Revenue Diversification and Financial Sustainability of Microfinance Institutions.

Revenue Diversification and Financial Sustainability of Microfinance Institutions.

ABSTRACT

The primary objective of Microfinance Institutions (MFIs) is extending credit to poor households. However, with the declining funding from government and NGOs, the emphasis is on their financial sustainability. Therefore, this article examines whether revenue diversification impacts on the financial sustainability of microfinance institutions. Using a worldwide panel data set of 443 MFIs in 108 countries, data for the period 2013-2018, and a two-step system GMM estimation model, we find that revenue diversification positively affects the financial sustainability of MFIs.

Keywords. Microfinance · Revenue Diversification · Financial Sustainability ·

JEL Classification G21 · L31

Introduction

Microfinance institutions (MFIs) have emerged as essential catalysts of financial inclusion and socio-economic development. MFIs provide credit to the poor financially excluded by mainstream banking institutions because they are considered high-risk borrowers. Thus, the main objective of MFIs is the eradication of poverty by fostering entrepreneurial activities through the provision of microcredit services and other non-financial services to the poor. Unlike conventional banking, MFIs lend small uncollateralized loans to the unbanked population using innovative lending strategies such as group lending and progressive loans. While microfinance institutions’ primary objective is to help eradicate poverty through expanded outreach (social performance), this goal is only attainable if they are financially sustainable.

Furthermore, increased outreach exposes an MFI to credit risks negatively affects financial performance and sustainability (Quayes,2012). Thus, MFIs that are not financially sustainable cannot fight poverty since the transaction cost of serving poor people is higher than focusing on the wealthier people. Also, the financial intermediation model of MFIs is quite different from that of traditional banking institutions. Microfinance institutions are not dependent on customers’ deposits as their prime source of funds; they are mainly funded through retained earnings, members’ savings, subsidies, and support from individuals, government, and NGOs (Al-Azzam, 2019). Despite the growth in the microfinance industry in the recent past, funding has decreased significantly as donors and government have shifted attention to other priority areas. Therefore, internal resources and subsidies are no-longer sufficient for a sustained level of social outreach, and countermeasure MFIs are commercializing their services. For instance, MFIs are gradually using commercial funding and mobilizing interest-earning deposits, which studies claim is likely to lead to a mission drift; lower the level of outreach (Beisland, D’Espallier, & Mersland, 2019).

The move towards financial sustainability has also been catalyzed by the growing competition (particularly from mobile lenders and large domestic banks), technological advancements, financial liberalization, and government regulation (Hermes, Lensink, & Meesters, 2011). Besides reaching as many poor people as possible, MFIs should strive to attain financial sustainability and efficiency for their continued existence. Microfinance institutions should cover the cost of lending out of the income generated from the outstanding loan portfolio and minimize operational costs as much as possible to be financially sustainable (Bayai, & Ikhide, 2018).

With the increased push for financial sustainability and self-sufficiency, MFIs are no longer viewed as a platform for poverty eradication and socio-economic but a key player in the formal financial sector. Thus, MFIs should apply market-based principles to achieve their duo goals of maximizing social wealth (serving more poor people) and economic prosperity (financial sustainability). Again financial sustainability is an essential ingredient for firms’ competitiveness and long-term survival. Given these facts, policy-makers and academia continue to interrogate determinants of MFIs financial sustainability; however, this research area remains amongst the virgin in microfinance studies. Furthermore, recent studies have overemphasized on a few factors such as outreach (Quayes, 2012; Churchill, 2020), capital structure (Bayai & Ikhide, 2018; Bogan, 2012), and quality credit portfolio (Ayayi & Sene,2010). A sustainable approach to MFIs’ financial sustainability would be to avoid over-dependence on one single revenue source. Marwa Aziakpono (2015) examined the relationship between profitability and financial sustainability of Saving and Credit Cooperatives (SACCOs) in Tanzania and concluded that microfinance institutions could attain financial sustainability by reducing their cost per loan and increasing their net revenue.

Consequently, MFIs must find innovative ways of generating income to attain financial sustainability; this entails diversifying into non-lending activities such as advisory services, custodial services, underwriting, and securities brokerage that earn non-loan based income. Revenue diversification affects the performance of both for-profit and non-profit organizations; savings co-operative societies (Mathuva, 2016), credit unions (Vieira, Bressan & Bressan, 2019), commercial banks (Hamdi, Hakimi, & Zaghdoudi, 2017), and the financial health of non-profit entities (Hung & Hager, 2019; Ahmad, Siraj & Ismail, 2019). Some scholars have argued that revenue diversification leads to cross-selling and cross-subsidization, implying that revenue diversification can improve MFIs lending business. Though revenue diversification is a probable adaptive response to the challenges facing MFIs, there is a lack of evidence concerning the relationship between revenue diversification and the financial sustainability of MFIs.

Consequently, this study seeks to fill this gap in the literature. The remainder of this study is organized as follows. The next section presents an overview of the related empirical literature. Section 3 discusses the methodology, while Section 4 presents the findings and discussion of the results. Section 6 concludes.

Review of the empirical literature

The financial sustainability of MFIs is subject to extensive debate between two competing theories; the welfarist theory and the institutional theory. On the one hand, the welfarist view that claims MFIs success is gauged by the number of poor people they serve. This theory is grounded on the premise that MFIs were established to fight poverty by empowering the economically active poor (Marwa & Aziakpono, 2015; Chattopadhyay & Mitra, 2017). On the other hand, the institutional approach suggests that the primary objective of MFIs is to create sustainable financial intermediation(Bhanot & Bapat, 2015; Chattopadhyay & Mitra, 2017). Their assertion is grounded on the understanding that financially sustainable MFIs can provide long-term financial services to more poor people without depending on subsidies and grants, which will ultimately stimulate the financial system (Morduch, 2000).

Similarly, it has also been argued that providing credit to the poor is very expensive due to the high transaction costs and risks associated with information asymmetry moral hazard (Hermes & Lensink, 2011). As MFIs expand to reach more clients, more information and resources are needed to screen, monitor, and enforce loans. Studies have also shown a trade-off between breadth of outreach and financial sustainability of MFIs; aggressive commercialization, targeting profitability, and sustainability is likely to compromise MFIs’ social mission of reaching out the unbanked people in the world (Awaworyi & Churchill, 2018). The trade-off between outreach and financial sustainability of MFIs has dominated in microfinance literature.

A study by Churchill (2020), based on a sample of 1595 MFIs in 109 countries, found that an increase in outreach breadth for profit-making MFIs led to improved financial sustainability and vice versa, but led to a decline in financial sustainability for not-for-profit MFIs and vice versa. Churchill’s (2020) findings suggested complementariness between breadth of outreach and sustainability and a trade-off between depth of outreach and financial sustainability. Based on a sample of 217 MFIs in 101 countries for 1998-2006, Ayayi and Sene (2010) found that MFIs’ financial sustainability is influenced by the quality of credit portfolios, interest rates, and client outreach, and the age of MFIs. Furthermore, Quaeyes (2012), employing data from 702 MFIs operating in 83 countries and found a positive complementary relationship between financial sustainability and depth of outreach. In the same research line, Quaeyes (2015), based on a sample of 764 microfinance institutions (MFIs) from 87 countries, investigated the possible trade-off between outreach and performance and found that greater depth of outreach has a positive impact on the financial performance of an MFI, thus a key driver of financial sustainability. A few studies have also explored the effect of commercialization on MFIs’ financial sustainability and outreach. A survey by Bayai and Ikhide (2018) that sought to examine the impact of financing structure on financial sustainability, using a sample of 60 SADC MFIs and data for the period 2005-2010, found that financing structure influenced financial sustainability; however, the impacted varied across countries. The study further found that portfolio at risk, cost efficiency, and costs linked to deposit attraction were significant determinants of financial sustainability. Hoque, Chishty, and Halloway (2011) examined the impact of commercialization on capital structure, mission, and microfinance institutions (MFIs). The authors used panel data for the years 2003-2008. They found that increased use of commercial debt (leverage) decreased outreach to the poor due to the increased cost of capital, leading to higher borrowing costs and ultimately higher default rate and increased credit risk.

The studies mentioned above points to an ongoing debate aimed at unearthing MFIs’ financial sustainability drivers. Fundamentally, financial sustainability denotes the ability of MFIs to comprehensively cover all their operational and administrative costs, including losses from bad loans, from their revenues from operations. As hybrid entities, MFIs pursue both social and economic objectives. Hence, as they seek to enhance the impoverished population’s social welfare, they should also strengthen their financial sustainability by minimizing costs and maximizing revenue (Ndiege, Qin, Massambu, & Towo, 2016). Therefore, MFIs should diversify their revenue activities, preferably by offering related financial services to improve financial profitability and achieve financial sustainability. The theoretical foundation of revenue diversification is Markowitz’s (1952) Modern Portfolio Theory. According to this theory, revenue diversification benefits might arise from engaging in different uncorrelated income-generating activities. It has also been argued that interest and non‐interest income are uncorrelated (or negatively correlated). Therefore, lending institutions with a high share of non‐interest income are less exposed to income variability, as possible cyclical declines to interest income are be compensated by a stable or an increasing non‐interest income (Chiorazzo et al., 2008; Sharma & Anand, 2018). Although the concept of revenue diversification has been examined extensively in the banking sector, just a few studies have focused on the microfinance sector.

Doan, Lin, and Doong (2018), considering 83 countries’ data over 2003–2012 assessed the relationship between income diversification and bank efficiency, found that increased income diversification improved bank efficiency. Similar findings were reported by Alhassan (2015), based on a sample of 26 Ghanaian banks and data for the period 2003 to 2011, and Nguyen and Pham (2020). They looked at Vietnamese commercial banks over the period 2005–2017. Empirical studies have also shown a positive association between revenue diversification and bank performance. A survey by Chiorazzo, Milani, and Salvini (2008) employed a sample of 85 Italian banking firms, and a panel dataset over the period 1993– 2003 found a positive and significant relationship between revenue diversification and bank performance. Similar results were reported by Meslier, Tacneng, and Tarazi’s (2014), who considered a sample of 39 universal and commercial banks in the Philippines and data over the period 1999 -2005 and, Hamdi, Hakimi, and Zaghdoudi (2017) in their paper that used annual data of 20 Tunisian banks during the period 2005-2012 and Dynamic Panel Data model. Again, revenue diversification has been linked to cross-subsidization and cross-selling, which improves the lending business and eventually improve profitability (Cosci, Meliciani, & Sabato, 2009; Abedifar, Molyneux, & Tarazi, 2018). In the context of MFI, Bergsma (2011) found that MFIs that offer microsavings are more financially sustainable than those that do not. Additionally, the author found no significant evidence to suggest that by providing microsavings, MFIs abandoned their most impoverished clients. Going by the extant literature, by engaging in revenue diversification, MFIs will benefit from increased efficiencies, improved financial performance, and achieve financial sustainability in the long run. Thus this study hypothesis as follows.

Ho: Revenue diversification positively and significantly affects the financial sustainability of MFIs

Methodology

3.1 Data and Sample

As our main objective is to assess the impact of revenue diversification on the financial sustainability of Microfinance institutions. Data is extracted from the MIX (Microfinance Information Exchange) Market database (www.mixmarket.org), a web-based platform that is maintained, supported by macroeconomic data from the World Bank. This database contains extensive financial and outreach information for MFIs. Once the MFIs submit their reports to the mix market, the data is converted into U.S. dollars using the prevailing exchange rate. At the time of data collection, it listed the profiles of over 3114 MFIs from over 122 countries. We chose the sample period from 2013 to 2018. At least two reasons to select this sample period: First, to isolate the effects of the global financial crisis of 2007-2009. Second, there are too many missing values in the database for earlier periods and after 2018. Too many missing values can create sample selection bias in favor of a few banks. After eliminating missing values and outliers, we left with an unbalanced panel of 443 MFIs with 2,664 MFIs-year observations

3.2. Definition and measurement of variables

In our empirical models, financial sustainability is used as dependent variables, while revenue diversification is the independent variable. The study also includes several control variables (depth of outreach, breadth of outreach, MFI firm size, and leverage), as argued in the empirical literature.

3.2.1Financial sustainability

Measurement of MFIs’ financial sustainability is a difficult task. However, various proxies have been used in previous studies. The widely used financial sustainability indicator is financial self-sufficiency (Ayayi & Sene, 2010; Kinde, 2012; Rahman and Mazlan, 2014; Tehulu, 2013). Financial self-sufficiency (FSS) is the ratio of adjusted operating income to adjusted operating expenses, and it is calculated as follows:

FSS denotes the financial self-sufficiency of microfinance institutions, and π is the total revenue generated by a microfinance institution. X indicates the total expenses for a microfinance institution i in the period. FSN is expressed in ratio form, where an institution with the financial sustainability of greater than one is considered sustainable. Other measures of financial sustainability used in previous studies include return on asset (ROA) and return on equity (ROE) (Bayai & Ikhide, 2018; Omri & Chkoundali, 2011; Meyer, 2019; Babajide, Taiwo, & Adetiloye, 2017). ROA is measured by dividing the net operating income by the institution’s total assets in the period. This measure shows the extent to which the institution uses its assets to generate profit. ROE is measured by dividing the institution’s net operation income by average equity of the period. Thus, to check for the results’ robustness, we use ROA and ROE, which are also MFIs performance measures, as dependent variables in the alternative models.

3.2.2 Revenue diversification

Following Stiroh and Rumble (2006), Meslier et al. (2014), Edirisuriya et al. (2015), we use the Herfindahl-Hirschman Index (HHI) to measure revenue diversification (R.D.). MFIs revenue is derived from lending activities (interest income on the loan portfolio, fee and commission income on the loan portfolio, income from penalty fees on loan portfolio), and non-lending operations (investing in government securities, underwriting, and consultancy services). Thus, revenue diversification is measured as follows;

Where; HHI= Herfindahl-Hirschman Index, FRL= Financial revenue from loans, NLFR= Non-loan financial revenue, and TFR= Total financial revenue. A higher value of R.D. indicates a more diversified revenue mix; however a value of zero means all revenue comes from one source (complete concentration), 0.5 is an even split (Doan, Lin & Doong, 2018).

3.2.3. Control Variables

To isolate the effect of revenue diversification on the financial sustainability of MFIS, we control for several relevant factors, as suggested by the empirical literature. There is a trade-off between microfinance institutions outreach, depth of outreach and breadth of outreach, and financial sustainability (Churchill, 2020). The breadth of outreach shows the extent to which microfinance promotes financial inclusion, and thus it is often measured by the number of clients served. Following previous studies, we take the natural logarithm of the number of active borrowers (Memon, Akram & Abbas, 2020). Depth of outreach attempts to capture the number of poor people in society that MFIs have reached, and it is also referred to as the quality of outreach (Quayes 2012). Depth of outreach is measured as the average loan size divided by the annual GDP per capita, all in $ U.S.; a smaller value is an indicator of greater depth of outreach (Hartarska, 2005; Louis, Seret & Baesens 2013). Studies have shown that the financial performance of MFIs is positively affected by the ratio of debt to equity; less leveraged MFIs have better operational self-sufficiency. The debt measures leverage to equity ratio (Quayes, 2012; Bayai & Ikhide, 2018). Firm size is likely to affect sustainability as large MFIs have the advantage of economies of scope. MFIs size is measured as the natural logarithm of total assets (Lensink, Mersland, Vu, & Zamore, 2018)

3.3. Empirical specifications

In this paper, we apply the system Generalized Method of Moment (GMM) suggested by Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998). The GMM estimation model has several advantages. First, the GMM estimator addresses the endogeneity problem, common in panel data estimation (Arellano and Bover, 1995; Blundell and Bond, 1998). Second, the GMM estimator also considers the biases that appear due to country-specific effects. Third, GMM helps avoids potential simultaneity or reverse causality among variables. Thus, our model takes the following form;

Where i indexes MFIs, j indexes country, and t indexes year and Y _i,t is the financial sustainability of MFI _j in year t. At the same time, Y_i,t_−1,and Y_i,t₋₂ are the one and two periods of lagged financial sustainability. X_i,trepresents a vector of MFI revenue diversification in year t of an active MFI i; Zi,t represents a vector of control variables in year t. ε_i,t= υ _i + γ _t + μ_i,tis the disturbance: γ_t is the unobservable time effects, υi is the unobserved complete set of country and MFI-specific effects, and μi,t is the idiosyncratic error.

The validity of the GMM estimation model depends on two conditions. First is the validity of the variables used as instruments. Second, lack of second-order serial correlation among residuals. Therefore, we conduct the Hansen test of overidentification restrictions. The null hypothesis (Ho) is that all the restrictions of overidentification are valid. Criteria of rejection/acceptation P rob > x2 ≥ 0.05(5%) If the probability is close to 1, it means that the test’s asymptotic properties have not been applied. Therefore we also must reject Ho (Roodman 2009). Additionally, we report the and the Arellano–Bond test of second-order correlation. The null hypothesis: Ho: of this test is that autocorrelation doesn’t exit. However, AR (1) is usually significant at 5% (AR (1) Pr > z < 0.05). Thus, the criteria of rejecting or failing to reject the null hypothesis we will be AR (2); probability (Pr> z) of AR (2) should be higher than 0.05, implying that the error term is not serially correlated.

Results and discussion

Before investigating the effects of revenue diversification on the financial sustainability of MFIs, we calculated the most commonly used descriptive statistics for the variables: financial sustainability (FSN), ROA, ROE, revenue diversification (R.D.), depth and breadth of outreach, firm size and leverage. As shown in Table, the financial sustainability of MFIS is 1.150 and ranges between -1.030 and 1.994. Comparing the mean FSN and the recommended threshold of 1.00 (100%), the findings of this study indicate that the global sample of MFIs can be said to be financially sustainable. However, the value is higher than 1.06 reported by Ayayi and Sene (2010) but lower than the figures reported by Marwa and Aziakpono (2015), 1.33 and 1.27. Similarly, the minimum value of -1.304 suggests that some MFIs are financially unstainable. The mean return on assets is 2.1%, which is slightly lower than the international (MIX) benchmark of 3% (ACCION, 2004).

Similarly, the mean ROE of 9.4% further confirms that microfinance institutions reported relatively satisfactory financial performance over the study period. Revenue diversification has a mean value of 0.132, which is significantly very low compared to 0.3 reported in the banking sector (Sharma & Anand, 2018; Elsas, Hackethal, & Holzhäuser, 2010). While most of the microfinance institutions are highly leveraged, as demonstrated by the mean value of debt to equity ratio of 4.164, the standard deviation of 2.717 coupled with the minimum 0.0136 and maximum values of 19.7 confirms a widespread dependence on debt financing as opposed to equity. The mean value of MFI firm size, measured by the natural logarithm of the institutions’ total asset, is 17.403, which transformed into their real values they will become $US36,147,427.33, $US 206,918.82 and $US8,100,183,205.34 for the mean, minimum and maximum values respectively. On average, MFIs are large enough to cover for their operational costs and be financially sustainable. The mean, minimum, and maximum breadth of outreach, measured as the natural logarithm of the number of active borrowers’ natural logarithm, was 10.484, 4.883, and 16.005, respectively. The translated values show an average of 35,739 clients per MFI, with some MFIs serving a few as 132 clients and others a large number such as 8,930,652. The average depth of outreach, the average outstanding loan balance per GDP, is 0.634, and the lowest value is 0.011, while the maximum value is approximately 7.678, which is a very extreme case. The high average outstanding loan balance per GDP indicates that MFIs are making far larger average loans, thus less poor clients are being served.

Table1. Descriptive Statistics

Variable	Obs	Mean	Std. Dev.	Min	Max
Financial sustainability	2664	1.150	0.243	-1.304	1.994
ROA	2664	0.021	0.065	-0.880	0.328
ROE	2664	0.094	0.198	-0.997	0.999
Revenue Diversification	2664	0.132	0.116	0.004	0.500
Depth of Outreach	2664	0.634	1.023	0.011	7.678
Breadth of Outreach	2664	10.484	1.832	4.883	16.005
Leverage	2664	4.164	2.717	0.013	19.730
Firm Size	2664	17.403	1.889	12.246	22.815

Table 2 presents the results for the association between financial sustainability and revenue diversification. In model I, the regression results of the association between financial sustainability (MFI SI) and revenue diversification (R.D) are presented. Then Model 2 presents the results of the regression of the ROA and R.D., while model 3 shows the regression analysis of ROE and R.D.

Table 2. Dynamic panel-data estimation, two-step system GMM

	Model 1	Model 2	Model 3
	FSN	ROE	ROA
L1.	0.437(0.111)**	0.389(0.061)**	0.506(0.057)**

RD	0.146(0.047)**	0.207(0.041)**	0.037(0.008)**
Depth	0.014(0.007)**	0.112(0.005)**	0.002(0.001)**
Breadth	0.026(0.007)**	0.028(0.006)**	0.006(0.001)**
Leverage	-0.013(0.003)**	-0.009(0.002)**	-0.002(0.001)**
Size	-0.004(0.005)	-0.006(0.005)	-0.003(0.001)**
_cons	0.484(0.098)	-0.081(0.052)	0.014(0.009)**
F value (6,441)	39.71	48.19	42.16
Prob >F	0.000	0.000	0.000
Observations	1710	1710	1710
No. Instruments	11	11	11
No. Groups	442	442	442
Hansen J-test chi2	7.51	7.28	7.01
Prob > chi2	0.111	0.122	0.136
AR(2) test	-1.07	0.50	0.75
Prob > chi2	0.286	0.618	0.453
No of MFIs	443	443	443
Notes: FSN denotes financial sustainability; ROE is the return on equity; ROA is the return on the asset; R.D. is the revenue diversification; Depth is the depth of outreach; Breadth is the breadth of outreach. The values in parentheses are standard errors. Hansen J-test denotes to the over-identification test for the restrictions in GMM estimation. The AR(2) test is the Arellano–Bond test for the existence of the second-order autocorrelation in first differences of residuals *p < 0.01, p < 0.05, *p < 0.1

This study’s main objective is to examine the effect of revenue diversification on the financial sustainability of MFIs. Table III presents the results of the GMM estimation model. Model 1 shows the results of financial sustainability as the dependent variable, while model 2 and model 3 show the regression results of ROE and ROA as dependent variables, respectively.

In Table III, we report the results of the dynamic panel-data estimation (two-step system GMM), and the specification reports of the Hansen J-statistic and the AR (2) and their corresponding p-values, which are the basis of test the null hypothesis on whether the instruments are uncorrelated with the error term or not. In our cases, we reject the null hypothesis and conclude that the GMM approach is well specified. Additionally, in the three models, the value of AR (2) is insignificant, implying that we cannot reject the second-order correlation’s null hypothesis.

Overall, Table III provides consistent and robust evidence that revenue diversification in MFIs income is associated with increased profitability and improved financial sustainability. These findings are consistent with those reported by Luu, Nguyen, and Vu (2019) and Chiorazzo et al., (2008), who focused on the banking sector. The probable reasons for the positive causality between revenue diversification and financial sustainability include; first, by engaging in non-lending activities, MFIs can exploit idle resources and leverage their intangible assets such as human capital for sustained competitive advantage. Second, diversification has also been linked to cross-selling and cross-subsidization strategies (Abedifar et al., 2018). MFIs can offer a mix of financial services using the existing client information; again, the income from non-lending businesses can improve the lending business by reducing the interest margins. Third, the economies of scope exist between lending and non-interest activities, which reduces the average cost of production and enhances cost efficiency. Depth of outreach has a positive effect on the financial sustainability of MFI, and the findings are consistent with those of Quayes (2012) and Quayes (2015) that found a positive complementary relationship between financial sustainability and depth of outreach. At the same time, leverage negatively affects (Quayes, 2012; Hartarska (2009). The breadth of outreach had a positive impact on financial sustainability. The findings are similar to those of Churchill (2020) that found complementarity between sustainability and outreach breadth though a trade-off between MFIs sustainability and outreach depth.

Contrary to the trade-off theory, the positive causality between the two dimensions of outreach and financial sustainability suggests that it is possible for MFIs to simultaneously achieve their two objectives- financial sustainability and serving as many the poor people as possible. Additionally, the findings show a negative and significant relationship between leverage and financial sustainability, which is in line with Hartarska and Nadolnyak’s (2007) results. Hence less leveraged MFIs are more financially sustainable than highly leveraged ones. Hartarska and Nadolnyak (2007) attributed this result to a possible link between donors’ willingness to provide equity to financially sustainable MFIs and extend loans to unsustainable MFIs. Again, unlevered MFIs with bigger endowments would be more efficient in their operations since they do not need to drift from their mission to get additional capital.

Contrary to Bogan (2012), the results indicate robust empirical evidence of a negative relationship between MFI size and financial sustainability. The findings suggest that smaller institutions (based on assets) are more financially sustainable, which might be attributed to the first small MFIs serving a few more efficient clients. Second, serving a small group of customers builds strong customer relationships, which lower credit risks. Third, very large MFIs suffer from diseconomies of scale associated with the managerial cost of coordinating the expanded span of control, ultimately compromising performance and sustainability.

Conclusion

This paper aims to provide empirically examine the revenue diversification-financial sustainability of microfinance institutions. Previous studies have primarily focused on depth and of as the main determinants of MFIs’ financial sustainability. In contrast, this study focuses on revenue diversification. Using a sample of 444 MFIs and a panel dataset from 2013 to 2018 and the two-step system GMM estimation methods, the study finds a positive and significant association between revenue diversification and financial sustainability of MFIs. Specifically, revenue diversification improves both performance and financial sustainability of microfinance institutions. The findings also suggest complementariness between outreach (depth the and breadth) and financial sustainability. However, the impact of leverage (commercial debt) on financial sustainability is negative. The results reported in this study offer important managerial and policy lessons on MFIs’ financial sustainability. Microfinance practitioners and policy-makers should consider revenue diversification as a strategy through which MFIs can attain financial sustainability. However, some previous studies that focused on the banking sector linked revenue diversification to income volatility; hence care should be taken to ensure the safety and soundness of individual MFIs and the whole financial system. Despite the novelty of the findings, the study had several limitations. First, the study considers the aggregate non-loan financial revenue. Thus, future studies can look deeper into the various non-loan financial revenue components that influence MFIs’ financial sustainability.

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List of ountries

Country

1. Afghanistan

2. Albania

3. Angola

4. Argentina

5. Armenia

6. Azerbaijan

7. Bangladesh

8. Belarus

9. Belize

10. Benin

11. Bhutan

12. Bolivia

13. Bosnia and Herzegovina

14. Brazil

15. Bulgaria

16. Burkina Faso

17. Burundi

18. Cambodia

19. Cameroon

20. Central African Republic

21. Chad

22. Chile

23. China, People’s Republic of

24. Colombia

25. Comoros

26. Congo, Democratic Republic of the

27. Costa Rica

28. Cote d’Ivoire (Ivory Coast)

29. Croatia

30. Dominican Republic

31. East Timor

32. Ecuador

33. Egypt

34. El Salvador

35. Ethiopia

36. Fiji

37. Gabon

38. Gambia, The

39. Georgia

40. Ghana

41. Grenada

42. Guatemala

43. Guinea

44. Guinea-Bissau

45. Guyana

46. Haiti

47. Honduras

48. Hungary

49. India

50. Indonesia

51. Iraq

52. Israel

53. Jamaica

54. Jordan

55. Kazakhstan

56. Kenya

57. Kosovo

58. Kyrgyzstan

59. Laos

60. Lebanon

61. Liberia

62. Macedonia

63. Madagascar

64. Malawi

65. Malaysia

66. Mali

67. Mexico

68. Moldova

69. Mongolia

70. Montenegro

71. Morocco

72. Mozambique

73. Myanmar (Burma)

74. Namibia

75. Nepal

76. Nicaragua

77. Niger

78. Nigeria

79. Pakistan

80. Palestine

81. Panama

82. Papua New Guinea

83. Paraguay

84. Peru

85. Philippines

86. Poland

87. Romania

88. Russia

89. Rwanda

90. Senegal

91. Serbia

92. Sierra Leone

93. Slovakia

94. Solomon Islands

95. South Africa

96. South Sudan

97. Sri Lanka

98. Sudan

99. Suriname

100. Swaziland

101. Syria

102. Tajikistan

103. Tanzania

104. Thailand

105. Togo

106. Tonga

107. Trinidad and Tobago

108. Tunisia

109. Turkey

110. Uganda

111. Ukraine

112. United States

113. Uruguay

114. Uruguay

115. Uzbekistan

116. Vanuatu

117. Venezuela

118. Vietnam

119. Yemen

120. Zambia

121. Zimbabwe