Immigration, opportunity, and assimilation in a technology economy

Abstract

We examine access to institutions and opportunity for entrepreneurs in a rising tech economy. A significant proportion of entrepreneurs and CEOs of tech firms in the American economy are either first- or second-generation immigrant minorities. Are these minority entrepreneurs assimilating into a rising economic elite? To what extent is the technology economy segmented by ethnic boundaries and sectors? On a range of empirical measures, including access to financial and social capital, firm performance, and normative beliefs on fairness and cooperation, we find second-generation immigrant minority tech entrepreneurs to be strikingly similar to their white counterparts. This study sheds new light on the institutional environment of a new regional technology economy, whereby barriers of entry are high in terms of human capital but economic competition is structurally and culturally open to immigrant minority entrepreneurs.

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Notes

  1. 1.

    In general, we use “immigrant minority entrepreneurs” and “minority entrepreneurs” to refer to non-White entrepreneurs who are either 1st or 2nd generation immigrants, while “white immigrant entrepreneur” refers to White immigrants from both generations. We use more precise language regarding generation when relevant to our analysis. “Native” entrepreneurs refers to entrepreneurs who were born in the United States from US-born parents (3rd + generation) regardless of race.

  2. 2.

    Due to anonymity issues, we refrain from discussing national origins in more detail when considering the small number of individuals from certain countries, potentially making our survey’s respondents identifiable.

  3. 3.

    We check the robustness of our results to different specifications of race in the robustness checks section.

  4. 4.

    For the series of ANOVA tests that follow, we maximize the number of observations per test rather than keeping it constant with listwise deletions for the few missing observations across some tests but not others. If intergroup differences do exist, maximizing the number of observations per test is the more cautious and conservative approach given the small size of our population and some of our subgroups of interest. This being said, all the tests presented here were re-run with a listwise deletion approach to keep the sample constant across tests, and the results are identical to those presented here, save for two ANOVA tests—the proportion of those with a white collar father and the use of accelerator programs—for which significance fell below 0.05. These are available from the authors upon request.

  5. 5.

    Cramér’s V for the association between nativity (first/s-generation/natives) and economic sector is 0.19. For the association between a binary specification of nativity (born abroad/born in the US) and economic sector, it is 0.187. For the association between a binary specification of race (White/non-White) and economic sector, it is 0.134. For the association between a 5-way specification of race (White/Black/Hispanic/Asian/Other) and economic sector, it is 0.179.

  6. 6.

    In addition, we compared annual salaries among 154 respondents who had another job before founding a tech firm. The resulting graph (available upon request) is substantively identical to Figure 3.

  7. 7.

    Regression analyses without inverse weighting show virtually identical results.

  8. 8.

    These results are not included in this article due to space constraints but are available upon request.

  9. 9.

    In these series of tests, note that we do not apply Bonferroni corrections since they make for more conservative tests. Applying Bonferroni corrections would be substantively meaningful in cases of apparently strong intergroup difference across our series of test, but since we do not find them here, we do not need to resort to that more conservative strategy. Had we found strong intergroup differences, we would have applied Bonferroni corrections as a robustness check.

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Acknowledgments

We thank Richard Alba, Daniel DellaPosta, Lisha Liu, Mario Molina, and two anonymous reviewers for useful comments on an earlier draft. Victor Nee gratefully acknowledges funding from the John Templeton Foundation, and he thanks Sonja Opper, principal investigator of a grant from the Marianne and Marcus Wallenberg Foundation.

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Appendix

Appendix

Table 5 provides descriptive statistics on gender, age, parental occupation (whether or not the respondent’s parents were white-collar workers or professionals), family income at age sixteen (ranging from 1 “far below average” to 5 “far above average”), and whether or not the respondent holds a master’s degree or above. Most of these tech entrepreneurs are male; this is true across subgroups. Most come from socioeconomically advantaged families, with above-average family income and at least one parent—sometimes both—in white-collar or professional occupations. They are, unsurprisingly, highly educated; a high proportion holds a master’s degree or more.

Table 5 Mean values and (standard deviations) for background characteristics of tech entrepreneurs in NYC, by race & nativity (for F-tests: * = p < 0.05, ** = p < 0.01)

Analyses of variance and pairwise t-tests show significant differences in age, father’s occupation, and proportion of those holding a master’s degree or more. In particular, second-generation minority entrepreneurs are younger than all other subgroups, while first-generation immigrant minority entrepreneurs are more educated than all other subgroups. This latter difference, however, might reflect selection effects for immigrant tech entrepreneurs educated abroad.

Comparison of study sample to data from the American Community Survey

We ascertain the quality of our sample by comparing its demographic composition against that of self-employed individuals working in the software, data processing and internet and internet media industries in New York City in the 2013–2017 pooled data from the American Community Survey (see Table 6). This comparison is necessarily imperfect insofar as certain sectors of the tech economy in New York City (e.g., medical technology or advertising) are likely to get classified with non-tech equivalent in the ACS data, but the results are nevertheless useful.

Table 6 Mean values for demographic variables in the NY Tech Survey and the 2013–2017 pooled data from the American Community Survey for self-employed individuals working in select tech industries in New York City (for T-tests for cross-mean difference: * = p < 0.05, ** = p < 0.01). *

Our sample of CEOs is slightly younger and more male than the comparison population in the American Community Survey, but appears broadly comparable on other dimensions which are central to our study, such as nativity and race. While the ACS does not have data on parental nativity, such demographic similarity suggests our sample to be a robust empirical basis for our study.

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Nee, V., Drouhot, L.G. Immigration, opportunity, and assimilation in a technology economy. Theor Soc 49, 965–990 (2020). https://doi.org/10.1007/s11186-020-09414-0

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Keywords

  • Assimilation
  • Entrepreneurs
  • Immigrant minority
  • Institutional change
  • Technology economy