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Do earnings by college major affect graduate migration?

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Abstract

College graduates are considerably more mobile than non-graduates, and previous literature suggests that the difference is at least partially attributable to college graduates being more responsive to employment opportunities in other areas. However, there exist considerable differences in migration rates by college major that have gone largely unexplained. This paper uses microdata from the American Community Survey to examine how the migration decisions of young college graduates are affected by earnings in their college major. Results indicate that higher major-specific earnings in an individual’s state of birth reduce out-migration suggesting that college graduates are attracted toward areas that especially reward the specific type of human capital that they possess.

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Notes

  1. This paper follows most of the previous literature and uses the term “college graduates” to refer to persons whose highest completed education is a bachelor’s degree or higher.

  2. For example, Moretti (2004) suggests that the share of the local population with a college degree creates positive human capital externalities by increasing wages for both college graduates and non-graduates in the same area. Similarly, Winters (2013) finds that a more educated local population increases labor force participation and employment probabilities for both college graduates and non-graduates.

  3. This framework often makes several simplifying assumptions. For example, the discussion herein largely ignores complexities related to informational uncertainty, risk aversion, dual-earner households, financing constraints, etc.

  4. Studies empirically examining the effects of expected earnings differentials on migration decisions include Treyz et al. (1993) and Kennan and Walker (2011).

  5. For example, recent studies include Faggian et al. (2006, 2007), Waldorf (2009), Busch and Weigert (2010), Corcoran et al. (2010), Dahl and Sorenson (2010), Scott (2010), Brown and Scott (2012), Haapanen and Tervo (2012), Winters (2012), Böckerman and Haapanen (2013), Di Cintio and Grassi (2013), Faggian et al. (2013), Marinelli (2013), Knapp et al. (2013), Carree and Kronenberg (2014), Liu and Shen (2014), Nifo and Vecchion (2014), Tano (2014), Winters (2014), Abreu et al. (2015), Betz et al. (2015), and Leguizamon and Hammond (2015).

  6. The ACS also asked individuals to report their location one year prior to the survey, which can be used to measure 1-year migration. However, 1-year migration is moderately noisy for many purposes and may be driven by short-run migration decisions. Lifetime migration should depend on long run factors.

  7. In particular, birth-state fixed effects in Eq. (1) control for statewide differences in aggregate earnings, cost of living, and amenities. Of course, there are likely some differences in these across areas within states, but the implicit assumption is that cost of living and amenities are conditionally uncorrelated with major-specific earnings.

  8. The mean residuals are computed by state of residence for workers in each state and college major and then merged to individuals based on their birth state. This measures the earnings of workers currently residing in one’s birth state and not the mean earnings of workers born in one’s birth state. It measures the earnings differential one might expect if they resided in their birth state. Notice also that birth-state fixed effects in Eq. (1) remove statewide differences across birth states in cost of living and amenities. Of course, there may be differences in these across areas within states

  9. Result for the demographic characteristics are reported in “Appendix” Table 6. Results for birth state, year, and college major fixed effects are not reported to conserve space but are available from the author by request.

    Table 3 Effects of major-specific earnings in birth state on birth-state out-migration, ages 22–30
    Full size table

  10. Such a policy would also be costly because of a general inability to distinguish graduates making marginal location decisions from those who are infra-marginal. Thus, only a small percentage of subsidy recipients would alter their location decisions because of the subsidy.

  11. The IV coefficient was \(-\)0.086 for men and \(-\)0.205 for women, possibly suggesting a greater responsiveness for women, but the imprecision of the estimates prevents making inferences.

  12. Another approach considered to address measurement error was to use the broad major categories in order to increase sample sizes for which earnings are measured. Doing so yielded moderately larger OLS coefficients and similar IV coefficients as the results using detailed majors, consistent with expectations. However, the heterogeneity in majors within many broad categories makes this approach less desirable than using detailed majors.

  13. Groups are defined to be mutually exclusive based on primary race and excluding Hispanics from other groups. Also, recall that the sample includes only persons born in the US.

    Table 5 Effects of major-specific earnings on out-migration by race/ethnicity, ages 22–30
    Full size table

  14. This is especially problematic for small majors, which often have zero observations for various flows and even among the “origin” population of some majors in given states. Having large numbers of zeros complicates analyses that take logarithmic transformations of migration flows. Having missing and/or noisy origin populations complicates analysis based on in-migration and net migration rates. Given the difficulties with examining in-migration, the current study maintains a focus on birth-state out-migration for simplicity.

  15. In results not shown, I also explored using 1-year state out-migration as a binary dependent variable. Regressing a dummy for leaving the state of residence 1 year prior on major-specific regression-adjusted mean log earnings in the state 1-year prior yields a small negative coefficient that is not statistically significant at conventional levels. Unfortunately, 1-year migration is a noisy measure for the current analysis because relatively few people move across states in a given year. Furthermore, individual locations in the prior year are likely not independent of recent earnings in the state. For example, recent graduates may have already left areas paying especially low salaries to persons with their major.

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Authors and Affiliations

  1. Oklahoma State University, 331 Business Building, Stillwater, OK, 74078-4011, USA

    John V. Winters

  2. IZA, Bonn, Germany

    John V. Winters

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  1. John V. Winters
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Correspondence to John V. Winters.

Appendix

Appendix

See Appendix Tables 6 and 7.

Table 6 Additional results for primary analysis, ages 22–30
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Table 7 Robustness to controlling for earnings in destination states, ages 22–30
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Winters, J.V. Do earnings by college major affect graduate migration?. Ann Reg Sci 59, 629–649 (2017). https://doi.org/10.1007/s00168-016-0748-7

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