Killing two birds with one stone? Sound investment with social impact

Abstract

This paper uses a novel dataset on investments by the European Bank for Reconstruction and Development to quantify a (sizeable) trade-off between the impact and financial objectives of a large lender. The unique feature of this dataset is ex ante records of impact. These are made at the early stages of work on each transaction alongside probability-of-default scores. Impact scores are further updated at the final approval stage with around 55 percent of transaction concepts translating into signed deals. We show that this approach delivers a simultaneous selection of debt investments on the quality of credit and impact with a sizable trade-off between pursuing commercial and development objectives. For commercially riskier investments, impact characteristics have a greater bearing on the probability of an investment going ahead. We further use machine-learning analysis to show that the impact of some investments is strengthened prior to project approval.