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“Misfits,” “stars,” and immigrant entrepreneurship

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Abstract

Prior research has shown that immigrants are more likely than natives to become entrepreneurs, and that entrepreneurs are disproportionately drawn from the extremes of the ability distribution. Using a large panel of US residents with bachelors’ degrees in scientific fields, we ask whether higher rates of entrepreneurship among immigrants can be explained by their position on the ability spectrum and establish four new facts about science-based and immigrant entrepreneurship. First, in this sample, an immigrant entrepreneurship premium exists only in science-based entrepreneurship. Second, this premium persists after controlling for ability (measured by paid employment wage residuals.) Third, a U-shaped relationship between ability and entrepreneurship exists only in non-science entrepreneurship; for science entrepreneurship, the relationship is increasing. Finally, the immigrant premium in science entrepreneurship is largest among immigrants with non-US degrees and those from non-English-speaking or culturally dissimilar countries. Stated preferences for self-employment do not explain the immigrant premium. The results suggest that immigrants may on average have higher levels of unobservable skills related to entrepreneurship.

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Notes

  1. On immigrant entrepreneurs, see, e.g., Borjas (1986), Fairlie (2008), Hart and Acs (2011). On the U-shape in wages, see e.g., Hamilton (2000), Hipple (2004), Poschke (2013), Astebro et al. (2011). The latter source uses the term “misfits.”

  2. For example, immigrants who entered on student or temporary visas have been shown to have higher rates of education and patenting (Hunt 2011). At the other extreme, Ferrer and Riddell (2008) show that immigrants have lower returns to education and to work experience than natives.

  3. Hunt and Gauthier-Loiselle (2010).

  4. Jovanovic (1994) develops a model that generates predictions about which types of workers become entrepreneurs depending on the correlation between skills related to managing others (x) and those related to working for a wage (y).

  5. Poschke (2013) finds this using data from NLSY but also reports this from calculations he did from data used by Borjas and Bronars 1989, Hamilton 2000, and Hipple (2004) among others; Astebro et al. (2011) has also found a bimodal relationship between entrepreneurship and education.

  6. While Braguinsky et al. (2012) do not characterize their evidence as showing the relationship to be U-shaped, their table shows a clear U-shaped relationship for older scientists and a J-shaped relationship for younger ones.

  7. E.g., Astebro et al. (2011), Astebro and Thompson (2011).

  8. Fairlie and Lofstrom (2015) and Kerr and Kerr (2016) summarized the literature on immigrant entrepreneurship; in two recent reviews, Kerr (2013) and Nathan (2014) focused on the contribution of high-skilled immigrants to innovation and entrepreneurship.

  9. Starting in 2013, new SESTAT entrants are drawn from the American Community Survey and added each survey year. The NSRCG discussed below has been discontinued.

  10. Note that the weights allow us to deal with biases that might derive from this over-sampling.

  11. More specifically, the Merged Outgoing Rotation Groups of the CPS follow the same individuals for 16 months but do not provide information on field of degree, work activity, and place of highest degree. The NSCG and the American Community survey contain information on field of bachelor degree but do not have a longitudinal dimension. Kerr and Kerr (2016) propose a data platform based on the Longitudinal Employer-Household Dynamics (LEHD). While the depth of the data is impressive, the LEHD does not identify firms’ founders and owners. The authors’ definition of entrepreneurship is based on the initial earnings of employees who work in newly entered firms, which may lead to measurement error.

  12. Carnahan et al. (2012) also used wage residuals to study the relationship between ability in previous employment and entrepreneurship.

  13. However, those with Ph.D.s surveyed in the SDR were continued from the 1990s to the 2000s and therefore were not dropped if first seen in 1999.

  14. Here, we report the coefficients (as odds ratios) on the immigrant dummy only. Full regression results from this and all tables are available upon request.

  15. We do not feel that it would be appropriate to control for region, because the choice of region often follows from the decision to become an entrepreneur. It would be interesting to test whether the region of residence matters differently for immigrants and natives in entrepreneurship but our sample is too small to provide robust results.

  16. Note that although the wage equation was calculated based on natives only, the deciles were based on the predicted wages for both natives and immigrants. It is for this reason that the native distribution is not a flat line at 10%.

  17. In other words, we reject the joint hypotheses that the immigrant and native coefficients equal each other at each decile.

  18. Immigrants also have a significantly higher tendency than natives to be non-science entrepreneurs, controlling for preferences, whereas they had similar tendencies when preferences were not controlled for (Table 3 column 10) for the whole sample; further analysis (not shown) indicates that the 1997 subset was somewhat different than the entire sample on this point.

  19. Empirical research on alertness is scarce as alertness is difficult to measure. One exception is Tang et al. (2012), who developed an alertness scale based on 13 items. They show that alertness is positively correlated with “prior knowledge” (Shane 2000).

  20. Progress in this direction has recently been made by Kerr and Mandorff (2016) who examine the concentration of different ethnic groups in specific sectors.

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Acknowledgements

This project was funded by National Science Foundation Grant SBE-0738371. We thank Donna Ginther who gave invaluable assistance with the dataset construction and the definition of entrepreneurship. We also thank Meg Blume-Kohout, TszKin Julian Chan, Jed DeVaro, Robert Fairlie, Dilip Mookherjee, Daniele Paserman, Claudia Olivetti, conference participants at the 2011 Southern Economic Association Annual meeting, and the participants and attendees at the 2012 SOLE session “The Economics of Science” for their helpful comments. A previous version of this paper was part of Giulia La Mattina’s Ph.D. dissertation at Boston University.

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Authors and Affiliations

  1. Boston University Questrom School of Business, Boston, MA, USA

    Shulamit Kahn

  2. University of South Florida Department of Economics, Tampa, FL, USA

    Giulia La Mattina

  3. Boston University Questrom School of Business and the NBER, Boston, MA, USA

    Megan J. MacGarvie

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  1. Shulamit Kahn
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  2. Giulia La Mattina
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Correspondence to Giulia La Mattina.

Appendix: Definition of “science entrepreneur”

Appendix: Definition of “science entrepreneur”

We define an indicator for being an entrepreneur (self-employed incorporated) in science. The indicator takes the value of 1 if any one of the following criteria is met:

  • The individual has a job in bio/med science, chemistry, chemical engineering, computer/math sciences, civil engineering, electrical engineering, mechanical engineering, other engineering, other physical sciences, physics or other life sciences and his/her primary work activity is not professional services.

  • The individual has a job as a manager and his/her primary work activity is research (Design of Equipment, Processes, Development, Computer Applications, Programming, Basic research, Applied Research); the individual is a manager and his/her primary work activity is management but his secondary work activity is research.

1.1 Definition of “non-science entrepreneur”

We define an indicator for being an entrepreneur (self-employed incorporated) but not in science. The indicator takes the value of 1 if any one of the following criteria is met:

  • The individual has a job in non-science or has a job as a teacher.

  • The individual has a job as a manager and neither his/her primary nor secondary work activity is research.

  • The individual has a job in bio/med science, chemistry, chemical engineering, computer/math sciences, civil engineering, electrical engineering, mechanical engineering, other engineering, other physical sciences, physics, or other life sciences and his/her primary work activity is professional services.

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Kahn, S., La Mattina, G. & J. MacGarvie, M. “Misfits,” “stars,” and immigrant entrepreneurship. Small Bus Econ 49, 533–557 (2017). https://doi.org/10.1007/s11187-017-9848-8

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