Where are all the self-employed women? Push and pull factors influencing female labor market decisions

Abstract

Previous research focuses on factors that influence self-employment participation, in part because entrepreneurship has been associated with economic growth. This literature has tended to focus only on men or the comparison of women to men, while ignoring substantial heterogeneity in employment decisions among women. By investigating the impact of individual, household, and local economic and cultural characteristics on the labor market outcomes of different groups of women, we get a more comprehensive picture of their self-employment decision. Recognizing self-employment as one of multiple labor market choices, we use multinomial logit and two confidential, geocoded micro-level datasets to study women`s career choices in urban areas. We find that the effects of various push and pull factors differ between married and unmarried women. In particular, more progressive gender attitudes pull married women into self-employment, while household burdens associated with children push them into self-employment. For unmarried women, the local business climate and individual characteristics have the strongest influence. In both cases, the motivations for women are quite different than men.

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Notes

  1. 1.

    Calculated using IPUMS CPS data on non-agricultural self-employment and non-agricultural employment.

  2. 2.

    Similarly, Booth and van Ours (2013) and Gianelli (1996) separate the employment choices facing women into full-time, part-time, and non-employment. They find that some women prefer part-time employment and are not simply using it as a step to full-time employment. Thus, the availability of part-time employment increases the labor force participation of women who would otherwise choose non-employment.

  3. 3.

    Our definition of not working is similar to the definition of non-employment in the above-referenced studies and includes persons not in the labor force as well as unemployment. The overwhelming majority of “not working” respondents are out of the labor force. In the pooled female sample, for example, 1.5 % of the estimation sample is unemployed, while approximately 18 % is out of the labor force. We also estimated versions of our models where the choice set is expanded to differentiate between respondents that are out of the labor force and unemployed. Our primary results remain unchanged. The results of this exercise are available from the authors upon request.

  4. 4.

    We also estimated versions with individual fixed effects on the pooled data with interaction terms. We employed Pforr’s (2014) femlogit implementation of a solution to avoid the incidental parameters problem (explained further in footnote 12). Although this approach prevents identifying important interactions with time-invariant individual characteristics and consumes substantial degrees of freedom, the marriage interactions remain statistically significant and further indicate that separate examination of married and unmarried women is warranted.

  5. 5.

    For more information on the multinomial logit approach, see Wooldridge (2010).

  6. 6.

    We also explored looking at the self-employment decision through an entry specification (the choice to enter self-employment from either salary employment or not working). Because of the small sample sizes associated with this specification, married and unmarried women must be pooled together (though we include interaction variables between marriage and some other variables). Though the results of this specification are largely similar to our preferred methodology, this specification cannot eliminate concerns about masking the heterogeneity in the employment decisions between married and unmarried women.

  7. 7.

    Unlike the conditional logit approach, multinomial logit does not account for differences between the actual choices, just the factors that are important to the choice. As long as the choice set is fully specified then we do not need to make the Independence of Irrelevant Alternatives (IIA) assumption as in conditional logit (Wooldridge 2010).

  8. 8.

    Clustering the standard errors at the individual level produces non-symmetric or highly singular variance matrices when the number of observations within a cluster becomes too small or there are too few observations within some cells. Although this is not a problem for most of our specifications, it presents an issue with the unmarried sample and some of the extension samples. In general the cause is too few observations in some of the industry cells. Table notes include information on the standard errors.

  9. 9.

    Marginal effects for indicator variables are calculated using discrete differences rather than derivatives.

  10. 10.

    Expected wages are predicted using estimates from Heckman selection-corrected, expanded wage equations that include age, experience, experience squared, education, sex, race, marital status, ability, MSA of residence, industry, and time dummies. The selection equation includes age, experience, experience squared, education, marital status, number of children, children under five, citizenship, and health limitations.

  11. 11.

    In order to limit the influences of outliers in an MSA biasing our gender attitude measure, we aggregate the data from our sample period to generate a single “fem_factor” observation for each MSA. Even if there are changes in attitudes over time within an MSA, the relative gender attitudes between cities are likely to remain relatively constant. In other words, while a city like Dallas, Texas, may become more progressive about gender roles during our time period, it is likely to remain more conservative than a city like Los Angeles. We also estimated models with the time-varying fem_factor variable. The results were similar and omitted for brevity.

  12. 12.

    We do not report results for specifications using individual or MSA fixed effects because there are several significant drawbacks to this approach within our context. Notably, our variables of interest include both time-varying and time-invariant characteristics. Ability, pre-labor market characteristics, health limitations, and a number of other important time-invariant factors that our results suggest differentially influence married and unmarried women do not change over time. Effects associated with the smoothed gender-role attitude metric could not be separately identified with MSA fixed effects and would be identified only from women who change MSAs with an individual fixed effect. Employing the time-varying fem_factor does not substantially improve our statistical power as the vast majority of variation in gender-role attitudes is between MSAs, rather than within MSAs over time. Fixed effects estimation within a multinomial logit framework also creates the well-known incidental parameters problem when implemented by including individual or MSA indicator variables. Instead, multinomial fixed effects estimation requires the Chamberlain (1980) solution. Recently, Pforr (2014) operationalized the Chamberlain solution for multinomial logit with the Stata command femlogit. Unfortunately, the command has not been extended to allow for the estimation of marginal effects. Thus, another drawback of individual fixed effects is that we can only obtain coefficient estimates relevant to the base outcome. With these issues in mind, we estimated fixed effects versions of our primary models with the time-varying fem_factor using femlogit. While we were unable to directly compare the marginal effects estimates due to the computational limitations described, comparing the coefficient estimates revealed no significant differences in our primary findings.

  13. 13.

    We use the phrases “not working” and “not employed” synonymously in what follows. However, technically, we are referring to women who are “not employed” by the definitions used in Table 1—those women who did not work at least 10 h per week for at least ten weeks in the year.

  14. 14.

    Our findings provide additional clarification to the results from Taniguchi (2002) and Renzulli et al. (2000) who find that education does not affect women’s self-employment rates after controlling for other factors. These authors compare self-employment to wage and salary employment, and find the education effect for married women appears relevant on the labor force participation margin only.

  15. 15.

    Because the difference is the predicted salaried employment wage minus the predicted self-employment wage, we would expect that if that is positive, it would make salaried employment more attractive or self-employment less attractive relative to salaried employment.

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Acknowledgments

This research was supported in part by funding from the Coca-Cola Critical Difference for Women Graduate Studies Research Grant and the Department of Women’s, Gender and Sexuality Studies at The Ohio State University.

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Correspondence to Amanda Weinstein.

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Patrick, C., Stephens, H. & Weinstein, A. Where are all the self-employed women? Push and pull factors influencing female labor market decisions. Small Bus Econ 46, 365–390 (2016). https://doi.org/10.1007/s11187-015-9697-2

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Keywords

  • Female labor force participation
  • Self-employment
  • Gender
  • Culture

JEL Classifications

  • J22
  • R23
  • J70
  • L26