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The Long-Term Labor Market Consequences of Graduating from College in a Bad Economy*
Lisa B. Kahn Yale School of Management First Draft: March, 2003 Current Draft: August 13, 2009

Abstract This paper studies the labor market experiences of white male college graduates as a function of economic conditions at time of college graduation. I use the National

Longitudinal Survey of Youth whose respondents graduated from college between 1979 and 1989. I estimate the e¤ects of both national and state economic conditions at

time of college graduation on labor market outcomes for the …rst two decades of a career. Because timing and location of college graduation could potentially be a¤ected by economic conditions, I also instrument for the college unemployment rate using year of birth (state of residence at an early age for the state analysis). I …nd large,

negative wage e¤ects to graduating in a worse economy which persist for the entire period studied. I also …nd that cohorts who graduate in worse national economies are in lower level occupations, have slightly higher tenure and higher educational attainment,
I am grateful for helpful comments from George Baker, Dan Benjamin, James Heckman, Caroline Hoxby, Larry Katz, Kevin Lang, Fabian Lange, Steve Levitt, Derek Neal, Chris Nosko, Emily Oster, Yona Rubenstein, Hugo Sonnenschein, Mike Waldman and seminar participants at Harvard University, the University of Chicago, Yale University, and the Midwest Economic Association 2003 annual meetings. email:

while labor supply is una¤ected. Taken as a whole, the results suggest that the labor market consequences of graduating from college in a bad economy are large, negative and persistent.




The immediate disadvantage of graduating from college in a poor economy is apparent. Even among employed persons, those who graduate in bad economies may su¤er from underemployment and are more likely to experience job mismatching since they have fewer jobs from which to choose. What is less clear is how these college graduates will fare in the long run relative to their luckier counterparts. The disadvantage might be eliminated if workers can easily shift into jobs and career paths they would have been in, had they graduated with more opportunities. However the disadvantage may persist if the importance of early labor market experience outweighs the later bene…t of a better economy for factors such as promotions and training. If this is the case, we might expect to see long-run di¤erences in labor market outcomes. A poor early economy can also a¤ect educational

attainment. If there are fewer jobs (or worse jobs) available, then the opportunity cost of staying in school is lower. Thus it is reasonable to expect that graduates in a poor economy will return to school at higher rates than graduates in a better economy. This paper studies the long-term consequences of graduating from college in a bad economy. Speci…cally I examine workers who graduate before, during and after the recession of the early 1980’ s. Since college graduates are skilled workers, using them makes it

more feasible to test di¤erent training and human capital investment models. This could potentially result in more interesting outcomes than using a group with fewer training opportunities (especially given the large scale and scope of the recession I am exploiting). In addition, studying college graduates allows for an analysis of the graduate school decision as a function of economic conditions at the time of college graduation. Prior research has linked schooling choice to decreased labor market opportunities, however, focus has been


primarily on the decision to complete high school or attend college.1 To my knowledge no work has been done on the graduate school decision. I use the National Longitudinal Survey of Youth (NLSY79) to study labor market outcomes and educational attainment for white males who graduated from college between 1979 and 1989. The NLSY79 allows me to follow participants for at least 17 years post

college graduation, and contains a wealth of information on individuals (including an aptitude test score and year-by-year, detailed work and school information). I analyze wages, labor supply, occupation, and educational attainment as a function of economic conditions in the year an individual graduated from college. Both national unemployment rates as

well as state unemployment rates are used. The state regressions include state and year …xed e¤ects so are useful in providing variation that is independent of national trends.2 However, these unemployment rate measures potentially su¤er from an endogeneity problem: students may take into account business cycle conditions when choosing the time and place of college graduation. I thus instrument for the national unemployment rate with

birth year and for the state unemployment rate with birth year and state of residence at age fourteen. I …nd persistent, negative wage e¤ects using both the national and state unemployment rates lasting for almost the entire period studied. Using national rates, both OLS and IV estimates are statistically signi…cant and imply an initial wage loss of 6 to 7% for a 1 percentage point increase in the unemployment rate measure. This e¤ect falls in magnitude by
Gustman and Steinmeier (1981) …nd that higher relative wage o¤ers reduce the probability of school enrollment for high school students and graduates. In addition, Card and Lemieux (2000) …nd a small positive correlation between local unemployment rates and college attendance. 2 National unemployment rates are advantageous since the national labor market is likely the most relevant one for college graduates. However, one might worry that the national unemployment rate e¤ect subsumes other cohort-speci…c factors. Cohort size is of particular importance since cohorts are getting smaller throughout the sample at the same time as the national unemployment rate is falling. Falaris and Peters (1992) …nd that demographic cycles can be important for labor-market outcomes and can a¤ect timing of school exit.


approximately a quarter of a percentage point each year after college graduation. However, even 15 years after college graduation, the wage loss is 2.5% and is still statistically signi…cant. Using state rates, the OLS results are insigni…cant but the IV estimates imply a 9% wage loss which persists, remaining statistically signi…cant 15 years after college graduation. Looking at other labor market outcomes, I …nd that labor supply (weeks supplied per year, and the probability of being employed) is largely una¤ected by economic conditions at the time of college graduation (both national and state). However, I do …nd both a

negative correlation between the national unemployment rate and occupational attainment (measured by a prestige score) and a slight positive correlation between the national rate and tenure. This is suggestive that workers who graduate in bad economies are unable to fully shift into better jobs after the economy picks up. Lastly, years enrolled in school post college and the probability of attaining a graduate degree increase slightly for those who graduate in times of higher national unemployment. This paper adds to previous work in several areas. A small but growing literature looks at the e¤ects of …nishing schooling during recessions and …nds persistence to varying degrees. Oyer (2006a) and (2006b) look at the e¤ects of completing an MBA or an economics Ph.D., respectively, during a recession and …nd persistent, negative e¤ects in both of these niche markets. Oreopoulos, von Wachter and Heisz (2006), the closest to the current paper, study the e¤ects of graduating from college in a recession using Canadian university-employeremployee matched data and …nd strong initial negative e¤ects which remain for up to ten years before dissipating. However, though they exploit an extremely rich data set, Canada has di¤erent institutions making it di¢ cult to determine the relevance of their work to the US labor market. For example, Murphy et al. (1998) and DiNardo and Lemieux (1997) point out that the US and Canada experienced diverging trends in wage inequality during


the 1980’ and 1990’ the period both papers study. The US saw a sharper rise in wage s s; inequality. Given a major driver of rising inequality has been a rise in residual inequality, it is reasonable to expect wage di¤erentials across college graduation cohorts to di¤er across countries, both in magnitude and persistence. This paper is also relevant to the cohort e¤ects literature (see Baker, Gibbs and Holmstrom (1994) and Beaudry and DiNardo (1991)) which looks within …rms and …nds that the average starting wage of a cohort or national unemployment rate when a cohort enters is negatively correlated with wages years later.3 Lastly, the current paper is applicable

to the literature on youth unemployment, which seeks to disentangle the e¤ects of state dependence (early unemployment) on adult outcomes from individual heterogeneity. Neumark (2002) studies this in the NLSY79, instrumenting for early job attachment with local labor market conditions at time of entry, and …nds positive e¤ects of early job stability on adult wages.4 I …nd that young workers su¤er persistent, negative wage e¤ects when

experiencing turmoil upon entering the labor market. This suggests that state dependence is important, supporting the previous literature. This paper contributes new results on the long-term e¤ects of cohort-level market shocks. It is the only paper, to my knowledge, that looks at this e¤ect for college graduates, an important share of the labor market, in the United States. I isolate a signi…cant shock,

the 1980’ recession, as well as cross-sectional state variation, and …nd that luck truly does s
3 However, Beaudry and DiNardo (1991) …nd that when they control for the lowest unemployment rate since the individual started the job, the initial unemployment rate becomes insigni…cant. This is not the case in my data. That is, when I control for both the national unemployment rate at college graduation and the minimum unemployment rate since college graduation, the coe¢ cient on the college unemployment rate is still negative and signi…cant while the coe¢ cient on the minimum rate is insigni…cant. Because Beaudry and DiNardo are interested in testing implicit contract models, they do not look at the wage e¤ect for workers who move …rms. My analysis allows workers to move across …rms which might be driving the di¤erence. 4 Unlike Neumark (2002), the previous literature in this area (e.g., Ellwood (1982) and Gardecki and Neumark (1998)) does not make a strong attempt to control for the endogeneity of early job attachment and typically …nds that the e¤ects do not last into adulthood.


matter for these workers. The remainder of the paper is structured as follows. Section 2 reviews existing theories that can explain long lasting e¤ects from a poor early labor market experience. Section

3 provides a brief description of data and methods, more of which can be found in the appendix. Section 4 presents results for wages, educational attainment, occupation and

labor supply. Section 4 also includes two robustness checks, one addresses whether there is di¤erential selection into college across cohorts and the other comparing these …ndings to an analysis of the 1990’ recession using the March CPS. Section 5 discusses the results in s relation to the theories outlined in section 2 and concludes.



Di¤erent theories lead to di¤erent expectations about the long run e¤ects of a poor early experience in the labor market. If a person experiences initial unemployment or job mismatching and is able to switch to the "correct" job when the economy picks up, he or she will have lost only a year or two of accumulated labor market experience. This loss can potentially be overcome quite quickly if we assume diminishing marginal returns to experience. Search theory provides a possible explanation for this scenario.5 It suggests that job shopping is bene…cial to future wage growth. If job changes are common and bene…cial then it is possible that an exogenous impediment to the job matching process (such as graduating from college in a bad economy) can easily be overcome. In fact, Topel and Ward (1992) …nd that 66% of lifetime wage growth occurs in the …rst ten years of a career. They largely attribute this to the fact that a similar proportion of lifetime job changes occurs in
There are, of course, other scenarios which predict only short-term e¤ects. For example, in a spotmarket economy there should be no lasting e¤ects from entering the market in a recession, as long as no productivity disparities arise.


the same period. Alternatively, if workers who graduate in bad economies develop disparities in human capital accumulation then they will be less productive than their luckier counterparts, even years after graduation, and we will see long-term e¤ects. The disparity could arise through general human capital investment or some kind of speci…c investment.6 Consider a matching model of the labor market (a la Jovanovic (1979a)). If a college graduate enters the labor force in a thin market then the job matching process could take longer because there are fewer options available. These individuals should have lower average wages controlling

for experience (relative to graduates who entered in a thick market and may have found matches more quickly) because they have spent more time in bad matches (i.e., where they are less productive).7 In addition, they would have spent time investing in the wrong

types of human capital either through …rm (Jovanovic (1979a)), career (Neal (1999)), or task-speci…c human capital – since workers who enter …rms in downturns may initially be placed in lower-level jobs with less important tasks (Gibbons and Waldman 2003). Studies showing that early training has positive e¤ects on future wages (e.g., Gardecki and Neumark (1997)) support this theory.8
Becker (1967) emphasizes the importance of early investment because the individual can reap the bene…ts of investment over a longer period of time. Workers who graduate in bad economies will have no investment if they are initially unemployed, or might have the wrong kind of investment if they su¤er job mismatching or are forced to take a lower level job. They will thus lag far behind their luckier counterparts who were probably investing heavily in the …rst few years. In addition, when workers do shift into the "correct" jobs it may no longer be worthwhile to train them since they are older and future bene…ts are lower. 7 Evidence is mixed on whether matches are better or worse when workers enter …rms in recessions. Bowlus (1995) …nds employment relationships are shorter when workers enter in recessions, implying worse matches. However, Kahn (2008) …nds that …rms that hire in recessions have unconditionally higher turnover and, controlling for this, matches are actually longer lasting when workers enter in recessions. She also …nds that these …rms tend to be lower paying, on average. This is consistent with both the wage and tenure results in the current paper. 8 Devereux (2002) presents a stigma model to explain cohort e¤ects. If information is imperfect and employers take a worker’ current wage as a signal of ability then exogenously being forced to take a lower s wage (due to business cycle shocks) could have lasting e¤ects. He shows this is true using the state unemployment rate as an exogenous source of variation in starting wages. This model does not apply to the current paper because the business-cycle shocks should be visible to employers. Thus the signalling equilibrium should shift: During negative business-cycle shocks, being unemployed or earning a lower wage should be less of a negative signal.


Thus theory is ambiguous about how long-lasting the e¤ects of graduating in a bad economy will be. If disparities in human capital (both general and various types of speci…c) are important then the e¤ects could be quite persistent. However if human capital is less important and job shopping is common then we will not see long-lasting e¤ects. It is

necessary to take this question to the data to gain more insight about the experience of these college graduates.


Data and Methods

The data set used in this paper is the National Longitudinal Survey of Youth (NLSY79).9 In 1979, 12,686 youths between the ages of 14 and 22 were interviewed and followed annually until 1994 and biennially thereafter. The most recent data available is from the 2006

survey. In this paper, the sample is restricted to the cross-section white male sample because their labor supply decisions are least sensitive to external factors such as childbearing or discrimination. Starting from a sample of 2,236 individuals, I restrict attention to the 631 of these with at least a college degree. Of the 596 of these where year of college graduation can be determined, I focus on the 529 people who graduated from college between 1979 and 1989 to avoid selection issues of those who graduated before or after, a rare group.10 Lastly, I drop 16 individuals who do not have an AFQT score, resulting in a panel of 513 individuals with labor force outcomes for a minimum of 17 years post-college graduation. Table 1 shows panel sample sizes by college graduation year. Appendix table A1 has more details about the data construction but I brie‡ describe y
The NLSY79 survey is sponsored and directed by the U.S. Bureau of Labor Statistics and conducted by the Center for Human Resources at The Ohio State University. Interviews are conducted by the National Opinion Research Center at the University of Chicago (BLS 2008a). 10 Restricting the sample by age at time of college degree to a resonable window (e.g., 21-25) yields very similar results.


the key dependent variables here.

The wage is an NLSY79 measure of hourly rate of

pay at main job and has been in‡ ation adjusted to 2000 dollars using the Consumer Price Index. I drop observations where the worker was enrolled in school in that year and Employment

drop wage values that are less than $1 or greater than $1000 per hour.

is restricted to non-enrolled persons while all other dependent variables are restricted to observations with a wage.11 Occupation is measured by a prestige score taken from the This score is a measure ranging from approximately 0

Duncan Socioeconomic Index.12

to 100 utilizing survey responses to questions on prestige of occupations as well as the average income and education requirements of the occupations.13 shows summary statistics for the sample. As an indicator of the economy in the year a worker graduated from college, I use both an annual average of national monthly unemployment rates and the state unemployment rate (hereafter collectively referred to as the college unemployment rates and individually as the national rate and the state rate, respectively). Values and means for each cohort are shown in table 1. There was substantial variation in the national unemployment rate from 1979-1989, the time period in which the sample graduated from college, making this a useful measure for my purposes. However there are only 11 cohorts of college graduates which raises the possibility of other explanations for my results. For example, di¤erences in outcomes could be driven by changes in cohort size over the sample period (Falaris and Peters (1992)), extensive deregulation that was occurring during the 1980’ (Card (1997)), s or changes in the wage structure (rising wage inequality) throughout the 1980’ (Katz and s
No comparable measure of employment is available in 2000-2004 so these years are excluded from the employment analysis. 12 Since occupation information is not comparable for 2002 onwards, these years are excluded from the occupation analysis. 13 See Duncan (1961) for more information.

Appendix table A2


Autor (1999)). An alternative method to gain more variation within the same sample is to look at the state rates. I can determine the state of college graduation and contemporaneous state This provides

of residence using the NLSY79 restricted-access geocodes (BLS 2008b).14

potentially …fty-one di¤erent data points (…fty states and Washington, DC) within each of 11 years.15 State unemployment rates, taken from the BLS, are measured in the state All regressions

in which an individual resided in the year he graduated from college.

using state rates include state and year …xed e¤ects, providing substantial variation that is independent of the national rates.16 In addition, when summary statistics are reported for the state rate groups, they will always have been adjusted for state and year …xed e¤ects. It is worth noting that while the state rates are useful in providing more variation than the national rates, they may not yield as large an e¤ect. Previous literature (e.g., Wozniak

(2006)) …nds that highly educated workers may be less sensitive to local labor markets since they can smooth shocks through migration. To gain a general sense of the unemployment rate e¤ects on future labor market outcomes, the state and national rates are categorized into three groups: high, medium and low unemployment rates. The breakdowns are chosen so that each group contains roughly a third of the sample and will be used throughout the paper. The national rate groupings (shown in table 1) are as follows: high includes 1981-1983, medium includes 1980, 1984 and 1985, and low includes 1979 and 1986-1989.

The ranges for the low, medium and high

When the state of college graduation is missing, I use the state from the nearest previous observation. This is done to maintain sample size, though results are not sensitive to the exclusion of these observations. 15 In practice, these data contain 239 state-year graduation cohorts. Appendix table A3 shows the sample distribution of year and state of college graduation. 16 With only a small number of observations in some states, it is unlikely that I have the power to identify all the state …xed e¤ects. These states would not be driving the analysis since state …xed e¤ects absorb almost all the variation in college unemployment rates. However, results are not sensitive to the exclusion of states with fewer than 5 graduates.


state rate groups are 2.9-6.4, 6.5-8.3, and 8.4-15.6, respectively. Table A2 shows summary statistics by both state and national rate groups. The largest problem with these data is a decreased sample size as potential experience increases. There are two reasons for this, in addition to general attrition problems. First, the most recent cohort graduated from college in 1989, giving only a maximum of 17 years of post-college observations. One cohort of college graduates drops out each year, as potential experience increases from 17 to 27. Second, the NLSY79 became a biennial survey after 1994 leaving holes in the odd years starting in 1995. I therefore restrict labor market

outcomes to the …rst 17 years after college graduation, since all cohorts can be observed for this length of time.17 Appendix table A4 shows the number of valid wage observations by experience year and college graduation year. It is worth noting that consistent sample sizes exist across cohorts for most of the experience years.18 For an individual, i, in year, t, I estimate equation 1, a standard Mincer earnings function augmented with college unemployment rate variables. The dependent variables, described above, are log wage, weeks worked per year, weeks tenure at current job, occupation prestige score, and a dummy for being employed.19

dep varit =



1 collegei


2 college 1 Expit

Expit + AF QTi +
2 2 Expit


+ 0 Yt + Stateue + it

+ uit

Including later experience years for older cohorts would have the bene…t of bringing these cohorts into the more recent labor market where the younger cohorts are observed. Results are similar when later years are included, but I believe it is more conservative to censor the data to a consistent window of observation post-college. 18 In addition, all regressions have been estimated with a balanced panel (only including individuals with observations where they could potentially have been observed in each of the …rst 17 years) with no substantial di¤erence in the results. 19 Regressions have also been estimated with hours worked per week and being in a professional or technical occupation as dependent variables. Results are very similar to weeks worked per year and occupation prestige score, respectively, and are thus not reported.


AF QT is the age-adjusted AFQT score;20 Exp is the number of years since college graduation (hereafter potential experience)21 ; Exp2 is its square; college is the college unemployment rate. Y is a a vector of contemporaneous year indicators and stateue is the state unemployment rate in individual i’ state of residence in year t, when the dependent variable s was measured. These variables ensure that I do not spuriously attribute the e¤ects of a subsequent economic shock to the college unemployment rate.22 As noted above, the state rate regressions also include year of college graduation and state of college graduation …xed e¤ects. The relevant explanatory variables are college and college Exp, the interaction of the college unemployment rate with potential experience.

provides the initial e¤ect of

the unemployment rate on a labor market outcome. By interacting the unemployment rate with potential experience,

shows how the e¤ect changes over time.23 The error term, u,

is clustered by year of college graduation in the national rate regressions and by state-year in the state rate regressions.24 As mentioned above, the timing and location of college graduation might be endogenous with respect to current labor market conditions. To correct for these endogeneity problems, I instrument for the college unemployment rate with indicators of exogenous timing (and location in the state case) of college graduation. Since 22 is the modal graduation age,

20 The Armed Forces Qualifying Test score (AFQT) is a measure of ability. In 1980, the US Departments of Defense and Military Services asked the NLSY to administer the test to its respondents so they could have a nationally representative sample to use in renorming the test. The measure used in this paper is standardized by subtracting the age-speci…c mean and dividing by the age-speci…c standard deviation. 21 Actual labor market experience could be a¤ected by the college unemployment rate, thus the results are measured using potential experience. 22 In cases of missing state of residence, I impute using the state of residence in the previous year so as not to lose sample size, though results are similar when actual state is used. 23 Here I have assumed that potential experience interacts with the college unemployment rate linearly. The results do not change substantially when I estimate nonlinear speci…cations, both including a quadratic interaction with potential experience and using dummy variables for each year of potential experience (or group of years) and interacting these dummies with the college unemployment rate. The linear interaction is chosen because it is the most parsimonious. 24 In each case clustering is done at the level of variation that is identifying the college unemployment rate e¤ect. It might also be desirable to cluster by individual, since there could be correlation across observations on the same person. Results are similar when the errors are clustered in this way. I present results clustered by year or state-year because it is a higher level of aggregation and is thus a more conservative speci…cation.


I instrument for the national rate using the unemployment rate in the year an individual turned 22 and the state rate using the age 22 unemployment rate in the state if residence at age 14 (hereafter the national proxy and state proxy, respectively). While a college

graduate arguably has control over where he or she resides, it is unlikely that a 14 year old does.25 In both the …rst and second stages of the regressions in the state analysis, I

control for state at age 14 and birth-year …xed e¤ects, instead of state and year of college graduation …xed e¤ects, so that the state proxy can be properly adjusted. A further endogeneity problem is potential experience. If the date of college graduation is endogenous then so is time since graduation. I therefore instrument for the quadratic in potential experience with a quadratic in age (or more speci…cally, years since age 22). In addition, I instrument for the interaction of the college unemployment rate and experience by interacting the national or state proxy with age.26 Note, this means that age is excluded from the second stage equation. There are many instances in which age should be important in an earnings (or other labor market outcome) regression. However, in this case, the

exclusion restriction should be valid. I have restricted the sample so that everyone is fairly close in age when graduating from college. It is unlikely, in this sample of white male

college graduates, that graduating a year or two older would have a signi…cant e¤ect on wages once experience and contemporaneous year e¤ects are controlled for.27 I predict that correcting for these endogeneity problems should yield e¤ects that are
For the 10 cases where state of residence at age 14 is missing, I instead use state of residence in 1979 (the earliest opportunity to observe location). All state regressions include a dummy variable indicating whether this person has an imputed age 14 state. They are included to increase sample sizes but results are not sensitive to their exclusion. 26 It might have been desirable to use birth year as an instrument, rather than the age 22 unemployment rate, because it would have allowed for more ‡ exibility in predicting timing of college graduation. Results are similar with this approach, but it becomes an extremely cumbersome equation to estimate in the state case, especially considering the instruments (year of birth dummies and state at age 14 dummies) need to be interacted with age in the …rst stage. 27 A more important exclusion restriction in the national regression is that I cannot control for cohort e¤ects. There could be other cohort-speci…c factors (such as cohort size) driving my results. This will be addressed in more detail below.


larger in magnitude than the OLS estimates for two reasons.

First, it is possible that

endogenous timing or migration could arbitrage away the negative e¤ects of graduating from college in a bad economy. Identifying o¤ of people who did not exhibit this type

of optimization should increase the magnitude of the college unemployment rate e¤ect. Second, as with all survey data, there could be measurement error in the variables indicating time and place of college graduation. Instrumenting should reduce measurement error It is reasonable to expect that these

leading to e¤ects that are larger in magnitude.

e¤ects will be larger in the state regressions since youths arguably have more choice over college location than timing of completion and there is plausibly more measurement error in location than year. Appendix table A5 summarizes the …rst-stage regression for each college unemployment rate measure. As can be seen, the age 22 unemployment rates are excellent predictors of the college unemployment rate; the F-statistics for the instruments are quite high.28 As

the table indicates, standard errors are clustered by birth cohort or state 14-birth cohort, since that is the level of variation I am exploiting. Standard errors in the second stage (all results labelled IV) are clustered in the same way.



Table 2 shows means of selected variables in the …rst full year after college graduation by unemployment rate group for both national and state rates. Clustered standard errors are in parentheses. Statistical signi…cance between the high and low groups and the medium
A point of concern is that in the national case, age is predictive of the college unemployment rate. This is because the higher unemployment rates occurred earlier in the sample period. College graduates from these years, having been born earlier, are more likely to be observed at older ages for two reasons. At a given age, they are probably less likely to su¤er from attrition, and fewer of their experience years are missing due to the NLSY changing to a biennial survey. This is another reason why have state-level variation is useful. Also, as noted above, the results are robust to using a balanced panel.


and low groups is indicated in the high and medium columns, respectively, while statistical signi…cance between high and medium is indicated in the far-right columns. Looking …rst at the national rate groups, it is clear that in the …rst year after college graduation workers in the high and medium groups earn substantially less than those in the low unemployment rate group. The high group earns 0.35 log points less than the low group while the medium group earns 0.2 log points less and each e¤ect is statistically signi…cant at the 1% level. The probability of being employed does not statistically di¤er across groups, but weeks supplied di¤ers signi…cantly across all comparisons. For example, the high group works almost a

month less in the …rst year out of school (conditional on not being enrolled in a graduate program). This suggests that workers are able to …nd jobs but those graduating in worse economies perhaps take longer. Both the high and medium groups are approximately twice as likely to be enrolled in school, relative to the low group one year after graduating from college (20% are enrolled in the high and medium groups, relative to 11% in the low group). The high group also su¤ers from lower occupational attainment. Finally, small tenure

di¤erences (approximately equal in size to the weeks-worked di¤erences) exist but are not statistically signi…cant. There are no outcomes with statistically signi…cant comparisons across state unemployment rate groups. Wage exhibits somewhat sizeable point-estimate di¤erences, though not signi…cant; the high and medium groups each earn 0.10 log points less than the low group.29 Table 2 su¤ers from a potential selection bias in that all of the wage and labor supply variables are restricted to individuals not enrolled in school. Since we saw that those who graduated in the medium and high national groups were more likely to be enrolled in school
The mean unemployment rate in the high state group is approximately 10 and the mean in the low state group is 5, implying a wage loss elasticity of -0.1. This elasticity is exactly in line with the wage curve literature (see Blanch‡ ower and Oswald (1994), e.g.).


one year after college graduation, it is worth examining whether the enrollment di¤erences lead to disparities in educational attainment. Table 3 reports the impact of unemployment rate group category on the probability of attaining a further degree and the number of years enrolled in school for both national and state rates.30 Regressions control for age-adjusted AFQT score since ability is an important determinant of educational attainment. The

analysis using national unemployment rates does yield signi…cant di¤erences in educational attainment. The high group is 7 percentage points more likely to attain a further degree and has on average a third of a year more schooling, both relative to the low group. Both di¤erences are statistically signi…cant at the 1% level and are important in magnitude (the base rate of attaining a further degree is 25% and the average number of years enrolled postcollege is 1.5). The point-estimates for the medium group, relative to the low, are positive and actually larger in magnitude than those for the high group but are not statistically signi…cant.31 The second set of columns in table 3 show that the state unemployment rate at time of college graduation is not signi…cantly correlated with educational attainment, though the estimates are quite noisy. Perhaps local labor market shocks are not large

enough to in‡ uence the graduate school decision.



Above we saw the negative wage e¤ects of graduating in a bad economy in the short run. Table 4 address the long-run wage e¤ects.

Columns 1 and 2 summarize wage regression

Both variables only include education obtained within 17 years of college graduation since that is the maximum length of time the youngest cohort can be followed. 31 A possible explanation for why we see larger e¤ects in the medium group is that the U.S. saw increasing returns to skills in the 1980’ which led to increased educational attainment in the population (see for s example DeLong, Goldin, and Katz (2003)). Roughly speaking, the high group graduated at the beginning of the sample and the low group graduated at the end. Thus due to secular trends, graduates in the low group may be getting more education than they otherwise would have while those in the high group may be getting less. Unfortunately the data are not rich enough to identify this time trend, so it is not possible to ascertain the importance of this hypothesis in explaining the educational attainment …ndings.


results using national rates and columns 3 and 4 summarize the state rate results. Panel A shows both OLS and IV regression coe¢ cients for the college unemployment rate and its interaction with potential experience. Panel B shows these values …tted for 1, 5, 10

and 15 years since college graduation. Looking …rst at the national rate e¤ect, I …nd that the college unemployment rate does indeed have a signi…cant negative impact on log wages. The initial e¤ect is a wage loss of 0.062 log points (in response to a 1 percentage point increase in the national rate), statistically signi…cant at the 5% level. Each year this e¤ect dissipates by 0.002 log points. Thus, some catch up occurs and, as panel B indicates, the …tted college unemployment rate e¤ect is small by 15 years out (0.026), and only signi…cant at the 10% level. However, it is large in magnitude and statistically signi…cant at the 1% level through the tenth year after college graduation. The IV estimates are similar to the OLS but larger in magnitude; the initial e¤ect is a 0.07 wage loss. This is consistent with the above hypothesis that the OLS estimates are biased downward in magnitude.32 Columns 3 and 4 in table 4 show estimates from the state regressions. These regressions are particularly stringent because the state and year …xed e¤ects absorb most of the stateyear variation. In fact the OLS results are smaller in magnitude (log wage falls by 0.024 in response to a 1 percentage point increase in the state unemployment rate) and insigni…cant. However the IV estimates are larger in magnitude and the e¤ect is persistent. The initial e¤ect is a wage loss of 0.091 log points and panel B indicates that the e¤ect remains similar in magnitude and statistically signi…cant at the 5% level for the full 15 years after college graduation.33 These state rate results provide support for the national wage results.
In my sample, lower national rates are associated with smaller cohorts, on average. Larger cohorts may fare worse in the labor market because of excess labor supply or "crowding out" e¤ects. Thus one might worry that cohort size is driving the persistent wage e¤ect. However, I have also estimated wage regressions which directly control for birth-cohort size and …nd no substantial change in the coe¢ cients or statistical signi…cance. 33 We might be surprised by the magnitude of the IV state rate results. One explanation is the IV helps


Despite the initial expectation that state labor markets should have only a small e¤ect on educated workers (and this is indeed the pattern for the other outcomes analyzed below), we still see a signi…cant wage loss in the IV. Recall from table 3 that the medium and high national unemployment rate groups had slightly higher educational attainment. Increased education might be one way for workers to mitigate the e¤ects of a poor early experience. We might expect the college unemployment rate e¤ect to be larger in magnitude for those who did not go on to graduate school. Wage equations similar to those reported in table 4 were estimated on the restricted sample of workers with exactly a bachelor’ degree. The college unemployment rate e¤ects are similar s in magnitude, signi…cance and persistence and are thus not reported here.34 It is useful to calibrate these results to the observed unemployment rates in the sample. The national rates range from 5.3% to 9.7% for this sample while the state rates range from 2.9 to 15.6. The average wage loss in response to a 1 percentage point increase in the

national unemployment rate for the …rst 17 years after college graduation is 4.4%, while the average for the state rates is 2.0% (using OLS estimates to be conservative).35 Thus the full e¤ects of the national unemployment rate range from a wage loss of 1.3% (for the second lowest national rate) to 20% (for the highest national rate) per year (relative to the luckiest group who graduated in 1989 with an unemployment rate of 5.3%). The OLS e¤ects for reduce measurement error in the college unemployment rate as discussed above. Another explanation is that by treating the unemployment rate as endogenous, the regression estimates a local average treatment e¤ect. Recall the instrument is the state unemployment rate in the year and state in which an individual should have graduated from college. Thus the estimate is identi…ed o¤ of stayers who did not endogenously alter the time or place of college graduation. We might think that this is a less-able group who would fare less well under poor economic conditions. 34 It is worth noting that even if the wage e¤ect were reduced by educational attainment, there could still be negative e¤ects of graduating in a bad economy. Consider a worker who would have preferred to take a job immediately out of school if more jobs had been available but instead went back to school for a graduate degree. The degree may help mitigate earnings losses but the worker would probably not be brought back to the same lifetime utility level as if he could have chosen to take a better job right away. 35 Average is obtained from converting the coe¢ cient for the college unemployment rate when I do not allow the e¤ect to vary over time to a percent. That is log wages are regressed on the college rate plus all other covariates except the interaction of the college unemployment rate and experience.


the state rate, though insigni…cant, range from a wage loss of 4.7% for the lowest decile to 19% wage loss for the highest decile unemployment rate (both relative to the minimum, 2.9). These calculations represent the average wage loss for each year for more 17 years after college graduation.


Labor Supply and Occupation

Table 5 summarizes regression results for other labor market outcomes. This table reports only OLS estimates since IV estimates yield qualitatively similar results. Turning …rst

to labor supply, I study the probability of being employed (excluding those enrolled in school) and weeks worked per year conditional on earning a wage. The probability of

being employed (shown in columns 1 and 5) is raised by approximately 0.01 in response to a 1 percentage point increase in either the national or state unemployment rate, remains fairly constant as experience accumulates and is signi…cant for the national rate at the 10% level.36 However, this e¤ect is quite small in economic signi…cance, considering the mean in the sample is 0.92. The e¤ects for weeks worked, shown in columns 2 and 6, move around somewhat. In the …rst year after college graduation, the e¤ect is half a week less work and moves to a third a week more work by 15 years out. The positive e¤ect on labor supply could be evidence that workers who graduate in worse economies try to make up some of the wage di¤erence by working more hours. However, the magnitudes are quite small, so one should not draw too much from these results. That labor supply is only slightly a¤ected is perhaps not surprising given the sample I analyze, white males with at least a college degree. This group is highly unlikely to be unemployed or out of the labor force.

Since other demographic groups likely have more

Results are similar when a probit model is estimated instead of this linear probability model.


elastic labor supply, it is possible that the college unemployment rate e¤ect for these groups would manifest itself to a greater extent through labor supply outcomes and that the wage e¤ect would be smaller. This is an interesting empirical question that should be examined in the future.37 Turning next to occupation-related outcomes, I present analyses of weeks of tenure at the current employer and occupation prestige score (which ranges in value from 7 to 82 in this sample).38 Tenure provides an indirect measure of how often each cohort changed employers. First, looking at the state results, I …nd a small negative tenure e¤ect (15 weeks) that dissipates after the …rst 5 years. In contrast, the national rate has no initial e¤ect

on job tenure but its impact becomes positive and statistically signi…cant starting 10 years after college graduation. The e¤ect, which ranges from 1 to almost 15 weeks tenure gain, is modest in size considering the sample mean for tenure 15 years after college is 362 weeks. However, it seems that small di¤erences in job tenure over the …rst ten years of a career accumulate and become important later on. Given that we think job changes are associated with wage growth (Topel and Ward 1992), and those who graduated in worse economies have a slight tendency to stay in their jobs, this might explain some of the wage e¤ect. However, it is important to bear in mind that the tenure e¤ects are small in magnitude. Also, in a previous version of this paper I looked at job changes directly and found very little di¤erence across college graduation cohorts. Columns 4 and 8 show occupational prestige score results. Here there is no e¤ect

using state rates, but results are negative and statistically signi…cant when national rates
See Kondo (2008) for a similar analysis across across race and gender. Hershbein (2009) studies the e¤ects of graduating from high school in a recession for women and …nds persistent, negative e¤ects on labor supply, but not on wages. 38 I have also analyzed the probability of being in a professional or technical occupation. These e¤ects are very similar to the prestige score; results are thus not included here.


are used.

In response to a 1 percentage point increase in the national rate, occupation

prestige score falls by almost 1 point. This e¤ect is modest (the sample average is 50) but statistically signi…cant and remains fairly constant throughout the entire period studied. Thus it seems that workers who graduate from college in bad economies are unable to fully shift into better jobs, at least over the …rst 15 years of their careers.


Robustness Checks

A potential confounding factor when studying college graduates is selection that di¤ers across cohorts. One might worry that the decision to enter college is a¤ected by labor

market conditions at time of high school completion. Since the economy moves cyclically, it is not unreasonable to think that economic conditions today and four years from today are correlated. So, if the economy induces some people to attend college who otherwise would not and these people complete college, college graduation cohorts could be di¤erentially selected. I address this in two ways. First, I look at the probability of completing college as a function of labor market conditions at age 18. Second, I look at the di¤erence in

characteristics between college completers and non-completers to determine whether there is a di¤erential selection across cohorts. Table 6 shows results on the probability of completing college.39 Columns 1 and 2 show results using the national unemployment at age 18 while columns 3 and 4 use the state. For the state results, I use the unemployment rate at age 18 in the state an individual resided
Here I analyze the unconditional probability of completing college for the whole sample of white males. I could instead look at the probability conditional on completing high school and results are similar. Using the entire sample avoids the problem that high school completion could also be endogenous with respect to labor market conditions at a young age.


at age 14 and also control for year and state …xed e¤ects.40 The …rst column in each set reports a basic speci…cation while the second additionally controls for AFQT score. This is important since cohorts di¤er in ability; higher unemployment rates at age 18 are associated with lower test scores. In fact, not taking this into account yields insigni…cant results for both the national and state rates. However, controlling for ability, the unemployment rate at age 18 does have a small, positive e¤ect on the probability of completing college. In

response to a 1 percentage point increase in the national or state unemployment rate at age 18, the probability of completing college increases by 0.008 and 0.02, respectively. These e¤ects are quite small, given 30% of the sample completes college, but are both signi…cant at the 5% level. In the data, economic conditions at time of high school completion are negatively correlated with economic conditions at time of college completion. So, those induced to attend college based on a bad economy at age 18 are more likely to have graduated from college in a better economy. In order to determine what type of bias this may cause, I look at the characteristics of college completers –relative to non-completers –across cohorts. I regress a characteristic on an indicator for whether or not the individual completed college, the unemployment rate at age 18, and the interaction of the two.41 I also control for year of birth and state …xed e¤ects in the state analysis and a time trend in the national analysis. Table 7 reports these regression results for AFQT score and several family background characteristics including age at birth for both parents, years of schooling for both parents and whether someone in the family had a library card at age 14. Panel A reports national results while panel B reports state. The main e¤ect for college degree shows that college
To address the fact that educational attainment is increasing for the population as a whole during the sample period, I control for a linear time trend in the national analysis (since year dummies are perfectly collinear with the unemployment rate at age 18). Results are not sensitive to its exclusion, however. 41 Again results are similar when I restrict the sample to those who have completed high school.


graduates are of course positively selected. For example, column 1 shows that the average college graduate has a higher AFQT score by almost 0.8, signi…cant at the 1% level in both panels. The other characteristics reveal that college graduates come from positively selected families, on average. The unemployment e¤ect is meant to control for di¤erences in cohorts. These might be more important in the national results since there fewer birthyear cohorts but should be small in the state analysis after controlling for state and year …xed e¤ects. The interactions reveal whether college graduates who experienced worse economic conditions at age 18 look di¤erentially selected above and beyond the college graduate main e¤ect. For most characteristics, the e¤ects are small and insigni…cant. There is some evidence for positive selection in that for both national and state the probability of a library card increases slightly (by 1.4 to 2 percentage points, compared to a base probability of 0.75). In addition AFQT score is 0.02 points higher for college graduates whose age 18 state unemployment rate was 1 percentage point higher. Because of the negative correlation between the economy at age 18 and the economy at college completion, the positively-selected college graduates would be graduating in good economies. This would bias me towards …nding a college unemployment rate e¤ect. However, the evidence presented here is comforting in that the di¤erences are quite small in magnitude and few are statistically signi…cant. The state of the economy at age 18 is probably the most relevant for an individual’ s decision to attend college. However, many college entrants never graduate and many Therefore I also analyze observable characteristics Table 8

graduates did not enter at age 18.

based on predicted year of graduation (in addition to predicted entry year).

reports regression results similar to those in table 7, instead using the age 22 unemployment rate and its interaction with college completion. As can be seen, college graduates are


similarly positively selected, but the interaction terms are all negative and insigni…cant – both economically and statistically. I conclude that there is no di¤erential selection in

college completion based on predicted exit date.


What About Other Recessions?

The focus of this paper has been an analysis of the early 1980’ recession. The NLSY79 is s ideal for this study because I can observe exactly when a person graduated from college, the same individuals can be tracked for almost 20 years and there is a wealth of information on labor market experiences and family background. However, in the NLSY79, I am restricted to these 11 cohorts and one might wonder whether the results extend to other recessions. The Current Population Survey (CPS) is a natural place to extend this research. The

Annual Supplement to the March CPS consists of repeated cross-sections with demographic information and labor market experiences in the prior calendar year. In this section, I use the March CPS to see whether the subsequent recession of the early 1990’ had a similar s impact on workers. Table 9 presents results using March CPS data re‡ ecting the calendar years 1987 through 2006 (i.e., survey years 1988-2007), again restricting the sample to white males with at least a bachelor’ degree.42 s Because I cannot observe the exact year in which a worker grad-

uated from college, I employ the reduced form of my instrument and assign everyone the year he turned 22 as the graduation year. I restrict the sample to workers who turned 22 between 1986 and 1996, so that each synthetic cohort can be followed for at least 10 years post-college.43 I estimate log wage regressions44 similar to the speci…cation in equation 1,
42 For most years in the CPS, this means the individual reported completing at least 4 years of college, though starting in 1992, individuals can report completing a bachelor’ degree. s 43 This window was chosen to surround the 1990’ recession, which saw its highest unemployment rate in s 1992. Results are not sensitive to shifting these cuto¤s, on either end, by a few years. 44 Wage is total income in the previous calendar year divided by usual hours worked per week times weeks


controlling for a quadratic in potential experience (de…ned here as years since age 22), contemporaneous year and state …xed e¤ects45 , and the contemporaneous state unemployment rate. Since I cannot observe and individual’ enrollment status, I restrict the sample to s

full-time workers. The …rst column of table 9 reports results on the full sample while the second restricts the sample to a consistent window of 10 years post-"graduation". Panel B again shows the college unemployment rate e¤ect …tted for 1, 5 and 10 years post-"graduation" (but not year 15 as it is really an out-of-sample prediction). The e¤ect on the full sample is a wage loss of almost 3% in response to a 1 percentage point increase in the national unemployment rate at age 22. When …tted one year out of college, this e¤ect is signi…cant at the 1% level. The second column shows even larger e¤ects when the sample is restricted to a narrow window, which would be expected if the e¤ects die o¤ over time. wage loss of 4%. These e¤ects are slightly smaller than the national analysis in the NLSY. Further, panel B reveals that the wage e¤ect disappears after 10 years. One reason for this could be measurement error introduced by the fact that I cannot determine the exact year of college graduation, I cannot follow the same individuals over time and I cannot control for AFQT score. It could also be driven by the fact that the recession in the 1990’ was smaller. s The initial e¤ect is a

The unemployment rate reached as high as 7.5, compared to 9.7 in 1982. Hence we might expect college graduates to be hit less hard. I take this CPS exercise as complementary evidence. It supports a robust …nding that workers who graduate from college in a bad economy experience large wage losses that worked. It is in‡ ation adjusted to 2000 dollars using the CPI. 45 Because I have ample sample sizes, I include state …xed e¤ects, while I did not in the NLSY estimates. Results are not sensitive to their exclusion.


persist for several years post-college graduation.



The results in this paper strongly support the hypothesis that graduating from college in a bad economy has a long-run, negative impact on wages. I also …nd a negative e¤ect on occupational attainment and slight increases in both educational attainment and tenure for those who graduate in worse national economies. Labor supply is essentially una¤ected

using both national and state unemployment rates. Wage loss ranges from 1%-20% each year, relative to the cohorts with the minimum state and national unemployment rates. Recalling that these …gures, which come from OLS estimates, are lower bounds (the IV results are larger in magnitude), this is quite striking. Given these wage …ndings, one might wonder if workers would be better o¤ waiting to enter the labor market until the economy picks up. This would mean forgoing a year or two of earnings, which is possibly a smaller loss than the losses I …nd. It does not In

seem to be the case in the real world, but this can be reconciled with my …ndings.

equilibrium individuals who graduate in worse economies but do their best given their restricted options should not carry a negative signal since employers are informed about business-cycle conditions. If many workers waited to enter the labor market as suggested here, then in the current equilibrium, they would probably be sending a negative signal since they did not even venture into the labor market to try to …nd a job. How this equilibrium became established is an open question. Section 2 outlined several theories that could potentially explain long-run, negative effects of a poor early labor market experience. General human capital di¤erences might

partially explain the wage e¤ect using national rates since I …nd that workers who gradu-


ated in worse economies are in lower-level occupations, on average. However, occupation di¤erences cannot fully account for the wage di¤erences. I have estimated wage equations also controlling for occupation prestige score and its square and I …nd that approximately one sixth of the wage e¤ect is eliminated in the national rate regressions, but a substantial portion remains and the e¤ect is still statistically signi…cant. The matching models which predict wage di¤erences because of less …rm or career-speci…c human capital are also not su¢ cient to explain my …ndings. I …nd only small e¤ects on tenure and I have also looked at career and job changes directly and …nd no signi…cant di¤erences across college graduation cohorts (although it is di¢ cult to be conclusive here since tenure and career-change variables are notoriously noisy). In addition, controlling for tenure in a wage regression

has no e¤ect on the college unemployment rate coe¢ cient. Surprisingly, the wage di¤erentials persist, both within job and within occupation. Thus a more complicated model is probably necessary to explain my …ndings. For example,

Gibbons and Waldman (2003) present a task-speci…c human capital model to explain cohort e¤ects. If a portion of skills developed on the job is only relevant to tasks pertaining to that job then after promotion, some of the worker’ skills will be wasted. s Entering the

…rm in a worse economy and starting at a lower-level job would mean more time spent investing in skills that will go unutilized later when the worker is promoted. Thus Gibbons and Waldman predict wage di¤erences within job level, career and …rm, consistent with my …ndings. However, it would be di¢ cult to establish a particular theory, given the small

sample size in these data. It is worth thinking about how extrapolatable these results are to other cohorts graduating in di¤erent recessions. In all outcomes, we see more signi…cant e¤ects in the national regressions than in the state regressions (with the exception of the instrumented state wage


regressions). As noted above this could be because college graduates are not as sensitive to local labor markets as to the national market. However, I cannot rule out the possibility that some cohort-speci…c factor other than the college unemployment rate is driving the national results to some extent. In addition, the 1982 recession may have been particularly damaging since it was quite large and was followed by another important recession just ten years later. The CPS results show that the early 1990’ recession may have had a smaller s impact on college graduates. Lastly, Oreopoulos et al. (2006), who use Canadian data

to study the e¤ects of state-level recessions on college graduates, …nd wage e¤ects that are similar in magnitude to my state-level OLS results but dissipate within ten years of leaving school. However, as noted above, several labor market institutions in Canada, as well as a more compressed wage distribution in general might ease the catching up process there, making their study less comparable with studies using US data. Also, Oyer (2006a and

2006b), who looks at the e¤ects of national economy on specialized groups (MBA’ and s economics Ph.D.’ …nds long-lasting e¤ects. Given the magnitude of my …ndings in the s) national results, the support of the state-level results and the CPS analysis, it is plausible that the wage e¤ects to graduating from college in a bad economy would be sizeable, at least in the medium-term horizon, for most groups of college graduates. This paper provides evidence that the business cycle e¤ects on recent labor market entrants are signi…cant and persistent. Further study, perhaps focusing on other demo-

graphic groups and observing cohorts for a longer period of time, will continue to illuminate business-cycle e¤ects and help determine what policy measures (if any) are appropriate to correct for these e¤ects.


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[24] Kondo, Ayako (2008). "Di¤erential E¤ects of Graduating during Recessions across Race and Gender." mimeo, Columbia University. [25] Murphy, Kevin M., W. Craig Riddell and Paul M. Romer (1998): "Wages, Skills, and Technology in the United States and Canada," NBER working paper #6638. [26] Neal, Derek (1999), "The Complexity of Job Mobility among Young Men." Journal of Labor Economics, Vol. 17, No. 2 (Apr), 237-261. [27] Neumark, David (2002), "Youth Labor Markets in the United States: Shopping Around Vs. Staying Put." The Review of Economics and Statistics, 84(3) (August): pp. 462482. [28] Oreopoulos, Phil, von Wachter, Till and Andrew Heisz (2006). "The Short- and LongTerm Career E¤ects of Graduating in a Recession: Hysteresis and Heterogeneity in the Market for College Graduates." Mimeo, Columbia University. [29] Oyer, Paul (2006a). "The Making of an Investment Banker: Macroeconomic Shocks, Career Choice and Lifetime Income." NBER Working Paper No. 12059. [30] Oyer, Paul (2006b). "Inital Labor Market Conditions and Long-Term Outcomes for Economists." The Journal of Economic Perspectives, 20(3) (Summer): pp. 143-160. [31] Topel, Robert H. and Michael P. Ward (1992), "Job Mobility and the Careers of Young Men" Quarterly Journal of Economics, 107, May, pp. 441-479. [32] Wozniak, Abigail (2006), "Why are College Graduates More Responsive to Distant Labor Opportunities?," mimeo, Notre Dame.


Table 1: Sample Sizes of College Graduation Cohorts NLSY79 White Males with at least a BA/BS Year College Frequency National National UE State UE 1 Graduation Rate Group Rate2 (Mean) UE Rate 19 5.72 1979 5.8 Low 44 7.04 1980 7.1 Medium 58 7.98 1981 7.6 High 64 10.00 1982 9.7 High 66 10.02 1983 9.6 High 56 7.53 1984 7.5 Medium 60 7.08 7.2 Medium 1985 73 7.13 1986 7.0 Low 43 6.63 1987 6.2 Low 18 5.32 1988 5.5 Low 12 5.35 1989 5.3 Low Total: 513 mean = 7.62 mean = 7.78 1. From: 2. From: Bureau of Labor Statistics, Local Area Unemployment Statistics. Notes: Sample is restricted to the cross-section, white-male sample who graduated from college between 1979 and 1989 and have valid AFQT score and state unemployment rate.


Table 2: Means of Selected Variables 1 Year After College Graduation by UE Rate Group NLSY79 White Males with at least a BA/BS National State1 UE Rate Group2 Log Wage Currently Employed3 Annual Weeks Worked4 Currently Enrolled5 High Medium 2.317 2.44 [0.042]** [0.020]** 0.845 [0.007] 0.852 [0.046] Low 2.647 [0.041] * 0.866 [0.012] 47.840 [0.526] + High 2.755 [0.223] 0.984 [0.101] 53.707 [2.429] Medium 2.785 [0.231] 0.964 [0.101] 51.508 [2.258] Low 2.893 [0.244] 0.988 [0.092] 53.266 [2.165]

44.426 46.286 [0.852]** [0.515]+

0.209 0.190 0.118 0.064 0.075 0.096 [0.018]** [0.063] [0.013] [0.104] [0.097] [0.092] 42.745 45.241 45.345 46.149 45.698 45.848 Occupation Prestige [0.180]** [2.862] [0.663] [5.008] [5.221] [4.923] 56.21 55.17 60.03 28.307 31.979 34.166 Tenure (weeks) [6.687] [8.381] [10.344] [25.871] [28.113] [30.849] Standard errors in brackets, clustered by college graduation year or state and year Statistical significance relative to Low indicated: + significant at 10%; * significant at 5%; ** significant at 1%. Statistical significance between High and Medium indicated in right column. 1. Adjusted for State and Year Fixed 2. National Groups by year: High - 1981, 1982, 1983; Medium - 1980, 1984, 1985; Low - 1979, 1986, 1987, 1988, 1989. State groups by UE Rate Range: High - >8.5, Medium - 6.6-8.5, Low -8.5, Medium - 6.6-8.5, Low -…...

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