Impact studies on microfinance using panel data

Researchers have devoted a large amount of effort to examining the incidence, correlates and dynamics of poverty among microfinance clients and the impact of microfinance services. Here we examine some of the rigorous studies that have been done on the impact of MFIs, primarily on credit services, based on panel data surveys. Panel data studies where baseline data are based on long recall and studies using qualitative methods are excluded in this review. Table 6.1 lists some

CONSUMPTION DYNAMICS OF MICROFINANCE CLIENTS: TWO PERIOD PANEL DATA

74

of the studies conducted on the impact of credit services and programs using different methods. We review the findings of these studies to investigate whether or not microfinance is effective in helping poor clients.

The notable study of KHANDKER (2005) using the fixed effects method suggests

that access to microfinance services contributes to poverty reduction, especially for female participants, and also to the local economy. Microfinance raised per capita household consumption of participants and also benefitted nonparticipants through growth in local income. Similarly, NGUYEN (2007) found that formal

credit in Vietnam positively affects borrowers’ consumption. In India, participation in self-help group microfinance programs reduces the vulnerability of households, largely because of poverty reduction (SWAIN & FLORO, 2008).

Using the concept of future counterfactuals to assess the long-term impact of farm households’ participation in microcredit, BERHANE &GARDEBROEK (2009)

reported that the timing of membership matters - the earlier the onset of membership the better the effect on household consumption. Up until now, little attention has been given to the time effect of microfinance services. In terms of income and assets, MOSLEY (2001) reported that the net impacts of borrowing are

positive. Net impact on wealthier borrowers was greater than that which poorer borrowers experienced. Using Analysis of Covariance (ANCOVA) procedures, DUNN & ARBUCKLE (2001) reported that microfinance has a positive impact on

income, income diversification and poverty reduction. The impact evaluation in Bolivia (MKNELLY &DUNFORD, 1999) gives evidence that credit and education

services, when provided together, can increase income and savings, improve household health and nutrition, and empower women.

In terms of the effect of microfinance on entrepreneurs, TEDESCHI (2008) found

that credit was assisting small business owners in Lima, Peru. Using quasi-experimental techniques and household fixed effects, impact estimates were robust for weekly and monthly enterprise profits. In an earlier study, Dunn & ARBUCKLE JR. (2001) found that a microcredit program in Peru had a positive

impact at the enterprise level (net revenues, fixed assets, employment, business ownership, input supplies, and business licenses). BANERJEE ET AL. (2009) and

BARNES ET AL. (2001) also found significant impact on start-up businesses and

profitability of existing businesses in India and Uganda, respectively. In Kenya, access to interest-free accounts had a positive impact on productive investments among market women (DUPAS &ROBINSON, 2010). Likewise, TAKAHASHI ET AL.

(2010) suggested that microcredit programs contribute to increased business size in Indonesia, but a decomposition of the sample showed that the enlargement of businesses occurred only for non-poor participants. Contrary to TEDESCHI (2008),

CONSUMPTION DYNAMICS OF MICROFINANCE CLIENTS: TWO PERIOD PANEL DATA 75

on business sales and profit using difference-in-difference analysis of respondents in Lima and Cuzco, Peru. However, they found significant effects on both individual and household income.

HUSSAIN & NARGIS (2008) found evidence against the popular belief that

microcredit is instrumental in elevating the rural poor to a higher economic status. Regular microcredit group participants experienced the lowest welfare gain, while non-participants gained the most. DUNN & ARBUCKLE (2001) found no

indication that microcredit had an impact on per capita food expenditures for all respondents, but did find positive impacts on poor households, albeit with rather

weak results. As found by TAKAHASHI (2010), the impact of microfinance varies

between levels of poverty. LENSINK &PHAM (2008) revealed that neither access

to microcredit nor participation in microcredit programs significantly affects household self-employment profits in Vietnam. Using multiple indicators, COLEMAN (1999) found that program loans in Thailand made minimal impact.

Studies have also analysed the impact of microfinance by comparing household outcomes differentiated by credit limit, while controlling for various factors that affect the outcome. DIAGNE &ZELLER (2001) found that access to credit had no

significant impact on the per capita incomes, food security, and nutritional status of credit program members in Malawi. They found that borrowers may be worse off (in terms of net crop incomes) after repaying the loan. Loan use may also be a determining factor in the effects of microfinance services. IMAI & AZAM (2010)

found that loans did not increase per capita household income significantly, but that household access to loans from MFIs for productive purposes significantly increased the per capita household income.

As for studies that used descriptive statistics on panel data, MUSTAFA ET AL.

(1996), HUSAIN (1998) and CHOWDHURY &BHUIYA (2004) found positive impact

of credit at the household and individual level in Bangladesh. CHOWDHURY &

BHUIYA found wider impacts of microfinance on child survival and nutritional

status, family planning practices, and children’s education.

Overall, the studies present differences in results of the impact of microfinance on various household social indicators. Several studies have raised doubts about the positive effect of microfinance services. Nevertheless, the majority of the studies found evidence that microfinance has a positive impact on its clients. The overview of methods and results reveal three major research gaps. First, more attention should be given to the impact variations across different poverty levels. Second, very few studies look into the temporal and long-term effects of microfinance or the difference in impact on new entrants and long-term clients. Third, many studies do not include the dropouts in their sample, which can result

CONSUMPTION DYNAMICS OF MICROFINANCE CLIENTS: TWO PERIOD PANEL DATA

76

in an overestimation of the impact of the program (KARLAN, 2001). This paper

contributes to filling these three research gaps.

Methodology, self-selection, non-random placement of microfinance services and incomplete selection bias are issues that need to be considered in impact studies. Recent studies have focused on propensity score matching to address these issues. In our study, we employ several techniques to test for these biases. Also, fixed-effect estimation has been the popular choice in impact analyses. Other models that capture change should also be tested in future impact studies. Here, we use the conditional change score method and the fixed-effects model with interaction variables to assess impact on household food consumption.