Entrepreneurship is thought to be a key driver of economic growth. While there are myriad forms of entrepreneurship, ranging from self-employment to small and medium size enterprises to technology- and innovation-driven startups, recent research provides evidence that the relationship between entrepreneurship and economic growth is driven not by overall quantity of new firm entry, but rather by a small subset of high-growth startups that are primarily categorized as innovation-driven. This paper provides a survey of the growing literature on the economics of such innovation-driven entrepreneurship. We begin by distinguishing between the various forms of entrepreneurship, which are often confounded in both theory and empirical work. We lay out the current state of knowledge, and describe the challenges faced by researchers in the field, particularly around measurement, data and identification. We conclude with an overview of the major open questions and directions for future research in the area.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Abstract A common concern surrounding minimum wage policies is their impact on independent businesses, which are often feared to be less able to bear or pass on cost increases. We examine how these typically small and medium-size firms accommodate minimum wage increases along product and labor market margins using a matched owner-firm-worker panel data set drawn from the universe of U.S. tax records over a 10-year period, and using state minimum wage changes as identifying variation. We find that on average, firms in highly exposed industries do not substantially reduce employment—they do not lay off workers but moderately reduce part-time hiring. Instead, these firms are able to fully finance the new labor costs with new revenues, leaving average owner profits unchanged. Higher wage floors, however, forestall entry, particularly for less productive firms, reducing the number of independent firms operating in these industries by roughly 2%. Yet these industries do not shrink; instead, incumbent responses and strong positive selection among entrants reshape industries that rely heavily on low-wage workers, yielding fewer but more productive firms after the cost shock. We also take a worker-level perspective to examine how potentially vulnerable individuals are affected by minimum wage increases. Using panels of low-earning and young workers, we find that their average earnings rise substantially with the minimum wage, while they are no less likely to be employed. Worker transitions indicate that minimum wage increases boost retention and that worker reallocation from independent firms toward corporations buffers disemployment impacts from reduced hiring at independent firms.

Abstract We develop a satisficing model of choice in which the available alternatives differ in their inherent complexity. We assume—and experimentally validate—that complexity leads to errors in the perception of alternatives’ values. The model yields sharp predictions about the effect of complexity on choice probabilities, some of which qualitatively contrast with those of maximization-based choice models. We confirm the predictions of the satisficing model—and thus reject maximization—in a novel data set with information on hundreds of millions of real-world chess moves by highly experienced players. Looking beyond chess, our work offers a blueprint for incorporating complexity at the level of individual objects into models of choice and for detecting satisficing outside of the laboratory.

Abstract Gun violence is a major problem in the United States, and extensive prior work has shown that higher temperatures increase violent behavior. We consider whether restricting the concealed carry of firearms mitigates or exacerbates the effect of temperature on violence. We use two identification strategies that exploit daily variation in temperature and variation in gun control policies between and within states. We provide evidence that more-prohibitive concealed-carry laws attenuate the temperature-homicide relationship. Our findings are consistent with more-prohibitive policy regimes reducing the lethality of altercations.

ABSTRACT The digital evolution in auditing has triggered a rapid shift in auditors' required skill sets, with audit firms heavily investing in and extolling advanced data analytics and artificial intelligence (AI) capabilities. However, this strong emphasis on newly required digital skills can lead many experienced auditors, who perceive these competencies as their weaker areas, to feel disaffirmed in their abilities. We predict and find, across two experiments, that auditors who feel disaffirmed in their digital skills more defensively discount specialist advice that places higher versus lower reliance on AI, but that an intervention in which auditors affirm their traditional audit skills mitigates this defensive reaction. Absent self‐affirmation, higher specialist reliance on AI results in auditors denigrating the competence and quality of advice that specialists provide. These findings suggest that disaffirmation escalates AI aversion, offering important insights into how audit firms can foster less defensive decision‐making in the rapidly evolving audit environment.

Abstract Using sales and leasing data, this paper finds three novel effects of a higher property transaction tax: higher buy-to-rent transactions alongside lower buy-to-own transactions despite both being taxed, a lower sales-to-leases ratio, and a lower price-to-rent ratio. This paper explains these facts by developing a search model with entry of investors and households, households choosing to own or rent in the presence of credit frictions, and homeowners deciding when to move house. A higher transaction tax reduces homeowners’ mobility and increases demand for rental properties, which explains the empirical facts and leads to a lower homeownership rate. The deadweight loss is large at 111% of tax revenue, with more than half of this due to distorting decisions to own or rent.

Abstract We develop a model-free measure of the variance premium by constructing option portfolios whose returns are highly correlated with realized stock variance. This effectively decomposes returns into realized variance minus implied variance. We apply this decomposition to document a novel quarterly cross-sectional continuation pattern in both realized variance and implied variance of individual stocks. Implied variance underanticipates the seasonality of realized variance, so options that performed well at quarterly lags continue to earn high returns in the future. Quarterly periodicity in realized stock variance only occurs on days with analyst earning revisions, suggesting an informational channel for this pattern. (JEL G12, G13, G40)

Abstract Using a novel measure of investment companies’ reliance on generative artificial intelligence (GenAI), we document a sharp increase in GenAI usage by hedge funds after ChatGPT’s 2022 launch. A difference-in-differences test shows that hedge funds adopting GenAI earn 2-4% higher annualized abnormal returns than nonadopters, while non-hedge funds do not benefit. The outperformance originates from funds’ AI talent and ChatGPT’s strength in analyzing firm-specific information. We conduct a new survey of fund managers’ GenAI usage to provide direct validation of our measure and offer additional new insights on how managers adopt GenAI tools in their practice.