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Arabian Journal of Business and Management Review
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The Gender Role on MFIs Performance: A Distinctive Trend

Monica Verma*

Faculty of Management, Marwadi University, Rajkot, Gujrat, India

*Corresponding Author:
Monica Verma
Faculty of Management, Marwadi University
Rajkot, Gujrat, India
Tel: 0281 292 4155
E-mail: [email protected]

Received Date: April 29, 2017; Accepted Date: May 04, 2017; Published Date: May 14, 2017

Citation: Verma M (2017) The Gender Role on MFIs Performance: A Distinctive Trend. Arabian J Bus Manag Review 7: 300.

Copyright: © 2017 Verma M. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Regardless of social goals strived by the MFIs they need to be economically viable and sustainable in long run. The efficiency of MFIs is a key in achieving sustainability. Literature reveals that presence of increased number of women in board and at middle level and in the total workforce has a positive impact on financial performance of an organization. The present study has been undertaken to examine the performance of the MFIs worldwide based on the data collected from micromix web and also try to establish whether gender diversity impacts the financial performance. Data have been analyzed using SPSS. Statistical tools like correlations and linear regressions have been used to ascertain relationship between gender diversity in staff of MFIs and performance.

Keywords

Microfinance; Micro financial institutions; ROE; OSS

Introduction

Banking sector has envisaged tremendous growth over time yet it has failed to fill the demand and supply of credit and reach the poor’s. An experiment in 1970s by Professor Mohammad Yunus, an economics lecturer at the University of Chittagong in Bangladesh and its success started a new wave in the world of credit. It became the world's favorite development idea, a means to cure world poverty and spread the wealth-creating force of capitalism across the globe. This led to the remarkable growth of Microfinance industry world over and micro finance institutions (MFIs) were established to make up for the gap that existed between demand and supply. At the end of 2013, the microfinance community reached 211 million clients, 114 million of whom were living in extreme poverty. The growth in clients for microfinance has occurred primarily among those who live above US$1.90 a day.

Regardless of social goals strived by the MFIs they need to be economically viable and sustainable in long run. The efficiency of MFIs is a key in achieving sustainability. Earlier researches on general business indicate that organizations with greater female participation perform better. Against this backdrop in this paper we examine the performance of the MFIs worldwide based on the data collected from micromix web and also try to investigate if at all there is any relation between gender diversity in staff and the financial performance of microfinance organization.

Literature Review

MFI and performance

MFIs need to be economically viable and sustainable in long run. However, the welfarists favor poverty lending approach [1] and are critical of organizations moving from not-for-profit to for-profit and emphasizing on financial sustainability and efficiency rather than outreach [2,3]. Efficiency in MFIs refers to how well these institutions utilize their inputs to produce optimal outputs. In order to finance their growth and attain financial sustainability many MFIs restructured themselves. Sustainability is the ability of a program to stay financially feasible even if subsidies and financial aids are withdrawn [4]. It emphasizes on generating sufficient profit so that all the expenses are met and at the same time eliminating all subsidies, even those lessobvious subsidies, such as loans made in hard currency with repayment in local currency. Tucker and Miles [5] analyzed three data series for the period between March 1999 and March 2001 and establish that self-contained MFIs are profitable and their return on equity (ROE) and return on assets (ROA) is perform better. Tucker [6] suggests that benchmarking and competition improves business practices. Financial performance of a MFI is depending on management ratings [7], which in turn is greatly influenced by level of regulation, size (regulated and larger MFIs in terms of loan portfolio, total assets or borrowers) have significantly better management ratings than non-regulated ones) and top management. The top management is a key indicator of financial success and seems to have an upbeat influence on the number of subsidies received. Contrarily, younger MFIs may be more profitable, as suggested by Stephens [8] but not necessarily better managed. In the process to exhort sustainability the many MFIs charge very high interest but that in no way ensures greater profitability or minimization of cost. Similarly, the institutions that make smaller loans are not necessarily less profitable [9].

MFI and gender

Microfinance has a beguiling simplicity and a record of success not just in promoting financial resilience but in achieving other social objectives – reaching the excluded, empowering women and developing the capacity of small groups of people to take control of their own lives. Women have always been primarily linked to microfinance [10]. About 82 percent of the total borrowers are women. (Microcredit Summit Campaign (2012) reports). According to Mayoux [11] three foremost wiles which support the thought of focusing on women: aim of poverty eradication, the principle of gender equality, and the MFIs ‘efficiency. In fact, the success of microfinance is credited to its focus on women [12].

Past researches indicate a positive linkage between presence of increased number of women on board, in middle management and in total workforce to the organizations financial performance, market value and competitive advantage. Women at the top are driving force to the career ambition of young women in lower position, which in turn positively impacts financial performance of the firm. It is also confirmed that women are more cooperative and collaborative than men. They use participative managerial style, opposite to the competitive style of man [13-25]. This has been supported by Rosener’s [26] findings that women are more inclined towards interaction between workers and inclusion by involving the colleagues and sharing information and communicating with subordinates. Some [27-30] draw that there is positive relation between gender diversity and firm's likelihood and propensity to innovation and creativity. Compared to men women have a higher propinquity to customer and greater capability to understand and meet their needs [31-40].

Research Gap

Most of the earlier studies on microfinance industry referred to gender diversity as a measure of outreach of MFI and discussed the women empowerment process as a result of microcredit or microfinance's projects. In this study, an attempt is made to draw a relation between gender diversity in workforces and MFIs ‘financial performance [41-45].

Objectives

To analyze the trend of MFIs

To analyze performance of the MFIs and

To analyze the gender diversity and its impact on financial Performance of MFIs

Research Methodology

This is a quantitative study, based purely on secondary data collected from MIX Market Inc. website over the period of 2008-2010. Our sample consists of 185 MFIs (observations) in 41 countries. To evaluate the financial performance the three-year average of return on equity (ROE) and operational self-sufficiency (OSS) have been used. ROE demonstrates an MFIs ability to generate income from its core financial service activity. ROE: (Adjusted Net Operating Income - Taxes)/Adjusted Average Total Equity. OSS is the most basic measurement of sustainability that indicates whether revenues from operations are sufficient to cover all operating expenses. Operational Self- Sufficiency Financial Revenue/(Financial Expense+Impairment Losses on Loans+Operating Expense). Linear regressions are applied to establish the relationship between gender diversity and financial performance. Three models have been developed. These models consist of the following (Table 1).

Control variable(y1) eqaution
Total staff
Growth in GDP per capita
Spread of interest
Inflation rate
Type of organization
Inception
Gender diversity(y2)
Ratio of female in staff
Organizational efficiency(y3)
Average loan
Operating expense ratio
Asset/liability management(y4)
Debt to equity
Yield on gross loan portfolio
Cost of fund ratio
Outreach (y5)
Average loan to borrower
Ratio of women borrower
Quality of portfolio(y6)
PAR30

Table 1: Quantitative study.

Multiple regressions have been used to test the increase of explained variance moving from a basic model, which includes only the control variables, to models which consider also gender diversity and covariates. We estimated regression for both the dependent variables using general control variables and different potential variables which could affect the financial performance reached by a MFI. Data have been analyzed using SPSS.

Analyses

Table 2 shows the trend of the sample of 185 MFIs in 41 countries. The selected MFIs have an average of 131 employees and 16,681 active borrowers. Both the lending style (individual, group and both) and geographical spread (urban, rural and both) suggest that on an average they are distributed uniformly and equitably.

S.no. Country Obv. (n.) Average staff (n.) Average active borrowers (n.) Lending (%) Total assets (.000 US$) Location (%)
          Individual Group Both   Urban Rural Both
1 Afghanistan 2 553 28,282 50 50 - 26,232 50 - 50
2 Albania 4 183.3 11,329 25 - 75 522 25 25 50
3 Angola 2 185 8,904 50 50 - 1,310 50 50 -
4 Armenia 3 63.7 5,092 33.3 - 66.7 2,819 33.3 33.3 33.4
5 Azerbaijan 7 54.2 5,731 31.8 22.7 45.5 2,809 27.3 31.8 40.9
6 Bolivia 2 64.5 7,248 50 50 - 195 50 50 -
7 Bosnia and Herzegovina 1 72 - - -   919,347 - - 100
8 Bulgaria 2 38.2 1,438 50 - 50 324,706 50 50 .
9 Burkina Faso 3 35.3 22,211 - 33.3 66.7 545,698 0 33.3 66.7
10 Burundi 2 25.5 317 50 - 50 252,575 0 50 50
11 Cambodia 5 545.1 58,270 40 40 20 12,657 20 40 40
12 Colombia 3 46.3 9,014 33.3 33.3 33.4 1,075 33.3 33.3 33.4
13 D. R. of Congo 3 21.9 1,700 33.3 33.3 33.4 682 33.3 0 66.7
14 Ecuador 28 99.5 12,580 53.6 26.2 20.2 17,361 42.9 48.8 8.3
15 El Salvador 3 27 3,342 33.3 - 66.7 504 33.3 33.3 33.4
16 F.Y.R. of Macedonia 1 7 3 - - 100 1,402 - - 100
17 Georgia 3 198.7 18,298 33.3 - 66.7 701 33.3 33.3 33.4
18 Ghana 2 280.8 30,910 50 50 - 4,336 50 - 50
19 Haiti 3 76.6 5,389 33.3 33.3 33.4 3,774 66.7 - 33.3
20 Honduras 13 109.2 9,909 46.1 23.1 30.8 33,375 38.5 30.8 27.5
21 Kazakhstan 4 35 2,299 50 25 25 1,346 25 25 50
22 Kenya 8 69.4 10,533 12.5 29.2 58.3 8,631 29.2 29.2 41.6
23 Kosovo 4 41.2 1,749 25 - 75 42 25 25 50
24 Kyrgyz Republic 6 73.6 3,142 33.3 16.6 50.1 22,719 33.3 16.6 50.1
25 Mali 3 397.3 53,318 33.3 33.3 33.4 5,847 33.3 33.3 33.4
26 Mexico 2 78.9 5,780 50 50 - 11,491 50 50 -
27 Mongolia 3 31.7 1,721 33.3 - 66.7 33 33.3 - 66.7
28 Morocco 3 283.3 47,583 33.3 33.3 33.4 6,476 - 33.3 66.7
29 Nicaragua 8 186.4 20,897 50 25 25 11,809 50 50 -
30 Niger 3 17 15,155 33.3 - 66.7 610 - 33.3 66.7
31 Nigeria 3 89 1,625 33.3 - 66.7 1,490 33.3 33.3 33.4
32 Peru 5 86.2 11,617 40 20 40 3,767 40 40 20
33 Philippines 5 543.2 58,051 40 20 40 26,782 40 20 40
34 Romania 8 27.4 1,233 37.5 - 62.5 2,444 25 37.5 37.5
35 Russian Federation 3 306.5 10,271 66.7 33.3 - 20,322 66.7 - 33.3
36 Senegal 3 79.3 8,144 33.3 - 62.5 393 33.3 - 66.7
37 Tajikistan 8 237 7,838 37.5 25 37.5 19,341 37.5 25 37.5
38 Tanzania 3 25.3 2,078 33.3 33.3 33.4 312 33.3 33.3 33.4
39 Togo 4 46 3,563 33.3 25 41.7 1,517 - 25 75
40 Uganda 4 69.5 15,042 25 25 50 305 25 25 50
41 Zambia 3 30.7 6,496 33.3 33.3 33.4 821 33.3 33.3 33.4
  Total 185 131.2 16,681 39.2 21.8 38.9 116,756 33.6 31.5 34.9

Table 2: General information.

From Table 3 it can be seen that most of the parameters are negatively correlated. And over all the maximum correlation is between OSS and ROE at 0.67 and least correlated is between inflation and average loan for staff i.e., when inflation least loan is disbursed to the staff in the organization. High positive relation between OSS & ROE indicate that the organization with high ROE have high operational self-sufficiency.

 Variables Mean Std. dev. 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Y1 0.04 0.54 0.67 -0.12 0.07 0.08 0.15 0.35 -0.07 0.02 -0.15 -0.13 -0.09 0.02 -0.14 0.34
Y2. -0.01 0.06   -0.14 0.14 -0.03 0.09 0.3 -0.09 0.06 0.09 -0.11 -0.14 0.03 -0.03 0.06
Y3. 131.23 81.27     -0.16 0.1 0.07 -0.1 -0.16 0.08 0.08 0.25 -0.1 -0.09 0.22 -0.05
Y4. 0.09 0.09       -0.04 0.04 -0.17 0.23 -0.21 -0.04 -0.09 -0.03 -0.01 0.09 -0.21
Y5. 0.09 0.05         0.39 -0.06 0.09 0.13 -0.15 0.25 0.19 -0.09 0.22 -0.01
Y6. 0.07 0.04           0.41 -0.49 0.24 -0.28 0.64 0.46 -0.21 -0.19 0.22
Y7. 0.14 0.66             0.03 -0.05 -0.12 -0.03 0.08 0.11 -0.08 -0.04
Y8. 131.41 198.26               -0.26 -0.01 -0.28 -0.22 0.25 -0.36 -0.02
Y9. 0.22 0.15                 -0.05 0.6 -0.15 -0.12 0.24 0.02
Y10. 3.23 4.31                   -0.08 -0.24 0.02 -0.06 0.05
Y11. 0.32 0.13                     0.33 -0.01 0.31 -0.14
Y12. 0.08 0.04                       0.17 0.27 -0.06
Y13. 6.09 29.74                         -0.45 -0.03
Y14 0.56 0.2                           -0.2
Y15. 0.07 0.13                            

Table 3: Descriptive statistics and standard deviations.

Table 4 shows that there is a positive relation between number of women in staff and the ROE and OSS and that too significant. Analysis of regression models shows a positive impact of diversity in gender in the total workforce on performance both in terms of operation activities and on overall return for owners. Though the value of R is low but there is statistically significant predictor. We see that ΔR-square increase for OSS to 12% and 8% for ROE as we include ratio of women to staff (from basic model i.e., 1 to 2) and there is an additional variance of 10.6% for OSS and 8.1% for ROE as we consider the collateral variable i.e., from Model 2 to Model 3. The results show that the impact of gender diversity on financial performance is positive and shows robustness in the case of inclusion of collateral variables.

  Operational self-sufficiency(OSS) Return on equity(ROE)
Model   Model
1 2 3   1 2 3
β β β Sig. β β β
Constant (y0) -4.24 10.85 2.81 -4.24 -2.04 -0.73 -0.33
Control variable(y1)              
Total staff -0.11 -0.08 -0.07 -0.11 -0.06 -0.04 -0.07
Growth in gdp per capita 0.05 0.06 0.08 0.05 0.1 0.11 0.12
Spread of interest 0.09 0.12 0.19 0.09 -0.02 0.01 0.1
Inflation rate 0.04 0.05 0.01 0.04 0.06 0.07 0.13
Type of organization 0.02 0.07 0.02 0.02 0.11 0.15 0.15
Inception 0.02 -0.04 -0.01 0.02 0.07 0.02 0.01
Gender diversity(y2)              
Ratio of female in staff   0.35 0.34     0.29 0.31
Organizational efficiency(y3)              
Average loan     -0.21       -0.21
Operating expense ratio     0.03       0.12
Asset/liability management(y4)              
Debt to equity     -0.14       0.11
Yield on gross loan portfolio     -0.14       -0.18
Cost of fund ratio     -0.16       -0.13
Outreach (y5)              
Average loan to borrower     0.11       0.11
Ratio of women borrower     -0.02       0.02
Quality of portfolio(y6)              
Par30     0.17       -0.04
F .388 2.074 1.725   .654 1.720 1.322
?F   1.686 1.337     1.066 .668
R-square .026 .146 .252   .044 .124 .205
?R-square   .120 .226     .080 .161

Table 4: Linear regressions on OSS & ROE.

Conclusion

OSS is a reliable measure goodness of financial performance and analysis shows a positive relation between female in staff and ROE and also to OSS there by indicating positive and strong impact of gender on financial performance. Thus, MFIs may hire women as they are not only fair and ethic but also convenient from financial and economic point. It would also serve the goal of the MFI institutions of targeting the women [46-51].

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