Publications

Operations research, originating during World War II, is the scientific study of operations aimed at improving decision-making and organizational performance. Initially focused on military logistics, its scope has expanded to address diverse operational problems in business, government, and non-profit sectors. These include scheduling, capacity planning, routing, and resource allocation. Through mathematical modeling and analysis, operations research seeks not just to describe but to optimize operations by aligning them with organizational goals such as maximizing profit, minimizing costs, or enhancing effectiveness. The field has evolved from simple problem identification to complex mathematical modeling, emphasizing the importance of framing the right problem within a well-understood system context. Applied operations research assumes that the identified problem can be modeled mathematically, that the models and assumptions are valid, and that organizational objectives and constraints are clearly defined and quantifiable. The ultimate aim is actionable recommendations that improve real-world decision-making. Grounded in the belief in mathematical rigor, operations research integrates objectives and constraints to deliver feasible solutions. By leveraging analytical tools, it supports better decision-making, ensuring that operations are not only efficient but also aligned with strategic priorities, making it a practical and impactful discipline across sectors.

The policies pursued by the second Trump administration have complicated the Federal Reserve’s monetary policy decision making in two ways. First, substantial uncertainty regarding a range of administration policies, particularly regarding trade, and their potential effects on the economy has made it more difficult to judge the appropriate path for policy. Second, the administration has subjected the Federal Reserve (Fed) to greater political pressure than has been seen in decades, pressing the Fed to ease monetary policy significantly. Thus far, the Fed has followed a cautious path for policy, and the protections for Fed independence in the Federal Reserve Act have provided a buffer against political pressures. While the Fed should be able to continue to set monetary policy to best foster its mandates from Congress for maximum employment and stable prices, the challenges to the Fed’s independence pose a significant risk.

In contrast to the results and conclusions by Xiang et al. (2025), reanalyzing their data we find
significant and substantive evidence of gambler's fallacy beliefs. Our results differ from theirs
because our analytical approach differs from theirs. While they average probability estimates
across judgments, we count probability estimates exhibiting the gambler's fallacy. For example,
after a streak of length four, 57% of responses deem a streak as overly likely to end, compared to
31% that do the opposite. Moreover, Xiang et al. report that the median participant doesn't exhibit
the gambler's fallacy, while we find that a substantial minority of participants do exhibit it.

Firms' diversity, equity, and inclusion (DEI) policies have received significant scrutiny in recent years, including their efficacy and role in long-term value creation. We provide new evidence on these issues by studying what is arguably the most important group of firms-those with identified problems. We find that in the wake of DEI controversies, firms shift their hiring practices toward recruiting diverse employees to presumably improve public perception of their DEI profiles. However, these effects are economically small and largely superficial. Despite these limited firm responses, we find these controversies have important negative stock price implications, which are largely offset when firms make more meaningful DEI investments. Our findings suggest firms currently often do little to address public concerns regarding their DEI activities, despite their significant value implications.

The world has seen a stunning rise in the willingness of great powers to use their trade and financial relationships for geopo- litical ends. This rise of “Geoeconomics” has the potential to reshape the interna- tional trade and financial system. Geoe- conomic policies include not only sanctions but also the strategic use of export controls, efforts to reshape supply chains for secu- rity purposes, the provision of foreign aid to secure political alignment, and the en- couragement or pressure on domestic and foreign firms to alter their business rela- tionships. While the foundation of a na- tion’s geoeconomic power is its economic strength, size and connections alone do not automatically translate into geoeconomic power. Instead, governments seeking to project geoeconomic power abroad need to be able to credibly co-opt or coerce their do- mestic firms and citizens, and perhaps crit- ical foreign allies, to take part in this power projection. Achieving this involves navigat- ing a range of political economy constraints at home, including legal restrictions, do- mestic political objectives, interest groups and other forces that limit a government’s ability to exert its influence. An important question for geoeconomic power projection is how far a government can push its own firms or allies to act against their private interests in pursuit of the country’s geopo- litical goals.

The transparency and control of earnings are major concerns for gig economy workers across platforms such as ride-hailing and food delivery. While workers advocate for greater transparency, platforms selectively disclose information, shaping workers' decision-making and earnings. Recently, the Federal Trade Commission (FTC) has highlighted lack of transparency as a key issue, and platforms have responded by introducing upfront pay quotes that provide pertrip compensation details for workers. Using a game-theoretic model, we analyze the strategic interactions between the platform and workers, incorporating tools from information design to examine how different transparency policies-specifically, a fixed commission rate versus upfront pay quotes-shape equilibrium outcomes. We find that greater transparency can paradoxically increase platform control, as it allows platforms to fine-tune pay structures in ways that ultimately reduce worker autonomy. Moreover, while full information benefits the platform when it has flexibility in commission setting, it can backfire under commitment constraints, leading to lower profits than a no-information policy. Our findings highlight that transparency is not inherently beneficial for workers. Instead, its effects depend on how it interacts with pay policies. In particular, simple mechanisms, such as a fixed commission rate, can provide workers with more stability and bargaining power than per-trip transparency. These insights offer important guidance for policymakers and platform designers navigating the trade-offs of transparency in the gig economy. Keywords: Platforms, transparency, queueing, gig eco

Vital Farms operated in the premium egg market. It was founded in 2007 by Matt O'Hayer, who predicted a growing market of consumers willing to pay a premium price for ethically produced foods. O'Hayer built a network of small family farms committed to strict environmental standards, ensuring humane treatment of the hens and ethical practices. The company's high quality and clever marketing led to significant growth.

O'Hayer based the organization on "Conscious Capitalism" as espoused by John Mackey, co-founder of Whole Foods Market, and Professor Raj Sisodia. Under this model, the company sought to support the interests of diverse stakeholders — including investors, farmers, employees, consumers, communities, the environment, and the hens and cows that produced Vital Farms' products. To formalize its commitment to stakeholders, Vital Farms received certification as a B Corp and reincorporated as a Delaware Public Benefit Corporation.

Initially funded by O'Hayer's resources, the company attracted private investors and impact funds aligned with its mission as it grew. In 2020, Vital Farms recognized the need for a significant capital infusion to maintain its growth trajectory. As it looked to raise capital, O'Hayer and Vital Farms CEO Russell Diez-Conseco faced the challenge of balancing its stakeholder interests against the pressures from capital markets, where investor priorities often dominated, while maintaining its commitment to conscious capitalism values. Potential options included seeking additional impact investors or venture capital, taking on debt, or considering public market offerings. Given the benefits and challenges inherent in each option, what path was best for the company, its investors, its stakeholders, and its mission?

The founders of Warby Parker had a clear vision of the kind of company they wanted to build: a novel business model that would disrupt a long-entrenched industry. The company’s values would create a culture that the founders themselves—and, they hoped, many others—would find meaningful and even fun. Their social impact mission wouldn’t be a philanthropic afterthought but an integral part of the business’s core. Purpose and profit would be pursued simultaneously, along with a commitment to building mutually beneficial relationships with customers, partners, communities, and other key stakeholders. They debated these and other critical details two years before the company was operational or even had a name.

The case examines the founders’ original business design for Warby Parker—a holistic approach that aligned and integrated purpose, a values-based culture, and business strategy with a commitment to building trusted relationships with stakeholders. The case also explores the actions taken by leadership to preserve this design as the company scaled, adapted and, ultimately, became a public corporation.

Consideration set models generalize discrete-choice models by relaxing the assumption that consumers consider all available options. Determining which options were considered has previously required either survey data or restrictions on how attributes affect consideration or utility. We provide an alternative route. In full-consideration models, choice probabilities satisfy a symmetry property analogous to Slutsky symmetry in continuous-choice models. This symmetry breaks down in consideration set models when changes in characteristics perturb consideration. We show that consideration probabilities are constructively identified from the resulting asymmetries. We validate our approach in a lab experiment where consideration sets are known and then apply our framework to study a “smart default” policy in Medicare Part D, wherein consumers are automatically reassigned to lower-cost prescription drug plans with the option of opting out. Full-consideration models imply that such a policy will be ineffective because consumers will opt out to avoid switching costs. Allowing for inattention, we find that defaulting all consumers to lower-cost options produces negligible welfare benefits on average, but defaulting only consumers who would save at least $300 produces large benefits.

This paper identifies which investments in school facilities help students and are valued by homeowners. Using novel data on school district bonds, test scores, and house prices for 29 U.S. states and a research design that exploits close elections with staggered timing, we show that increased school capital spending raises test scores and house prices on average. However, impacts differ vastly across types of funded projects. Spending on basic infrastructure (such as HVAC) or on the removal of pollutants raises test scores but not house prices; conversely, spending on athletic facilities raises house prices but not test scores. Socio-economically disadvantaged districts benefit more from capital outlays, even conditioning on project type and the existing capital stock. Our estimates suggest that closing the spending gap between high- and low-SES districts and targeting spending towards high-impact projects may close as much as 25% of the observed achievement gap between these districts.

In collaboration with Feeding America, we aim to redesign Real-Time—a tool on its food sourcing and rescue platform, MealConnect—that facilitates the connection of ad-hoc, time-sensitive food donations to local agencies (e.g., meal programs) through an offer process. In making offer decisions, Real-Time and similar food rescue platforms face a challenge in balancing efficiency and equity due to heterogeneity in response rates across agencies: offering to many agencies upfront improves efficiency (the likelihood of acceptance) but may disadvantage those with lower response rates. On top of this, the ad hoc nature of donations introduces future uncertainty, adding to the challenge of achieving the dual goals of efficiency and equity. Motivated by these challenges, we study a sequential offer scheduling problem in which donations arrive sequentially and are connected through a multi-stage offer process. The goal is to maximize an objective that balances efficiency and equity, promoting allocations proportional to agencies needs. We first develop a dynamic programming (DP)-based algorithm that optimally solves the one-donation problem and yields an intuitive nested offer schedule. Moving beyond one donation and motivated by the ad hoc nature of donations, we take a robust approach to designing sequential offer policies that do not rely on any knowledge about future donations. We design a penalty-based offer policy that solves a modified one-donation DP by properly penalizing current allocation to hedge against future uncertainty. We establish strong (and optimal in asymptotic regimes) performance guarantees for our policy. We further demonstrate the importance of hedging: a greedy policy that solves each one-donation DP without accounting for future arrivals fails to achieve a comparable guarantee. Numerical results on real data from Feeding America’s MealConnect platform demonstrate that our proposed approach significantly improves both efficiency and equity relative to current practice and several benchmarks.

This study examines how and why the effectiveness of business and social restrictions evolved during the first year of the COVID-19 pandemic, using weekly county-level data from the United States in 2020. Panel data regressions with county-level fixed effects reveal that business restrictions became less effective the longer they remained in place. Residents progressively spent less time at home under sustained restrictions, with this decline accelerating when neighboring counties maintained more lenient policies. The analysis further shows that any initial correlation between policy stringency and COVID-19 fatalities, whether positive or negative, dissipated over time. Estimates indicate this erosion occurred within 20 weeks for many interventions. To explain this declining effectiveness, several hypotheses are tested. The data most strongly support "lockdown fatigue," whereby individuals became progressively less willing to modify their behavior in response to government restrictions over time. This explanation better accounts for the observed patterns than alternative mechanisms, such as improvements in mitigation technology or adaptive behaviors. Point estimates suggest that business restrictions became disassociated from deaths after four to five months, yet many jurisdictions maintained them considerably longer. More broadly, the findings indicate that certain policies were either ineffective from day 1 or counterproductive, calling into question their use during the pandemic response.

AI is increasingly used to guide high-stakes educational decisions, yet its effectiveness depends on whether people follow its advice. We present the first large-scale field experiment with adolescents in this context, conducted across 14 public high schools. We compare the adoption of identical college-application recommendations from human counselors versus an AI-based algorithm. Contrary to the common assumption that objective, data-based recommendations favor AI, we find that algorithm aversion intensifies when recommendations are based on objective criteria (e.g., grades, admission chances) and dissipates when criteria are more subjective. We find that student perceptions of the recommender's intent strongly drive this aversion, consistently across scenarios and statistical approaches; perceptions of alignment with personal goals, ability, and comprehension also play significant roles. The results further reveal substantial heterogeneity in recommendation adoption rates. We observe stronger aversion among female students, students from rural schools, lower-GPA students, and those with stronger prestige-seeking attitudes. Using an optimization approach, we demonstrate how a policymaker can navigate the heterogeneity in recommendation adoption rates to optimally prioritize the assignment of human versus AI-based algorithmic recommenders, under varying social priorities and limited capacity of human counselors. We find that a targeting policy relying on few readily available student and school features can approximate the first-best, personalized targeting policy effectively. These insights underscore the importance of understanding student preferences and trust in an AI system's intent, and of adopting hybrid approaches that blend human guidance with AI tools to design effective recommendation systems.

Many industries, including the NFL with the Rooney Rule and law firms with the Mansfield Rule, have adopted interview-stage diversity interventions requiring a minimum representation of disadvantaged groups in the interview set. However, the effectiveness of such policies remains inconclusive. In light of this, we develop a framework of a two-stage hiring process, where rational firms, with limited interview and hiring capacities, aim to maximize the match value of their hires. The labor market consists of two equally sized social groups, m and w, with identical ex-post match value distributions. Match values are revealed only post-interview, while interview decisions rely on partially informative pre-interview scores. Pre-interview scores are more informative for group m, while interviews reveal more for group w; as a result, if firms could interview all candidates, both groups would be equally hired. However, due to limited interview capacity and information asymmetry, we show that requiring equal representation in the interview stage does not translate into equal representation in the hiring outcome, even though interviews are more informative for group w. In certain regimes, with or without intervention, a firm may interview more group w candidates but still hire fewer. At an individual level, we show that strong candidates from both groups benefit from the intervention as the candidate-level competition weakens. For borderline candidates, only group w candidates gain at the expense of group m. To understand the impact of non-universal interview-stage interventions on the market, we study a model with two vertically differentiated firms, where only the top firm adopts the intervention. We characterize the unique equilibrium and demonstrate potentially negative effects: we show that in certain regimes the lower firm hires fewer group w candidates due to increased firm-level competition for them, and further find examples where overall fewer group w candidates are hired across the market. At an individual level, while superstar candidates in both groups benefit, surprisingly the impact on borderline candidates may reverse: the lower firm may replace borderline group w candidates with borderline group m candidates in its interview set, effectively reducing the chance of those borderline group w candidates being hired. Overall, our findings highlight challenges in diversifying the labor market at early hiring stages due to information asymmetry, filtering, and competition. Beyond our context, our natural framework of a market with two-stage hiring may be of independent interest.

Governments use their countries’ economic strength from financial and trade relationships to achieve geopolitical and economic goals. We provide a model of the sources of geoeconomic power and how it is wielded. The source of this power is the ability of a hegemonic country to coordinate threats across disparate eco- nomic relationships as a mean of enforcement on foreign entities. The hegemon wields this power to demand costly actions out of the targeted entities, including mark-ups, import restrictions, tariffs, and political concessions. The hegemon uses its power to change targeted entities’ activities to manipulate the global equilib- rium in its favor and increase its power. A sector is strategic either in helping the hegemon form threats or in manipulating the world equilibrium via input-output amplification. The hegemon acts a global enforcer, thus adding value to the world economy, but destroys value by distorting the equilibrium in its favor.

We develop a tractable dynamic contracting framework to study bank bail-in regimes. In the presence of a repeated monitoring problem, the optimal bank capital structure combines standard debt, which induces liquidation and provides strong incentives, and bail-in debt, which restores solvency but provides weaker incentives. When there are fire sales, optimal policy entails joint regulation: a bail-in regime reduces standard debt while leverage regulation reduces total debt. Bail-ins replace bailouts as a recapitalization tool.

We develop an economic model of autonomous vehicle (AV) ride-hailing markets, in which uncertain aggregate demand is served with a combination of a fixed fleet of AVs and a flexible pool of human drivers (HVs). Dispatch efficiencies increase with scale because of density effects. We analyze market outcomes in this setting under four market configurations, defined by two dispatch platform structures (common platform versus independent platforms) and two levels of supply competition (monopoly AV versus competitive AV). A key result of our analysis is that the lower cost of AVs does not necessarily translate into lower prices; the price impact of AVs is ambiguous and depends critically on both the dispatch platform structure and the level of AV supply competition. In the extreme case, we show that if AVs and HVs operate on independent dispatch platforms, there is a monopoly AV supplier, and labor supply elasticity is sufficiently high, then prices are even higher than in a pure-HV market. Indeed, to guarantee consistently lower prices (relative to a pure HV market) in all scenarios and under all supply and density elasticities, a common dispatch platform between AVs and HVs is required. Furthermore, competitive AVs lead to lower prices than monopoly AVs in every such scenario. Our results illustrate the critical role that market configuration plays in realizing potential welfare gains from AVs.

This paper examines the welfare implications of third-party informational interme- diation. A seller sets the price of a product that is sold through an intermediary, who discloses information about the product to consumers. In a model where the inter- mediary is consumer-minded—has a payoff that depends on both the seller’s revenue and the consumer surplus, we show that total welfare may decrease in the Pareto sense, as the intermediary’s consumer-mindedness increases. Furthermore, we show that consumer-mindedness emerges endogenously when a revenue-maximizing interme- diary is forward-looking and the consumer base is increasing in past consumer surplus.