Publications

Pandemic restrictions targeted non-essential businesses that required in-person contact to operate. We document the impact of these restrictions county-by-county on directly regulated businesses and on businesses not subject to restrictions. Through 2023, a one standard deviation increase in restrictions increased business closures by 3% and reduced net job creation by 18%. These adverse effects extended beyond regulated firms to those exempt from regulations. We attribute this transmission at least in part to the loss of face-to-face interactions.

Making sense of massive, individual-level data is challenging: marketing researchers and analysts need flexible models that can accommodate rich patterns of heterogeneity and dynamics, work with and link diverse data types, and scale to modern data sizes. Practitioners also need tools that can quantify uncertainty in models and predictions of consumer behavior to inform optimal decision-making. In this paper, we demonstrate the promise of probabilistic machine learning (PML), which refers to the pairing of probabilistic modeling and machine learning methods, in pushing the frontier of combining flexibility, scalability, interpretability, and uncertainty quantification for building better models of consumers and their choices. Specifically, we overview both PML models and inference methods, and highlight their utility for addressing four common classes of marketing problems: (1) uncovering heterogeneity, (2) flexibly modeling nonlinearities and dynamics, (3) handling high-dimensional and unstructured data, and (4) addressing missingness, often via data fusion. We also discuss promising directions in enriching marketing models, reflecting recent developments in representation learning, causal inference, experimentation and decision-making, and theory-based behavioral modeling

Problem definition. Expanding infrastructure in transport, energy, and digital sectors is important for achieving the economic and sustainability goals of many developing economies. The values of projects involving such infrastructure expansion can be interdependent due to inherent network effects. However, institutional constraints and limited coordination capacity often force governments to award projects through separate auctions. Brazil’s National Logistics Plan (PNL) exemplifies this setting, where interdependent transport investments are procured separately despite strong network effects across projects. We study how a government buyer should design separate per-project procurements when the overall value depends on the resulting infrastructure network, and bundling is not feasible. Methodology/results. We analyze: (i) parallel procurement, in which auction rules are fixed in advance; (ii) sequential procurement, in which their sequence is fixed, but later rules can adapt to earlier executed projects. In both settings, we show that the optimal mechanism retains a simple form akin to canonical results in auction theory: each project is awarded to the lowest virtual-cost bidder if its virtual cost falls below a project-specific threshold that reflects its expected contribution to the equilibrium-induced network. Beyond the optimal mechanism, we propose a simple and interpretable ``credit-aware'' heuristic that adjusts each project's threshold by a simple one-step estimate of its expected network contribution. To demonstrate the importance of network effects in procurement design, we construct a calibrated case study of a major railroad corridor expansion under the PNL. Modeling the Brazilian infrastructure as a hub--spoke network, and the expansion as two key added segments, we estimate their network effect by solving a congestion game aligned with current practice. We show that joint commissioning of these segments adds 16.4% additional economic value, and the common practice of ignoring network effect in procurement design leaves 30–36% of attainable surplus unrealized. Our credit-aware heuristic recovers about 90% of the optimal surplus. Managerial implications. As developing economies invest in infrastructure expansion, it is important to evaluate these projects holistically, as network effects can be strong and first-order. Even when institutional constraints require projects to be procured separately, network effects can still be incorporated effectively through careful adjustments to existing mechanisms, and even simple changes can yield substantial economic and environmental gains.

From disparities in the number of exhibiting artists to auction opportunities, there is evidence of women’s under-representation in visual art. Here we explore the exhibition history and auction sales of 65,768 contemporary artists in 20,389 institutions, revealing gender differences in the artist population, exhibitions and auctions. We distinguish between two criteria for gender equity: gender-neutrality, when artists have gender-independent access to exhibition opportunities, and gender-balanced, that strives for gender parity in representation, finding that 58% of institutions are gender-neutral but only 24% are gender-balanced, and that the fraction of man-overrepresented institutions increases with institutional prestige. We define artist’s co-exhibition gender to capture the gender inequality of the institutions that an artist exhibits. Finally, we use logistic regression to predict an artist’s access to the auction market, finding that co-exhibition gender has a stronger correlation with success than the artist’s gender. These results help unveil and quantify the institutional forces that relate to the persistent gender imbalance in the art world.

Nature provides essential inputs to the economy through ecosystem services. In Asia and the Pacific, accelerating biodiversity loss is eroding these services, increasing vulnerability to shocks, and creating new risks for investors and governments.

The Kunming-Montreal Global Biodiversity Framework (GBF) identifies an annual biodiversity funding gap of $700 billion. Given insufficient public funds, GBF Target 19 seeks to leverage private finance to fill this funding gap. Yet viable and scalable private conservation strategies remain uncommon (1). Here, we provide evidence that changes in local biodiversity can affect municipal borrowing costs using a case study of how a sudden decrease in bat populations, which weakens natural pest-control services provided to farms, affects the finances of US rural county governments. This connection between biodiversity and government borrowing costs creates a promising pathway for private conservation investment. Profit-seeking investors may strategically invest in a county’s municipal bonds when valuations are likely to change owing to shifts in the condition of local biodiversity. This investment strategy may allow investors to monetize conservation efforts by aligning their bond investment strategy with conservation actions.

Trump’s Ten Commandments: Strategic Lessons from the Trump Leadership Toolbox offers a provocative and penetrating look inside the mind of one of history’s most controversial leaders. Written by Jeffrey Sonnenfeld, celebrated Yale leadership scholar and advisor to five U.S. presidents, this book reveals the ten guiding principles—or “commandments”—that define Donald Trump’s decision-making across business, media, and politics.

Drawing from decades of personal interactions with Trump as advisor, critic, and confidant, Sonnenfeld moves beyond gossip and ideology to decode the predictable patterns behind Trump’s seemingly chaotic style. There are critiques and salutes through history of the impact of the imperial presidency but no existing current or past analysis of how such leadership operates until this deep dive into Trump’s toolkit. Sonnenfeld exposes how Trump’s playbook—built on domination, disruption, and relentless self-promotion—breaks every conventional rule of leadership yet often achieves results through sheer force of will.

From the boardroom to the Oval Office, Sonnenfeld distills lessons in power, persuasion, and performance—lessons that illuminate not only Trump’s successes and failures but also timeless truths about human ambition, influence, and control. Whether you admire or abhor him, understanding Trump’s Ten Commandments reveals how he built empires, dismantled institutions, and redefined leadership in his own image.

Part insider analysis, part leadership case study, Trump’s Ten Commandments helps readers grasp how Trump thinks, how he leads, and what his methods teach us—about both the dangers and the undeniable magnetism of power used without restraint.

For anyone seeking to understand America’s most unpredictable leader, this is the definitive guide to Trump’s strategy, psychology, and legacy.

Women in masculine-typed roles often experience their gender identity as a barrier to proving themselves by the ideal-worker norms of their male-dominated occupations. Yet, these women often internalize these experiences, blaming themselves for their struggles. They rarely identify as members of a disadvantaged identity group and often distance themselves from other women at work. How and when might such women externalize their struggles as gendered and collective? Drawing on data from a qualitative field study of staff working in many masculine-typed roles across various male-dominated occupations at a U.S. public-lands management organization, I develop grounded theory suggesting when and how some women might come to reappraise some of their struggles as rooted in the gendered cultures of their occupations rather than in their own deficiencies or idiosyncratic circumstances. I find that “parallel-peer connections” between similarly situated women outside their local tokenized work groups can spark transformative mindset shifts when these encounters occur under the right conditions: during a window of sensemaking about a career impasse and in a less competitive context that is conducive to sharing confidences. Some women credited these shifts with prompting them to shed years of self-doubt and to promote gender equality at work. This study contributes to our understanding of supportive workplace relations among tokenized women and mindset shifts at work.

The design of algorithms for political redistricting generally takes one of two approaches: optimize an objec- tive such as compactness or, drawing on fair division, construct a protocol whose outcomes guarantee partisan fairness. We aim to have the best of both worlds by optimizing an objective subject to a binary fairness constraint. As the fairness constraint we adopt the geometric target, which requires the number of seats won by each party to be at least the average (rounded down) of its outcomes under its worst and best possible partitions of the state.

Local, state, and federal governments offer firms combinations of tax incentives and financial intermediation to help attract or retain jobs and investment for their constituents. Tax breaks are often implemented as place-based policies (PBPs), for which firms must allocate resources to a particular locality to maximize subsidy amounts. Tax instruments underlying PBPs can take many forms, including tax breaks for specific firms in critical sectors, broad-based subsidies for hiring and capital expenditures, industrial policies which operate through intergovernmental development plans, and local revitalization programs targeting neighborhoods which appear to be distressed based on measures such as unemployment and poverty rates. While there is a large body of research examining the equity-efficiency tradeoffs inherent in PBPs based on aggregated real economic outcomes and via quantitative spatial models, less is known about how firms alter their production processes and corporate strategy in responding to policy nudges. Data limitations, especially in contexts with small, privately held firms, prevent comprehensive analyses of these margins of adjustment. On the labor side, firms can use subsidies to engage in labor hoarding; for multi-plant firms, PBPs induce firms to shift the spatial distribution of worker skills within the firm's internal network, with implications for regional inequality. Firms also alter their investment plans over time, across space, and between physical and intangible capital inputs; funds obtained through place-based programs may substitute for external financing sources. Metrics for scoring PBPs aimed at firms range from ex post partial equilibrium cost per job or general equilibrium NPV calculations to ex ante criteria based on compatibility of firms' incentives with policymakers' objectives and the scope for welfare losses from inter-jurisdictional tax competition. Large variation in the same metric across existing studies focusing on the same type of corporate tax instrument underscores challenges in extrapolating the successes and failures of any one PBP into general policy design principles.

We document an understudied form of discrimination based on femininity expressed in headshots. To do so, we use a Generative Adversarial Network to create realistic headshots of people and then manipulate their femininity independently of other attributes like pose, general facial expression, background, hairstyle, and clothes. Then, we devise an experimental design that allows us to identify the separate and combined causal impact of femininity and gender identity (proxied by gender pronouns) on real-life outcomes. In a field experiment within a naturalistic advertising environment, we find that prospective customers of an education company discriminated against femininity at various stages of the purchase funnel, to a largely similar extent for men, women, and non-binary people. The findings inform managers and policymakers of an important form of discrimination that would otherwise be underestimated by disregarding headshots. Methodologically, we introduce a novel framework for testing specific hypotheses pertaining to causal effects of treatments which are derived from unstructured data. The approach involves using natural text to readily identify hypothesis-relevant features from a generative AI model (in a particularly disentangled and interpretable representation space) and then creating realistic stimuli that vary controllably in those features.

Freemium upgrade is a primary monetization lever for mHealth apps, yet whether paying for premium features actually sustains user engagement remains an open question. We study this question using large-scale data from a fitness-tracking mobile app and a staggered difference-in-differences design. Premium adoption generates an immediate increase in food and exercise tracking, caloric budget adherence, and exercise calories, but these effects attenuate within several weeks and do not translate into sustained weight loss. Sensitivity analysis and matched-sample designs confirm that the engagement responses are not solely driven by time-varying selection. Heterogeneous engagement lifts by pre-upgrade exposure are more consistent with hedonic decline than with sunk-cost effects or motivational mean reversion: users with limited prior exposure to the free version exhibit substantially larger post-upgrade engagement lifts than those who have already interacted extensively with it. We further demonstrate that failing to account for endogeneity and selection in premium adoption leads to a substantial overstatement of its effects on both engagement and health outcomes.

This chapter argues that municipal debt monitoring is an important but underappreciated component of retirement security. Municipalities promise pensions and retiree health benefits, debt repayment, public services, and tax stability from the same fiscal capacity, so fiscal deterioration forces tradeoffs among these claimants. Effective monitoring requires acquiring, verifying, using, and acting on fiscal information before imbalances become acute. We argue that monitoring failures in this market are structural rather than incidental, reflecting fragmented principals, weak incentives, opaque disclosure, and limited enforcement. Professional intermediaries lower information costs but hold narrow mandates, leaving no actor with both the incentive and authority to safeguard fiscal sustainability.

This paper studies how local competition affects pricing and product assortment decisions. We leverage a unique circumstance in the context of the legalized cannabis industry in Washington State, where retail licenses were allocated via a lottery, which generates quasi-random variation in the number and proximity of competitors. This natural experiment helps address the endogeneity issues commonly encountered in causal analyses of competitive effects. The analyses yield three key findings: first, additional nearby competitors reduce markups, with nearly all of the effect concentrated in the first two competitors. Second, retailers facing more nearby competition differentiate their assortments. Third, this differentiation helps mitigate the intensity of price competition among retailers. In a partial equilibrium simulation, we evaluate a 10% increase in the number of licensed retailers and estimate that it would reduce markups and generate approximately $7.5 million in annual consumer savings. These findings shed light on the nature of localized competition and the implications of entry restrictions in regulated retail markets.

A seller wants to sell an item to n buyers. Buyer valuations are drawn i.i.d. from a distribution unknown to the seller; the seller only knows that the support is included in [a,b]. To be robust, the seller chooses a DSIC mechanism that optimizes the worst-case performance relative to the ideal expected revenue the seller could have collected with knowledge of buyers' valuations. Our analysis unifies the regret and the ratio objectives. 
For these objectives, we derive an optimal mechanism and the corresponding performance in quasi-closed form, as a function of the support information [a,b] and the number of buyers n. Our analysis reveals three regimes of support information and a new class of robust mechanisms. i.) When a/b is below a threshold, the optimal mechanism is a second-price auction (SPA) with random reserve, a focal class in earlier literature. ii.) When a/b is above another threshold, SPAs are strictly suboptimal, and an optimal mechanism belongs to a class of mechanisms we introduce, which we call pooling auctions (POOL); whenever the highest value is above a threshold, the mechanism still allocates to the highest bidder, but otherwise the mechanism allocates to a uniformly random buyer, i.e., pools low types. iii.) When a/b is between two thresholds, a randomization between SPA and POOL is optimal. 
We also characterize optimal mechanisms within nested central subclasses of mechanisms: standard mechanisms that only allocate to the highest bidder, SPA with random reserve, and SPA with no reserve. We show strict separations in terms of performance across classes, implying that deviating from standard mechanisms is necessary for robustness.

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.

ob seekers often rely on help from social ties in the search for employment. Yet the job search is characterized by meritocratic ideals according to which candidates should be selected based on their qualifications, not their connections. How do people justify the use of connections given the conflicting cultural logics of social capital and meritocracy? We conduct an inductive analysis of 56 interviews with young Spaniards experiencing a difficult labor market and identify a novel process of justification, situational alignment, that reconciles these conflicting logics. Respondents justified situations in which connections provided assistance as legitimate when they perceived alignment among the job seeker, job, and type of help that connections provided. Respondents deemed illegitimate the situations in which these were not aligned. These justifications allowed respondents to embrace the social capital logic’s prescription to use connections, while upholding the meritocratic principle that jobs be awarded based on qualifications. We further find that situations involving close ties were more readily justified than those involving distant others. We test this inductively derived process using a survey experiment with 1,536 young Spaniards. This study demonstrates that perceptions of merit are situated, and advances the understanding of social capital by identifying a novel process of justification that contributes to labor market inequality.

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.