Profits and Social Impacts: Complements vs. Tradeoffs for Lenders in Three Countries

The canonical approach to corporate governance posits a tradeoff between maximizing shareholder profits and maximizing other aspects of shareholder welfare such as social impact. Advances in machine learning that enhance the firm's ability to target specific customers may exacerbate or ameliorate this tradeoff. We estimate these tradeoffs using data from randomized microcredit approvals in South Africa, the Philippines, and Bosnia. We examine social impact on two dimensions: credit access (i.e., reaching disadvantaged groups that typically have less access to financial markets) and impact (i.e., treatment effects on household income). Two of the three lenders could have increased average loan profit margin by 7–9 percentage points through machine learning-based targeting. However, such targeting pushes against social impact goals vis-à-vis credit access; specifically, women and lower-income households would have been more excluded (the implied impact on borrowers' income, also central to the dual goal of social impact, is too imprecisely estimated to draw an inference). To gauge the magnitude of the first tradeoff, we examine how profits would change if the lenders altered whom they lent to within each quintile of baseline borrower income, thus holding the distribution broadly similar but fine-tuning the targeting within income bands. Such a constraint would lower the profit gains of targeting by about half. These findings highlight the importance of quantifying tradeoffs and complementarities when deciding what to maximize.