




































Bangladesh Journal of Multidisciplinary Scientific Research 

 Vol. 3, No. 1; 2021 

ISSN 2687-850X   E-ISSN 2687-8518  

Published by CRIBFB, USA 

 

24 

EFFECT OF MICROFINANCE ADOPTION ON RURAL 

HOUSEHOLD INCOME IN SELECTED UPAZILA OF KUSHTIA 

DISTRICT OF BANGLADESH 
 

 

Bilkish Banu 

Assistant Professor 

Department of Economics 

Hajee Mohammad Danesh Science & Technology University  

 Dinajpur-5200, Bangladesh 

E-mail: bilkishbanuu@gmail.com 

 

Mohammad Mukul Hossain 

MBA Student 

Department of Management Studies 

University of Rajshahi, Bangladesh 

E-mail: mukul101ru@gmail.com 

 

Mohammad Samiul Haque 

MBA Student 

Department of Finance and Banking 

Hajee Mohammad Danesh Science & Technology University  

Dinajpur-5200, Bangladesh 

E-mail: samiulhaque445@gmail.com 

 

Babor Ahmad 

MSS Student 

Department of Economics 

Hajee Mohammad Danesh Science & Technology University  

Dinajpur-5200, Bangladesh 

E-mail: baboraswapon@gmail.com 

 

ABSTRACT 

This present study aims at analyzing how microfinance affects household income of its 

participants as well as to find out the factors that influence the participants to adopt loan for 

which a logit model was applied. This study was run on three villages of Daulatpur Upazila of 

Kushtia district namely Khalishakundi, Malipara and Silimpur from which 350 respondents were 

chosen randomly. The result reveals that Age (p<0.05), household size (p<0.01) and credit 

amount (p<0.05) from microfinance had significant negative impact on average household 

income and spouse’s income had a positive sign with coefficient of 0.867 which was significant 

(p<0.01). Logit model expresses that average income, age of respondent and household size all 

had negative sign implying that an increase of one of them will discourage the respondents 

tending towards borrowing loans from microfinance which were also significant (p<0.01). This 

study also found several problems faced by the participants of microfinance such as high interest 

rate, delay of credit, rude behavior of representatives, short recovery time, influence of old 

members etc. as well as recommend the policy implication regarding them. 



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Keywords: Microfinance, Household Income, Credit, Logit Model, Policy Implication.  

JEL Classification Codes: G21, Q52, O18, D13 

 

INTRODUCTION 

Microfinance is the provision of savings, credit, deposit, insurance and repayment services to the 

low-income households to enable them to create employment opportunities and reduce poverty 

by starting their own businesses and thus generating income. In Bangladesh there are mainly four 

types of institutions involved in microfinance activities. These are Grameen Bank (GB), around 

1500 non-government organizations (NGO), commercial and specialized banks like Bangladesh 

Krishi Bank (BKB), Rajshahi Krishi Unnayan Bank (RAKUB), and government-sponsored 

microfinance projects (Bangladesh Bank). The NGOs started credit programme in the mid-1980s 

and their activities increased noticeably after 1990. With the increasing number of collateral-free 

micro credit disbursement by the MFIs, some nationalized commercial banks (NCBs), and 

specialized banks were encouraged to provide a considerable amount of their rural credit to the 

poor without security. Today, some of the private commercial banks (PCBs) have also started 

direct and linkage programmes with the NGOs. Microfinance provides households with loan to 

group of borrowers with peer monitoring to secure loan for exchange of physical collateral. Low-

income households take loan for accumulation assets, emigration assets, emigration to abroad, 

small business, cultivation, reinvestment, betroth, education for children, dowry, smooth 

consumption, land purchase, health and housing, women empowerment and employment 

opportunities for the borrowers households. Microfinance improves the standard of living of the 

poor by creating income and employment opportunities. Microfinance gives not only loan but 

also training and social activities to the household for support self-employment and small 

business.  However, transformation of the MFIs was inevitable considering rural economic 

growth and financial inclusion requirements of Bangladesh where some 50 per cent of the adult 

population remain unbanked. Microcredit Regularity Authority' (MRA) was formed in 2006 to 

ensure transparency in activities of the micro-lending institutions. The high interest rates of 

microcredit were widely criticised at home and abroad. The government fixed the interest ceiling 

at 27 per cent in 2010. It was said that gradually the rates would be reduced further, but nothing 

has been done about this over the last one decade. The government has recently decided to slash 

down interest rates on microcredit provided by the NGOs by 3.0 per cent in order to attain the 

Sustainable Development Goals (SDGs). The finance ministry approved the interest rates at 24 

per cent after the authorities introduced the recommendation. Overall, Microfinance increases 

household income and the standard of living of the poor households (The Financial express). 

The study found out that the microcredit programmes of ASA helps both rural and urban 

poor households to improve their standard of living by increasing income, expenditures, saving 

and poverty reduction. Besides, the microcredit institutions gain competitive advantage by 

providing both micro financing and other various services such as education, skill-based 

programmes, and training sessions (Ariful et al., 2017). MFI loans impact on both household 

income and food consumption. A productive loan reduces the income poverty trends of 

households and non-productive loans minimize consumption poverty and BMI (Imai & Azam, 

2012). Microfinance access improves the consumption, household income and poverty status but 

microfinance members take loans to smooth consumption rather than investing in petty business 

(Bhuiya et al., 2016). Microfinance has a significant positive impact on increasing household 

income label and reducing widespread poverty from the rural areas of Bangladesh and age, 

family member and loan amount role in household income determination (Basu et al., 2020). The 



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26 

microfinance of BRAC has diverted borrowers (93% of respondents) to self-employed activities 

which develop high income and the contribution to the family expenditure and 7% of 

respondents have employed in low paid works after joining. (Hossain, 2012). Despite higher 

loans to cumulative borrowing microfinance continues to reduce poverty among poor borrowers 

and increase per capita household consumption within the local economy for participation and 

non-participants (Khandker, 2005). A utilized extensive microfinance operation helps increasing 

informal employment and achieving goals of poverty reduction as well as leading of greater 

economic development (Shahnaz et al., 2018). Microfinance empower the poor, reduces 

catastrophic income loss and poverty, increase employment by diversifying their income source 

(Wright, 1999). Microcredit mostly impresses the poor for poverty reduction, savings, 

microfinance and training in Bangladesh (Chowdhury et al., 2020). Microfinance increases 

household income after persistence, participation in the programmes. All households both 

participants and non-participants of microfinance registered increases in household welfare 

(Islam, 2016). The age, monthly income, of borrowers, interest rate and loan duration help the 

credit accessibility of rural households in Bantul. Borrower’s income, total household assets and 

changes in expenditure measure the assessment relates to the credit impacts. Microfinance 

reduces poverty and increases consumption but microcredit borrowers have higher consumption 

than non-borrowers, besides microfinance influences the standard of living and property level. 

 

MATERIALS AND METHODS 

This present study used data collected from three villages of Daulatpur Upazila in Kushtia 

district namely Khalishakundi, Malipara and Silimpur. The questionnaire was prepared in 

English and filled up with face to face interview and collected from September 2020 to 

December 2020. The study considered each household as the unit of analysis for evaluating the 

effect of microfinance on household income (Zewde & Tollens, 2008; Panda & Atibuddi, 2010). 

Following Panda and Atibuddi (2010) two group was made one is treatment group indicating 

those who adopted microfinance as beneficiary households from undertaking microfinance (more 

than two years till this survey) and the second is controlled group indicating those who are new 

entrants in microfinance programme and did not receive any loan (Kondo et al., 2008 & Al-

Azzam, 2006). To analyze the effect of microfinance the multivariate regression model with 

double logs was used as below. 

 

               lnYi = lnα0 + lnβ1X1i + lnβ2X2i + lnβ3X3i + lnβ4X4i + lnβ5X5i + lnβ6X6i + lnβ7X7i + i        

Where, 

ln = Natural logarithm; 

Y = Monthly average income measured in taka; 

X1 = Age of the respondent measured in years; 

X2 = Adoption of microfinance loan by participant measured by dummy variable as 1 for  

        adopt loan and 0 for otherwise; 

X3 = Education level of participant measured by years of schooling; 

X4 = Size of household measured in numbers; 



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27 

X5 = Spouse’s income (monthly) measured in taka; 

X6 = Amount of credit measured in taka; 

X7 = Agricultural holdings measured in hectare. 

α and β’s are the unknown parameters to be estimated,  refers to error term and i indicates 

1,2,3,……., n. 

There are several factors influencing the farmers to participate microfinance that were analyzed 

with limited variable model namely logit model.  

Li = ln⁡ 
Pi

1−Pi
 = Zi = β1 + β2X2 +…….…+ βnXn 

Here, 
𝑃𝑖

1−𝑃𝑖
 is the odd ratio in favor of occurring an event (Gujarati, 2009). 

Then for our study, 

Li = ln⁡ 
Pi

1−Pi
 = ln β0 + β1 ln X1i + β2 ln X2i + β3 ln X3i + β4 ln X4i + β5 ln X5i + β6 ln X6i +  ui    

           

Where,  

𝑃𝑖

1−𝑃𝑖
 is the odd ratio in favor of adopting loan from microfinance by the respondent measured by 

1 for took a loan and 0 for otherwise. X1 = Average income of household measured in taka; X2 = 

Age of respondent measured in years; X3 = Educational level of the respondent measured by 

years of schooling; X4 = Household size measured in numbers; X5 = Land holdings by 

respondent measured in hectare; X6 = Availability of credit taken as dummies measured by 1 for 

easier to grant a loan compared to banks and 0 for otherwise. u was the stochastic disturbance 

term including all other factors that may affect the model and in equation (5), i represented 

number as 1,2,3,….,n. 

Pseudo R
2 

(McFadden’s) was computed as  

R2 = 1 −
𝐿  𝑀𝑓𝑢𝑙𝑙  

𝐿  𝑀𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡  
 

Where, 

  𝑀𝑓𝑢𝑙𝑙   = Model with predictors;  𝑀𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡   = Model without predictors and 𝐿  = Estimated 

likelihood. This represents the strength of the offered model. The higher the value of Pseudo R
2
, 

the more fitted model is. 

RESULT AND DISCUSSION 

Effect of Participation in Microfinance on income 

Participation in the microfinance has a great impact on the household income shown in table 2 by 

comparison of average income between control and treatment group where the average income 

of control group is higher than that of the treatment group indicated by the mean difference of 

2161.50 which was statistically significant (p<0.01) recorded by t ratio of 4.786. This result was 



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28 

similar with the findings of Jasmine (2008); Panda and Atibudhi (2010); Kundu, (2012); Sharma 

(2008); and Sarania (2015). 

 

Table 1. Descriptive Statistics (N=350) 

Variables Measurement Mean Std. Dev. Min. Max. 

Marital status  Measured in three points; 1 

for single, 2 for married and 3 

for widow 

2.022 0.149 2 2 

Age Measured in years 32.442 8.599 18 56 

Educational level Years of education 5.845 3.465 0 12 

Family size Numbers of persons 3.605 1.042 1 6 

Average income Taka (Monthly) 5675.262 3155.258 1000 43333.33 

Spouse income Taka (Monthly) 19612 10682.38 5000 130000 

Land holdings Hectare 0.135 0.216 .01 1.32 

Membership of 

Microfinance 

Measured in Months 68.240 60.255 .12 360 

loan Dummy variables as 1 for 

took load and 0 for otherwise 

0.282 0.451 0 1 

Loan amount Taka 39934.29 32774.56 3000 200000 

Loan availability Dummy variables as 1 for 

easier compared to 

Government and Private 

banks and 0 for otherwise 

0.888 0.315 0 1 

Interest rate Dummy variables as 1 for 

lower compared to 

Government and Private 

banks and 0 for otherwise 

0.034 0.182 0 1 

Source: Authors’ own computation from survey data of 2020 

Table 2. Comparison of average income (monthly) between control and treatment group 

Variable Treatment group Control group Mean differences t- ratio 

Average income 6527.50 4366.00 2161.5*** 4.786 

Standard deviation 4222.961 2052.52   

Standard Errors 

differences 

451.586*** 

*** Refers to 1 % level of significance. 

Table 3 represents how much income of household was affected by microfinance 

participation analyzing with Ordinary Least Squares (OLS) where average income was positively 

affected having significant at 5% level with catirus paribus that is if all the other factors were 

held constant. Age, household size and credit amount all had significant negative impact on 

average income implying that an increase of one of them will reduce the average income of the 



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29 

household and vice versa where household size was significant at 1% level and the rests were at 

5% level. On the contrary spouse’s income had a positive sign with coefficient of 0.867 

indicating an 1% increase of spouse’s income will raise the average family income by 86.7% 

which was significant (p<0.01). 

 Table 3. Effect of microfinance participation on the household income (N=350) 

Variables Parameters Coefficients Standard 

Errors 

t- ratios 

Constant α0   2.516** 1.026  2.45 

Age of respondent β1  -0.332** 0.159 -2.09 

Education level β2   0.054 0.049  1.11 

Size of household β3  -0.912*** 0.095 -9.58 

Spouse’s income  β4   0.867*** 0.071 12.16 

Amount of credit β5  -0.022** 0.009 -2.40 

Agricultural holdings β6  -0.004 0.026 -0.16 

F 100.18*** 

R-squared 0.320 

Number of Observations 350 

Note: *, ** and *** Asterisks refer 1%, 5% and 10% level of significance respectively. 

 

Factors influencing participants to adopt loan 

Table 4 shows the determinants of adopting microfinance loan by its participants. Pseudo R
2 

represents how the model is fitted similarly as of R
2
 in case of OLS. LR chi

2 
of 108.06 shows 

that the model is statistically significant (p<0.01).The result reveals that 242.9 percent loan 

adoption rises with catirus paribus at 1% level of significance. It is also seen that the average 

income, age of respondent and household size all had coefficient with  negative sign implying 

that an increase of one of them will discourage the respondents tending towards borrowing loans 

from microfinance and all of them were significant at 1% level. The other factors such as 

educational level, land holding and availability of credit were not significant.  

 

Table 4. Factors affecting participants in adopting loan 

 

Variables Coefficients Standard 

Errors 

Z- Statistics 

Constant 24.296*** 4.028  6.03 

Average income (Tk./Monthly) -1.774*** 0.311 -5.69 

Age of respondent (Years) -2.005*** 0.744 -2.69 

Educational level (Years of schooling) -0.110 0.244 -0.45 

Household size (No.) -2.776*** 0.5004 -5.55 

Land holding (Hectare) -0.042 0.131 -0.33 

Availability of credit (Dummy measured as 1 

for easier to grant a loan compared to banks 

and 0 for otherwise) 

 0.425 0.485 

 

 0.88 

Log likelihood -155.365 



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LR chi
2
  108.06*** 

Pseudo R
2
 0.2580 

Number of Observations 350 
Note: *** Asterisk refers 1% level of significance respectively. 

From our field surveys and findings, several problems related to the microfinance participation 

have been identified such as  

 High interest rate of microcredit 

 Those who earn low income, MFIs don’t provide large amount of loans. 

 Those who are referenced by old borrowers, they can get loan early. 

 Generally, allowable Loan repayment time is too short. 

 Educated persons get loan easier than uneducated persons. 

 Those who have no income MFIs don’t give loan to them. 

 DPS can be formed by the borrowers to get loan from MFIs. After opening DPS, they are 

forced/ propelled to withdraw loan from MFIs. 

 Most of the time, borrowers expect as much as loan but they can’t get the expected 

amount. 

 The family which has no male income person, MFIs don’t want to access female into 

microcredit  

 Sometimes, the uneducated borrowers are fraud by field workers. 

 Unmarried girls cannot allow getting loan by self-reference. 

 Most of the time, MFIs collect their loan amount weekly basis. 

 

CONCLUSION AND RECOMMENDATION 

From the findings of this study it was clear that the participation in microfinance was recorded as 

a positive significant effect on the average income in its participants. This study also identified 

several factors which had negative effect on average household income and spouse’ income was 

found as a key contributor on family income that could not be ignored. This study found that age, 

household size and other factors which had great influence on the decision of participants 

whether to adopt a loan or not. However several problems were recorded facing by the 

participants and arose by the system of microfinance whose overcoming might make 

microfinance closer to its goals. 

This study suggests the following policies to better the management and credit policy of the 

microfinance. 

 Reduction of interest rate of microcredit. 

 Providing education loan at low interest rate. 

 MFIs should provide the unemployed young people of the society with credit to make 

them self-employed. 

 MFI should give the widows loan. 

 MFIs should give loan to the unmarried girl to make them employed. 

 Most of the borrowers want to repay their loan monthly basis. 

 MFIs authority should supervise the field workers’ activity and communicate the 

borrowers separately to avoid the chances of frauds. 

 MFIs should increase the loan amount as well as loan repayment time to the low income 

borrowers. 



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31 

 Loan providing criteria should be minimized including educated persons with fewer 

deposits. 

 

After having all of the above mentioned policies adoption, we can hope to have a better 

credit management of microfinance for the wellbeing’s of general people and another well 

managed movement towards its goals. 

 

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