How do students form beliefs about how their future career will depend on their choice of college major? Using both nationally representative survey data and surveys that we administered among undergraduates at the Ohio State University, we document that U.S. freshmen hold systematically incorrect beliefs about the relationship between majors and occupations. Students appear to stereotype majors, greatly exaggerating the likelihood that they lead to their most distinctive jobs (e.g., counselor for psychology, journalist for journalism, teacher for education). A stylized model of major choice suggests that stereotyping boosts demand for "risky" majors: ones with rare stereotypical careers and low-paying alternative jobs. In a field experiment among the same Ohio State sample, providing statistical information on career frequencies to first-year college students has significant effects on their intended majors (and, less precisely, on their choices of which classes to enroll in), with larger effects on students considering risky majors. Finally, we present a model of belief formation in which stereotyping arises as a product of associative memory. The same model predicts—and the survey data confirm—that students also overestimate rare non-stereotypical careers and careers that are concentrated within particular majors. The model also generates predictions regarding role model effects, with students exaggerating the frequency of career-major combinations held by people they are personally close to.

How do we predict which asset-price booms go bust? We develop a model of financial markets with investor heterogeneity that yields a summary statistic for the degree to which an asset price overreacts to news: the gap in holdings of the asset by oversensitive investors versus rational investors. We use quarterly institutional holdings data to measure investors' news sensitivity according to their tendency to purchase stocks after positive news, and compute from this measure the asset-level holdings gaps between oversensitive and rational investors. We find that investor news sensitivity is persistent over time, with the holdings gap measure able to forecast reversals or continuation of asset-price run-ups. Furthermore, the holdings gap measure serves as a powerful aggregator of different channels of overreaction, reflecting not only price extrapolation but also overreaction to various sources of non-price information, such as industry winners and fundamental growth.

Forthcoming

Why do retail vacancies persist for more than a year in some of the world’s highest-rent retail districts? To explain why retail vacancies last so long (16 months on average), we construct and estimate a dynamic, two-sided model of storefront leasing in New York City. The model incorporates key features of the commercial real estate industry: tenant heterogeneity, long lease lengths, high move-in costs, search frictions, and aggregate uncertainty in downstream retail demand. Consistent with the market norm in New York City, we assume that landlords cannot evict tenants unilaterally before lease expiration. However, tenants can exit leases early at a low cost, and nearly 55% of tenants with ten-year leases exit within five years. We estimate the model parameters using novel data on storefront occupancy and micro data on commercial leases. Move-in costs and heterogeneous tenant quality give rise to heterogeneity in match surplus, which generates option value for vacant landlords. Both features are necessary to explain long-run vacancy rates and the length of vacancy spells: in a counterfactual exercise, eliminating either move-in costs or tenant heterogeneity results in vacancy rates of close to zero. We then use the estimated model to quantify the impact of a retail vacancy tax on long-run vacancy rates, average rents, and social welfare. Vacancies would have to generate negative externalities of $29.68 per square foot per quarter (about half of average rents) to justify a 1% vacancy tax on assessed property values.

Forthcoming

Allocating innovation resources to their most productive uses is a challenge for innovators because they have incomplete information about which projects will be most productive. I empirically study how a group of large scientific labs traded off the exploitation of existing opportunities versus the exploration of new ones, that is whether they pursued safe projects to maximize short-term productivity or undertook high-variance projects to acquire information and improve long-term productivity. To recover how these labs made the exploitation-exploration tradeoff, I estimate a dynamic model of decision-making, assuming the labs approximated the value of exploration with a simple Upper Confidence Bound (UCB) index. The type of index is well-studied in theory and well-used in practice but has not been applied to estimation of empirical decision models. The index model captures the labs’ decision-making well. Estimates of its free parameters suggest that the labs explored extensively. Counterfactual simulations show that, had the labs not explored, their output quantity would have decreased by 51%, and their citations would have decreased by 57%.

Seven preregistered studies (N = 2,890) conducted in the field, lab, and online document opportunity neglect: a tendency to reject opportunities with low probability of success, even when they come with little or no objective cost (e.g., time, money, reputation). In Study 1, participants rejected a low-probability opportunity in an everyday context. Participants also rejected incentive-compatible gambles with positive expected value–for both goods (Study 2), and money (Studies 3-7)–even with no possibility of monetary loss and non-trivial stakes (e.g., a 1% chance at $99). Participants rejected low-probability opportunities more frequently than high-probability opportunities with equal expected value (Study 3). While taking some real-life opportunities comes with costs, we show that people are even willing to incur costs to opt out of low-probability opportunities (Study 4). Opportunity neglect can be mitigated by highlighting that rejecting an opportunity is equivalent to choosing a zero probability of success (Studies 6-7).

Forthcoming.

Forthcoming

The role of growth strategy for innovative startups is theoretically ambiguous and much debated among practitioners. I interviewed executives of 253 scaling software ventures from 34 countries and scored the internal consistency of their market and organizational plans to measure growth strategy, developing the first dataset of its kind. US startups have a 0.3 standard deviation higher growth strategy score than others. Yet, having a growth strategy predicts performance more for non-US startups, for which a one standard deviation increase in the growth strategy score is associated with an increase in valuation by over a third and an increase in the probability of a successful exit by over four percentage points. Additional analyses suggest that mistakes are more costly in non-US contexts because of financial, talent, and cultural differences, making growth strategy more important for anticipating sources of failure. Yet it is often through prior mistakes that entrepreneurs build knowledge to develop a growth strategy in the first place. Together, this research suggests that in institutional contexts where mistakes are more costly, growth strategy matters more, but is also harder to develop.

A long tradition in innovation research asserts that data-driven organizations excel at incremental innovation, but allocate resources away from less-measurable breakthrough innovations. Questioning this premise, I distinguish the magnitude of an organization’s use of quantitative analysis from the methodological pluralism of its organizational culture (the extent to which members use different kinds of analyses). I argue that organizations using more quantitative analysis will actually produce more breakthrough innovations—provided that they use qualitative analysis liberally as well. To test my theory, I measure innovation performance using product-level sales and attribute data for over 3,500 consumer product launches from 61 organizations between 2010 and 2016; I measure use of qualitative and quantitative analyses using natural language processing on employee résumés. I find that increased reliance on quantitative analysis decreases innovation performance when qualitative analysis is low, and, conversely, increases when qualitative analysis is high. Additional analyses show that this relationship is particularly strong for novel products, and in markets characterized by high uncertainty. I also explore antecedents: management fads, not organizational learning, appear to account for excessively data-driven cultures. The paper contributes to organizational theories of innovation, and to research linking organizational culture to strategic performance