GENDER DISPARITY IN FUNCTIONALITY AND CONSEQUENCE OF MICROFINANCE: DOES IT FUNCTION CURRENTLY?

Protap Kumar Ghosh^1† --- Sutap Kumar Ghosh²

¹Assistant professor, Business Administration Discipline, Khulna University, Khulna-9208, Bangladesh , ²Assistant professor , Finance and Banking Department, Islamic University, Kusthia, Bangladesh

Keywords: Microcredit, Motivating factors, Poverty alleviation, Formalities, Funding adequacy, Gender disparity.

Received: 30 September 2016/ Revised: 25 October 2016/ Accepted: 2 November 2016/ Published: 9 November 2016

Contribution/ Originality

This study is one of the few studies that have investigated gender inequality in different issues in microfinance and how this disparity results in consequence of microfinance so that the policymaker can develop appropriate policy to create level playing field for male and female.

1. INTRODUCTION

Microfinance was started its journey as a poverty alleviating tool since its incorporation in 1976 by Professor Mohammad Yunus in Bangladesh. But now microfinance is not only used in Bangladesh but also used across the world. Still now, the role of microfinance in alleviating poverty and enhancing living standard is in question. A series of studies have been conducted to reveal the role of microfinance on the poor people and both positive and negative outcomes have been found out. A study was conducted by {Morris, 2005 #18}Morris and Barnes (2005) on the impacts of microfinance and found that microfinance programs helped to reduce financial vulnerability of poor individuals through the diversification of available income sources and the accumulation of assets. Khandker (2005); Anuradha and Ganessan (2010) revealed that living standard had been increased because of using microfinance and microfinance helped to eradicate extreme poverty and hunger at both individual and household levels. Mckernan (2002) found that people participated in microfinance program could exert a large positive impact on self- employment profits. Pitt and Khandker (1998) disclosed that program credit had a significant impact on the well-being of poor households and its impact was greater when credit was targeted to the women. They also found that female borrowing had a larger impact on children’s school enrollment than male borrowing. Groetz and Gupta (1996) disclosed that microfinance leaded to the empowerment of women breaking down of gender inequalities through providing opportunities for women to take on leadership roles and responsibilities. Banjeree et al. (2010) inversely concluded that microfinance had positive impact on small business creation and expansion, but insignificant effects on health, education and women empowerment in the short-run.

Mourduch (1998) disclosed that microfinance programs had increased access to healthcare and as more children were being sent to school and staying enrolled longer, this indicated long-term welfare of the society. Pitt et al. (2003) also revealed that female borrowing significantly increased children’s height-for-age and arm circumference whereas male borrowing had no statistically significant effect on these factors. Morrision et al. (2007) represented the impact of women’s access to markets and women’s decision-making power within households on poverty reduction and productivity at the individual and household level. Sarumathi and Mohan (2011) disclosed that there was a definite progress in psychological enhancement and social empowerment among rural women as a result of participating into micro finance. Anuradha and Ganessan (2010) conducted their study to focus on social and economic issues and the use of microfinance as a strategy for inclusive growth and sustainable development. Rajendran and Raya (2011) concluded that there was an appreciable empowerment in political space than economic empowerment and negligible social empowerment of women because of using microfinance

Pitt and Khandker (1998) concluded that involvement with microfinance had increased annual consumption expenditures of the participants. Chemim (2008) also stated that participants of microfinance programs achieved 3 percent additional income for their expenditures than similar non-participants in this type of initiatives in Bangladesh. Microfinance was successful in building and growing small businesses, but in the short run, no effect was found on health, education and the empowerment of women entrepreneurs. Most microfinance programs assisted poor people, particularly women, to become entrepreneurs by taking risks and supportive enterprise start-ups (Haque and Harbin, 2009). Helms (2006) claimed that microfinance can only serve a limited number of clients and many potential clients remained beyond coverage. Hermes and Lensink (2007) also claimed that microfinance had a positive impact on economic development, but it could not reach the poorest of the poor people. They concluded that microfinance could only become an effective device to fight against poverty if it could reach the poorest user of microfinance. Opponents of microfinance argued that Microfinance institutes had to distinguish between “marginally poor” and “very poor” (Sengupta and Aubuchon, 2008). Because of gender disparities in distribution of earnings, access to credit, lack of control over property and earnings, gender preferences in labor markets, women encountered many more dimensions of poverty in their real life (Cagatay, 1998). In Bangladesh a study was conducted by Lucy et al. (2008) and eventually concluded that people of this country more or little suffered from poverty but women and children were the most sufferer because of discrimination in health, nutrition, education, employment and political participation. Jahiruddin et al. (2011) concluded that microcredit holder having minimal or no surplus financial capacity are prone to contrary effect of microcredit. Haque and Harbin (2009) reported that Microfinance institutions provided small sized loans to women who had little access to institutional formal credit.

Critics of microfinance claimed that poverty could not be reduced by microfinance as it was
extra burdensome for the poor with additional debt. A study was conducted by Coleman (1999) on microcredit programs of northeast Thailand and reported that only an insignificant effect on physical assets accumulation, production, productive expenses & sales, savings and expenditures on health care and education. Later on, Coleman (2006) concluded that microcredit programs were biased towards wealthier users and also reported that Microfinance had a positive influence on the household welfare of the better-off members and insignificant effect on the poor members of microfinance institutions. Hermes and Lensink (2007) stated that although microfinance had a positive effect on economic development, it could not reach the poorest of the poor and also recommended that microfinance could be an effective tool to combat against poverty if it could reach the poorest user of microfinance. Mohmad et al. (2014) concluded that size of microfinance influenced differently on income, expenditure, health and children’ education of women entrepreneurs. They concluded that relatively small sized loan had more significant impact on income, expenditure, health and children’ education than that of big sized loans.

From the above discussion, it is found that although the success of microfinance is not beyond doubt, to a great extent it can be used as an effective tool for reducing poverty and increasing economic development of a country. There are some factors that encourage users of microfinance to use it to reduce their level of poverty but the access to microfinance and it effects of on different type of users are different. In this study, we have tried to identify whether gender disparity affects different aspects and consequence of microfinance or not in order to making it more effective.

The objectives of our study are:

1. to identify and rank motivating factors of microfinance and identify gender disparity in it.
2. to identify the effect of gender disparity on adequacy of loan, Interest rate, formalities to take loan and poverty alleviation.

2. RESEARCH QUESTIONS AND HYPOTHESIS DEVELOPMENT

To limit our research scope, we have developed the following research questions and hypothesis.

2.1. Research Question

1. What factors direct people using microfinance and what are the comparative rank of those factors?
2. Is there any difference between male and female in ranking motivating factors?
3. What is the current perception of the borrowers relating to adequacy of loan amount, formalities to take loan, rate of interest and effect on poverty alleviation?
4. Is there any difference in the effect of gender disparity on adequacy of loan amount, Interest rate, formalities to take loan and poverty alleviation?

2.2. Research Hypothesis

3. METHODOLOGY

This study is mainly an empirical study based on primary data. The variables of this study are designed from literature review and pilot survey. During pilot survey, fifteen microcredit holders are surveyed to recognize the factors that motivate them to hold microcredit from various NGOs. Then from literature review and pilot survey, a detailed questionnaire is developed for data collection. There are three types of question in the questionnaire. In the first part, four questions are designed for general information and in the second part, seven motivating variables are designed that are ranked by respondents and in the last part, we set four variables to collect perception regarding different issues and consequence of microfinance in order to detect gender inequality in it. The respondents are asked to rank these motivating factors based on their given importance. Most important factor is ranked one and in the same way up to seven. The perception of the last four variables are encoded using 5-point Likert Scale technique. Where I denotes “Strongly disagree”, 2 denotes “Disagree”, 3 denotes “ Neutral”, 4 denotes “Agree” and 5 denotes “ Strongly disagree” . Our population is all the borrowers in South-west side in Bangladesh who take microcredit form various NGOs and 100 respondents are selected using judgment sampling method to ensure content validity and demographic balance among the respondents. One of the reasons of using judgment sampling is that microcredit holders are very poor and illiterate. To ensure content validity, we have selected those respondents that can reply correctly and confidently. To analyze our collected data and to resolve our research questions, we have used descriptive and inferential statistical tools i.e. mean, standard deviation, mode, Independent T-test, Chi-square test and ordered logistic regression. The ordered logistic regression has been used to check gender difference into the perception of the respondent regarding functionality and consequence of microfinance as our dependent variables perception regarding adequacy of loan amount, formalities to take loan, rate of interest and poverty alleviation are designed based on five point likert scale. To resolve research question one, mean, standard deviation, mode, maximum value and minimum values are used. To resolve research question two, independent t-test is used. Lastly to resolve research question three and four, descriptive statistics, cross-tabulation and inferential statistical tool ‘chi-square test’ and ordered logistic regression are used.

4. FINDINGS OF THE STUDY

4.1. Ranking Motivating Factors of Microfinance

From our pilot survey, we have identified seven major factors that direct people to use microcredit to reduce their poverty. Respondents are asked to rank the following factors based on their preference of importance. According to the mean value, it is found that “mortgage free loan” factor has got the first position having mean value 1.96 where it is found that 45% of our respondents have ranked this factors one. “Suitable installment system” factor has got position 2 having mean value 2.55 but 30 % of the respondents ranked it 1 and modal value is also 1. In the Same way “less formalities to take loan” has got the position 3 having mean value 3.11 where mode is 3 and 31% respondents have ranked it 3.

Table-1. Ranking of motivating factors of microfinance

Source: Primary data and statistical output

“Transactions at doorstep” variable has got position 4 having mean value 3.92 but mode is 5, that is, 23% respondents ranked it 5. “Lower interest compare to local lender” variable has got position 5 and its mode is also 5, that is, 24% respondents have ranked it 5. In the same way last two variables “Non availability of micro finance from bank” and “More formalities to take bank loan” have got position 6 and 7 positions respectively.

4.2. Difference between Men and Women Rank Scoring

To resolve research question two, we have used inferential statistical tool independent t-test. The table -2 reveals that all these seven factors have got same position according to men’s and women’s mean scores of ranking. But out of seven factors, only two factors represent significant difference in rank scoring. From table-2, it is found that variable “Less formality to take loan” has a men’s mean rank score value 2.75 having standard deviation 1.218 whereas women’s mean rank scoring value is 3.28 having standard deviation 1.118. The “Less formality to take loan” has got same rank as per men and women mean values. But the associated P-value is 0.034 and at 5% level of significance, it indicates a significance difference between men and women mean values. That is, there is a significant difference between men and women perception regarding formalities to take loan.

Table-2. Difference between men and women ranking of motivating factors.

Source: Primary data and statistical output

The variable “Non availability of microfinance from banks” represents men’ mean value 6.25 and women’ mean value 5.72 and the associated p-value is .015 which indicates a significance difference between men mean value and women mean values. It denotes this factor is more important to women than men and women have less access to institutional finance than that of men as their mean value is lower than men’ mean value. For the rest of the variables, P - values denotes insignificance difference between Men and Women rank mean values but little bit difference remain between man and women mean scores which indicates perceptional difference between male and female regarding driving forces microfinance .

4.3. Overall Perception of the Borrowers Relating to Microfinance

4.3.1. Adequacy of Loan Amount

From the table-3, it is found that the overall mean value of the first variable is 3.47 having standard deviation 1.029416 which indicates that overall perception of the borrowers regarding the amount of money received as loan was adequate to serve their purposes lies between middle of neutral and agree level but the mode is 4, that is, majority group of respondents agree with it. If we critically analyze table-3, we see that 45% of our respondents agree and 13% strongly agree with the statement that they can meet their purposes by the amount they received as loan. Whereas 3% of our respondents strongly disagree and 18% respondents disagree with the same. 20% respondents are at neutral level. The value of chi-square is 17.134 and associated p-value is 0.002 which denotes that at 5% level of significance, we cannot accept our null hypothesis (H₂). That is, there is a significant difference between male and female responses. If we judgmentally analyze the differences between male and female responses, it is clear that there is no male respondent that strongly agree and only 31.2% male respondents agree with the statement that the amount of money paid as loan is sufficient to serve their purposes. Whereas out of total female respondents, 19.1% strongly agree and 51.5% agree with the statement that the amount of money paid as loan is sufficient to serve their purposes.

The output of regression analysis represents that the value of chi-square is 14.10 and the associated p-value is 0.000.

So, our regression model is statically fitted. The table-3.2 denotes that the odds ratio is 0.2242 and resulting p-value is 0.000. So, the difference between male perception and female perception is statistically significant and one unit change in gender status, that is, from female (0) to male (1), the possibility of having the odds of strongly agree reply versus combined agree, neutral, disagree and strongly disagree reply categories for male than female is 0.2242 times.

Table-3.1. Cross tabulation between gender and adequacy of loan amount

a. 3 cells (30.0%) have expected count less than 5. The minimum expected count is .96.

Similarly, the possibility of having the odds of the combined strongly agree and agree reply versus combined neutral, disagree and strongly disagree reply categories for male than female is 0.2242 times if one unit change in gender status and so on. So, it can be concluded that male borrowers are relatively less satisfied with the adequacy of loan amount than that of female borrowers. Haque and Harbin (2009) concluded that Microfinance institutions provided small sized loans to women who had little access to institutional formal credit although an inverse conclusion was found by Rahman et al. (2013) where they concluded that Bank officials found difficulties because of lack of collateral, lack of training, loan recover problem and use of loan for other purposes of selecting women entrepreneurs for financing.

Table-3.2. Output of ordered logistic regression between adequacy of loan and gender

Source: Statistical output

4.3.2. Formalities to Take Loan

The variable “formalities to take a loan is easy” has a gross mean value 3.74 along with a standard deviation 1.02119. The mean value of this variable lies between neutral and agree level but this value is closer to agree level. And, the mode is 4, that is, majority group of respondents agree with the statement. The table-4 represents that 46% of the respondents agree and 23% respondents strongly agree with the statement. 14% respondents remain at neutral level and only 1% respondents strongly disagree and 16% respondents disagree with the statement. While making difference between male and female responses, the table-4 represents that 3.1% male whereas 32.4% female respondents strongly agree with the statement “Formalities to take loan is easy” and 34.4% male where as 51.5% female respondents agree with the same statement. The value of chi-square is 28.214 and its associated p-value is 0.000. So, at 5% level of significance, we can conclude that there is a significant difference between male and female responses regarding formalities to take loan.

Table-4.1. Cross tabulation between gender and formalities to take loan

a. 3 cells (30.0%) have expected count less than 5. The minimum expected count is .32.

The output of ordered logistic regression represents chi-square value of the model 4.30 having a p-value 0.04. So, the regression model between perception regarding formalities to take loan and gender status is statistically fitted. The odds ratio in table-4.2 is statistically significant at 5% level of significance as the resulting p-value of the odds ratio is 0.040. Similarly as we explain in 4.3.1, the women is more positive with formalities to take loan than that of men as the odds ratio is 0.4396.

Table-4.2. Output of ordered logistic regression between formalities and gender

Source: Statistical output

4.3.3. Rate of Interest

The variable “The rate of interest is reasonable” denotes a mean value 3.2 having standard deviation 1.154701 and the mode is 3, that is, majority of the respondents remains neutral about the rate of interest. Although the mean value lies between Neutral and Agree level, it is very much close to neutral level. 36% respondents are at neutral level, that is, neither agrees nor disagrees. But 20% of the total respondents agree and 17% strongly agree with the statement. Whereas 7% respondents strongly disagree and 20 % respondents disagree with the statement. That is, 27% (20%+7%) respondents directly claim that the rate of interest is not feasible for them. If we want to measure disparity between male and female respondents, we see that the value of chi-square and the resulting p-value is 0.000. So, at 5% level of significance, it can be claimed that our null hypothesis (H₄) relating to rate of interest cannot be accepted. That is, at 95% level of confidence, we can conclude that there is a significant difference between male and female perception regarding reasonable rate of interest. In details, we see that 50% (25% SD + 25% A) of total female respondents have positive attitude toward rate of interest whereas only 9.4% (0% SD + 9.4% A) of total male respondents have positive attitude for the same. The reasons behind this difference might be because of limited access of women to other institutional finance than that of men (Alsos et al., 2006; Itani et al., 2011; Zororo, 2011). So, whatever they (female) receive, they become happy. Female borrowers have little chance to compare rate of interest with other financial institutions.

Table-5.1. Cross tabulation between gender and satisfaction with the rate of interest

a. 2 cells (20.0%) have expected count less than 5. The minimum expected count is 2.24.

The chi-square value of ordered logistic regression model is 3.25 and the associated p-value is 0.07. Eventually, we can conclude that this model is marginally fitted. The resulting odds ratio and its p-value are 0.5108 and 0.07 respectively presented in table-5.2. Although the odds ratio is marginally significant as per resulting P-value, combine analysis of table-5.1 and 5.2 clearly denote substantial differences between female and male perception regarding rate of interest of microcredit.

Table-5.2. Output of ordered logistic regression between interest rate perception and gender

Source: Statistical output

4.3.4. Poverty alleviation

Microfinance program was started as a tool of alleviating poverty in 1976 in Bangladesh. Since its incorporation as a tool of poverty alleviation, the effectiveness of microfinance has been evaluated by numerous researchers over this long period of time. Our study reveals that the mean value of this variable is 3.48 having a standard deviation 1.321768 which indicates that the mean value exactly lies between the middle of neutral and agree level. But, the value of mode is 4, that is, majority of the respondents (i.e. 33.0%) agree with the statement and 59% (33% agree + 26% strongly agree) of the total respondents have positive attitude toward the effect of microfinance in poverty alleviation. Whereas 26% (11% strongly disagree+15% disagree) of the total respondents have negative attitude toward microfinance as a tool of poverty alleviation. While investigating differences between male and female perception regarding microfinance’s capability to reduce poverty from the society, the value of chi-square and its associated p-value don’t provide, at 5% level of significant, any statistical evidence to accept null hypothesis (H₅). That is, there is a significant difference between male perception and female perception regarding microfinance as a poverty alleviation tool. This study reveals that 81.3% (46.9% agree + 34.4% strongly agree) of the male respondents have positive attitude about the capability of microfinance reducing poverty whereas only 48.6% (26.5% agree + 22.1% strongly agree) female respondents have the same attitude.

Lastly, when we want to reveal capability of microfinance reducing poverty of the borrowers and impact of gender difference into this consequence, we have run ordered logistic regression between perception of the borrowers regarding poverty reduction as a consequence of microfinance and their gender status. The output of our logistic regression represents that the value of chi-square is 8.96 and the associated p-value is 0.003. So, the fitted model is statistically sound. The table 6.2 represents that the resulting odds ratio is statistically significant at 5% level of significance as the p-value of this odds ratio is 0.003.

Table-6.1. Cross tabulation between gender and perception regarding poverty alleviation

a. 3 cells (30.0%) have expected count less than 5. The minimum expected count is 3.52.

One unit change in gender status, that is, from female (0) to male (1), the possibility of having the odds of strongly agree reply versus combined agree, neutral, disagree and strongly disagree reply categories for male than that of female is 3.1725 times. So, the table-6.1 and 6.2 represent that Male respondents are more positive about the consequence of microfinance as a tool of poverty alleviation than that of female which is completely inverse into their perception relating to adequacy of loan amount, rate of interest and formalities to take loan. The causes of this perceptional difference might be the reasons of different social, cultural, educational and technological challenges and difficulties of women in acquiring and using economic resources than male entrepreneurs in which they operate (Mayoux, 2001; Shaw, 2004; Makena et al., 2014) and because of discrimination in health, nutrition, education, employment and political participation (Lucy et al., 2008) that make them less successful.

Table-6.2. Output of ordered logistic regression poverty alleviation and gender

Source: Statistical output

5. CONCLUSION

This study is designed to investigate the gender disparity in different issues and consequence of microfinance. From this study, our cross tabulation analysis and ordered logistic regression represent that female microcredit holders have more positive attitude regarding formalities to take loan, rate of interest and adequacy of loan amount than that of men. The reasons behind this more positive attitude than men are just because of less access of women to institutional finance (Alsos et al., 2006; Itani et al., 2011; Zororo, 2011; Rahman et al., 2013). So, what they received , they became happy. Most of them have little chance to think about loan amount, formalities to take loan and rate of interest. Whenever the question of the consequence of microfinance on poverty alleviation arises, the output of ordered logistic regression denotes that the male respondents are more positive than that of female. The reasons behind less positive attitude of women than men toward capability of microfinance as a tool of poverty alleviation are just because of gender biasness in socio-economic environment in which they operate (Mayoux, 2001) limited access to skills and technology; financial resources and services; and market information and business services (Makena et al., 2014). Sometimes, microcredit firms show favorable attitude toward providing small sozed loan to women (Haque and Harbin, 2009) but Sinha (2005) concluded that credit facilities to women might not be fruitful if familial and social condition restricted independence and mobility of women. That is, this study reveals that although microfinance ensures little access of women to institutional finance, they are less successful because of gender biasness in socio-economic environment .Even though, the function and consequence of microfinance are not beyond debate and free from gender biasness, few motivating factors (i. e. Mortgage free loan, Suitable installment system, less formality to take loan etc.) make it quite attractive to the microcredit holders.

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