Impact Assessment Methodologies for Microfinance: Theory, Experience and Better Practice
Introduction
In recent years impact assessment has become an increasingly important aspect of development activity as agencies, and particularly aid donors, have sought to ensure that funds are well spent. As microfinance programs and institutions have become an important component of strategies to reduce poverty or promote micro and small enterprise development then the spotlight has begun to focus on them. But knowledge about the achievements of such initiatives remains only partial and is contested. At one end of the spectrum are studies arguing that microfinance has very beneficial economic and social impacts Holcombe, 1995, Hossain, 1988, Khandker, 1998, Otero and Rhyne, 1994, Remenyi, 1991, Schuler et al., 1997. At the other are writers who caution against such optimism and point to the negative impacts that microfinance can have Adams and von Pischke, 1992, Buckley, 1997, Montgomery, 1996, Rogaly, 1996, Wood and Sharrif, 1997. In the “middle” is work that identifies beneficial impacts but argues that microfinance does not assist the poorest, as is so often claimed Hulme and Mosley, 1996, Mosely and Hulme, 1998.
Given this state of affairs the assessment of microfinance programs remains an important field for researchers, policy-makers and development practitioners.1 This paper reviews the methodological options for assessing the impacts of such programs drawing on writings on microfinance and the broader literature on evaluation and impact assessment. Subsequently it explores ways in which impact assessment practice might be improved. It views impact assessment (IA) as being “...as much an art as a science...” (a phrase lifted from Little, 1997, p. 2). Enhancing the contribution that impact assessment can make to developmental goals requires both better science and better art. The scientific improvements relate to improving standards of measurement, sampling and analytical technique. Econometricians and statisticians are particularly concerned with this field. Improving the “art” of impact assessment has at least three strands. One concerns making more systematic and informed judgements about the overall design of IAs in relation to their costs, specific objectives and contexts. The second is about what mixes of impact assessment methods are most appropriate for any given study. The third relates to increasing our understanding of the ways in which the results of IA studies influence policy-makers and microfinance institution (MFI) managers.
Section snippets
Impact assessment: objectives
Impact assessment studies have become increasingly popular with donor agencies and, in consequence, have become an increasingly significant activity for recipient agencies. In part this reflects a cosmetic change, with the term IA simply being substituted for evaluation. But it has also been associated with a greater focus on the outcomes of interventions, rather than inputs and outputs. While the goals of IA studies commonly incorporate both “proving” impacts and “improving” interventions, IAs
Assessing impact: the choice of conceptual frameworks
All impact assessment exercises have a conceptual framework at their heart. In well-planned and well-resourced IAs with long “lead-in” times such frameworks are usually explicitly identified Khandker, 1998, Sebstad et al., 1995, Schuler and Hashemi, 1994. By contrast, in many smaller scale exercises the framework is implicit and may be seen as “common sense.” There are three main elements to a conceptual framework:
—a model of the impact chain that the study is to examine,
—the specification of
The three paradigms of impact assessment: problems of attribution and fungibility
The major methodological problems that confront the IA of microfinance relate to attribution and fungibility. At the heart of impact assessment is the attribution of specific effects (i.e., impacts) to specific causes (i.e., interventions). From the vast literature on microfinance IA it is possible to draw out three very different paradigms by which authors seek to demonstrate attribution. The first is the conventional scientific method with its origins in the natural sciences. The second has
Knowledge creation: the methodological menu17
Over the last decade microfinance impact assessment studies have increasingly moved away from single method approaches Hossain, 1988, Fuglesang and Chandler, 1986 to multimethod or pluralist approaches Hulme and Mosley, 1996, Mustafa et al., 1996. The introduction of participatory approaches to impact assessment has extended the methodological menu for data collection and knowledge creation. While sample surveys remain a common model, rapid appraisal, participant-observation and participatory
Effective impact assessment: achieving “fit”
The key task for the IA designer is to select an approach that can meet the objectives of the specific assessment at an acceptable level of rigor, that is compatible with the program’s context, that is feasible in terms of costs, timing and human resource availability and that avoids the problems identified in earlier sections. Wherever possible an IA methodology should be piloted before full implementation. The questions that s/he must answer can be summarized as follows.
—What are the
Conclusion
In recent years donors have been keen to assess the impact of their programs. The initial emphasis on “scientific” sample surveys and statistical analyses has shifted as multimethod impact assessment studies and most recently participatory approaches have been utilized. Microfinance programs and institutions have experienced these shifts and examples of IAs on this topic provide a resource from which this paper has sought to draw out lessons for future practice. Much further work will be needed
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2020, Journal of Cleaner ProductionCitation Excerpt :Demographic factors include age, gender, and occupation (Elsayed and Paton, 2009; Jawahar and McLaughlin, 2001; Moore, 2001). Meanwhile, social factors include culture, local wisdom and social capital (Bédécarrats et al., 2012; Moore, 2001) while economic factors consist of poverty, entrepreneurship and financial inclusion (Elsayed and Paton, 2009; Hulme, 2000; Kabeer, 2005; Mosley, 2001; Orlitzky, 2001; Robinson, 2002; Stanwick and Stanwick, 1998). Lastly, food security and global warming (deforestation, fire management), biodiversity, and local wisdom represent environmental factors (Allet, 2011; Allet and Hudon, 2013; Boons and Lüdeke-Freund, 2013; Brown et al., 1987; Nishat, 2004; Scholtens, 2008; Starik and Kanashiro, 2013; Tilbury, 1995; van Marrewijk, 2003).