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Income, Wealth, and Environmental Inequality in the United States
October 2024
Working Paper Number:
CES-24-57
This paper explores the relationships between air pollution, income, wealth, and race by combining administrative data from U.S. tax returns between 1979'2016, various measures of air pollution, and sociodemographic information from linked survey and administrative data. In the first year of our data, the relationship between income and ambient pollution levels nationally is approximately zero for both non-Hispanic White and Black individuals. However, at every single percentile of the national income distribution, Black individuals are exposed to, on average, higher levels of pollution than White individuals. By 2016, the relationship between income and air pollution had steepened, primarily for Black individuals, driven by changes in where rich and poor Black individuals live. We utilize quasi-random shocks to income to examine the causal effect of changes in income and wealth on pollution exposure over a five year horizon, finding that these income'pollution elasticities map closely to the values implied by our descriptive patterns. We calculate that Black-White differences in income can explain ~10 percent of the observed gap in air pollution levels in 2016.
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Temperature and Local Industry Concentration
October 2023
Working Paper Number:
CES-23-51
We use plant-level data from the US Census of Manufacturers to study the short and long run effects of temperature on manufacturing activity. We document that temperature shocks significantly increase energy costs and lower the productivity of small manufacturing plants, while large plants are mostly unaffected. In US counties that experienced higher increases in average temperatures between the 1980s and the 2010s, these heterogeneous effects have led to higher concentration of manufacturing activity within large plants, and a reallocation of labor from small to large manufacturing establishments. We offer a preliminary discussion of potential mechanisms explaining why large manufacturing firms might be better equipped for long-run adaptation to climate change, including their ability to hedge across locations, easier access to finance, and higher managerial skills.
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Technology Lock-In and Costs of Delayed Climate Policy
July 2023
Working Paper Number:
CES-23-33
This paper studies the implications of current energy prices for future energy efficiency and climate policy. Using U.S. Census microdata and quasi-experimental variation in energy prices, we first show that manufacturing plants that open when electricity prices are low consume more energy throughout their lifetime, regardless of current electricity prices. We then estimate that a persistent bias of technological change toward energy can explain the long-term effects of entry-year electricity prices on energy intensity. Overall, this 'technology lock-in' implies that increasing entry-year electricity prices by 10% would decrease a plant's energy intensity of production by 3% throughout its lifetime.
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Fatal Errors: The Mortality Value of Accurate Weather Forecasts
June 2023
Working Paper Number:
CES-23-30
We provide the first revealed preference estimates of the benefits of routine weather forecasts. The benefits come from how people use advance information to reduce mor tality from heat and cold. Theoretically, more accurate forecasts reduce mortality if and only if mortality risk is convex in forecast errors. We test for such convexity using data on the universe of mortality events and weather forecasts for a twelve-year period in the U.S. Results show that erroneously mild forecasts increase mortality whereas erro neously extreme forecasts do not reduce mortality. Making forecasts 50% more accurate would save 2,200 lives per year. The public would be willing to pay $112 billion to make forecasts 50% more accurate over the remainder of the century, of which $22 billion reflects how forecasts facilitate adaptation to climate change.
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Is Air Pollution Regulation Too Lenient? Evidence from US Offset Markets
June 2023
Working Paper Number:
CES-23-27R
This paper describes a framework to estimate the marginal cost of air pollution regulation, then applies it to assess whether a large set of existing U.S. air pollution regulations have marginal benefits exceeding their marginal costs. The approach utilizes an important yet under-explored provision of the Clean Air Act requiring new or expanding plants to pay incumbents in the same or neighboring counties to reduce their pollution emissions. These "offset" regulations create several hundred decentralized, local markets for pollution that differ by pollutant and location. Economic theory and empirical tests suggest these market prices reveal information about the marginal cost of abatement for new or expanding firms. We compare estimates of the marginal benefit of abatement from leading air quality models to offset prices. We find that, for most regions and pollutants, the marginal benefits of pollution abatement exceed mean offset prices more than ten-fold. In at least one market, however, estimated marginal benefits are below offset prices.
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Long-Run Adult Socio-economic Outcomes
from In Utero Airborne Lead Exposure
November 2022
Working Paper Number:
CES-22-53
As a neurotoxin, early exposure to lead has long been assumed to affect socioeconomic out-comes well into adulthood. However, the empirical literature documenting such effects has been limited. This study documents the long-term effects of in utero exposure to air lead on adult socio-economic outcomes, including earnings, disabilities, employment, public assistance, and education, using US survey and administrative data. Specifically, we match individuals in the 2000 US Decennial Census and 2001-2014 American Community Surveys to average lead concentrations in the individual's birth county during his/her 9 months in utero. We find a 0.5 'g/m3 decrease in air lead, representing the average 1975-85 change resulting from the passage of the U.S. Clean Air Act, is associated with an increase in earnings of 3.5%, or a present value, at birth, of $21,400 in lifetime earnings. Decomposing this effect, we find greater exposure to lead in utero is associated with an increase in disabilities in adulthood, an increase in receiving public assistance, and a decrease in employment. Looking at effects by sex, long-term effects for girls seem to fall on participation in the formal labor market, whereas for boys it appears to fall more on hours worked. This is the first study to document such long-term effects from lead using US data. We estimate the present value in 2020, from all earnings impacts from 1975 forward, to be $4,230 Billion using a discount rate of 3%. In 2020 alone, the benefits are $252 B, or about 1.2% of GDP. Thus, our estimates imply the Clean Air Act's lead phase out is still returning a national dividend of over 1% every year.
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Exploring New Ways to Classify Industries for Energy Analysis and Modeling
November 2022
Working Paper Number:
CES-22-49
Combustion, other emitting processes and fossil energy use outside the power sector have become urgent concerns given the United States' commitment to achieving net-zero greenhouse gas emissions by 2050. Industry is an important end user of energy and relies on fossil fuels used directly for process heating and as feedstocks for a diverse range of applications. Fuel and energy use by industry is heterogeneous, meaning even a single product group can vary broadly in its production routes and associated energy use. In the United States, the North American Industry Classification System (NAICS) serves as the standard for statistical data collection and reporting. In turn, data based on NAICS are the foundation of most United States energy modeling. Thus, the effectiveness of NAICS at representing energy use is a limiting condition for current
expansive planning to improve energy efficiency and alternatives to fossil fuels in industry. Facility-level data could be used to build more detail into heterogeneous sectors and thus supplement data from Bureau of the Census and U.S Energy Information Administration reporting at NAICS code levels but are scarce. This work explores alternative classification schemes for industry based on energy use characteristics and validates an approach to estimate facility-level energy use from publicly available greenhouse gas emissions data from the U.S. Environmental Protection Agency (EPA). The approaches in this study can facilitate understanding of current, as well as possible future, energy demand.
First, current approaches to the construction of industrial taxonomies are summarized along with their usefulness for industrial energy modeling. Unsupervised machine learning techniques are then used to detect clusters in data reported from the U.S. Department of Energy's Industrial Assessment Center program. Clusters of Industrial Assessment Center data show similar levels of correlation between energy use and explanatory variables as three-digit NAICS codes. Interestingly, the clusters each include a large cross section of NAICS codes, which lends additional support to the idea that NAICS may not be particularly suited for correlation between energy use and the variables studied. Fewer clusters are needed for the same level of correlation as shown in NAICS codes. Initial assessment shows a reasonable level of separation using support vector machines with higher than 80% accuracy, so machine learning approaches may be promising for further analysis. The IAC data is focused on smaller and medium-sized facilities and is biased toward higher energy users for a given facility type. Cladistics, an approach for classification developed in biology, is adapted to energy and process characteristics of industries. Cladistics applied to industrial systems seeks to understand the progression of organizations and technology as a type of evolution, wherein traits are inherited from previous systems but evolve due to the emergence of inventions and variations and a selection process driven by adaptation to pressures and favorable outcomes. A cladogram is presented for evolutionary directions in the iron and steel sector. Cladograms are a promising tool for constructing scenarios and summarizing directions of sectoral innovation.
The cladogram of iron and steel is based on the drivers of energy use in the sector. Phylogenetic inference is similar to machine learning approaches as it is based on a machine-led search of the solution space, therefore avoiding some of the subjectivity of other classification systems. Our prototype approach for constructing an industry cladogram is based on process characteristics according to the innovation framework derived from Schumpeter to capture evolution in a given sector. The resulting cladogram represents a snapshot in time based on detailed study of process characteristics. This work could be an important tool for the design of scenarios for more detailed modeling. Cladograms reveal groupings of emerging or dominant processes and their implications in a way that may be helpful for policymakers and entrepreneurs, allowing them to see the larger picture, other good ideas, or competitors. Constructing a cladogram could be a good first step to analysis of many industries (e.g. nitrogenous fertilizer production, ethyl alcohol manufacturing), to understand their heterogeneity, emerging trends, and coherent groupings of related innovations.
Finally, validation is performed for facility-level energy estimates from the EPA Greenhouse Gas Reporting Program. Facility-level data availability continues to be a major challenge for industrial modeling. The method outlined by (McMillan et al. 2016; McMillan and Ruth 2019) allows estimating of facility level energy use based on mandatory greenhouse gas reporting. The validation provided here is an important step for further use of this data for industrial energy modeling.
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The U.S. Manufacturing Sector's Response to Higher Electricity Prices: Evidence from State-Level Renewable Portfolio Standards
October 2022
Working Paper Number:
CES-22-47
While several papers examine the effects of renewable portfolio standards (RPS) on electricity prices, they mainly rely on state-level data and there has been little research on how RPS policies affect manufacturing activity via their effect on electricity prices. Using plant-level data for the entire U.S. manufacturing sector and all electric utilities from 1992 ' 2015, we jointly estimate the effect of RPS adoption and stringency on plant-level electricity prices and production decisions. To ensure that our results are not sensitive to possible pre-existing differences across manufacturing plants in RPS and non-RPS states, we implement coarsened exact covariate matching. Our results suggest that electricity prices for plants in RPS states averaged about 2% higher than in non-RPS states, notably lower than prior estimates based on state-level data. In response to these higher electricity prices, we estimate that plant electricity usage declined by 1.2% for all plants and 1.8% for energy-intensive plants, broadly consistent with published estimates of the elasticity of electricity demand for industrial users. We find smaller declines in output, employment, and hours worked (relative to the decline in electricity use). Finally, several key RPS policy design features that vary substantially from state-to-state produce heterogeneous effects on plant-level electricity prices.
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The Grandkids Aren't Alright: The Intergenerational Effects of Prenatal Pollution Exposure
November 2020
Working Paper Number:
CES-20-36
Evidence shows that environmental quality shapes human capital at birth with long-run effects on health and welfare. Do these effects, in turn, affect the economic opportunities of future generations? Using newly linked survey and administrative data, providing more than 150 million parent/child links, we show that regulation-induced improvements in air quality that an individual experienced in the womb increase the likelihood that their children, the second generation, attend college 40-50 years later. Intergenerational transmission appears to arise from greater parental resources and investments, rather than heritable, biological channels. Our findings suggest that within-generation estimates of marginal damages substantially underestimate the total welfare effects of improving environmental quality and point to the empirical relevance of environmental quality as a contributor to economic opportunity in the United States.
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What Caused Racial Disparities in Particulate Exposure to Fall? New Evidence from the Clean Air Act and Satellite-Based Measures of Air Quality
January 2020
Working Paper Number:
CES-20-02
Racial differences in exposure to ambient air pollution have declined significantly in the United States over the past 20 years. This project links restricted-access Census Bureau microdata to newly available, spatially continuous high resolution measures of ambient particulate pollution (PM2.5) to examine the underlying causes and consequences of differences in black-white pollution exposures. We begin by decomposing differences in pollution exposure into components explained by observable population characteristics (e.g., income) versus those that remain unexplained. We then use quantile regression methods to show that a significant portion of the 'unexplained' convergence in black-white pollution exposure can be attributed to differential impacts of the Clean Air Act (CAA) in non-Hispanic African American and non-Hispanic white communities. Areas with larger black populations saw greater CAA-related declines in PM2.5 exposure. We show that the CAA has been the single largest contributor to racial convergence in PM2.5 pollution exposure in the U.S. since 2000 accounting for over 60 percent of the reduction.
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