CONCLUSIONES GENERALES

To answer the third resarch question which seeks to analyse the impact of firm-level determinants on MFIs capital structure, by testing the conventional theoretical framewok on capital structure choice of firms. The study run an ordinary least-squares regressions with the two different capital structure variables of MFIs (leverage and subsidies) as dependent variables and firm-specific factors as explanatory variables for each of the 56 countries (leverage) and 54 countries (subsidies) in the sample as follows. Take leverage for example.

𝐿𝐸𝑉_𝑖𝑡= 𝛽_0𝑖𝑡+ 𝛽₁𝑇𝐴𝑁𝐺_𝑖𝑡 + 𝛽₂𝑅𝐼𝑆𝐾_𝑖𝑡 + 𝛽₃𝑃𝑅𝑂𝐹_𝑖𝑡 + 𝛽₄𝐿𝐼𝑄𝑈𝐼_𝑖𝑡 + + 𝛽₅𝑆𝐼𝑍𝐸_𝑖𝑡+ 𝛽₆𝐴𝐺𝐸_𝑖𝑡 + Ɛ_𝑖𝑡 (5.4)

Where 𝑖̇ denotes an individual MFI and 𝑡 denotes time. It is important to note that observed countries in slightly lower when the dependent variable is subsidies. This is due to the fact MFIs

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in some countries have not benefited from subsidized funding over the sample period. Next, the study conduct a few statistical test. First, it test whether the coefficients of Firm-specific determinants are equal across countries. The procedure includes six diffrent test to examine whether one or more of the six firm-specific coefficients, namely, tangibility, risk, profitability, liquidity, size and age have the same value for all countries in the sample. To test that each explanatory factor is same across countries, the study first conduct an unrestricted regression and then calculate the average coefficients across observations to find the mean value. The observed explanatory factor is further deducted from the average coefficient to examine how far it is from the mean value. Then test whether it is significantly different from zero. If it is statistically and significantly different from zero, then the coefficients are not same across countries otherwise it is.

Second, the study use a different approach to test whether the coefficients of all Firm-specific determinants are equal across all the 56 countries for both models. The study make use of the joint test of significance of regression coefficients proposed by Verbeek (2004, p.27), where a single ristricted regression model imposes that all the 6 Firm-specific coefficients are the same across all countries. This test can help in deciding whether it is appropriate to use a single model for MFIs in all countries. Where the research question is not rejected, it is asumed that Firm-specific coefiicients are same across countries. The foregoing test conducted on the equality of Firm- specific coefficients are meant to provide additional evidence to either reject or accept the second approach. However, in the case of rejection, it helps identify which Firm-specific variables determines such a rejection. The statistics of joint test of significance of regression coeffcients is defined as follows;

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𝑓 =

(𝑆

− 𝑆

)/𝐽

𝑆

/(𝑁 − 𝐾)

(5.5)

Where 𝑆_𝑅 is the sum squared residuals of the restricted model, and 𝑆_𝑈𝑅 is the sum squared residuals of the unrestricted models. 𝑁 is the number of observations, 𝐽 is the number of regressors omitted in the restricted models (It shows the difference in degree of freedom df between restricted and unrestricted models). 𝐾 is the number of regressors remaining in the restricted model including the intercept. Using the Seemingly Unreleted Regression (SUR) estimation method, the study obtains 𝑆𝑈𝑅 by adding all the SSR generated from the 56 and 54 equations of Firm-specific

determinants of leverage and subsidies respectively as in Equation (5.4). Furthermore, the study obtain the 𝑆_𝑅 using the SUR method as well. The SSR added to the system was derived from a single restricted equation that assumes Firm-specific coefficients are the same across countries. The value of the f-statistics determines whether to reject or accept the equality of Firm-specific coefficients across countries in both leverage and subsidies model respectively.

5.7.2.2. The impact of institutional-specific factors

Turning to the fourth and fifth research question (i.e., do the institutional-specific determinants affects MFIs capital structure directly and indirectly).This study used a procedure similar to de Jong et al (2008) who estimate the direct and indirect impact of country specific factors on firms financing choices. MFI capital structure variables can be described as leverage and subsidies. In the first step, we run a pooled OLS regression for all MFIs in all countries, for each fiscal year, considering cross-country diffreneces through country dummies. The equation can be described as follows, for example leverage.

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𝐿𝐸𝑉_𝑖𝑗𝑡=∑11_𝑡=1∑_𝑗=156 𝛼𝑗𝑡𝑑_𝑗𝑡+∑_𝑡=111 ∑_𝑗=156 𝛽₁𝑑_𝑗𝑇𝐴𝑁𝐺_𝑖𝑗𝑡+∑_𝑡=111 ∑56_𝑗=1𝛽₂ 𝑑_𝑗𝑅𝐼𝑆𝐾_𝑖𝑗𝑡+

∑11_𝑡=1∑56_𝑗=1𝛽₃𝑑_𝑗𝑃𝑅𝑂𝐹_𝑖𝑗𝑡+∑_𝑡=111 ∑_𝑗=156 𝛽₄𝑑_𝑗𝐿𝐼𝑄𝑈𝐼_𝑖𝑗𝑡+∑11_𝑡=1∑56_𝑗=1𝛽₅𝑑_𝑗𝑆𝐼𝑍𝐸_𝑖𝑗𝑡+

∑11_𝑡=1∑56_𝑗=1𝛽₆𝑑𝑗𝐴𝐺𝐸𝑖𝑗𝑡+ 𝑢𝑖𝑗𝑡 (5.6)

Where 𝐿𝐸𝑉_𝑖𝑗𝑡 is one of the capital strucure variable of MFIs. 𝑇𝐴𝑁𝐺_𝑖𝑗𝑡, 𝑅𝐼𝑆𝐾_𝑖𝑗𝑡, 𝑃𝑅𝑂𝐹_𝑖𝑗𝑡,

𝐿𝐼𝑄𝑈𝐼𝑖𝑗𝑡, 𝑆𝐼𝑍𝐸𝑖𝑗𝑡, 𝐴𝐺𝐸𝑖𝑗𝑡 are Firm-specific variables of MFI 𝑖̇ in country 𝑗̇ and time t: 𝑑𝑗

represents the country dummies. The equation yields results for each of the Firm-specific variables and cross country dummies. The country dummy coefficients represent the capital structure (which are the countries leverages and subsidies after correcting for the impact of Firm-specific factors). For example, The country dummy coefficients of leverage is the overall measure of indebtedness of MFIs in a particular country for a particular year . In other words, it measures the level of intensity of capital strcture undertakings in a particular country.

The analyses the impact of intitutional-specific variables on MFI capital structure using the Weighted Least Squares (WLS) regressions. Weighted Least Squares can often be used to maximize the efficiency of parameter estimation. This is done by attempting to give each data point its proper amount of influence over the parameter estimates (Wooldridge, 2010). As Gujarati (2003) argued that any procedure that estimate data equally would probably give less precise measured points more influence than they should have and would give highly precise points too little influence. Compared to other least squares method, weighted least squares is an effcient method that makes use of small data sets (Wooldridge, 2010). Therefore, weighted Least squares is an estimator used to adjust for a known form of heteroskedasticity, where each squared residual is weighted by the inverse of the (estimated) variance of the error (Wooldridge, 2010)

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In the second stage, the study first examine the direct impact of institutional factors in explaining the values of country dummy coefficients 𝛼_𝑗𝑡 generated in Equation (5.6). The value of the country dummy coefficients represents the countries capital structure variables (in this case leverage) after correcting for the impact of Firm-specific factors. We apply the Weighted Least Squares regression where the weight used is the inverse standard errors of the corresponding countries dummies. The weight is inversely proportional to the variance of the observation. These weights ensures that MFIs in each country are given the proper amount of influence over the parameter of estimates. The regression specification in the case of leverage is writen as follows:

𝛼_𝑗𝑡= 𝛾₀+ 𝛾₁𝐶𝑅𝐸𝑅𝐼𝐺𝐻𝑇_𝑗𝑡 +𝛾₂𝐶𝑅𝐸𝐼𝑁𝐹𝑂_𝑗𝑡+ 𝛾₃𝐿𝐸𝐺𝐴𝐿_𝑗𝑡+ 𝛾₄𝐹𝐼𝑁𝐷𝐸𝑉_𝑗𝑡+ 𝛾₅𝐶𝑂𝑅𝑅𝑈𝑃_𝑗𝑡+

𝛾₁𝑃𝑂𝐿𝑆𝑇𝐴_𝑗𝑡 + 𝛾₁𝑅𝐸𝐺𝑈𝐿_𝑗𝑡+ 𝛾₁𝐺𝐷𝑃_𝑗𝑡+ 𝑤_𝑗𝑡 (5.7)

Where the dependent variable is the estimated values of country dummy coefficients (𝛼_𝑗𝑡) in the Equation (5.6). 𝐶𝑅𝐸𝑅𝐼𝐺𝐻𝑇_𝑗𝑡, 𝐶𝑅𝐸𝐼𝑁𝐹𝑂_𝑗𝑡, 𝐿𝐸𝐺𝐴𝐿_𝑗𝑡, 𝐹𝐼𝑁𝐷𝐸𝑉_𝑗𝑡, 𝐶𝑂𝑅𝑅𝑈𝑃_𝑗𝑡, 𝑃𝑂𝐿𝑆𝑇𝐴_𝑗𝑡,

𝑅𝐸𝐺𝑈𝐿𝑗𝑡, and 𝐺𝐷𝑃𝑗𝑡 are institutional-specific variables defined in Table 3.5. Unlike de Jong et al.,

(2008) etimation that used single capital structure for each country, the estimation in Equation (5.7) was able to capture a single capital structure (leverage and subsidies) for each year in each country, allowing for the fact that Firm-specific coefficients are different across countries. The study test various reduced forms of this equation.

Similarly, in the spirit of de Jong et al (2008) the study analyse the indirect impact of intitutional specific variables on MFI capital structure using the Weighted Least Squares (WLS) regressions. Due to data limitations, the study use a single capital structure for each country as observation4_.

4 _{For example, Countries like Nigeria and Jordan have 6 and 1 data point in a particular year. Therefore, applying the method in Equation (3.3) to}

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We determine the indirect impact of institutional factors by estimating its effect on Firm-specific variables. This procedure is straight forward and simple, we first estimate the regression coefficients of all our Firm-specific variables as in Equation (5.4) for each country in our sample. The values of the generated coefficients of Firm-specific variables is then regressed against our institutional variables using the weighted least square regression discussed above. The weight used in Equation (5.8) is the inverse standard errors of the corresponding Firm-specific coefficients. Typically, the observation represent averages and the weight are the number of elements that gave rise to the average.The equation can be described as follows;

𝛽_𝑘𝑗= 𝜆₀+ 𝜆₁𝐶𝑅𝐸𝑅𝐼𝐺𝐻𝑇_𝑗 +𝜆₂𝐶𝑅𝐸𝐼𝑁𝐹𝑂_𝑗+ 𝜆₃𝐿𝐸𝐺𝐴𝐿_𝑗+ 𝜆₄𝐹𝐼𝑁𝐷𝐸𝑉_𝑗+𝜆₁𝐶𝑂𝑅𝑅𝑈𝑃_𝑗 + 𝜆₁𝑃𝑂𝐿𝑆𝑇𝐴_𝑗 +

𝜆1𝑅𝐸𝐺𝑈𝐿𝑗 + 𝜆1𝐺𝐷𝑃𝑗+ ℯ𝑘 (5.8)

Where k denotes the estimated betas (𝛽𝑘𝑗, j= 1, 2, ..., 6) in Equation (5.4). 𝐶𝑅𝐸𝑅𝐼𝐺𝐻𝑇𝑗,

𝐶𝑅𝐸𝐼𝑁𝐹𝑂_𝑗, 𝐿𝐸𝐺𝐴𝐿_𝑗, 𝐹𝐼𝑁𝐷𝐸𝑉_𝑗, 𝐶𝑂𝑅𝑅𝑈𝑃_𝑗, 𝑃𝑂𝐿𝑆𝑇𝐴_𝑗, 𝑅𝐸𝐺𝑈𝐿_𝑗 and 𝐺𝐷𝑃_𝑗 are institutional5 characteristics defined in Table 3. Various reduced form of this equation is tested as well. This analysis is the second stage of a two-stage procedure.

To test the reliability of our results, the study use annual average data from the inception. In the first instance, we take the average of all the Firm-specific data for each particular country. This leaves us with an average single observation for each country. However, due to data limitations, some countries were dropped after implementing this approach. For example, Jordan has one MFI with eleven firm year observations. After taking an average of these observations, we are left with one observation for Jordan which is not enough to run the first stage of the regression on Jordan.

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Therefore, countries with insufficient data were eliminated, leaving the study with 30 countries from the original 56 countries as presented in Figure 5.1 below.

Figure 5.1. Differences in the composition of sample based on full data and annual average data.

Figure 5.1 shows the differences in spread of MFIs across regions. In the full data, MFIs are spread across all the six regions, with the highest concentration in Latin America and the lowest in East Asia. However, in the annual average data, Latin America is still the region with the highest concentration of MFIs, folllowed by Eastern Europe. Middle East is having zero observation while Africa has the lowest concentration of MFIs.

In the first stage, the study run a pooled OLS regression as in Equation (5.6) conisdering cross- country differences through country dummies. The values of the dummy coefficients 𝛼_𝑗 generated in the first step is run against the institutional specific factors in Equation (5.7). The equation can be described as follows, for instance leverage.

Africa 14% East Asia 7% Eatern Europe 29% Latin America 30% Middle East 9% South Asia 11%

Full data

Annual average data

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𝛼_𝑗= 𝛾₀+ 𝛾₁𝐶𝑅𝐸𝑅𝐼𝐺𝐻𝑇_𝑗 +𝛾₂𝐶𝑅𝐸𝐼𝑁𝐹𝑂_𝑗+ 𝛾₃𝐿𝐸𝐺𝐴𝐿_𝑗+ 𝛾₄𝐹𝐼𝑁𝐷𝐸𝑉_𝑗+ 𝛾₅𝐶𝑂𝑅𝑅𝑈𝑃_𝑗+ 𝛾₁𝑃𝑂𝐿𝑆𝑇𝐴_𝑗 +

𝛾₁𝑅𝐸𝐺𝑈𝐿_𝑗+ 𝛾₁𝐺𝐷𝑃_𝑗+ 𝑤_𝑗 (5.9)

Where the dependent variable is the estimated values of country dummy coefficients for each country (𝛼_𝑗). 𝐶𝑅𝐸𝑅𝐼𝐺𝐻𝑇_𝑗, 𝐶𝑅𝐸𝐼𝑁𝐹𝑂_𝑗, 𝐿𝐸𝐺𝐴𝐿_𝑗, 𝐹𝐼𝑁𝐷𝐸𝑉_𝑗, 𝐶𝑂𝑅𝑅𝑈𝑃_𝑗, 𝑃𝑂𝐿𝑆𝑇𝐴_𝑗, 𝑅𝐸𝐺𝑈𝐿_𝑗, and

𝐺𝐷𝑃_𝑗 are institutional-specific variables defined in Table 3.5. Using the annual average data in the first stage, the estimation in Equation (5.9) was able to capture a single capital structure (leverage and subsidies) for each country, allowing for the fact that Firm-specific coefficients are different across countries. Note that, the study runs the first and second stage regression using average country values for the sample period. The study test various reduced forms of this equation.