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Investigating the unobserved heterogeneity effect on outreach to women: lessons from microfinance institutions

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Abstract

The main objective of this study is to assess the impact of unobserved heterogeneity on microfinance social efficiency analysis. Based on recent nonparametric techniques and directional distances, we identify a latent heterogeneity factor related to the microfinance institute (MFI) manager’s ability to promote women, independent of MFI size. We test for the significance of this unobserved factor and analyze the impact of MFI social inefficiency measures. Using a cross-country sample of 501 MFIs in 2011 from six main regions of the world, our findings reveal a significant effect of unobserved heterogeneity on the frontier and hence stress the importance of subjective factors in defining the set of production possibilities. We assess the robustness of our findings with the considered profit-oriented status and analyze the link between our unobserved heterogeneity factor and institutional and socioeconomic indicators.

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Notes

  1. Note that several papers have documented the methodological issues raised by two-step methods, as in Simar and Wilson (2007) or Simar and Wilson (2011).

  2. Note that the analysis performed below has also been repeated in 2010 and 2012 to check for robustness of our results. The results were consistent with 2011 but we chose not to include them in the paper to save space.

  3. This explains in particular why our sample size is smaller than in Fall et al. (2021). Indeed, we have introduced several new variables to characterise the unobserved heterogeneity (one auxiliary variable and six external socio-economic variables) and datapoints for these variables were sometimes missing for the year 2011. Dropping cross-sections with missing data lead us to base our analysis on a sample of 501 MFIs.

  4. Note that, as suggested by a peer reviewer, we could have completed the analysis by using the number of female loan officers as an additional observed heterogeneity factor [as in Fall et al. (2021)]. We chose a single heterogeneity factor and favored the representation of women on the boards of directors for one main reason: targeting women is a strategic decision in MFIs and this strategic decision must be ratified by the board of Footnote 4 Continued

    directors. Having women represented on the boards of directors can therefore encourage MFIs to deliver more financial services to women (financial inclusion of women).

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Appendix: Tables and Figures

Appendix: Tables and Figures

See Tables 1, 2 and 3.

Table 1 MFI distribution by institutional form and region
Table 2 Variable definition
Table 3 Descriptive evidence

1.1 Impact of heterogeneity variables on the frontier and inefficiency

See Figs. 1, 2 and 3.

Fig. 1
figure 1

Gender effect (unobserved and observed heterogeneity) for the full sample. The three figures on the left correspond to the effect on the frontier, and the three figures on the right correspond to the effect on the inefficiency level. The two figures on the top correspond to the 3D plot with the observed and unobserved heterogeneity (observed heterogeneity is % of Female board members). The two figures in the middle (in the bottom) correspond to the 2-D plot with the observed heterogeneity (unobserved heterogeneity) for the quantile values of the unobserved heterogeneity (observed heterogeneity). The symbols for the three quantile values are \(\triangledown \) for Q1, * for Q2 and o for Q3. The pvalues correspond to the test evaluating the impact of the heterogeneity variable on the frontier or on the inefficiency level

Fig. 2
figure 2

Gender effect (unobserved and observed heterogeneity) for the for-profit sample. The three figures on the left correspond to the effect on the frontier, and the three figures on the right correspond to the effect on the inefficiency level. The two figures on the top correspond to the 3D plot with the observed and unobserved heterogeneity (observed heterogeneity is % of Female board members). The two figures in the middle (in the bottom) correspond to the 2-D plot with the observed heterogeneity (unobserved heterogeneity) for the quantile values of the unobserved heterogeneity (observed heterogeneity). The symbols for the three quantile values are \(\triangledown \) for Q1, * for Q2 and for Q3. The pvalues correspond to the test evaluating the impact of the heterogeneity variable on the frontier or on the inefficiency level

Fig. 3
figure 3

Gender effect (unobserved and observed heterogeneity) for the nonprofit sample. The three figures on the left correspond to the effect on the frontier, and the three figures on the right correspond to the effect on the inefficiency level. The two figures on the top correspond to the 3D plot with the observed and unobserved heterogeneity (observed heterogeneity is % of Female board members). The two figures in the middle (in the bottom) correspond to the 2-D plot with the observed heterogeneity (unobserved heterogeneity) for the quantile values of the unobserved heterogeneity (observed heterogeneity). The symbols for the three quantile values are \(\triangledown \) for Q1, * for Q2 and o for Q3. The pvalues correspond to the test evaluating the impact of the heterogeneity variable on the frontier or on the inefficiency level

1.2 Characterization of the unobserved heterogeneity

See Fig. 4.

Fig. 4
figure 4

Link between the unobserved heterogeneity and external variables: a Government effectiveness; b Voice and accountability; c Rule of law; d Gender Inequality Index (GII); e Gender Development Index (GDI); and f Human Development Index (HDI). The inverted triangles represent the local linear fit to the sample of points. The pvalues correspond to the test evaluating the impact of the external variable on the unobserved heterogeneity

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Fall, F.S., Tchakoute Tchuigoua, H., Vanhems, A. et al. Investigating the unobserved heterogeneity effect on outreach to women: lessons from microfinance institutions. Ann Oper Res 328, 1365–1386 (2023). https://doi.org/10.1007/s10479-023-05353-y

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