CREAT: Census Research Exploration and Analysis Tool

Impact Investing and Worker Outcomes

May 2025

Working Paper Number:

CES-25-30

Abstract

Impact investors claim to distinguish themselves from traditional venture capital and growth equity investors by also pursuing environmental, social, and governance (ESG) objectives. Whether they successfully do so in practice is unclear. We use confidential Census Bureau microdata to assess worker outcomes across portfolio companies. Impact investors are more likely than other private equity firms to fund businesses in economically disadvantaged areas, and the performance of these companies lags behind those held by traditional private investors. We show that post-funding impact-backed firms are more likely to hire minorities, unskilled workers, and individuals with lower historical earnings, perhaps reflecting the higher representation of minorities in top positions. They also allocate wage increases more favorably to minorities and rank-and-file workers than VC-backed firms. Our results are consistent with impact investors and their portfolio companies acting according to non-pecuniary social goals and thus are not consistent with mere window dressing or cosmetic changes.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
investment, company, earnings, employed, financial, investing, venture, entrepreneur, minority, investor, financing, impact, workforce, equity, disadvantaged, funding, security, earner

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

The model is able to label words and phrases by part-of-speech, including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are identified to contain references to specific institutions, datasets, and other organizations.
:
Longitudinal Business Database, North American Industry Classification System, Alfred P Sloan Foundation, Longitudinal Employer Household Dynamics, Census Bureau Disclosure Review Board, Integrated Public Use Microdata Series

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