Gender effect on microfinance social efficiency: A robust nonparametric approach

Abstract

The main objective of this study is to assess the impact of gender on microfinance social efficiency. Our methodology is based on the most recent nonparametric techniques to estimate the gender effect. We use a conditional directional free disposal hull (FDH) approach as well as its robust version of order-α; we study the effect of the heterogeneity factor on the difference of conditional and non conditional inefficiencies as well as on the inefficiency level using a local linear regression and we test the significance of its effect using a wild double bootstrap procedure. Using a cross-country sample of 680 microfinance institutes (MFIs) in 2011 from six main regions of the world, our findings suggest that gender diversity has globally a positive impact on the microfinance social efficiency. However, the nature of the effect depends on the considered heterogeneity factor and we find that the boardroom gender diversity effect is linear, whereas the effect of the percentage of women loan officers is non linear (U-shaped on the difference of inefficiencies and inverted U-shaped on the inefficiency levels). We assess the robustness of our findings on various subsamples (global or regional scale, and also depending on the considered profit oriented status). Our findings reinforce the importance of the role played by women in MFI social efficiency.

Are donor funds used effectively? An examination of Islamic and conventional microfinance institutions

“…As the poor tend to require the smallest amounts of financing, lower values indicate greater outreach to the poor (Al-Azzam, 2019). We use the number of borrowers served by the MFI to measure their breadth of outreach (Fall et al , 2021; Mohamed and Elgammal, 2023b). Together, these two measures capture whether an MFI serves a greater number of clients overall and whether the MFI serves poorer clients specifically, both of which would contribute to enhancing financial inclusion.…”

Efficiency of zakat institutions in Indonesia: data envelopment analysis (DEA) vs free disposal hull (FDH) vs super-efficiency DEA

“…By comparing the super-efficiency DEA model allows units to have values greater than 1, thus making it possible to differentiate their performance. The used common method to Previously used to analyze the public sector (Sherman and Zhu, 2006) The efficiency value result is between 0 and 1 (Charnes et al, 1978) Convexity of production possibilities (Charnes et al, 1978) Compares the most efficient DMU group to determine efficiency (benchmark) (Sherman and Zhu, 2006) Recommends input and output targets to maximize efficiency (potential improvement) (Sherman and Zhu, 2006) Measures efficiency with multiple inputs and outputs but does not require precise relationships between inputs and outputs (Sherman and Zhu, 2006) Free disposal hull (FDH) Relevant for public sector efficiency analysis because it does not require many assumptions (Borger et al, 1994) The efficiency value result is between 0 and 1 (Tulkens, 1993) Nonconvexity of production possibilities (Fall et al, 2021) Efficiency analysis is generated by comparing the input and output of each DMU (Tulkens, 1993) Recommends input and output targets to achieve efficiency (potential improvement) (Cherchye et al, 2000) Measures efficiency with several inputs and outputs but does not require precise relationships between inputs and outputs (Tulkens, 1993) Super-efficiency DEA Relevant for analyzing the public sector (Atiya et al, 2020) The efficiency value result is more than 1 for DMU, which is considered efficient to determine the efficiency ranking of each DMU (Andersen and Petersen, 1993) Convexity assumption (Chen and Du, 2015) Compares the most efficient DMU group to determine efficiency (benchmark) (Andersen and Petersen, 1993) Recommends input and output targets to achieve efficiency (potential improvement) (Andersen and Petersen, 1993) Measures the efficiency with several inputs and outputs but does not require precise relationships between inputs and outputs (Andersen and Petersen, 1993) Allows for infeasible result (Lee and Zhu, 2012) JIABR 14,8 measure efficiency is DEA. However, DEA method does not accommodate units that have efficient status (Rusydiana and Hasib, 2020).…”

“…In addition, the computational technique used to solve the FDH program considers more mixed programming than the DEA model with linear programming (Lim et al , 2016). The FDH method is a nonconvex efficiency measurement method that does not require certain assumptions (Fall et al , 2021). The FDH method can analyze special case data (Fall et al , 2021).…”

A Study on Funding and Financial Strategy Followed by Women Entrepreneurs: A New Way to Entrepreneurial Growth of Bangladesh

“…This research also suggested to MFIs for distinguishing biased culture that can aid financiallyimpeded women. Fall, Tchuigoua, Vanhems, and Simar (2021) studied and assessed the effect of gender on the social competence of microfinance by applying nonparametric techniques to assess the influence of gender. They used 680 microfinance organizations as a cross-country database from six countries of the world.…”