Recent Working Papers

Driving Behavior and the Price of Gasoline: Evidence from Fueling-Level Micro Data

Christopher R. Knittel (MIT) and Shinsuke Tanaka (Tufts)

We use novel microdata on on-road fuel consumption and prices paid for fuel in Japan to estimate short-run price elasticities of demand for gasoline consumption. We have three main findings. First, our elasticity estimates of roughly -0.37 are in orders of magnitude larger than previously estimated using more aggregate data. Second, we are one of the first papers to separately estimate both the price elasticities of kilometers driven (-0.30) and on-road fuel economy (0.07). Lastly, we find that on-road fuel economy is determined by recent prices than distant past prices paid, suggesting limited habit formation of fuel-conserving driving behaviors.

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Using Machine Learning to Target Treatment: The Case of Household Energy Use

Christopher R. Knittel (MIT) and Samuel Stolper (Michigan)

We use causal forests Athey et al (2018) to evaluate heterogenous treatment effects within a series of large-scale randomized experiments. Our application is the Home Energy Report, a widely-used behavioral nudge that relies on repeated social comparison to encourage household energy efficiency. Our data consist of monthly electricity consumption, treatment assignment, and cross-sectional characteristics of 902,581 New England households. The program-wide average treatment effect is a monthly reduction in electricity consumption of approximately 9 kilowatt-hours (kWh), or 1 percent; the full range of type-specific impacts, however, runs from -30 to +10 kWh. The forest algorithm uses two variables with disproportionately high frequency in developing predictions: pre-treatment consumption and home value. In addition, we find evidence of a ``boomerang effect'': households with relatively lower consumption than neighbors of the same type are the ones for which we estimate positive treatment effects. Finally, using forest results to selectively target treatment increases annual social welfare by 12-120 percent, depending on the program year and parameterization of the welfare function.

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The Use of Regression Statistics to Analyze Imperfect Pricing Policies

Forthcoming, The Journal of Political Economy

Mark R. Jacobsen (UCSD), Christopher R. Knittel (MIT), James M. Sallee (Berkeley), Arthur A. van Benthem (Wharton)

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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Attribute Substitution in Household Vehicle Portfolios

Revised and resubmitted to The Rand Journal of Economics

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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