Geoffrey Heal (11/20/19)

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 (09/18/19)

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 (10/30/19)

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 (10/02/19)

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 (05/01/19)

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.

Kelsey Jack (04/17/19)

Kelsey Jack

University California of Santa Barbara
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Paying for Power: Prepaid Electricity and the Spending Patterns of the Poor

(Joint with Kathryn McDermott and Anja Sautmann)

Abstract: Revenue recovery is a challenges for electricity providers in developing countries. Poor customers often struggle to pay monthly bills, and providers face both cost and political economy barriers to enforcing payment. Prepayment is increasingly seen as a solution to this problem. However, little work has been done to date to understand how this affects poor consumers. The research is motivated by two empirical facts observed in the study setting in Cape Town (South Africa). First, electricity use falls by around 13 percent when households are switched from monthly billing to prepaid metering. Second, low income customers on prepaid metering purchase electricity in small quantities and at very high frequencies (every 3 days, on average), reminiscent of the purchasing patterns of poor consumers in other domains. These patterns may reflect liquidity and other constraints on poor households or deliberate choices, with very different welfare implications. We combine analysis of administrative data on electricity purchases with interventions that manipulate liquidity and transaction costs in order to understand the welfare implications of prepayment, as well as the drivers of the high frequency transactions observed in the expenditure patterns of poor households across many domains and settings  We find little evidence for a demand for self- or other-control to explain these patterns. At the same time, we find evidence of meaningful transaction costs associated with purchases, which suggests a high liquidity cost of larger and less frequent expenditures. A model of credit constraints on cash combined with transaction costs on electricity helps reconcile the observational data with our experimental results.

 

Karen Clay (03/20/19)

Karen Clay

Carnegie Mellon University Heinz College
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“Short-Run and Long-Run Impacts of Environmental Regulations on Firm Productivity: Evidence from the U.S. Electricity Sector, 1938-1999”

Abstract: Although economic costs of environmental regulations are widely debated, there is limited empirical evidence on the magnitude of these economic costs and the extent to which these costs persist over time. This paper quantifies the short-run and long-run efficiency costs of air quality regulations on the U.S. electricity production sector. The analysis draws on newly digitized annual, plant-level data on the vast majority of U.S fossil fuel fired power plants from 1938-1999. This sample allows us to examine the U.S. electricity industry both before and after the implementation of the National Ambient Air Quality Standards (NAAQS) in 1972. To estimate the economic costs, we utilize a difference-in-differences framework where counties face different environmental regulations as they move in and out of attainment with NAAQS over time. We find that plants located in non-attainment counties experienced declines in TFP and production of 6.3\% and 7.0\%. The effects of nonattainment on TFP and generation are persistent over time. This suggests that existing plants did not adapt to environmental regulations such as NAAQS even in the long run.

Paulina Oliva (3/13/19)

Paulina OlivaUniversity of Southern CaliforniaPaulina Oliva

University of Southern California
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“The Effect of Air Pollution on Migration: Evidence from China”

Abstract: This paper looks at the effects of air pollution on migration in China using changes in the average strength of thermal inversions over five-year periods as a source of exogenous variation for medium-run air pollution levels. Our findings suggest that air pollution is responsible for large changes in inflows and outflows of migration in China. Specifically, we find that a 10 percent increase in air pollution, holding everything else constant, is capable of reducing population through net outmigration by about 2.8 percent in a given county. We find that these inflows are primarily driven by well-educated people at the beginning of their professional careers, leading to substantial changes in the sociodemographic composition of the population and labor force of Chinese counties. We also find strong gender asymmetries in the response of mid-age adults that suggests families are splitting across counties to protect vulnerable members of the household. Our results are robust to different specifications, including a spatial lag model that accounts for localized migration spillovers and spatially correlated pollution shocks.

Stephie Fried (03/06/19)

Stephie Fried

Arizona State University
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“Seawalls and Stilts: A Quantitative Macro Study of Climate Adaption”

Abstract: Investment in adaptation capital reduces the damage from extreme weather, mitigating the welfare cost of climate change. Federal aid for disaster relief reduces the net costs to localities that experience extreme weather, decreasing their incentives to invest in adaptation capital. We develop a heterogenous-agent macro model to quantify the relationship between adaptation capital, federal disaster policy, and climate change. We find that federal aid for disaster relief substantially reduces adaptation investment. However, the federal subsidy for adaptation more than offsets this moral hazard effect. We introduce climate change into the model as a permanent, increase in the severity of extreme weather. We find that adaptation reduces the welfare cost of this climate change by 15-20 percent. Full Paper