Publications

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.

This article empirically examines the “drip” pricing behavior of firms, where consumers purchase a base product and may later buy an add-on, often without anticipating the need for it. A loss leader pricing strategy emerges, with firms pricing the base product below, and the add-on above, standalone levels. Using data from the Portuguese driving instruction market, we find that base course prices decrease with competition, while prices for add-on repeat courses do not. A survey reveals that at least 25% of students are inattentive to repeat fees, driven by underestimating failure rates and being unaware of repeat test costs.

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.

Multiple extensions of the federal student loan forbearance program that began in March 2020 resulted in a temporary payment pause that lasted more than 3 years. We examine the impact of long-term forbearance on the evolution of borrowing by distressed individuals. Compared to distressed borrowers not in forbearance, we observe a 13.4-point credit score increase within a year of forbearance, followed by 12.3% more credit card debt and 4.6% more auto loans, but significantly less total mortgage debt. By year 3, student loan balances are 12.1% higher for the forbearance sample and delinquencies on nonstudent debt are also higher. In the absence of policy interventions, our results suggest that the extended breathing room that the program allowed could accelerate post-forbearance financial distress.

What is keeping Lidl, Europe’s preeminent grocery chain, from gaining similar success in the U.S?

Upon entry to the U.S. market in 2017, Lidl US aspired to open 1,000 stores in its first years. But the company was unable to attract US consumers. By 2024, Lidl had managed to open fewer than 200 stores, capturing less than 1% of the market. To right the expansion effort in 2023, Joel Rampoldt became Lidl US’s fourth president. In 2024, he faced critical issues in the selection of store locations, assortment, customer relationship management, and effectively communicating Lidl US’s value proposition in a crowded and competitive market.

When Kinya Seto became CEO of LIXIL in 2016, he faced two major challenges: integrating a workforce spread across several acquired brands and differentiating LIXIL in a housing and water technology industry where products were seen as commodities. This case explores how LIXIL developed and activated its corporate purpose—"To Make Better Homes a Reality for Everyone, Everywhere"—as a unifying and strategic force. The purpose became a guiding “North Star,” brought to life through an ambitious new business unit focused on delivering low-cost, eco-friendly toilets to underserved communities around the world. This purpose-driven approach also helped unite employees, spark innovation, and address the challenge of product commoditization.

Machine learning tools have been remarkably successful at using published anomalies for creating portfolios with extremely high returns. However, the underlying economic mechanisms behind their performance remains unclear. This paper proposes a theory-based dynamic arbitrage trading strategy to interpret how neural networks select among anomalies over time. Using 153 firm characteristics (anomalies), this strategy ranks them similarly to neural networks and absent the use of microcaps explains nearly 40% of their monthly performance. When unpublished anomalies and microcap stocks are excluded, an economic model based algorithm fully explains almost all of the neural network’s return performance and largely duplicates the anomaly selection. Additionally, we show how the performance of neural networks can be further improved by incorporating aspects of economic principles.

Over the past two decades, the European Commission's enforcement activities against abusive practices often came too late. Investigations only started when companies had already acquired a strong dominant position. It proved difficult to restore competition. In this paper, we advocate an earlier intervention, namely when undertakings start to monopolize markets. This is possible under Art. 102 TFEU with a return to the original definition of dominance and less emphasis on market shares. In line with cases like Hoffman-LaRoche, dominance must be defined as the ability to steer the market into a direction that is detrimental to competition and trading partners. This will allow to investigate the behaviour of firms at an earlier point of time, particularly in markets prone to tipping.

We re-analyze data from four recent papers to demonstrate that the traditional approach for probing
interactions—linear regression followed by Simple-Slopes ("spotlight") or the Johnson-Neyman
procedure ("floodlight")—can lead to qualitatively incorrect conclusions when true relationships
are not linear. Building on proposals to use GAMs (Generalized Additive Models) to probe
interactions, we introduce the Johnson-Neyman 2.0 procedure (JN2). This procedure involves: (1)
using GAMs to probe an interaction of interest, and then (2) verifying the key conclusion of interest
with a t-test or linear regression run on the subset of relevant data. For example, if a GAM
determines an experimental manipulation has a negative effect for moderator values 0.67 SD below
the mean, a t-test is run only among those observations to verify the negative effect.

This paper examines which municipal disclosures provide informational value to investors. Using the entire universe of post-issuance financial and event disclosures from 2009 to 2022, we find that most municipal bonds do not trade in the weeks following a disclosure. However, some disclosures do provide enough new information to increase trading. Investors trade more on credit-relevant disclosures, such as adverse credit event disclosures, and less on required annual financial statements. Trading after disclosures also increases more when a bond is large or risky. Moreover, we find that credit rating agencies do respond to disclosures, lending support to the idea that some disclosures have informational value. In further analyses, we find that trading before the disclosure, lack of timeliness, illiquidity, and information processing constraints contribute to the limited trading on the average disclosure. The findings suggest that reconsidering a one-size-fits-all approach to regulating post-issuance municipal disclosures may be worthwhile.

Nike describes its purpose with reference to three “Purpose Pillars”: People, Planet, and Play. Each pillar sets targets, tracks progress, and assesses outcomes tied to compensation. This approach helps Nike navigate complex social issues and stakeholder relationships, often translating corporate principles into actionable strategies.