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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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Addressing Data Gaps:
Four New Lines of Inquiry in the 2017 Economic Census
September 2019
Working Paper Number:
CES-19-28
We describe four new lines of inquiry added to the 2017 Economic Census regarding (i) retail health clinics, (ii) management practices in health care services, (iii) self-service in retail and service industries, and (iv) water use in manufacturing and mining industries. These were proposed by economists from the U.S. Census Bureau's Center for Economic Studies in order to fill data gaps in current Census Bureau products concerning the U.S. economy. The new content addresses such issues as the rise in importance of health care and its complexity, the adoption of automation technologies, and the importance of measuring water, a critical input to many manufacturing and mining industries.
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Regulating Mismeasured Pollution: Implications of Firm Heterogeneity for Environmental Policy
August 2018
Working Paper Number:
CES-18-03R
This paper provides the first estimates of within-industry heterogeneity in energy and CO2 productivity for the entire U.S. manufacturing sector. We measure energy and CO2 productivity as output per dollar energy input or per ton CO2 emitted. Three findings emerge. First, within narrowly defined industries, heterogeneity in energy and CO2 productivity across plants is enormous. Second, heterogeneity in energy and CO2 productivity exceeds heterogeneity in most other productivity measures, like labor or total factor productivity. Third, heterogeneity in energy and CO2 productivity has important implications for environmental policies targeting industries rather than plants, including technology standards and carbon border adjustments.
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Relative Effectiveness of Energy Efficiency Programs versus Market Based Climate Policies in the Chemical Industry
April 2018
Working Paper Number:
CES-18-16
This paper addresses the relative effectiveness of market vs program based climate policies. We compute the carbon price resulting in an equivalent reduction in energy from programs that eliminate the efficiency gap. A reduced-form stochastic frontier energy demand analysis of plant level electricity and fuel data, from energy-intensive chemical sectors, jointly estimates the distribution of energy efficiency and underlying price elasticities. The analysis controls for plant level price endogeneity and heterogeneity to obtain a decomposition of efficiency into persistent (PE) and time-varying (TVE) components. Total inefficiency is relatively small and price elasticities are relatively high. If all plants performed at the 90th percentile of their efficiency distribution, the reduction in energy is between 4% and 13%. A modest carbon price of between $9.48/ton and $14.01/ton CO2 would achieve reductions in energy use equivalent to all manufacturing plants making improvements to close the efficiency gap.
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Air Quality, Human Capital Formation and the Long-term Effects of Environmental Inequality at Birth
May 2017
Working Paper Number:
carra-2017-05
A growing body of literature suggests that pollution exposure early in life can have substantial long term effects on an individual's economic well-being as an adult, however the mechanisms for these effects remain unclear. I contribute to this literature by examining the effect of pollution exposure on several intermediate determinants of adult wages using a unique linked dataset for a large sample of individuals from two cohorts: an older cohort born around the 1970, and a younger cohort born around 1990. This dataset links responses to the American Community Survey to SSA administrative data, the universe of IRS Form 1040 tax returns, pollution concentration data from EPA air quality monitors and satellite remote sensing observations. In both OLS and IV specifications, I find that pollution exposure at birth has a large and economically significant effect on college attendance among 19-22 year olds. Using conventional estimates of the college wage premium, these effects imply that a 10 'g/m3 decrease in particulate matter exposure at birth is associated with a $190 per year increase in annual wages. This effect is smaller than the wage effects in the previous literature, which suggests that human capital acquisition associated with cognitive skills cannot fully explain the long term wage effects of pollution exposure. Indeed, I find evidence for an additional channel working through non-cognitive skill -pollution exposure at birth increases high school non-completion and incarceration among 16-24 year olds, and that these effects are concentrated within disadvantaged communities, with larger effects for non-whites and children of poor parents. I also find that pollution exposure during adolescence has statistically significant effects on high school non-completion and incarceration, but no effect on college attendance. These results suggest that the long term effects of pollution exposure on economic well-being may run through multiple channels, of which both non-cognitive skills and cognitive skills may play a role.
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Longitudinal Environmental Inequality and Environmental Gentrification: Who Gains From Cleaner Air?
May 2017
Working Paper Number:
carra-2017-04
A vast empirical literature has convincingly shown that there is pervasive cross-sectional inequality in exposure to environmental hazards. However, less is known about how these inequalities have been evolving over time. I fill this gap by creating a new dataset, which combines satellite data on ground-level concentrations of fine particulate matter with linked administrative and survey data. This linked dataset allows me to measure individual pollution exposure for over 100 million individuals in each year between 2000 and 2014, a period of time has seen substantial improvements in average air quality. This rich dataset can then be used to analyze longitudinal dimensions of environmental inequality by examining the distribution of changes in individual pollution exposure that underlie these aggregate improvements. I confirm previous findings that cross-sectional environmental inequality has been on the decline, but I argue that this may miss longitudinal patterns in exposure that are consistent with environmental gentrification. I find that advantaged individuals at the beginning of the sample experience larger pollution exposure reductions than do initially disadvantaged individuals.
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