Publications

An influential emerging literature documents strong correlations between carbon emissions and stock returns. We re-examine those data and conclude that these associations are driven by two factors. First, stock returns are correlated only with unscaled emissions estimated by the data vendor, but not with unscaled emissions actually disclosed by firms. Vendor-estimated emissions systematically differ from firm-disclosed emissions and are highly correlated with financial fundamentals, suggesting that prior findings primarily capture the association between such fundamentals and returns. Second, unscaled emissions, the variable typically used in academic literature, is correlated with stock returns but emissions intensity (emissions scaled by firm size), an equally important measure used in practice, is not. While unscaled emissions represent an important metric for society, we argue that, for individual firms, emissions intensity is an appropriate measurement choice to assess carbon performance. The associations between emissions and returns disappear after accounting for either of the issues above.

We present a new database of banking-crisis interventions from the Roman Empire
to the present, covering 1,946 interventions in 20 categories across 143 countries.
We demonstrate that crisis-intervention patterns are significantly related to income
and fiscal variables and to measures of the political system and currency regime.
GDP losses following crises are economically significant and are larger for
wealthier countries, with some evidence that these losses are mitigated by
democratic political systems and liberal currency regimes. Finally, intervention
frequencies reached an apex during the post-Bretton Woods era, continuing a
secular increase since at least the late 17th century.

A buyer procures a good owned by a group of sellers whose hetero- geneous cost of trade is private information. The buyer must either buy the whole good or nothing, and sellers share the transfer in pro- portion to their share of the good. We characterize the optimal mech- anism: trade occurs if and only if the buyer’s benefit of trade exceeds a weighted average of sellers’ virtual costs. These weights are endog- enous, with sellers who are ex ante less inclined to trade receiving higher weight. This mechanism always outperforms posted-price mechanisms. An extension characterizes the entire Pareto frontier.

Can information from a credible messenger shift behavior in an information-saturated environment? In a randomized controlled trial involving twenty-eight million individuals in West Bengal, we find that SMS-delivered video messages containing information about COVID-19 symptoms and health-preserving behaviors recorded by a credible messenger increased adherence to targeted and non- targeted preventive behaviors, measured by two objective measures (symptoms reported to a health worker, and phone usage at home), as well as self-reported behaviors. We find large spillovers onto non- targeted recipients. Credible light-touch messaging can play an important role in crisis response, even when similar information is widely available.

This book offers a radical thesis: Technological innovation often benefits elites while worsening conditions for workers, challenging the common view that technology always improves living standards. Through historical transitions like the Industrial Revolution, the authors illustrate how innovations have frequently led to worker exploitation. They argue that governance, rather than competition, determines whether technological advances benefit society. In the digital age, platforms exploit user data without fair compensation, causing harm through addictive services and poor regulation. The book calls for stronger regulations to protect consumers and ensure that innovation aligns with societal well-being, especially as artificial intelligence spreads.

I study the long-term effects of landing a first job at a large firm versus a small one using Spanish administrative data. Size could be a relevant employer attribute for inexperienced workers since large firms are associated with greater productivity, wages, and training. The key empirical challenge is selection into first jobs based on unobserved worker characteristics. I develop an instrumental variable approach that, keeping business cycle conditions fixed, leverages variation in the composition of labor demand that labor market entrants face. Initially matching with a larger firm persistently improves long-term outcomes, even through subsequent jobs. Mechanisms suggest better skill development at large firms.

This paper proposes a theory of large-scale government bond purchases by central banks in an environment with endogenous information acquisition. Information acquisition by private investors lowers sovereign bond yields by reducing uncertainty, and makes prices more responsive to new information. I show that asset purchases by the central bank discourage information acqui- sition. Using the case of Italian bonds and the start of ECB purchases in 2015, I document through various measures that price informativeness indeed sig- nificantly declined with purchases. When the sovereign can be subject to self- fulfilling debt crises, however, this reduction in information acquisition can be beneficial. I show that by impairing price informativeness, asset purchases can avoid the occurrence of roll-over crises, generating large welfare gains. A key property of the model is that substantial purchases may be required, while small interventions have ambiguous welfare consequences. When the sovereign expects the central bank to carry such programs, it leads to exces- sive indebtedness, forcing the central bank to run an inflated balance sheet to avoid roll-over crises.

This article summarizes empirical research on the interaction between monetarypolicy and asset markets, and reviews our previous theoretical work that capturesthese interactions. We present a concise model in which monetary policy impactsthe aggregate asset price, which in turn ináuences economic activity with lags. Inthis context: (i) the central bank (the Fed, for short) stabilizes the aggregate assetprice in response to Önancial shocks, using large-scale asset purchases if needed(ìthe Fed putî); (ii) when the Fed is constrained, negative Önancial shocks causedemand recessions, (iii) the Fedís response to aggregate demand shocks increasesasset price volatility, but this volatility plays a useful macroeconomic stabilizationrole; (iv) the Fedís beliefs about the future aggregate demand and supply drivethe aggregate asset price; (v) macroeconomic news ináuences the Fedís beliefs andasset prices; (vi) more precise news reduces output volatility but heightens assetmarket volatility; (vii) disagreements between the market and the Fed microfoundmonetary policy shocks, and generate a policy risk premium.

The Chicago Fire is a Major League Soccer (MLS) team acquired by Andell Holdings, an investment firm led by Andrew Hauptman. Purchased for $35 million in 2007, the Fire experienced significant struggles with its home venue, Toyota Park in Bridgeview Illinois, due to poor public transport access and suboptimal maintenance. Despite growing league fortunes and increased sponsorships, the team’s location in suburban Bridgeview posed a significant strain on the popularity of the club.

To effectuate its expansion plans and address these challenges, Andell Holdings must make two critical decisions regarding its ownership of the Chicago Fire. First, Hauptman needs to evaluate the financial and operational ramifications of relocating the team to Soldier Field in downtown Chicago despite the large payments the village of Bridgeview was demanding to break the team’s long-term lease on Toyota Park. This decision hinges on calculating the required increase in attendance to offset the financial burden and considering other non-economic benefits such as brand visibility and fan engagement. Second, Hauptman must determine the optimal timing and strategy for Andell’s exit from Fire ownership. The sale of Andell’s stake required assessing when the team’s valuation would peak and the impact of any decisions regarding the team’s home venue. 

Health centers are vital primary care safety nets for underserved populations, but optimal clinician staffing associated with quality care is unclear. Understanding the association of clinician staffing patterns with quality of care may inform care delivery, scope-of-practice policy, and resource allocation.

Problem definition: Volunteer crowdsourcing platforms match volunteers with tasks that are often recurring. To ensure completion of such tasks, platforms frequently use a lever known as “adoption,” which amounts to a commitment by the volunteer to repeatedly perform the task. Despite reducing match uncertainty, high levels of adoption can decrease the probability of forming new matches, which in turn can suppress growth. We study how platforms should manage this trade-off. Our research is motivated by a collaboration with Food Rescue U.S. (FRUS), a volunteer-based food recovery organization active in more than 30 locations. For platforms such as FRUS, effectively using nonmonetary levers, such as adoption, is critical. Methodology/results: Motivated by the volunteer management literature and our analysis of FRUS data, we develop a model for two-sided markets that repeatedly match volunteers with tasks. We study the platform’s optimal policy for setting the adoption level to maximize the total discounted number of matches. When market participants are homogeneous, we fully characterize the optimal myopic policy and show that it takes a simple extreme form: depending on volunteer characteristics and market thickness, either allow for full adoption or disallow adoption. In the long run, we show that such a policy is either optimal or achieves a constant-factor approximation. We further extend our analysis to settings with heterogeneity and find that the structure of the optimal myopic policy remains the same if volunteers are heterogeneous. However, if tasks are heterogeneous, it can be optimal to only allow adoption for the harder-to-match tasks. Managerial implications: Our work sheds light on how two-sided platforms need to carefully control the double-edged impacts that commitment levers have on growth and engagement. Setting a misguided adoption level may result in marketplace decay. At the same time, a one-size-fits-all solution may not be effective, as the optimal design crucially depends on the characteristics of the volunteer population.

When consumers encounter unfamiliar products, they often face difficulty in understanding
which attributes are crucial, leading to challenges in product comparison and potential di-
minished interest in the category. This study examines how firms strategically communicate
the importance of product attributes in a competitive environment. Despite consumer aware-
ness of attributes and their levels, ambiguity regarding their relative importance remains.
We analyze a situation where two firms each receive a noisy signal about the true attribute
importance and convey this information to consumers through cheap-talk messages. Follow-
ing these communications, consumers decide whether to incur a cost to further explore the
category by visiting stores. Our findings reveal a truthful equilibrium where firms honestly
report their received signals. In this equilibrium, when both firms’ messages align, their
collective messages can credibly convey information about the more important attribute,
thereby encouraging store visits and purchase. Interestingly, firms may still find it advan-
tageous to truthfully highlight an attribute, even if it doesn’t align with their competitive
advantage. Moreover, we show that without competition (i.e., a single firm communicating),
this truthful equilibrium does not exist. Thus, the presence of the competition enables the
credible communication of information about attribute importance, benefiting both firms
by enhancing consumer engagement with the product category

We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias. We show that these regressions generally fail to estimate convex averages of heterogeneous treatment effects—instead, estimates of each treatment’s effect are contaminated by non-convex averages of the effects of other treatments. We discuss three estimation approaches that avoid such contamination bias, including the targeting of easiest-to-estimate weighted average effects. A re-analysis of nine empirical applications finds economically and statistically meaningful contamina- tion bias in observational studies; contamination bias in experimental studies is more limited due to idiosyncratic effect heterogeneity

This paper considers the problem of offering a scarce object with a common unobserved quality to strategic agents in a priority queue. Each agent has a private signal over the quality of the object and observes the decisions made by other agents. We first show that, under the widely-used first-come-first-served sequential offering mechanism, herding behavior emerges: initial rejections create an information cascade resulting in inefficient waste. To address this issue, we then introduce a class of batching mechanisms. Agents in each batch report whether they would be willing to accept or reject the object based on their private signals and prior information. If the majority opts to accept, the object is randomly allocated within that batch. We prove that suitable batching mechanisms are incentive-compatible and improve efficiency. A key property of the mechanism is the gradual increase of the batch size after each failed allocation; the size is chosen so that it elicits as much information as possible without distorting the incentives of agents to report truthfully. Additionally, from a healthcare policy perspective, our results can shed light on the large wastage in organ allocation. In particular, wastage that arises due to herding may be reduced by applying adaptive simultaneous offering mechanisms.

This paper examines the current state of social innovation and entrepreneurship programming, courses, and centers within schools of public health through a survey data analysis. This report presents a cross-sectional survey conducted among faculty members of public health schools in the United States. The survey aims to determine the availability and current state of student-centered programs and courses centered around social innovation and entrepreneurship within schools of public health. Insights were drawn from 19 professionals across 15 schools of public health. Uncertainties surround the sustainability of current programs, with insufficient funding, human resources, and the need to teach more pressing topics identified as the most significant obstacles. Key areas identified as opportunities for growth were faculty engagement, expertise, and funding to expand more structured programming.

Using data from 15 countries, this article investigates whether descriptive and prescriptive gender norms concerning housework and child care (domestic work) changed after the onset of the COVID-19 pandemic. Results of a total of 8,343 participants (M = 19.95, SD = 1.68) from two comparable student samples suggest that descriptive norms about unpaid domestic work have been affected by the pandemic, with individuals seeing mothers’ relative to fathers’ share of housework and child care as even larger. Moderation analyses revealed that the effect of the pandemic on descriptive norms about child care decreased with countries’ increasing levels of gender equality; countries with stronger gender inequality showed a larger difference between pre- and post-pandemic. This study documents a shift in descriptive norms and discusses implications for gender equality—emphasizing the importance of addressing the additional challenges that mothers face during health-related crises.

n response to intense pressure from policy makers and the public, technology companies have enacted a range of policies aimed at reducing the spread of misinformation online 1 - 3 . The enforcement of these policies has , however, led to technology companies being regularly accused of political bias 4 - 6 . W e argue that even under politically neutral anti - misinformation policies, such political asymmetries in enforcement should be expected , as there is a political asymmetry in the sharing of misinformation 7 - 12 . We support this argument with an analysis of Twitter data from 9,000 politically active users during the U.S. 2020 presidential election . While users on the political right were indeed substantially more likely to be suspended than those on the left , users on the right also shared far more links to low quality news sites – even when news quality was determined by politically - balanced groups of laypeople , or groups of only Republicans – and were estimated to have a far higher likelihood of being bots. We find similar associations between conservatism and low quality news sharing (based on both expert and politically - balanced layperson ratings) in seven other dat asets of sharing from Twitter, Facebook, and survey experiments, spanning 2016 to 2023 . These results demonstrat e that political im balance in enforcement need not imply bias, and should not dissuade technology companies from taking action against the spread of misinformation

This paper provides a framework in which a multiproduct ecosystem competes with small single-product firms in both price and innovation. The ecosystem is able to use data collected on one product to improve the quality of its other products. We study the impact of regulation which either restricts the use of data across the ecosystem’s business units, or which requires the ecosystem to share data with small firms. This regulation induces small firms to innovate more and set higher prices; it also dampens data spillovers within the ecosystem, reduces the ecosystem’s incentive to innovate, and potentially increases its prices. As a result, the impact of data regulation on consumers is ambiguous. Small firms do not necessarily benefit from sharing data with each other via a data cooperative, because doing so triggers more aggressive pricing by the ecosystem. A data cooperative can also harm consumers by inducing the ecosystem to innovate less.