What is a Fair Level of Profit for Social Enterprise? Insights from Microfinance


Although microfinance organizations are generally considered as inherently ethical, recent events have challenged the legitimacy of the sector. High interest rates and the excessive profitability of some market leaders have raised the question of how to define a fair profit level for social enterprise. In this article, we construct a fair profit framework based on four dimensions: profitability, social mission, pricing, and surplus distribution. We then apply this framework using an empirical sample of 496 microfinance institutions (MFIs). Results indicate that satisfying all four criteria is a difficult, although not impossible, task. According to our framework, 24 MFIs emerge as true double bottom line organizations. These MFIs are characterized by higher outreach to women, lower portfolio risk, and higher productivity in high-density environments such as South Asia. We argue that excessive profits can be better understood relative to pricing, the outreach of the MFI, and organizational commitment to clients in the form of reduced interest rates.


Social enterprises aim to incorporate a social mission through commercial activities. Microfinance institutions (MFIs) are one of the most well-known examples of social enterprise (Battilana and Dorado 2010; Cobb et al. 2016). A central question that has persisted in microfinance—and social entrepreneurship more generally—is the level of profit that can be considered fair, non-exploitative, or acceptable. Can a social enterprise simultaneously make substantial profits and serve the poor?

Social enterprises such as MFIs are hybrid organizations that combine multiple institutional logics, i.e., development or social logics and the market logic of profit generation (Di Domenico et al. 2010). These various logics co-exist and sometimes conflict, forcing managers to make difficult decisions (Huybrechts and Nicholls 2012). The pricing of microcredit is a key management decision for MFIs. Setting interest rates has financial implications since interest payments are a key source of revenue, but also carries an ethical dimension since microcredit clients are poor. Pache and Santos (2013) have argued that the banking logic would require profit-maximizing interest rates, whereas the development logic would suggest low interest rates for the purpose of poverty alleviation. MFIs often try to compromise by setting interest rates at an intermediate level (Pache and Santos 2013). Balancing the two goals is obviously a challenge.

There is a growing body of literature on ethical issues within microfinance. A first stream of the literature addresses the ethical aspects of management decisions, such as interest rate fairness (Sandberg 2012) or potentially discriminatory and harmful practices (Hulme and Arun 2011; Agier and Szafarz 2013; Labie et al. 2015; Cozarenco and Szafarz 2018). A second stream tackles the ethical implications of the entry (or interactions with) more commercially minded actors (Chiu 2014; Brière and Szafarz 2015), such as investment funds and the State (Olsen 2017). A third stream analyzes, or suggests, tools designed to curb ethical lapses. These tools include codes of ethics (Chakrabarty and Bass 2014; Kleynjans and Hudon 2016); social and environmental performance reporting (Casselman et al. 2015; Allet 2014; Forcella and Hudon 2016; Gutierrez-Nieto et al. 2016); and new approaches to better integrate microfinance into community empowerment (Tavanti 2013).

However, the literature is relatively silent on key ethical discussions relating to business models and profitability. In this paper, we address the issue of what can be considered a fair level of profitability in the microfinance sector. We therefore focus on MFIs that have been able to break even and generate a profit. Indeed, therein lies another key question: to which extent can we say that profitable MFIs remain true to their original double bottom line objective, i.e., simultaneously achieving financial sustainability and social impact? Or, to phrase it differently, how can we determine whether an institution has drifted toward dedicating more attention to profit generation than to social outcomes?

In order to analyze the fairness of microfinance profits, we constructed a framework based on four dimensions. The first and most obvious dimension is the profitability of the MFI. We assumed that MFIs with the most severe ethical problems would be those charging high prices in relation to their operating structure (Hudon and Sandberg 2013). The second dimension is adherence to the core social mission of the MFI, i.e., the poverty outreach of the organization. MFI missions are primarily related to poverty alleviation (Morduch 1999a). MFIs focusing on wealthier clients have frequently been accused of drifting away from their original mission, a phenomenon called “mission drift” (Armendariz and Szafarz 2011; Beisland et al. forthcoming). The third dimension is the price that borrowers have to pay for microcredit, i.e., the absolute value of the interest rates. The interest rate charged to micro-borrowers is a central issue since it not only carries an ethical dimension, but also directly affects profitability and constitutes a key management decision by MFIs (Dorfleitner et al. 2013). The fourth dimension is the distribution of the surplus generated by the MFI, namely, the extent to which it favors its clients. The distribution or reinvestment of the surplus is a core element of social enterprise and has even been formalized within the definition of social enterprise by the Department of Trade and Industry in the UK (DTI 2002). Based on the categorization of MFIs across these four dimensions, we will suggest which management practices are ethically condemnable.

This paper contributes to the literature both in theoretical and empirical terms. First, it provides a framework—based on existing literature—that addresses the fairness of profits made by social enterprises. This framework goes beyond simple concepts or metrics to suggest a more comprehensive approach dealing with the question of excessive profit. Second, it provides empirical evidence for this debate by applying the framework to the microfinance industry. Whereas most of the literature on ethical issues in microfinance or social enterprise is of a theoretical nature, we applied our four-dimensional framework to a large dataset provided by the Microfinance Information Exchange (MixMarket), which includes information on 2479 MFIs.

Using a balanced panel of 496 MFIs for the years 2009–2010, our empirical results indicate that satisfying all four criteria is a difficult, although not impossible task; indeed only 4.8% of MFIs emerged as best-in-class, or true double bottom line organizations. These MFIs tended to be relatively young, to operate in high population density environments and to benefit from substantial economies of scale. Conversely, 9.3% of the sample emerged as exploitative MFIs, or organizations that only fulfilled the sustainability dimension of our framework. Between these two extremes, we found MFIs that made trade-offs between the poverty level of their clientele, the interest rates they charged to clients, and the amount of surplus they made available to borrowers over time.

In the next section, we will review the literature on fair pricing and profitability in microfinance. Fair profits in microfinance: a categorization section will present the criteria that we suggest to determine profit fairness in microfinance. Fair profits in microfinance: empirical application section will apply the fair profit framework to an empirical case. Conclusion section will present our conclusions.

Profit and Pricing in Social Enterprises: The Case of Microfinance

Profit maximization is the standard strategy for most financial institutions operating in market economies. Miles (1993) argued that profit maximization strategies were not only financially optimal but also helped firms to deliver social value. By focusing on the creation and supply of unique products and services, a firm would deliver something useful in addition to the financial benefits that accrued to shareholders. However, the scientific literature on business ethics has challenged the necessity of profit maximization. For instance, Kolstad (2007, p. 144) argues that “corporations should in certain cases deviate from profit maximization, from maximizing returns to owners, to pursue ends that are more important from a social point of view.” In some cases, societal interests may take precedence over profit maximization and its efficiency-enhancing effects.

Graafland (2002) addressed the relationship between profit and what he called “principles,” i.e., the social and environmental performance of firms, through an economic framework that differentiates between four perspectives: the win–win perspective assumes a positive relationship between profit and principles; in the license-to-operate perspective, firms need a “minimum value of principles”; in the acceptable profit perspective, firms aim to maximize principles but profitability must reach an acceptable level; and, finally, the integrated perspective calls for firms to attach an intrinsic value to both profit and principles and select an optimal balance.

Hybrid organizations, or social enterprises, pursue both social and financial objectives; hence they are closer to Graafland’s first and third perspectives—win–win and license-to-operate. Historically, most MFIs have generally operated within the license-to-operate perspective and tried to alleviate poverty under financial constraints. These MFIs started as non-profit organizations with a strong social mission and considered profitability merely as a necessary condition to become sustainable. Wealth creation, or profit, served only as an instrument to fulfill the social mission (Dees 1998). Apart from a few extreme cases, profits were not subjected to much scrutiny as long as they were reinvested in the activities of the social enterprise (Barboza and Trejos 2009). Appropriate levels of profitability were hardly discussed in the social enterprise and microfinance literature. This may have been due to the fact that the vast majority of social enterprises charged low prices or because the centrality of their social mission provided them with moral legitimacy.

The entrance of new financial actors, together with commercialization trends, have led to the emergence of actors who consider that profitability is inherently related to outreach (Lehner and Nicholls 2014)—or principles in Graafland’s terminology. For instance, Rhyne (1998) argued that the profit motive of commercial microfinance would make the sector more efficient, more willing to seek out new products or markets, and eventually increase its outreach. Some critics, however, fear that higher profits and commercialization may lead to lower outreach (Mersland and Strøm 2010; Postelnicu and Hermes, forthcoming), highlighting the fact that many decisions entail a trade-off in the microfinance business model between financial and social performance (Copestake 2007; Zhao and Wry 2016; Wry and Zhao 2018).

The microfinance business model shares many similarities with traditional financial institutions. For instance, staff costs are typically the largest organizational expense. Nevertheless, like other types of social enterprises, MFIs differ from traditional firms due to their social mission and the substantial subsidies that they receive from various mission-oriented actors, such as international donors, local government bodies, socially responsible investors or philanthropists. These subsidies have enabled MFIs to start up their operations and have allowed many MFIs to become more socially efficient (D’Espallier et al. 2013). The Grameen Bank, founded by Mr. Yunus, illustrates this powerfully; Morduch (1999b) showed that Grameen could not cover its operating costs with its revenues if implicit subsidies (through soft loans) were included in their financial reports.

The presence of subsidies also increases the complexity of profitability analysis. As argued by Morduch (1999b, p. 236), “it is not clear what ‘profit’ really means when a large fraction of inputs are subsidized.” The disbursement of millions of dollars in grants and soft loans has also created some fear of over-subsidization. After several decades, a large number of MFIs are still reliant upon subsidies and external support. In response, there has been a sectoral push to commercialize microfinance activities (Kent and Dacin 2013). This trend is not restricted to microfinance; various types of social enterprise have experienced some kind of “commercial turn” or “marketization” (Child 2010). In the microfinance sector, commercialization has often been accompanied with worries about large-scale mission drift or even allegations of exploitative practices (Hudon and Sandberg 2013). Yunus has warned that commercialization would lead to profit maximization and high interest rates (Yunus 2011).

According to D’Espallier et al. (2013), subsidies have frequently allowed MFIs to achieve better social performance, either in terms of poverty or gender outreach under local conditions. Indeed in the absence of subsidies, MFIs modify their business model. For instance, unsubsidized African and Asian MFIs tend to charge higher interest rates than their subsidized counterparts (D’Espallier et al. 2013).

Interest rates are the cost of credit borne by micro-borrowers and are therefore scrutinized by many external stakeholders, including regulators and local governments. Contrary to other organizations in social finance that differentiate their price according to social criteria, MFIs typically charge similar rates to all borrowers (Cornée and Szafarz 2014). Moreover, interest rates differ according to the type of MFI. The microcredit interest rates of non-profit MFIs are usually higher than those of microfinance banks, partly because they offer smaller loans that are more costly to administer (Cull et al. 2009). It is also well known that when the loans are not appropriately used or when clients are too poor (Mosley and Hulme 1996), they may encounter severe socio-economic problems. For instance, Montgomery (1996), and more recently Schicks (2014), have argued that microcredit can push poor borrowers into over-indebtedness, or into businesses that can hardly ensure their subsistence. These practices, along with some other factors, have led to major crises in microfinance (Guérin et al. 2015).

Many MFIs are unprofitable and it is likely that some will never post positive financial results. Nevertheless, in an increasing number of countries, microfinance has at times been criticized for its “excessive profits,” lack of social impact, and stringent operating practices (Banerjee and Duflo 2011). Critics argue that the profit generation objective was integral to the success of microfinance according to the following rationale: people excluded from formal financial intermediation are extremely numerous and, therefore, if MFIs aspire to meet this unmet demand, they should aim for rapid growth. Achieving long-run growth can only be done through a self-sustaining business model; therefore, being profitable is justified in order to generate the surplus needed to sustain this growth process.

To find a balance between growth and excessive profits, microfinance practitioners have suggested possible benchmarks focusing on either the return on equity (ROE) or the return on assets (ROA). Under the ROE approach, an ROE below 5% could be “insufficient for long term sustainability,” between 6 and 15% would match the double bottom line objective, 16–25% would fall within a “gray area,” and anything above 25% could clearly be considered as “excessive” (Waterfield 2012). Following up this approach, a “responsible profit matrix” using ROA and interest rates has been suggested; it measures “acceptable profits,” whereby interest rates above 25% could be considered problematic once the ROA is higher than 6% (Rozas 2012).

In the next section, we will review four criteria that advance a better understanding of what may be considered fair profits in microfinance or, conversely, what constitutes exploitative profits.

Fair Profits in Microfinance: A Categorization

Applying a form of consequentialist reasoning, we argue that the fairness of profit should be based on some key characteristics and policies of MFIs. In order to address fairness, we classify MFIs according to the following four dimensions, which are central to the identity of social enterprises: profitability, poverty outreach, pricing, and surplus distribution.

The first dimension is related to the profitability of the MFI, i.e., the ratio between its revenues and (operating) expenses. Organizations that are unprofitable or are unable to cover their costs can hardly be accused of booking unfair profits. We calculated the ratio between financial revenue and the sum of all operating expenses, financial expenses and loan-loss provisions which, in microfinance, is commonly known as the operational self-sufficiency (OSS) ratio (Cull et al. 2007). An OSS greater than 100% means that an MFI is able to cover all its costs of doing business, while a ratio under 100% indicates that the MFI is making losses and may therefore have to rely upon subsidies to continue operating. In the context of our framework, the first step creates an OSS threshold to identify potentially profitable MFIs, i.e., those MFIs with an OSS greater than 100%. Below, we briefly explain why looking at the OSS is necessary but insufficient as a stand-alone indicator to assess MFI profitability.

The profitability indicator is insufficient because of its inability to account for the large operational inefficiencies of MFIs. For instance, the largest MFI in Mexico, Compartamos, was initially criticized for charging high interest rates, which were required to cover their relatively high operating expenses (Armendáriz and Morduch 2010). This policy looked particularly unfair because poor borrowers had to pay high prices as a result of managerial inefficiency. In competitive markets, inefficient organizations will be forced to increase their efficiency or decrease their prices if they wish to survive. Nevertheless, many microfinance markets are not fully competitive, and many MFIs still enjoy a great deal of freedom to exploit the inelastic demand of their clients (Karlan and Zinman 2008). Discriminating between cases where high interest rates are charged due to market conditions and those where high interest rates hide more contestable motivations should therefore be a priority. However, there are many legitimate reasons for high interest rates in microfinance.

The main reason is that operational procedures in standard microfinance are expensive. To date, successful MFIs have tended to be based on the effective decentralization of loan screening and client follow-up, which requires time-intensive field work by credit officers. This is a fundamental feature of the industry and has allowed MFIs to develop the knowledge that traditional financing and banking institutions lacked. However, as one can imagine, this is also quite expensive since a large amount of time is dedicated to interacting with customers who, ultimately, take relatively small loans. This leads to the second fundamental cause of why microcredit is expensive. In microfinance, loans are—by definition—small while often involving the same fixed costs as larger loans (in terms of salary, office maintenance, etc.). As a result, for an MFI to cover its costs, it needs to charge higher rates on smaller loans. Last but not least, in many cases, MFIs have little or no access to prime rates for their own funding. Therefore, obtaining resources tends to cost them more relative to larger, more traditional financial institutions.

The second dimension of our framework is the social mission of the organization. Considering the developmental efforts involved in microfinance, social performance variables attempt to capture the following factors: poverty level of clients; a focus on women, rural or disabled clients; the number of clients; the type of products delivered; and the cost of microfinance services (Schreiner 2002; D’Espallier et al. 2011; Randøy et al. 2015; Beisland and Mersland 2017). Although it is not a perfect indicator, the most widely used proxy among practitioners and academics to assess the poverty level of an MFI’s clientele, or depth of outreach, is the average loan size (Cull et al. 2007). For international comparisons, average loan size is taken as a ratio over per capita GNI (gross national income) (Olivares-Polanco 2005). A ratio under 20% is often used to determine the poverty profile of microfinance clients (Morduch 1999a; Olivares-Polanco 2005). Therefore, the second step in our fair profit framework uses this threshold to distinguish between MFIs serving poor clients and those with relatively better-off clients. Below, we discuss the rationale for using average loan size in more detail and suggest other variables that could potentially complement loan size as a measure of social performance.

MFIs offering large loans will be more likely to exclude very poor clients and thus drift from their original social mission. Over-indebtedness among microfinance clients often concerns the most vulnerable or poorest borrowers—and thus MFIs that offer very small loans. Working with very poor clients should therefore imply a higher sense of responsibility. This leads to an apparent paradox for people outside the microfinance community: poorer customers pay more for their loans. However, this is perfectly understandable due to the cost structure that we have detailed previously.

In relative terms, it costs more for MFIs to service poorer customers, and therefore poorer customers pay higher interest rates. However, this rationale no longer holds once the profit margins of an MFI are above the break even point. Indeed, once organizational sustainability has been reached, MFIs claiming to be double bottom line should arguably prioritize smaller margins for poorer clients. In fact, for MFIs that have a diversified portfolio in terms of customers, it could be argued that no margin should be taken on the poorer (or poorest) customers. Cross-subsidization in terms of “margin generation” could even be considered between the poor, poorer, and poorest customers (Armendariz and Szafarz 2011). By doing so, an MFI would engage in a sort of “affirmative action” where the “best” credit conditions are provided to the “worst-off” customers.

Of course, these are sensitive issues that leave space for open discussion. Finding the optimum is not a simple task and would force MFIs to simultaneously manage different interest rates for different customers. Effectively, this change would push MFIs away from current practice, which is to apply a high degree of standardization, including the pricing of microloans. Nevertheless, for an industry intent on fighting poverty, charging poorer customers as little as possible above the break even rate should be prioritized. Average loan size has proven to be a popular social performance indicator thanks to its standardization, its applicability to both urban and rural markets, and the fact that it can easily be extracted from existing data. However, there are other metrics that also warrant consideration when it comes to measuring the social commitment of MFIs.

For instance, another widely used social indicator is gender distribution of an MFI’s portfolio. Social conventions in base-of-the-pyramid (BoP) markets often promote gender inequality, resulting in the exclusion of women from market-based activities (Chakrabarty and Bass 2014). Hence a metric often applied in microfinance is the percentage of women borrowers.

A similar dichotomy—between rural and urban markets—is also often used in microfinance. It has been suggested that rural clients are a more socially desirable client base for MFIs since credit imperfections are more acute in rural markets and lead to tighter credit-rationing conditions by credit institutions (Ali et al. 2014). The difficulties involved in serving rural markets are accentuated by higher operational costs in low population density environments and the higher risks associated with agriculture-based lending (Lopez and Winkler 2018).

Finally, social performance measurement may also deal with the targeting of persons with disabilities (PWDs). Research on PWDs in microfinance has typically been underexamined, in part due to the severe discrimination faced by PWDs in credit markets (Beisland and Mersland 2017). Nevertheless, since research strongly supports that PWDs have lower-income levels and fewer assets than non-PWDs, MFIs might be inclined to adapt their products to serve this vulnerable social group (Beisland and Mersland 2017).

Indicators such as the percentage of women and rural clients have been widely used in microfinance research and are often readily available in datasets such as the MixMarket and reports by specialized ratings firms. To reduce the complexity of our model, we only took into account average loan size since the introduction of additional social dimensions creates a trade-off between the interpretability and granularity of the framework (with the number of emergent groups increasing exponentially with each additional factor). However, we did examine how the percentage of women borrowers differed across the emergent categories within our framework in the exploratory results.

A final option, incorporating the many facets of social performance into a single dimension, could be the construction of a “social index” that would combine rural and female client indicators with average loan size to produce a single unit of analysis. However, the creation of a social index would be associated with much added complexity and we lacked an ex ante assessment of what may be considered a threshold for “good” social performance in terms of percentage of women and rural clients. For these reasons, we proceeded with average loan size as a social indicator within our framework.

The third dimension is the price that borrowers have to pay for microcredit, i.e., the absolute value of the interest rates. Contrary to the most expensive MFIs, such as Banco Compartamos, MFIs charging low interest rates have not been subjected to much criticism (Cull et al. 2009). The full cost for borrowers includes not only interest rates, but also mandatory savings, upfront fees, and other commissions. Unfortunately, it is very difficult to obtain all of these figures. Portfolio yield is therefore frequently used as a proxy for interest rates (Cull et al. 2009).

We assumed that MFIs facing the strongest ethical problems would be those charging high prices relative to their operating structure (Hudon and Sandberg 2013). As a result, the third step in our fair profit framework uses the portfolio yield (in real terms) of MFIs to differentiate between MFIs that charge high and low interest rates. Lacking an objective benchmark for acceptable portfolio yield, we arrived at this measurement empirically by splitting the sample into a high interest rate group, i.e., MFIs with a portfolio yield higher than the sample median, and a low interest rate group, i.e., MFIs with a portfolio yield lower than the sample median. Interest rates in microfinance are discussed in greater depth below.

Since the inception of the microfinance industry, a central question has been “what is an acceptable level for microcredit interest rates?” This question has been widely discussed both in the literature and within the industry itself. For some, the key benchmark should be interest rates charged by informal lenders, since these are usually the incumbent lenders for microfinance clients. Although this benchmark appears very logical at first glance, at least two objections can be raised. First, informal market rates are heavily segmented and not everyone has access to the same type of money at the same conditions. As a result, using some sort of “average informal market rate” as a point of comparison may be quite misleading. Second, it should be understood that MFIs benefit from external funding sources, economies of scale, standardization processes, and, often, subsidies; this justifies the expectation that MFIs should be able to deliver microcredit at a lower total cost (for the borrower) than informal markets, inclusive of borrowers’ transaction costs. Consequently, we argue that any comparison with informal markets should be to identify the “absolute threshold” for acceptable interest rates charged by MFIs.

However, this approach is in no way sufficient. Indeed, for microcredit to effectively contribute to poverty alleviation, interest rates should be as low as feasible. How low is this? For income-generating activities, the answer is quite simple: it should at least be low enough to make the typical activities of micro-entrepreneurs profitable. Indeed, if the cost of repaying a microloan exceeds the returns to the entrepreneurial activity, MFI clients actually become poorer by taking a loan. In theory, this type of situation should not occur since the client would not willingly engage in loss-making activities. However, given the high risks of many micro-entrepreneurial activities, some micro-entrepreneurs are not only working poor but also getting poorer by working.

The question is even more difficult when considering the other uses of microfinance loans. Further exacerbating the issue, a substantial portion of the “productive” loans taken by microcredit customers are either for social purposes (school fees, health costs, etc.) or consumption. The industry is deeply divided on how to deal with this issue. Some practitioners suggest that only the ability of a client to repay from various sources of income is of importance. Others are more reserved, concerned that “consumption credit” leads to a higher chance that customers will be pushed into over-indebtedness.

Without entering this debate, let us just point out that when loans are not used for productive purposes, establishing a “fair price” becomes even more difficult. Ultimately, if we wish to prioritize the borrower, the cheaper the loan, the better. If the loan is for a productive activity, a lower interest rate increases the margin for the micro-entrepreneur. If the loan is for a non-productive activity, a cheaper interest rate will reduce the amount of outside resources that need to be reallocated to service the loan. In both cases, a lower interest rate unsurprisingly results in more disposable income for the client.

The fourth dimension of our fair profit framework is the distribution of the surplus generated by the MFI. In a number of economies, the pattern of distribution of the surplus often characterizes the social enterprise sector. For instance, the UK Department of Trade and Industry defines social enterprise as “a business with primarily social objectives whose surpluses are principally reinvested for that purpose in the business or in the community, rather than being driven by the need to maximize profit for shareholders and owners” (DTI 2002, p. 14). The distribution of the surplus is also a central element in the governance of MFIs (Labie and Mersland 2011). Hudon and Ashta (2013) have argued that fairness in microfinance relies on the equitable distribution of the surplus generated by the financial transaction. This echoes theoretical considerations on exploitation, which is rooted in unfair and disproportional benefits accruing to the manager (Snyder 2010; Zwolinski 2007).

The distribution of surplus attempts to answer which stakeholders receive a benefit from the value created by an organization. Put more simply, we ask whether clients pay lower prices, employees receive higher wages, or shareholders receive higher dividends whenever an organization creates value. Since value creation takes place over time, this fourth dimension is dynamic, whereas the previous three dimensions are static. In the context of our framework, global surplus is understood as net output (at constant price), which lends itself to the use of the “global productivity surplus” (GPS) methodology developed by the French Centre d’Etude des Revenus et des Coûts (CERC 1969).

This method considers the “marginal” effect. That is, we start from an initial (static) condition of an organization and then study the surplus (dynamic) distribution among stakeholders. In some cases, surplus gains accrue to a stakeholder thanks to an increase in global surplus. However, surplus gains can also accrue to a stakeholder in a negative global surplus situation if losses are borne by a different stakeholder in the surplus distribution process. For example, borrowers may receive surplus in the form of reduced interest rates at the expense of employees (who receive lower wages). As a result, even if the global surplus is negative, some stakeholders may still benefit (Hudon and Périlleux 2014).

We adopted the GPS specification as previously applied in microfinance by Périlleux et al. (2012) and Hudon and Périlleux (2014); it is represented by the equation:

$${\text{GP}}{{\text{S}}_{\text{t}}}=[\Delta {\text{O}}{{\text{L}}_{\text{t}}} \times {i_{{\text{t}} - 1}} - \Delta {\text{O}}{{\text{L}}_{\text{t}}} \times {\text{p}}{{\text{r}}_{{\text{t}} - 1}}] - [\Delta {\text{D}}{{\text{E}}_{\text{t}}} \times {i^{\prime\prime}_{{\text{t}} - 1}}+\Delta {D_{\text{t}}} \times {i^{\prime}_{{\text{t}} - 1}}+\Delta {N_{\text{t}}} \times {w_{{\text{t}} - 1}}]=S_{{\text{t}}}^{1}+S_{{\text{t}}}^{2}+S_{{\text{t}}}^{3}$$

where GPSt corresponds to the net output by an MFI, or the difference between an MFI’s output (O) and inputs (I) (Périlleux et al. 2012). The output of an MFI is obtained by taking the variation in the MFI’s outstanding loan portfolio ΔOLt at the previous year’s interest rate charged to clients (it−1). The previous year’s interest rate is calculated by dividing the financial revenue by the outstanding loan portfolio. An adjustment is made for loan losses, which reduce the MFI’s output. We account for this by subtracting ΔOLt × prt−1, where prt−1 is the provisioning rate of the MFI for bad debts.

MFI inputs include fund providers, workforce providers and other providers (Périlleux et al. 2012). In microfinance, there are two primary fund providers: savers and lending institutions. Inputs by savers can be summarized by the variation in deposits ΔDEt at the previous year’s deposit rate (iʺt−1). Similarly, inputs by lending institutions can be represented as the change in debt (ΔD) taken at the previous year’s external funding rate (iʹt−1). Workforce inputs are denoted by the change in the number of employees (ΔNt) multiplied by the previous year’s average salary (wt−1). Because it is impossible to differentiate between price and quantity variations, other input providers are not included in the calculation of the GPS (Périlleux et al. 2012).

Surpluses for the various MFI stakeholders are represented by S1 (borrower surplus), S2 (supplier surplus), and S3 (MFI surplus, inclusive of shareholders) (Périlleux et al. 2012). Since we are focusing on profit fairness in relation to clients, in this article we will only deal with S1. The borrower surplus can be estimated as follows: change in interest rate multiplied by the loan portfolio minus any surplus gained or lost through loan losses, such that

$$S_{{\text{t}}}^{1}= - [\Delta {i_{\text{t}}} \times {\text{O}}{{\text{L}}_{\text{t}}} - \Delta {\text{p}}{{\text{r}}_{\text{t}}} \times {\text{O}}{{\text{L}}_{\text{t}}}].$$

The presence of a negative sign indicates that a decrease in the interest rate (Δi < 0) results in a borrower surplus. This also means that an increase in the provision rate generates gains for borrowers, since they will potentially reimburse less to the MFI (Périlleux et al. 2012).

Surplus transfers are a central issue in microfinance, particularly considering how the microfinance industry has evolved over the last 10 years. Indeed, while many MFIs still rely on subsidies, some have been able to generate profits as double bottom line organizations. To fuel client growth, it makes perfect sense that some profits should be generated, notably in order to solidify the equity base over time. As a result, profits should not automatically be condemned—they are part of sound management practice. However, as in other industries, the question centers upon how much profit is acceptable and what is it used for. As argued in previous sections of this article, when profits are large, they deserve to be questioned. Of course, how they are reallocated may be even more crucial.

There are many cases where the debate becomes far more nuanced. Is it acceptable that MFI shareholders occasionally make more money by investing in microfinance than other profit-maximizing industries? Is it acceptable that some managers of MFIs make as much (or more) money working in microfinance than they would working for a traditional financial firm? Is it acceptable that when the portfolio yield covers more than an MFI’s cost structure, the debate often defaults to a choice between distributing higher dividends to shareholders or reinvesting the profits into growth opportunities, without even considering the possibility of lowering the interest rates charged to customers?

The debate on surplus distribution forces social organizations to consider which stakeholders benefit when value is created by the firm. We suggest that emphasizing profitability indicators alone drives social industries, such as microfinance, closer to a paradigm benefitting profit-maximizing shareholders rather than a stakeholder perspective focusing on a key social objective: “improving the situation of customers.” Taking the four dimensions of our fair profit framework into account, we propose a double bottom line approach, which is illustrated in Table 1.

Table 1 Fair profit framework

From Table 1, we suggest that the most problematic cases from an ethical standpoint are MFIs with only one star, called “Group H.” These are profitable MFIs that charge high interest rates, do not serve poor clientele and do not transfer any surplus to their borrowers. Conversely, the best case (four stars), named “Group A,” are profitable MFIs but charge low rates to poor clientele and even transfer some of their surplus to their clients when available.

Fair Profits in Microfinance: Empirical Application

Dataset Description

We used a dataset provided by the Microfinance Information Exchange (MixMarket). MixMarket is the largest industry data source providing information on the financial performance of microfinance institutions (Cull et al. 2016). The full dataset includes information on 2479 MFIs from 121 countries from 1995 to 2010. In the present sample, we used a balanced panel data structure focusing on the two most recent years in the dataset: 2009–2010. Hence, our dataset contains 992 observations from 496 MFIs. Our sample includes a diverse range of MFI profiles: 39% are NGOs, 14% are cooperatives, 6% are banks, and 37% are non-banking financial institutions (NBFIs). The remaining 4% have another legal status, such as state bank or regional rural bank. Geographically, 11% are located in Africa, 9% in East Asia and Pacific, 20% in Eastern Europe and Central Asia, 38% in Latin America, 5% in North Africa and the Middle East, and 17% in South Asia. Table 2 provides descriptive statistics of the main variables used in this study.

Table 2 Descriptive statistics

An important limitation of the MixMarket dataset is that data are voluntarily self-reported by MFIs, which could lead to some self-selection bias (D’Espallier et al. 2017). Although the dataset may not be fully representative of all microfinance institutions, scholars have typically noted that the MixMarket is skewed toward institutions that emphasize financial objectives and profitability (Cull et al. 2009).

To check the representativeness of our sample, we compared some basic sample statistics to the 890 MFIs in the 17th MicroBanking Bulletin (MBB; MIX Market 2008). We obtained similar results. The average OSS ratio was 115% in the MBB (2008) and 103% in our sample. The average number of borrowers was 11,041 in the MBB (2008) and 13,767 in our sample. The average nominal yield was 30% in the MBB (2008) and 29.6% in our sample. Finally, the average staff productivity was 112 in the MBB (2008) and 112 in our sample.

Empirical Application of the Fair Profit Framework

In this section, we will apply the fair profit framework to the dataset. First, we will identify operationally sustainable MFIs. Second, we will distinguish between MFIs serving poor clients and MFIs serving somewhat better-off clients. Third, we will identify MFIs charging relatively high interest rates. Fourth, we will examine whether any surplus was transferred to borrowers in the form of reduced interest rates. Combining these four criteria, Table 3 reports the number of MFIs for each category within our fair profit classification. After briefly discussing the results of the empirical classification, we will conduct an exploratory analysis of the institutional characteristics and operating environments to contextualize our taxonomy.

Table 3 Application of fair profit framework

Table 3 shows that 199 (or 40%) of the full sample of 496 MFIs were operationally unsustainable.Footnote 1 Since unsustainable MFIs can hardly be accused of making exorbitant profits, we focused on the remaining 297 sustainable MFIs. A first, perhaps unsurprising, observation is that satisfying all four criteria of the framework is not an easy task. “Best-in-class” MFIs (Group A) numbered only 24, or 8.1% of the 297 sustainable MFIs, and a mere 4.8% of the total 496 MFIs in our sample. MFIs that met three of the four criteria (Groups B, C, and D) were classified as “Acceptable” and accounted for 93 of the 297 sustainable MFIs (or 31.3%). “At-risk” MFIs fulfilled two of the four criteria (Groups E, F, and G) and numbered 134, or 45.1% of sustainable MFIs. Finally, “Exploitative” MFIs (Group H) did satisfy the sustainability criteria but tended to serve better-off clients at higher interest rates; while exploitative MFIs generated a surplus from efficiency gains, they did not transfer any of this surplus to borrowers. Exploitative MFIs numbered 46 and made up 15.5% of the sustainable MFI sample.

Table 4 shows our first exploration of the data. Similar to Gutiérrez-Nieto and Serrano-Cinca (2007), given our small sample sizes, we merged the eight groups into four categories as detailed previously: Best-in-class (4 stars—Group A), Acceptable (3 stars—Groups B, C, D), At-risk (2 stars—Groups E, F, G), and Exploitative (1 star—Group H).

Table 4 Performance measurement, organizational features, and operating environment: non-parametric tests

Table 4 also allows us to conduct a first exploration of average values for each category by determining whether differences exist across MFI performance, organizational features, and operating environment. To test whether these differences were statistically significant, we first performed the non-parametric Kruskal–Wallis Test and then applied the Dunn Test to identify statistically significant pairwise category relationships. The Kruskal–Wallis Test is one of the most popular options to perform a means test in most statistical software (see Kruskal and Wallis 1952). Although the Kruskal–Wallis test identified statistically significant differences at the median for at least one of our categories, it did not indicate which category was driving the statistically significant relationships. To remedy this, we applied the Dunn Test to statistically significant Kruskal–Wallis variables and reported the results in Table 5. The Dunn Test is a popular post-hoc pairwise test; it is suitable since it uses the same rankings as Kruskal–Wallis and also accounts for pooled variance implied by the null hypothesis of the Kruskal–Wallis Test (Dinno 2015). To account for multiple comparisons, we adjusted p values using the Bonferroni correction (Miller 1981).

Table 5 Post-hoc pairwise Dunn Tests

Regarding MFI performance, we could not observe any statistically significant differences across framework categories as regards the traditional profitability indicators, ROA and ROE. A first reaction might be to wonder why the MFI group with the second lowest ROE figures could be considered exploitative. We imagine two scenarios to explain this result. First, MFIs that emerged as “exploitative” in our framework may simply have strategically targeted wealthier clients at higher interest rates. Not all MFIs share the same mission of poverty alleviation, thus prioritizing the poorest borrowers by offering them low interest rates may not be a top issue for these MFIs. A second possibility could be that “exploitative” MFIs operate inefficiently in uncompetitive environments, and thus are unable to lower interest rates or serve a poorer clientele.

Ultimately, we suggest that since there was no statistically significant difference between the ROA and ROE of the emergent groups, additional measurements will likely be needed to identify exploitative MFI practices.

With respect to social performance, we found a statistically significant difference at the .01 level between the categories for the percentage of women, indicating that there was a difference in medians for at least one of the categories. The post-hoc Dunn Test results given in Table 5 were driven entirely by Best-in-class MFIs, which served more women clients than the other categories on average. This finding suggests that substituting the percentage of women clients as an alternative proxy for the second framework dimension (i.e., commitment to the social mission) would produce similar results as using average loan size.

We also examined to what extent firm efficiency and productivity varied across the framework categories. Perhaps unsurprisingly, the results presented in Table 4 were statistically significant for all indicators and appeared to be driven primarily by Best-in-class MFIs (Table 5). These MFIs not only achieved better portfolio quality in terms of portfolio at-risk and write-off ratios, but also utilized their staff more efficiently as measured by the number of borrowers per staff member and the cost per borrower. The results also held when looking at a wider efficiency indicator: the operational expense ratio.

We also observed a few organizational differences across the categories. The age of an MFI was found to be statistically significant (Table 4) and to be driven by the differences between Best-in-class MFIs and the Acceptable and At-risk categories. The size of the organization was also found to vary significantly across categories as measured by the number of borrowers, although no significant differences were detected when MFI size was measured by the gross loan portfolio. We also do not report any significant differences with respect to the amount of donated equity, suggesting that good performance can be achieved independently of the amount of direct subsidy received by an MFI.

We imagine two potential explanations for these results. The first interpretation would suggest that Best-in-class MFIs tended to operate through a group-loan methodology in densely populated areas. While we lack details on loan methodology in our dataset, the geographical and operating environment indicators suggest that, on average, Best-in-class were indeed more likely to operate in areas of high population density. This is reinforced by the geographical region variables, whereby Best-in-class MFIs predominantly came from South Asia.

A second interpretation might simply be, as already outlined in Profit and pricing in social enterprises: the case of microfinance section, that more exploitative MFIs operated in more uncompetitive environments that allowed them to exploit the inelastic demand of clients. Although we cannot rule out this possibility, our evidence is substantially weaker in this aspect. It might be possible to establish a link between monopolistic power and employee surplus, given that we found that Best-in-class MFIs transferred substantially more surplus to employees than At-risk or Exploitative MFIs—but were indistinguishable from the Acceptable category. Nevertheless, we believe that further investigation of MFIs operating in uncompetitive environments is warranted and constitutes an avenue ripe for further research.


Ethical debates on business models and profitability of social enterprises are scarce. In this paper, we addressed the fairness of profits made by social enterprises, analyzing what can be considered a fair level of profitability in the microfinance sector. Assessing what constitutes “fair profits” is far from easy due to the complexity and variation of social enterprise business models and their simultaneous integration of social and economic objectives. However, according to the four criteria that we defined, there are some clear-cut differences in the case of microfinance. We were able to identify two “key exemplary cases”: on the one hand, a fair MFI (in terms of profits) has its cost structure under control (to avoid inefficiencies) and charges interest rates that allow it to cover costs while making a relatively low margin. This margin is not fully absorbed by shareholders but benefits other stakeholders as well—with special attention to lowering the price of microcredits for the poorest customers. On the other hand, an unfair MFI can be inefficient or efficient, but is either forced to charge high interest rates to cover up its operational inefficiencies or, conversely, generates huge margins. Most profits are absorbed by shareholders and little consideration is given to lowering the costs to poorer customers.

Of course, few institutions perfectly match these “cliché profiles” and many cases fall between these two extremes. Our results show that only a small share of the 496 MFIs (9.3%) were in the “exploitative” category. This empirical evidence stands in contradiction to the concern that a large-scale mission drift is underway in microfinance due to commercialization of the sector. Our findings could indicate that concerns over mission drift and exploitative practices may not be empirically validated, at least at a global level.

This paper provides both theoretical and empirical terms. The main theoretical contribution creates an ethical framework that addresses the fairness of profits made by social enterprises. Although the framework is applied to microfinance, we believe it could be relevant for various types of social enterprises. The main empirical contribution is to highlight the difficulty in satisfying all four criteria of the fair profit framework, although it is not an impossible task. While expectations of ethical conduct are particularly high for mission-driven organizations such as social enterprises, our results show that it is particularly difficult in microfinance to perform well on all dimensions of the fair profit framework. Fewer than 5% of in-sample MFIs are classified as Best-in-class organizations. These organizations tend to be highly efficient South Asian MFIs operating in high-density environments that have achieved substantial economies of scale. Their efficiency gains appear to be distributed to clients through interest rate reductions and to other stakeholders (such as employees, in the form of higher wages), rather than accruing to MFI shareholders, as measured by ROA or ROE. Given the relative youth of the Best-in-class MFIs, our results stress the potential importance of “smart subsidies”, or subsidies that help MFIs build infrastructure and develop institutional capacity during the initial growth phase (Armendáriz and Morduch 2010). These targeted subsidies often include provisions for technical assistance, staff training, or the implementation of information management systems.

No defining features of MFIs at the other end of the spectrum jump out of the data and the majority of MFIs fall between the two extremes, essentially making trade-offs between the poverty level of their clientele, interest rates and the amount of surplus they make available to clients over time. One interpretation would be that MFIs must make decisions on whether to target poorer clientele, to charge lower interest rates, or to transfer any global surplus to borrowers in the form of reduced interest rates. These choices could possibly be linked to the lending methodology of specific MFIs; we can observe some clustering in categories where MFIs offer high proportions of female clients relatively small loans, which is a popular microfinance lending method in high-density South Asia. Future research could investigate this topic in more detail. Alternatively, the weaker operational efficiency of more exploitative MFIs may well illustrate a lack of innovation in places where these face relatively little competition.

Of course, our fair profit framework is subject to some limitations. Empirically, the framework lacks an objective way to measure pricing within microfinance institutions. We partially addressed this issue when constructing the framework by discussing other options such as a comparison with informal market rates. However, this was not practically feasible given that our database covered several continents and diverse lending contexts—where informal credit conditions vary a great deal. Future studies might consider the integration of various social criteria into an index, or further developing a more objective measurement related to price setting in social entrepreneurship.

Second, our empirical strategy only covered a period of two-years. In practice, transfers of borrower (or employee) surplus may take longer to materialize into reduced interest rates (or higher wages). Future studies could look at a longer time horizon to understand how MFIs, or social enterprises more generally, transfer operational efficiencies to clients in the face of increasing competition. Moreover, our empirical strategy was unable to incorporate soft subsidies into the analysis. Although we did report direct subsidies to MFIs, concessionary finance (in the form of below-market rate loans, credit guarantees, or preferential equity) was unobservable in our dataset.

A final limitation is related to the dataset itself. The MixMarket is skewed toward institutions that emphasize financial objectives and profitability. Adding more socially driven MFIs would probably decrease the prevalence of exploitative MFIs.

Despite these limitations, we believe that using this four-dimensional approach could help structure the debate that boards, investors, and donors should have about what constitutes an acceptable level of profit in microfinance—should the industry wish to preserve what it created in the first place, namely, the will to contribute to the common good.


  1. 1.

    Appendix Table 6 reports the mean and standard deviation of the fair profit indicators for each emergent group.


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Correspondence to Marek Hudon.

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See Tables 6 and 7.

Table 6 Fair profit framework classification
Table 7 Best-in-class MFIs

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Hudon, M., Labie, M. & Reichert, P. What is a Fair Level of Profit for Social Enterprise? Insights from Microfinance. J Bus Ethics 162, 627–644 (2020). https://doi.org/10.1007/s10551-018-3986-z

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  • Microfinance
  • Fairness
  • Exploitation
  • Profit
  • Social enterprise

JEL Classification

  • F35
  • G21
  • G28
  • L31
  • M14