Nonlinear pricing and taxation complicate economic decisions by creating multiple marginal prices for the same good. This paper provides a framework to uncover consumers’ perceived price of nonlinear price schedules. I exploit price variation at spatial discontinuities in electricity service areas, where households in the same city experience substantially different nonlinear pricing. Using household-level panel data from administrative records, I find strong evidence that consumers respond to average price rather than marginal or expected marginal price. This suboptimizing behavior makes nonlinear pricing unsuccessful in achieving its policy goal of energy conservation and critically changes the welfare implications of nonlinear pricing.

This paper analyzes "attribute-based regulations," in which regulatory compliance depends upon some secondary attribute that is not the intended target of the regulation. For example, in many countries, fuel-economy standards mandate that vehicles have a certain fuel economy, but heavier or larger vehicles are allowed to meet a lower standard. Such policies create perverse incentives to distort the attribute upon which compliance depends. We develop a theoretical framework to predict how actors will respond to attribute-based regulations and to characterize the welfare implications of these responses. To test our theoretical predictions, we exploit quasi-experimental variation in Japanese fuel economy regulations, under which fuel-economy targets are downward-sloping step functions of vehicle weight. Our bunching analysis reveals large distortions to vehicle weight induced by the policy. We then leverage panel data on vehicle redesigns to empirically investigate the welfare implications of attribute-basing, including both potential benefits and likely costs.

We develop a framework to characterize strategic behavior in sequential markets under imperfect competition and restricted entry in arbitrage. Our theory predicts that these two elements can generate a systematic price premium. We test the model predictions using microdata from the Iberian electricity market. We show that the observed price differences and firm behavior are consistent with the model. Finally, we quantify the welfare effects of arbitrage using a structural model. In the presence of market power, we show that full arbitrage is not necessarily welfare-enhancing, reducing consumer costs but increasing deadweight loss.

We develop a framework to estimate willingness to pay for clean air from defensive investments on differentiated products. Applying this framework to scanner data on air purifier sales in China, we find that a household is willing to pay $1.34 annually to remove 1 μg/m3 of air pollution (PM10) and $32.7 annually to eliminate the pollution induced by the Huai River heating policy. Substantial heterogeneity is explained by income and exposure to information on air pollution. Using these estimates, we evaluate various environmental policies and quantify the value of recent air quality improvements since China declared a war on pollution in 2014.

We develop an optimal policy assignment rule that integrates two distinctive approaches commonly used in economics—targeting by observables and targeting through self‐selection . Our method can be used with experimental or quasi‐experimental data to identify who should be treated, be untreated, and self‐select to achieve a policymaker's objective. Applying this method to a randomized controlled trial on a residential energy rebate program, we find that targeting that optimally exploits both observable data and self‐selection outperforms conventional targeting. We use the Local Average Treatment Effect (LATE) framework (Imbens and Angrist (1994)) to investigate the mechanism in our approach. By estimating several key LATEs based on the random variation created by our experiment, we demonstrate how our method allows policymakers to identify whose self‐selection would be valuable and harmful to social welfare.

We study the investment effects of market integration on renewable energy expansion. Our theory highlights that market integration not only improves allocative efficiency by gains from trade but also incentivizes new investment in renewable power plants. To test our theoretical predictions, we examine how recent grid expansions in the Chilean electricity market changed electricity production, wholesale prices, generation costs, and renewable investments. We then build a structural model of power plant entry to quantify the impact of market integration with and without the investment effects. We find that the market integration in Chile increased solar generation by around 180%, saved generation costs by 8%, and reduced carbon emissions by 5%. A substantial amount of renewable entry would not have occurred in the absence of market integration. Our findings suggest that ignoring these investment effects would substantially understate the benefits of market integration and its important role in expanding renewable energy.

International transboundary air pollution poses a significant threat to the global economy and health, yet conventional economic analyses seldom incorporate this phenomenon. By integrating transboundary particle trajectory data with individual-level mortality and emergency department visit records, we find that air pollution from China significantly increases mortality and morbidity in South Korea. We evaluate the spillover benefits of recent Chinese environmental regulations and find that a country's environmental policies could generate substantial hidden benefits for neighboring countries. Finally, we demonstrate that China's potentially strategic reductions in pollution could have undermined these benefits, highlighting the implications for additional gains through Coasian bargaining.

We study a problem in which policymakers need to screen self-selected individuals by unobserved heterogeneity in social welfare gains from a policy intervention. In our framework, the marginal treatment effects and marginal treatment responses arise as key statistics to characterize social welfare. We apply this framework to a randomized field experiment on electricity plan choice. Consumers were offered welfare-improving dynamic pricing with randomly assigned take-up incentives. We find that price-elastic consumers—who generate larger welfare gains—are more likely to self-select. Our counterfactual simulations quantify the optimal take-up incentives that exploit observed and unobserved heterogeneity in selection and welfare gains.