Endnotes

¹ An earlier helpful survey published by ADBI is Meyer (2002). It draws out some of the methodological problems in assessing impact and surveys a number of important studies available at the time of writing (around 2001). Morduch (1999) is an extremely authoritative earlier survey focusing on both conceptual and empirical questions.

² Microbanking Bulletin reports only data on a limited number of MFIs who choose to participate. Those reporting to the Bulletin are thought to be amongst the best and are therefore unlikely to be representative of the industry as a whole (Meyer 2002: 14).

³ Patten et al (2001) find evidence that the microfinance side of the Indonesian banking system performed much more robustly during the macro crises of the late 1990s than did the commercial banking sector.

⁴ In Sri Lanka, the microfinance sector is highly subsidized, discouraging entry by private commercial banks, but Hatton National Banks (HNB), Seylan Bank and Sampath Bank have become involved in the sector. However, Charitonenko, Campion and Fernando (2004) report that their combined microloans accounted for just 1.2% of the industry total at the end of 2000 and that none of the microfinance programs are profitable. Thus, the future of the involvement of private commercial banks in microfinance in Sri Lanka is questionable.

⁵ An important attempt to address this problem has been the Income Generation for Vulnerable Group Development (IGVGD) program run by BRAC in Bangladesh, which combines measures of livelihood protection (food aid) with livelihood promotion (skills training and micro credit). Hence micro credit is provided as part of a package approach. Matin and Hulme (2003) survey the evidence on how far the benefits of this program actually reach the core poor and conclude that although the program was more successful than more conventional micro credit schemes, nonetheless many target households were still missed.

⁶ CGAP reports that the CGAP-PAT has been used to assess the relative poverty level of clients of 7 MFIs—2 of these are in Asia and 2 in Latin America. Three of these MFIs who explicitly identify serving the poorest of the poor as an institutional mission appear to be succeeding in that goal. Institutions with broader goals tended to serve a clientele that is more representative of the communities in which they operate, which may or may not be poorer than the national average.

⁷ The analysis of covariance (ANCOVA) essentially allows separate parallel regression lines to be fitted through the data for the treatment (borrower) and control groups. The regression lines measure the outcome variable for a given year (t + n) relative to an earlier year (t). Insofar as a program like microcredit has a tangible effect this will be picked up by the distance between the two lines, that is by the difference in intercept terms. The statistical significance of this distance gives a test for the impact of the program.

⁸ This discussion draws extensively on Coleman (2001).

⁹ See Hulme (1999) for a discussion of different approaches to impact.

¹⁰ Poverty is based on a daily calorie intake of 2112 and extreme poverty on one of 1739.

¹¹ This debate, which in part centers around details of econometric estimation, has not been resolved. An unpublished paper by Pitt reworks the original analysis to address the concerns of Morduch and is said to confirm the original results (Khandker 2003, footnote 1).

¹² Unlike the Khandker studies, these data choose households before they join a micro credit scheme. Their vulnerability measure is broader than simply fluctuations in consumption.

¹³ There is some ambiguity in the interpretation of poverty impact since the definition of the headcount poverty index in the notes to Table 5 in Mosley (2001) does not seem to match the explanation in the text. This refers to between 10 and 20 per cent ‘of borrowers’ crossing the poverty line as a consequence of microfinance. We take this to mean ‘of poor borrowers’ given the low poverty outreach reported in Table 5 [ PDF 91.1KB | 1 page ].

¹⁴ To explain this worrying result, the authors suggest that as the poverty measure is expenditure based, new borrowers may curtail their consumption in the short-term to invest in their micro-enterprise at the same time as they take out a new loan, and that this lower consumption may show up as higher poverty in the short-term.

¹⁵ Fujita (2000) makes this point in the context of Bangladesh.

¹⁶ The study on this scheme by Wodon (1998) appears considerably more sophisticated than the other studies and compares costs with the future stream of estimated benefits to the poor in terms of gains from education. The ratio for this activity may not be directly comparable with the other figures in the table.

¹⁷ It should be noted that the benefits from Grameen lending found in Khandker (2003), which are almost half of those found in his earlier study, imply considerably higher cost effectiveness ratios than those reported in Table 5 [ PDF 91.1KB | 1 page ], unless there has been a corresponding rise in the efficiency of operations.

¹⁸ As defined in Table 5 [ PDF 91.1KB | 1 page ] of Mosley (2001), the indicators for the MFIs and the Social Fund programs are not directly comparable as the former are cost per person brought out of poverty and the latter are cost per income benefit received by the poor. Additional assumptions would have be used to convert the ratios for the Social Fund programs to cost per person brought out of poverty, but these are not referred to.

The views expressed in this paper are the views of the author/s and do not necessarily reflect the views or policies of the Asian Development Bank Institute nor the Asian Development Bank. Names of countries or economies mentioned are chosen by the author/s, in the exercise of his/her/their academic freedom, and the Institute is in no way responsible for such usage.

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