442

American Economic Review: Papers & Proceedings 2011, 101:3, 442–447

http://www.aeaweb.org/articles.php?doi=10.1257/aer.101.3.442

Environmental regulations limiting emissions 

of harmful ambient air pollution are designed 

with health benefits in mind. Associated with 

these benefits are the costs of abating pollution. 

The Pollution Abatement Costs and Expenditure 

Survey suggests that regulations pertaining to 

the Clean Air Act (CAA) in the United States 

account for almost $5 billion in capital expenditures and $20 billion in annual operating costs 

within the manufacturing industry alone (United 

States Census Bureau 2008). As a result, manufacturers often argue that these regulations place 

plants and industries at a competitive disadvantage, forcing plants to downsize or close. 

Implicit in this argument is that environmental regulations lead to job loss associated with 

industry-wide reductions in output.

Accordingly, various papers have looked at 

the implications of environmental regulation for 

regulated industries, generally finding negative 

effects of regulations on industry employment (J.

Vernon Henderson 1996; Michael Greenstone 

2002). However, regulation typically affects the 

distribution of employment among industries 

rather than the economy-wide employment level 

(Kenneth J. Arrow et al. 1996). As a result, the 

appropriate measure of regulatory costs to the 

workforce should not be characterized by jobs 

lost but by any transitional costs associated with 

reallocating production or workers (Arrow et 

al. 1996; Greenstone 2002). To the extent that 

workers simply transition from one employer to 

the next without losses pertaining to job-specific 

human capital or unemployment, it is not clear 

that job loss should be a net cost when evaluating regulations. Even though this fact has been 

Environmental Regulation and Labor Reallocation:

Evidence from the Clean Air Act

By W. Reed Walker*

*Columbia University, Department of Economics, 

420 West 118th Street, New York, NY 10027 (e-mail: 

rw2157@columbia.edu). I thank Janet Currie, Lucas Davis, 

Wolfram Schlenker, Till von Wachter, and discussant Olivier 

Deschenes for helpful discussions and comments. Any 

opinions and conclusions expressed herein are those of the 

author(s) and do not necessarily represent the views of the 

US Census Bureau. All results have been reviewed to ensure 

that no confidential information is disclosed.

pointed out in numerous papers, little to no work 

has attempted to understand the magnitudes of 

these frictions in the context of environmental 

regulation.

The goal of this paper is to begin to understand the degree to which changes in regulatory 

stringency over time from the CAA have contributed to costly job transitions by the affected 

workforce. Recent work linking plant-level 

job flow statistics to worker-level job turnover 

surveys has found a strong link between plantlevel job destruction and involuntary workerlevel job loss (Steven Davis, Jason Faberman, 

and John Haltiwanger 2006), where layoffs 

are likely to result both in significant nonemployment spells and earnings losses (see Till 

von Wachter, Jae Song, and Joyce Manchester 

2007 for recent evidence). Thus, the margins 

of adjustment at the firm level have important 

distributional implications for the affected 

workforce. To the extent that firms adjust labor 

demand by increasing firing rates (job destruction), decreasing hiring rates (job attrition), 

reducing plant entry rates, or increasing plant 

exit rates, workers will be more or less affected 

in terms of job loss and/or losses pertaining to 

job-specific human capital.

This is the first paper to decompose net 

changes in employment due to environmental 

regulations into job flow components, offering 

new insight into the distributional impacts of 

regulation on the affected workforce. In doing 

so, I draw upon the most detailed and comprehensive data available on plant-level regulatory 

status over time: a confidential establishmentlevel, longitudinal database from the United 

States Census Bureau that I am able to link 

to a plant-level regulatory database from the 

Environmental Protection Agency. I can explicitly observe plant-level regulatory status over 

time and observe how these plants respond to 

environmental regulatory changes. Previous 

work inferring regulatory stringency is based 

on two- and four-digit nationwide industry 

classifications.
VOL. 101 NO. 3 Environmental Regulation and Labor Reallocation 443

A further contribution is that no research 

has evaluated the most recent amendments and 

changes to the CAA on employment. Previous 

work estimating the costs of the CAA focuses 

on earlier time horizons, when pollution levels 

were much higher and technological constraints 

greater. Thus, previous estimates of the cost 

of regulation may no longer be applicable in 

today’s economy. I exploit changes in regulations following the CAA Amendments of 1990, 

in which the Environmental Protection Agency 

(EPA) adopted new and more stringent pollution 

standards. My estimates from these most recent 

revisions are arguably more applicable to current 

policy debates, and are particularly important 

in light of the EPA’s recent proposal to further 

strengthen emissions standards (Environmental 

Protection Agency 2010).

The results suggest that the recent strengthening of emissions standards in the early 1990s led 

to a persistent decline in employment in affected 

sectors. Sector-level models suggest that the size 

of the newly regulated, polluting sector fell by 

more than 15 percent in the ten years following the change in regulation. These changes in 

employment are driven primarily by an increase 

in the plant-level job destruction rate, suggesting 

that these plant-level downsizings are associated 

with significant worker-level adjustment costs 

pertaining to involuntary job loss.

I. Environmental Regulations

in the United States

Air pollution regulation in the United States 

is coordinated under the CAA, where regulatory 

stringency varies at the county level. Regulations 

primarily affect densely populated areas where 

people live and work and where the potential 

benefits from abatement are larger. While regulation varies across counties, it also varies across 

seven “criteria air pollutants.” Areas with high 

levels of a specific air pollutant are regulated 

only for that pollutant. The threshold for excessive pollution varies for each pollutant but is 

applied uniformly across the United States. In 

any given year, some counties find themselves 

over these thresholds, while others do not.

Ambient air pollution is measured by EPA pollution monitors that take hourly/daily pollution 

readings for various pollutants. Monitor location 

is not subject to manipulation by local authorities. When a county is out of attainment for one 

of the regulated pollutants, the EPA forces local 

plants that emit that pollutant to adopt “lowest 

achievable emission rates” (LAER) technologies without regard to costs. Furthermore, the 

EPA forces any new polluting plants that wish 

to locate in that particular county to offset their 

emissions from another polluting source within 

the county. In contrast, in areas designated as 

“attainment,” large polluting plants must use 

“best available control technology” (BACT), 

which is significantly less costly than LAER 

technology (Randy Becker 2005). In summary, 

in nonattainment areas, firms are subject to regulations designed to reduce emissions without 

regards to costs; counties in attainment are more 

lightly regulated.

In 1990, Congress passed the CAA 

Amendments of 1990, which created a new criteria pollutant class for particulates, PM-10.1

Furthermore, the EPA formally reviewed all 

county nonattainment designations for all criteria pollutants. As a result, 177 counties switched 

into nonattainment in 1991.

II. Longitudinal Plant-Level Employment

and Regulatory Data

The primary source of data for this project 

is the Census Bureau Longitudinal Business 

Database (LBD), an establishment-level database that covers the universe of US establishments. Annual information on employment, 

payroll, and firm age, and detailed industry, 

location, and entry/exit years for the respective 

establishments are all included. I link the LBD 

to plant-level regulatory and permit data from 

the EPA, formally known as the Air Facility 

Subsystem (AFS), using a name and address 

matching algorithm.2

The AFS provides permit information detailing the regulatory program(s) for which the plant 

is regulated, as well as the specific pollutants for 

which the permit is issued. A limitation of the 

AFS is that it does not provide any information as 

to when these permits were issued. Fortunately, 

the regulatory structure of the CAA allows one to 

infer the timing based on county nonattainment 

1 Previously, the EPA regulated particulates in the form 

of Total Suspended Particulates (TSPs), which are larger 

and thought to be less pernicious than the smaller forms of 

particulates. 2 See Reed Walker (2010) for details.
444 AEA PAPERS AND PROCEEDINGS MAY 2011

status. Specifically, I define a plant as regulated 

if the plant has an operating permit in the AFS 

database and resides in a county that is in nonattainment for the specific pollutant on the permit. 

This is the first longitudinal, national dataset that 

includes plant-level regulatory status. Previous 

research proxied regulation by two- and fourdigit SIC level national pollution estimates 

(Greenstone 2002; Becker 2005).

I limit the sample to establishments within the 

manufacturing and utility sectors.3 I also exclude 

establishments with a maximum employment of 

fewer than 50 employees over the sample frame 

and any establishments with a lifespan of fewer 

than 3 years. Since EPA regulations primarily 

apply to major sources with potential to emit 

of more than 100 tons per year, excluding these 

smaller establishments has little effect on the 

estimates.

I create a second dataset that aggregates this 

plant-level micro data to the county by sector 

(i.e., polluting or nonpolluting) and by year for 

the years 1985 to 2005. This eases the computational burden and provides aggregate statistics 

that reflect both changes in employment for continuing plants as well as any changes pertaining 

to plant entry or exit.

A. Summary Statistics

Since the CAA is administered on a county 

by year basis and only polluting plants are regulated in that county-year, there is a tremendous 

3 Specifically, one-digit and two-digit 1987 SIC code categories 2 (manufacturing), 3 (manufacturing), and 49 (electric, gas, and sanitary services). These are the most widely 

represented sectors in the AFS.

amount of regulatory variation in the data. It is 

therefore instructive to understand the degree to 

which these sources of variation are orthogonal to plant or county observables. If there are 

significant differences across counties or plants 

pertaining to preregulatory observed differences, then the nature of these differences 

should motivate the choice of a proper empirical specification. Table 1 presents sample statistics by polluting and nonpolluting sectors 

for both county and plant samples, where each 

column shows the characteristics for attainment counties, nonattainment counties, and 

counties that switched nonattainment status in 

1991. Nonattainment counties tend to be more 

urban and economically larger, and relying on 

cross-sectional variation alone might confound 

regulations with other sources of heterogeneity across counties. Similarly, purely relying 

on time-series variation in regulated plants is 

suspect, given that the recession in the early 

1990s occurred at the same time as the CAA 

Amendments of 1990. Within-county comparisons between plants in the same county rely on 

the fact that polluting and nonpolluting plants 

are similar except for regulatory status. Table 1 

shows that polluting plants tend to be older and 

larger than their counterparts, and have slower 

growth rates ex ante. Failing to account for these 

differences might lead to confounding regulation with plant age or plant vintage effects, a 

point not addressed in the previous literature.

Credible identification requires accounting 

for all these sources of observed and unobserved 

confounders. Fortunately, the richness of the 

data permit me to flexibly control for most unobserved shocks, while still being able to recover 

precise estimates.

Table 1—County and Plant Characteristics in Year Prior to CAA Amendments

Polluting sector Nonpolluting sector

Attain Nonattain Switch Attain Nonattain Switch

County employment (1990) 1,249.6 6,421.1 6,041.2 1,798.2 13,522.6 17,514.5

Plant employment (1990) 281.3 289.7 285.2 152.0 132.2 137.4

Employment growth (1985–1990) 0.408 0.280 0.333 0.199 0.107 0.170

Job creation rate (1985–1990) 0.511 0.433 0.456 0.581 0.553 0.592

Job destruction rate (1985–1990) 0.104 0.153 0.123 0.382 0.446 0.422

Plant age (1990) 9.874 10.74 10.63 8.257 8.492 8.075

Plants (1990) 8,507 9,488 3,915 38,692 59,755 34,599

Notes: Plant-level growth rates and labor reallocation rates are constructed using denitions from Davis and Haltiwanger (1992), 

and are described further in Section IV. Number of counties per category: attain 2,265, nonattain 392, switch 177.
VOL. 101 NO. 3 Environmental Regulation and Labor Reallocation 445

III. Sector-Level Dynamics

In order to understand the dynamic effects of 

CAA regulations on sector-level employment, I 

turn to a generalized triple-difference model of 

the form

(1) Ljct = ∑

k=−5

10

 βk (Nc × Pj × 1(τt = k))

+ δjc + ηct + ρjt +γt + ujct,

where Ljct represents the log of sector j employment in county c for year t. Nc are indicators for 

those counties that switched nonattainment after 

the 1990 CAA Amendments, Pj is a sector-level 

indicator for the polluting sector, and τt and represents years before and after the policy change. 

The lower-order interaction terms of a standard 

triple-difference estimator are implicit in the 

“switching county” by year fixed effects (ηct), 

polluting sector by year fixed effects (ρjt), and 

the county-by-sector fixed effects (δjc). The 

excluded time category is k = −1 so that estimates are measured relative to the year before 

the change in policy.

The parameters of interest are the βks which 

provide an estimate of the semi-elasticity of 

employment with regard to changes in environmental regulations. Estimates of the βks are 

identified by within-sector comparisons over 

time for those sectors that experienced changes 

in regulatory stringency. This specification controls for any observed or unobserved permanent county-by-sector characteristics as well as 

any unobserved shocks to those counties that 

switched nonattainment or unobserved shocks to 

the polluting sector. To account for potential correlation across sectors within the same county, 

standard errors are clustered at the county level. 

Lastly, regressions are weighted by the total 

employment for each county/sector in 1985.

Figure 1 plots the coefficients from a version of 

equation (1) for the five years prior and ten years 

after the changes to the 1990 CAA Amendments. 

Specifically, the plotted coefficients are the difference in event time indicators for the polluting 

sectors in counties that switched nonattainment 

status in 1991 relative to the polluting sector in 

those counties that did not switch.4

4 The online Appendix presents the estimated parameters and standard errors, as well as employment trends for 

There are two important features from this 

figure. First, the trends in employment in the 

polluting sectors for the years prior to the 

change are remarkably similar (as reflected by 

the zero pretrend differences). Second, beginning with the year of the regulatory change, 

the employment of polluting sectors in newly 

regulated counties begins to fall for the next 

eight years to 15 percent below 1990 employment levels. Recall that these estimates are all 

relative to polluting sectors in counties that did 

not switch regulatory status in 1990, and thus 

any cyclical differences pertaining to the recession in the early 1990s should be accounted 

for (conditional on the control group evolving 

similarly). Figure 1 summarizes the paper’s 

primary finding: namely, that there is a strong 

and persistent relationship between nonattainment designation and sector-level employment.

I next turn to plant-level data to look at differences in employment growth and labor 

reallocation rates over five-year intervals for 

affected plants. The focus on medium- to longrun differences abstracts away from the shortrun dynamics, while allowing me to compare 

my estimates to the previous literature using 

the Census of Manufacturers (Henderson 1996; 

Greenstone 2002).

each sector, rather than the difference in trends presented 

here. http://www.aeaweb.org/articles.php?doi=10.1257/

aer.101.3.442.

Figure 1. Sector-Level Employment Estimates before 

and after Regulation

Note: Plotted are the coefficient estimates from a version of 

equation (1). See text for details. The dashed lines represent 

95 percent conficence intervals.

−0.30

−0.20

−0.10

0.00

0.10

Employment

−5 0 5 10

Years before and after regulation
446 AEA PAPERS AND PROCEEDINGS MAY 2011

IV. Plant-Level Labor Reallocation Rates

In keeping with the literature, I define 

plant employment growth as the difference in 

employment between t and t − 5, divided by 

the average employment in those two periods. 

To better understand the margins of firm adjustment, employment growth is decomposed into 

two separate components: one measuring the 

growth rate from expanding establishments 

(i.e., the job creation rate) and the other measuring the rate at which contracting establishments are shrinking (i.e., the job destruction 

rate).

5

 I estimate various forms of the following 

plant level model:

(2) yijct = α + θ(Nct−5 × Pi)

+ δjt + ηi + ζct + γit + uijct ,

where Nct−5 is a lagged indicator for whether 

county c is in nonattainment five years prior, and 

Pi is a plant-level indicator for polluting status. 

5 These statistics are created using the standard job flow 

definitions from Davis and Haltiwanger (1992). Importantly, 

all of these measures account for entry and exit at the plant 

level, whereas measures using log differences are not defined 

for plants entering or exiting the sample.

The parameter, θ, provides an estimate of the 

effect of plant-level nonattainment designation 

on the five-year plant employment growth 

and labor reallocation rates. Equation (2) also 

controls for annual fluctuations by industry 

with two-digit SIC × year fixed effects (δjt); 

any permanent observed or unobserved plant 

characteristics with plant fixed effects (ηi); and 

any local economic shocks to plants that affect 

both polluting and nonpolluting plants similarly 

by including county-by-year fixed effects (ζct). 

Since plant age is an important determinant of 

growth rates and job flows, I also include a set 

of plant age indicators (γit). Lastly, estimates are 

weighted by plant-specific median employment 

over the sample.

Panel A of Table 2 presents regression estimates pertaining to equation (2). Similar to the 

findings in the previous literature, plant-level 

employment growth declines with changes to 

plant-level regulatory status. Interestingly, the 

results suggest that most of this adjustment is 

occurring through increases in the job destruction rate (i.e., the rate at which plants with negative employment growth shed jobs). The job 

destruction rate nearly doubles for newly regulated plants, suggesting there may be significant 

costs to the affected workforce from these plantlevel reductions in employment.

Table 2—Effect of Nonattainment Designation on Plant Level Five-Year Labor 

Reallocation Rates

Employment Job creation Job destruction

growth rate rate

Panel A. Model 1

Nonattainmentct−5 × Polluteri −0.142*** −0.037*** 0.105***

(0.043) (0.014) (0.039)

Panel B. Model 2

Nonattainment(CO)ct−5 × Polluteri −0.031 −0.012 0.019

(0.076) (0.023) (0.063)

Nonattainment(O3)ct−5 × Polluteri −0.099*** −0.035* 0.064 *

(0.035) (0.019) (0.033)

Nonattainment(PM10)ct−5 × Polluteri −0.133*** −0.007 0.126*** 

(0.030) (0.013) (0.024)

Nonattainment(SO2)ct−5 × Polluteri −0.246** −0.114*** 0.132* 

(0.118) (0.044) (0.077)

Notes: This table reports several estimates pertaining to equation (2), where each column of each 

panel is a separate regression. See text for details. N = 470,958. Reported in parentheses are 

robust standard errors that are clustered at the county level.

***Significant at the 1 percent level.

**Significant at the 5 percent level.

 *Significant at the 10 percent level.
VOL. 101 NO. 3 Environmental Regulation and Labor Reallocation 447

Since counties can be regulated for various sources of pollution, and the regulations 

are applied such that only plants emitting the 

pollutant in question are regulated, I can estimate a model that jointly identifies the regulatory effects for each pollutant (Greenstone 

2002). I estimate a version of equation (2)

where there are four parameters of interest, 

θ p for p ∈ {CO, PM10, O3, SO2}, conditional 

on the respective nonattainment indicators, 

Nct−5 p being equal to one.6 Thus, the regulatory effects are identified off of changes to the 

plant-level regulatory status for each pollutant 

p, conditional on that plant emitting pollutant 

p. Panel B of Table 2 presents results from 

the joint pollutant estimation. Changes in the 

plant-level regulatory status have significant 

effects on plant-level employment growth and 

labor reallocation rates across most pollutant 

classes.

V. Conclusion

Evidence here suggests that firms primarily 

respond to regulatory pressure by destroying 

jobs rather than reducing their hiring rates. This 

has important distributional implications for 

the affected workforce. It is not entirely clear, 

however, how to monetarize these effects. 

While the jobs might disappear, the workers 

certainly do not, and thus the true costs should 

be characterized by the adjustment costs associated with reallocating the workforce.

Future work should try to explicitly estimate 

these costs. Specifically, longitudinal micro 

data could yield considerable insight into the 

magnitude of the earnings losses pertaining to 

reallocation for the affected workforce in both 

the short and long run. Since most of these regulations occur in relatively thick labor markets, 

and since these shocks are very sector specific, 

the actual costs to the workforce could be quite 

modest relative to the estimated benefits of the 

policy. See Walker (2010) for evidence suggesting this to be the case. 

6 Only the pollutants CO, PM-10, O3, and SO2 experience 

significant amounts of regulatory variation in the sample.

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