Bloom-Smith: Identifying the Drivers of Social Entrepreneurial Impact/Test of SCALERS

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Abstract

The scaling of social entrepreneurial impact is an important issue in the field of social entrepreneurship. While researchers have focused relatively little theoretical and empirical attention on scaling, a recently proposed set of drivers of scaling – incorporated into what has been labeled the SCALERS model – may provide guidance for new theoretical and empirical work on scaling of social impact. In this study, prior work on the drivers of scaling is extended by adding to the theoretical foundations upon which the SCALERS model is developed and by providing an initial empirical test of the SCALERS model. Initial empirical support is found for the SCALERS model of scaling social entrepreneurial impact.

KEY WORDS: Social entrepreneurship, scaling, organization capabilities, social impact, nonprofit, social enterprise, social capital, human capital, political capital, new venture growth

Introduction

Nearly every problem has been solved by someone, somewhere. The frustration is that we can’t seem to replicate [those solutions] anywhere else. (Former US President, Bill Clinton, quoted in Olson 1994, p. 29)

The challenge of how to scale social impact efficiently and effectively has become a key issue for both practitioners and researchers of social entrepreneurship (Bradach 2003, Dees et al. 2004, Bloom and Dees 2008).

Managers of social entrepreneurial organizations – and the donors and agencies that fund and support them – are eager to learn how to take a program that has helped to resolve a social problem in a limited way and then scale it up so that the program’s impact on society becomes wider (i.e. helps more people in more places) and deeper (i.e. reduces the problem’s negative effects more dramatically). Can a high-quality, cost-effective, local program that fosters, for example, drug rehabilitation or recycling be scaled up to create significantly less drug abuse or solid waste around the world?

To date, the field of social entrepreneurship has dedicated relatively little theoretical and empirical work to the study of scaling of social impact. The theoretical work has largely focused on the development of practitioner frameworks. In the same way, the empirical work that has been done, specifically to understand the drivers of successful scaling for social entrepreneurial organizations, has been limited, with most of it utilizing comparative case-study approaches (Alvord et al. 2004, LaFrance et al. 2006, Sharir and Lerner 2005, Grant and Crutchfield 2007). While this work has generated some provocative theoretical insights, more complete theorizing and empirical tests of theories have been relatively limited (Sherman 2007). The limited theoretical and empirical work is regrettable since the scaling of a social innovation offers the potential to greatly expand the social value of the innovation to a greater number of beneficiaries. In this way, it is arguably one of the most, if not the most, important dependent variables in the field of social entrepreneurship.

One important exception to the limited theoretical work is the emerging work of the SCALERS model (Bloom and Chatterji 2009). Drawing on previous research on scaling and on case studies and theoretical notions from strategic management, organizational behavior and marketing, the SCALERS model, shown in Figure 1, identifies seven different potential drivers of scaling social impact. These drivers of social impact are: Staffing, Communicating, Alliance-building, Lobbying, Earnings-generation, Replicating and Stimulating market forces, and form the acronym, SCALERS.

Similar to the PIMS research agenda, which focused on identifying factors associated with differences in business performance (Buzzell 2004, Farris and Moore 2004), the SCALERS model has the potential to open up important opportunities for both theoretical and empirical work on scaling of social impact. However, to provide the platform for a research stream of scaling social impact, the SCALERS model needs additional theoretical work and the initial development and testing of measures to assess its predictive validity. In this study, prior work on the identification of drivers of scaling is extended in two important ways. First, this paper adds to the theoretical foundations upon which the SCALERS model is developed. In the process, the similarities and differences between scaling within social entrepreneurship and growth within commercial entrepreneurship are also highlighted. Second, the paper provides an initial empirical test of the SCALERS model. Specifically, it examines both the reliability and validity of the measures and the predictive validity of the constructs of the SCALERS model with a large-scale sample of more than 500 social enterprises in the United States.

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Hulme: Impact Assessment Methodologies for Microfinance

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Summary

Microfinance programs and institutions are increasingly important in development strategies but knowledge about their impacts is partial and contested. This paper reviews the methodological options for the impact assessment (IA) of microfinance. Following a discussion of the varying objectives of IA it examines the choice of conceptual frameworks and presents three paradigms of impact assessment: the scientic method, the humanities tradition and participatory learning and action (PLA).

Key issues and lessons in the practice of microfinance IAs are then explored and it is argued that the central issue in IA design is how to combine different methodological approaches so that a “fit” is achieved between IA objectives, program context and the constraints of IA costs, human resources and timing. The conclusion argues for a greater focus on internal impact monitoring by microfinance institutions.

Keywords: methods, microfinance, credit, impact assessment, monitoring and evaluation, poverty reduction

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 microfnance has very beneficial economic and social impacts (Holcombe, 1995; Hossain, 1988; Khandker, 1998; Otero & Rhyne, 1994; Remenyi, 1991; Schuler, Hashemi & Riley, 1997). At the other are writers who caution against such optimism and point to the negative impacts that microfinance can have (Adams & von Pischke, 1992; Buckley, 1997; Montgomery, 1996; Rogaly, 1996; Wood & 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 & Mosley, 1996; Mosely & Hulme, 1998).

Given this state of affairs the assessment of microfinance programs remains an important field for researchers, policy-makers and development practitioners. 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 policymakers and microfinance institution (MFI) managers.

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