Publications

Doctors often treat similar patients differently, which affects health outcomes and medical spending. We assess the recent literature on doctor decision-making through the lens of a model that incorporates diagnostic and procedural skills, beliefs, incentives, and differences in patient pools. Decision-making is affected by beliefs, training, experience, peer effects, financial incentives, and time constraints. Interventions to improve decision-making include providing information, guidelines, and technologies like electronic medical records and algorithmic decision tools. Economists have made progress in understanding doctor decision-making, but applications of that knowledge to improving health care are still limited.

We consider an economy in which long-lived experts are matched with short-lived clients. Experts choose the type of client with whom they match, unobserved by the market. The interaction outcome depends on both the expert's and the client's type. We study the effects of supplying information about otherwise unobservable outcomes, such as "medical report cards," to help clients identify better experts. Such information can lead to inefficient matches, as experts reject risky clients to build their reputation. Hence, information can reduce welfare. Withholding information can mitigate these perverse incentives at the cost of misallocating experts known to be inept.

We develop a model of multi-dimensional misspecified learning in which an overconfident agent learns about groups in society from observations of his and others’ successes. We show that the average person sees his group relative to other groups too positively, and this in-group bias exhibits systematic comparative-statics patterns. First, a person is most likely to have negative opinions about other groups he competes with. Second, while information about another group’s achievements does not lower a person’s prejudice, information about economic or social forces affecting the group can, and personal contact with group members has a beneficial effect that is larger than in classical settings. Third, the agent’s beliefs are subject to “bias substitution”, whereby forces that decrease his bias regarding one group tend to increase his biases regarding unrelated other groups.

Modern macroeconomics ignores the recent proliferation of new monies. We show in our model that new monies like credit cards or stable coins or crypto currencies or helicopter money can cause a huge increase in prices, like the 1970s inflation when credit cards emerged in full use. These monies are not perfect substitutes, so shrinking conventional money supply to compensate for the growth of new monies comes at a welfare cost. Price levels are determined by money chasing goods, measured by the separate quantities of each kind of money and the scale of individual transactions. In Part I we introduce a one period version of our model in which we concentrate on the transactions role of monies. We show how fiat wealth (net of taxes) can be positive if there are enough gains to trade. Monies that raise fiat wealth (such as helicopter money) cause more inflation—eventually even hyperinflation—by increasing the interest rate, which reduces transactions. In contrast, credit cards (and central bank purchases of bonds) also cause inflation, but they enhance transactions and welfare. In Part II we present a multiperiod version in which the store-of-value role of money, and expectations about future policy, also affect inflation.

Global warming poses substantial risks to natural and human systems worldwide. Understanding the complex interactions between climate change and the economy is essential for designing effective policies and mitigation strategies. Yet, existing modeling tools are often limited by coarse spatial aggregation, simplified climate representation, or lack of interaction between climate and the economy. To address these gaps, we develop a novel framework that couples an Earth System Model (ESM) – the Norwegian Earth System Model version 2 (NorESM2) – with a spatially disaggregated Integrated Assessment Model (IAM), the Disaggregated Integrated Assessment Model (DIAM). The resulting modeling tool, NorESM2–DIAM, incorporates state-of-the-art climate and weather dynamics, allows economic impacts to depend on the full distribution of weather outcomes, and captures realistic spatial heterogeneity. To our knowledge, it is the first framework to fully couple an ESM with a high-resolution cost-benefit IAM. The primary contribution of this paper is to develop and implement the methodology that enables this coupling. We demonstrate the utility of NorESM2–DIAM through a baseline simulation. The results show that the economic impacts of global warming vary dramatically across space and that internal climate variability generates substantial volatility in regional GDP, highlighting the importance of high-resolution economic impact assessments. Although the baseline simulation focuses on regional temperature, the framework can be easily extended to incorporate additional variables such as precipitation and extreme events. It can also be applied to study a wide range of climate policies. NorESM2–DIAM represents an important step towards improving the understanding of economic impacts of climate change and can ultimately become an important source of information for decision-makers.

We analyze a nonlinear pricing model where the seller controls both product pricing (screening) and buyer information about their own values (persuasion). We prove that the optimal mechanism always consists of finitely many signals and items, even with a continuum of buyer values. The seller optimally pools buyer values and reduces product variety to minimize informational rents. We show that value pooling is optimal even for finite value distributions if their entropy exceeds a critical threshold. We also provide sufficient conditions under which the optimal menu restricts offering to a single item.

During adolescence, peer interactions become increasingly central to children’s development, whereas the direct influence of parents wanes. Nevertheless, parents can continue to exert leverage by shaping their children’s peer groups. We construct and estimate a model of parenting with peer and neighborhood effects where parents intervene in peer formation and show that the model captures empirical patterns of skill accumulation, parenting style, and peer characteristics among US high school students. We find that interventions that move children to better neighborhoods lose impact when they are scaled up, because parents’ equilibrium responses push against successful integration with the new peer group.

Many mental health disorders start in adolescence, and appropriate initial treatment may improve trajectories. But what is appropriate treatment? We use a large national database of insurance claims to examine the impact of initial mental health treatment on the outcomes of adolescent children over the next 2 years, where treatment is either consistent with US Food and Drug Administration guidelines, consistent with looser guidelines published by professional societies (gray area prescribing), or inconsistent with any guidelines (red-flag prescribing). We find that red-flag prescribing increases self-harm, use of emergency rooms, and health care costs, suggesting that treatment guidelines effectively scale up good treatment in practice.

We study identification and inference in first-price auctions with risk-averse bidders and selective entry, building on a flexible framework we call the Affiliated Signal with Risk Aversion (AS-RA) model. Assuming exogenous variation in either the number of potential bidders (N) or a continuous instrument (z) shifting opportunity costs of entry, we provide a sharp characterization of the nonparametric restrictions implied by equilibrium bidding. This characterization implies that risk neutrality is nonparametrically testable. In addition, with sufficient variation in both N and z, the AS-RA model primitives are nonparametrically identified (up to a bounded constant) on their equilibrium domains. Finally, we explore new methods for inference in set-identified auction models based on Chen et al. (2018, Econometrica, vol. 86, 1965–2018), as well as novel and fast computational strategies using Mathematical Programming with Equilibrium Constraints. Simulation studies reveal the good finite-sample performance of our inference methods, which can readily be adapted to other set-identified flexible equilibrium models with parameter-dependent support.

This paper proposes a framework for the global optimization of possibly multimodal continuous functions on bounded domains. The authors show that global optimization is equivalent to optimal strategy formation in a two-armed decision model with known distributions, based on a strategic law of large numbers. They establish asymptotically optimal strategies and introduce a class of Strategic Monte Carlo Optimization (SMCO) algorithms that rely on sign-based decisions rather than gradient magnitudes. Theoretical results provide local and global convergence guarantees, and extensive numerical experiments demonstrate strong performance of the proposed algorithms in high-dimensional and challenging optimization settings.

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 economic relationships as a means 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 equilibrium 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.

Private information on car quality means the sale price reflects the average quality of cars sold, which can be lower than the average quality in the population. This difference is the lemons penalty imposed on holders of high-quality cars. The authors estimate the evolution of the lemons penalty through an equilibrium model of car ownership with private information using Danish linked registry data on car ownership, income, and wealth. They examine the aggregate implications and distributional consequences of these penalties, finding that the penalty is largest early in ownership, declines with ownership duration, reduces transaction volumes and car turnover, and weakens the self-insurance role of cars, though the market does not collapse because income shocks induce sales.

This paper estimates the consumer surplus from using alternative payment methods. We use evidence from Uber rides in Mexico, where riders have the option to use cash or cards to pay for rides. We design and conduct three large-scale field experiments, which involved approximately 400,000 riders. We also build a structural model which, disciplined by our new experimental data, allows us to estimate the loss of private benefits for riders when a ban on cash payments is implemented. We find that Uber riders who use cash as means of payment either sometimes or exclusively suffer an average loss of approximately 40–50% of their total trip expenditures paid in cash before the ban. The magnitude of these estimates reflects the intensity with which cash is used in the application, the shape of the demand curve for Uber rides, and the imperfect substitutability across means of payments. Welfare losses fall mostly on the least-advantaged households, who rely more heavily on the cash payment option.

We exploit a unique event to study the extent to which popular attitudes towards trade are driven by economic fundamentals. In 2007, Costa Rica put a free trade agreement (FTA) to a national referendum. With a single question on the ballot, 59% of Costa Rican adult citizens cast a vote on whether they wanted an FTA with the U.S. to be ratified or not. We merge disaggregated referendum results, which break new ground on anonymity-compatible voting data, with employer–employee, customs, and firm-to-firm transactions data, and data on household composition and expenditures. We document that a firm’s exposure to the FTA, directly and via input–output linkages, significantly influences the voting behaviour of its employees. This effect dominates that of sector-level exposure and is greater for voters aligned with pro-FTA political candidates. We also show that citizens considered the expected decrease in consumer prices when exercising their vote. Overall, economic factors explain 7% of the variation in voting patterns, which cannot be accounted for by non-economic factors such as political ideology, and played a pivotal role in this vote.

Noncarceral conviction is a common outcome of criminal court cases: for every person incarcerated, there are approximately three who were recently convicted but not sentenced to prison or jail. We extend the binary-treatment judge IV framework to settings with multiple treatments and use it to study the consequences of noncarceral conviction. We outline assumptions under which widely used 2SLS regressions recover margin-specific treatment effects, relate these assumptions to models of judge decision-making, and derive an expression that provides intuition about the direction and magnitude of asymptotic bias when a key assumption on judge decision-making is not met. We find that noncarceral conviction (relative to dismissal) leads to a large and long-lasting increase in recidivism for felony defendants in Virginia. In contrast, incarceration (relative to noncarceral conviction) leads to a short-run reduction in recidivism, consistent with incapacitation. Our empirical results suggest that noncarceral felony conviction is an important and overlooked driver of recidivism.