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We use causal forests to evaluate the heterogeneous treatment effects (TEs) of repeated behavioral nudges toward household energy conservation. The average response to treatment is a monthly electricity reduction of 9 kilowatt-hours (kWh), but the full distribution of responses ranges from -40 to +10 kWh. Households learn to reduce more over time, conditional on having responded in year one. Pre-treatment consumption and home value are the most commonly used predictors in the forest. The results suggest the ability to use machine learning techniques for improved targeting and tailoring of treatment.

© 2018 by The Association of Environmental and Resource Economists. Despite widespread agreement that a carbon tax would be more efficient, many countries use fuel economy standards to reduce transportation-related carbon dioxide emissions. We pair a simple model of the automakers’ profit maximization problem with unusually rich nationally representative data on vehicle registrations to estimate the distributional impact of US fuel economy standards. The key insight from the model is that fuel economy standards impose a constraint on automakers that creates an implicit subsidy for fuel-efficient vehicles and an implicit tax for fuel-inefficient vehicles. Moreover, when these obligations are tradable, permit prices make it possible to quantify the exact magnitude of these implicit subsidies and taxes. We use the model to determine which vehicles are most subsidized and taxed, and we compare the pattern of ownership of these vehicles between high-and low-income census tracts. Finally, we compare these distributional impacts with existing estimates in the literature for a carbon tax.

© 2018 by The Association of Environmental and Resource Economists. Despite widespread agreement that a carbon tax would be more efficient, many countries use fuel economy standards to reduce transportation-related carbon dioxide emissions. We pair a simple model of the automakers’ profit maximization problem with unusually rich nationally representative data on vehicle registrations to estimate the distributional impact of US fuel economy standards. The key insight from the model is that fuel economy standards impose a constraint on automakers that creates an implicit subsidy for fuel-efficient vehicles and an implicit tax for fuel-inefficient vehicles. Moreover, when these obligations are tradable, permit prices make it possible to quantify the exact magnitude of these implicit subsidies and taxes. We use the model to determine which vehicles are most subsidized and taxed, and we compare the pattern of ownership of these vehicles between high-and low-income census tracts. Finally, we compare these distributional impacts with existing estimates in the literature for a carbon tax.

© 2019 American Economic Association. Exploiting geological variation and timing in the initiation of hydraulic fracturing, we find that fracking leads to sharp increases in oil and gas recovery and improvements in a wide set of economic indicators. There is also evidence of deterioration in local amenities, which may include increases in crime, noise, and traffic and declines in health. Using a Rosen-Roback-style spatial equilibrium model to infer the net welfare impacts, we estimate that willingness-to-pay (WTP) for allowing fracking equals about $2,500 per household annually (4.9 percent of household income), although WTP is heterogeneous, ranging from more than $10,000 to roughly 0 across 10 shale regions.

© 2020 by The University of Chicago. All rights reserved. Corrective taxes can solve many market failures, but actual policies frequently deviate from the theoretical ideal because of administrative or political constraints. We present a method to quantify the efficiency costs of constraints on externality-correcting policies or, more generally, the costs of imperfect pricing, using simple regression statistics. Under certain conditions, the R2 and the sum of squared residuals from a regression of true externalities on policy variables measure relative welfare gains frompolicies.We illustrate via four empirical applications: Random mismeasurement of externalities, imperfect electricity pricing, heterogeneity in the longevity of energy-consuming durable goods, and imperfect spatial policy differentiation.

Electricity tariffs typically charge residential users a volumetric rate that covers the bulk of energy, transmission, and distribution costs. The resulting prices, charged per unit of electricity consumed, do not reflect marginal costs and vary little across time and space. The emergence of distributed energy resources—such as solar photovoltaics and energy storage—has sparked interest among regulators and utilities in reforming electricity tariffs to enable more efficient utilization of these resources. The economic pressure to redesign electricity rates is countered by concerns of how more efficient rate structures might impact different socioeconomic groups. We analyze the bill impacts of alternative rate plans using interval metering data for more than 100,000 customers in the Chicago, Illinois area. We combine these data with granular Census data to assess the incidence of bill changes across different socioeconomic groups. We find that low-income customers would face bill increases on average in a transition to more economically efficient electricity tariffs. However, we demonstrate that simple changes to fixed charges in two-part tariffs can mitigate these disparities while preserving all, or the vast majority, of the efficiency gains. These designs rely exclusively on observable information and could be replicated by utilities in many geographies across the U.S.