Recent Working Papers

The Use of Regression Statistics to Analyze Imperfect Pricing Policies

Mark R. Jacobsen, Christopher R. Knittel, James M. Sallee, Arthur A. van Benthem

Corrective taxes can completely solve a variety of market failures, but actual policies are commonly forced to deviate from the theoretical ideal due to administrative or political constraints. This paper presents a method that requires a minimum of market information to quantify the efficiency costs of constraints on the design of externality-correcting tax schemes, or more generally the costs of imperfect pricing, using simple regression statistics. We demonstrate that, under certain intuitive conditions, standard output from a regression of true externalities on policy variables, including the R2 and the sum of squared residuals, has an immediate welfare interpretation—it characterizes the relative welfare gains achieved by alternative policies. We utilize our approach in four diverse empirical applications: random mismeasurement in externalities, imperfect spatial policy differentiation, imperfect electricity pricing, and heterogeneity in the longevity of energy-consuming durable goods. In two cases, we find that policy constraints are relatively harmless, while in the other two cases, the constraint induces large inefficiencies. Regarding the case of durable longevity, we find that policies that regulate vehicle fuel economy, but ignore the differences in average longevity across types of automobiles, recover only about one-quarter to one-third of the welfare gains achievable by a policy that also takes product longevity into account.

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Machine Learning from Schools about Energy Efficiency

Fiona Burlig, Christopher R. Knittel, David Rapson, Mar Reguant, Catherine Wolfram

In the United States, consumers invest billions of dollars annually in energy efficiency, often on the assumption that these investments will pay for themselves via future energy cost reductions. We study energy efficiency upgrades in K-12 schools in California. We develop and implement a novel machine learning approach for estimating treatment effects using high-frequency panel data, and demonstrate that this method outperforms standard panel fixed effects approaches. We find that energy efficiency upgrades reduce electricity consumption by 3 percent, but that these reductions total only 24 percent of ex ante expected savings. HVAC and lighting upgrades perform better, but still deliver less than half of what was expected. Finally, beyond location, school characteristics that are readily available to policymakers do not appear to predict realization rates across schools, suggesting that improving realization rates via targeting may prove challenging.

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Attribute Substitution in Household Vehicle Portfolios

James Archsmith (UC Davis), Kenneth Gillingham (Yale), Christopher R. Knittel (MIT), and David Rapson (UC Davis)

Household preferences for goods with a bundle of attributes may have complex substitution patterns when one attribute is changed. For example, a household faced with an exogenous increase in the size of one television may choose to decrease the size of other televisions within the home. This paper quantifies the extent of attribute substitution in the context of multi-vehicle households. We deploy a novel identification strategy to examine how an exogenous change in the fuel economy of a kept vehicle affects a household's choice of a second vehicle. We find strong evidence of attribute substitution in the household vehicle portfolio. This effect operates through car attributes that are correlated with fuel economy, including vehicle footprint and weight. Our findings suggest that attribute substitution exerts a strong force that may erode a substantial portion of the expected future gasoline savings from fuel economy standards, particularly those that are attribute-based. Elements of our identification strategy are relevant to a broad class of settings in which consumers make sequential purchases of durable portfolio goods.

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