Vice Capital

Academic and market interest in environmental, social, and governance (ESG) investing has grown markedly in recent years. Although less prominent, a substantial literature also explores whether “sin pays” in the public capital markets. This literature’s underlying theory is that social norms discourage the funding of businesses that promote vice. According to this theory, some investors—particularly institutions sensitive to social norms, such as pension funds and foundations—will shun vice investments. A consequence of this aversion is a “vice premium” for those investors who will invest in such companies. Largely unexplored, however, is what industries or business models qualify as “vice,” how this definition is constructed and changes, how vice aversion affects startup corporate governance and finance, and what consequences vice aversion holds for the real economy. We address these gaps through a series of interviews with startup founders, venturecapital (VC) and angel investors, and legal and financial practitioners. Descriptive data from commercial VC databases supplement our interviews. We find that the definition of “vice” is nuanced and shifts over time as the subjective preferences of investors and their constituents adapt to changing regulatory environments and social mores. Our respondents report that vice startups face heightened regulatory and business-infrastructure hurdles compared to non-vice startups. This experience is especially true for women and other minoritized vice entrepreneurs and those serving minoritized customer bases. These challenges implicate entrepreneurship, society, and capital markets, including by complicating the concept of the vice premium in finance theory and by showing the potential for vice aversion to shape both the vice and nonvice sides of the real economy.

Publication Details

Journal
eScholarship (California Digital Library)
Published
2025-08-31
Primary Topic
Data Mining Algorithms and Applications
Type
article
Field-Weighted Citation Impact
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article

Vice Capital

eScholarship (California Digital Library)
Data Mining Algorithms and Applications
article

Vice Capital

article en

Abstract

Academic and market interest in environmental, social, and governance (ESG) investing has grown markedly in recent years. Although less prominent, a substantial literature also explores whether “sin pays” in the public capital markets. This literature’s underlying theory is that social norms discourage the funding of businesses that promote vice. According to this theory, some investors—particularly institutions sensitive to social norms, such as pension funds and foundations—will shun vice investments. A consequence of this aversion is a “vice premium” for those investors who will invest in such companies. Largely unexplored, however, is what industries or business models qualify as “vice,” how this definition is constructed and changes, how vice aversion affects startup corporate governance and finance, and what consequences vice aversion holds for the real economy. We address these gaps through a series of interviews with startup founders, venturecapital (VC) and angel investors, and legal and financial practitioners. Descriptive data from commercial VC databases supplement our interviews. We find that the definition of “vice” is nuanced and shifts over time as the subjective preferences of investors and their constituents adapt to changing regulatory environments and social mores. Our respondents report that vice startups face heightened regulatory and business-infrastructure hurdles compared to non-vice startups. This experience is especially true for women and other minoritized vice entrepreneurs and those serving minoritized customer bases. These challenges implicate entrepreneurship, society, and capital markets, including by complicating the concept of the vice premium in finance theory and by showing the potential for vice aversion to shape both the vice and nonvice sides of the real economy.

eScholarship (California Digital Library)
Decent work and economic growth
Openalex Percentile: Top 34%
Data Mining Algorithms and Applications
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