Research

We document the role of intangible capital in manufacturing firms' substantial contribution to non-manufacturing employment growth from 1977-2019. Exploiting data on firms' "auxiliary" establishments, we develop a novel measure of proprietary in-house knowledge and show that it is associated with increased growth and industry switching. We rationalize this reallocation in a model where firms combine physical and knowledge inputs as complements, and where producing the latter in-house confers a sector-neutral productivity advantage facilitating within-firm structural transformation. Consistent with the model, manufacturing firms with auxiliary employment pivot towards services in response to a plausibly exogenous decline in their physical input prices.

A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market wherein firms and consumers tailor their choices to the demand data. The social dimension of the individual data—whereby a consumer's data are predictive of others' behavior—generates a data externality that can reduce the intermediary's cost of acquiring the information. The intermediary optimally preserves the privacy of consumers' identities if and only if doing so increases social surplus. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large.

This paper uses a college-by-graduate degree fixed effects estimator to evaluate the returns to 19 different graduate degrees for men and women. We find substantial variation across degrees, and evidence that OLS overestimates the returns to degrees with high average earnings and underestimates the returns to degrees with low average earnings. Second, we decompose the impacts on earnings into effects on wage rates and effects on hours. For most degrees, the earnings gains come from increased wage rates, though hours play an important role in some degrees, such as medicine, especially for women. Third, we estimate the net present value and internal rate of return for each degree, which account for the time and monetary costs of degrees. We show annual earnings and hours worked while enrolled in graduate school vary a lot by gender and degree. Finally, we provide descriptive evidence that gains in overall job satisfaction and satisfaction with contribution to society vary substantially across degrees.
 

This paper documents differences across higher education courses in the coverage of frontier knowledge. Applying natural language processing (NLP) techniques to the text of 1.7M syl- labi and 20M academic articles, we construct the “education-innovation gap,” a syllabus’s rel- ative proximity to old and new knowledge. We show that courses differ greatly in the extent to which they cover frontier knowledge. Instructors play a big role in shaping course content; instructors who are active researchers teach more frontier knowledge. More selective and bet- ter funded schools, and those enrolling socio-economically advantaged students, teach more frontier knowledge. Students from these schools are more likely to complete a doctoral degree, produce more patents, and earn more after graduation.

We obtain a necessary and sufficient condition under which random-coefficient discrete choice models such as the mixed logit models are rich enough to approximate any nonparametric random utility models across choice sets. The condition turns out to be very simple and tractable. When the condition is not satisfied and, hence, there exists a random utility model that cannot be approximated by any random-coefficient discrete choice model, we provide algorithms to measure the approximation errors. After applying our theoretical results and the algorithms to real data, we find that the approximation errors can be large in practice.

This paper studies optimal second-best corrective regulation, when some agents/activities cannot be perfectly regulated. We show that policy elasticities and Pigouvian wedges are sufficient statistics to characterize the marginal welfare impact of regulatory policies in a large class of environments. We show that a subset of policy elasticities, leakage elasticities, determine optimal second-best policy, and characterize the marginal value of relaxing regulatory constraints. We apply our results to scenarios with unregulated agents/activities, uniform regulation across agents/activities, and costly regulation. We illustrate our results in applications to financial regulation with environmental externalities, shadow banking, behavioral distortions, asset substitution, and fire sales.

Incomplete vaccine uptake and limited vaccine availability for some segments of the population could lead policymakers to consider re-imposing restrictions to help reduce fatalities. Early in the pandemic, full business shutdowns were commonplace. Given this response, much of the literature on policy effectiveness has focused on full closures and their impact. But were complete closures necessary? Using a hand-collected database of partial business closures for all U.S. counties from March through December 2020, we examine the impact of capacity restrictions on COVID-19 fatality growth. For the restaurant and bar sector, we find that several combinations of partial capacity restrictions are as effective as full shutdowns. For example, point estimates indicate that, for the average county, limiting restaurants and bars to 25% of capacity reduces the fatality growth rate six weeks ahead by approximately 43%, while completely closing them reduces fatality growth by about 16%. The evidence is more mixed for the other sectors that we study. We find that full gym closures reduce the COVID-19 fatality growth rate, while partial closures may be counterproductive relative to leaving capacity unrestricted. Retail closures are ineffective, but 50% capacity limits reduce fatality growth. We find that restricting salons, other personal services and movie theaters is either ineffective or counterproductive.

We analyze whether receiving care from higher-priced hospitals leads to lower mortality. We overcome selection issues by using an instrumental variable approach which exploits that ambulance companies are quasi-randomly assigned to transport patients and have strong preferences for certain hospitals. Being admitted to a hospital with two standard deviations higher prices raises spending by 52% and lowers mortality by 1 percentage point (35%). However, the relationship between higher prices and lower mortality is only present at hospitals in less concentrated markets. Receiving care from an expensive hospital in a concentrated market increases spending but has no detectable effect on mortality.