Publications

A critical element of word of mouth (WOM) or buzz marketing is to identify seeds, often central actors with high degree in the social network. Seed identification typically requires data on the relevant network structure, which is often unavailable. We examine the impact of WOM seeding strategies motivated by the friendship paradox, which can obtain more central nodes without knowing network structure. Higher degree nodes may be less effective as seeds if these nodes communicate less with neighbors or are less persuasive when they communicate; therefore, whether friendship paradox–motivated seeding strategies increase or reduce WOM and adoption remains an empirical question. We develop and estimate a model of WOM and adoption using data on microfinance adoption across village social networks in India. Counterfactuals show that the proposed strategies with limited seeds are about 13%–30% more effective in increasing adoption relative to random seeding. These strategies are also on average 5%–11% more effective than the firm’s leader seeding strategy. We also find these strategies are relatively more effective when we have fewer seeds.

We hypothesize that “low-contrast categories” (those lacking sharp differentiation from adjacent categories) catalyze the creation of groundbreaking inventions by influencing two key stages in the life of an invention: (1) idea-creation and (2) idea-positioning. During “idea-creation,” low-contrast categories increase the likelihood that descendant inventions will combine the focal invention with more (a) boundary-spanning, (b) novel, (c) original, and (d) atypical knowledge inputs. During “idea-positioning,” they allow greater leeway in articulating how descendant inventions depart from the focal invention’s lineage and chart new technological directions. We find robust support for our hypothesis using data from the United States Patent and Trademark Office’s classification system spanning nearly four decades. Further analyses demonstrate that the catalyzing effect of low-contrast categories has important material consequences: inventions classified in low-contrast categories spur descendant inventions that generate substantially higher economic value and exert more enduring technological impact than those in high-contrast categories. By introducing the concept of catalyzing categories, this study offers a novel theoretical perspective on the genesis of groundbreaking inventions and the role of categorical structures in the inventive process.

The 2020 revisions to the Federal Reserve’s monetary policy framework included a shift in the Fed’s policy focus to shortfalls (rather than deviations) from maximum employment and a commitment to “flexible average inflation targeting.” The new framework, and the associated guidance and asset purchases with which it was implemented, were tested by the surge in inflation in 2021 and 2022. We consider the lessons learned from this experience. We conclude that the changes to the framework were too focused on the experience following the financial crisis and hence were not robust in the face of unexpected changes in economic circumstances. We also argue that the Fed made mistakes with the calibration and communication of the tools used to implement the framework—the forward guidance on the policy rate and the asset purchase program. We recommend a broad framework that would be appropriate in a wide range of policy environments, with the specific policy approach to be taken in any given circumstance to be communicated through forward guidance and asset purchase announcements. We suggest ways in which the Fed could implement these tools with better calibration and communication, in order to avoid having its policy commitments exacerbate costly economic outcomes.

Should Microsoft join a global moratorium on underwater mineral mining until the harm mining could do to the oceans is thoroughly assessed?

Global technology giant Microsoft designs, manufactures, and sells a range of software, services, and devices, including the Windows operating system, Microsoft Office, Azure cloud computing services, and hardware products such as Surface tablets and Xbox gaming consoles. Sourcing the cobalt for the lithium-ion batteries in its hardware products presents Microsoft with a dilemma. Terrestrial cobalt mining ravages environments and produces considerable emissions. In the Democratic Republic of the Congo, home of most of the world's cobalt, the metal is extracted with forced labor, child labor, and exploitation. Cobalt is also found in the deep oceans. However, environmentalists and many global governments decry undersea cobalt mining as unnecessary and damaging to precious deep ocean biospheres. Microsoft must balance its need for reliable cobalt supplies with company ethical and environmental standards, though terrestrial and ocean cobalt are both imperfect sources with ethical and environmental risks and controversies.

The credit-implied volatility (CIV) surface is introduced as an organizing framework for analysis of credit spreads, providing a description of CDS spreads for firms across the credit spectrum, of varying maturities, and at all points throughout the credit cycle.

We model the optimal resolution of insolvent firms in general equilibrium. Collateral- constrained banks lend to (i) solvent firms to finance investments and (ii) distressed firms to avoid liquidation. Liquidations create negative fire-sale externalities. Liquidations also re- lieve bank balance-sheet congestion, enabling new firm loans that generate positive collateral externalities by lowering bank borrowing rates. Socially optimal interventions encourage liqui- dation when firms have high operating losses, high leverage, or low productivity. Surprisingly, larger fire sales promote interventions encouraging more liquidations. We study synergies be- tween insolvency interventions and macroprudential regulation, bailouts, deferred loss recog- nition, and debt subordination. Our model elucidates historical crisis interventions.

Oprea argued that prospect theory’s risk attitudes reflect ''complexity'' rather than underlying risk preferences. A re-analysis reveals that this claim is driven by measurement error caused by a confusing experimental design. Participants valued risky lotteries and riskless ''mirrors'' similarly, but this was largely due to the 75% who failed comprehension checks. These subjects had noisy valuations and frequent dominance violations, which artifactually produce a fourfold pattern, loss aversion, and positive correlation between mirrors and lotteries. In contrast, participants who likely understood the task valued mirrors at expected value and lotteries in line with prospect theory.

Researchers were already expecting a gradual enrollment slowdown before the onset of COVID-19. Public school enrollment edged up only 2% between 2012 and 2019, holding near 50 million students, while the U.S. total fertility rate had slipped to 1.71 births per woman—well below the replacement level—foreshadowing a smaller school-age cohort. The pandemic turned that slow decline into a sudden shock. Studies document steep post-2020 losses in Massachusetts, Virginia, Michigan, and California. Research at the national level shows similar trends in urban and high-poverty districts, and a surge in both homeschooling and private-schooling that still leaves millions of children “missing” from any formal roll. In addition to concerns around student progress, shrinking headcounts also create immediate fiscal stress because most state and federal aid flows on a per-pupil basis. District leaders have already considered adjusting school capacity, redistricting, or even closing campuses to balance budgets— steps that are often politically sensitive but considered in response to fiscal pressures. Recent evidence confirms that steeper enrollment losses measurably raise the odds of permanent closure. Enrollment shifts have not fallen evenly across student groups. Recent evidence shows that kindergarten enrollment fell most sharply for black and low-income children, whereas the smaller declines observed in later grades were concentrated among white and higher-income students already enrolled in public schools. Such patterns may heighten long-standing worries about potential re-segregation and resource inequality. Against this backdrop, policymakers and district officials are experimenting with strategies to stem further enrollment losses or mitigate their effects. Some, like New York City, have pledged to preserve school budgets even as rolls shrink. Others hope new curricula or enhanced parent outreach will attract students back. This report provides detailed estimates of recent shifts in public school enrollment. By linking the latest National Center for Education Statistics data with federal population estimates, the report shows how enrollment shifts differ across districts with distinct racial and economic profiles. In addition, it projects how continued enrollment declines could drive future school closures and alter the number of seats traditional districts will need through 2050.

The origin of the modern joint-stock company is typically traced to the concomitant appearance of large-scale maritime trading companies in England and the Netherlands in the early seventeenth century. Highlighting medieval cases in southern Europe, we claim that the joint-stock company emerged earlier in history. These prior appearances support the theory of convergent evolution towards the joint-stock company. We document alternative and largely independent developmental paths that suggest the joint-stock company can emerge in a variety of legal, political and socioeconomic contexts. This evidence has implications for identifying the necessary background underlying the emergence of the joint-stock company, and for the debate regarding the link between business institutions and economic growth.

Growing socio-economic inequality poses one of the greatest challenges to society, thereby raising new questions about the responsibility of corporations to address its effects. Inequality also poses material risks to business performance. Like climate risk, inequality can impact business across a broad set of sectors and economies on a global scale. To mitigate risks and leverage opportunities to generate positive outcomes from corporate sustainability investments, managers and investors need better data on the business risks posed by inequality and the impact of corporate conduct on it. However, the current transparency infrastructure is inadequate to meet this need. This article reviews the current state of corporate disclosure on inequality and assesses its utility to companies as well as investors and other stakeholders. Drawing on innovations in climate disclosure, we suggest a path forward for companies and investors to drive improved disclosure from companies on the risks presented by socio-economic inequality.

We examine whether and how granular, real-time predictive models should be in- tegrated into central banks’ macroprudential toolkit. First, we develop a tractable framework that formalizes the tradeoff regulators face when choosing between imple- menting models that forecast systemic risk accurately but have uncertain causal content and models with the opposite profile. We derive the regulator’s optimal policy in a set- ting in which private portfolios react endogenously to the regulator’s model choice and policy rule. We show that even purely predictive models can generate welfare gains for a regulator, and that predictive precision and knowledge of causal impacts of policy interventions are complementary. Second, we introduce a deep learning architecture tailored to financial holdings data—a graph transformer—and we discuss why it is op- timally suited to this problem. The model learns vector embedding representations for both assets and investors by explicitly modeling the relational structure of holdings, and it attains state-of-the-art predictive accuracy in out-of-sample forecasting tasks including trade prediction.

We analyze survey data on ESG beliefs and preferences in a large panel of retail investors linked to administrative data on their investment portfolios. The survey elicits investors’ expectations of long-term ESG equity returns and asks about their motivations, if any, to invest in ESG assets. We document four facts. First, investors generally expected ESG investments to underperform the market. Between mid-2021 and late-2022, the average expected 10-year annualized return of ESG investments relative to the overall stock market was –1.4%. Second, there is substantial heterogeneity across investors in their ESG return expectations and their motives for ESG investing: 45% of survey respondents do not see any reason to invest in ESG, 25% are primarily motivated by ethical considerations, 22% are driven by climate hedging motives, and 7% are motivated by return expectations. Third, there is a link between individuals’ reported ESG investment motives and their actual investment behaviors, with the highest ESG portfolio holdings among individuals who report ethics-driven investment motives. Fourth, financial considerations matter independently of other investment motives: we find meaningful ESG holdings only for investors who expect these investments to outperform the market, even among those investors who reported that their most important ESG investment motives were ethical or hedging reasons.

We examine whether and how granular, real-time predictive models should be in- tegrated into central banks’ macroprudential toolkit. First, we develop a tractable framework that formalizes the tradeoff regulators face when choosing between imple- menting models that forecast systemic risk accurately but have uncertain causal content and models with the opposite profile. We derive the regulator’s optimal policy in a set- ting in which private portfolios react endogenously to the regulator’s model choice and policy rule. We show that even purely predictive models can generate welfare gains for a regulator, and that predictive precision and knowledge of causal impacts of policy interventions are complementary. Second, we introduce a deep learning architecture tailored to financial holdings data—a graph transformer—and we discuss why it is op- timally suited to this problem. The model learns vector embedding representations for both assets and investors by explicitly modeling the relational structure of holdings, and it attains state-of-the-art predictive accuracy in out-of-sample forecasting tasks including trade prediction.

We ask when additional data collection by a monopolist to engage in price discrimi- nation monotonically increases or decreases weighted surplus. To answer this question, we develop a model to study endogenous market segmentation subject to residual un- certainty. We give a complete characterization of when data collection is good or bad for surplus, which consists of a reduction of the problem to one with only two demand curves, and a condition for the two-demand-curves case that highlights three distinct ef- fects of information on welfare. These results provide insights into when data collection and usage for price discrimination should be allowed.

This chapter reviews major theories of emotion and decision making, concentrating on developments within the disciplines of psychology, economics, and decision science. These theories naturally cluster into two sets of theories – one set that views emotional valence (i.e., positivity versus negativity) as the primary factor for predicting decision outcomes, and a second set of theories that views valence as one of multiple factors for making predictions. Often known as “emotion-specific models”, theories in this latter set propose that emotions of the same valence can have opposing (rather than similar) effects on certain decisions. After describing strengths and weaknesses of each approach, the chapter offers a review of the Emotion-Imbued Choice model (EIC) – a unified, meta-level model of emotion and decision making.

While traditional change management emphasizes gradual tactics like pursuing small wins and building coalitions, in turbulent times these gradual tactics aren’t necessary—and they can hold leaders back from taking advantage of bigger opportunities. Research from healthcare settings during Covid show that both senior leaders and frontline managers are more successful at prompting change during turbulent times when they do three things: 1. Selecting a shovel-ready idea and reframing it as a solution to a problem at hand as well as long-term success, 2. Moving quickly to take advantage of a window in time when people are more open to change, and 3. Thinking more expansively about what’s possible.

How does market power in the corporate banking sector influence the effects of interest rate cap policies on credit allocation? We study this question using administrative credit registry data in Bangladesh, where the Central Bank capped the interest rate on corporate loans at 13% in 2009, relative to a pre-reform average interest rate of 14.5%. We apply a difference-in-differences design with variation in pre-regulation, branch-level interest rates as an exposure measure and find that a one percentage point cap-induced drop in rates increased lending amounts by 30% over the two years of the cap regime. This increase in lending is not driven by banks’ costs to supplying credit, as proxied by the riskiness of the borrower pool or deposit funding costs. Our results point to substantial credit under-provision due to banks’ market power in an emerging markets context, even in the presence of relationship lending.

We empirically characterize how China is internationalizing its bond market by staggering the entry of different types of foreign investors into its domestic market and propose a dynamic reputation model to explain this strategy. Our framework rationalizes China's strategy as trying to build credibility as a safe issuer while reducing the cost of capital flight. We use our framework to shed light on China's response to episodes of capital outflows.