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Gender, formality, and entrepreneurial success

Abstract

In this paper, we address two entrepreneurship puzzles prevailing in developing countries. First, field experiments on business training programs and grants have shown that it is much more difficult to improve business outcomes for female entrepreneurs than for their male counterparts. Second, empirical studies have revealed that it is difficult to increase entrepreneurial performance in the informal sector. We argue that an extended version of the entrepreneurship model in Lucas (Bell Journal of Economics, 9, 508–523, Lucas 1978) can provide insights into these recurrent puzzles. In particular, if female entrepreneurs are time constrained, interventions that only target business ability and credit constraints may not be sufficient to raise the entrepreneurial outcomes of female entrepreneurs. In addition, if informal entrepreneurs face business constraints in terms of both their access to credit and entrepreneurial ability, interventions that target these constraints together can have a potentially greater impact than those that target either in isolation. We support our theoretical predictions using data from a field experiment with microfinance clients, conducted in Tanzania.

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Notes

  1. 1.

    See, for instance, Field et al. (2010), Karlan and Valdivia (2011), Banerjee and Duflo (2011), Giné and Mansuri (2011), Klinger and Schündeln (2011), Bruhn and Zia (2012), de Mel et al. (2013, 2014), Drexler et al. (2014), Karlan et al. (2015), Berge et al. (2015a), Bulte et al. (2015), Higuchi et al. (2015), Higuchi and Sonobe (2015), and Angelucci et al. (2015). In addition, several studies reveal that female entrepreneurs tend to have worse entrepreneurial outcomes than males (see Fairlie and Robb 2009; Lee and Marvel 2014). For a review of female entrepreneurship in developing countries, see Minniti and Naudé (2010).

  2. 2.

    See, for instance, Morduch (1999), Khandker (2005), Hermes and Lensink (2007), Cull et al. (2009), Rijkers and Costa (2012), and Cintina and Love (2017). The usual definition of the formal sector is firms that are officially registered, organized, and regulated in a country. In practice, this means that firms that are legally registered with authorities keep records and pay taxes. The opposite is the case for informal firms.

  3. 3.

    On the importance of credit constraints on entrepreneurship, see, for instance, Cotler and Woodruff (2008), Brown et al. (2005), de Mel et al. (2011), Bruhn and Love (2011), and Kairiza et al. (2017). For the significance of business knowledge on entrepreneurship, see, for example, Jäckle and Li (2006).

  4. 4.

    For an early analysis of the role of social norms and gender on economic development, see, for instance, Field (1984) and Lele (1986), respectively.

  5. 5.

    See, for instance, Potash (1986), Agarwal (1994), Saito (1994), Udry (1996), Dey-Abbas (1997), Johnson (2004), Dasgupta (1993), Pitt and Khandker (1998), Mammen and Paxson (2000), Van Tassel (2004), Munshi and Myaux (2006), and Alesina et al. (2013).

  6. 6.

    See, for instance, Boeke (1953), Lewis (1954), Agénor (2005), Mandelman and Montes-Rojas (2009), Vollrath (2009), and Fergusson (2013).

  7. 7.

    Emran et al. (2006) explain why women are more common in the informal sector (and particularly in microfinance) using non-existent or “missing” labor markets for women. This argues that as women suffer discrimination in the formal labor market, they have no other choice than to work or become entrepreneurs in the informal sector.

  8. 8.

    Obviously, there are other interventions besides business grants and training that can affect entrepreneurial outcomes. For example, Drexler et al. (2014) focus on improving record keeping using simple rules of thumb, while Karlan et al. (2015) analyze the impact of 1:1 consulting/advising.

  9. 9.

    Note that our results differ from those in Fafchamps et al. (2014), where business grants only had an impact on female entrepreneurs already earning higher profits at the commencement of the intervention.

  10. 10.

    Bandiera et al. (2011) focus differently on time constraints by considering the way CEOs allocate time at work. They show that the division of time between different activities (spending time with insiders versus outsiders of the firm) is central for CEO productivity and firm performance.

  11. 11.

    See also Evans and Jovanovic (1989), Mesnard and Ravallion (2006), Alby et al. (2013), De Mel et al. (2016), and McKenzie (2015).

  12. 12.

    Obviously, other explanations are possible, and we discuss some below.

  13. 13.

    Note that in the Lucas (1978) model, credit constraints are also exogenous.

  14. 14.

    See, for instance, Jovanovic (1982), Cabral and Mata (2003), Hurst and Lusardi (2004), Atolia and Prasad (2011), Calá et al. (2015), and Berge et al. (2015b).

  15. 15.

    For an analysis of microfinance lending outside loan groups, see De Quidt et al. (2016) and Fischer and Ghatak (2011).

  16. 16.

    The average attendance rate per session was 70%, while 83% of the clients qualified for a diploma (after participating in 10 or more sessions).

  17. 17.

    On the importance of business contacts for entrepreneurship, see McAdam et al. (2018).

  18. 18.

    Note that working hours are a good proxy for domestic work for women. Women in our sample report that they do most of the domestic work in their households. This is possibly the reason why female entrepreneurs work closer to their homes than male entrepreneurs.

  19. 19.

    As discussed, we could well believe that the bargaining power inside the household also influences female entrepreneurial activity, business time constraints, and profits. However, our measures of bargaining power (who makes decisions in the household and controls the savings of the family) are uncorrelated with female profits and working hours.

  20. 20.

    In the appendix, we provide the correlates of working hours by gender (see Appendix Table 8). As shown there, working hours for the full sample positively and statistically significantly correlate with formality, the service sector, and the level of education, but negatively and statistically with loans (indicating the substitutability between capital and labor) and female entrepreneurs. Dividing by gender, a similar pattern arises with the exception that the loan variable is no longer statistically significant.

  21. 21.

    The exception is for female entrepreneurs who received the business grant and work more than 30 or 40 h a week. However, the magnitude of the coefficients is larger in this interval, before it again declines and increases again, and even though the coefficients in all regressions are economically significant, the standard errors are quite large. In this sense, it is difficult to rule out the possibility that the coefficients are identical as well as positive. Note also that the theoretical model does not exclude the possibility of a positive impact on entrepreneurial activities of the grant-only treatment.

  22. 22.

    For entrepreneurial aspirations, we posed five questions during the baseline survey to capture to what extent the clients had entrepreneurial “potential.” If anything, the results based on an index of these questions suggest that female entrepreneurs are borderline significantly more “entrepreneurial” than males. Conversely, when the enumerators were asked to subjectively judge the entrepreneurial potential of the subjects in our study, males scored better, although not significantly (p value = 0.132).

  23. 23.

    Using data on 100 self-employed/small-scale entrepreneurs from another experiment we have conducted in Dar es Salaam, including both microfinance members (not necessarily members of PRIDE) and non-microfinance members, we find only modest evidence that microfinance members are different from non-microfinance members. In particular, looking at income stability, mathematical ability, willingness to compete, risk aversion, trust, and patience, we identify no significant differences between microfinance members and non-members. Though, we note that microfinance members are more likely to report that they have a stable income than non-microfinance members (although the difference is not significant). If we look at happiness, microfinance members report they are significantly happier with their lives than non-microfinance members.

  24. 24.

    McMullen (2011) argues that institutional and cultural interventions should complement a market-based approach to entrepreneurs in developing countries. This seems to be in accordance with our findings.

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Correspondence to Armando José Garcia Pires.

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The study was organized by The Choice Lab and financed by grant 204691 from The Research Council of Norway.

Appendix

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Table 8 Hour correlations, by gender

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Oppedal Berge, L.I., Garcia Pires, A.J. Gender, formality, and entrepreneurial success. Small Bus Econ 55, 881–900 (2020). https://doi.org/10.1007/s11187-019-00163-8

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Keywords

  • Field experiments
  • Entrepreneurship
  • Gender
  • (In)formal sector

JEL classification

  • A13
  • L26
  • O12
  • O16
  • O17
  • O55