Publications

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.

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.

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.