Educational Attainment, College Selectivity, and Job Separation

Transcription

1 Educational Attainment, College Selectivity, and Job Separation Michael Bates March 1, 2016 Abstract It is well known that in the United States there is a lower unemployment rate among those with higher levels of education. This may lead some to conclude that higher educational attainment provides job security as an additional benet. Using evidence from the 1979 National Longitudinal Survey of Youth (NSLY 79), this work shows that ability bias may play a large role in the dierent incidence rates of unemployment across levels of educational attainment. In some specications, when controlling for ability as measured by the Armed Forces Qualifying Test (AFQT) those with more education are more mobile, both through job-to-job and job-to-unemployment transitions. 1 Introduction There is a robust extant literature documenting the return to education on future earnings. 1 There is a smaller but growing literature documenting the return to educational quality on earnings [Dale and Krueger, 2011, Hastings et al., 2013]. However, less is known about the return to educational attainment and quality on job security. As gure 1 shows unemployment rates dier greatly between those with less education and the more educated [of St. Louis, 2016]. This gap in unemployment rates has lead some researchers to conclude that lower risk of unemployment is an additional return to education [Mincer, 1991]. However, as with estimating the return to education in earnings, we must be wary of potential omitted variable bias on the extensive margin of working; an important potential source coming from unobserved ability dierences between the populations who choose higher and lower levels of education. 1 Card [1999] provides an excellent summary of this literature 1

2 Dierences in unemployments rates across levels of education may be driven by either dierences in the incidence rates of job loss or by dierences in duration of unemployment. Riddell and Song [2011] nds that additional education shortens duration of unemployment, whereas the evidence on incidence rates is mixed. Using the 1979 National Longitudinal Survey of Youth (NSLY 79), this work examines the degree to which dierences in incidence of job separation between groups of workers by level and quality of education is driven by ability. I use age adjusted percentile z-scores from the Armed Forced Qualifying Test (AFQT) to proxy for intellectual ability, and distinguish between job-to-job and job-to-unemployment transitions in light of disparate macroeconomic conditions. I nd that controlling for AFQT reverses the correlation of education with the probability of job separation. While unconditionally educational attainment is a strong negative correlate with mobility, conditioning on AFQT makes the relationship more tenuous. College selectivity becomes more positively related to mobility with the inclusion of individuals AFQT scores. This seems to be driven by college graduates from selective universities for job-to-job transitions and job-to-unemployment transitions during an economic recessions. Put more precisely, conditional on an individual's AFQT score, those who graduate from a selective university are 0.4 percentage points more likely to switch jobs and are 1.4 percentage points more likely to move from employment to unemployment. 2 Data and Estimation I draw my evidence from the National Longitudinal Survey of Youth of 1979 (NLSY79), since it contains, which college respondents attended, an often missing measure measure of intellectual ability in the percentile score on the Armed Forces Qualifying Test (AFQT), and the dates of hiring and termination of each job respondents held over their careers. I focus on men to minimize instances of job separation due to child rearing. The remaining sample is composed of observations of 6,403 males over 22 years. Additionally, I exclude 452 men 2

3 for whom the NLSY79 contains no AFQT score. I also drop all individuals for whom there is missing data for more than a quarter of the time periods. Following Pinkston [2009] and arcidiacono2010beyond, I further restrict the analysis to men who obtained at least a high school degree, dropping 889 men who did not complete their secondary education. I end the sample in 2000 to bring my estimates in line with the existing literature and to reduce issues related to non-random attrition, which begins to become more problematic in subsequent years. Since all individuals completed the AFQT in 1979, the AFQT test was administered when participants were between the ages of 14 and 22. Consequently, in order to correct for developmental inuences, I follow the age adjustment of Altonji and Pierret [2001] by subtracting the average percentile score of all those who were the same age when they took the test and dividing by the standard deviation. Table 1 provides the average standardized AFQT percentile scores for workers of each level of education and by college selectivity. I will use this as a measure that captures both quality and quantity of education received. Education is measured as the highest grade completed at the age of 25. College competitiveness is measured according to the Barron's index of degree granting institution or most recent school attended at the age of 25. As mentioned, the NLSY79 records employment status covering this 22 year period of observation. Analysis is restricted to periods in which the participants were working at least 30 hours in a week and to jobs initiated after the survey began. After matching employers across years and NLSY job lines, using employer start and stop dates, this study constructs measures of experience, tenure, and job separations. Experience is measured as the number of quarters an individual reports working up to the current period. Because employers may infer additional information about the worker from experience, following Pinkston [2009], I use potential experience instead of actual experience for all single-step estimation and as an instrument for actual experience in the control function estimation. 3

4 As mentioned in Bratsberg and Terrell [1998], the tenure variable recorded in the NLSY79 is inconsistent in accounting for the start and stop week of jobs. Consequently, I generate tenure using the dierence between the start date and beginning date of each quarter individual reports working for a particular employer, subtracting periods the worker reports being temporarily out of work or on active call in the military. Terminal tenure uses the date the respondent reports leaving the employer. As noted in Light [2005], the young workers in the NLSY79 are highly mobile. Table 3 provides a rough distribution of the terminal tenure length of job spells within the sample measured in quarters. Notice that roughly 55% of all employment relationships end within the rst year and 76% end within the rst two years. From the third year onward the drop-o is less dramatic. Job separations serve as the primary outcome variable, and following Schönberg [2007], I decompose separations into job-to-job and job-to-unemployment transitions. I further separate analysis on moves to unemployment between economic expansions and contractions. This is aimed to distinguish between voluntary and involuntary separations. I also uses respondents' self reported reasons for leaving as an additional check. Separations are taken directly from the quarter in which respondents reported to leave their primary employer. Separations in which the respondent reported working for a new employer during the same or next quarter without reporting to have looked for a job or spent more than a full quarter out of the labor force, I dene as job-to-job moves. Separations during which the respondent reports having looked for a job, I dene as job-to-unemployment transitions. Table 4 provides this breakdown in job separations. Overall, it seems the quarterly termination rate is nearly twelve percent with the majority of moves coming from job-to-unemployment transitions. I estimate the impact of educational attainment on job separations using standard normal maximum likelihood (probit) estimation according to equation 1 below. 2 y it = Φ {τ t + β 1 AF QT i + β 2 Attainment i + β s3 Selectivity i + β s4 X it }, (1) 2 Robustness analysis explores other functional forms. 4

5 where yit stands for the latent probability of a job separation for individual i in time t. Naturally, I only observe whether or not a separation occurs, which I implicitly model as, y it = 1 if yit y it, and y it = 0 otherwise. τ t represents year xed eects and X it represents a vector of covariates including potential experience, tenure with current employer, an indicator for whether current residence is urban, and the state unemployment rate. to capture the total signaling role of education, in some specications I summarize education according to the average AFQT score of those with the same level of education who attended a school of similar selectivity. 3 Results Table 5 shows the evolution of estimated average partial eects (APEs) on the probability a job separation occurs as more regressors are included in the model. In the rst four columns, AFQT is omitted, and in the last four it is included. Comparing the APEs on educational attainment, column 1 shows that without conditioning on college competitiveness or AFQT, an additional year of education is associated with 0.1 percentage point decrease in the incidence rate of job separation. Conditioning on college competitiveness magnies this eect in column 3. However, controlling for AFQT causes the the sign to ip in column 5 and in from column 3 to 7 the magnitude of the APE on educational attainment falls by 55%. Column 2 shows that by itself attending a competitive college has very little connection to the probability of separation. It is surprising that before controlling for individual ability attending a competitive college is associated with a 0.6 percentage point (5%) increase in the probability of a job separation. When controlling for AFQT in column 7 the APE increases by 24%. Naturally, attending a competitive college is highly correlated with educational attainment. Education AFQT measures the average AFQT score of those with the same level 5

6 of education who attend a college of comparable competitiveness. Since the AFQT scores were generated before schooling is completed for this sample, it may capture the aggregate signal of that educational attainment and college competitiveness provide. Column 4 shows that without conditioning on individual AFQT scores, a one standard deviation higher Education AFQT score is associated with 0.3 percentage point decrease in the probability of job separation. Column 8 shows that controlling for individuals' AFQT causes the APE on Education AFQT to ip signs. With each of these measures of education, the inclusion of AFQT in the regression leads to a positive economically and statistically signicant change in the relationship between education and the probability of job loss. This points to the importance of ability bias in considering the return to education on job separation. From columns 4-8, regarding the relationship between individuals' AFQT scores and the probability of separation, I nd that having one standard deviation higher AFQT score is associated with a decrease in the probability of job separation in a given quarter by 0.61 to 0.65 percentage points (p-values less than.001). Given that the base probability of separating is about 12% within sample, this is about a 5% increase in the probability of terminating an employment match. Given that these results come from estimating equations that do not account for dierences in job separations, they are more useful to provide a setting rather than direct evidence. The implications of the role of education in determining worker mobility depend greatly upon the conditions surrounding job separations, specically regarding whether employers or employees initiate the separation. Table 6 separates moves into job-to-job transitions and job-to-unemployment transitions during economic recessions and job-to-unemployment transitions during economic expansions. The thought is that job-to-job moves are most likely initiated by the worker, whereas job-to-unemployment moves during recessions are most likely initiated by the employer. Job-to-unemployment transitions during expansions are less clear as there are many factors that may be at play.they are included primarily for completeness. The rst three columns provide APEs for individual AFQT, educational 6

7 attainment, college competitiveness, race, length of work spell, and time in the labor market separately for each type of move. The last three columns provide estimates of the average partial eects (APEs) of the individual's and the educational references group's AFQT on the probability of job-to-job moves and job-to-unemployment separations both during economic expansions and recessions. Table 6 reveals that the adverse selection on the basis of an individual's AFQT score is driven by job-to-unemployment transitions, with the strongest eects during recessions. I nd that a one standard deviation increase in AFQT conditional on observables is associated with a full percentage point decrease in the probability of experiencing a move from employment to unemployment during a recession. During economic expansions, the same increase in an individual's AFQT is associated with around a 0.6 percentage point drop in the probability of separation. Each of these results are statistically signicant with p-values less than 0.001, though they are not statistically dierent from one another. These results are suggestive of asymmetric learning. As in Gibbons and Katz [1991], the rationale here is that rms layo their least protable workers. During job-to-job moves, though the the asymmetric learning hypothesis predicts similar adverse selection, in keeping with Schönberg [2007], I nd no evidence of negative selection for these types of moves. Neither Gibbons and Katz [1991] nor Schönberg [2007] analyze the selection with regard to reference group, conditional on individual ability.columns 8 and 9 indicate positive selection into job-to-job and job-to-unemployment transitions during recessions. Being a member of a reference group with a one standard deviation higher AFQT is associated with an increase in the probability of a job-to-job transition of 0.4 percentage points or about 8% (p-value<0.001). The point estimate for Educational AFQT is also positive for job-tounemployment transitions during recessions. Taken literally, this positive point estimate indicates that those with a one standard deviation higher reference group AFQT are 1.5 percentage points (13%) more likely to move from employment to unemployment during a recession, conditional on individual ability (p-value<0.001). For job-to-unemployment 7

8 transitions during recessions, Educational AFQT is associated with a 0.5 percentage point decrease in the probability of transitioning (p-value<0.001). Turning to the components of the educational reference groups, column 1 shows that conditional on individual AFQT an additional year of education is associated with a 0.1 percentage point increase in the probability of undergoing a job-to-job transition (p-value<0.01). Column 2 shows that an additional year of education is associated with a 0.4 percentage point increase in the probability of undergoing a or job-to-unemployment move during a recession (p-value<0.01). During an economic expansion, the sign of the APE is reversed, as an additional year of education is associated with 0.2 percentage point decrease in the probability of a job-to-unemployment separation. However, columns 4-6 show that including college competitiveness drive each of these estimated APEs more negative. Though for both job-to-job and job-to-unemployment transitions during recessions, the point estimates remain positive they both lose statistical signicance at any meaningful level. Columns 4-6 show that attending a competitive colleges is associated with increases in the probability of separation, all else equal. These results are strongest job-to-unemployment separations during recessions. Attending a competitive, very competitive, highly competitive, or most competitive undergraduate institution increases the probability of switching jobs or moving to unemployment during an economic expansion by about 0.3 percentage points (p-values<0.05 and 0.1 respectively) and the probability of a job-to-unemployment transition during a recession by 1.2 percentage points (p-value<0.1). Cumulatively, these point estimates indicate education is positively selected in job switches and job-to-unemployment transitions during recessions, where involuntary separations are more compose a larger share of the job separations. Here, I briey summarize the relationship between other covariates and the probability of separation. White and Hispanic respondents are conditionally more likely than Black respondents to experience a separation in each environment with the largest point estimates coming from job-to-unemployment separations during recession. As expected, tenure and 8

9 time in the labor market (potential experience) are both negatively related to the probability of separation. Similarly expected, job-to-job transitions are relatively less common when the state unemployment rates are high just as job-to-unemployment moves are more likely. The division of separations into job-to-job and job-to-unemployment during recessions and during expansions is an attempt to show potential dierences in the relationship between education, ability, and separations that are initiated by workers as opposed to employers. Another way to reach this end may be to ask respondents the reason behind their job separation. I separate the moves into: quits for another job in which respondents reported that they left to take or look for another job; layos in which layos, plant closings, rings, and program ending are aggregated; and quits for other reasons, which include quits for any reason other than obtaining another job. There is reason to believe this analysis suers from a signicant amount of measurement error. First, there may be a stigma associated with involuntarily losing one's job. Secondly, nearly half of the reported job separations are not accompanied by a reason for the separation. For these reasons, the observation based approach is preferred. Table 7 presents the results from using these self reported reasons for leaving. 3 As in columns 4-6 of table 6, educational attainment is largely unrelated to the probability of a quit for another job or layos when controlling for college competitiveness and individual's AFQT. As for college competitiveness, the magnitudes are still positive but signicantly attenuated. College competitiveness is only signicantly related to the probability of a layo. In this framework, Education AFQT is unrelated to the probability of a layo or quit for another job, conditional on college competitiveness and individual AFQT. Individual AFQT is negatively related to the probability of both layos and quits for other reasons. Table 8 is similar to table 6 except that it adopts a linear probability model. Here the results are remarkably similar in magnitude and statistical signicance to the ndings in table 6 for Years of Education, College Competitiveness, Education AFQT, and individual 3 Separations without reason given are not treated as a separation of any kind 9

10 AFQT. Thus, it does not seem that the results are driven by the choice of functional form. 4 Discussion Whether higher rates of job separation into unemployment erode or bolster the return to education hinge upon the mechanisms underlying them. There are, of course, two sides to the market. Consider rst the employee side. It is possible that the more or more selectively educated workers do not face the same damages of unemployment, and consequently maintain a higher reservation wage. Thus, during economic declines, more educated workers may be more willing to become unemployed rather than face a wage reduction. In support of this hypothesis Riddell and Song [2011] nds that the those with more education do faces shorter spells of unemployment and have a higher reentry rate into the working population. Conversely, Brunello [2001] notes that those with more education sacrice more future earnings when experiencing a spell of unemployment, which may point against these higher reservation wages. From the employer side of the market, asymmetric employer information may explain both higher job-to-job and job-to-unemployment mobility among the more educated. In this framework, current employers have better information about the quality of their workers than do any other potential employer. 4 Thus, they place less emphasis on the more public signals of worker quality and place more emphasis on their workers' true quality than do other potential employers. Thus, those with high education conditional on ability are more likely to be bid away by another employer in a job-to-job move. Asymmetric employer information may also explain higher rates of job-to-unemployment transitions for more educated workers. Because potential employers value education as an important signal, they bid up the wages of more educated workers close to those workers' true productivity levels. Thus, conditional on true ability, there is a larger buer between 4 For more detail on asymmetric employer learning, please see Waldman [1984], Greenwald [1986], Schönberg [2007], Pinkston [2009], and Kahn [2013]. 10

11 the wages and productivity levels of less educated workers. During an economic recession, those with more education may become costly to keep whereas those with less education may remain protable, leading to disparate separation rates by education, conditional on ability. More work is necessary to disentangle these possible mechanisms in order to more fully understand the role of education in job security. For the time being, while I will continue to show my undergraduates the dierence in unemployment rates by levels of educational attainment, I will tone down my language regarding the extent to which this relationship is causal. References Joseph G. Altonji and Charles R. Pierret. Employer learning and statistical discrimination. The Quarterly Journal of Economics, 116(1):313350, February ISSN , doi: / URL Bernt Bratsberg and Dek Terrell. Experience, tenure, and wage growth of young black and white men. The Journal of Human Resources, 33(3):658682, July ISSN X. doi: / URL ArticleType: research-article / Full publication date: Summer, 1998 / Copyright  c 1998 The Board of Regents of the University of Wisconsin System. Giorgio Brunello. Unemployment, education and earnings growth David Card. The causal eect of education on earnings. Handbook of labor economics, 3: , Stacy Dale and Alan B Krueger. Estimating the return to college selectivity over the career using administrative earnings data. Technical report, National Bureau of Economic Research, Robert Gibbons and Lawrence Katz. Layos and lemons. Journal of Labor Economics, 9 (4):351380, Bruce C Greenwald. Adverse selection in the labour market. The Review of Economic Studies, pages ,

12 Justine S Hastings, Christopher A Neilson, and Seth D Zimmerman. Are some degrees worth more than others? evidence from college admission cutos in chile. Technical report, National Bureau of Economic Research, Lisa B Kahn. Asymmetric information between employers. American Economic Journal: Applied Economics, 5(4):165205, October ISSN , Audrey Light. Job mobility and wage growth: Evidence from the NLSY79. Monthly Labor Review, 128:33, Jacob Mincer. Education and unemployment. Technical report, National bureau of economic research, Federal Reserve Bank of St. Louis. Unemployment rate by education, URL Joshua C. Pinkston. A model of asymmetric employer learning with testable implications. The Review of Economic Studies, 76(1):367394, doi: /j X x. W Craig Riddell and Xueda Song. The impact of education on unemployment incidence and re-employment success: Evidence from the us labour market. Labour Economics, 18(4): , Uta Schönberg. Testing for asymmetric employer learning. Journal of Labor Economics, 25(4):651691, October ISSN X. URL ArticleType: research-article / Full publication date: October 2007 / Copyright  c 2007 The University of Chicago. Michael Waldman. Job assignments, signalling, and eciency. The RAND Journal of Economics, 15(2):255267, Tables Table 1: AFQT Percentiles (Standardized by Age) by Race and Education Count Mean SD High school Graduate Uncompetitive College Attendee Competitive College Attendee Uncompetitive College Graduate Competitive College Graduate Total

13 Table 2: Work History Mean SD Min Max Experience Potential Experience Tenure All variables are measured in quarters. Table 3: Terminal Tenure Year N Share Mean SD Min Max > Total Figures 13

14 Table 4: Job Separations Table 4: Job Separations Mean SD Recessions Job Separation Job-to-Unemployment Move Job-to-Job Transition Observations 29,550 Expansions Job Separation Job-to-Unemployment Move Job-to-Job Transition Observations 202,686 Full Sample Job Separation Job-to-Unemployment Move Job-to-Job Transition Observations 232,236 Civilian Unemployment Rate Unemployment Rate: High School Graduates, No College, 25 years and over Unemployment Rate: College Graduates: Bachelor's Degree and Higher, 25 years and over Unemployment Rate: Some College or Associate Degree, 25 years and over (Percent) research.stlouisfed.org Figure 1: Unemployment by educational attainment 14

15 Table 5: Changes in the eects of easy to observe characteristics on the probability of moving VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) Years of Education -0.12*** -0.22*** * (0.037) (0.052) (0.044) (0.055) Competitive College *** 0.58*** 0.82*** (0.156) (0.218) (0.174) (0.220) Education AFQT -0.34*** 0.20 (0.130) (0.153) AFQT -0.57*** -0.67*** -0.62*** -0.60*** (0.090) (0.086) (0.091) (0.091) White 0.50*** 0.43** 0.50*** 0.49** 0.80*** 0.82*** 0.81*** 0.80*** (0.192) (0.191) (0.192) (0.192) (0.197) (0.197) (0.197) (0.197) Hispanic 1.01*** 1.00*** 1.03*** 1.00*** 0.76*** 0.74*** 0.77*** 0.75*** (0.211) (0.211) (0.211) (0.211) (0.214) (0.213) (0.214) (0.214) Tenure -0.23*** -0.23*** -0.23*** -0.23*** -0.22*** -0.22*** -0.22*** -0.22*** (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) Potential Experience -0.14*** -0.14*** -0.14*** -0.14*** -0.15*** -0.15*** -0.15*** -0.15*** (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) State Unemployment -0.17*** -0.17*** -0.17*** -0.17*** -0.17*** -0.17*** -0.17*** -0.17*** (0.041) (0.041) (0.041) (0.041) (0.041) (0.041) (0.041) (0.041) Observations 232, , , , , , , ,236 Standard errors are in parentheses. Average Partial Eects are from Normal MLE (Probit). Regressions include full set of year indicators and urbanicity. *** p<0.01, ** p<0.05, * p<

16 Table 6: Education, ability, and the probability of each type of move VARIABLES Job-to-Job Job-to-Unemployment Job-to-Job Job-to-Unemployment Job-to-Job Job-to-Unemployment Full Sample Recession Expansion Full Sample Recession Expansion Full Sample Recession Expansion Years of Education 0.10*** 0.40*** -0.16*** *** (0.029) (0.128) (0.036) (0.037) (0.162) (0.045) Competitive College 0.34** 1.22* 0.34* (0.146) (0.636) (0.181) Education AFQT 0.40*** 1.53*** -0.49*** (0.102) (0.444) (0.126) AFQT *** -0.59*** *** -0.61*** *** -0.60*** (0.061) (0.254) (0.073) (0.062) (0.256) (0.074) (0.062) (0.256) (0.074) White 0.41*** ** 0.42*** ** 0.41*** ** (0.133) (0.571) (0.159) (0.134) (0.571) (0.159) (0.133) (0.571) (0.159) Hispanic *** 0.66*** *** 0.67*** *** 0.65*** (0.148) (0.628) (0.169) (0.148) (0.628) (0.169) (0.148) (0.627) (0.169) Tenure -0.08*** -0.11*** -0.15*** -0.08*** -0.11*** -0.15*** -0.08*** -0.11*** -0.15*** (0.003) (0.023) (0.004) (0.003) (0.023) (0.004) (0.003) (0.023) (0.004) Potential Experience -0.06*** -0.30*** -0.06*** -0.06*** -0.30*** -0.06*** -0.06*** -0.30*** -0.06*** (0.005) (0.023) (0.006) (0.005) (0.023) (0.006) (0.005) (0.023) (0.006) State Unemployment -0.24*** 0.25** *** 0.25** *** 0.25** (0.029) (0.104) (0.034) (0.029) (0.104) (0.034) (0.029) (0.104) (0.034) Observations 232,236 29, , ,236 29, , ,236 29, ,686 Standard errors are in parentheses. Average Partial Eects are from Normal MLE (Probit). Regressions include full set of year indicators and urbanicity. *** p<0.01, ** p<0.05, * p<

17 Table 7: Education, ability, and the probability of each type of move using self reported reason for leaving Quit for Layo Quit for VARIABLES another job other reason Years of Education *** (0.022) (0.028) (0.013) Competitive College * 0.05 (0.089) (0.112) (0.053) Education AFQT *** (0.061) (0.077) (0.037) AFQT *** -0.17*** -0.05** -0.05** (0.037) (0.037) (0.045) (0.046) (0.021) (0.021) White 0.38*** 0.37*** (0.082) (0.082) (0.099) (0.099) (0.045) (0.045) Hispanic 0.18** 0.18** (0.089) (0.089) (0.107) (0.107) (0.047) (0.047) Tenure -0.05*** -0.05*** -0.08*** -0.09*** -0.02*** -0.02*** (0.002) (0.002) (0.003) (0.003) (0.001) (0.001) Potential Experience -0.03*** -0.03*** -0.04*** -0.04*** -0.01*** -0.01*** (0.003) (0.003) (0.004) (0.004) (0.002) (0.002) State Unemployment -0.21*** -0.21*** 0.15*** 0.15*** -0.02** -0.02** (0.016) (0.016) (0.016) (0.016) (0.010) (0.010) Observations 232, , , , , ,236 Standard errors are in parentheses. Average Partial Eects are from Normal MLE (Probit). Regressions include full set of year indicators and urbanicity. *** p<0.01, ** p<0.05, * p<

18 Table 8: Education, ability, and the probability of each type of move using OLS Job-to-Job Job-to-Unemployment Job-to-Unemployment VARIABLES Full Sample Recession Expansion Years of Education *** (0.037) (0.160) (0.043) Competitive College 0.35** 1.28** 0.36** (0.148) (0.630) (0.175) Education AFQT 0.41*** 1.68*** -0.44*** (0.103) (0.441) (0.122) AFQT *** -0.96*** -0.63*** -0.62*** (0.062) (0.062) (0.256) (0.256) (0.073) (0.073) White 0.41*** 0.40*** ** 0.33** (0.133) (0.133) (0.557) (0.557) (0.158) (0.158) Hispanic *** 2.71*** 0.80*** 0.78*** (0.146) (0.146) (0.629) (0.628) (0.173) (0.173) Tenure -0.06*** -0.06*** -0.03* -0.04* -0.10*** -0.10*** (0.003) (0.003) (0.019) (0.019) (0.003) (0.003) Potential Experience -0.06*** -0.06*** -0.31*** -0.31*** -0.07*** -0.07*** (0.005) (0.005) (0.023) (0.023) (0.006) (0.006) State Unemployment -0.26*** -0.26*** 0.29*** 0.29*** (0.030) (0.030) (0.109) (0.109) (0.037) (0.037) Observations 232, , , , , ,236 Standard errors are in parentheses. Average Partial Eects are from OLS regressions. Regressions include full set of year indicators and urbanicity. *** p<0.01, ** p<0.05, * p<