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Sam Kortum

Yale University
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“Optimal Unilateral Carbon Policy”

Abstract: We consider climate policy by one country in a world with international trade in energy and in manufactured goods produced with energy and labor. Assuming that the country’s trading partner is passive, we derive an optimal unilateral policy to confront the global externality from manufacturing’s combustion of carbon-based energy. Our solution strategy combines techniques from Markusen (1975) and from Costinot, Donaldson, Vogel, and Werning (2015). We interpret the optimal policy as a particular set of taxes and subsidies. The key features are: (i) the country institutes a carbon tax equal to its damages from emissions, raising the cost of energy for its manufacturers relative to the price received by its energy extractors; (ii) this Pigouvian tax is the sum of an extraction tax and a production tax on the use of energy, with a border adjustment on imports equal to the production tax; (iii) the mix between the extraction and production tax is optimized to reduce carbon leakage and to improve the country’s terms of trade; (iv) the border adjustment on the energy content of imported manufactured goods leaves the country’s consumption decisions undistorted; (v) energy taxes are not removed at the border for exports, but instead the country subsidizes exports of goods in which its comparative advantage is weak while taxing those where its comparative advantage is strong; (vi) the country expands exports of manufactures on the extensive margin, potentially even exporting goods that it also imports. Features (i)-(iii) of this optimal policy are reminiscent of Markusen, in a model with trade only in energy. Features (iv)-(v) are reminiscent of Costinot et al., in a model with no externalities. A novel property, captured in (vi), is how the country can exploit international trade in manufactured goods to expand the reach of its climate policy. Through its tax policy the country indirectly controls how energy is used in producing both its imports and its exports.

This seminar will be online. Zoom details will be provided a couple of days prior.

Tatyana Deryugina

University of Illinois at Urbana-Champaign
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Clare Balboni

Massachusetts Institute of Technology
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“In Harm’s Way? Infrastructure Investments and the Persistence of Coastal Cities”

Abstract: Coasts contain a disproportionate share of the world’s population, reflecting historical advantages, but environmental change threatens a reversal of coastal fortune in the coming decades as natural disasters intensify and sea levels rise. This paper considers whether large infrastructure investments should continue to favour coastal areas. I use a dynamic spatial equilibrium framework and detailed georeferenced data from Vietnam to examine this issue and find evidence that coastal favouritism has significant costs. Road investments concentrated in coastal regions between 2000 and 2010 had positive returns but would have been outperformed by al-locations concentrated further inland even in the absence of sea level rise. Future inundation renders the status quo significantly less efficient. Under a central sea level rise scenario, welfare gains 72% higher could have been achieved by a foresighted allocation avoiding the most vulnerable regions. The results highlight the importance of accounting for the dynamic effects of environmental change in deciding where to allocate infrastructure today. Full Paper

Lint Barrage

UC Santa Barbara
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“Climate Change, Directed Innovation, and Energy Transition: The Long-run Consequences of the Shale Gas Revolution”

Abstract: We investigate the short- and long-term effects of a shale gas boom in an economy where energy can be produced with coal, natural gas, or clean energy sources. In the short run, cheaper natural gas has counteracting effects on CO2 emissions: on the one hand it allows substitution away from coal which reduces CO2 emissions, ceteris paribus; on the other hand the shale gas boom may increase pollution as it increases the scale of aggregate production. We then empirically document another potentially important effect, namely that the shale boom was associated with a decline in innovation in green relative to fossil fuels-based electricity generation technologies. Introducing directed technical change dynamics in our model, we derive conditions under which a shale gas boom reduces emissions in the short-run but increases emissions in the long-run by inducing firms to direct innovation away from clean towards fossil fuels innovation. We further show the possibility of an infinitely delayed switch from fossil fuels to clean energy as a result of the boom. Finally, we present a quantitative version of the model calibrated to the U.S. economy, and analyze the implications of the shale boom for optimal climate policy.

Steve Cicala

University of Chicago
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“Advantageous Selection as a Policy Instrument: Unraveling Climate Change”

Abstract: This paper applies principles of advantageous selection to overcome obstacles that prevent the implementation of Pigouvian policies to internalize externalities. Focusing on negative externalities from production (such as pollution), we evaluate settings in which aggregate emissions are known, but individual contributions are unobserved by the government. The government provides firms with the option to pay a tax on their voluntarily and verifiably disclosed emissions, or an output tax based on the average of rate of emissions among the undisclosed firms. The certification of relatively clean firms raises the output-based tax, setting off a process of unraveling in favor of disclosure. We derive the conditions under which unraveling will yield an outcome close to the first best. We then implement our mechanism in an international setting with unilateral climate change policy as the motivation. We show how such a mechanism extends the reach of a carbon tax, and that the gains over a system of carbon tariffs depend on a small number of estimable parameters. Full Paper

Geoffrey Heal

Columbia Business School
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“Coase, Hotelling and Pigou: The Incidence of a Carbon Tax and CO2 Emissions”

Abstract: A carbon tax has been widely discussed as a way of reducing fossil fuel use and mitigating climate change, generally in a static framework. Unlike standard goods that can be produced, oil is an exhaustible resource. Parts of its price reflects scarcity rents, i.e., the fact that there is limited availability. We highlight important dynamic aspects of a global carbon tax, which will reallocate consumption through time: some of the initial reduction in consumption will be offset through higher consumption later on. Only reserves with high enough extraction cost will be priced out of the market. Using data from a large proprietary database of field-level oil data, we show that carbon prices even as high as 200 dollars per ton of CO2 will only reduce cumulative emissions from oil by 4% as the supply curve is very steep for high oil prices and few reserves drop out. The supply curve flattens out for lower price, and the effect of an increased carbon tax becomes larger. For example, a carbon price of 600 dollars would reduce cumulative emissions by 60%. On the flip side, a global cap and trade system that limits global extraction by a modest amount like 4% expropriates a large fraction of scarcity rents and would imply a high permit price of $200. The tax incidence varies over time: initially, about 75% of the carbon price will be passed on to consumers, but this share declines through time and even becomes negative as oil prices will drop in future years relative to a case of no carbon tax. The net present value of producer and consumer surplus decrease by roughly equal amounts, which are almost entirely offset by increased tax revenues. Full Paper

Katherine Meckel

UC San Diego
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“Are Inspections Going to Waste? Using Machine Learning to Improve EPA Inspection Targeting of Hazardous Waste Facilities”

Abstract: Machine learning (ML) algorithms are increasingly used to model and predict economic outcomes. Using 15 years of data and nearly 10,000 variables, we build an ML model to predict the likelihood that manufacturing facilities will violate EPA regulations on hazardous waste. Given that the EPA can inspect a limited number of these facilities per year, we simulate the case in which the EPA’s inspection choices are replaced by facilities predicted to be high risk by our model. The results suggest that our model’s predictions improve on the EPA’s rate of finding violations by 50%. To validate our estimates of the model’s efficacy at improving targeting, we run a multi-year field test in which the EPA and the model each choose half of the facilities to be inspected. A field test that incorporates real world implementation challenges is critical for agency adoption. Ours is the first direct test of potential for machine learning to improve on the decision based targeting of government resources in the U.S.

Panle Jia Barwick

Cornell University
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“From Fog to Smog: the Value of Pollution Information”

Abstract: During 2013-2014, China launched a nation wide real-time air quality monitoring and disclosure program, a watershed moment in the history of its environmental regulations. We present the first empirical analysis of this natural experiment by exploiting its staggered introduction across cities. The program has transformed the landscape of China’s environmental protection, substantially expanded public access to pollution information, and dramatically increased households’ awareness about pollution issues. These transformations in turn triggered a cascade of behavioral changes in household activities such as online searches, day-to-day shopping, and housing demand when pollution was elevated. As a result, air pollution’s mortality cost was reduced by nearly 7% post the program, amounting to an annual benefit of RMB 120 billion. The resulting benefit is an order of magnitude larger than the cost of the program and the associated avoidance behavior. Our findings highlight considerable benefits from improving access to pollution information in developing countries, many of which are experiencing the world’s worst air pollution but do not systematically collect or disseminate pollution information. Full Paper

Jacquelyn Pless

MIT Sloan
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“Are “Complimentary Policies” Substitutes? Evidence from R&D Subsidies in the UK” 

Abstract: Governments subsidize R&D through a mix of interdependent mechanisms, but subsidy interactions are not well understood. This paper provides the first quasi-experimental evaluation of how R&D subsidy interactions impact firm behavior. I use funding rules and policy changes in the UK to show that direct grants and tax credits for R&D are complements for small firms but substitutes for larger firms. An increase in tax credit rates substantially enhances the effect of grants on R&D expenditures for small firms. For larger firms, it cuts the positive effect of grants in half. I explore the mechanisms behind these findings and provide suggestive evidence that complementarity is consistent with easing financial constraints for small firms. Substitution by larger firms is most consistent with the subsidization of infra-marginal R&D expenditures. I rule out some alternative explanations. Subsidy interactions also impact the types of innovation efforts that emerge: with increases in both subsidies, small firms steer efforts increasingly towards developing new goods (i.e., horizontal innovations) as opposed to improving existing goods (i.e., vertical innovations). Accounting for subsidy interactions could substantially improve the effectiveness of public spending on R&D. Full Paper

Jeremy West

University of California, Santa Cruz
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“Product Quality Disclosure with Uninformed Sellers”

(Joint with Erica Myers and Steve Puller)

Abstract: This study examines markets in which both buyers and sellers may not fully observe transacted product quality. Using a behavioral model, we illustrate how ignorance can influence sellers’ quality disclosure decisions. Empirically, we leverage a natural policy experiment that encourages homeowners to provide potential buyers with certified measurements of energy efficiency. Using similar nearby homes to form a counterfactual, we find that credible disclosure significantly increases price capitalization of and investments in energy efficiency. Despite very heterogeneous price benefits from disclosure, we show that properties’ relative energy efficiency only weakly predicts disclosure propensities. Connecting our empirical findings to the model, we demonstrate using a computational simulation that a substantial share of homeowners are apparently uninformed about the relative energy efficiency of their own properties. Our findings yield insights about the energy efficiency gap and hold implications for disclosure policies in real estate markets and in other settings.