Micro-credit programmes of different NGOs/MFIs: A comparative study

This study evaluated micro-credit programmes of six leading NGOs/MFIs (BRAC, Grameen Bank, PROSHIKA, ASA, SSS and TMSS) in terms of the clients’ views towards the programmes. The objective of this project is to develop a selection criterion to identify the efficient programme. A total of 406 members from the selected NGOs/MFIs operating micro-credit programmes in Dhaka, Mymensingh, Sherpur, Netrokona, Kishoreganj, Norshingdi, Sylhet and Lalmonirhat were interviewed. The data analyses reveal that the majority of the large amount of loans (above Tk.15000) was taken by GB members (33.7 percent). The respondents who knew the actual interest rate were more in BRAC (42.9 percent). Workers of GB ranked first in terms of field visit (24.9 percent). Request needed to get loan was the highest in BRAC (53.0 percent). The satisfaction level of respondents was more in GB and low in SSS. Poverty alleviation due to micro-credit was the highest among GB members (22.0 percent). Logistic regression analysis suggests that amount of loan taken, experience of poverty alleviation and NGO membership are three important determinants of satisfaction level on the micro-credit programme. A set of characteristics were chosen to find out the best performing micro-credit operating NGOs/MFIs. The data analysis suggests that TMSS is the best performing NGOs/MFIs. The successive NGOs/MFIs in order of rank are GB, SSS, PROSHIKA, BRAC and ASA, respectively.

Many Non-Government Organisations (NGOs)/Micro Finance Institutes (MFIs) in Bangladesh are running micro-credit programmes in rural Bangladesh. Programme structure, coverage, client type, terms and condition and interest rate vary from programme to programme as well as between NGOs/MFIs. Despite reported success of micro-credit programmes, differences in the programmes will lead to different outcomes. There have been limited accessible studies comparing the similarity and dissimilarity between the programmes run by different NGOs/MFIs. It is important to identify the best practised method for policy purposes as well as for academic interest. Such information will guide the new comers in this sector. Given that micro-credit programmes are widely accepted and worldwide practised method of rural development, no single structure of the programme is yet to be identified as the most efficient to ensure rapid rural development. Moreover, there is a huge criticism of high interest rate. This research aims to address the issues by comparing the micro-credit programme activities of leading NGOs/MFIs in this sector. The specific objectives of the project were to i) evaluate the clients' views regarding the credit programmes they are involved with and compare the results between different NGOs and ii) identify the efficient micro-credit programme through development of selection criterion.

Data
Members of Bangladesh Rural Advancement Committee (BRAC), Grameen Bank, PROSHIKA, Association for Social Advancement (ASA), Society for Social Service (SSS) and Thengamara Mohila Sabuj Sangha (TMSS) were chosen for the study. Field work was conducted in different locations in Mymensingh, Sherpur, Netrokona, Kishoreganj, Dhaka, Narshingdi, Sylhet and Lalmonirhat where the selected NGOs/MFIs operate their micro-credit programmes. In these eight districts, twenty-seven villages were selected purposively and among these villages, 406 micro-credit clients were chosen randomly for the study ( Table 1). The survey was conducted during July 2011-June 2012.

Binary Logistic Regression Model
For a dichotomous dependent variable multiple linear regression modelling is not possible as the assumption that the dependent variable is continuous is violated. In such situation, binary logistic Regression model is a suitable alternative. Let Y be a dichotomous dependent variable, say client's satisfaction with micro-credit programme taking values 1 and 0 and suppose that Y=1, if the client is satisfied and Y=0, otherwise. Also let X be an independent variable. Then the form of the logistic regression model is Then a transformation of P known as the logit transformation and is defined as For more than one independent variable the model can be generalised as

Comparison between NGOs and MFIs
A set of characteristics were chosen to categorise the best performing NGO. These were i) if the clients knew actual interest rate, ii) visit by the NGO workers, iii) if any request was needed to get loan, iv) if the client thought that the loan was affordable, v) if the clients were satisfied, vi) if micro-credit brought any positive change in poverty situation, and vii) if the organogram of the NGO (as a proxy of operating cost) was simple/complex. Each of the NGOs was assigned with a score ranging from 1 to 6 for their ranks of performances for the characteristics considered. The lower the score the better the performance. The lowest aggregate score obtained by the NGO will identify the best performing NGO.

Bi-variate analysis
Amount of loan was significantly associated with NGOs/MFIs. Most of the respondents took loan within the range of Taka 6000 to 10000. The majority of respondents who took loan above Tk.15000 were GB members (33.7%) ( Table 2). Most of the respondents did not know the actual interest rate. These types of respondents (21.6%) were more in ASA. The respondents who knew the actual interest rate were more in BRAC (42.9%). Visit of the field workers has significant effect on the membership of different NGOs/MFIs. In GB, the field workers' activities were good (24.9%) whereas these activities were low in TMSS (10.4%). In some NGOs/MFIs clients required some type of request to get loan. Most of the clients who needed request to get loan belong to BRAC (53.0%). Respondents who reported that loan was affordable the highest 20.6% were members of GB. Among the clients who reported difficulty to repay loan 25.5 percent were members of ASA. Respondents satisfied with loan were more in GB (21.5%) and low in SSS (9.8%). Most of the respondents improved their poverty situation by using micro-credit loan. This corroborates the findings of other studies (for example, Hossain, 1984;Hossain, 1988;Akter, 1996;Hashemi et al., 1996;Humal and Mosley, 1996;Khandker and Chowdhury, 1996). The highest number of micro-credit clients who were able to change their poverty situation belonged to GB (22.0%).

Regression analysis
Logistic regression analysis suggests that amount of loan taken, experience of poverty alleviation and NGO membership are three important determinants of satisfaction level on the micro-credit programme ( Table 3). The analysis suggests that members of BRAC were 0.242 times significantly less likely to be satisfied with the micro-credit programmes than the members of Grameen Bank. Respondents taking loan upto Tk 5000 were 19.6 times significantly more likely to be satisfied with micro-credit than the members taken loan more than Tk 15000. Micro-credit clients who did not have any change in their poverty situation were 0.215 times less likely to be satisfied with micro-credit.

Comparison between NGOs/MFIs
Performances in different assssment indicators for different NGOs/MFIs were ranked in Table 4. Table 4 reveals that different NGOs/MFIs are performing differently for different assessment indicators. The score for the simplicity of the organogram was judgmental based on the perception of the cost that would be incurred as operating cost (organograms of different NGOs/MFIs were compared; not provided in the manuscript). The data analysis suggests that TMSS is the best performing NGO/MFI. The successive NGO/MFI in order of rank are GB, SSS, PROSHIKA, BRAC and ASA.

Conclusion
Though the goals of the micro-credit programmes of different NGOs/MFIs are almost similar the implementation and achievements are some what different. This study evaluated the NGOs/MFIs in terms of their field management, clients' perception and change of the poverty situation of the clients. This study set the criteria to find out the efficient micro-credit programme and identified TMSS as the best performing NGO among the selected NGOs/MFIs. This study is the first of its kind and hence limited accessible literature can be compared with the findings of this study. Though the study is not a nationally representative one, the findings can still be generalised and future policies may be formulated. Government can adopt the criteria suggested to identify efficient micro-credit programme.