Internal Determinants of the Microfinance

Internal Determinants of the Microfinance Institutions Performances in Ivory Coast: A Panel Data Analysis

Jean Michel Banto1 Marc-Arthur Diaye2 Eric Paget-Blanc3 Boubakar Habib Sarr4

Summary This article aims to identify and analyze the internal determinants of the financial and social performance of microfinance institutions (MFI) in Ivory Coast. For this purpose, we have collected a database of twenty-two Ivorian MFI with financial data covering the period 2011-2014. Our results highlight the positive influence of the legal form on the financial performance of MFI. Specifically, we show that MFI with the status of "public limited companies" have higher profit margins than mutual and cooperative savings and credit institutions (IMCEC). On the other hand, the prudential ratios, the maturity of the MFI, the size of customer deposits and the number of points of service seem to have no influence on MFI’s financial performance. Regarding social performance, the maturity of the MFI, the numbers of points of service and the geographical coverage of the MFIs play a positive role in the attractiveness of the customers. Lastly, the capitalization ratio, geographical coverage and deposit size play a major role in the size evolution of the loans granted to customers. Keywords : Microfinance, performance, governance JEL codes : C51, G21, G32, P27

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LITEM, Univ Evry,IMT-BS, Université Paris-Saclay, 91025, Evry, France, [email protected] CES, Université Paris 1 Panthéon-Sorbonne, 75013, Paris, France, [email protected] 3 LITEM, Univ Evry,IMT-BS, Université Paris-Saclay, 91025, Evry, France, [email protected] 4 Ministry of Finance, Abidjan, Ivory Coast, [email protected] 2

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1. Introduction The mission of MFI – also referred as decentralized financial systems – DFS – by regulators in West Africa - is to make savings and credit services available to people who are generally excluded from the traditional banking system. It includes a wide range of financial services such as deposits, loans, credits, payment services and, recently, money transfers, insurance or micro insurance supply to low-income households as well as micro-enterprises. However, because of their ability to serve a poor customer base, microfinance institutions not only aim for financial performance but also have social objectives. This duality of objectives is the subject of debate in the academic literature. Some research work advocates for MFI to be focused primarily on financial goals (Jacquand, 2005), while others support the need to pursue a dual purpose, both financial and social (Christen, Rosenberg and Jayadev, 2006; Bédécarrats, 2010). The financial performance of MFI is apprehended by conventional financial ratios that have been harmonized at the level of the West African Economic and Monetary Union (WAEMU). These ratios are indicators of profitability, efficiency-productivity, portfolio quality, and management. The measure of social performance, meanwhile, is not unanimous. It is indeed the subject of a debate between two main approaches, namely the ‘welfarist’ approach and the ‘institutionalist’ approach. For welfarists, microfinance should aim to fight against poverty and improve the well-being of populations excluded from conventional banks. In addition to providing financial services, microfinance must provide non-financial services such as training and technical assistance to microentrepreneurs, literacy and women's empowerment. This approach has been supported by the research of Khandker et al. (1998); Morduch (1998); Morduch and Roodman (2014) and Banerjee et al. (2015). In practice, this line of thinking is reflected in the application by MFI of lower interest rates than the market and a large dependence on public or private subsidies. For advocates of this approach, the social performance of an MFI is measured by changes in the level of income, nutrition and education of the poor, as well as access to health and insurance services. However, the welfarist approach has been criticized in two ways. First, MFI that have adopted this approach have been faced to a high level of delinquency rates and very high operating costs, leading to the gradual disappearance of many microcredit programs. Secondly, research in this field faces the methodological difficulty of measuring the social impact of MFIs. These criticisms favored the emergence of the institutionalist approach. This is based on two requirements: the credit massification and the institution sustainability. The corollary of these two requirements is the improvement of performance indicators and the respect of prudential ratios. This generally implies the application of higher interest rates than in MFI that have opted for the welfarist approach and even than in conventional banks. The goal of this approach is not to be focused on improving the overall well-being of the poor peoples, but rather on improving access to financial services for the low-income population which are excluded peoples of traditional banking sector. To measure the social impact, proponents of the institutionalist approach use variables such as the number of poor people with access to banking services, the size of credit granted, or the quality of services offered. The institutionalist approach seems to prevail today, even though it is not yet unanimous. It is, for example, supported by international organizations such as the World Bank through CGAP (The Consultative Group to Assist the Poor), international development banks, or state regulatory agencies. Several research articles have attempted to identify the

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determinants of the financial and social performance of MFIs. Overall, the main determinants are governance, legal form, maturity and geographical coverage. In the following analysis, we consider these differents explanatory factors of the financial and social performance of MFI, and include two other factors, namely prudential ratios and the size of deposits. Indeed, prudential ratios and the size of deposits are important for the financial and social viability of MFI. The rest of the article is organized as follows. The Section 2 reviews the literature review, Section 3 presents the data, variable choices, and the econometric method, while Section 4 presents the results of the estimates and Section 5 concludes.

2. Literature Review 2.1 Review of empirical studies The purpose of the study is to identify and to analyze the influence of the internal determinants of financial and social performance of MFIs from a sample of institutions in Côte d'Ivoire. A number of empirical studies have already been conducted I this field, and have identified MFIs key performance indicators. Empirical experiences led in developing countries have shown that microfinance can help poor people to increase their income, consumption, and viable businesses (Shahidur et al., 1998, Choudhoury, 2003, Giraud and Renouard, 2013, Banerjee et al, 2015). Moreover, others papers such as Mees (2013) and Gubert Roubaud (2005) showed respectively that microfinance can improve the country's welfare (health and education) and also can make a positive change in microenterprises of low-income populations. However, although microfinance is an opportunity for those most in need to have access to funding sources (Robinson and Fidler, 2001), some authors are more critical about its management, especially its governance (Guérin and Servet, 2004; Guérin and Palier 2006; Ordioni 2005; Fernando 2004). According to the work of Banerjee et al. (2015) carried out in India, microfinance has a positive impact on the creation of activities generating income. However, Banerjee et al. (2015) argue that microfinance has no impact on social indicators such as education, health or women's empowerment. Moreover, according to Guérin and Palier (2006) and Guérin and Servet (2005), while microfinance allows 50% of MFIs’ clients to reduce the constraint of their family budget, and 25% to increase their entrepreneurial activity generating of income, it results in a financial and social failure for 25%. Finally, for Coleman (2008), the inefficiency of MFIs is largely due to the use of products that are unsuited to the needs of their clients. The literature on the governance of MFIs remains limited, unlike the one on corporate governance (Van den Berghe and Levrau, 2004, Meisel, 2004, Guedri, 2008, Gillette et al., 2008) and banking governance (Eichengreen and Gibson, 2001, Aizenman and Marion, 2003, Goddard et al, 2004, Bates et al., 2009). Works on MFIs’ governance has focused on managers' practices and on the impact on their performance (Chrysten et al., 2003, Peck and Rosenberg, 2004, Satta, 2004, Lapenu and Pierret, 2005, Sabana, 2006, Labie, 2007, 2009). In addition, Hartarska (2005) and Mersland and Strøm (2009) questioned about the role played by governance mechanisms on the social and financial performance of MFIs in Central and

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Eastern European countries. As for Tchuigoua (2011), he analyzed the influence of governance mechanisms on the performance of MFIs in Sub-Saharan Africa on a sample of 64 African MFI between 2001 and 2005. Mersland and Strøm (2009) and Tchuigoua (2011) did not find evidence of the influence of board size and legal form on the financial and social performance of MFIs. However, Mersland and Strøm (2009) found a negative impact of MFIs maturity on financial performance measured by operational selfsufficiency and a positive influence of maturity on loan size. According to them the volume of the credit decreases when the IMFs grew. Indeed, when MFIs reach maturity, they tend to attract poor customers who have a low repayment capacity, hence the decline in the credit size. In addition, Mersland and Strøm (2009) analyzed the role played by geographic coverage on financial and social performance variables. They did not find an influence of geographic coverage on social performance, but they showed that the performance of the loan portfolio increases as MFIs operate in urban areas. In sub-Saharan Africa, particularly in Côte d'Ivoire, the microfinance sector is highly attractive to the population and is showing encouraging results. However, there are many challenges for the MFIs: lack of qualified staffs, insufficient external funding and mainly the governance failures. 2.2 Key performance indicators for MFI The review of literature on MFIs has led us to identify the key performance indicators. Financial performance is apprehended by two indicators: the profit margin and the index of dependence on subsidies. Regarding social performance, we also used two performance indicators: the number of MFI’s active customers (translating the width of their customer base) and the size of the loans granted. This choice is in line with the recommendations of Manos et al. (2008) and Schreiner (2002). In fact, according to Manos et al., the subsidy dependency indicator considers the direct impact of subsidies, unlike other measures of financial performance, such as Return on Equity (ROE) and Return on Asset (ROA), which takes it only into account partly. In addition, Schreiner (2002) shows that the width indicator corresponding to the number of customers served should not be examined alone but in combination with that of the depth corresponding to the size of the loans granted (because a shallow depth can be compensated by more width and vice versa). If there is no consensus in the literature about the amount from which a loan from an MFI can be classified as social, we adopt the same approach as the Consultative Group to Assist the Poorest Populations (CGAP) which proposes the following definition: an MFI targets a poor customer base if the amounts of credit granted to its customers are less than 20% of the Gross National Product per capita (Acclassato, 2006, Ndour and Paget-Blanc, 2014). Applied to Côte d'Ivoire, this definition gives the sum of 306 euros (200,000 FCFA) per customer. Regarding the explanatory variables, based on the literature (see the following subsections), we identify seven types of variables that can influence the financial and social performance of MFI: the legal form (public limited company, mutual institutions, savings and credit cooperative -IMCEC), the prudential ratios they use, the size of their board , the maturity of the MFI, the number of points of service in the territory, the geographical coverage and the size of the deposits of customers.

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The legal form of MFI

MFIs can opt for different legal forms. Some have opted for non-governmental organization (Servet, 2015). Others have the status of public limited companies (LC), which make them less dependent on donations, according to some authors (Hardy et al., 2003, Jansson et al., 2004, Fernando, 2004, Ledgerwood and White, 2006). For the latter, the transformation of non-profit microfinance institutions into LC makes it possible to raise the capital needed for their activities. For Varottil (2014), the for-profit form allows MFI to make low-cost loans, which ultimately goes in the direction of their social function. Today, some MFIs are publicly traded (Littlefield and Rosenberg 2005, Ponsot 2007). Other MFI have chosen a cooperative or mutual type of legal form (Lelart 2006, Servet 2015). However, Hartarska (2005), Mersland and Strøm (2009), Sinclair (2012), Tchuigoua (2010) do not seem to find any influence of the form of legal status on the performance of MFI. Finally, Hansmann (1996), analyzing the advantages and disadvantages of governance systems in the framework of agency theory, shows that the NGO status makes it possible to minimize agency costs related to contracts concluded by MFI with the different parties such as employees, customers, donors, because of a better local anchorage. -

The size of the board

Boards of Directors are at the center of debates on economic governance reforms (Jensen 1993, Dalton et al., 1998, Hermalin and Weisbach 2003, Adams and Mehran 2003, and Zhao and Peters 2009). For Jensen (1993), the size of the board plays an important role in the effectiveness of an organization's governance system. According to him, the optimal size for effective operation of the board is eight. For Hermalin and Weisbach (2003), the increase in the size of the board has a negative effect on the performance of firms. Dalton et al. (1998) and Adams and Mehran (2003), however, yield different results. Adams and Mehran (2003) find no negative relationship between board size and firm performance as measured by Tobin's Q ratio. Dalton et al. (1998) believe that a broad board of directors increases the pool of expertise and resources needed for the company. -

The composition of the board

The composition of the Board of Directors has long been of particular interest, notably in the corporate governance literature (Maginson et al., 2009, Hollandts and Guedri, 2008, Kramarz and Thesmar 2013, Gillette et al., 2008). Indeed, the study of the composition of the board of directors makes it possible to evaluate, on one hand, the effectiveness of the proper functioning of the board of directors, and on the other hand its capacity to have an impact on the performance companies. For Hollandts et al. (2008), the opening of turnover to employee shareholders has no effect on corporate performance. However, Klein (1998) establishes a positive relationship between the presence of internal business people in the board's finance committee and the performance of firms. It appearq that the presence of external members improves the effectiveness of decisions, ie the impact on strategic choices (Gillette et al., 2008). In addition, gender diversity in the board of directors (Smith et al., 2006, Carter et al., 2003) appears to have a positive impact on corporate performance.

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In general, the question of whether the composition of the board has an impact on the financial and social performance of MFIs should be linked to the normative debate on the objectives of microfinance. Indeed, development agencies, social and commercial investors, individuals and NGOs (Goodman, 2003) are now investing in microfinance institutions. But all these entities with different interests, try to influence the orientations and decisions in the governance of microfinance institutions. For example, the presence of banks in the capital of an MFI may give rise to some doubts as to the MFI's mission (Köhn and Jainzik, 2007), since the MFI can then favor the achievement of financial objectives to the detriment of social objectives. -

Prudential ratios

Prudential standards are management indicators that assess the ability of MFIs’ managers to conduct healthy savings and lending activities. Initially, they were aimed at improving the governance mechanisms of conventional banking organizations (Macey and O'Hara, 2003). They have been adopted by bank regulators, and now constitute one of the key elements of the supervisory tools aimed at ensuring the creditworthiness and liquidity of credit institutions (Mishkin F., 2013). At the level of microfinance, the Central Bank of West African States (Banque Centrale des Etats d’Afrique de l’Ouest – BCEAO) has published a set of prudential ratios that the DFS operating in WAEMU must comply with. These ratios are instruments for managing and assessing the financial position of MFIs.

3. Empirical study The objective of this study is to identify the determinants of financial and social performances of MFIs. 3.1. The data We have retained a database made up of twenty-two Ivorian MFIs, observed over the period 2011 to 2014. It includes indicators relating to the internal governance of MFIs (size of the board , prudential regulation, composition of the board of directors, administration, governance system), to the financial performance (profit margin, subsidy dependency index) and the social performance (outstanding credit granted to customers, size of credit granted to customers). This dataset has been provided by the National Commission for Microfinance, the regulator of the microfinance sector in Ivory Coast. This commission depends on the Ministry of Economy and Finance of Ivory Coast, whose president is the General Director of the Treasury and Public Accounting. All microfinance institutions have the obligation to transmit their financial statements to the secretariat of the National Commission for Microfinance which is the Microfinance Department (MD). The data collected by the Microfinance Department is subject to several statistical treatments before being validated and submitted to the Central Bank of West African States. In other words, the data we have is the same as the BCEAO. Initially, the sample consisted of about 60 MFI, but some observations were missing. Thus, we

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made a selection of the MFI that provided the maximum amount of information in the financial statements submitted and whose various reports were available. The variables selected and the descriptive statistics of the sample are presented in Tables 1 and 2. 3.2 Choice of variables Table 1: List of variables

IDENTIFICATION

VARIABLE

DEFINITION AND MEASUREMENT

CODE Microfinance Institutions

MFI

The name of the microfinance institution

Profit Margin

MB

Ratio A / B with A = Net Exploitation Result and B = Exploitation Products. The standard to be respected is Ratio A / B> = 20%

Grant Dependency Index

IDS

Ratio A / B with A = Amount of grants and B = Income from loans. The lower the ratio, the better the independence of grants

Total

Number

of

CLTS

Number of customers of the institution

TACDTS

Amount of the size of the credits granted = A / B ratio with A = outstanding

Customers Credit size

credits and B = total number of clients. Maturity of the institution

MATUR

Number of years of activity of the MFI

Liquidity ratio

LQ

Prudential Ratio A / B with A = Realizable values available in amounts and B = Liabilities payable. The standard to be respected is Ratio A / B> = 100%

Capitalization ratio

CAP

Prudential ratio A / B with A = own funds and B = active end-of-period totals in amounts. The standard to be respected is Ratio A / B> = 15%

Limitations of the risks to

LRI

Prudential ratio A / B with A = Risks borne by an institution: Net amounts

which an MFI is exposed

of provisions and deposits of guarantees B = resources. The standard to be

ratio

respected is Ratio A / B> = 200%

Ratio of medium and long-

CEMLT

term liability coverage by

Prudential A / B ratio with A = stable resources and B = medium and longterm jobs. The standard to be respected is Ratio A / B> = 100%

stable resources The size of the deposits

LOG(TADEP)

Ratio Ln (A / B) with A = deposit amounts of the period and B = Total number of customers

Points of service

PTS

Number of points of service throughout the national territory

Number of board members

NCA

Number of MFI board members

Governance system or legal

SGV

Binary variable; 1 if the MFI is a public limited company and owns

status

institutional entities (banks, investment funds, development agencies) in its board of directors (SA), 0 otherwise

MFI activity area inside the

RINTER

Binary variable; 1 if the MFI exercises within the country, 0 otherwise

RABIDJ

Binary variable; 1 if the MFI practices in the economic capital (Abidjan), 0

country Activity

area

economic capital

in

the

otherwise

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Table 2 : Descriptive statistics of quantitative variables Variables

Number of

Average

Observations

Standard

Médian

Minimum

Maximum

deviation

MB

88

-194,46

975,36

-6,28

-8691,88

2,35

IDS

88

8,28

44,32

0

0

399,42

MATUR

88

7,91

4,06

7

1

16

CAP

88

0,05

9,77

0,23

-18,85

5,03

LQ

88

0,67

1,7

0,79

-14,11

3,92

CEMLT

88

1,33

4,47

1,13

-16,91

19,89

LRI

88

0,976

0,93

0,82

0,003

7,29

TACDTS

88

359040

2300404

56315

751

21618273

CLTS

88

32405

113615

3310

95

594010

PTS

88

11

28

1

1

126

NCA

88

9

6

7

3

36

Table 2 shows that Ivorian MFIs have negative profit margins due to their high operating costs. Negative profit margins can be explained by the opening of many agencies by MFIs, by overstaffing and by the large overhead costs necessary to carry out their savings and credit activities. However, we note specificities depending on the area of activity. Indeed, the profit margin for MFIs operating only in rural areas is more degraded than for MFIs operating only in the Ivorian capital city. This result may be due to a size effect of the market. It can also be caused by a difference in the managerial quality of agency managers and / or a difference in the skills of employees in MFI. Ivorian MFIs, however, are trying to reduce their staff cost, following the injunctions and recommendations of the National Commission for Microfinance. In fact, too fast growth in MFI credit activity leads to a rapid increase in staff costs and overheads. In addition, most MFIs do not receive operating subsidies, so they use other sources of funding outside of farm subsidies to carry out their activities.

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Descriptive statistics show that Ivorian MFI have on average a depressed capitalization ratio, and some have negative equity. The average capitalization ratio is 5%, well below the prudential standard of 15%. In addition, Ivoirian MFIs do not have the capacity to cope with an urgent demand for credit as shown by the average LQ ratio of 67.62%, which is well below the norm (which is 80% for IMCEC and 100% for SA). We also observe that medium and long-term jobs are sufficiently covered by the stable resources of Ivorian MFI, as shown by the average CEMLT ratio of 133.75% and above the norm (of 100% in the UMOA area). Their credit risk exposure is also insufficiently provisioned, as shown by the LRI ratio which is 97.58% and is below the norm imposed by regulators (which is 200%). Table 3: Descriptive statistics of qualitative variables Variables

Number of

Mode

Frequency

1

26,14%

0

73,86%

1

37,5%

0

62,5%

1

72,72%

0

27,28%

observation SGV RINTER

RABIDJ

88 88

88

Table 3 indicates that the vast majority (73.86%) of MFIs in Côte d'Ivoire have opted for the mutual or cooperative legal form. A relatively small number (26.14%) chose the form of public limited company and have institutional investors (banks, insurance companies, investment funds) seating in the board of directors. The legislation on the creation of the legal form public limited company is more recent. This is an innovative approach that has been introduced in the countries of Central and Eastern Europe (Lélart, 2006), then transposed to sub-Saharan Africa, particularly in Côte d'Ivoire. Finally, we note that MFIs are mainly concentrated in the Ivorian economic capital (Abidjan) and its surroundings, which is challenging because one of the objectives of MFIs is to improve the financial inclusion of rural populations. 3.3 Performance Analysis Model for Microfinance Institutions The objective is to identify the determinants of financial and social performances of MFIs. We are using a panel model with individual unobserved heterogeneity taken into account by specifying the error term εit as the sum of an individual specific effect αi and a random term uit. Specifically, we consider the following model: Yit= b0 +b1X1it+…+bpXpit +eit With : eit=αi + uit

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In this model, αi represents the unobserved heterogeneity. We tested the following four models: Model (1): MBit= b0 +b1MATURit +b2LQit +b3CAPit + b4LRIit + b5CELMTit +b6log(TADEP)it+ b7PTSit + b8NCAit + b9SGVit+ b10RINTERit+b11RABIDJit+ eit Model (2): IDSit= b0 +b1MATURit +b2LQit +b3CAPit + b4LRIit + b5CELMTit +b6log(TADEP)it+ b7PTSit + b8NCAit + b9SGVit+ b10RINTERit+b11RABIDJit+ eit Model (3): Log(CLTSit) = b0 +b1MATURit +b2LQit +b3CAPit + b4LRIit + b5CELMTit +b6log(TADEP)it+ b7PTSit + b8NCAit + b9SGVit+ b10RINTERit+b11RABIDJit+ eit Model (4): Log(TACDTSit)= b0 +b1MATURit +b2LQit +b3CAPit + b4LRIit + b5CELMTit +b6log(TADEP)it+ b7PTSit + b8NCAit + b9SGVit+ b10RINTERit+b11RABIDJit+ eit Where MB represents the profit margin, defined as the ratio of net operating income excluding grants to operating income; IDS is the grant dependency index of a microfinance institution; CLTS represents the number of clients of a microfinance institution; TACDTS represents the size of the credit granted (i.e. the average credit granted to clients). The four models differ in their dependent variables. Models (1) and (2) focus on the internal determinants of financial performance with two types of financial performance indicators: profit margin and dependence on subsidies. Models (3) and (4) aim at identifying the internal determinants of social performance with two types of social performance indicators: the total number of clients and the average amount of credit granted to clients.

4. Econometric tests, analysis of results and discussion Before performing the econometric tests, we will check, using a Student’s test, if the relations between the variables are not too important, which could have the effect of distorting the variance-covariance matrix of the estimated parameters and raise questions about the reliability of the variables. 4.1 Correlation matrix We Note (see Table 4) that, in general, the explanatory variables do not seem to have strong correlations between them. There are only 20 significant correlations out of the 55 possible and these significant correlations have amplitudes in absolute value lower than 0.7

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Table 4: Pearson Correlation Matrix

MATUR

LQ

CAP

LRI

CEMLT

LOG(TADEP)

PTS

MATUR

1

LQ

-0,045

1

CAP

-0,0081

0,23**

1

LRI

0,12

0,34***

0,11

1

CEMLT

-0,21*

0,66***

0,44***

0,16

1

LOG(TADEP)

0,24**

0,07

-0,0012

0,12

0,056

1

PTS

0,44***

-0,087

-0,029

-

-0,056

0,12

1

NCA

SGV

RINTER

RABIDJ

0,032 NCA

0,21**

-0,12

-0,022

0,15

-0,073

-0,019

-0,12

1

SGV

-0,44***

0,26**

0,1

0,11

0,34***

0,062

-0,11

-0,26**

1

RINTER

0,31***

-0,053

-0,041

0,21*

-0,14

0,18*

0,33***

0,14

-0,44***

1

RABIDJ

-0,18

-0,078

0,015

-0,15

0,1

-0,13

0,15

-0,3***

0,35***

-0,7***

*, **, *** respectively significant at 10%, 5% and 1% 4.2 Fischer test: Are the models individually specific or without individual effects? To verify the existence of individual specific effects in a panel model, we used Fisher's test to choose between a pooled model (MCO model) or a specific effects model. The principle of this test is as follows: Ho: pooled model H1: model with individual specific effects If the p value associated with the test statistic is> α%, then the null hypothesis of no effect specific to the α% threshold cannot be rejected. The following table gives us the results of the Fischer test.

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Table 5: Fischer tests of existence of individual specific effects Model

F statistics

P value

Model (1) : MB

16,91

0,00

Model (2) : IDS

38,52

0,00

Model (3) : Log(CLTS)

33,67

0,00

Model (4) : Log(TACDTS)

8,27

0,00

For models (1), (2), (3) and (4), the basic assumption is rejected at the 1% threshold. The choice of the individual effect model seems appropriate. It remains to be clarified whether this specific effect is random or fixed, which is the object of the Hausman test. 4.3 Hausman specification test The Hausmann test allows specifying whether or nor there is a correlation in the random effects model between specific effect αi and the independent variables. Moreover, Hausmann test allows mainly to choose the best model between random effect model and fixed effect model. In fact, according to the hypothesis below, whether the null hypothesis (Ho) is not rejected, which means that the specific effect of αi is correlated with the independent variables of the model, the independent variables are endogenous (Mundlak, 1981). Therefore, within model or fixed effects model is better than random effect model. Otherwise, in the case that null hypothesis is not rejected, the random effects model is better ( Greene, 2011). The hypothesis tested concerns the correlation of individual effects and explanatory variables: Ho: E (αi | X) = 0 (correlation between the individual specific effect and the explanatory variables of the model) H1: E (αi | X) ¹ 0 If the p value of the statistic of this test is less than or equal to α%, then the fixed effects model is preferable to the aleatory effects model at the threshold of α%. But when the probability of the test is strictly greater than α%, then the Hausman test does not make it possible to differentiate the fixed effects model from the random effects model. The results of this test applied to each of the models are available in Table 6.

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Table 6: Hausman test results

Model

F statistic

P value

Model (1) : MB

137,47

0,00

Model (2) : IDS

629,5

0,00

Model (3) : log(CLTS)

76,836

0,00

Model (4) : log(TACDTS)

6,0226

0,87

For models (1), (2) and (3), the basic assumption is rejected at the 1% threshold. This argues for a fixedeffect model. As for model (4), the high value of p-value indicates that we cannot reject the basic assumption. In sum, three out of four models favor a fixed effects model. A final argument seems to argue in favor of a fixed effects model (Trognon 2003, Greene 2011, Wooldridge 2015). Indeed, unlike Hartarska (2005), Hartarska and Nadolnyak (2007), Mersland and Strøm (2009) or Tchuigoua (2011), who worked on samples drawn randomly from a population, our sample almost exhaustive in the sense that it covers almost all (80%) of the population of MFI in Côte d'Ivoire. In addition, we performed heteroscedasticity and autocorrelation tests to ensure the validity of the Within estimator. Table 7: Heteroscedasticity and autocorrelation tests Heteroscedasticity tests

Auto-correlation tests

(studentized Breusch-Pagan test)

(Breusch-Godfrey/Wooldridge test)

Models BP

P-value

Chisq

P-value

MB

49.425

0,0253

30.464

0,00

IDS

42.442

0,1026

37.041

0,00

Log(CLTS)

56.285

0,005

17.091

0,00

Log(TACDTS)

78.837

0,00

18.331

0,00

The results of the Breusch-Pagan tests show that the errors of the models (1), (3) and (4) are heteroscedastic because the p-values associated with these tests are less than 1% contrary to the model (2) whose errors are homoscedastics with a p-value greater than 1%. As for the results of the tests of Breusch-Godfrey and

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Wooldridge, they show that the p-value of all models are all less than 1%. We can conclude with autocorrelation of errors. Thus, at the end of the two tests we can conclude that the errors are heteroscedastic and self-correlated. To control heteroscedasticity and autocorrelation of errors we use a sandwich estimator. We obtain a robust estimate of the variance of the parameters for the construction of the tests and the confidence intervals associated with the parameters of the different models. 4.4 Results of econometric tests Table 8: Estimation of the performance of MFI:

Financial Performance VD VI MATUR LQ CAP

MB (1) -0,88 (30.97) 0,12 (22,44) 0,68 (5,81)

Social Performance

IDS (2)

Log(CLTS) (3)

Log(TACDTS) (4)

1,07 (1,55) 0,97 (1,12) -0,88 (0,79)

1,79* (0,03) 0,38 (0,02) -1,13* (0,02)

-0,73 (0,08) -0,23 (0,06) 2,1** (0,04)

LRI

-0,26 (73.49)

-1,42 (3.68)

-0,74 (0,09)

- 0,17 (0,19)

CEMLT

-1,64 (11.13) 0,82 (37,42)

1,02 (0,55) -0,43 (1,87)

0,88 (0,01) -18,73*** (0,04)

0,07 (0,02) 8,61*** (0,1)

PTS

-0,06 (4,57)

-0,08 (0,22)

0,29* (0,005)

-0,06 (0,01)

NCA

1,92 (15,15)

-0,91 (0,75)

0,3 (0.02)

0,1 (0,04)

SGV

3,76*** (329.01)

-24,09*** (16.49)

RINTER

-14,47*** (567,83)

1,11 (28,46)

2,26** (0.71)

0,94*** (1,51)

RABIDJ

-12,12*** (672.93)

0,89 (33,73)

2,17** (0.84)

0,76*** (1,80)

R2

92,76%

92,53%

89,27%

64,54%

Pvalue

0,00

0,00

0,00

0,00

Log(TADEP)

-3,95*** (0,41)

-1,61*** (0,88)

(Fischer Test )

*, **, *** significant respectively at 10%, 5% and 1% VD: Dependent Variables VI: Independent Variables

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4.5 Analysis and discussion of the results Results related to financial performance: According to the estimation of the model (1), the legal form and geographical coverage have a significant influence on the profit margin. According to our results, the profit margin increases when the MFI is a private company (which is in line with Ledgerwood and White (2006) on the financial viability of private MFI). Our results show that the profit margin of MFIs operating in rural areas is very poor. However, we note specificities depending on the area of activity. Indeed, the profit margin for MFIs operating only in the country (in the provinces) is weaker than that of MFIs operating only in the Ivorian capital. This result may be due to a size effect of the market. It can also be caused by a difference in the managerial quality of agency managers and / or a difference in the skills of employees in MFIs. The results of model (2) estimation show that the legal form also plays a role in subsidy dependence. Indeed, the private companies have less and less state subsidies. Then, they are turning more towards private capitals. These results confirm the arguments of Hardy et al. (2003), Janson et al. (2004), by Fernando (2004), as well as those of: Ledgerwood and White (2006), who argue that MFIs with the legal form of public limited companies are independent of donations and have a facility to capture private resources. The IMCECs need subsidies because it does not weigh on the financial resources they have to carry out easily their activities. Results related to social performance: The results of the model (3) show that the maturity of the MFI positively influences one of the social performance variables, namely the number of customers. However, it can also be a learning effect, ie MFIs learn over time to be effective, in particular by better controlling their environment and their activity. It can also be an institutional effect. Indeed, institutions such as the NCM (through its injunctions and recommendations), the World Bank, and AFD (through their methodological seminars) contribute to the effectiveness over time of MFIs. Except from the maturity, the MFIs with broad geographic coverage are those with lot of customers. In addition, we observe that the number of customers decreases when the size of the deposits increases. This result is explained by the fact that deposit rates at MFIs are not attractive. In fact, customers with large amounts of deposits prefer to deposit their money with banks that charge more attractive rates. We also observe a negative relation between the number of clients and MFIs capital. Indeed, MFI whose shareholders (individuals or individuals) can quickly mobilize resources do not implement strategies (e.g. marketing) to attract new customers and capture their deposits. Of course, these deposits will be then transformed into microcredits for customers. Model (4) shows that the capitalization ratio (CAP) and the size of deposits in an MFI have a positive impact on the size of loans granted to customers. In other words, MFIs increase the size of credits as and when their own funds and deposits of their clients increase, which could be interpreted as a "mission drift" phenomenon. However, this is difficult to demonstrate. According to Armendariz et al. (2009), MFIs can increase the size of loans by making soft loans, i.e. MFIs increase the size of new loans as the client repays the previous loan. In addition, MFIs can use the cross-subsidization technique of targeting a well-off segment of the population to then finance a poor population Moreover, our results show that the IMFs increase overtime the credit proposed to their customer throughout the country. Lastly, the size of the

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board, the points of service and the prudential ratios (excluding the capitalization ratio) do not seem to play any role in the evolution of the size of the loan granted over the period studied. 5. Conclusion We have shown in this article that the legal form chosen by an MFIs has a significant influence on their financial performance: MFIs with the status of "Anonymous company" have higher financial performance. On the other hand, prudential ratios, the maturity of MFI, the size of deposits and the number of points of service do not appear to have any influence on the financial performance. The conclusions about the determinants of social performance are mixed: the maturity of the MFI, the points of service and the geographical coverage of the MFI play a positive role in the attractiveness of the customers. Lastly, capitalization ratio, geographical coverage and deposit size play a major role in the evolution of the size of loans granted to customers. Beyond analyzing the determinants of performance, our article provides evidence that Ivorian MFIs are faced with many difficulties, which have a negative impact on their financial and social performance. These difficulties are due to the weakness of MFIs’ financial income and their high operating expenses. This is attributable to the fact that almost all MFIs reach poor customers in the sense that they grant loans lower than 20% of the Ivorian gross national product per capita over the period studied (i.e. less than 306 euros on average per customer). Lending activity to this category of population generates low margins since the granting of small loans involves relatively high operating costs compared to the granting of large

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