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Mobility, education and labor market outcomes for U.S. graduates: Is selectivity important?

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Abstract

The literature on human capital, and its positive effects on individuals and regional economies, is now vast. The linkages between human capital and migration have also found a fertile ground in recent years especially in Europe where many studies have focused on interregional migration of graduates and highly skilled individuals. However, the literature on this phenomenon in the USA is less developed. Using the SESTAT database from NSF, this paper aims at contributing to the understanding of inter-state migration behavior of graduates in the USA and its effects on their career outcomes. It builds on the existing literature not only by focusing specifically on the US context, but also incorporating into the empirical model a correction for the possible selection bias that arises from the dual relationship between migration propensity and human capital endowment. Our estimated Mincerian earning equations, corrected for migrant self-selectivity, show that indeed repeat migration is associated with higher average salaries, while late migration is associated with a salary penalty. As for the other control variables, our results are consistent with what has been found in the labor economics literature. Female workers suffer from a salary penalty, while experience, level of education and employer size are all associated with higher average salaries. The labor market also rewards different fields of study differently.

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Notes

  1. Although, ideally, we would have liked to classify individuals defining migration at county-level, location was only available at state level, so migration is defined as a change of state. This leaves out any interstate migration consideration for which data were not available.

  2. Notice that, as Deb and Trivedi (2006) discuss, the model requires normalization restrictions on the scale of each choice equation and on the variance-covariance parameters. The last condition can be met when \( \delta _{{jk}} = 0~\forall ~j~ \ne k \). Thus if this holds, then each choice is affected by a unique latent factor. Furthermore, in order to normalize the scale of each choice equation, you need to set \( \delta _{{jj}} = 1~\forall ~j \).

  3. To check for the robustness of our results, we also estimated our models for individuals aged 25–40. Results are reported in Table 6. The restriction to younger individuals does not change the results for the large majority of our regressors, and selection and endogeneity are still important factors. However, some of the selection terms do change sign, the “late-migrant” dummy, for instance, is now significant and positive for younger individuals, confirming the finding of Faggian et al. (2007a, (2007b) for the UK.

  4. A complete list is provided in “Appendix” (Table 3).

  5. Data from the World Bank in current prices.

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Author information

Authors and Affiliations

  1. Agricultural, Environmental and Development Economics (AEDE) Department, The Ohio State University, Columbus, OH, 43210, USA

    Pantelis Kazakis & Alessandra Faggian

Authors
  1. Pantelis Kazakis
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  2. Alessandra Faggian
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Corresponding author

Correspondence to Alessandra Faggian.

Additional information

We are indebted to the National Science Foundation and specifically the National Center for Science and Engineering Statistics for providing the SESTAT data. They bear no responsibility for the analysis conducted or interpretation of the data. All remaining errors are ours.

Appendix

Appendix

See Tables 3, 4, 5 and 6.

Table 3 List of prestigious occupations
Full size table
Table 4 First-stage (multinomial logit) results: all sample
Full size table
Table 5 Summary of variables used in both first-stage and main equations
Full size table
Table 6 Results on the restricted sample of individuals aged 25–40
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Kazakis, P., Faggian, A. Mobility, education and labor market outcomes for U.S. graduates: Is selectivity important?. Ann Reg Sci 59, 731–758 (2017). https://doi.org/10.1007/s00168-016-0773-6

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