Publications

We provide large-sample evidence on whether U.S. publicly traded corporations opportunistically use voluntary disclosures about their commitments to employee diversity. We document significant discrepancies between companies' disclosed commitments and their hiring practices and classify firms that discuss diversity more than their actual employee gender and racial diversity warrants as “diversity washers." We find diversity-washing firms obtain superior scores from environmental, social, and governance (ESG) rating organizations and attract investment from institutional investors with an ESG focus. These outcomes occur even though diversity-washing firms are more likely to incur discrimination violations and pay larger fines for these actions. Our study highlights the consequences of selective ESG disclosures on an important social dimension of employee diversity, equity, and inclusion.

Several firms claim to be socially responsible. We confront these claims with data using the most notable recent proclamation, the Business Roundtable’s (BRT) 2019 Statement on the Purpose of a Corporation. The BRT is a large, influential business group containing many of America’s largest firms; the Statement proclaimed a corporation’s purpose as delivering value to all stakeholders, rather than only shareholders. However, we find no evidence that signatories – who voluntarily signed – engaged in such stakeholder-centric practices before or after signing. Relative to peers, signatories violate environmental and labor laws more frequently, have higher carbon emissions, rely more on government subsidies, and are more likely to disagree with proxy recommendations on shareholder proposals. We also do not observe post-signing improvements along these dimensions, suggesting that the Statement was not a credible commitment to improve. Our results suggest that firms’ proclamations of stakeholder-centric behavior are not backed up by hard data.

This paper examines the effect of state-level monitoring on municipal governance, focusing on outcomes in financial reporting quality, local corruption, political entrenchment, and municipal financial soundness. I exploit the staggered adoption of fiscal monitoring policies that entail a regular review of municipal financial reports for signs of fiscal distress. I find that introducing these monitoring policies is associated with an increase in the proxies for reporting quality, a decrease in the number of corruption convictions, and a reduction in re-election likelihood for incumbent politicians. Consistent with the purpose of the policies, my evidence shows that fiscal health ratios of municipalities improve after initiating state monitoring. Collectively, my results are consistent with state fiscal monitoring improving several important aspects of municipal governance.

This paper proposes a tractable model of the Global Financial Cycle and study its welfare implications for emerging market economies (EMEs). When local firms issue debt denominated in dollars, central banks must increase their policy rate as the U.S. tightens in order to offset balance sheet effects stemming from the depreciation of their currency. When global financial mar- kets are imperfect, this synchronized policy response has negative spillovers: all individual countries seek to attract capital inflows at the expense of one another, exacerbating the Global Financial Cycle. This congestion externality requires further tightening and results in inefficiently low levels of output and employment in EMEs, and generates gains from coordination. On the con- trary, discouraging debt issuance in dollars through macroprudential policy has positive spillovers, and does not necessarily require coordination between EMEs. Its optimal use dampens the Global Financial Cycle and its inefficien- cies.

We introduce a model of dynamic pricing in perishable goods markets with competition and provide conditions for equilibrium uniqueness. Pricing dynamics are rich because both own and competitor scarcity affect future profits. We identify new competitive forces that can lead to misallocation due to selling units too quickly: the Bertrand scarcity trap. We empirically estimate our model using daily prices and bookings for competing U.S. airlines. We compare competitive equilibrium outcomes to those where firms use pricing heuristics based on observed internal pricing rules at a large airline. We find that pricing heuristics increase revenues (4-5%) and consumer surplus (3%).

We investigate an unexplored mechanism of earnings management: income shifting from not-wholly-owned subsidiaries to help the parent company avoid losses at the expense of subsidiaries. Consolidated net income attributable to the parent company (i.e., net income) increases through this mechanism, as the parent company enjoys the full amount of the shifted earnings rather than sharing them with minority investors. We design an empirical model to directly estimate the amount of income shifted from subsidiaries to parent firms. Employing this measure, we find that firms opportunistically decrease earnings of their not-wholly-owned subsidiaries to manage net income upward to avoid losses. The results are stronger for firms with high noncontrolling ownership, firms with large subsidiaries, firms with strong influence over not-wholly-owned subsidiaries, and firms with a high level of related-party transactions. Our results are robust to alternative research designs, including controls for within-firm variations, alternative earnings thresholds, propensity score matching, and entropy balancing techniques. Our mechanism of earnings management is generalizable to other earnings management scenarios, such as share pledging.

Violence against civilians has killed nearly one million people worldwide and displaced
millions more over the past three decades. This paper examines the economic forces driving
civilian persecution in Myanmar. Using a difference-in-differences approach, we show that
violence against civilians increases in rice-suitable townships when rice prices rise, the op-
posite pattern of that documented in the literature on two-sided conflicts. We argue that
large power asymmetries inherent in civilian persecution explain this difference. Higher re-
turns from expropriating rice harvests and rice-growing inputs during these periods drive
the pattern we observe in Myanmar, which we corroborate with an original survey of Ro-
hingya refugees forcibly displaced to Bangladesh. Our work demonstrates how to generate
systematic and representative evidence on civilian persecution in politically sensitive and
data-poor contexts.

Elemental Excelerator is a not-for-profit fund established in 2017 by Dawn Lippert. With $36 million of funding, the organization focuses on investing in for-profit businesses that aim to mitigate climate change while embedding equity and social justice into their solutions. 

The fund faces a dilemma in selecting the last companies for its 2023-2024 cohort of climate tech businesses. With only one or two slots remaining, Elemental must decide how to balance its portfolio. Options include prioritizing technologies in the electric power and transportation sectors favored by the Inflation Reduction Act, or continuing to diversify in buildings, agriculture, and industry, sectors with few sustainable climate solutions. Another consideration is whether to focus on companies that already attract venture capital or support those overlooked by investors.  Yet another concern is balancing sustainability with social justice. Six very different companies are presented as finalists.

The DMA raises tantalising opportunities for app stores innovation, making it the most exciting area of digital regulation.

Facing the 2018-19 school year, Michael Garcia, the budget director of the Spring Valley School District in California (a disguised name), confronted challenges in changing the district’s accounting system to comply with the federal Every Student Succeeds Act (ESSA).

A successor to the Elementary and Secondary Education Act (also known as the No Child Left Behind Act), ESSA was signed by President Obama in 2015. ESSA governed multiple programs providing federal funding to states, as well as requirements for school district reporting to the U.S. Department of Education. Among other revisions, ESSA required for the first time that districts report the per-pupil spending for each school in the district, including allocated central costs. It also required that the data be available to the public. This requirement was designed to give parents and community members more knowledge about relative spending for the schools within the district.

Before ESSA, Spring Valley, like many districts, had done its budgeting on a functional basis, dividing district-wide spending into various operational areas, like instruction, maintenance, and central office costs. However, it only had to report a consolidated number for district-wide costs: the total district-wide costs divided by the number of students in the district. Faced with the new school-level reporting requirement, Garcia was inclined to track the school-level costs in as much detail as possible. Spring Valley schools were a heterogeneous collection of old and new facilities in a city with widely disparate income levels and educational needs, and Garcia believed that additional information could only help the district and its students.

However, Jessica Adams, the newly installed superintendent, clashed with Garcia on how to implement several ESSA accounting requirements. In her short tenure, Adams had already faced criticism from parents at several schools. She did not want the school-level spending report required by ESSA to become another divisive flashpoint in the already contentious school board meetings. In private conversations, she told Garcia that the new allocation scheme should show as little spending difference as possible between the schools.

Garcia and Adams had to resolve several allocation issues. One concern was allocating staff salaries and related benefits to individual schools. These costs were a large portion of the district budget – Spring Valley spent around 81% of its total operational budget on employee salaries and related benefits for instructional, support, and administrative costs.

Another area of concern was allocating the district-wide costs related to the maintenance of plant facilities. Should they allocate maintenance of plant facilities by school square footage, or look for more granular indicators of the actual costs in each school? Finally, what about busing costs? Should they allocate costs to each school on a per-pupil basis across the system or assign busing costs to each location?

Garcia understood Adams’s reluctance to implement the more detailed approach, but he still thought a more granular approach could provide valuable information. The Spring Valley School District faced several important issues in the next few years. A new contract with the teachers’ union was looming. Declining enrollments meant that the district might need to close one or more schools. Would a more precise calculation of costs for each school lead to better decisions?

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.

We consider the problem of explaining models to a decision maker (DM) whose payoff depends on a state of the world described by inputs and outputs. A true model specifies the relationship between these inputs and outputs, but is not intelligible to the DM. Instead, the true model must be explained via a simpler model from a finite- dimensional set. If the DM maximizes their average payoff, then an explanation using ordinary least squares is as good as understanding the true model itself. However, if the DM maximizes their worst-case payoff, then any explanation is no better than no explanation at all. We discuss how these results apply to policy evaluation and explainable AI.

We propose and implement a procedure to optimally hedge climate change risk. First, we construct climate risk indices through textual analysis of newspapers. Second, we present a new approach to compute factor-mimicking portfolios to build climate risk hedge portfolios. The new mimicking portfolio approach is much more efficient than traditional sorting or maximum correlation approaches by taking into account new methodologies of estimating large-dimensional covariance matrices in short samples. In an extensive empirical out-of-sample performance test, we demonstrate the superior all-around performance delivering markedly higher and statistically significant alphas and betas with the climate risk indices.

We present evidence that noisy financial flows influence financial conditions and macroeconomic activity. How should monetary policy respond to this noise? We develop a model where it is optimal for the central bank to target and (partially) stabilize financial conditions beyond their direct effect on output and inflation gaps, even though stable financial conditions are not a social objective per se. In our model, noise affects both financial conditions and macroeconomic activity, and arbitrageurs are reluctant to trade against noise due to aggregate return volatility. Our main result shows that Financial Conditions Index (FCI) targeting—announcing a (soft and temporary) FCI target and setting the policy rate in the near future to maintain the actual FCI close to the target—reduces the FCI volatility and stabilizes the output gap. This improvement occurs because a more predictable FCI enables arbitrageurs to trade more aggressively against noise shocks, thereby" recruiting" them to insulate FCI from financial noise. FCI targeting is similar to providing forward guidance about the FCI, and in our framework it is strictly superior to providing forward guidance about the policy interest rate. Finally, we extend recent policy counterfactual methods to incorporate our model's endogenous risk reduction mechanism and apply it to US data.

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.