Publications

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.

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 propose a method for constructing conditional option return distributions. In our model, uncertainty about the future option return has two sources: Changes in the position and shape of the implied volatility surface that shift option values (holding moneyness and maturity fixed), and changes in the underlying price which alter an option's location on the surface and thus its value (holding the surface fixed). We estimate a joint time series model of the spot price and volatility surface and use this to construct an ex ante characterization of the option return distribution via bootstrap. Our "ORB" (option return bootstrap) model accurately forecasts means, variances, and extreme quantiles of S&P 500 index conditional option return distributions across a wide range of strikes and maturities. We illustrate the value of our approach for practical economic problems such as risk management and portfolio choice. We also use the model to illustrate the risk and return tradeoff throughout the options surface conditional on being in a high or low risk state of the world. Comparing against our less structured but more accurate model predictions helps identify misspecification of risks and risk pricing in traditional no-arbitrage option models with stochastic volatility and jumps.

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.

The real estate market is highly intermediated, with 90 percent of buyers and sellers hiring an agent to help them transact a house. However, low barriers to entry and fixed commission rates result in a market where inexperienced intermediaries have a large market share, especially following house price booms. Using rich micro-level data on 8.5 million listings and a novel instrumental variables research design, we first show that houses listed for sale by inexperienced real estate agents have a lower probability of selling, and this effect is strongest during the housing bust. We then study the aggregate implications of the distribution of agents’ experience on housing market liquidity by building a dynamic entry and exit model of real estate agents with aggregate shocks. We find that 3.7% more listings would have been sold in a flexible commission equilibrium. Eighty percent of this improvement comes from competition driving down overall seller commissions, while the remaining share can be attributed to commission variation across experience levels.

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.

Sparsity is a central aspect of interpretability in machine learning. Typically, sparsity is measured in terms of the size of a model globally, such as the number of variables it uses. However, this notion of sparsity is not particularly relevant for decision making; someone subjected to a decision does not care about variables that do not contribute to the decision. In this work, we dramatically expand a notion of decision sparsity called the Sparse Explanation Value (SEV) so that its explanations are more meaningful. SEV considers movement along a hypercube towards a reference point. By allowing flexibility in that reference and by considering how distances along the hypercube translate to distances in feature space, we can derive sparser and more meaningful explanations for various types of function classes. We present cluster-based SEV and its variant tree-based SEV, introduce a method that improves credibility of explanations, and propose algorithms that optimize decision sparsity in machine learning models

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.

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.

We propose that investment strategies should be evaluated based on their net-oftrading- cost return for each level of risk, which we term the “implementable efficient frontier.” While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading costs leads to excessive reliance on fleeting small-scale characteristics, resulting in poor net returns. We develop a framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning. The superior net-of-cost performance is achieved by learning directly about portfolio weights using an economic objective. Further, our model gives rise to a new measure of “economic feature importance.”