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Racial differences in self-employment exits

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Abstract

Using detailed work history data in the 1979 National Longitudinal Survey of Youth, I investigate the reasons behind the racial gap in self-employment. My analysis of an “age uniform” sample of men, all of whom are observed from age 22 to 40 years, reveals that racial differences in cross-sectional self-employment rates are largely due to the fact that minority workers’ self-employment spells are relatively short-lived. Moreover, I find that minority workers’ relatively high exit rates are driven primarily by transitions to nonemployment. Estimates from a multinomial logit model of self-employment exits suggest that minority workers’ weak attachment to the labor market prior to entering self-employment is an important determinant of their transition from self-employment to nonemployment, while lack of prior industry and self-employment experience contributes to minorities’ transitions to wage employment. When I assign blacks and Hispanics the same (mean) work histories as whites, the predicted black–white gap in the first-year self-employment survival rate decreases by 31% and the Hispanic–white gap decreases by 14%.

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Notes

  1. They define an established business as one that has paid wages or profits for more than 42 months.

  2. The 4-week criterion that distinguishes between transitions to nonemployment and wage employment is based on Royalty (1998). While choosing this 4-week interval in determining types of transitions may seem somewhat arbitrary, using different intervals (e.g., from 1 week to 3 months) did not change the estimated results in any important way.

  3. From 2002 onward, in addition to the class of worker question, respondents were also asked a series of questions that determine the type of job (traditional, nontraditional or self-employed). To maintain comparability across years, I ignore this additional information and use the class of worker definition of self-employment for all years.

  4. Class of worker information is missing for 12.6% (4,332) of the 34,251 jobs reported by my 4,036 sample members between ages 22 and 40 years. I am able to impute self-employment status for 487 of those missing cases; the remaining 3,845 jobs are dropped from the sample. The descriptive statistics and estimated parameters were virtually unaffected by eliminating those 487 imputed cases.

  5. The variable on the time spent in nonemployment is to measure the degree of attachment to the labor market prior to the current self-employment spell. Hence, the case with a long spell of nonemployment and the case with frequent nonemployment can be treated equally as representing weak attachment to the labor market.

  6. Detailed information on personal assets is only available from 1985 onward in the NLSY79. Thus, I impute net assets for the years before 1985 with predicted values based on a person-specific asset-year equation. The asset measure is deflated by the CPI-U and expressed in hundreds of thousands of 2002 US dollars.

  7. Given that the work history variables are key to my analysis, I interact each of them with both race/ethnicity dummies regardless of the statistical significance of the estimated coefficients.

  8. For jobs that start before age 22 years, actual starting date is used. For jobs that end before the last interview, true duration is used. If the job is right-censored, the last interview date is used as the ending date for Table 4 only.

  9. The predicted 1-year survival (SE-to-SE) probabilities in Table 6 are lower than the sample transition rates in Table 1. Part of the difference between the conditional and unconditional predictions is due to the fact that both SE-to-NE and SE-to-WE transition probabilities decrease with years in the current self-employment job (i.e., reflect negative duration dependence) as shown by the coefficient estimates for duration dummies in Table 7. As a result, the unconditional probability of 1-year survival is higher than the predicted probability of 1-year survival conditioned on a worker having become self-employed in the current year.

  10. In making these computations, I set all other covariates, including the three work history variables, equal to the race/ethnicity-specific group means or modes.

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Acknowledgements

This article contains work from my doctoral dissertation submitted to the Ohio State University. I would like to thank Audrey Light for her guidance and all-aspect supports. I also would like to thank Bruce Weinberg, Joe Kaboski, Maria Minniti, and two anonymous referees for providing valuable comments and suggestions. All remaining errors are mine.

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Correspondence to Taehyun Ahn.

Appendix

Appendix

See Table 7.

Table 7 Random-effects multinomial logit estimates of exit from self-employment

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Ahn, T. Racial differences in self-employment exits. Small Bus Econ 36, 169–186 (2011). https://doi.org/10.1007/s11187-009-9209-3

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