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Social capital and small informal business productivity: the mediating roles of financing and customer relationships

Abstract

How does an entrepreneur’s social capital improve small informal business productivity? Although studies have investigated this relationship, we still know little about the underlying theoretical mechanisms driving these findings. Using a unique Zambian Business Survey of 1971 entrepreneurs administered by the World Bank, we find an entrepreneur’s social capital facilitates small business productivity through the mediating channels of firm financing (i.e., credit from suppliers, credit to customers, loans from friends and family) and customer relationships (i.e., more customers). Our findings, thus, identify specific mechanisms that channel social capital toward an informal business’ productivity, which prior studies have overlooked.

Plain English Summary

Small informal businesses are more productive when they build relationships with customers, extend credit to customers, and receive credit from suppliers and friends and family. Using a unique survey of 1,971 Zambian entrepreneurs administered by the World Bank, we observe that belonging to a business or social association enables these businesses to extend credit, receive credit, and build relationships. Our findings provide more specific mechanisms that channel social capital toward an informal business’ productivity, which prior studies have overlooked. Thus, our main policy recommendation is that entrepreneurs, especially from informal businesses and developing contexts, should focus on networking to foster customer and professional relationships and enhance access to capital, which can help foster firm productivity.

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Fig. 1

Notes

  1. 1.

    However, there are studies showing how the effect of the structural dimension of social capital on firm performance is mediated by relational and resource dimensions (e.g., Castro and Roldán (2013).

  2. 2.

    Although there are many different definitions of entrepreneurship, we consider these Zambian small business owners entrepreneurs because they are operating their enterprises to maintain or build their business. Although most of these business owners are not high tech or growth oriented, they are all trying to operate in the economy, and many have very high entrepreneurial aspirations. This is consistent with calls to embrace entrepreneurial diversity (Welter et al., 2017).

  3. 3.

    One of the co-authors was part of the team that wrote the survey instrument.

  4. 4.

    Fafchamps and Minten (2002) discuss in more detail how the norms and network dimension of social capital are related.

  5. 5.

    https://www.sba.gov/advocacy/bank-credit-trade-credit-or-no-credit-evidence-surveys-small-business-finances

  6. 6.

    https://www.zendesk.com/resources/customer-service-and-lifetime-customer-value/

  7. 7.

    One common concern for papers that use data from a single source is common method variance (Podsakoff et al., 2003). Although it is difficult to rule out this problem entirely, it might not be a major concern in this case because the main dependent and independent variables, including the measures of firm performance and social capital are measured in objective terms. The productivity measure is based on the number of workers the firm has and the firm’s sales. The social capital variable is based on whether the firm’s owner belongs to any business or non-business associations. Fact-based data are generally seen as less susceptible to common method variance than perception or attitudinal variables are (Brannick et al., 2010; Chang et al., 2010). In addition, the variables use different scales, the questions are relatively straightforward, and the topics are not overly sensitive. Finally, the questions are spread out over a long (35 page), broad survey about the business environment.

  8. 8.

    See Clarke et al. (2010) for a more detailed description of the survey.

  9. 9.

    The data in this section refer to the non-agricultural firms in the survey that the empirical work focuses on.

  10. 10.

    Consistent with the idea that the estimates are imprecise, most estimated that self-consumption was a round number. Almost half said either 20, 30 or 40% of output.

  11. 11.

    Moreover, because this information is self-reported, it probably overestimates registration.

  12. 12.

    Moreover, the sample is based on a sampling frame given by the Zambia Central Statistical Office, which also implicitly excludes informal firms.

  13. 13.

    Biggs and Shah (2006), for example, use these data.

  14. 14.

    Van Biesebroeck (2005) reports that the “selection of informal firms was generally left to the interviewers” (p. 549).

  15. 15.

    https://www.gemconsortium.org/economy-profiles/zambia

  16. 16.

    https://oec.world/en/profile/country/zmb/

  17. 17.

    https://thegedi.org/downloads/

  18. 18.

    https://www.cia.gov/library/publications/the-world-factbook/geos/za.html

  19. 19.

    https://www.theigc.org/wp-content/uploads/2012/06/Kedia-Shah-2012-Working-Paper.pdf

  20. 20.

    In additional robustness checks, we separate this variable into business and non-business associations. Both measures are dummy coded (1 = yes; 0 = no).

  21. 21.

    Owners responded with ranges rather than exact numbers. The ranges are 0 customers, 1 to 5 customers, 6 to 10 customers, 11 to 50 customers, 51 to 100 customers, 101 to 500 customers, 501 to 1,000 customers, and over 1,000 customers.

  22. 22.

    We use the GSEM command in Stata because several important dependent variables including social capital are binary. This means that we cannot present standard goodness of fit measures. The main problem is that Stata does not calculate standard goodness of fit statistics for GSEM models because many standard goodness of fit measures are inappropriate for models with binary variables. Most notably, the chi-squared statistic assumes multivariate normality, which is violated in models with binary variables. When we estimate the model ignoring that many dependent variables are not continuous, however, the simpler model produces similar results to our main model. For this model, we can calculate several goodness of fit measures. Despite the obvious specification problems due to assuming continuity, the model performs relatively well. The root mean square residual is 0.03, the comparative fit index is 0.982, and the standard root mean square residual is 0.01. The Tucker-Lewis index, however, falls a little short of standard benchmarks (0.78).

  23. 23.

    Indirect effects are calculated by multiplying all coefficients along the path of analysis. For example, we calculate the indirect effect of social capital on labor productivity through the channel of loans from family and friends as follows: (.588 × .225 × .203) = 0.027.

  24. 24.

    Indirect effect = 0.091 = 0.034 + 0.013 +|− 0.013| +|− 0.004|+ 0.027; total effect = 0.362 = 0.271 + 0.091.

  25. 25.

    Proportion mediated = indirect effect / total effect (i.e., 25.14% = 0.091 / 0.362).

  26. 26.

    One remaining concern is that the percentage of other business owners in the same ethnic group might serve as a proxy for ethnicity. If ethnicity affects firm performance due to ethnic business networks, this could be a problem. However, the OLS and 2SLS results are robust to the inclusion of ethnicity dummies or to dropping the percentage of firms in the district with owners of the same ethnicity as an instrument (see the online appendix).

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Boudreaux, C., Clarke, G. & Jha, A. Social capital and small informal business productivity: the mediating roles of financing and customer relationships. Small Bus Econ (2021). https://doi.org/10.1007/s11187-021-00560-y

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Keywords

  • Entrepreneurship
  • Financing
  • Productivity
  • Small business
  • Social capital

JEL Classifications

  • D24
  • D71
  • G21
  • L26
  • M13