
interpreted as the long lasting effects of low economic activity on
workers' labor market performance.
Overeducation, or education mismatch, is measured in several ways
in this study. The main results are derived using two binary measures of
overeducation. These measures follow Verdugo and Verdugo (1989) and are
prevalent in the overeducation literature. Workers are assigned to the
overeducated group if their years of education exceed the median years
of education among workers in their occupation or industry by more than
a standard deviation. (6) The median (or "required" level of
education) is measured within 4-digit ISCO (International Standard
Classification for Occupations) codes and 2-digit NACE (Nomenclature
statistique des Activites economiques dans la Communaute Europeenne)
industry codes in each year. (7) Groups with fewer than ten observations
are excluded because the required level of education generated from such
small samples is unlikely to be representative. Because industry is
measured at the 2-digit level, almost all groups are large enough to
establish a required education level. Only 31 worker observations are
excluded from the sample. In the case of 4-digit occupations 559
observations are excluded, but this was found not to affect the main
results. (8) Table SI, Supporting Information, shows that the share of
overeducated workers for their occupation is not statistically different
between the samples that do and do not include these additional
observations.
The prevalence of overeducated workers in the German labor force is
also demonstrated in Table 1. Approximately 24% of workers in the sample
are overeducated according to the occupation measure and 35% are
overeducated according to the industry measure. These shares are higher
than those reported in Daly, Buchel, and Duncan (2000) and Bauer (2002)
because the analysis focuses on higher-education graduates. (9) For both
occupation and industry, the difference between actual and required
education in years is also measured with a continuous variable. These
alternative measures give a sense of the magnitude of overeducation and
may help to capture any effects that do not meet the threshold set in
the binary measure. The average difference between the occupation median
education and a worker's own actual education is 0.74 years,
whereas for the industry measure it is almost a year and a half.
This article focuses on the effect that economic conditions at
graduation have on the probability of someone being overeducated in
subsequent employment. To demonstrate the importance of this type of
mismatch relative to other measures, a subjective "horizontal"
measure of job match quality from the GSOEP is also included. This
binary indicator is based on whether individuals "work in the
occupation for which they are trained." In the sample of higher
education graduates, 79% of workers are well-matched according to this
horizontal measure. (10)
The data suggest that those who graduated during a recession are,
on average, more likely to be overeducated. Figure 1 plots the
probability of overeducation by 4-digit occupation against the regional
unemployment rate at graduation. Shares of workers that are overeducated
at every value of the unemployment rate are generated from the data. A
local moving average fitted through the scatterplot demonstrates a
positive and significant relationship between initial labor market
conditions and overeducation rates. Figure 2 shows a similar, although
less striking, relationship for overeducation rates defined within
2-digit industries. It is also informative to examine overeducation
rates in a way that accounts for the uneven grouping of individuals
across unemployment rates in the sample. Table 2 provides the shares of
workers who are mismatched across graduation unemployment rate groups of
similar sample sizes. Workers have an overeducation rate of
approximately 18% for their occupation if they entered the labor market
during the most favorable times (when the regional unemployment rate was
less than 6%). The share of overeducated rises with the unemployment
rates reaching 30% for those who graduated in labor markets with
state-level unemployment rates in the range of 11-15%. Similar results
are found for the industry measure where overeducation rates range from
about 27% to almost 42%, respectively. (11) Interestingly, it does not
appear that the likelihood of working in the occupation one is trained
for, a horizontal measure of mismatch, has the same cyclical property
found in the vertical overeducation measures. The relationship between
horizontal and vertical measures is further examined in Table S1. The
two measures are virtually uncorrected. It also turns out that, unlike
the vertical overeducation measures used in this study, the horizontal
measure does not exhibit cyclicality. This suggests that the
macroeconomy tends to affect vertical rather than horizontal mismatch,
at least in the case of Germany.
IV. EMPIRICAL APPROACH
The empirical analysis is based on a parsimonious specification
that is designed to separate the effects of initial labor market
conditions from the effects of a worker's human capital. For each
of the overeducation measures (OE), the baseline Equation (1) is
estimated using the linear probability model. (12)
(1) [OE.sub.irt] = [alpha] + [X'.sub.irt] [beta] +
[gamma][U.sub.rt-h] + [[delta].sub.r] + [[tau].sub.t] + [member of] +
[[epsilon].sub.irt].
The coefficient y is an estimate of the relationship between
region-level unemployment rates (U), in the region of gradation (r) and
at the time of graduation (t - h), on the overeducation measure (OE) of
worker i in period t. Estimates are weighted using the enumeration
weights provided in the GSOEP to give representative results for the
German population.
Labor market conditions vary at the regional level and by the time
of graduation. Thus, the empirical model relies on regional
fixed-effects to capture the group structure of the standard errors.
However, serial correlation within regions is still a concern (Bertrand,
Duflo, and Mullainathan 2004). Therefore, standard errors are clustered
on the region of graduation. Unfortunately, it is also true that
cluster-robust inference may lead to over rejection of the null in
t-tests when the number of clusters is low (Cameron and Miller 2015). In
the case of Germany there are 16 federal regions, which fall between the
potential thresholds of 42 suggested by Moulton (1986) and 10 suggested
by Angrist and Pischke (2009). (13) The wild-cluster bootstrap of
Cameron, Gelbach, and Miller (2008) is therefore employed to provide
robust inference for our variable of interest.
The model also includes the covariate vector (X). This vector
contains dummy variables for the highest completed education stream
(university, other tertiary, and apprenticeship), gender. marital
status, and German nationality. These demographic variables are usually
included in wage regressions and they are expected to play a role in
employment possibilities and therefore the probability of overeducation.
Continuous controls for age and full-time work experience measured in
years, as well as their quadratics, are included. Region of graduation
dummy variables ([delta]) and year dummy variables ([tau]) are also
included. Approximately 3% of the individuals in the sample have
relocated since graduation to a different region. (14) A dummy variable
that captures geographic mobility since graduation is also included to
account for the possibility that geographic mobility contributes to the
likelihood of overeducation. This may be important when considering
regional labor markets if those of higher ability, for example, are more
likely to avoid overeducation by relocating to a neighboring region.
A. Identification
Identifying the causal impact of region-level unemployment rates
requires that unemployment rates are exogenous. Certainly, macroeconomic
conditions at the regional level cannot be meaningfully influenced by
the decisions of any one individual worker. However, endogeneity could
be an issue because of graduation location or timing. Individuals may
attempt to time their graduation to coincide with improved labor market
conditions. This is especially true among university graduates in
Germany since many degree programs do not have fixed timelines and
tuition fees are relatively low. Any bias might therefore be expected to
be most significant for university graduates. Scrupulous students may
also choose to attend tertiary education or enrol in apprenticeship
programs in regions where jobs are more prevalent. This might be
particularly true in apprenticeship programs where connections are made
with future employers.
The data suggest that some workers do delay their graduation. Among
graduates with university education, 25% of the sample graduate beyond
age 29. The equivalent statistics for tertiary education and
apprenticeships occur at ages 23 and 21, respectively. The modal
graduation ages are 27 for university, 21 for other tertiary schooling,
and 20 for apprenticeships. The share of workers who switch region since
age 14 is low at only 3% of the sample. Results addressing endogeneity
with IV estimates are presented in Section V.B. The IV approach used in
this article exploits exogenous variation in labor market conditions
that originates from the accident of birth and therefore sidesteps
issues of endogenous gradation timing. However, these results suggest
that endogeneity bias in the OLS estimates is negligible.
V. RESULTS
A. Baseline Specification
Regional unemployment rates at the time of graduation have positive
and significant effects on the likelihood that a worker is
overqualified. Table 3 presents OLS estimates of Equation (1) using
various outcome measures. The graduation date and location from the
highest level of completed education is used in these results. Estimates
are presented with indicators for statistical significance from
cluster-robust standard errors, and wild-bootstrap p values are included
at the bottom of the table. Only those results that are significant with
both methods of inference are discussed.
Columns 1 and 2 show the effects of a downturn on the binary
measures of overeducation within occupation and industry, respectively.
A single percentage point increase in the regional unemployment rate at
labor market entry leads to a 1.2 percentage point increase in the
likelihood of overeducation within a worker's occupation. Given
that 18% of all workers in the GSOEP data are overeducated, this is a
significant result. A recession which increases regional unemployment by
4 percentage points could be expected to increase the share of
overqualified workers in the labor force by 4.8 percentage points, which
would represent an increase of about 25% in the average overeducation
rate. Columns 3 and 4 examine the difference between actual education
and required education arising from labor market entry conditions. These
linear measures provide insight about the extent of overeducation among
workers as a result of macroeconomic conditions. Column 4 shows that a
single percentage point increase in the region unemployment rate at
labor market entry increases the amount by which actual education
exceeds required education by about 0.7 years. (15)
The sizes of the coefficients in this study are somewhat lower
relative to earlier studies. For example, Liu, Salvanes, and Sorensen
(2016) find that a 3 percentage point increase in unemployment rates
leads to a 30% increase in mismatch. However, the results are not
directly comparable because Liu, Salvanes, and Sorensen (2016) measure
mismatch by comparing a worker's industry to their field of
education. The relatively small effects found here might be explained by
the fact that the incidence of mismatch is considered across a
worker's entire career, as observed in the data. Nevertheless, the
current findings are in line in terms of magnitude with predictions from
a structural model of the Canadian economy. Summerfield (2016) finds
that a single percentage point increase in regional unemployment at the
time of job creation leads to a 3 percentage point increase in the
probability of an individual being overeducated.
The results above demonstrate that overeducation, a measure of
vertical mismatch, responds to labor market entry conditions. To
investigate whether entry conditions also affect horizontal mismatch, a
GSOEP measure of specific job match is also used. Whereas the
overeducation measures may capture mismatch in general transferable
skill, this alternative measure may capture mismatch between training
and occupation or mismatch across educational fields. Estimates in
column 5 indicate that labor market entry conditions do not affect the
likelihood of a worker being employed in an occupation that they are
trained for. This result differs from Liu, Salvanes, and Sorensen (2016)
who do find some evidence of horizontal mismatch between field of study
and industry. The very low correlation between vertical and horizontal
measures used in this article (Table S1) suggest that horizontal and
vertical mismatch may arise for separate reasons. Overeducation, or
vertical mismatch, appears to be the more relevant measure for cyclical
mismatch in the labor market. Horizontal mismatch may reflect structural
change that brings about changes in the demand for specific skills.
However, the latter is not examined in this study. (16) Vertical
mismatch is indicative of a worker's position in the hierarchy of
general skills and it is expected to vary with the business cycle.
The vector of estimates [beta] is informative regarding factors
other than macroeconomic conditions that contribute to overeducation.
University graduates in general appear more likely to be overeducated
compared to those with "other" tertiary education, although
this estimate is insignificant for the occupation measure. In general,
apprenticeship graduates are as likely to be overeducated as workers
that have completed other tertiary education. Overeducation also
increases with age following a quadratic path and decreases with years
of work experience. The latter result implies that more experienced
workers rely on their experience and on-the-job learning as a source of
human capital rather than to formal education. An alternative
interpretation is that workers initially accept an overeducated role,
and later are promoted to a role commensurate with their skills. (17)
The positive effect of age on overeducation may reflect the depreciation
of human capital. With the advent and proliferation of computers and
technology in the workplace, older workers may find themselves relegated
to jobs which typically attract less-educated workers.
B. IV Estimates and Endogeneity Bias
Although individual workers cannot reasonably be expected to affect
the macroeconomy, they may be able to control when and where they
graduate. (18) Therefore, addressing potential endogeneity bias is
critical for the credibility of the findings. Unemployment rates at the
actual graduation location and time are instrumented with regional
unemployment rates in the location where a worker lived at age 14, and
at the modal graduation time for others within the same age cohort and
education stream. This approach is akin to instrumenting actual
graduation with the graduation path an individual would have followed if
they had not moved location or delayed their program completion. Three
separate instruments are created so that one represents each of the
three higher education streams analyzed in this article. These
instruments provide a source of exogenous variation in labor market
entry conditions. At age 14 it is likely that individuals are living in
the family household, yet it is unlikely that their decisions affect the
household's location. Economic conditions at the modal age of
graduation are also not within the realm of control of an individual.
Therefore, the instrument exploits changes in the regional unemployment
rate that new graduates face as a result of the accident of their birth.
Following Kahn (2010) experience is removed from the specification,
because experience is also endogenous if workers delay (or accelerate)
their education.
After controlling for endogeneity bias, the estimated effects of
entry conditions change little relative to the OLS estimates. Table 4
presents IV estimates that correspond to the OLS estimates in Table 3.
The effects are almost identical. A single percentage point increase in
the regional unemployment rate at graduation leads to an increase in the
probability of being overeducated by approximately 1.4 percentage points
for the occupation measure and a single percentage point for the
industry measure. The linear distance measures are also positive and
significant. As with all other results, there is no meaningful effect on
the horizontal measure of mismatch. Similar IV and OLS estimates are
justified in view of the low incidence of geographic relocation observed
in the data. The apparent delay in graduation among groups of workers
may simply reflect the education program duration.
The bottom panel of Table 4 summarizes the first stage results. All
three of the instruments correlate positively and strongly with the
endogenous unemployment rate at graduation. Indeed, it is reasonable
that the most common graduation date and location for an individual is
highly correlated with his or her actual graduation date and location.
Multivariate F-tests (Angrist and Pischke 2009) show that the null
hypothesis of weak instruments is rejected at the 1% level in all cases.
Because there are three instruments, it is also possible to test against
a null-hypothesis of exogenous instruments using the Sargan-Hansen
overidentification test. The test-statistic is insignificant across all
three specifications indicating that the null-hypothesis cannot be
rejected, the expected result given the intuition behind these
instruments.
C. Education Types and Overeducation
Overeducation may be more likely for graduates of certain education
streams. University graduates may accept employment opportunities that
do not strictly require university, while those without university
education are less likely to receive employment offers for positions
typically filled with university graduates. This section provides
estimates using an alternative specification that estimates the effect
of graduation conditions separately by education stream.
(2) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Graduation unemployment rates are interacted with indicators (E)
for university (UNI), other tertiary (TE), and apprenticeship (APR)
graduation, so that each individual is assigned a value only for the
variable corresponding to their own highest level of education. This
specification allows one to investigate whether initial labor market
conditions affect certain types of graduates in a different way compared
to others. Added flexibility by education type may be particularly
important in the case of Germany because it has a well-developed
apprenticeship system. Technical and theoretical education may provide
workers with more specific and more general human capital, respectively.
If this is the case, economic conditions may have different effects on
individuals with different educational attainment. For example, a
university-educated individual may be able to find a job for which she
or he is overqualified if her/his general skills are productive in other
fields of work, whereas an apprentice may be more likely to suffer
unemployment if her/his skills are not transferrable to other, perhaps
less suitable, jobs.
Table 5 shows that the effects above are predominantly driven by
the experience of university graduates. A single percentage point
increase in the regional unemployment rate would lead to a 1.6
percentage point increase in the probability of one's overeducation
within his/her occupation. Within one's industry, the impact is
slightly higher at 1.7 percentage points. These results are in line with
intuition if university education is expected to develop a more general
and transferrable form of human capital. However, specific human capital
that characterizes AP and other tertiary education has been shown to
vary with industry (Neal 1995), and/or occupation (Kambourov and
Manovskii 2009). There is also some evidence that AP graduates may be
affected by entry conditions. However, this effect is found only for the
occupation-based measure.
The instruments are highly significant in each of the three first
stages. (19) Coefficients are large and significant and the multivariate
F-tests suggest that the instruments are not weak. Because there are
three instruments and three endogenous unemployment rates, these models
are just-identified and it is not possible to test overidentifying
restrictions.
The IV estimates show that the effects of schooling type and age
are similar with respect to the initial specification and, in addition,
the coefficient for the married dummy variable is now significant. The
dummy variable that indicates residence in a different region than the
region of graduation becomes significant. Those who relocate to other
regions appear to be more likely to be overqualified in their job. The
coefficient for region switch is likely to capture those who move for
reasons such as the career of a spouse or a better wage or working
conditions. Workers who move to a new region may have less-developed
professional networks and might be expected to start lower on the career
ladder.
As a robustness test, two alternative measures of overeducation are
used. First, estimates corresponding to columns 1-4 in Table 5, that
evaluate a worker's overeducation based on the mean education
within the same year and occupation or industry, are presented in Table
S3. The magnitude of estimated impacts is very similar to the earlier
results. Second, in Table S4. results are presented that evaluate worker
education against the yearly mode for their occupation and industry. In
this measure a standard deviation is less intuitive and so workers with
years of education exceeding the mode are considered overeducated. The
results are again similar to the earlier results except that the effect
of university graduates in Column 1 turns out to be statistically
insignificant. (20)
D. Scarring Effects of Recessions on Job Matches
The costs of overeducation for workers may depend on the length of
time that workers remain "trapped" in jobs for which they are
overqualified. The career path of young workers is often characterized
by significant job mobility (Topel and Ward 1992). Job-to-job
transitions provide important sources of wage growth through
occupational upgrading (Devereux 2002). Temporary overeducation, as part
of a career path that is optimal over the life cycle, might not be
viewed as a negative situation. Frei and Sousa-Poza (2012), for example,
find that half of overqualified Swiss workers find a suitable match
within one year. Evidence that job match quality is procyclical also
suggests that overeducated workers might move to better matches when
conditions improve (Bowlus 1995; Carrillo-Tudela et al. 2016; Devereux
2000, 2004; Moscarini and Vella 2008). Still other findings suggest that
Flemish (Baert, Cockx, and Verhaest 2013), and Norwegian (Liu, Salvanes,
and Sorensen 2016) workers may get "trapped" in poor matches.
Overselling has been shown to be self-perpetuating in Australian data
(Mavromaras and McGuinness 2012). Fruhwirth-Schnatter et al. (2012) show
that adverse entry conditions cause unfavorable income trajectories.
This section presents estimates showing that scarring effect of
labor market entry conditions on job match quality lasts up to 9 years
after graduation. Equation (3) below builds on the baseline
specification:
(3) [OE.sub.irt] = [alpha] + [X'.sub.irt][beta] +
[gamma][U.sub.rt-h] + [Z'.sub.irt][pi] + ([U.sub.rt-h] x
[Z.sub.irt])' [rho] + [[delta].sub.r] + [[tau].sub.t] +
[[epsilon].sub.irt].
The vector Z, which is comprised of dummy variables for the year of
graduation, and 3-year groups for years thereafter, is included along
with its interaction with initial conditions. These variables are in
lieu of the continuous measures of experience. The dummy variables
continue to allow for non-linear effects related to experience while the
interaction terms allow the effect of entry conditions to vary across
the experience dimension.
Table 6 presents the marginal effect of labor market entry
conditions (U) on overeducation (OE), evaluated at the year of
graduation and 3-year intervals thereafter. The top panel includes
graduation from all types of education. Estimates for the probability of
overeducation by occupation suggest that labor market entry conditions
have persistent effects. The marginal effects are significant and
positive up to 9 years after graduation. It should be noted that
estimates are imprecise for the marginal effects beyond 9 years and so
there is no evidence that initial effect disappears, although it is also
not possible to reject the possibility of no effect. This 9-year effect
is similar in duration to the wage penalty scarring effects reported in
Oreopoulos, von Wachter, and Heisz (2012) and Liu, Salvanes, and
Sorensen (2016). The effects are similar when defining overeducation by
industry, also lasting for 6 years. The continuous measures capturing
the linear distance between actual and required education for an
industry suggest slightly longer persistence. This result shows that
some important variation in overeducation occurs late in the career even
if this variation is insufficient to meet the threshold of the binary
measures
Furthermore, it is important to disentangle the effect that initial
conditions have on future overeducation due to initial-job mismatch from
the effect that they have on subsequent overeducation through labor
market experience scarring effects. In an attempt to shed some light in
this issue, Table S5 presents auxiliary OLS results where there is a
control capturing whether or not individuals were overeducated in their
first job. These results suggest that initial-job mismatch plays an
important role, increasing the probability of overeducation by 38
percentage points and 45 percentage points for occupation and industry
measures, respectively. Yet, initial labor market conditions retain
their positive and significant contribution to overeducation conditional
on prior mismatch, implying the importance of the detrimental effect of
slack labor market conditions on future overeducation over and above
possible independent confounding effects of initial bad matches.
Since estimates by education type in Section V.C suggest that the
effect is strongest among university graduates, the bottom panel of
Table 6 presents marginal effects for university graduates only. These
results suggest that the negative impacts of graduating during a
recession are stronger later in the careers of university graduates. One
possible explanation of this finding may reflect the sorting mechanisms
of the graduate employment labor markets. A large number of new
graduates should be expected to be mismatched initially, regardless of
labor market conditions, as career path jobs often involve
on-the-job-training with lesser job titles and lower wages. (21) Thus
scarring effects may be masked in early job matches, only to become
visible later on when these graduates experience delayed career
advancement relative to their peers who graduated in better labor
markets. Workers that find initial jobs in a recession may find limited
opportunity in the future resulting from firms' unwillingness to
invest in their workforce when facing uncertainty about future demand.
It is also true that those graduates who do find a good match during a
recession are more likely to work initially in temporary jobs and
experience several early-career unemployment spells. Even if these
early-career jobs appear as good matches, this job history is likely to
be a negative signal to future employers and could adversely affect
future job prospects and job matches.
Thus, the finding that the negative impacts of graduating during a
recession are stronger later in the careers of university graduates is a
significant result as it highlights the potential of serious career
repercussions. (22) The finding is also consistent with the result from
Table S5, that labor market entry conditions affect overeducation
conditional on the match quality in a workers first job. Plots of these
marginal effects are presented in Figures SI and S2.
Table S6 includes an alternative set of estimations that address
the historical path of labor market conditions faced by workers. This
approach isolates the entry effect from the effects of exposure to
subsequent labor market conditions. Control variables for the average
regional unemployment rate at each of the time intervals are included in
place of the time dummy variables following the approach of Oreopoulos,
von Wachter, and Heisz (2012). Estimation is repeated on a sub-sample of
workers who do not switch regions and for whom these histories can be
reliably generated. The coefficient estimates are remarkably similar in
magnitude although the 3- and 9-year interaction effects are no longer
significant.
Several of the measures of overeducation are insignificant or
negative during the year of graduation. This implies that those workers
who end up overeducated, select into these work arrangements after
searching unsuccessfully for more suitable jobs. It is also interesting
to note that the horizontal mismatch estimate, which captures the
probability of employment in the occupation for which an individual is
trained, is significant and negative in the year of graduation only.
This suggests that the effect on horizontal mismatch is temporary and
implies that workers accept jobs outside their field as a stopgap
measure.
VI. CONCLUSIONS
This article examines the role of macroeconomic conditions at
graduation, or first labor market entry, on the mismatch of workers
throughout their careers. The mismatch is approximated with measures of
overeducation that compare the educational attainment of workers to the
median education within their occupation. Using an IV estimation to
control for the potentially endogenous timing of graduation the article
shows that increases in regional-level unemployment rates at graduation
affect the future probability of overeducation, and hence mismatch.
The findings in this article suggest that the costs of recessions
may extend to the future career of the affected workers. Whereas there
is a focus among policy-makers on unemployment statistics, unfavorable
labor market conditions are also costly for those who do find work. This
article also suggests that scarring effects are persistent because
estimates of the probability of overeducation are not restricted to
early career workers. Furthermore, the effects of initial labor market
conditions may last up to 9 years after graduation. The duration of
scarring effects suggested by these overeducation estimates is
consistent with the duration of scarring effects on wages in the
literature. This suggests that overeducation may help to explain why
workers graduating in a recession earn lower wages for several years
after they enter the labor market.
Therefore, the results in this article suggest that time does not
cure all evils. Although workers may be able to climb the ladder,
switching to better jobs as times improve, many workers cannot overcome
the initial scarring effect. Some workers may choose to remain
mismatched after the recession if they have developed specific human
capital that might be lost in transition to the "right" job.
However, there may be scope for training and job-search assistance
programs following recession periods to assist those who are better
served by returning to occupations or industries where their education
is fully utilized. These policies may benefit some more experienced
workers as well as recent graduates.
This study finds scant evidence that horizontal mismatch responds
to initial labor market conditions. Therefore, policy to improve job
matching may be more effective if it is directed at workers with
vertical mismatch. It appears that overeducation, that is an excess
level of schooling, rather than mismatch across fields of study, is more
likely to come about because of economic downturns. It is also more
likely to have significant and lasting effects.
ABBREVIATIONS
GSOEP: German Socio-Economic Panel
IV: Instrumental Variables
OLS: Ordinary Least Squares
doi: 10.1111/ecin.12446
FRASER SUMMERFIELD and IOANNIS THEODOSSIOU *
* The authors are grateful to the editor, two anonymous referees of
this journal, and seminar participants at the University of Aberdeen and
the CEDEFOP/IZA workshop on skills and skill mismatch for very helpful
comments and suggestions.
Summerfield: Assistant Professor. Department of Economics, Lakehead
University, Thunder Bay, ON P7B 5E1, Canada. Phone +1 807 343 8919, Fax
+1 807 346 7936, E-mail fraser.summerfield@lakeheadu.ca
Theodossiou: Professor, Business School and Centre for European
Labour Market Research (CELMR). University of Aberdeen, Aberdeen AB24
3QY. UK. Phone +44 1224 272183, Fax +44 1224 272159, E-mail
theod@abdn.ac.uk
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SUPPORTING INFORMATION
Additional Supporting Information may be found in the online
version of this article:
Table S1. Mismatch measure correlation matrix
Table S2. t-test for difference in the probability of overeducation
within occupation, by number of observations within cells
Table S3. Robustness check: IV impacts on overeducation measures,
comparing actual to mean education
Table S4. Robustness check: IV impacts on overeducation measures,
comparing actual to modal education
Table S5. Robustness check: OLS results conditional on match
quality in the first ever job
Table S6. The marginal effect of labor market entry conditions on
overeducation by grouped years since graduation
Figure S1. The effect of labor market entry conditions on
overeducation by years since graduation: all graduates
Figure S2. The effect of labor market entry conditions on
overeducation by years since graduation: university graduates
(1.) Labor mobility is much lower in Germany than in other
countries such as the United States. Only 3% of all observations in the
data studied amount to workers who have switched federal state since
their graduation date.
(2.) The authors thank Michael Stops at IAB for assistance locating
these data.
(3.) Results were also tested for robustness by generating
unemployment rates directly from the GSOEP data since 1990. when data
collection began for the former East German regions. The findings are
robust. These results are not presented here but they are available from
the authors on request.
(4.) For example, an apprenticeship graduate would be linked to the
graduation date of their apprenticeship program thereafter, until such
time as they graduate university in which case they would be associated
with the new graduation date. However, a university graduate who returns
to study in an apprenticeship program would continue to be associated
with their university graduation date.
(5.) On average these workers have about 4 years of tenure in the
current job and about 2% are in their first ever job.
(6.) Measures using the mean and the mode, instead of the median,
provided similar results. Robustness checks are provided in the
Supporting Information.
(7.) Overeducation within an occupation is a more straight forward
concept than overeducation within an industry. There are very few
studies on the relationship of industry and overeducation (e.g., Tarvid
2015). The industry measure may still provide an important robustness
check for the occupation measure and in the context of this paper it may
also be informative in its own right. Assuming that production processes
do not change over the business cycle, an increase in the education of
workers relative to the industry median reflects a change in the type of
worker hired and thus may reflect overeducation. Table SI confirms this
intuition by demonstrating a high correlation between the occupation-
and industry-based measures of overeducation.
(8.) Estimates that evaluate these additional observations against
the 3-digit occupation, or 1-digit industry median education were also
generated. Results are very similar and are available from the authors
upon request.
(9.) Bauer (2002) finds 12% of men and 11 % of women are
overeducated using measures based on the mean education of worker
groups. Daly, Buchel, and Duncan (2000) report that 14% of men and 20%
of women were overeducated in 1984 using a self-reported measure.
(10.) Other dimensions of mismatch may also be interesting
including mismatches across college major or other definitions of skill
(Allen and van der Velden 2001; Robst 1995). However, an extended
analysis is beyond the scope of the current paper.
(11.) The cyclical pattern does not hold for the group with
unemployment rates above 15%. Observations with these unemployment rates
represent mainly those workers graduating in the former East
Germany's poorest regions where workers may graduate into
unemployment rather than into employment associated with overeducation.
(12.) The linear probability model is chosen over the probit or
logit analysis because it permits more careful inference with
wild-cluster-robust inference. Probit models give similar results.
(13.) It should be noted, however, that cluster-robust standard
errors from popular statistics packages such as Stata might still behave
well with less than ten clusters (Angrist and Pischke 2009 citing Hansen
2007).
(14.) Regional mobility in Germany is also found to be low in other
studies. Arntz (2005) finds that only 7% of unemployed workers relocate
more than 80 km to take up a new job and, using the GSOEP, Resuchke
(2011) finds that only 10% of all relocation events cross regional
borders.
(15.) Estimates were also calculated where required education was
defined by both occupation and industry. Estimates are insignificant due
to reduced sample sizes, although coefficients are broadly similar.
These results are available from the authors upon request.
(16.) It should be noted that the GSOEP measure is subjective and
therefore it may be noisier than the other measures used in this paper.
Hence, this type of mismatch is not discussed further.
(17.) The authors thank an anonymous referee of this journal for
fruitful suggestions relating to this interpretation.
(18.) Recessions have also been linked to the decision to enrol in
post-secondary education (Alessandrini, Kosempel, and Stengos 2015;
Betts and McFarland 1995; Dellas and Sakellaris 2003). Although this is
an interesting issue, it is beyond the focus of this study.
(19.) Only the corresponding education type instrument is shown.
However all three instruments are part of all three first stages. In all
cases, among the three, only the instrumental variable coinciding with
the schooling stream of interest turns out to be statistically
significant in the first stage.
(20.) In many cases there is a tie for modal years of schooling,
more so within a yearly 4-digit occupation category than a 2-digit
industry category. In this case the higher value is used in order to
obtain conservative estimates. This may help to explain a smaller impact
for occupation measures while industry measures remain similar to Table
5.
(21.) Devereux (2000) finds that some firms reassign workers to
lower quality tasks (demote them) during recessions. However, this
effect is unlikely to be widespread, especially in European labor
markets, due to the strength of labor laws and collective agreements.
(22.) The authors thank an anonymous referee of this journal for
drawing our attention to the significance of this finding.
Caption: FIGURE 1 Entry Conditions and the Probability of
Overeducation within 4-Digit Occupations
Caption: FIGURE 2 Entry Conditions and the Probability of
Overeducation within 2-Digit Industries
TABLE 1
Summary Statistics
Variable M SD N
Male 0.483 0.500 13,563
Age in years 28.476 6.166 13,563
German citizen 0.936 0.245 13,563
Married 0.217 0.412 13,563
Years of experience 4.746 4.702 12,222
Years of education 13.673 2.926 13,563
Grad: University 0.367 0.482 13,563
Grad: Tertiary 0.123 0.329 13,563
Grad: Apprentice 0.509 0.500 13,563
Working in OCC trained for 0.787 0.409 11,819
Actual-median (OCC) 0.743 2.088 12,993
Actual-median (IND) 1.417 2.748 13,314
Overeducated (OCC) 0.243 0.429 12,993
Overeducated (IND) 0.345 0.475 13,314
Baden-Wiierttemberg 0.135 0.342 13,563
Bavaria 0.160 0.367 13,563
Berlin 0.045 0.207 13.563
Brandenburg 0.031 0.175 13,563
Bremen 0.007 0.084 13,563
Hamburg 0.026 0.158 13,563
Hesse 0.080 0.271 13,563
Mecklenburg-Western Pomerania 0.019 0.137 13,563
Lower Saxony 0.089 0.285 13,563
North Rhine-Westphalia 0.197 0.397 13,563
Rhineland-Palatinate 0.049 0.215 13,563
Saarland 0.007 0.084 13,563
Saxony 0.060 0.238 13,563
Saxony-Anhalt 0.032 0.178 13.563
Schleswig-Holstein 0.031 0.164 13,563
Thuringia 0.044 0.174 13,563
Switch Region 0.033 0.179 13,563
Notes: Years of experience for full-time work only. Switch
Region refers to workers sampled in a region different from
their region of graduation. IND, industry; OCC, occupation.
Source: GSOEP 1994-2012. graduates from university,
other tertiary education, and apprenticeships.
TABLE 2
Education Mismatch Shares and Regional
Unemployment Rates at Graduation
Region Share Share Share in Number of
Grad Over- Over- Occupation Graduates
Urate educated educated Trained For
OCC IND
3.8-5.9 0.179 0.269 0.774 1,901
6.0-7.4 0.197 0.295 0.787 2,026
7.5-8.9 0.270 0.375 0.768 2,374
9.0-10.9 0.283 0.404 0.834 2,491
11.0-14.9 0.299 0.420 0.803 2,033
15.0-20.5 0.205 0.265 0.746 2,489
Note: IND. industry; OCC, occupation.
Source: GSOEP 1994-2012. graduates from university,
other tertiary education, and apprenticeships. Mismatch
measures defined in the text. Section III. Weighted using GSOEP
enumeration weights.
TABLE 3
OLS Impacts of Regional Unemployment Rates at Graduation from
Highest Education Obtained on Various Mismatch Measures
(1) (2) (3)
Pr(OE) Pr(OE) Distance
Median (OCC) Median (IND) Median (OCC)
R. Grad Urate 0.012 ** 0.012 ** 0.032
(0.004) (0.005) (0.019)
University -0.008 0.321 ** 0.558 ***
(0.036) (0.058) (0.174)
Apprentice 0.004 -0.106 * 0.010
(0.050) (0.055) (0.181)
Age 0.087 *** 0.049** 0.364 ***
(0.014) (0.022) (0.105)
Age (2) -0.001 *** -0.000 -0.004 *
(0.000) (0.000) (0.002)
Experience -0.027 *** -0.011 -0.143 ***
(0.006) (0.008) (0.034)
Experience (2) 0.001 * -0.000 0.002
(0.000) (0.000) (0.002)
Married -0.020 -0.024 -0.281 **
(0.021) (0.021) (0.111)
Male -0.018 0.048 0.019
(0.024) (0.046) (0.108)
German 0.070 0.036 0.128
(0.050) (0.038) (0.266)
Region Switch 0.070 0.043 * 0.330 *
(0.046) (0.020) (0.158)
Constant -1.556 *** -1.018 ** -6.807 ***
(0.225) (0.374) (1.400)
WBoot p values
R. Grad Urate 0.058 0.108 0.177
N 11,892 12,215 11,892
[R.sup.2] 0.088 0.361 0.148
(4) (5)
Distance Pr(Work in the OCC
Median (IND) Was Trained For)
R. Grad Urate 0.073 *** -0.000
(0.021) (0.004)
University 2.536 *** -0.009
(0.217) (0.028)
Apprentice -0.355 -0.149 ***
(0.225) (0.028)
Age 0.373 *** -0.032
(0.109) (0.024)
Age (2) -0.004 * 0.000
(0.002) (0.000)
Experience -0.093 *** 0.015 *
(0.038) (0.007)
Experience (2) -0.000 -0.001 **
(0.002) (0.000)
Married -0.226 -0.031
(0.137) (0.033)
Male 0.203 -0.037 *
(0.119) (0.019)
German 0.208 0.089 ***
(0.297) (0.025)
Region Switch 0.287 ** 0.020
(0.087) (0.031)
Constant -7.727 ** 1.237 ***
(1.705) (0.357)
WBoot p values
R. Grad Urate 0.062 0.887
N 12,215 12.191
[R.sup.2] 0.477 0.055
Notes: Regional unemployment rates exclude self-employed and pool
the effects of graduation timing across all individuals using their
highest achieved education level. Omitted education dummy is other
tertiary-technical schooling, such as medical or teaching or other
vocational schooling. Estimates include dummies for region of
graduation. Region Switch is a dummy to indicate those who reside
in a different region relative to graduation date. Standard errors
in parentheses clustered on region of graduation. Estimates
weighted with enumeration weights. Wild-cluster bootstrap p values
at the region level impose the null hypothesis on the variable of
interest (y =0) using 999 repetitions. IND. industry: OCC.
occupation.
*** p < 0.01, ** p < 0.05, * p < 0.1 for all coefficients and
test statistics.
Source: GSOEP 1994-2012.
TABLE 4
IV Impacts of Regional Unemployment Rates at Graduation from
Highest Education Obtained on Various Mismatch Measures
(1) (2) (3)
Pr(OE) Pr(OE) Distance
Second Stage Median (OCC) Median (IND) Median (OCC)
R. Grad Urate 0.014 *** 0.010 ** 0.045 **
(0.005) (0.005) (0.023)
University 0.032 0.334 *** 0.771 ***
(0.034) (0.049) (0.152)
Apprentice -0.023 -0.121 ** -0.089
(0.058) (0.053) (0.196)
Age 0.071 *** 0.062 *** 0.396 ***
(0.010) (0.015) (0.053)
Age (2) -0.001 *** -0.001 *** -0.005 ***
(0.000) (0.000) (0.001)
Married -0.035 * -0.035 * -0.375 ***
(0.020) (0.021) (0.101)
Male -0.020 0.036 -0.007
(0.020) (0.038) (0.086)
German 0.065 0.028 0.070
(0.048) (0.037) (0.241)
Region Switch 0.089 ** 0.047 *** 0.427 ***
(0.040) (0.016) (0.159)
First Stage: R. Grad Urate
R14. Mod Urate (UNI) 0.892 *** 0.894 *** 0.892 ***
(0.017) (0.019) (0.017)
R14. Mod Urate (TE) 0.874 0.876 *** 0.874 ***
(0.039) (0.040) (0.039)
R14. Mod Urate (APR) 0.923 *** 0.923 *** 0.923
(0.012) (0.013) (0.012)
Multivariate F 2,120.39 1,909.44 2,120.39
Sargan-Hansen 1.210 4.095 0.906
[chi square]
N 12.993 13.314 12,993
[R.sup.2] 0.063 0.328 0.132
(4) (5)
Distance PrlWork in the
Second Stage Median (IND) OCC Was Trained For)
R. Grad Urate 0.068 *** -0.003
(0.019) (0.004)
University 2.673 *** -0.033
(0.175) (0.027)
Apprentice -0.410 * -0.152 ***
(0.216) (0.027)
Age 0.494 *** -0.002
(0.065) (0.012)
Age (2) -0.007 *** -0.000
(0.001) (0.000)
Married -0.291 ** -0.025
(0.119) (0.032)
Male 0.140 -0.031
(0.109) (0.021)
German 0.119 0.090 ***
(0.270) (0.029)
Region Switch 0.362 *** 0.030
(0.069) (0.030)
First Stage: R. Grad Urate
R14. Mod Urate (UNI) 0.894 *** 0.891
(0.019) (0.019)
R14. Mod Urate (TE) 0.876 *** 0.871*"
(0.040) (0.041)
R14. Mod Urate (APR) 0.923 *** 0.922 ***
(0.013) (0.013)
Multivariate F 1,909.44 1,836.86
Sargan-Hansen 1.155 3.224
[chi square]
N 13,314 12,529
[R.sup.2] 0.454 0.029
Notes: Regional unemployment rates exclude self-employed and pool
labor market entry effects across all individuals using their
highest achieved education level. Education levels: UNI-university
and APR-apprenticeship. Omitted education dummy is TE-technical
schooling, such as medical or teaching or other vocational
schooling. Estimates include dummies for region of graduation.
Region Switch is a dummy to indicate those who reside in a
different region relative to graduation date. Standard errors in
parentheses clustered on region of graduation. Estimates weighted
with enumeration weights. R. Grad Urate is instrumented with R14
Mod Urate (UNI I TE I APR), the unemployment rates specific to each
of the three education levels in the region where an individual
resided at age 14 at the modal graduation year for their age cohort
following Kahn (2010). [chi square] is the test statistic from the
Hansen J test for overidentification of all instruments with a
null-hypothesis that instruments are exogenous. F is the
multivariate F-test for joint significance of instruments from
Angrist and Pischke (2009). IND. industry; OCC. occupation.
*** p <0.01, ** p < 0.05, * p < 0.1 for all coefficients and test
statistics.
Source: GSOEP 1994-2012.
TABLE 5
IV Impacts of Regional Unemployment Rates at Graduation
from Specific Level of Education Obtained on
Various Mismatch Measures
Second Stage (1) (2) (3)
Pr(OE) Pr(OE) Distance
Median (OCC) Median (IND) Median (OCC)
R. Grad Urate (UNI) 0.016 *** 0.017 *** 0.035
(0.006) (0.005) (0.027)
R. Grad Urate (TE) 0.009 0.006 0.034 *
(0.006) (0.009) (0.020)
R.Grad Urate (APR) 0.013 ** 0.005 0.050 **
(0.005) (0.005) (0.023)
University -0.039 0.215 * 0.746 **
(0.074) (0.116) (0.336)
Apprentice -0.063 -0.124 -0.249
(0.103) (0.140) (0.363)
Age 0.071 *** 0.061 *** 0.397 ***
(0.010) (0.014) (0.053)
Age (2) -0.001 *** -0.001 *** -0.005 ***
(0.000) (0.000) (0.001)
Married -0.035 * -0.036 * -0.375 ***
(0.020) (0.021) (0.101)
Male -0.020 0.038 -0.010
(0.019) (0.037) (0.088)
German 0.067 0.033 0.068
(0.048) (0.038) (0.241)
Region Switch 0.090** 0.048 *** 0.427 ***
(0.040) (0.016) (0.158)
First Stage: R. Grad Urate (UNI)
R. Mod Urate 14 (UNI) 0.927 *** 0.928 *** 0.927 ***
(0.012) (0.013) (0.012)
Multivariate F 5,900.11 4.976.10 5,900.11
First Stage: R. Grad Urate (TE)
R. Mod Urate 14 (TE) 0.968 *** 0.969 *** 0.968 ***
(0.007) (0.006) (0.007)
Multivariate F 20.830.4 23,402.6 20,830.4
First Stage: R. Grad Urate (APR)
R. Mod Urate 14 (APR) 0.964 *** 0.963 *** 0.964 ***
(0.003) (0.004) (0.003)
Multivariate F 99.690.5 50.508.7 99.690.5
N 12.993 13,314 12,993
[R.sup.2] 0.063 0.329 0.133
Second Stage (4) (5)
Distance Pr(Work in
Median (IND) the OCC Was
Trained For)
R. Grad Urate (UNI) 0.091 *** 0.001
(0.026) (0.005)
R. Grad Urate (TE) 0.058 * 0.003
(0.034) (0.006)
R.Grad Urate (APR) 0.050 ** -0.008 *
(0.021) (0.004)
University 2.351 *** -0.019
(0.411) (0.076)
Apprentice -0.331 -0.050
(0.448) (0.069)
Age 0.491 *** -0.002
(0.063) (0.011)
Age (2) -0.006 *** -0.000
(0.001) (0.000)
Married -0.294 ** -0.026
(0.119) (0.032)
Male 0.146 -0.029
(0.108) (0.021)
German 0.135 0.092 ***
(0.276) (0.028)
Region Switch 0.365 *** 0.032
(0.067) (0.030)
First Stage: R. Grad Urate (UNI)
R. Mod Urate 14 (UNI) 0.928 *** 0.932 ***
(0.013) (0.012)
Multivariate F 4,976.10 5.939.14
First Stage: R. Grad Urate (TE)
R. Mod Urate 14 (TE) 0.969 *** 0.969 ***
(0.006) (0.006)
Multivariate F 23,402.6 24.723.4
First Stage: R. Grad Urate (APR)
R. Mod Urate 14 (APR) 0.963 *** 0.960 ***
(0.004) (0.005)
Multivariate F 50,508.7 34.361.0
N 13,314 12,529
[R.sup.2] 0.454 0.030
Notes: Regional unemployment rates exclude self-employed and are
specific to an individual's highest achieved education level.
Education levels: UNI-university. APR-apprenticeship. and
TE-technical schooling (omitted group), such as medical or teaching
or other vocational schooling. Estimates include dummies for region
of graduation. Region Switch is a dummy to indicate those who
reside in a different region relative to graduation date. Standard
errors in parentheses clustered on region of graduation. Estimates
weighted with enumeration weights. R. Grad Urate variables are
instrumented with R14 Mod Urate (UNI I TE I APR) variables, the
unemployment rates specific to each of the three education levels
in the region where an individual resided at age 14 at the modal
graduation year for their age cohort following Kahn (2010). Model
is just-identified. Multivariate F is the F-test for joint
significance of instruments from Angrist and Pischke (2009). IND,
industry; OCC. occupation.
*** p < 0.01, ** p < 0.05. * p< 0.1 for
all coefficients and test statistics. Source: GSOEP 1994-2012.
TABLE 6
The Marginal Effect of Labor Market Entry Conditions on
Overeducation by Grouped Years Since Graduation
(1) (2) (3)
Years Since Pr(OE) Pr(OE) Distance
Graduation Median OCC Median IND Median OCC
All schooling types
0 0.010 ** 0.003 0.012
(0.004) (0.006) (0.021)
1-3 0.009 ** 0.011 ** 0.022
(0.004) (0.004) (0.021)
4-6 0.014 ** 0.012 ** 0.035
(0.005) (0.005) (0.023)
5-9 0.014 * 0.007 0.054*
(0.008) (0.006) (0.027)
10-12 0.012 0.010 0.063
(0.008) (0.008) (0.039)
13-15 0.004 0.011 0.051
(0.009) (0.008) (0.048)
16-18 0.024 0.008 0.119
(0.016) (0.010) (0.091)
University only
0 0.006 -0.017 ** -0.020
(0.006) (0.006) (0.025)
1-3 0.002 0.007 * -0.022
(0.004) (0.004) (0.016)
4-6 0.010 0.020 *** 0.008
(0.006) (0.003) (0.026)
5-9 0.016* 0.027 *** 0.044
(0.009) (0.005) (0.040)
10-12 0.018* 0.025 *** 0.050
(0.010) (0.007) (0.045)
13-15 0.033* 0.032 ** 0.121*
(0.015) (0.012) (0.063)
16-18 0.030 0.034 * 0.147
(0.022) (0.019) (0.123)
N 13,386 13,710 13,386
(4) (5)
Years Since Distance Pr(Work in the
Graduation Median IND OCC Was
Trained For)
All schooling types
0 0.020 -0.018 *
(0.021) (0.009)
1-3 0.071 *** -0.004
(0.020) (0.005)
4-6 0.071 *** 0.001
(0.019) (0.004)
5-9 0.075 ** -0.002
(0.029) (0.005)
10-12 0.063* -0.003
(0.035) (0.006)
13-15 0.031 0.0004
(0.058) (0.010)
16-18 0.063 -0.012
(0.067) (0.021)
University only
0 -0.064 * -0.025 ***
(0.033) (0.006)
1-3 0.033 -0.001
(0.028) (0.006)
4-6 0.076 ** 0.009
(0.026) (0.005)
5-9 0.118 *** 0.012 *
(0.032) (0.006)
10-12 0.105 ** 0.014 **
(0.045) (0.006)
13-15 0.194 ** 0.009
(0.070) (0.014)
16-18 0.271 * -0.011
(0.132) (0.030)
N 13,710 12,733
Notes: Marginal effects from OLS regressions including dummies for
grouped years since graduation, the regional unemployment rate at
graduation and their interactions. All schooling types from
regressions with pooled unemployment rates from all post-secondary
graduates. University only marginal effects calculated for
university graduates from regressions with unemployment rates split
by education type. Regional unemployment rates exclude
self-employed. Other control variables include education levels:
UNI-university, TE-technical schooling such as medical or teaching
or other vocational schooling. APR-apprenticeship, dummies for
region of graduation, year dummies, dummies for German nationality,
gender and marital status, and age in years and its quadratic.
Standard errors in parentheses clustered on region of graduation.
Estimates weighted with enumeration weights. IND, industry: OCC.
occupation.
* p < 0.01, ** p < 0.05, *** p < 0.1 for all coefficients and test
statistics. Source: GSOEP 1994-2012.
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