Publications

Objectives Previous studies have reported inconsistent findings regarding the relationship between COVID-19 restrictions and suicide rates, particularly concerning business and social restriction policies. This study aimed to address this gap by analyzing detailed US county-level restriction and suicide death data. Study Design Data from the US Centers for Disease Control and Prevention (CDC) were obtained for county-level suicide rates by race, sex, and age from 2016 to 2023. Yale School of Management-Tobin Center State and Local COVID Restriction Database provided data on COVID-19 social and business restrictions. These datasets were combined with other relevant data on county-level demographics, gross domestic product (GDP), unemployment, and population density. Methods Poisson interrupted time-series regression was employed to assess whether these restrictions were associated with changes in suicide rates during pandemic (2020 and 2021) and post-pandemic (2022 and 2023) periods. Results During the pandemic restriction era of 2020–2021, stricter business capacity limits were linked to lower suicide rates overall (Poisson coefficient: -0.90 [95% CI -1.54, -0.25, p = 0.006]), and in particular among males (Poisson coefficient: -1.13 [-1.94, -0.32, p = 0.006]). The estimated coefficient was not statistically significant for females. Among age groups, individuals aged 25–34 and 35–44 experienced lower suicide rates in counties with tighter restrictions, while other age and sex groups did not show similar trends. Additionally, no statistically significant correlations were found across racial groups. In contrast, social gathering restrictions had a less consistent relationship with suicide rates; while those aged 15–24 experienced an increase in rates under tighter restrictions, those aged 25–34 had a decrease. No other demographic groups yielded statistically significant coefficient estimates. Conclusions Results underscore the importance of considering differential effects of business and social restrictions on suicide rates, and to tailor interventions to address the unique needs of specific populations during public health crises.

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.

In late 2018, GigaCloud CEO Larry Wu (Yale MBA, 2002) sat reviewing his company’s operating metrics with senior managers at the company’s headquarters in Los Angeles. Wu and his team had begun selling furniture from Asia to the US in 2014. As a pioneer in cross-border e-commerce, Wu’s company, GigaCloud, procured directly from Asian manufacturers, taking advantage of the rich SKU assortment and competitive price to attract a wide array of U.S. customers.

All went well until mid-2018 when demand suddenly dipped amid higher interest rates and rising tariffs. Not only did demand decline, but the Asian manufacturers also started to sell directly to other U.S. retailers to stay profitable during the challenging time. Operating income slipped; the company went from earning hundreds of thousands of dollars per month to going into the red by August.

Reviewing the sales data and the company’s cash position, the senior management team agreed to sell inventory at a loss to pull GigaCloud through the crisis. Then, looking to the future, Wu proposed a rather radical pivot: build a B2B service business in addition to the company’s existing B2C business by attracting Asian manufacturers to offer their wares through the GigaCloud website to other U.S. retailers. Many of GigaCloud’s senior management team were opposed to the proposal, believing the service would cause GigaCloud’s retail business to collapse. These managers thought GigaCloud could weather the storm and return to the business as usual – nothing fundamental needed to change.

However, Wu believed the business would inevitably run into trouble down the road, and it was the right time to make some fundamental changes. Wu and his team debated the sharp change in the business model late into the night. Even if the company decided to shift its business model, how would GigaCloud implement the change?
 

Public discourse often framed artificial intelligence (AI) as a threat to jobs and livelihoods. Yet in the social-services sector, AI held the potential to raise pay and ease workloads for frontline care teams. This insight was not lost on the team at Lawrence Hall.

Founded in 1865, Lawrence Hall had grown into a leading child and family services agency in Chicago, providing foster and residential care for youth. Originally an orphanage, it evolved through eras of social and technological change into a trauma-informed non-profit with programs for young people across the city. 

Central to its model: residential facilities where young people (ages 8–21) lived and received round-the-clock support. Best practice in child welfare was to keep youth safely in family or family-like settings, reserving residential care as a last-resort intervention. Yet in the U.S., more than 30% of teenagers in foster care were housed in group or institutional settings. Once in residential care, the average youth had little chance at adoption. Of those older than 13, nearly half remained in state custody until “aging out” of the system at age 21. 

For youth in residential care, enduring, supportive adult relationships are among the strongest predictors of positive outcomes. Yet the child welfare system was plagued by employee turnover. Residential staff managed heavy caseloads and administrative demands, while earning modest wages in resource-constrained facilities. The ensuing cycle of departures came at a substantial cost to children and the agencies that served them.

Generative AI stood to shift this dynamic by reducing administrative burden for both the care team and back-office departments. The result would be a double dividend: care teams could spend more quality time with the young people they serve, while money saved from back-office automation boosted their compensation.

The executive team—Kara Teeple (Chief Executive Officer), Sean McGinnis (Chief Program Officer), and Devan Hughes (Chief Financial Officer)—met to strategize. Some sectors could afford speculative AI experimentation, but foster agencies could not. Per-resident reimbursement rates were capped by state policy, and any additional costs had to be covered by grants or individual donors. The agency would need to select a narrow set of high-yield use cases.

The executive team identified residential facilities as an optimal starting point. This setting was home to the full spectrum of agency personnel, from case managers and therapists to child-care aides and administrative staff. Insights from this environment could later be applied across Lawrence Hall’s broader continuum of care. The executive team resolved to pilot AI tools at two residential programs: a campus for youth aged 8–17 and a transitional living program for adolescents aged 17–21.

But which personnel or departments were best suited for the pilot? Should the rollout differ for each program? To answer this, the team needed to map costs to each program, identify which AI deployments were likely to deliver the greatest efficiency, and model resource reallocation across the care team.

This paper studies how mandatory greenhouse gas disclosure affects new business formation. We find a significant increase in business entry following the implementation of the Greenhouse Gas Reporting Program in affected industries, relative to unaffected controls. We propose two channels. First, through a production channel, disclosure pressures incumbent firms to reduce emissions by scaling back production or reallocating resources toward cleaner technologies, weakening incumbents’ competitive positions and creating space for new entrants. Second, through an information channel, public disclosure of previously proprietary emissions data helps potential entrepreneurs identify viable entry opportunities. We present evidence consistent with both channels. Incumbent firms reduce economic activity and experience declines in profitability, and entry is concentrated in industries facing greater emissions reductions and public scrutiny. Additionally, regulatory and industry commentary highlights concerns over the proprietary nature of disclosed emissions data. Overall, our findings reveal an unintended yet economically meaningful consequence of environmental disclosure mandates.

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.

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.

In 2015, Yara International faced simultaneous financial, legal, and reputational pressures as industry conditions deteriorated. Newly appointed CEO Svein Tore Holsether concluded that restoring performance required more than cost-cutting and reputation repair, and instead moved to future-proof the firm by embedding sustainability into Yara’s purpose, strategy, and operating model. This case examines the rationale, execution, and 2025 outcomes of Yara’s purpose-led transformation amid evolving market and societal constraints.

The Securities and Exchange Commission (SEC) reviews firms’ financial reports and issues comment letters to ensure compliance with applicable disclosure and accounting requirements. We explore the nature, determinants, and consequences of SEC comment letters that refer to information disclosed in voluntary earnings conference calls. Using hand-collected data, we document that the SEC primarily references these voluntary disclosures to illustrate insufficiencies and, less commonly, inconsistencies in mandatory filings across a wide range of topics. These letters are more likely to be issued when filing reviews are more complex, SEC staff are less resource constrained, and for firms with more institutional investors and analysts. Conference call-related comments tend to occur during higher-quality review processes and require greater remediation costs than other comments. The SEC’s use of call disclosures also leads to more pronounced changes in firms’ subsequent mandatory filings, particularly when the firm indicates agreement with SEC comments. However, we observe a mixed effect on the overall information environment, consistent with possible unintended consequences for the quality of firms’ voluntary disclosures.

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.

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.