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What influences entrepreneurship among skilled immigrants in the USA? Evidence from micro-data

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Abstract

Self-employment among immigrants is a key source for income and social assimilation with natives. Rate of self-employment is significantly higher for immigrants than for native-born individuals, and the causal reasons behind this differential are still not well understood. We hypothesize that a key factor is that domestic employers often cannot accurately assess the quality of higher education received by the immigrants in their home countries. This lowers immigrants’ return to human capital in the traditional job market relative to natives. Our hypothesis predicts that this factor should be reflected in higher relative rates of self-employment for immigrants that rises with the level of education. We test and confirm this hypothesis using IPUMS micro-data from the USA.

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Notes

  1. The impact of immigration, including that of assimilation, on the natives has been a recurrent theme in the literature (Dustmann et al. 2005; Akay et al. 2014; Constant et al. 2009).

  2. For evidence regarding the impact of language proficiency and skill see Borjas (1994), Dustman and Fabbri (2003), and Card (2001, 2005) for the USA, and Barrett et al. (2012), for the European Union.Coate and Tennyson (1992) provide an explanation of self-employment in the presence of labor market discrimination. Yuengert (1995) finds that one determinant of immigrant self-employment was the self-employment rates at the immigrant’s home country. Constant and Zimmermann (2003) find occupational choice of immigrants is influenced by the choices of their parents, especially mothers.

  3. Whether these differences disappear (converge with native-born wages) after long periods in the USA is still debated in the empirical literature. Lofstrom (2002) finds that even after long periods in the United States these wage differentials persist, while Borjas (1994) finds that immigrant earnings converge to natives in about 15 years and may eventually get reversed. For wage gaps in other countries see the Global Wage Report 2014/15 (ILO 2016) and Barrett et al. (2012).

  4. The manifestation of asymmetric information as low skill workers flooding the wage employment is analogous to Akerlof’s (1970) classic discussion of ‘lemons’ in the market for automobiles.

  5. Statistical discrimination occurs when distinctions between groups are made on the basis of real or imagined statistical distinctions (e.g., immigrant workers being offered lower wages because they are perceived as less productive, on average, than native workers).

  6. The actual output or productivity of low-skilled workers is also easier to monitor and quicker to ascertain relative to the corresponding variables for high-skilled, high human capital, workers.

  7. In addition to racial discrimination and other social and demographic effects that differ, there are other factors that may specifically, and differentially, impact self-employment and the returns to higher education including affirmative action programs (Yuengert 1995; Fairlie and Meyer 1996; Fairlie et al. 2012). Due to these differences across races, we focus on white individuals which is the largest racial group in the sample. Seeing how these results hold up for other racial groups and for other countries would be an interesting future avenue for research. It is worthwhile to note that of the immigrants living in the USA, those classified as white are the largest single group, accounting for almost 46% of the total.

  8. Individuals are classified based on their highest degree earned. So for example a person who started but did not finish college would be coded with a value of 1 for earning a high school degree only. The literature has found important nonlinear effects of degree completion (e.g., large discontinuities based on years of education surrounding the degree completion events), and since our hypothesis is about the value of these credentials, coding degree completion is preferable to a variable simply based on the years of education.

  9. In the probit model, the inverse standard normal distribution of the probability is modeled as a linear combination of the predictors. Our results are robust to using linear probability (LPM) or Logistic specifications.

  10. The specifications using two subsamples also allow the coefficients on the other control variables to differ across the groups as well, although given the smaller sample sizes they are estimated with lower precision, especially for the smaller immigrant subsample. The combined specification assumes these other coefficients are similar across the groups with the exception of the education effect. The key advantage of the combined model is the direct ability to test for the statistical significance of the differential educational effect.

  11. Again, however, because industry choice can be endogenous, while we wish to show our results are robust to including these controls, we are more confident in our estimates from specification 4 than specification 5 due to potential endogeneity issues affecting all coefficient estimates in that specification.

  12. Using our preferred specification, college-educated immigrants have a 2.3% higher probability of self-employment, of which 0.9% is a constant effect for all immigrants unrelated to education, and the remaining 1.4% is due to the education variable interaction. Immigrants with a high school education have a 1.6% higher probability of self-employment, of which 0.9% is a constant effect for all immigrants unrelated to education, and the remaining 0.7% is due to the education variable interaction.

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Acknowledgements

The authors are grateful to two anonymous reviewers for insightful comments on an earlier draft and the Editor for prompt support. The usual disclaimer applies.

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Authors and Affiliations

  1. Department of Economics, College of Business Administration, University of Wisconsin – La Crosse, La Crosse, Wisconsin, USA

    Nabamita Dutta

  2. Centre for Studies in Social Sciences, Calcutta, R 1, B.P. Township, Kolkata, 700 094, India

    Saibal Kar

  3. IZA, Bonn, Germany

    Saibal Kar

  4. Baker School of Business, The Citadel, Charleston, South Carolina, USA

    Russell S. Sobel

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  1. Nabamita Dutta
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Correspondence to Saibal Kar.

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Dutta, N., Kar, S. & Sobel, R.S. What influences entrepreneurship among skilled immigrants in the USA? Evidence from micro-data. Bus Econ 56, 146–154 (2021). https://doi.org/10.1057/s11369-021-00220-9

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