Knowledge that Transforms

Mechanisms used to derive optimal allocations are typically designed assuming agents fully know their own preferences. It is often impossible to duplicate optimal allocations when agents imperfectly observe object characteristics. I present a crowdsourcing mechanism to approximate optimal allocations under imperfect observations. To ensure truthtelling, agents are punished when their reports differ from the “wisdom of the crowd.” Under mild conditions, this crowdsourcing-with-punishment mechanism replicates the full-information optimal allocation with probability exponentially converging to one in the market size, with small waste. No alternative mechanism meaningfully does better. The proposed mechanism can be applied in many settings, including matching markets.

We examine the effect of job displacement on mortality, hospitalization, biomarkers, and health behaviors in South Korea. We find that the impact on health differs between severe and less severe outcomes and also by gender. Men experience little impact on mortality and hospitalization except for an increase in suicide deaths, whereas their biomarkers and health behaviors improve. Women experience an increase in mortality and hospitalization due to cancer, but no significant effects on biomarkers or health behaviors. The study emphasizes the need to consider a comprehensive range of outcomes to accurately evaluate the effect of job loss on health.

Many public programs let individuals choose between publicly provided benefits and a subsidized private alternative. We investigate health insurance choice in Medicare—a setting with vast geographic variation in the uptake of the private alternative. We analyze insurance decisions among individuals who move to quantify the relative importance of individual-specific factors (e.g., preferences or income) and place-specific factors (e.g., local health insurance options). We find roughly 40% of the geographic variation in the share selecting private coverage is due to place-based factors, while the remainder due to individuals. Our findings inform the potential for place-based policy to address geographic disparities.

We develop models that take point forecasts from the Survey of Professional Forecasters (SPF) as inputs and produce estimates of survey-consistent term structures of expectations and uncertainty at arbitrary forecast horizons. Our models combine fixed-horizon and fixed-event forecasts, accommodating time-varying horizons and availability of survey data, as well as potential inefficiencies in survey forecasts. The estimated term structures of SPF-consistent expectations are comparable in quality to the published, widely used short-horizon forecasts. Our estimates of time-varying forecast uncertainty reflect historical variations in realized errors of SPF point forecasts, and generate fan charts with reliable coverage rates.

International trade has rapidly increased in the past decades, affecting production and labor demand across various economic sectors. The impact of trade on employment and welfare relies heavily on data about worker reallocation, which often contains coding errors. This study demonstrates that such errors bias the estimated effects of trade and structural parameters in standard models. An econometric framework is developed to estimate misclassification probabilities, correct mobility matrices, and structural parameters. The findings reveal that the true effects of trade shocks differ significantly from those estimated using uncorrected data, highlighting the importance of addressing coding errors in economic analyses.

Many economic panel and dynamic models, such as rational behavior and Euler equations, imply that the parameters of interest are identified by conditional moment restrictions. We introduce a novel inference method without any prior information about which conditioning instruments are weak or irrelevant. Building on Bierens (1990), we propose penalized maximum statistics and combine bootstrap inference with model selection. Our method optimizes asymptotic power by solving a data-dependent max-min problem for tuning parameter selection. Extensive Monte Carlo experiments, based on an empirical example, demonstrate the extent to which our inference procedure is superior to those available in the literature. [C12, C36].

Countries with greater commodity export intensity have more concentrated markets for imported goods. Import market concentration is associated with higher domestic prices, suggesting that markups due to greater concentration outweigh any potential cost efficiency. Tariffs, non-tariff measures, and tariff evasion are mechanisms that concentrate import markets. These results suggest a novel channel for the resource curse stemming from the monopolization of imports.

We propose a new inference method in high-dimensional regression models and high-dimensional IV regression models. The method is shown to be valid without requiring the exact sparsity or Lp sparsity conditions. Simulation studies demonstrate superior performance of this proposed method over those based on LASSO or random forest, especially under less sparse models. We illustrate an application to production analysis with a panel of Chilean firms.

This paper examines how to bundle household health insurance, treating each member’s policy as a product in the bundle. Two regimes are analyzed: pure bundling and bundle discounts. Pure bundling is socially optimal when willingness to pay differs across members, when the bundle is valued more than individual components, or when households prefer insuring costlier members, implying within-household adverse selection. Using Vietnam’s Social Health Insurance data, structural estimates show substantial heterogeneity in willingness to pay, driven mainly by health-type differences. A pure bundling policy increases consumer surplus by 43% relative to bundle discounts.

We present a statistical test for the hypothesis of (approximate) utility maximization on the basis of nonparametric revealed preference conditions. We take as null hypothesis that the consumer behaves randomly, and we reject this hypothesis only if the data provides sufficient evidence to support the alternative hypothesis of approximate utility maximization. Our statistical test uses a permutation method to operationalize the principle of random consumption behavior. We show that our test (i) is valid for any sample size under the null and (ii) has an asymptotic power of one. We also provide simulated power results and two empirical applications.