




































Indian Journal of Finance and Banking 

 Vol. 7, No. 1; 2021 

                                       ISSN 2574-6081   E-ISSN 2574-609X 

Published by CRIBFB, USA 

 

13 

EXTERNAL SOURCES OF FUND OF MICROFINANCE 

INSTITUTIONS (MFI) IN BANGLADESH: DO INSTITUTIONAL 

CHARACTERISTICS MATTER? 

 

Serajul Islam 

Associate Professor 

Department of Business Administration 

International Islamic University Chittagong, Chittagong, Bangladesh 

E-mail: serajulislamiiuc@gmail.com 

 

 

ABSTRACT 

The external sources of fund for the micro-finance institutions (MFIs) include various loans and donor’s 

fund. The loan financing consists of loan from the government, the loan from other micro credit financial 

institutions, the loan from the commercial bank and the loan from Palli Karma Sahayak Foundation 

(PKSF). There is the impact of capital resources on the profitability performance. Therefore, it is 

important for a firm to know about the significant influences of institutional characteristics on external 

sources of fund.  Hence, this study investigates the driven factors of the sources of funds of microfinance 

institutions, the effect of institutional characteristics on sources of fund especially on external sources. 

The study is conducted by examining longitudinal data of 169 microfinance institutions (MFIs) from 

Bangladesh covering a period of six years from 2009 to 2014. This study employs relevant data from 

the Mix market and Microcredit Regulatory Authority (MRA) annual reports. Results show that a 

reliance on external sources of the fund (ESF) has a significant correlation with interest rate cap, 

inflation rate, ROA, number of branches (Size) and age of the MFIs. Donations have a significant 

correlation with the regularity variable and size of the MFIs. Additionally, apart from location, the rest 

of the institutional variables significantly influence the external sources of funds (ESF). 

 

Keywords: Micro Finance Institution, External Sources of Fund, Capital Structure, and Profitability, 

Institutional Characteristics, Regularity, Macroeconomic Factor.  

JEL Classification Codes: G2, G21, G23, O16.  

 

INTRODUCTION 

The sources of funds for MFI play a vital role on their overall performance. The microfinance industry 

in Bangladesh optimally utilizes the sources of funds (Mia & Rana, 2018).Costs of funds depend on the 

sources of funds ,and the fund’s sources affect the rate of interest of MFIs (Al-Azzam & Mimouni, 2016; 

Assefa & Meesters, 2013; Cotler & Almazan, 2013; Dorfleitner & Mosch, 2013; Fernando, 2006; 

Ghosh, 2013, Heinen & Baquero, 2013; Janda & Zetek, 2018; Roberts, 2013). Policymakers and 

academicians have been considering interest rates as a major issue in microfinance since its inception 

(Fernando, 2006, Mersland & Khafagy, 2013; Rosenberg et al., 2013). If MFI uses external debt 

(commercial bank’s debt) as the source of funds, it increases the cost of funds and increases the interest 

rate. And it could detain MFI’s outreach goal, and clients face financial trouble (Sun & Im, 2015). Cull 

and Morduch (2007) considered MFIs as micro banks that aim to gain profitability and poverty 

alleviation. For promoting these targets, fund’s external sources should be used by MFIs (Tchuigoua, 

2015). However, it has a small research work regarding the effect of institutional characteristics on 

sources of funds in MFIs. Therefore, empirically testing the issues driving the fund sources is the 



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principal focus of this study. So, answering two questions is the attempt of this study. First, what are the 

influential factors of the capital structure of MFIs? Second, do institutional characteristics matter on 

sources of the fund? Institutional characteristics influence the sources of funds of large organizations 

(Frank & Goyal, 2009; Rajan & Zingales, 1995), SMEs (Degryse et al., 2012), and little business (Aktas 

et al., 2011). But do institutional characteristics matter in the financing choice of MFIs? 

There is very limited empirical literature on sources of funds in MFIs. For instance, the effects 

of institutional life cycle and maturity on financing choice to the MFIs have been identified (De Sousa- 

Shields & Frankiewicz, 2004; Ledgerwood & White, 2006).It has a study on factors influencing funding 

from abroad for the MFIs. The study found two determinants like return and good outreach, have a 

positive relationship with international commercial borrowing (Mersland & Urgeghe, 2013). 

Hence, there is a research gap investigating the factors influencing MFI's fund choices in 

Bangladesh. The study's main findings reveal that regularity variable, macroeconomic variable and 

institutional characteristics influence the external sources of funds of MFIs in Bangladesh.  

This study will help policymakers and managers minimize the cost of funds and get sufficient 

funding from both the debt and equity sources. The rest of the study's sections are conceptual framework, 

research methodology, results and findings and concluding remarks. 

 

LITERATURE REVIEW 

In the present day, the increasing number of microfinance NGOs are focusing more on the deposit 

collections than giving credit for providing saving service to low-income people. So, they increase their 

capacity to mobilize and intermediate voluntary savings (Ledgerwood & White, 2006). But most of the 

MFIs are trying to eradicate poverty by giving a loan to poor people mostly based on subsidies 

(Morduch, 1999). 

Client savings is one of the sources of fund of MFIs. Microfinance is a growing flexible means 

of widening access to financial services to alleviate poverty (Armendariz de Aghion & Morduch, 2010). 

Accessing medium to long term sustainable commercial sources of the fund is one of the major 

challenges of MFIs. The sources of funds of MFIs are shareholders fund, customer’s deposit, debenture, 

qualifying medium to long-term loans, grants or donations from individuals, organizations, government 

and international sources (Anyanwu, 2004). 

Hasan and Ahmed (2009) said that members’ savings, commercial banks and the stock market 

are the future sources of MFIs. According to his study, there is a lack of efficiency in the system of 

mobilizing funds to MFIs. It is very difficult to give figures on how much donor funds go to the 

poor.  Donor funds are mainly allocated for administrative uses, and only a few percent is spent on 

the poor (Yunus, 1999). 

Hartarska and Nadolnyak (2008) found the negative relationship between the leverage of MFIs 

and their sustainability. Currently, the source of funds of MFIs is a vital issue in finance though there 

are several barriers to raising capital, which hamper the growth of MFIs (Bogan, 2012). Hartarska and 

Nadolnyak (2008) mentioned that all the rating agencies do not influence MFIs' funds' sources. But the 

commercial investment is necessary to fund the continued expansion of microfinance (Cull, Demirguc-

Kunt, & Morduch, 2009). 

Garmaise and Natividad (2010) mentioned two reasons for differing financing sources of MFIs 

from banks. First, the demand deposit is not the main source of finance. Second, noncommercial lenders 

provide the debt financing of MFIs. Adongo and Stork (2006) found that a positive relationship between 

donor involvement in providing start-up funds for the loan portfolio and financial sustainability. 

The cost of funds is an important issue for all MFIs in Bangladesh. For example, Rosenberg, 

Gonzalez, and Narian (2009) found that cost of funds is one of the four influencing factors of interest 

rates in MFIs. They also found that most of the total cost is operating depending on organizational 

features like the age of the MFI, location and loan size etc. Also, Jayadev and Rao (2012) opined that 

funds' cost is the most influencing element for fixing the MFIs' interest rate. They also mentioned that 

MFIs are less leveraged than commercial banks. 



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Sources of funds are debt and equity. The unique nature of MFI's sources of the fund is the sponsored 

external financing (Tchuigoua, 2015). A lot of MFIs still use subsidized funds as their sources of capital 

(Armendariz de Aghion & Morduch, 2010).The sources of fund composition of MFIs in Bangladesh are 

the client's savings, the government's loan, loans from other MFIs, the loan from commercial banks, 

other loans, cumulative surplus, other funds, and the loan PKSF, and donor's fund. 

 

METHODOLOGY 

The methodology part includes modeling the determinants of funds and data sources.  

 

Modeling Determinants of Sources of Funds 

Janda and Zetek (2018) used both the factors (internal & external) to develop an econometric model. 

For deriving such a model, the same method has been followed here. Therefore, the model can be 

expressed as follows:   

 

1 it 2 it 3 it 4 it 5 it it i
2

t      = 0 + lnNOB  + ROA  + LOC  + lnAGE  + (lnAGE )  + ZX + (1)itESF         

Here 'i' stands for the microfinance institutions with a time, 't' and €it expresses the stochastic 

error. The dependent variable, external sources of the fund (ESF), has been used as a proxy to capture 

all the major external sources. The major external sources of funds are a loan from the government 

(GOVT), the loan from other MFI (MFIB), the loan from the commercial bank (BANK), the loan from 

PKSF (PKSF), and donors' fund (DON). The exclusive dependent variables are only CSAV and CUMS 

two internal sources of funds.    

The model includes the size of MFI that is represented by the number of branches (B), 

profitability measured by return on assets (ROA), location (LOC), and age of MFI (AGE). Hence, the 

model focuses on five institutional characteristics. Generally, there is a positive relationship between 

profitability and debt financing. So, ROA affects sources of funds. Location is included to examine what 

location chooses what type of financing more. The number of branches, i.e., the size of MFI, also affects 

capital structure. Normally, it has a positive relation with external financing. Besides, the age (AGE) of 

MFIs represents the consequence of experience and long life on the financing policy. Usually, the 

superior the age, the bigger the practice of a microfinance institution. So it has a hands-on experience 

that may affect the external financing policy. Furthermore, the present study has also included AGE2 to 

explore the opportunity of a non-linear correlation with external funding policy to confine the 'learning 

curve' effect (Mia & Rana, 2018). Also, to overcome simultaneity bias and improve the regression 

model's goodness of fit, the model transferred AGE and B into natural logarithms (De Bandt & Davis, 

2000).   

The model also includes three macroeconomic issues and one regulatory factor. These variables 

are the control variables to estimate the model (Ahlin, Lin, & Maio, 2011). Since an interest rate cap is 

introduced for controlling the rates of interest in micro-financing, the analysis anticipates the negative 

impact of INTCP on external funding. Moreover, the GDP growth rate reveals the economic condition 

that may positively relate to the funds' external sources. Another control variable, the inflation (INF) 

rate of the model to be expected negatively related with the funding from external sources. However, 

the model's exchange rate (EXC) may positively correlate with the donation and negatively with the 

capital's remaining external sources. The definitions and measurement units of the variables are given 

in the table below: 

 

 

 

 

 

 

 



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Table 1. Definitions and measurement units of the variables 

 

Variable Definition Unit 

External Sources of fund 

(ESF): dependent variables 

  

Government funds (GOVT) Amount of government loans (state-owned bank) and 

concessionary funds divided by the total amount of 

fund of an MFI*100 

% 

Peer borrowing (MFIB) Total amount of borrowed capital from peer MFIs 

divided by the total amount of fund of an MFI*100  

% 

 

Loan from commercial bank 

(BANK) 

Total amount of borrowed capital from commercial 

banks divided by the total funds of an MFI*100 

% 

Loan from Palli Karma Sahayak 

Foundation (PKSF) 

Amount of PKSF funds divided by the total funds of 

an MFI*100 

% 

Donors’ fund (DON) Total amount of donations divided by the total funds 

of an MFI*100 

% 

Independent Variables   

Number of Branches (B) Size of an MFI is based on the total number of 

branch 

Number 

Return on Asset (ROA) Total earnings divided by total asset  Ratio 

Location (LOC) Dummy variable-1, if the MFI was registered in the 

capital city of Dhaka, 0 otherwise  

0,1 

Age of MFI (AGE) Year of establishment (registration) of an MFI Number 

Interest rate caps (INTCAP)  0 before the interest rates cap in 2011 and 

1 after, dummy variable 

0, 1 

GDP growth rate (GDPGR) Annual Gross Domestic Product growth % 

Inflation (INF) Rate of price change in the economy as a whole % 

Exchange rate (EXC) Exchange rate between Bangladeshi Taka and US 

dollar 

Ratio 

Exclusion Variables   

Savings (CSAV) Amount of savings divided by the total amount of 

fund of an MFI*100 

% 

Cumulative Surplus (CUMS) Amount of cumulative surplus divided by the total 

amount of fund of an MFI*100 

% 

 

Data Sources 

This research used only secondary information, including annual reports of the Microcredit Regulatory 

Authority (MRA), Bangladesh. This study has included a period of six years, from 2009 to 2014. During 

this period, those MFIs existed and completed datasets are considered for the study as the number of 

MFIs is not equal. Accordingly, one hundred sixty-nine samples are finalized for the study. 

 

FINDINGS AND DISCUSSION 

The winsorized observations are used in descriptive statistics and regressions. Descriptive Statistics of 

the variables are given in the table-2.  

 

 



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Table 2. Descriptive statistics of the variables 

  

Variable Obs Mean Std. Dev. Min Max       

INTCAP 1014.000 0.500 0.500 0.000 1.000 

INF 1014.000 7.498 1.678 5.423 10.705 

EXC 1014.000 78.483 8.973 65.558 86.742 

GDPGR 1014.000 5.950 0.525 5.000 6.500 

DON 1011.000 2.097 7.453 0.000 43.990 

BANK 1011.000 4.627 11.082 0.000 54.130 

GOVT 1011.000 1.546 4.706 0.000 28.360 

MFIB 1011.000 1.585 4.832 0.000 28.420 

PKSF 1011.000 22.610 27.618 0.000 89.540 

ROA 1012.000 3.226 3.956 -10.170 16.000 

AGE 1014.000 16.878 7.582 4.000 38.000 

B 1014.000 56.512 229.056 1.000 2029.000 

LOC 1014.000 0.290 0.454 0.000 1.000 

Source: Authors 

From the descriptive statistics table (table#2), we find that PKSF is the largest source among all 

external sources. It has a mean value of 27.62%, whereas its maximum value is 89.54%. The second 

highest external source of funds is BANK, and its mean value and highest value are 4.63% and 54.13%, 

respectively.  The other external sources are DON, MFIB and GOVT, and their mean values are 2.1%, 

1.59% and 1.55%, respectively. It is also found that some of the MFIs only depend on internal sources 

of funds. 

On the contrary, some MFIs depend mostly on external sources because their maximum values 

are more than 50%.  The average growth rate of gross domestic product (GDP) has been found the 

medium rate of 5.95% during the sample period. 

 

  Table 3. Pair wise correlation 

  

          lnB  lnAGE  INTCAP     INF    EXC  GDPGR        ROA      LOC          

lnB 1 
       

lnAGE 0.4086 1 
      

INTCAP 0.024 0.2204 1 
     

INF 0.0033 0.0184 -0.3496 1 
    

EXC 0.0203 0.1907 0.6191 0.399 1 
   

GDPGR 0.0186 0.1641 0.4762 0.5176 0.901 1 
  

ROA 0.079 0.0277 0.0632 0.011 0.0576 0.06 1 
 

LOC -0.09 -0.1619 0 0.0002 0.0003 -0.0001 0.017 1 

 

From the pair wise correlation table, it is found that there is multicollinearity between GDPGR 

and EXC. The correlation of only these independent variables (0.901) exceeds the highest limit of 0.80 

(Kennedy, 2008).Therefore, these two variables are not used together in the same model.  

The mostly used systems for analyzing panel data are fixed effect (FE) analysis and random 

effect (RE) analysis (Torres-Reyna, 2007). This study is based on panel data. Hence, random effect 



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analysis has been chosen through the Hausman (1978) test, and the discussion is based on the results of 

RE analysis. Robust standard errors have been measured in all the models.  

 

Table 4. Determinants of External Sources of Funds (Random effect) 

 

 (1) (2) (3) (4) (5) 

 DON BANK GOVT MFIB PKSF 

INTCAP -1.1901** 

(0.4938) 

4.4997*** 

(0.8566) 

0.1181 

(0.3749) 

-0.3510 

(0.3689) 

-7.6903*** 

(1.3972) 

INF -0.1981 

(0.1282) 

0.5537** 

(0.2381) 

0.0339 

(0.1039) 

0.0053 

(0.1005) 

-1.0956*** 

(0.3347) 

GDPGR 0.3055 

(0.4135) 

-0.6244 

(0.7873) 

0.0109 

(0.3433) 

-0.4713 

(0.3300) 

1.2231 

(1.0408) 

ROA -0.0117 

(0.0359) 

-0.1568** 

(0.0674) 

0.0078 

(0.0294) 

-0.0007 

(0.0284) 

-0.2395*** 

(0.0902) 

LOC -0.5931 

(1.1575) 

0.6692 

(1.4999) 

 

-0.7194 

(0.6659) 

-0.6076 

(0.7148) 

-2.1209 

(4.1738) 

lnAGE 3.8966 

(3.7356) 

1.9699 

(6.5830) 

12.7062*** 

(2.8834) 

-0.1796 

(2.8426) 

37.5285*** 

(9.8969) 

lnB 0.8014*** 

(0.3059) 

1.5626*** 

(0.4361) 

-0.2817 

(0.1930) 

-0.3428* 

(0.2025) 

2.5350*** 

(0.9515) 

lnAGE2 -0.7772 

(0.7936) 

-0.6757 

(1.3364) 

-2.4494*** 

(0.5863) 

0.0216 

(0.5846) 

-5.5776** 

(2.2312) 

_cons -3.9232 

(4.8487) 

-1.5115 

(8.7333) 

-13.8632*** 

(3.8214) 

5.8148 

(3.7444) 

-36.5324*** 

(12.5414) 

N 1009 1009 1009 1009 1009 

F      

r2_a      

N_g 169.0000 169.0000 169.0000 169.0000 169.0000 

Standard errors in parentheses 
*p< 0.10, **p< 0.05, ***p< 0.01 

 

Since the fitness on the whole of the guesstimates is rationally fine, the modeling of determinants 

of external sources of funds (ESF) is robust. Though the data has a different scale of importance, F-

statistics is significant here. 

The result of the donation model (model 1) indicates that the regulatory variable, INTCAP, 

harms the DON, one external source of fund. This result is inconsistent with the findings of a positive 

relationship between regulation and donation, and the regulatory control administers the activities of 

MFIs that offer intrinsic assurances (Demirguc-Kunt & Maksimovic, 1999). But institutional variable 

lnB has a positive effect on DON. It suggests that donors pay attention to the size of the MFIs, which is 

supported by the findings that donors' attention is high on the effective use of their grants and expansion 

(Tchuigoua, 2015). The result is also supported by the experimental (Buchheit & Parsons, 2006) and 

pragmatic study (Tinkelman, 1998; Trussel & Parsons, 2007). There is no significant impact of 

macroeconomic variables INF and GDPGR on the DON. Profitability (ROA) does not have a significant 

effect on DON. The findings are the direct opposite of the literature, which maintains the affirmative 

signal of financial soundness with donations (Trussel & Parsons 2007) and two specific institutional 

characteristics LOC and AGE, do not have any significant influence on DON. 

The result of the bank model (model 2) indicates that the regulatory variable, INTCAP has a 

positive effect on the bank loan, which is an interesting finding because it controls interest rate that 



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makes a barrier for external sourcing of funds. This result is inconsistent with the expectation. 

Additionally, macroeconomic variable INF (inflation) has a positive effect on a bank loan that is opposite 

to the initial expectation. The most striking finding reveals that inflation positively influences MFIs for 

borrowing from commercial banks. But another macroeconomic variable, GDPGR, harms bank loans 

though it loses its statistical significance. ROA has a negative influence on loans from commercial 

banks. It suggests that MFIs should use retained earnings as the new financing to reduce the dependency 

on borrowing. The result is supported by the profitability that is negatively associated with borrowings 

(Tchuigoua, 2015).There is no significant impact on the location and age of the MFIs on debt from 

commercial banks. However, another institutional variable (LnB), the size of MFI, positively influences 

commercial banks' loans. Hence, the size of MFI is a determinant of debt by bank-loan, where the 

organization is small or has a small number of branches (Almeida & Campello, 2007). 

The result of the GOVT model (model 3) indicates that the regulatory variable, INTCAP, has a 

positive effect on the loan from the government (GOVT), but it is statistically insignificant. Also, 

macroeconomic variables INF (inflation) and GDPGR have a positive effect on GOVT that is the 

opposite of the initial expectation though the variables lose their statistical significance. ROA has a 

positive influence on government loans, and it is also statistically insignificant. There is no significant 

impact of location and number of branches of the MFIs on government debt. However, another 

institutional variable (LnAge), the age of MFI, positively influences the government's loans. Hence, the 

age of MFI is an important determinant for MFI debt financing from government loans. 

The result of the MFIB model (model 4) indicates that the regulatory variable, INTCAP, hurts 

the loan from other MFI (MFIB), but it is statistically insignificant. Besides, macroeconomic variables 

INF (inflation) and GDPGR have a statistically insignificant impact on MFIB. Profitability (ROA) 

negatively influences loans from other microfinance institutions, and it is also statistically insignificant. 

There is no significant impact of location and age of the MFIs on debt from other MFIB. However, 

another institutional variable (LnB), the number of branches of MFI, has a positive influence on loan 

from other MFIs. Therefore, the size of MFI is an important determinant for MFI debt financing from 

MFIB. 

The result of the PKSF model (model 5) indicates that the regulatory variable, INTCAP harms 

the PKSF loan, which is very much consistent with the prior expectation because it controls interest rate 

that makes a barrier for external sourcing of fund. Besides, the macroeconomic variable INF (inflation) 

also harms the PKSF loan consistent with the initial expectation. But another macroeconomic variable 

GDPGR has a positive impact on PKSF loan though it loses its statistical significance. ROA has a 

negative influence on loan from PKSF. This result proposes that MFIs should use equity as the new 

financing to reduce debt financing dependency. Although the negative relation of ROA with the debt 

financing is consistent with the previous studies (Hartaska & Nadolnyak, 2008; Gropp & Heider, 2010), 

the role of operating efficiency or profitability on loan from PKSF is significant. There is no significant 

impact on the location of the MFIs on debt from PKSF. However, other institutional variables (LnAge), 

age of MFI, and the number of branches (LnB), have positive influences on loan from PKSF. Therefore, 

age and number of branches of MFIs are the important determinants for MFI debt financing from PKSF 

loans. 

Additionally, the outcome reveals that the size of MFI has a significant association with debt 

from PKSF. Reputations of big MFIs are higher, and such institutions can manage their risks efficiently. 

As a result, they are less risky than small MFIs.  

 

CONCLUSION 

This research has contracted with a vital micro-finance issue by recognizing the elements influencing 

external sources of funds. Some necessary policy propositions for MFIs have been found from the 

discussion and analysis. GDPGR and LOC have no significant influence on the ESF. So, other 

significant determinants of ESF are INTCAP, INF, ROA, Size and AGE.  Except for LOC, all the 

institutional characteristics influence the external sources of funds of microfinance institutions (MFIs) 

significantly, particularly in Bangladesh.  In the DON model, findings prove that the regulatory variable 



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and size of the MFIs have a significant influence on the donors' fund. It reveals a positive relationship 

between the number of donations and the size of MFIs, suggesting that large MFIs attract more 

donations. Also, for the bank and PKSF model, the size of MFI has the likely impact on the loan from 

the bank and PKSF.  However, there is no consistency with the previous study (Tchuigoua, 2015) that 

states that donors provide higher for sustaining tiny microfinance institutions' growth. 

Additionally, it is found from the donation model that an interest rate cap has a negative effect 

on the donors' decision.  The same negative effect of interest rate cap has been found for the PKSF 

model, and the relationship is also significant between this regulatory intervention and PKSF's fund. 

However, the interest rate cap has a significant positive role on the bank loan. This finding reveals that 

the bank is willing to give a loan if there is an interest ceiling for controlling excessive interest rates as 

it reduces their default risk. One interesting finding is that this intervention policy is ineffective for the 

fund from the government and other microfinance institutions. The result is supported by the empirical 

study that there is an ineffectiveness of interest rate cap policy on the portfolio yield of MFIs (Mia & 

Rana, 2018).  

The macroeconomic factor, inflation, has a significant positive effect on a bank loan and a 

negative impact on PKSF's fund. Profitability has no significant impact on donations, the government's 

fund, and the loan from other MFIs supported by Tchuigoua (2015). It claimed that donors do not mainly 

expect a rate of return. But it has a significant negative impact on loans from commercial banks and 

PKSF, which is a very interesting finding. The study reveals that commercial lenders are more sensitive 

to profitability than noncommercial lenders. The finding is inconsistent with the previous study, as 

mentioned by Matth¨aus-Maier and Von Pischke (2006) that commercial banks are making more 

investments in microfinance not only for charity but also for getting returns. There is a positive 

relationship between profitability and commercial bank loan (de Mel, McKenzie, & Woodruff, 2008). 

The study also found that the government and PKSF encourage small MFIs for their sustainability by 

giving more funds than the larger MFIs. This negative relation among the age of the MFIs, GOVT and 

PKSF are statically significant.  

Future studies could be done to find the determinants of cross-border funding of microfinance 

and examine the effect of the lawful organizational and regulatory framework on the capital formation 

of MIFs in Bangladesh. 

 

REFERENCES 
Ahlin, C., Lin, J., & Maio, M. (2011). Where Does Microfinance Flourish? Microfinance Institution 

Performance in Macroeconomic Context. Journal of Development Economics, 95(2), 105-120. 

 

Adongo, J., & Stork, C. (2006). Factors influencing the financial sustainability of selected microfinance 

institutions in Namibia (No. 39). Namibian Economic Policy Research Unit. 

 

Aktas, N., Bellettre, I., & Cousin, J. G. (2011). Capital structure decisions of French very small 

businesses. Finance, 32(1), 43-73. 

 

Al-Azzam, M., & Mimouni, K. (2016). Is Exchange Rate Risk Priced in Microfinance? Research in 

International Business and Finance, 36, 520-531. 

 

Almeida, H., & Campello, M. (2007). Financial constraints, asset tangibility, and corporate investment. 

The Review of Financial Studies, 20(5), 1429-1460. 

 

Anyanwu, C. M. (2004, November). Microfinance institutions in Nigeria: policy, practice and 

potentials. In G24 Workshop on “Constraints to Growth in Sub Saharan Africa,” Pretoria, 

South Africa (pp. 1-31). 

 



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21  

Armendariz de Aghion, B., & Morduch, J. (2010). The Economics of Microfinance (2nd ed.). 

Cambridge, MA: MTT Press. 

 

Ahmed, S. (2009). Microfinance institutions in Bangladesh: achievements and challenges. Managerial 

Finance, 35(12), 999-1010. 

 

Assefa, E. N., & Meesters, A. (2013). Competition and the Performance of Microfinance Institutions. 

Applied Financial Economics, 23(9), 767-782. 

 

Bogan, V. L. (2012). Capital structure and sustainability: An empirical study of microfinance 

institutions. Review of Economics and Statistics, 94(4), 1045-1058. 

 

Buchheit, S., & Parsons, I. M. (2006). An Experimental Investigation of Accounting Information's 

Influence on the Individual Giving Process. J Account Public Policy, 25, 666-686. 

 

Cotler, P., & Almazan, D. (2013). The Lnding Interest Rates in the Microfinance Sector: Searching for 

its Determinants. Journal of CENTRUM Cathedra: The Business and Economics Research 

Journal, 6(1), 69-81. 

 

Cull, R., & Morduch, J. (2007). Financial Performance and Outreach: A Global Analysis of Leading 

Microbanks. The Economic Journal, 117, F107-F133. 

 

Cull, R., Demirguc-Kunt, A., & Morduch, J. (2009). Microfinance Meets The Market. J Econ Prospect, 

23, 167-192. 

 

De Bandt, O., & Davis, E. (2000). Competition, Contestability and Market Structure in European 

Banking Sectors on the Eve of EMU. Journal of Banking and Finance, 24(6), 1045-1066. 

 

de Mel, S., McKenzie, D., & Woodruff, C. (2008). Returns to Capital in Microenterprises: Evidence 

from a Field Experiment. Quarterly Journal of Economics, 123, 1329-1372. 

 

De Sousa -Shields., M., & Frankiewicz., C. (2004). Financing Microfinance Institutions: The Context 

for Transition to Private Capital. USAID, Washington DC , 174-178. 

 

Degryse., H., de Goeij., P., & Kappert., P. (2012). The impact of firm and industry characteristics on 

small firms' capital structure. Small Bus Econ, 38, 431-447. 

 

Demirguc-Kunt, A., & Maksimovic, V. (1999). Institutions , Financial Markets, and Firm Debt 

Maturity. J Financ Econ, 54, 295-336. 

 

Dorfleitner, G. M., & Mosch, J. V. (2013). What Dettermines Microcredit Interest Rates? Applied 

Financial Economics, 23(20), 1579-1597. 

 

Frank, M. Z., & Goyal, V. K. (2009). Capital structure decisions: which factors are reliably important?. 

Financial management, 38(1), 1-37. 

 

Fernando, N. A. (2006). Understanding and Deealing with High Interest Rates on Microcredit. Asian 

Development Bank, 13, 1-13. 

 

Garmaise, M. J., & Natividad, G. (2010). Infomation, the Cost of Credit and Operational Efficiency: 

An Empirical Study of Microfinance. The Society for Financial Studies , 2560-2587. 



https://www.cribfb.com/journal/index.php/ijfb                           Indian Journal of Finance and Banking                        Vol. 7, No. 1; 2021 
 

 

22  

Ghosh, J. (2013). Microfinance and the Challenge of Financial Inclusion for Development. Cambridge 

Journal of Economics, 37, 1203-1219. 

 

Gropp, R., & Heider, F. (2010). The Determinants of Bank capital Structure. Rev Financ, 14, 587-622. 

 

Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica : Journal of the 

Econometric Society, 46, 1251-1271. 

 

Heinen, A. H., & Baquero, G. (2013). Competition, Loan Rates and Information Dispersion in 

Microcredit Markets. 

 

Hartarska, V., & Nadolnyak, D. (2008). Does rating help microfinance institutions raise funds? Cross-

country evidence. International Review of Economics & Finance, 17(4), 558-571. 

 

Janda, K., & Zetek., P. (2018). Macroeconomic Factors Influencing Interest Rates of Microfinance 

Institutions in the Latin America and Caribbean. Agricultural Economics, 60(4), 159-173. 

 

Jayadev, M., & Rao, R. N. (2012). Financial Resources of the Microfinance Sector: Securitization 

Deals-Issues and Challenges Interview with the MFIs Grameen Koota and Equitas. IIMB 

Management Review, 24(1), 28-39. 

 

Kennedy, P. (2008). A Guide To Econometrics (6th ed.). New Jersy: Wiley-Blackwell. 

 

Ledgerwood., J., & White., V. (2006). Transforming Microfinance Institutions: Providing Full 

Financial Services To The Poor . World Bank, Washington DC , 205-210. 

 

Matth¨aus-Maier, I., & Pischke, J. V. (2006). Microfinance Investment Funds: Leveraging Private 

Capital. Berlin: Springer. 

 

Mersland, R., & Khafagy, A. (2013). Towards Reasonably Priced Microcredit Analysing Egyptian  

NGO-MFIs' Cost Structure and Financial Performance. Enterprise Development and 

Microfinance, 24(4), 328-345. 

 

Mersland, R., & Urgeghe, I. (2013). International Debt Financing and Performance of Microfinance 

Institutions. Strat Change, 22(1-2), 17-29. 

 

Mia, M. A., & Rana, M. S. (2018). What affects portfolio yield of microfinance institutions? Evidence 

from Bangladesh. African Journal of Science, Technology, Innovation and Development, 10(3), 

345-353. 

 

Morduch, J. (1999). The Mcrofinance Promise. Journal of Economic Literature, 37, 1569-1614. 

 

Rajan, R. G., & Zingales, L. (1995). What do we know about capital structure? Some evidence from 

international data. The journal of Finance, 50(5), 1421-1460. 

 

Roberts, P. W. (2013). The Profit Orientation of Microfinance Institutions and Effective Interest Rates. 

World Development, 41, 120-131. 

 

Rosenberg, R., Gaul, S., Ford, W., & Tomilova, O. (2013). Microcredit interest rates and their 

determinants: 2004–2011. In Microfinance 3.0 (pp. 69-104). Springer, Berlin, Heidelberg. 

 



https://www.cribfb.com/journal/index.php/ijfb                           Indian Journal of Finance and Banking                        Vol. 7, No. 1; 2021 
 

 

23  

Rosenberg, R., Gonzalez, A., & Narain, S. (2009). The New Moneylenders: Are The Poor Being 

Exploited by High Microcredit Inteest Rates? In Moving Beyond Storytelling: Emerging 

Research in Microfinance , 145-181. 

 

Sun, S. L., & Im, J. (2015). Cutting microfinance interest rates: An opportunity co–creation perspective. 

Entrepreneurship Theory and Practice, 39(1), 101-128. 

 

Tchuigoua, H. T. (2015). Capital Structure of Microfinance Institutions. Journal of Financial  Services  

Research, 47(3), 313-340. 

 

Tinkelman, D. (1998). Differences in sensitivity of financial statement users to joint cost allocations: 

The case of nonprofit organizations. Journal of Accounting, Auditing & Finance, 13(4), 377-

393. 

 

Torres-Reyna, O. (2007). Panel Data Analysis Fixed and Random Effects Using Stata (V. 4.2) . 

 

Trussel, J., & Parsons, L. (2007). Financial Reporting Factors Affecting Donations to Charitable 

Organizations. Adv Account, 23, 263-285. 

 

Yunus, M. (1999). The Grameen Bank. Scientific American, 281(5), 114-119. 

 

APPENDICES 

 Appendix: Table A1: OLS for model-1 

 

Source SS        df                MS Number of obs = 1009.00 

      F(  8,  1000) 4.64 

Model 2009.26 

8              

251.157744 Prob > F 0.00 

Residual 54087.87 

1000       

54.0878711 R-squared 0.04 

      Adj R-squared 0.03 

Total 56097.13 

1008       

55.6519177 Root MSE 7.35 

 
DON Coef. Std. Err.                t P>t         [95% Conf. Interval] 

 

INTCAP -1.10 

.8774924         -

1.26 0.210      -2.823678 0.62 

INF -0.19 

.2653463          -

0.71 0.478      -.7090914 0.33 

GDPGR 0.37 

.8998563           

0.41 0.684      -1.399465 2.13 

ROA 0.12 

.0588385            

2.04 0.041       .0048019 0.24 

lnAGE -8.51 

4.121602          -

2.06 0.039      -16.59886 -0.42 

lnB 0.63 

.166162              

3.80 0.000       .3059085 0.96 

lnAGE2 1.55 

.7972955           

1.95 0.052      -.0137182 3.12 



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LOC -0.73 

.5170545          -

1.40 0.161      -1.740735 0.29 

_cons 11.57 

6.291318            

1.84 0.066       -.779339 23.91 

 

Table A2: OLS for model-2 

 

            

Source SS df                  MS 
 

Number of obs 1009.00     
F(  8,  1000) 12.68 

Model 11228.15 8            

1403.519 

 
Prob > F 0.00 

Residual 110729.05 1000      110.729 
 

R-squared 0.09     
Adj R-squared 0.08 

Total 121957.20 1008      120.989 
 

Root MSE 10.52       

      

BANK Coef. Std. Err.           T P>t [95% Conf. Interval]       

INTCAP 5.05 1.255522       

4.02 

0.00 2.58 7.51 

INF 0.68 .3796592       

1.78 

0.08 -0.07 1.42 

GDPGR -0.88 1.28752      -

0.68 

0.50 -3.40 1.65 

ROA -0.32 .0841865      -

3.82 

0.00 -0.49 -0.16 

lnAGE 4.82 5.897214        

0.82 

0.41 -6.75 16.39 

lnB 1.88 .2377456       

7.90 

0.00 1.41 2.34 

lnAGE2 -1.55 1.140775      -

1.36 

0.18 -3.79 0.69 

LOC 0.58 .7398049       

0.78 

0.43 -0.87 2.03 

_cons -2.51 9.001657    -  

0.28 

0.78 -20.18 15.15 

 

Table A3: OLS for model-3 

 

Source SS df                   

MS 

  Number of obs 1009 

    
F(  8,  1000) 6.67 

Model 1132.734 8          

141.591797 

 
Prob > F 0 

Residual 21227.89 1000   

21.2278932 

 
R-squared 0.0507 

    
Adj R-squared 0.0431 



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Total 22360.63 1008   

22.1831623 

 
Root MSE 4.6074 

      

      

GOVT Coef. Std. Err.         T P>t [95% Conf. Interval]       

INTCAP 0.248425 .5497268       

0.45 

0.651 -0.8303257 1.327175 

INF 0.052803 .1662328       

0.32 

0.751 -0.2734025 0.3790076 

GDPGR 0.024483 .5637372       

0.04 

0.965 -1.081761 1.130726 

ROA -0.09621 .0368608      -

2.61 

0.009 -0.1685393 -0.0238724 

lnAGE 8.372216 2.582079       

3.24 

0.001 3.305302 13.43913 

lnB -0.39641 .1040963      -

3.81 

0 -0.6006824 -0.192138 

lnAGE2 -1.62487 .4994854      -

3.25 

0.001 -2.605034 -0.6447143 

LOC -0.75834 .3239216      -

2.34 

0.019 -1.39398 -0.1226917 

_cons -8.04466 3.941351      -

2.04 

0.042 -15.77892 -0.3103893 

 

Table A4: OLS for model-4 

 

Source SS df       MS   Number of obs 1009     
F(  8,  1000) 3.59 

Model 658.360957 8  82.2951196 
 

Prob > F 0.0004 

Residual 22916.614 1000   

22.916614 

 
R-squared 0.0279 

    
Adj R-squared 0.0201 

Total 23574.975 1008   

23.387872 

 
Root MSE 4.7871 

      

      

MFIB Coef. Std. Err.      T P>t [95% Conf. Interval]       

INTCAP -0.5140698 .5711743    -0.90 0.368 -1.634907 0.6067679 

INF -0.0141708 .1727183    -0.08 0.935 -0.3531027 0.3247611 

GDPGR -0.4985114 .5857313    -0.85 0.395 -1.647915 0.650892 

ROA 0.1120502 .0382989     2.93 0.004 0.0368947 0.1872058 

lnAGE 3.136983 2.682819     1.17 0.243 -2.127617 8.401583 

lnB -0.3190772 .1081576    -2.95 0.003 -0.5313191 -0.1068354 

lnAGE2 -0.5563466 .5189728    -1.07 0.284 -1.574747 0.4620541 

LOC -0.5572619 .3365593    -1.66 0.098 -1.217705 0.1031816 

_cons 1.163388 4.095123     0.28 0.776 -6.872632 9.199407 



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Table A5: OLS for model-5 

 

Source SS df                         

MS 

 
Number of 

obs 

1009 

    
F(  8,  

1000) 

25.21 

Model 129095.522 8           16136.9403 
 

Prob > F 0 

Residual 640014.746 1000       

640.014746 

 
R-squared 0.1679 

    
Adj R-

squared 

0.1612 

Total 769110.268 1008       

763.006218 

 
Root MSE 25.299 

      

      

PKSF Coef. Std. Err.      t P>t [95% Conf. Interval]       

INTCAP -8.59246 3.018481    -2.85 0.005 -14.51574 -2.669177 

INF -1.298126 .9127632    -1.42 0.155 -3.089277 0.4930252 

GDPGR 1.510438 3.09541     0.49 0.626 -4.563806 7.584683 

ROA -1.142845 .2023982    -5.65 0 -1.540019 -0.7456709 

lnAGE 85.6115 14.17787     6.04 0 57.78971 113.4333 

lnB 4.193141 .5715795     7.34 0 3.071509 5.314774 

lnAGE2 -14.32086 2.742612    -5.22 0 -19.70279 -8.938924 

LOC -1.032405 1.778613    -0.58 0.562 -4.522647 2.457836 

_cons -101.4312 21.64147    -4.69 0 -143.8991 -58.9633 

 

Table A6: Robust Check for model 1 

 

Linear regression       Number of 

obs 

1009.00 

    
F(  8,  1000) 4.32     

Prob > F 0.00     
R-squared 0.04     
Root MSE 7.35       

      

 
Robust 

    

DON              Coef. Std. Err. t P>t [95% Conf. Interval]       

INTCAP      -1.10 0.81 -1.36 0.17 -2.69 0.48 

INF            -.188 0.25 -0.76 0.45 -0.68 0.30 

GDPGR       .366 0.81 0.45 0.65 -1.22 1.96 

ROA           .120 0.06 2.14 0.03 0.01 0.23 

lnAGE       -8.510 6.10 -1.39 0.16 -20.49 3.47 

lnB             .632 0.14 4.45 0.00 0.35 0.91 

lnAGE2       1.550 1.14 1.37 0.17 -0.68 3.78 



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LOC            -.726 0.51 -1.43 0.15 -1.72 0.27 

_cons         11.566 8.51 1.36 0.17 -5.13 28.26 

 

Table A7: Robust Check for model 2 

 

Linear regression       Number of obs     
F(  8,  1000)     

Prob > F     
R-squared     
Root MSE      

     

 
Robust 

   

BANK       Coef. Std. Err. t P>t [95% Conf.      

INTCAP    

5.046054 

1.36 3.70 0.00 2.37 

INF     .677068 0.39 1.74 0.08 -0.09 

GDPGR    -

.878072 

1.34 -0.65 0.51 -3.51 

ROA   -.3211897 0.09 -3.54 0.00 -0.50 

lnAGE    

4.817849 

5.79 0.83 0.41 -6.54 

lnB    1.877266 0.31 6.01 0.00 1.26 

lnAGE2   -

1.547518 

1.08 -1.43 0.15 -3.67 

LOC    .5799564 0.73 0.80 0.43 -0.84 

_cons   -2.513548 8.75 -0.29 0.77 -19.69 

 

Table A8: Robust Check for model 3 

 

Linear regression       Number of obs 1009.00     
F(  8,  1000) 7.60     
Prob > F 0.00     
R-squared 0.05     
Root MSE 4.61       

      

 
Robust 

    

GOVT       Coef. Std. Err. t P>t [95% Conf. Interval]       

INTCAP        .25 0.55 0.45 0.65 -0.83 1.33 

INF                .05 0.18 0.30 0.77 -0.29 0.40 

GDPGR         .02 0.61 0.04 0.97 -1.16 1.21 

ROA             -.10 0.04 -2.44 0.02 -0.17 -0.02 

lnAGE           8.37 1.50 5.59 0.00 5.43 11.31 



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lnB                 -.40 0.09 -4.40 0.00 -0.57 -0.22 

lnAGE2         -1.62 0.29 -5.63 0.00 -2.19 -1.06 

LOC                 -.76 0.29 -2.63 0.01 -1.32 -0.19 

_cons            -8.04 2.76 -2.92 0.00 -13.45 -2.64       

  
 

        

 

Table A9: Robust Check for model 4 

 

            

Linear regression 
   

Number of obs 1009.00     
F(  8,  1000) 3.32     

Prob > F 0.00     
R-squared 0.03     
Root MSE 4.79       

      

 
Robust 

    

MFIB       Coef. Std. Err. t P>t [95% Conf. Interval]       

INTCAP   -

.5140698 

0.52 -0.98 0.33 -1.54 0.52 

INF          -

.0141708 

0.16 -0.09 0.93 -0.32 0.29 

GDPGR   -

.4985114 

0.51 -0.98 0.33 -1.49 0.50 

ROA         

.1120502 

0.05 2.27 0.02 0.02 0.21 

lnAGE     

3.136983 

2.18 1.44 0.15 -1.13 7.41 

lnB          -

.3190772 

0.08 -4.03 0.00 -0.47 -0.16 

lnAGE2   -

.5563466 

0.41 -1.35 0.18 -1.36 0.25 

LOC         -

.5572619 

0.32 -1.72 0.09 -1.19 0.08 

_cons    1.163388 3.67 0.32 0.75 -6.03 8.36 

 

Table A10: Robust Check for model 5 

 

Linear regression       Number of obs 1009.00     
F(  8,  1000) 38.71     

Prob > F 0.00     
R-squared 0.17     
Root MSE 25.30       

      



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29  

 
Robust 

    

PKSF       Coef. Std. Err. t P>t [95% Conf. Interval]       

INTCAP    -

8.59246 

2.91 -2.96 0.00 -14.29 -2.89 

INF        -1.298126 0.90 -1.45 0.15 -3.05 0.46 

GDPGR    

1.510438 

3.00 0.50 0.62 -4.37 7.39 

ROA        -

1.142845 

0.22 -5.27 0.00 -1.57 -0.72 

lnAGE     85.6115 12.90 6.64 0.00 60.30 110.92 

lnB          4.193141 0.59 7.08 0.00 3.03 5.36 

lnAGE2   -

14.32086 

2.66 -5.38 0.00 -19.54 -9.10 

LOC         -

1.032405 

1.78 -0.58 0.56 -4.52 2.46 

_cons     -101.4312 18.92 -5.36 0.00 -138.56 -64.30 

 

Table A11: Hausman Test 

  
---- Coefficients -

--- 

     

 
(b)                       (B)                (b-B)             sqrt(diag(V_b-V_B)) 

 

 
f4           .                              Difference                     

S.E. 

  

        

INTCAP -1.628999    -1.190124       -.4388745        

.7756078 

  

INF -.2646382    -.1980557       -.0665825        

.1173038 

  

GDPGR .39186        .3054745              .0863855          

.17537 

  

ROA -.0209966    -.0116552       -.0093413        

.0069348 

  

lnAGE 4.498007     3.896566        .6014411        

3.239226 

  

lnB .9795708      .801363        .1782078        

.4625556 

  

lnAGE2 -.6103025    -.7771929        .1668904        

1.129302 

  

        

 
b = consistent under Ho and Ha; obtained from xtreg 

 

B = inconsistent under Ha, efficient under Ho; obtained from xtreg         

Test:  Ho: difference in coefficients not 

systematic 

   

        



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chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-

B) 

   

 
7.73 

      

 
Prob>chi2 =      0.2588 

    

                

 

Table-A12: Fixed effect: 

 (1) (2) (3) (4) (5) 

 DON BANK GOVT MFIB PKSF 

INTCAP -1.6290* 

(0.9187) 

-2.0512 

(1.7358) 

-0.4234 

(0.7673) 

0.6151 

(0.7368) 

-4.0123* 

(2.2764) 

INF -0.2646 

(0.1736) 

-0.4422 

(0.3281) 

-0.0482 

(0.1450) 

0.1510 

(0.1393) 

-0.5359 

(0.4302) 

GDPGR 0.3919 

(0.4487) 

0.8799 

(0.8478) 

0.1172 

(0.3748) 

-0.6760* 

(0.3599) 

0.4054 

(1.1119) 

ROA -0.0210 

(0.0365) 

-0.1373** 

(0.0690) 

0.0276 

(0.0305) 

-0.0168 

(0.0293) 

-0.2071** 

(0.0905) 

LOC 0.0000 

(.) 

0.0000 

(.) 

0.0000 

(.) 

0.0000 

(.) 

0.0000 

(.) 

lnAGE 4.4980 

(4.9401) 

-23.5443** 

(9.3341) 

12.3888*** 

(4.1263) 

2.2543 

(3.9624) 

47.9744*** 

(12.2415) 

lnB 0.9796* 

(0.5541) 

0.9699 

(1.0469) 

0.3492 

(0.4628) 

-0.5916 

(0.4444) 

0.7988 

(1.3730) 

lnAGE2 -0.6103 

(1.3791) 

8.7801*** 

(2.6057) 

-2.0764* 

(1.1519) 

-1.0837 

(1.1061) 

-9.9900*** 

(3.4173) 

_cons -7.1755 

(5.1103) 

-0.9695 

(9.6557) 

-17.2991*** 

(4.2685) 

7.7038* 

(4.0989) 

-29.1821** 

(12.6633) 

N 1009 1009 1009 1009 1009 

F 3.2021 14.0527 3.2088 3.6415 12.2856 

r2_a -0.1784 -0.0823 -0.1783 -0.1742 -0.0968 

N_g 169.0000 169.0000 169.0000 169.0000 169.0000 

Standard errors in parentheses 
*p< 0.10, **p< 0.05, ***p< 0.01 

 

 

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