Who Goes to College, Military, Prison, or Long-Term Unemployment? Racialized School-to-Labor Market Transitions Among American Men

Abstract

This paper analyzes the selection processes behind post-schooling transitions into college enrollment, military service, long-term unemployment, and incarceration relative to civilian employment, examining to what extent these processes are racialized. Rather than analyzing a complete set of alternatives, previous research typically focuses on a limited set of these alternatives at a time, and rarely accounts for incarceration or long-term unemployment. Using individual-level panel data on the first post-high school transition from the National Longitudinal Survey of Youth 1997 Cohort, results show that white men experience positive transitions (college enrollment and military service) at higher rates and for longer periods than black men, who experience negative transitions (long-term unemployment and incarceration) at higher rates for longer periods than whites. Competing risk Cox regression analyses reveal that blacks’ transitions are polarized, showing that blacks in the upper distributions of standardized test scores and socioeconomic status are more likely to pursue a college education relative to their white counterparts, whereas blacks in the bottom of the standardized test score and socioeconomic status distribution are more likely to experience negative transitions than whites. Unlike prior research finding that military service provided “bridging careers” for racial minorities, black men are no longer more likely to join the military than whites. Instead, blacks now face a much higher risk of incarceration. Implications for intra-generational mobility and changing opportunity structures for racial minorities are discussed.

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Fig. 1

Notes

  1. 1.

    For several reasons, I limit the study to young men. First, males disproportionately experience military service and incarceration, and the sample sizes for women are too small to analyze in these data. Second, there are different types of life events concerning labor market processes, such as motherhood for women, which are beyond the scope of this study, but deserve an analysis in their own right.

  2. 2.

    As MacLean and Elder (2007) document, the effects of military service are different across racial groups, depending on the timing of service in one’s life, exposure to war, and time of service, whether the Vietnam War, peacetime draft, or AVF eras. For instance, it has been found that the employment probability of veterans for entry-level jobs varies depending on the type of job held in the military and whether its skills are transferable to the civilian labor market, rather than on race of veterans (Kleykamp 2009). See also Phillips et al. (1992).

  3. 3.

    Since being unemployed means staying in the labor market while seeking a job, the selection into long-term unemployment is not a choice but a forced consequence of failure to secure a job. Thus, it may be argued that, unlike military service and higher education, long-term unemployment cannot be considered an institution. However, even the military is considered to be a best-available alternative to others, and the least-preferred experience of incarceration is a forced consequence.

  4. 4.

    Propensity is measured in nationally representative sample surveys as the percentage of youths willing to enlist.

  5. 5.

    After three years, in 2000, the census estimate of the sum of male non-Hispanic whites and African-American men between the ages of 15 and 19 is 8,151,427, close to the weighted population of the same groups in the NLSY97 sample, 8,113,858.

  6. 6.

    Data from the 15th round were recently released, but are not included in this analysis.

  7. 7.

    Sampling weights can be retrieved from https://www.nlsinfo.org/weights/nlsy97.

  8. 8.

    I define this in contrast to long-term unemployment, as either being employed or looking for a job while being unemployed for a short period (less than 6 consecutive months).

  9. 9.

    In the analysis, those who enrolled in a two-/four-year college for more than three consecutive months are coded as college enrollment. Considering Weiss and Roksa’s (2016) finding that an increasing number of college students work while attending college, the delineation of full-time students from part-time students is important to clearly define the reference state of being in the civilian labor market.

  10. 10.

    Cameron and Heckman (1993) find that the observed higher earnings of GED holders than of high school dropouts come from their vocational trainings or work experiences after leaving high school earlier rather than from the GED itself. Jepsen et al. (2012) also find that GED has no significant effect on employment and earnings, while it only slightly increases post-secondary education by 4% for male and 8% for female. More importantly, GED is not considered equivalent to high school diplomas in the enlistment application process.

  11. 11.

    It is necessary to note that there are considerable debates on the validity of AFQT scores. For example, Cordero-Guzmán (2001) argues that AFQT is influenced by cultural exposure to the white upper middle class, material resources, etc. On the contrary, Rodgers and Spriggs (1996) insist that AFTQ scores are a racially unbiased predictor of wage. Despite such debates, I use the standardized measure of AFQT rather than school grades since grading standards/expectations likely vary by schools.

  12. 12.

    Because it is technically difficult to run a Cox regression with values from multiple imputation and to adjust for sampling weight at the same time, although one at a time is possible, the mean values of ten times of imputation based on other covariates is imputed and a Cox regression is separately run. Covariates used for multiple imputations are race, educational attainment, AFQT score percentile, parent composition, log-household income, parents’ mean year of education, and regions. Random component is present in multiple imputations but restricted between maximum and minimum of the nonmissing values.

  13. 13.

    Erola et al. (2016) find that the combination of father’s and mother’s education levels better explain children’s occupational outcomes than either one alone, indicating that father’s and mother’s independent effects on children’s mobility are small and differ during the different life-course stages of the children. For respondents with only one parent’s education reported, father’s and mother’s education years are separately imputed using multiple imputation before calculating the mean between the imputed and reported values.

  14. 14.

    Because a competing risk Cox regression estimates the risk of the first transition while treating observations of other transitions as censored, logistic regression of each first transition is used to calculate its predicted probability.

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Acknowledgement

The author is grateful to Sanjiv Gupta, Jennifer Lundquist, Donald Tomaskovic-Devey, Meredith Kleykamp, Amy Bailey, Ken-Hou Lin, and anonymous reviewers from Population Research & Policy Review for their helpful comments on earlier drafts.

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Correspondence to JooHee Han.

Appendix

Appendix

See Tables 4, 5, 6, 7.

Table 4 Cox regression competing risk hazard ratios of college enrollment since age 16 (sampling weights adjusted)
Table 5 Cox regression competing risk hazard ratios of military service since age 16 (sampling weights adjusted)
Table 6 Cox regression competing risk hazard ratios of long-term unemployment since age 16 (sampling weights adjusted)
Table 7 Cox regression competing risk hazard ratios of incarceration since age 16 (sampling weights adjusted)

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Han, J. Who Goes to College, Military, Prison, or Long-Term Unemployment? Racialized School-to-Labor Market Transitions Among American Men. Popul Res Policy Rev 37, 615–640 (2018). https://doi.org/10.1007/s11113-018-9480-6

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Keywords

  • Military service
  • Incarceration
  • Long-term unemployment
  • Racial inequality
  • Life course
  • First life transition