Publications

This paper studies the dissemination of frontier knowledge through higher education. Apply- ing natural language processing (NLP) techniques to the text of 1.7M university course syllabi and 20M academic articles, we construct the “education-innovation gap,” a measure of a syl- labus’s distance from frontier knowledge. Using this measure, we document four new facts. First, courses differ greatly in their education-innovation gap, even after controlling for field, course-level, and time. Second, instructors play an important role in shaping course content. Research-active instructors teach more frontier knowledge, particularly when their research is close to the course topic. Third, access to frontier knowledge is unequal: Schools enrolling more socio-economically advantaged students offer courses with a lower gap. Lastly, students from lower-gap schools are more likely to complete a doctoral degree, produce more patents, and earn more after graduation

Using detailed transactions data across the United States, we find that single women earn 1.5 percentage points lower annualized returns on housing relative to single men. Forty-five percent of the gap is explained by transaction timing and location. The remaining gap arises from a 2% gender difference in execution prices at purchase and sale. Consistent with a negotiation channel, women list for less and experience worse negotiated discounts. The gender gap shrinks in tight markets, where negotiation is replaced by quasi-auctions. Overall, gender differences in housing explain 30% of the gender gap in wealth accumulation for the median household.

We examine the demand-side implications of Amazon's distribution and logistics investments. Our results indicate that online demand—transactions at Amazon and its competitors—does not respond to the consumer's proximity to Amazon's upstream fulfillment distribution facilities, suggesting that their densification did not differentially improve local shipping times and on-time delivery. Instead, we find that investments in last-mile delivery facilities and services allow the company to improve shipping times more directly in the urban markets served by these facilities, simultaneously increasing demand through the rollout of same-day service options and reducing the visits to traditional brick-and-mortar retail.

This paper seeks to demonstrate the impact of the gig economy on product qual- ity in seemingly unrelated local industries through the labor market. Our empirical context is the quality of service for restaurants in the city of Austin, and we examine how they were impacted by the exogenous exit and reentry of rideshare platforms, Uber and Lyft, because of regulatory changes. We leverage these exogenous shocks and combine them with sentiment-analyzed data from Yelp reviews that capture how customers assess the quality of service at each restaurant. We show that, compared with control cities, customers in Austin become more negative about service quality when Uber and Lyft are present in the city. Additionally, we use rich data on employee turnover and wages to demonstrate that service staff turnover increases in Austin when Uber and Lyft are present compared with the control cities. We also conduct several additional studies and robustness checks that are all congruent with our hypothesis that Uber and Lyft lower the quality of service in Austin restaurants by raising their staff turnover. Together, these results suggest signifi- cant ramifications of the gig economy on the broader industries through the labor market.

Environmental, Social, and Governance (ESG) has been a headline in financial circles for well over a decade.Footnote1 From private equity and publicly listed companies to bonds and loans, the interest in “ESG Investing” has expanded exponentially over the last 10 years, powered by empirical studies that, for the most part, suggest a correlation with improved financial performance.Footnote2 By some estimates, roughly a third of all assets under management now consider some aspects of ESG information in their investment strategy.Footnote3 However, in the first part of 2023, ESG has exploded from investor research onto the front pages of major media outlets such as the New York Times,Footnote4 the Wall Street Journal,Footnote5 and even USA Today.Footnote6 The sudden mass interest has been driven, in large part, from backlash to the idea of ESG investing.

The Sustainable Corporation is a guide for lawyers to effectively represent and advise their clients on the risks, opportunities, and rewards facing companies on important issues such as environmental impact, social responsibility, and corporate governance. This guide defines, explains, and provides strategies for a "business centric" approach to ESG for the corporation.

This paper studies the welfare effects of encouraging rural-urban migration in the develop-
ing world. To do so, we build a dynamic incomplete-markets model of migration in which
heterogeneous agents face seasonal income fluctuations, stochastic income shocks, and disu-
tility of migration that depends on past migration experience. We calibrate the model to
replicate a field experiment that subsidized migration in rural Bangladesh, leading to signif-
icant increases in both migration rates and consumption for induced migrants. The model’s
welfare predictions for migration subsidies are driven by two main features of the model and
data: first, induced migrants tend to be negatively selected on income and assets; second, the
model’s non-monetary disutility of migration is substantial, which we validate using newly
collected survey data from this same experimental sample. The average welfare gains are
similar in magnitude to those obtained from an unconditional cash transfer, and greater than
from policies that discourage migration, though migration subsidies lead to larger gains for
the poorest households, which have the greatest propensity to migrate.

The ability of recent Large Language Models (LLMs) such as GPT-3.5 and GPT-4 to generate human-like texts suggests that social scientists could use these LLMs to construct measures of semantic similarity that match human judgment. In this article, we provide an empirical test of this intuition. We use GPT-4 to construct a measure of typicality—the similarity of a text document to a concept. We evaluate its performance against other model-based typicality measures in terms of the correlation with human typicality ratings. We conduct this comparative analysis in two domains: the typicality of books in literary genres (using an existing dataset of book descriptions) and the typicality of tweets authored by US Congress members in the Democratic and Republican parties (using a novel dataset). The typicality measure produced with GPT-4 meets or exceeds the performance of the previous state-of-the art typicality measure we introduced in a recent paper [G. Le Mens, B. Kovács, M. T. Hannan, G. Pros Rius, Sociol. Sci. 2023, 82–117 (2023)]. It accomplishes this without any training with the research data (it is zero-shot learning). This is a breakthrough because the previous state-of-the-art measure required fine-tuning an LLM on hundreds of thousands of text documents to achieve its performance.

Consensus analyst target prices are widely available online at no cost to investors. In this paper we examine how the amount of dispersion in the individual target prices comprising the consensus affects the predictive association between the consensus target price and future returns. We find that returns implied by consensus target prices and realized future returns are positively correlated when dispersion is low, but they become highly negatively correlated when dispersion is high. Further analyses suggest that the differing effect of dispersion stems from incentive-driven staleness in price targets by some analysts after bad news. As a stock performs poorly and some analysts are slow to update their target prices, dispersion increases, and the consensus target price becomes too high. This has important implications for how consensus analyst target prices should inform investment decisions. We show that a hedge strategy taking a long (short) position in stocks with the highest predicted returns among stocks with the lowest (highest) dispersion earns more than 11% annually. Finally, we show that the negative correlation between consensus-based predicted returns and future realized returns for high-dispersion stocks exists mainly for stocks with high retail interest, suggesting that unsophisticated investors are misled by inflated target prices that are available freely online.

Though organizational scholars have studied punishment for decades, recent examples of punishment in organizations cannot be fully explained by the scholarly literature. This may be because much of our prior understanding of punishment has been based on studies in highly bounded organizations, but with the shift to remote work arrangements and contract or freelance work, modern organizations are becoming increasingly boundaryless. Does punishment in bounded organizations look different than punishment in boundaryless organizations? To answer this question, we review and categorize the literature on punishment in organizations according to the boundedness of the organization it examined. We find that though there are similarities in punishment in bounded and boundaryless organizations, punishment in boundaryless organizations involves different actors, punishing different situations, for different reasons, using different methods. However, many questions about punishment in boundaryless organizations remain, including about the pervasiveness, motivations, and impacts that punishment has in boundaryless organizations.

We study the impact of changing choice set size on the quality of choices in health insurance markets. Using novel data on enrolment and medical claims for school district employees in the state of Oregon, we document that the average employee could save $600 by switching to a lower cost plan. Structural modelling reveals large “choice inconsistencies” such as non-equalization of the dollar spent on premiums and out of pocket, and a novel form of “approximate inertia” where enrolees are excessively likely to switch to other plans that are close to the current plan on the plan design spreadsheet. Variation in the number of plan choices across districts and over time shows that enrolees make lower-cost choices when the choice set is smaller. We show that a curated restriction of choice set size improves choices more than the best available information intervention, partly because approximate inertia lowers gains from new information. We explicitly test and reject the assumption that this is because individuals choose worse from larger choice sets, or “choice overload”. Rather, we show that this feature arises from the fact that larger choice sets feature worse choices on average that are not offset by individual re-optimization.

Two years after COVID-19 vaccine rollouts began, COVID-19 vaccination rates in low- and middle-income countries (LMICs) continue to lag. Tracing individual country experiences with vaccine procurement in the early stages of the pandemic suggests that international supply chain failures caused initial delays. High vaccine hesitancy in the population and last-mile delivery challenges within LMICs were other possible limiting factors. This paper summarizes descriptive and experimental research on vaccine demand and supply in LMICs to evaluate these competing claims. The weight of the evidence suggests that external supply restrictions and internal distribution challenges (rather than vaccine hesitancy) appear to be paramount.

Effective teachers are a vital input for schools and students. Teachers can have important and long-lasting impacts on students’ learning, college attendance, and eventual earnings. They can also reduce teen pregnancy or incarceration. Attracting effective teachers into public schools and retaining them is thus a first-order policy goal. Changes in teacher compensation, for example across-the-board raises in salaries or pay plans that directly tie salaries to performance, are often proposed as ways to achieve this goal. The debate on these reforms, though, is very much open; some opponents argue that these changes would be ineffective because teachers are not motivated by money.

How does the exchange of employees between regulatory agencies and regulated firms (i.e., the firm-government revolving door) affect firm regulatory outcomes? Existing work has mostly found a positive impact of revolving door hiring on firm outcomes, but it has overlooked potential limitations of this corporate political activity (CPA) tactic. We argue that the advantages firms can gain from hiring former regulators are bound by the timing of revolving door employment relative to the regulatory process. Within the context of agribiotechnology and its main regulator, the U.S. Department of Agriculture, we study regulators who move to in-house and contract lobbying positions (i.e., exit revolving door). We find that firms receive better regulatory outcomes (i.e., faster regulatory approval for new crops) only prior to the regulators’ move to in-house lobbying, consistent with the regulatory capture perspective. Moreover, this revolving door was only valuable in the time period immediately before the mobility event. Additionally, contrary to the belief that former regulators provide firms with expertise and social capital as lobbyists, we find that firms did not gain any advantage after regulators became lobbyists. Taken together, our results suggest that revolving doors can be an effective business political mobilization strategy, albeit one that has limited success in shaping firm government outcomes, much like other types of CPA.