БЛИЖАЙШИЕ
МЕРОПРИЯТИЯ
АКТУАЛЬНАЯ НАУКА
международный научный журнал
Публикация научных статей в международном научном журнале "Актуальная наука" - ПИ № ФС77-70430 от 20.07.2017 - ISSN 2587-9022
№ 4 – 2023
ЭКОНОМИЧЕСКИЕ НАУКИ
UDC 336.77
Asooda Aziz
Department of Finance and Banking
Faculty of Economic
Kabul University
Kabul, Afghanistan
DETERMINANTS OF LOAN RECOVERY
IN MAIMANA ISLAMIC INVESTMENT
AND FINANCE COOPERATIVE MICROFINANCE INSTITUTION
MIIFC is one of the financial institutions operating in financial sector in Faryab province of Afghanistan.
One of the most important problems of financial institutions is the recovery of the loans from the borrowers. The purpose of this study is to find the determinants of loan recovery in MIIFC micro finance institution. The statistical population of this research is the borrowers of MIIFC micro finance institution, the sample size is 300 and the stratified random sampling technique was employed. The study used both descriptive and inferential statistic, the quantitative method has been analysed by descriptive statistic with statistical package SPSS (24) and empirical analysis is conducted by using binary logistic regression model to stablish of all explanatory variables.
The results show that from these variables, only economic and political situation, number of loan taken, penalty of delay, gender and debt guarantee have a significant relationship with loan repayment. The marital status, education level, age, monthly expenditure, loan utilization, literacy, income-increasing debt, relative location, type of loan, group loan, amount of repayment, repayment period, interest rate on the loan, number of families, loan utilization training and pre-loan purpose have no relationship with loan recovery.
Key words: loan recovery, financial institutions, economic factors, institutional factors, demographic factors, and loan factors.
INTRODUCTION
Financial institutions are poverty-reducing tools used around the world, including Islamic countries, like Pakistan, Egypt, Tajikistan, Indonesia. Bangladesh, a Muslim-majority country that is one of the poorest countries in the world is one of the country’s 30 years of microfinance services that has reduced its poverty level of this country today. Poor people in developing countries do not have access to formal financial services and the formal financial services are not available in the rural and remote areas. Informal financial services such as moneylenders provide financial services at highly unfavourable terms and conditions [8].
MFI is one of the tools used to alleviate poverty around the world. Poor people are bankable and they need adequate and timely access to financial services and are not necessarily dependent on subsidized funds because they are willing to pay services fee for timely and adequate access to credit. The microfinance serves poor, low-income people, self-employed entrepreneurs, livestock holders, people who are entrepreneurs but do not have access to capital, people in remote, isolated or underserved communities and those who do not meet the perquisites of formal financial institution, such as banks. Microfinance institutions play an increasingly important role in local financial economies and compete to obtain customers and resources. They get rewards for good performance and suffer loss for poor performance. Many microfinance institutions have grown rapidly, serving more customers and larger geographic areas, and offering a wide range of financial services and products [1]. The organizational structures and operating environments of microfinance institutions can provide unique challenges. They may be decentralized or too centralized, tend to be labor-and transaction-intensive have concentration on risk in certain regions or sectors due to their mission, and often operate in volatile and less financial markets. Microfinance institutions are striving for financial viability through cost-effective and efficient operations, making effective risk management essential to achieve better capital and cash management without undue risk [8].
Studies and reports show that microfinance clients with better access to livelihoods tend to invest in housing, health and education, thereby improving the family’s overall quality of life. The impact of microfinance in Afghanistan is considerable; according to initial impact study conducted by IDS UK1, 72 % of microfinance clients reported improvement in their economic situation; 64 % female and 74 % male microfinance clients generated employment opportunities. Nearly 45% of all microfinance clients generated employment for others [8]. According to Korankye [3] lack of control causes loan default. Control includes training of beneficiaries before and after disbursement, interest rate, monitoring of clients and proper loan appraisal.
Eno [4] stated that credit appraisal, credit risk control and collection policy is very important in influencing loan recovery. In addition, it was recommended that microfinance banks should ensure the experienced and competent credit officers do that credit appraisal, in order to body out those intolerable credit risk at the earliest possible opportunity. However, experiments over the years proved that inadequate credit analysis and sound judgment of loans applications have resulted loan defaults. The financial sustainability of microfinance institution is a necessary condition for institutional sustainability. Therefore, a thorough investigation of the various aspects of loan defaults, source of credits and conditions of loan provision are of great importance for both policy makers and microfinance institution. Hence, this study was undertaken to find the determinants of loan recovery in Maimana islamic investment and finance cooperative microfinance institution.
Material and Method
This study is categorized into applied research and in terms of types of data. The study is a quantitative approach and data is collected by questionnaires through interviews with key informant interviewees. The researcher used logistic regression model for analysing the data. The logistic regression deals with loan repayment performance which is factor (dependent) variable and 21 explanatory (independent) variables which are categorized in four main determinants of loan repayment named as demographic, institutional, economic and loan factors.
Research Population and Sampling
Sampling is a process by which a relatively small number of individual, objects or events is selected and analysed in order to find out something about the entire pupulation from which it was selected Denning 2005(as cited in [6]).
Kothari [5] defined the target pupulation as a complete set of individuals, cases or objects with the same common observal characcteristics. Therefore, the pupulation for this study is 300 borrowers of Maimana islamic investment and finance cooperative. The numbers of borrower’s data were collected from borrowers’ profile of the institution. During data collection process, borrowers have been classified as defaulters and non-defaulters. Currently MIIFC microfinance institution has 3 500 borrowers.
Data Analysis Method
Walsh & Wigens [9] asserted, the type of data analysis tool depends on type of data, was the data quantitative or qualitative. According to Wilson [10] to analyize quantitative data, frequency tables and statistical software package should be used. In this study the quantitative data has been analyzed by descriptive statistic after all the relevant data have been gathered from respondents. The collected data were edited, coded, and entered statistical package for social sciences SPSS (version 24). This package has the incredible capabilities and flexibilities af analyzing huge data within seconds and generating unlimited simple and sopthisticated statistical results including simple frequency distribution tables, graphs, pie charts, percentages,cummulative frequencies, binomal mean, mediam, standard deviation and other distributions. In additions, this study is conducted the empirical analysis by using binary logistic regression model to establish the effect of all four factors on the level of loan repayment among customers of MIIFC microfinance institution that deals with loan repayment performance in total of 20 explanatory variables included in this study.
The following regression model has been formulated for this study:
Y=B0+B1X1+B2X2+B3X3+B4X4+…+BnXn,
where Y = loan repayment performance
B0 = Constant
X1, X2, X3… Xn = independent variables such as family size, loan size, etc.
RESULT AND DISCUSSION
All 20 independent variables included in four categories of loan repayment determinants regressed by logistic regression. The result shows that five variables (economic and political situation, number of loans taken, penalty of delay, gender, and debt guarantee) out of 20 variables, have significant relationship with loan repayment. The rest of variables have no significant relationship with loan repayment.
Questionnaire reliability
The reliability of the instrument, also referred as accuracy is that if a measuring instrument used to measure variable and trait made under similar conditions at another time or place, It yields similar results; in other words, it is a reliable or valid instrument that has the same reproducibility. Cronbach's alpha method was used to determine the reliability of the questionnaire.
Table 1
Reliability Statistics
As shown in Table 1, Cronbach's alpha coefficient was obtained using SPSS24 statistical software, which was 0.809 higher than (0.7) for all variables, indicating optimum reliability of the questionnaire. It should be noted that the range of the Cronbach's alpha coefficient is between 0 and 1. The closer the coefficient is to 1, the more reliable the questionnaire will be.
Test Descriptive Statistics
The main objective of descriptive statistics is summarizing, arranging and presenting information in a clear and comprehensive manner, and if necessary determining the relationships between the information collected. In this part of the study, the researcher attempted to further specifying, arranging and presenting the collected data into tabular or graphical forms.
Gender of Respondents
As the table below shows, 236 (78.7 %) of the respondents are male and the rest of them, 64 (21.3 %) are female.
Table 2
Gender of respondents
Figure 1. Indicats the gender of paticipeint
Marital Status of Respondents
Data also shows that, most of the loan borrowers are married and just 9% of them are single.
Table 3
Marital status of respondents
Figure 2. Marital status of respondents
Family member
The table below shows the number of members in borrowers’ families. The mode of family members is five members in a family.
Table 4
Member of family
Figure 3. Indicts family member
Respondents' Education
As the table below shows, 47% of respondents are bachelors.
Table 5
Education level
Final Test Research Model Analysis
After identifying the effects of model variables that represent the model's validity, in order identify the factors affecting the accuracy of the model, the effect of each variable is identified as below:
The economic and political situation variable (B = –1.521);
Number of loans taken (.461);
Penalty of delay in loan repayment (2.157);
Gender (–1.772);
Debt guarantee (–17.449);
And the constant (C = 17.006).
All the figures analysed by 95 % confidence level and 5 % error level.
Table 6
Final Research Model (Beta value)
CONCLUSION
According to the logistic regression output, significant values of gender, debt, debt securities, debt penalties, economic and security conditions are less than 0.05 and have significant effect on loan repayment, supported by H1 research hypotheses. The other variables do not have a significant effect on the independent variable of loan repayment. Therefore, H1 hypothesis and research claim are rejected as follows: Marital status, education level, age, monthly expenditure, loan utilization, literacy, income-increasing debt, location, type of loan, group loan, amount of loan, repayment time, interest rate of the loan, the family member, education and the purpose of the loan before receiving the loan.
Logistic regression analysis is performed to identify repayment problem as a dependent variable and among factors affecting variables of economic and security conditions, number of times of loan, late payment of debt, gender, and type of guarantee as 5 independent variables identified. In total, 300 samples were included in the analysis and the complete model was statistically significant (Wald = 119.69, df = 4, sig = 0.000, EXP (B) = 0.159).
This model among the five independent variables in this study was able to explain between 34 % and 62 % of loan repayment changes. 63 % of people with difficulty in repayment were classified correctly and 96 % of forecasts for those with Problems have not been correct and 92 % of the predictions were correct.
According to the outputs of the logistic regression analysis, the results show that among all the variables tested, economic and security challenges have a negative impact on the loan repayment. This means that if the economic and security challenges increase, the loan repayment decreases by 1.5 and the guarantee. Debt is also negatively correlated with loan repayment, indicating that as loan conditions become easier to repay, the repayment ratio will be reduced to 0.08. The gender change from male to female will have a negative impact on loan repayment, indicating a shift towards gender. The man reduced the loan repayment ratio to 0.17 means that women are less likely to encounter problems with repayment of the loan. But the variables of the number of times the loan is received and the delayed penalties have a positive effect on the repayment of the loan for example by increasing penalties, the repayment loan rate increases by .461 and 2.157 units, respectively.
LIST OF REFERENCES
1. Arp F., Ardisa A., Ardisa A. Microfinance for poverty alleviation : Do transnational initiatives overlook fundamental questions of competition and intermediation ? – 2016. – February. – P. 27–30.
2. Haile F. Determinants of loan repayment performance: case study od Harari microfinance institutions // Journa of Agreicultural extention and rural development. – 2015. – № 7 (2). – P. 56–64.
3. Korankye A. Causes and control of loan default/delinquency in microfinance institution // Amirican international journal of contemporary research. – 2014. – № 4.
4. Eno G.U. Effect of credit management systems on the loan recovery efforts of microfinance banks // Research journal of finance and accounting. – 2018. – № 9. – P. 26.
5. Kothari C. Research methodology: method and tecniques. – Second ed. New Dehli: New age international (p) Ltd., publishers, 2004.
6. Mwangi C. Effect of loan collection procedures and loan default in microfinance institutons in Kirinyaga county // Global journal of manaement and business research. – 2016. – № 16 (8).
7. Onyeagocha S., Chidebelu D. Determinants of loan repayment of microfiance // International journal of science and humanities. – 2012. – № 3–6.
8. Osegge M. Risk managment course. – Kabul: Afghanistan institution banking and finance, 2012.
9. Walsh M., Wigens L. Introduction to research // Cheltenhm. – United kingdom: Thom ltd, 2003.
10. Wilson J. Esssential of business research: A guid to doing your research project. – London: Sage publication, 2010.
Асоода Азиз
Факультет финансов и банковского дела
Экономический факультет
Кабульский университет
Кабул, Афганистан
ФАКТОРЫ, ОПРЕДЕЛЯЮЩИЕ ВОЗВРАТ КРЕДИТА
В ИСЛАМСКОМ ИНВЕСТИЦИОННО-ФИНАНСОВОМ КООПЕРАТИВНОМ
МИКРОФИНАНСОВОМ ИНСТИТУТЕ МАЙМАНА
MIIFC является одним из финансовых учреждений, работающих в финансовом секторе провинции Фарьяб в Афганистане.
Одной из важнейших проблем финансовых учреждений является взыскание кредитов с заемщиков. Целью данного исследования является выявление факторов, определяющих возврат кредита в микрофинансовую организацию MIIFC. Статистическим объектом исследования являются заемщики микрофинансовой организации MIIFC, размер выборки составлял 300 человек, использовался метод стратифицированной случайной выборки. В исследовании использовалась как описательная, так и косвенная статистика, количественный метод был использован с помощью описательной статистики со статистическим пакетом SPSS (24), а эмпирический анализ проводился с использованием модели бинарной логистической регрессии для определения всех объясняющих переменных.
Результаты показывают, что из этих переменных только экономическая и политическая ситуация, количество взятых кредитов, штраф за просрочку, пол и долговая гарантия имеют значительную связь с погашением кредита. Семейное положение, уровень образования, возраст, ежемесячные расходы, использование кредита, грамотность, долг, увеличивающийся доход, относительное местоположение, тип кредита, групповой кредит, сумма погашения, период погашения, процентная ставка по кредиту, количество семей, обучение использованию кредита и цель предварительного кредита не имеют никакого отношения к возврату кредита.
Ключевые слова: возврат кредитов, финансовые институты, экономические факторы, институциональные факторы, демографические факторы, кредитные факторы.
