Publications

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.”

Medical crowdfunding is a popular channel for people in need of financial help with paying medical bills to collect donations from large numbers of people. However, large heterogeneity exists in the amount of donations each case receives and such uncertainty in fundraising outcomes hinders making timely treatment plans for patients. It is important to provide early and accurate predictions for medical crowdfunding performance, and help fundraisers engage in timely interventions. In this study, we propose a new approach that effectively combines time-varying features and time-invariant features in a deep learning model. This model provides dynamic predictions of fundraising outcomes, based on fixed case-level attributes and daily updated measures of social media activities. Compared with a rich set of baseline models, our model consistently demonstrates higher predictive accuracy while requiring a shorter observation window of data, thus achieving both accurate and early prediction objectives. We conduct a temporal clustering analysis to analyze the heterogeneous patterns in how the time-varying features relate to fundraising outcomes. In addition, we conduct a simulation analysis to demonstrate that interventions from fundraisers can significantly improve the fundraising performance of disadvantaged cases that are predicted to receive the lowest donation amounts, particularly when the interventions are taken early. These findings show that our deep learning prediction model and the actionable insights can provide timely feedback to fundraisers and promote equal access to resources for all. Our proposed approach is generalizable to different contexts, enabling effective processing of diverse sources of data and informing timely interventions early on.

We analyze optimal monetary policy and its implications for asset prices, when aggregate demand has inertia and responds to asset prices with a lag. If there is a negative output gap, the central bank optimally overshoots aggregate asset prices (asset prices are initially pushed above their steady-state levels consistent with current potential output). Overshooting leads to a temporary disconnect between the performance of financial markets and the real economy, but accelerates the recovery. When there is a lower-bound constraint on the discount rate, overshooting becomes a concave and non-monotonic function of the output gap: the asset price boost is low for a deeply negative initial output gap, grows as the output gap improves over a range, and shrinks toward zero as the output gap improves further. This pattern also implies that good macroeconomic news is better news for asset prices when the output gap is more negative. Finally, we document that during the Covid-19 recovery, the policy-induced overshooting was large—sufficient to explain the high levels of stock and house prices in 2021.

Demand rebounded more rapidly than expected after the COVID pandemic. This interacted with a series of unprecedented supply-side shocks around the shutdown and reopening of economies, along with broad-based increases in commodity prices following the invasion of Ukraine. Inflation spiked to the highest level in decades. In response, central banks tightened monetary policy sharply. This book explores the commonalities and differences in countries’ strategies, as well as lessons for the next inflationary episode.

Demand rebounded more rapidly than expected after the COVID pandemic. This interacted with a series of unprecedented supply-side shocks around the shutdown and reopening of economies, along with broad-based increases in commodity prices following the invasion of Ukraine. Inflation spiked to the highest level in decades. In response, central banks tightened monetary policy sharply. This book explores the commonalities and differences in countries’ strategies, as well as lessons for the next inflationary episode.

A monotone function interval is the set of monotone functions that lie pointwise between two fixed monotone functions. We characterize the set of extreme points of monotone function intervals and apply this to a number of economic settings. First, we leverage the main result to characterize the set of distributions of posterior quantiles that can be induced by a signal, with applications to political economy, Bayesian persuasion, and the psychology of judgment. Second, we combine our characterization with properties of convex optimization problems to unify and generalize seminal results in the literature on security design under adverse selection and moral hazard.

Nike, as a global leader in athletic footwear and apparel, experiences significant dilemmas when choosing which societal issues to confront. In the contemporary business landscape, stakeholders—including investors, customers, employees, and communities—expect companies to address a range of controversial topics such as racial justice, gender equality, climate change, and voting rights.

The challenges for Nike are multifaceted. Key decisions rest on whether an issue aligns with Nike’s “Purpose Pillars,” which include commitments to people, the planet, and play. These pillars guide Nike’s strategy and operations, helping determine whether to engage with a particular issue. For example, diversity and inclusion directly relate to Nike's core consumers and historical partnerships, making them integral to its brand identity.

However, Nike must navigate the risks associated with taking a public stance. Missteps can impact reputation, consumer loyalty, and sales. The company carefully assesses the potential impact on various stakeholders, including employees and athletes, before deciding to engage. This assessment is critical since different groups may have conflicting expectations or reactions to the company’s position on contentious issues.

A dedicated team, including CEO John Donahoe, evaluates these factors, exploring implications and weighing potential risks and benefits. This thorough vetting process ensures Nike’s responses are consistent with its core identity and capability to make a meaningful impact.

Nike, a global leader in athletic footwear and apparel, is committed to advancing sustainability through innovation. Its purpose is to inspire and innovate for every athlete while moving the world forward by building community, protecting the planet, and increasing access to sport. Responding to the industry's significant environmental impact, Nike has established ambitious goals under its "Move to Zero" initiative, aiming to power all owned facilities with 100% renewable energy by 2025 and divert 99% of its footwear manufacturing waste from landfills.

The Space Hippie project exemplifies Nike's commitment to sustainability by striving to create the lowest carbon footprint shoe in the company's history. In 2019, Nike tasked a small team with designing a shoe that would maintain performance, comfort, and aesthetic appeal while being manufacturable with readily available materials and contributing meaningfully to the company's climate goals. This project challenged Nike to reconsider all aspects of its established practices, from design and materials to manufacturing and marketing, emphasizing the importance of sustainability in innovation.

Networks across many different settings—including social, economic, and natural—are powerful tools for interventions due to the cascading impact of one individual node on others. All networks with degree variation exhibit the friendship paradox phenomenon. We demonstrate its multifaceted nature, and provide its foundations mathematically and empirically. We identify a network property—inversity—and propose network intervention strategies based on the friendship paradox. Inversity uniquely determines the best-performing strategy. These strategies provide a privacy-sensitive approach to obtaining highly connected individuals without knowing the network, and are guaranteed to obtain a greater than average degree for almost any network. Finally, we characterize the value of these strategies theoretically and with real-world networks.

In the optimal cohort partition problem, a planner seeks to distribute a heterogeneous population of individuals into a fixed number of distinct groups to optimize their objective. This problem is prevalent across a wide range of applications, including assigning students to classes, forming teams of experts in organizations, maximizing biodiversity, and designing contests among teams.

In this work, we approach partition problems from a majorization theory perspective and make two main contributions. At a conceptual level, we present a majorization-theoretic optimization framework for objectives that take a part-additive form, i.e., they can be written as a function of the summation vector of a partition. We show that the optimization of several common objectives reduces to a part-additive problem and characterize the structure of the optimal cohort partition policy across various objectives and applications.

At an application level, we focus on a common educational task---how to optimally partition students into classes in the presence of peer effects---and derive theoretical guarantees and new insights for this problem, thus making a theoretical contribution to the chiefly empirical literature on peer effects in education. In particular, we employ the workhorse empirical model, namely the linear-in-means (LIM) model, and study two different behavioral microfoundations proposed in the literature: the LIM spillover model and the LIM conformist model. We theoretically characterize the optimal partition for four distinct objectives---performance, welfare, diversity, and inequality.

We derive several policy-relevant insights. First, in both behavioral models, we show that as the planner's highest priority shifts from the top-performing class to the middle-performing class and then to the least-performing class, the optimal partition becomes progressively less assortative, ranging respectively from fully assortative to upper-uniform and then to fully uniform. Second, we show that, while both behavioral models yield identical optimal partition policies with respect to performance, diversity, and inequality, the welfare-optimal partition policy differs between the two LIM microfoundational models. Within the same behavioral model though, trade-offs are not always inevitable. For example, when student behavior aligns with the LIM conformist model and the planner weakly prioritizes the performance of the least-achieving classes, the uniform partition is optimal in most practical scenarios and across all four objectives. Third, we find that the uniform partition, despite achieving lower inequality than any integral partition, does not achieve perfect equality. We further illustrate these theoretical findings using calibrated simulations with student data from a novel dataset of public high schools.

Finally, we showcase how our theoretical framework extends beyond educational contexts by studying the optimal assignment of experts to teams, the design of Tullock contests, and non-linear peer effect models.

Reclassification risk is a major concern in health insurance where contracts are typ- ically one year in length but health shocks often persist for much longer. While most health systems with private insurers pair short-run contracts with substantial pricing regulations to reduce reclassification risk, long-term contracts with one-sided insurer commitment have significant potential to reduce reclassification risk without the negative side effects of price regulation, such as adverse selection. We theoreti- cally characterize optimal long-term insurance contracts with one-sided commitment, extending the literature in directions necessary for studying health insurance markets. We leverage this characterization to provide a simple algorithm for computing optimal contracts from primitives. We estimate key market fundamentals using data on all under-65 privately insured consumers in Utah. We find that dynamic contracts are very effective at reducing reclassification risk for consumers who arrive to the market in good health, but they are ineffective for consumers who come to the market in bad health, demonstrating that there is a role for the government insurance of pre-market health risks. Individuals with steeply rising income profiles find front-loading costly, and thus relatively prefer ACA-type exchanges. Switching costs enhance, while myopia moderately compromises, the performance of dynamic contracts.

Should policymakers adapt their macroprudential and monetary policies when the financial sector is vulnerable to belief-driven boom-bust cycles? I develop a model in which financial intermediaries are subject to collateral constraints, and that features a general class of deviations from rational expectations. I show that distinguishing between the drivers of behavioral biases matters for the precise calibration of policy: when biases are a function of equilibrium asset prices, as in return extrapolation, new externalities arise, even in models that do not have any room for policy in their ratio- nal benchmark. These effects are robust to the degree of sophistication of agents re- garding their future biases. I show how time-varying leverage, investment and price regulations can achieve constrained efficiency. Importantly, greater uncertainty about the extent of behavioral biases in financial markets reinforces incentives for preven- tive action.

In late 2023, Orange Grove Bio co-founder and CEO Marc Appel considered the future of his private drug development investment firm. Orange Grove Bio, (OGB) had finished its fourth- year nurturing promising early-stage drugs for new treatments of cancer, autoimmune, and inflammatory disease. The possibility of an influx of new capital was good. The company was thinking about launching additional rounds of private equity financing. Furthermore, Appel expected three of its portfolio companies to be cleared by the Federal Drug Administration for initial tests on people by 2025.

Teleconferencing with his executive team, CEO Appel gamed out possible future strategic directions for OGB. One possibility was building the company’s infrastructure. OGB could buy a contract research organization (CRO) to do bench work, sift through enormous data sets from lab tests, and conduct animal and human clinical trials on novel therapeutics. Having this technology in-house instead of paying outside companies for it would expand OGB’s capabilities and cut costs. Or the company could simply invest in building more wet labs for its portfolio companies.

Another possibility was building more relationships to increase OGB’s pipeline. The company had been built on the premise that valuable research to in-license was being ignored in universities located away from the biotech hot spots of Boston and San Francisco. OGB had invested in establishing connections with scientists in universities away from these locations, and even moved its headquarters to Cincinnati to take advantage of this opportunity. The team also had started looking internationally, considering relationships with universities and biotech firms outside the United States. However, creating productive alliances required resources and time. OGB had to consider the importance not only of the breadth but also the depth of its scientific alliances.

Appel also discussed the idea of starting an associated venture capital fund to leverage the firm’s relationships with investors and drug companies. OGB had been established to nurture promising scientific work ready for clinical trials on people. Its business model was to in-license biological discoveries from academia to build businesses around, with the plan of selling these for large profits to pharmaceutical companies when the underlying molecules showed promise of improving human health in tests on people. The company could also look further ahead in the development cycle and in-license medicines approaching the end of human clinical testing and possibly reap profits on the next blockbuster drug.

Or the company, for now, could just stand on its original business model and develop its existing portfolio of eight early-stage, preclinical subsidiaries. What would attract the most investor interest? OGB was building toward an eventual IPO, but in 2023, market conditions for biotech firms were deteriorating. The U.S. S&P 500 Biotechnology Industry index fell 50 percent in the third quarter of 2023 from its high in 2021.5 The sector was in its worst shape in 20 years, some analysts said.  And venture capital funding had sunk to a six-year low amid worries about high interest rates and inflation. Given the market conditions, what would be the smartest move?

Firms facing complex objectives often decompose the problems they face, delegating different parts of the decision to distinct sub-units. Using comprehensive data and internal models from a large U.S. airline, we establish that airline pricing is not well approximated by a model of the firm as a unitary decision-maker. We show that observed prices, however, can be rationalized by accounting for organizational structure and for the decisions by departments that are tasked with supplying inputs to the observed pricing heuristic. Simulating the prices the firm would charge if it were a rational, unitary decision-maker results in lower welfare than we estimate under observed practices. Finally, we discuss why counterfactual estimates of welfare and market power may be biased if prices are set through decomposition, but we instead assume that they are set by unitary decision-makers.

We develop a dynamic theory of “Partial Equilibrium Thinking” (PET), which micro-founds time-varying price extrapolation: extrapolative beliefs are present at all times, but only sometimes manifest themselves in explosive ways. To study this systematically, we formalize the distinction between normal times shocks and “dis- placement shocks” (Kindleberger 1978). In normal times, PET generates constant extrapolation, contrarian trading, and price momentum. Instead, following a dis- placement shock that increases uncertainty, PET leads to stronger and time-varying extrapolation with momentum trading, triggering bubbles and endogenous crashes. Our theory sheds light on both normal times dynamics and Kindleberger’s narrative of bubbles within a unified framework.

Using unique hand-collected data covering the political affiliation of 1,045 cities over the last two decades, this paper studies the implications of city-state partisan conflicts on the financing and provision of public goods. Cities with the same political affiliation as the state governor face 8 basis points lower borrowing costs, as compared to misaligned cities. These effects are stronger in states where governors are granted more powers, where cities are more fiscally dependent on the state, and for bonds issued by riskier borrowers. Consistent with state investments substituting for city-specific initiatives, we show that aligned cities reduce their investment in costly hazard preparedness projects when a same-party governor is elected.

Should Pepsi reconsider its long-term focus on the youth market for the company's signature brand?

For decades, PepsiCo, the largest food company in the United States, marketed its Pepsi-Cola brand with a primary focus on the youth demographic, leveraging advertisements featuring teens and young adults. However,  sales of the brand struggled; the company had seen its market share erode over the 2010s, dropping even faster than the overall cola segment. Amidst this backdrop, Todd Kaplan, PepsiCo's new Chief Marketing Officer for U.S. Pepsi, faced the challenge of revitalizing the brand:

Youth Targeting: Persist with Pepsi's traditional approach of appealing to youth, despite the drop in youth consumption of cola products and the inherent risks of marketing missteps. Look for new users to drink their brand.

Increasing Share of Wallet: Look to increasing consumption among existing customers, regardless of their age. 

Kaplan's team considered the options. What marketing strategy should Pepsi take to improve its brand position? Would Pepsi reconsider its long-term focus on the youth market for the company's signature brand? If so, what options would succeed in the market as well as with a skeptical internal audience?