Publications

We demonstrate how to obtain the distribution of consumer willingness to pay (WTP) for digital subscription products, where consumers pay a fixed price each period for potentially unlimited usage, for example, music streaming like Spotify. Typically, in such applications, usage data are observed and is critically valuable for the method here. We demonstrate how variation in usage and subscription choice together can identify the WTP distribution in the absence of price variation. Our framework accommodates and builds upon a range of utility specifications for usage, which is related to subscription decisions. We provide the conditions required on exogenous variation impacting usage, and prove how these lead to identification of the WTP distribution. We also investigate the conditions under which usage variation is not equivalent to price variation. We apply our method to an empirical application using the data from a music streaming service. Using the estimated WTP distribution, we obtain the revenue maximizing prices for different consumer segments.

Aid organizations, activists, and the media often use graphic depictions of human suffering to elicit sympathy and aid. While effective, critics have condemned these practices as exploitative, objectifying, and deceptive, ultimately labeling them ‘poverty porn.’ This paper examines people's ethical judgments of portrayals of poverty and the criticisms surrounding them, focusing on the context of charity advertising. In Studies 1 and 2, we find that tactics that have been decried as deceptive (i.e., using an actor or staging a photograph) are judged to be less acceptable than those that have been decried as exploitative and objectifying (i.e., depicting an aid recipient's worst moments). This pattern occurs both when evaluating the tactics themselves (Studies 1a-1c) and when directly evaluating critics' arguments about them (Study 2). Studies 3 and 4 unpack the objection to deceptive tactics and find that participants' chief concern is not about manipulating the audience's responses or about distorting perceptions of reality. Participants report less concern about non-deceptive manipulation (using emotion to compel donations) and ‘cherry-picked’ portrayals of poverty (an ad showing an extreme, but real image) so long as there is some truth to the portrayal. Yet they are more sensitive to artificial images (e.g., an actor posing as poor), even when the image resembles reality. Thus, ethical judgments hinge more on whether poverty portrayals are genuine than whether they are representative. This work represents the first empirical investigation into ethical judgments of poverty portrayals. In doing so, this work sheds light on how people make sense of morally questionable tactics that are used to promote social welfare and deepens our understanding of reactions to deception.

Adaptation is critical in reducing the inevitable impact of climate change. Here we study cities’ adaptation to elevated flood risk by introducing a linguistic measure of adaptation extracted from financial disclosures of 431 US cities over 2013-2020. While cities with a higher flood risk have higher adaptation, more than half of high-risk cities have below-average adaptation levels. We explore three factors associated with this adaptation gap, defined as a city’s adaptation being lower than predicted based on flood risk. We do not find that Republican cities are more likely to have an adaptation gap. Instead, our results point to the importance of financial constraints: cities with one standard deviation smaller unrestricted-fund-to-expense ratio are 6.6% more likely to have an adaptation gap. We also provide evidence on the importance of long-term planning: cities with a planning horizon shorter by one year are 4% more likely to have an adaptation gap.

Is there a gender gap in the serial founding of VC-backed startups? We address this question by introducing a new empirical design that exploits differences in future funding outcomes for men and women who co-founded the same startup. We document substantial gender gaps, both on average and following failure or success of the current startup. Following failure, our estimates imply that women are 22.5% less likely to found another VC-backed startup compared to their cofounders who are men.  Among those who do found another VC-backed firm, women raise 24.6% less capital. We investigate potential demand- and supply-side drivers of the gap.  We rule out lack of interest by women in founding new firms and we do not find evidence of gender differences in founder quality.  In fact, the results of an outcome test show no gender difference in the success probabilities of subsequent startups, despite the large funding gap that we document. The gender gaps that we observe appear to be driven by unequal treatment by investors.  Our analysis of potential supply-side channels reveals striking negative spillovers following investors’ experiences with other women-founded startups, but no positive spillovers following success of women-founded portfolio firms.

Organizational research increasingly relies on online review data to gauge perceived valuation and reputation of organizations and products. Online review platforms typically collect ordinal ratings (e.g., 1 to 5 stars); however, researchers often treat them as a cardinal data, calculating aggregate statistics such as the average, the median, or the variance of ratings. In calculating these statistics, ratings are implicitly assumed to be equidistant. We test whether star ratings are equidistant using reviews from two large-scale online review platforms: Amazon.com and Yelp.com. We develop a deep learning framework to analyze the text of the reviews in order to assess their overall valuation. We find that 4 and 5-star ratings, as well as 1 and 2-star ratings, are closer to each other than 3-star ratings are to 2 and 4-star ratings. An additional online experiment corroborates this pattern. Using simulations, we show that the distortion by non-equidistant ratings is especially harmful in cases when organizations receive only a few reviews and when researchers are interested in estimating variance effects. We discuss potential solutions to solve the issue with rating non-equidistance.

We derive a small open economy (SOE) as the limit of an economy as the number or size of its
trading partners goes to infinity and trade costs also go to infinity. We obtain this limit in the
Armington, Eaton-Kortum, Krugman, and Melitz models. In all cases, the trade of the SOE
with the foreign countries approaches a finite limit, and the domestic expenditure share for the
SOE approaches a limit that is not zero or unity. The foreign countries can be either infinitely
many SOEs, or alternatively, one or many large countries with domestic expenditure shares that
approach unity. We illustrate the usefulness of this framework by obtaining a formula for the
optimal tariff in the SOE – depending on the elasticity of domestic wages with respect to the
tariff – that is consistent with all models.

Collective evaluation processes, which offer individuals an opportunity to assess quality, have transcended mainstream sectors (e.g., books, restaurants) to permeate professional contexts from within and across organizations to the gig economy. This paper introduces a theoretical framework to understand how evaluators’ visibility into prior evaluations influences the subsequent evaluation process: the likelihood of evaluating at all and the value of the evaluations that end up being submitted. Central to this discussion are the conditions under which evaluations converge—are more similar to prior evaluations—or diverge—are less similar—as well as the mechanisms driving observed outcomes. Using a quasinatural experiment on a platform where investment professionals submit and evaluate investment recommendations, I compare evaluations that are made with and without the possibility of prior ratings influencing the subsequent evaluation process. I find that when prior ratings are visible, convergence occurs. The visibility of prior evaluations decreases the likelihood that a subsequent evaluation occurs by about 50%, and subsequent evaluations become 54%–63% closer to the visible rating. Further analysis suggests that peer deference is a dominant mechanism driving convergence, and only professionals with specialized expertise resist peer deference. Notably, there is no evidence that initial ratings are related to long-term performance. Thus, in this context, convergence distorts the available quality signal for a recommendation. These findings underscore how the structure of evaluation processes can perpetuate initial stratification, even among professionals with baseline levels of expertise.

I study whether firms that receive targeted U.S. state-level subsidies are more likely to subsequently engage in corporate misconduct. I find that firms are more likely to engage in misconduct in subsidizing states, but not in other states that they operate in, after receiving state subsidies. Using data on both federal and state enforcement actions, and exploiting the legal principle of dual sovereignty for identification, I show that this finding reflects an increase in the underlying rate of misconduct and that this increase is attributable to lenient state-level misconduct enforcement. Collectively, my findings present evidence of an important consequence of targeted firm-specific subsidies: non-financial misconduct that potentially could impact the very stakeholders subsidies are ostensibly intended to benefit.

Housing market conditions are often used as leading indicators of real business cycles. Does the housing market also lead the financial cycle? We address this question by applying deep learning OCR techniques to create a new hand-collected database spanning a century of monthly building permit quantities and valuations for all U.S. states and the 60 largest MSAs. We show that the option to build embedded in permits renders volatility in residential building permit growth (BPG) a strong predictor of aggregate and cross-sectional stock and corporate bond return volatility. This predictability remains even after conditioning on a battery of factors, including corporate and household leverage and firms' exposure through their network of plants to other localized physical risks like natural disasters. Cities with more elastic housing supply consistently predict stock market downturns at 12-month horizons, resulting in new trading strategies to hedge against overbuilding risk.

Limited capacity in the emergency department (ED) secondary to boarding and crowding has resulted in patients receiving care in hallways to provide access to timely evaluation and treatment. However, there are concerns raised by physicians and patients regarding a decrease in patient centered care and quality resulting from hallway care. We sought to explore social risk factors associated with hallway placement and operational outcomes.

We study the cross-sector allocation of R&D resources in a multisector growth model with an innovation network, where one sector's past innovations may benefit other sectors' future innovations. Theoretically, we solve for the optimal allocation of R&D resources. We show a planner valuing long-term growth should allocate more R&D toward central sectors in the innovation network, but the incentive is muted in open economies that benefit more from foreign knowledge spillovers. We derive sufficient statistics for evaluating the welfare gains from improving R&D allocation. Empirically, we build the global innovation network based on patent citations and establish its empirical importance for knowledge spillovers. We evaluate R&D allocative efficiency across countries using model-based sufficient statistics. Japan has the highest allocative efficiency among the advanced economies. For the U.S., improving R&D allocative efficiency to Japan's level could generate more than 19.6% welfare gains.

We study how countries compete to become an international safe asset provider. Gov- ernments in our model issue debt to a common set of investors, resulting in competition as issuance by one country raises required yields for all countries. Governments are tempted ex post to engage in expropriation or capital controls, and can build reputa- tion as a safe asset provider by resisting temptation to do so. We show how increased competition deters countries from building reputation, leaving more countries stuck at low reputation levels and unable to supply safe assets. We derive a model-implied measure of country reputation. We estimate this reputation measure using micro-data on investor portfolio holdings, and use it to track the evolution of countries’ reputa- tion over time. We study how an incumbent safe asset provider, like the U.S., uses its issuance strategy to deter the emergence of competitors.

Science, technology, engineering, and mathematics (STEM) education can be stressful, but uncertainty exists about (a) whether stressful academic settings elevate cortisol, particularly among students from underrepresented racial/ethnic groups, and (b) whether cortisol responses are associated with academic performance. In four classes around the first exam in a gateway college STEM course, we investigated participants' (N = 271) cortisol levels as a function of race/ethnicity and tested whether cortisol responses predicted students' performance. Regardless of race/ethnicity, students' cortisol, on average, declined from the beginning to the end of each class and across the four classes. Among underrepresented minority (URM) students, higher cortisol responses predicted better performance and a lower likelihood of dropping the course. Among non-URM students, there were no such associations. For URM students, lower cortisol responses may have indicated disengagement, whereas higher cortisol responses may have indicated striving. The implication of cortisol responses can depend on how members of a group experience an environment.

Individuals often turn to search engines like Google Search as their first source of health information. However, large language models (LLMs) are positioned to become another destination for those seeking medical information. Consequently, the readability of these materials becomes an important factor in ensuring their effectiveness in promoting health literacy, given that the average American reads at the eighth-grade level.1,2 We looked at the readability and word count of information produced by four publicly available LLMs – ChatGPT-3.5, ChatGPT-4, Google Bard (Gemini), and Microsoft Bing (set to precise) – across 4 months.

Multiple extensions of the federal student loan forbearance program that began in March 2020 resulted in a temporary payment pause that lasted more than 3 years. We examine the impact of long-term forbearance on the evolution of borrowing by distressed individuals. Compared to distressed borrowers not in forbearance, we observe a 13.4-point credit score increase within a year of forbearance, followed by 12.3% more credit card debt and 4.6% more auto loans, but significantly less total mortgage debt. By year 3, student loan balances are 12.1% higher for the forbearance sample and delinquencies on nonstudent debt are also higher. In the absence of policy interventions, our results suggest that the extended breathing room that the program allowed could accelerate post-forbearance financial distress.

Do corporate control transactions discipline the labor force? Consistent with synergies, new disclosures of employee misconduct in the investment advisory industry drop by between 17 and 22 percent following mergers. Both targets and acquirers have better pre-merger misconduct records than the industry’s average firm and, within the subsample of merging firms, there is assortative matching on misconduct. Merger events facilitate further reductions in misconduct through separations of target firm employees with high misconduct. Many of these employees remain in the industry, suggesting that consolidation plays an important role in the redistribution of misconduct across firms.