Recent Working Papers

Assessing the distribution of employment vulnerability to the energy transition using employment carbon footprints

PNAS, 121(7)

Kailin Graham (MIT) and Christopher R. Knittel (MIT)

As the world moves away from fossil fuels, there is growing recognition of the need for a just transition of those working in carbon-intensive industries and for policy to support this transition. While recent policies such as the U.S. Inflation Reduction Act have begun to incorporate support for energy-intensive regions, little work has thoroughly investigated which communities are most vulnerable to economic disruption in the energy transition and therefore require policy support. This paper analyzes the distribution of employment vulnerability in the United States by calculating the average "employment carbon footprint" of close-to every job in the U.S. economy at high geographic and sectoral granularity. The measure considers employment vulnerability across the entire economy and captures both fossil fuel consumption and production effects, with the sectors covered in our analysis accounting for 86% of total U.S. employment and 94\% of U.S. carbon emissions outside of the transportation sector. We find that existing efforts to identify at-risk communities both in the literature and the Inflation Reduction Act exclude regions of high employment vulnerability, and thereby risk leaving these communities behind in the energy transition. This work underscores the importance of proactive and continuous measures of employment vulnerability, presents policymakers with much-needed data to incorporate such measures into just transition policy, and makes the case for place-based policy approaches when considering how best to support communities through the energy transition.

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Designing Climate Policy Mixes: Analytical and Energy System Modeling Approaches

Energy Economics, 122, June 2023

Emil Dimanchev (NTNU) and Christopher R. Knittel (MIT)

A matter of debate in climate policy is whether lawmakers should rely on carbon pricing or regulations, such as low-carbon standards, to reach emission reduction goals. Past research showed that pricing is more cost-effective. However, previous work studied the two policies when implemented separately, in effect comparing two policy extremes. In contrast, we explore the full spectrum of climate policy mixes that include both types of policies but vary in how much they rely on each. We do this both analytically by extending previous theory and numerically with two energy system models. In line with past work, increasing reliance on pricing increases the cost-effectiveness of the policy mix. However, we show that this benefit exhibits diminishing marginal returns. Thus the gain in cost-effectiveness from complementing stringent standards with modest pricing is relatively large. Our results show that relying on pricing for 20% of emission reductions (and on a standard for 80%) reduces costs by 32%–57% compared to a standard-only approach. Importantly, trading off more of the standard for pricing delivers smaller and smaller gains in cost-effectiveness. For example, a policy mix that relies on each policy for 50% of emission reductions decreases costs by 60%–81%, which is already 71%–88% as cost-effective as the theoretically most cost-effective pricing-only policy.

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

Journal of Public Economics, October 2021

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

Conditionally accepted, The Economic Journal

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

The Journal of Political Economy, May 2020

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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