Publications

In most evaluation systems—such as those governing the allocation of prestigious awards—the evaluator’s primary task is to reward the highest quality candidates. However, these systems are imperfect; top performers may not be acknowledged and thus be underrecognized, and low performers may receive unwarranted recognition and thus be overrecognized. An important feature of many evaluation systems is that people alternate between being candidates and being evaluators. How does experiencing misrecognition as a candidate affect how people subsequently evaluate others? We develop novel theory that underrecognition and overrecognition lead people to reproduce those experiences when they are evaluators. Across three studies—a quasi-natural experiment and two preregistered, multistage experiments, we find that underrecognized evaluators are less likely to grant recognition to others—even to the highest-performing candidates. Conversely, overrecognized evaluators are more likely to grant rewards to others—even to the lowest-performing candidates. Whereas underrecognized evaluator behavior is driven by individuals’ perceptions that their experience was unfair, overrecognized evaluator behavior is driven by the informational cues people glean on how to evaluate others. Thus, in evaluation processes where people oscillate between being the evaluated and being the evaluator, we show how and why seemingly innocuous initial inefficiencies are reproduced in subsequent evaluations.

Should central banks’ inflation targets remain set in stone? We study a dynamic mechanism
design problem between a government (principal) and a central bank (agent). The central
bank has persistent private information about structural shocks. Firms learn the state from the
central bank’s reports and form inflation expectations. A dynamic inflation target implements the
full-information commitment allocation. The central bank is delegated the authority to adjust
the level and flexibility of its target as long as it does so one period in advance. All history
dependence of the mechanism is summarized by the current period’s target. We show that
a declining natural interest rate and a flattening Phillips curve imply opposite optimal target
adjustments. We leverage our framework to study longer-horizon time consistency problems
and speak to practical policy questions of inflation target design.

A strategic tension between consumers in a monopolistic market arises when many high-value consumers want to pool with a few lower-value consumers in order to obtain low prices from the seller. We study the interaction between consumers and the resulting market segmentation into consumer groups as the outcome of a cooperative game between the consumers. We introduce two new solution concepts, the weakened core and stability, which coincide with the core whenever it is nonempty. We show that these concepts are in fact equivalent and non-empty, and are characterized by efficiency and saturation. A segmentation is saturated if shifting consumers from a segment with a higher price to a segment with a lower price leads the seller to optimally increase the lower price. We show that stable segmentations that maximizes average consumer surplus (across all segmentations) always exist

This paper develops a theory-based, explainable deep learning convolutional neural network (CNN) classifier to predict the time-varying emotional response to music. We design novel CNN filters that leverage the frequency harmonics structure from acoustic physics known to impact the perception of musical features. Our theory-based model is more parsimonious, but it provides comparable predictive performance with atheoretical deep learning models while performing better than models using handcrafted features. Our model can be complemented with handcrafted features, but the performance improvement is marginal. Importantly, the harmonics-based structure placed on the CNN filters provides better explainability for how the model predicts emotional response (valence and arousal) because emotion is closely related to consonance—a perceptual feature defined by the alignment of harmonics. Finally, we illustrate the utility of our model with an application involving digital advertising. Motivated by YouTube’s midroll ads, we conduct a laboratory experiment in which we exogenously insert ads at different times within videos. We find that ads placed in emotionally similar contexts increase ad engagement (lower skip rates and higher brand recall rates). Ad insertion based on emotional similarity metrics predicted by our theory-based, explainable model produces comparable or better engagement relative to atheoretical models.

This article concerns itself with fees that Apple and Google might charge to business users in their respective mobile ecosystems. We lay out the economic analysis behind the goals of the DMA—contestability and fairness—as they apply to third-party app store access fees. We focus on the access fees for alternatives to the Apple App Store, as this has become contentious in the early enforcement of the DMA. Much of our analysis, however, also applies also to Google and/or any other designated gatekeeper.

For more than 150 years, the founding mission of the American Society for the Prevention of Cruelty to Animals (ASPCA) guided the organization while enabling it to address evolving challenges in animal welfare. Under Matt Bershadker, ASPCA’s president and CEO, the company faced mounting pressure to engage with a growing stream of societal matters far afield from its core purpose. The case explores Bershadker’s initiative to develop a clear, strategic framework for considering which societal issues to address. This effort would clarify the ASPCA’s approach to these issues by evaluating them against the organization’s strategy, history, policies, and key stakeholder relationships, ensuring consistency, transparency, and mission-driven decision-making.

We study a retirement savings plan with a default contribution rate of 12% of income, which is much higher than previously studied defaults. Twenty-five percent of employees had not opted out of this default 12 months after hire; a literature review finds that the corresponding fraction in plans with lower defaults is approximately one-half. Because only contributions above 12% were matched by the employer, 12% was likely to be a suboptimal contribution rate for employees. Employees who remained at the 12% default contribution rate had average income that was approximately one-third lower than would be predicted from the relationship between salaries and contribution rates among employees who were not at 12%. Defaults may influence low-income employees more strongly in part because these employees face higher psychological barriers to active decision making.

Bayer AG is a German multinational pharmaceutical and life sciences company founded in 1863. It operates globally in over 90 countries with approximately 100,000 employees as of 2024. Bayer is structured into three main business segments: Pharmaceuticals, Consumer Health, and Crop Science, with significant global operations and an extensive patent portfolio. The company is a leader in agricultural products, prescription medicines, and over-the-counter health products. It also focuses on innovative solutions for healthcare and agricultural challenges.

The current dilemma for students to address are issues related to Bayer’s transition to a new operating model called Dynamic Shared Ownership (DSO). This model, introduced under the leadership of CEO Bill Anderson, aims to flatten the corporate hierarchy and create a nimbler, customer-centric organization. The transition involves removing the existing hierarchical structure, which previously consisted of 12 management layers, and replacing it with self-managed teams. This structural change was referred to internally as the "hardware."

The initial steps under DSO included substantial organizational redesign, involving layoffs and the establishment of self-managed teams. The Board of Management successfully halved the number of management layers to five or six and replaced thousands of middle managers with self-managed teams. A complementary aspect of DSO, labeled the "software," focused on fostering cultural changes to promote new mindsets, norms, and behaviors among Bayer’s nearly 100,000 employees.

Notwithstanding several early wins, the Board of Management is now grappling with implementing new talent management and personnel policies that align with the DSO model. Existing HR processes and systems, which are designed for a hierarchical organization, need to be reinvented. Questions about compensation, career pathing, and performance metrics must be addressed to ensure these new systems support the DSO framework effectively.

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.

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.

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 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.