The Impacts of Environmental Regulations on Industrial Activity: Evidence from the 1970

and 1977 Clean Air Act Amendments and the Census of Manufactures

Author(s): Michael Greenstone

Source: Journal of Political Economy, Vol. 110, No. 6 (December 2002), pp. 1175-1219

Published by: The University of Chicago Press

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1175

[Journal of Political Economy, 2002, vol. 110, no. 6]

 2002 by The University of Chicago. All rights reserved. 0022-3808/2002/11006-0005$10.00

The Impacts of Environmental Regulations on

Industrial Activity: Evidence from the 1970 and

1977 Clean Air Act Amendments and the

Census of Manufactures

Michael Greenstone

University of Chicago, American Bar Foundation, and National Bureau of Economic Research

This paper estimates the impacts of the Clean Air Act’s division of

counties into pollutant-specific nonattainment and attainment categories on measures of industrial activity obtained from 1.75 million

plant observations from the Census of Manufactures. Emitters of the

controlled pollutants in nonattainment counties were subject to

greater regulatory oversight than emitters in attainment counties. The

preferred statistical model for plant-level growth includes plant fixed

effects, industry by period fixed effects, and county by period fixed

effects. The estimates from this model suggest that in the first 15 years

This research was carried out while I was a research associate at the Census Bureau’s

Center for Economic Studies in Suitland, Md. The opinions and conclusions expressed

in this paper are those of the author and do not necessarily represent the view of the

U.S. Bureau of the Census. All papers are screened to ensure that they do not disclose

confidential information. I am indebted to Orley Ashenfelter, David Card, Kenneth Chay,

James Heckman, David Lee, Paul Oyer, Katherine Ozment, Robert Topel, and an anonymous referee for especially valuable comments. The paper also benefited from discussions

with Tim Dunne, Henry Farber, Wayne Gray, John Haltiwanger, Vernon Henderson, Alan

Krueger, Helen Levy, John McClelland, Harvey Rosen, Michael Rothschild, Cecilia Rouse,

Christopher Timmins, and Ken Troske. Numerous seminar participants made very helpful

suggestions and comments. Robert Bechtold, John Haltiwanger, and Arnie Reznek were

especially generous with their time in helping me obtain access to the Longitudinal Research Database. Vernon Henderson and Randy Becker graciously allowed me to photocopy parts of the Code of Federal Regulations. The Environmental Protection Agency district

office in Philadelphia complied with my Freedom of Information Act request in a timely

fashion. Richard Brooks, Emily Johnson, Steve Luk, and Ryan Montgomery provided outstanding research assistance. Generous financial support was provided by the Alfred P.

Sloan Foundation’s Doctoral Dissertation Fellowship, Resources for the Future’s Joseph

L. Fisher Dissertation Award, and at Princeton University by the Center for Economic

Policy Studies, the Industrial Relations Section, and the Graduate School’s Summer

Fellowship.
1176 journal of political economy

in which the Clean Air Act was in force (1972–87), nonattainment

counties (relative to attainment ones) lost approximately 590,000 jobs,

$37 billion in capital stock, and $75 billion (1987 dollars) of output

in pollution-intensive industries. These findings are robust across

many specifications, and the effects are apparent in many polluting

industries.

I. Introduction

Efforts to regulate pollution are among the federal government’s most

controversial interventions into the marketplace. On the one hand, the

Pollution Abatement Costs and Expenditures Survey reports that manufacturing plants spend almost $30 billion a year to comply with environmental regulations (U.S. Bureau of the Census 1993). Manufacturers

contend that these expenditures place them at a competitive disadvantage in the global economy and that this leads to the loss of tens of

thousands of U.S. jobs. On the other hand, previous empirical research

fails to consistently document a negative association between environmental regulations and industrial activity (Bartik 1985; McConnell and

Schwab 1990; Gray and Shadbegian 1995; Jaffe et al. 1995; Henderson

1996; Levinson 1996; Becker and Henderson 2000, 2001). In fact, some

research suggests that environmental regulations do not harm regulated

firms or their workers and may even benefit them (Porter and van der

Linde 1995; Berman and Bui 1998, 2001). To set rational policy, it is

crucial to understand whether these regulations restrict economic progress.1 This paper presents new evidence about the relationship between

environmental regulations and industrial activity by focusing on the

Clean Air Act’s impact on polluting manufacturers.

The Clean Air Act, originally passed in 1963 and amended in 1970,

1977, and 1990, is one of the most significant federal interventions into

the market in the postwar period. Following the passage of the 1970

amendments, the Environmental Protection Agency (EPA) established

separate national ambient air quality standards—a minimum level of

air quality that all counties are required to meet—for four criteria pollutants: carbon monoxide (CO), tropospheric ozone (O3), sulfur dioxide (SO2), and total suspended particulates (TSPs). As a part of this

legislation, every U.S. county receives separate nonattainment or attainment designations for each of the four pollutants annually. The nonattainment designation is reserved for counties whose air contains concentrations of a pollutant that exceed the relevant federal standard.

Emitters of the regulated pollutant in nonattainment counties are sub1 See Chay and Greenstone (2000, 2002a) for estimates of the benefits associated with

the Clean Air Act Amendments.
environmental regulations 1177

ject to stricter regulatory oversight than emitters in attainment counties.

Nonpolluters are free from regulation in both categories of counties.

This paper brings together a variety of comprehensive data files to

empirically determine the effects of these federally mandated countylevel regulations on the activity of polluting manufacturers in the

1967–87 period. I compiled annual data on the four pollutant-specific,

nonattainment/attainment designations for each of the 3,070 U.S. counties from the Code of Federal Regulations and EPA pollution monitors. The

structure of these longitudinal regulation data allows for the identification of cross-sectional variation in these regulations, as well as changes

in counties’ pollutant-specific regulatory status over time. Despite the

centrality of these county-level regulations to environmental policy, this

is the first time that either a researcher or the EPA has produced a data

file with these designations for all four of these criteria pollutants.2 The

regulation file is merged with the 1.75 million plant-level observations

from the five Censuses of Manufactures in the 1967–87 period. These

censuses contain detailed questions about plants’ characteristics (including county of location), input usage, and output. The combined

data file is used to relate the growth of employment, investment, and

shipments of manufacturers to the federally mandated regulations

across the entire country.

The paper’s approach overcomes some of the objections to earlier

studies of the impact of environmental regulations. First, the preferred

specification includes plant fixed effects, industry by period fixed effects,

and county by period fixed effects in plant-level models for the growth

of employment, investment, and shipments. Consequently, the estimated

regulation effects are purged of all permanent plant characteristics that

determine growth, all transitory differences in the mean growth of plants

across industries, and all transitory determinants of growth that are

common to polluters and nonpolluters within a county. These controls

are important because this was a period of dramatic changes in the

manufacturing sector, including a substantial increase in competition

from foreign firms in some industries, a secular movement of plants

from the Rust Belt to the South, and two oil price shocks that had

differential effects on particular industries and regions.

Second, this paper uses the principal instruments of the Clean Air

Act Amendments (CAAAs), the pollutant-specific, county-level attainment/nonattainment designations, as its measures of regulation. These

four designations are the “law of the land” and capture the regional

2 McConnell and Schwab (1990), Henderson (1996), and Becker and Henderson (2000,

2001) use nonattainment status for O3 but did not collect information on nonattainment

status for the other pollutants.
1178 journal of political economy

and industry variation that Congress imposed with this legislation.3 In

fact, these designations govern the writing and enforcement of the plantspecific regulations that restrict the behavior of polluters. Moreover, the

simultaneous evaluation of all four regulations is important, because

many plants emit multiple pollutants and many counties are designated

as nonattainment for multiple pollutants. These regulations should address Jaffe et al.’s (1995) criticism that previous studies rely on measures

of regulation that are too aggregated (e.g., state-level measures) to detect differences in stringency.

Third, the detailed Census of Manufactures questionnaire allows for

an examination of regulation’s impact across a number of outcomes

and categories of plants. The previous literature generally focuses on

the effects of regulation on a single outcome variable (e.g., employment)

or on a particular category of plants (e.g., new plants and their location

decisions). This narrow focus may provide an incomplete picture of the

consequences of environmental regulations. In contrast, this paper examines the impacts of regulation on the growth of employment, capital

stock, and shipments. Moreover, its estimates are derived from a sample

that includes existing plants as well as newly opened ones.

The results indicate that the CAAAs substantially retarded the growth

of polluting manufacturers in nonattainment counties. The estimates

suggest that in the first 15 years after the amendments became law (i.e.,

1972–87), nonattainment counties (relative to attainment ones) lost

approximately 590,000 jobs, $37 billion in capital stock, and $75 billion

(1987 dollars) of output in pollution-intensive industries. Importantly,

these findings are robust across many specifications, and the effects are

evident across a wide range of polluting industries. Although the decline

in manufacturing activity was substantial in nonattainment counties, it

was modest compared to the size of the entire manufacturing sector.

The paper is organized as follows. Section II describes the statutory

requirements of the CAAAs and the variation in regulation that they

imposed. Section III describes the data and presents some summary

statistics on the regulations’ scope. Section IV presents the identification

strategy, and Section V discusses the estimation results. Section VI develops two measures of the magnitude of the regulations’ impacts and

interprets the results. Section VII concludes the paper.

II. The CAAAs and the Variation in Regulation

The ideal analysis of the relationship between industrial activity and

environmental regulations involves a controlled experiment in which

3 A few states and localities (e.g., California) have imposed clean air regulations that

are stricter than the federal ones. Any regulations over and above the federally mandated

ones are unobserved variables in the subsequent analysis.
environmental regulations 1179

environmental regulations are randomly assigned to plants. Then the

changes in activity among the regulated and unregulated can be compared with confidence that any differences are causally related to

regulation.

In the absence of such an experiment, an appealing alternative is to

find a situation in which similar plants face different levels of regulation.

The structure of the 1970 and 1977 CAAAs may provide such an opportunity. In particular, the amendments introduce substantial crosssectional and longitudinal variation in regulatory intensity at the county

level. This section describes the CAAAs and why they may offer the

opportunity to credibly identify the relationship between environmental

regulation and industrial activity.

A. The CAAAs and Their Enforcement

Before 1970 the federal government did not play a significant role in

the regulation of air pollution; that responsibility was left primarily to

state governments. In the absence of federal legislation, few states found

it in their interest to impose strict regulations on polluters within their

jurisdictions. Disappointed with the persistently high concentrations of

CO, O3,

4 SO2, and TSPs5 and concerned about their detrimental health

impacts,6 Congress passed the 1970 Clean Air Act Amendments.7

The centerpiece of this legislation is the establishment of separate

federal air quality standards for each of the pollutants, which all counties

are required to meet. Appendix table A1 lists these air quality standards.

The stated goal of the amendments is to bring all counties into compliance with the standards by reducing local air pollution concentrations. The legislation requires the EPA to assign annually each county

to either nonattainment or attainment status for each of the four pollutants, on the basis of whether the relevant standard is exceeded.

The CAAAs direct the 50 states to develop and enforce local pollution

abatement programs that ensure that each of their counties attains the

standards. In their nonattainment counties, states are required to de4 There are separate standards for O3 and nitrogen dioxide (NO2), and, in principle, a

county could meet one of these standards but not the other. However, O3 is the result of

a complicated chemical process that involves NO2, and the vast majority of counties that

were nonattainment for NO2 were also nonattainment for O3. As a result, I designated a

county nonattainment for O3 if the EPA labeled it nonattainment for either O3 or NO2.

All future references to O3 refer to this combined measure. 5 In 1987 the EPA changed its focus from the regulation of all particulates (i.e., TSPs)

to the smaller particulate matter (PM10s), which have an aerodynamic diameter equal to

or less than 10 micrometers. In 1997 the PM10 regulation was replaced with a PM2.5 one. 6 See Dockery et al. (1993), Ransom and Pope (1995), and Chay and Greenstone (2002a,

2002b) on the relationship between air pollution and human health. 7 See Lave and Omenn (1981) and Liroff (1986) for more detailed histories of the

CAAAs.
1180 journal of political economy

velop plant-specific regulations for every major source of pollution.

These local rules demand that substantial investments, by either new or

existing plants, be accompanied by installation of state-of-the-art pollution abatement equipment and by permits that set emissions ceilings.

The 1977 amendments added the requirement that any increase in

emissions from new investment be offset by a reduction in emissions

from another source within the same county.8 States are also mandated

to set emission limits on existing plants in nonattainment counties.

In attainment counties, the restrictions on polluters are less stringent.

Large-scale investments require less expensive (and less effective) pollution abatement equipment; moreover, offsets are not necessary.

Smaller investments and existing plants are essentially unregulated. Additionally, nonpolluters are free from regulation in both sets of counties.

Both the states and the federal EPA are given substantial enforcement

powers to ensure that the CAAAs’ intent is met. For instance, the federal

EPA must approve all state regulation programs in order to limit the

variance in regulatory intensity across states. On the compliance side,

states run their own inspection programs and frequently fine noncompliers. The 1977 legislation made the plant-specific regulations both

federal and state law, which gives the EPA legal standing to impose

penalties on states that do not aggressively enforce the regulations and

on plants that do not adhere to them. Nadeau (1997) and Cohen (1998)

document the effectiveness of these regulatory actions at the plant level.

Perhaps the most direct evidence that the regulations are enforced successfully is that air pollution concentrations declined more in nonattainment counties than in attainment ones during the 1970s and 1980s

(Henderson 1996; Chay and Greenstone 2000, 2002a; Greenstone

2002).

B. Which Industries Are Targeted by the CAAAs?

The manufacturing sector is a primary contributor of the four regulated

pollutants. Within this sector, the pollutant-specific regulations apply

only to emitters of the relevant pollutants. An official list of the emitting

industries is unavailable from the EPA, so it was necessary to develop a

rule to divide manufacturers into emitters and nonemitters for each of

the four pollutants. It is important that this assignment rule be accurate,

because the subsequent analysis compares the growth of emitters and

nonemitters, and misclassification will bias the estimated regulation

effects.

8 The reduction in pollution due to the offset must be larger than the expected increase

in pollution associated with the new investment. The offsets could be purchased from a

different facility or generated by tighter controls on existing operations at the same site

(Vesilind, Peirce, and Weiner 1988).
environmental regulations 1181

After exploring a number of alternatives, I use the EPA’s estimates

of industry-specific emissions (see App. table A2) to determine pollutantspecific emitter status. Industries that account for 7 percent or more of

industrial sector emissions of that pollutant are designated an emitter;

all other industries are considered nonemitters.9 This rule aims to mimic

the EPA’s focus on the dirtiest industries in the years in which the CAAAs

were first in force. Its application causes 12 separate industries to be

designated as emitters of at least one of the pollutants. The subsequent

analysis demonstrates that the estimated effects of the regulations are

largely insensitive to other reasonable definitions of emitter status.

Under any rule, each industry could emit any of the 16 (i.e., 24

)

possible combinations of the four pollutants. The 7 percent assignment

rule divides the manufacturing sector such that eight of the possible

combinations are represented. The seven polluting combinations (with

the relevant industry names and standard industrial classification [SIC]

codes in parentheses) are emitters of O3 (printing 2711–89; organic

chemicals 2861–69; rubber and miscellaneous plastic products 30; fabricated metals 34; and motor vehicles, bodies, and parts 371), SO2 (inorganic chemicals 2812–19), TSPs (lumber and wood products 24), CO/

SO2 (nonferrous metals 333–34), CO/O3/SO2 (petroleum refining

2911), O3/SO2/TSPs (stone, clay, glass, and concrete 32), and CO/O3/

SO2/TSPs (pulp and paper 2611–31 and iron and steel 3312–13 and

3321–25). The EPA’s estimates of emissions indicate that the remaining

industries are not major emitters of any of the four pollutants, and I

assign these industries to the clean category.10

C. Summarizing the Variation in Regulation Due to the CAAAs

The structure of the CAAAs provides three sources of variation in which

plants were affected by the nonattainment designations. This subsection

summarizes this variation and highlights its importance from an eval9 See the Data Appendix for further details on the determination of pollutant-specific

emitting status. 10 It is informative to compare this division of the manufacturing sector into polluters

and nonpulluters with those in the previous literature. In each of their papers, Henderson

(1996) and Becker and Henderson (2000, 2001) designate different sets of industries as

subject to O3 nonattainment status. The current paper’s set of ozone emitters spans the

intersection of their three sets, with the exception that the 7 percent rule excludes wood

furniture (SIC 2511) and plastic materials and synthetics (SIC 282). Berman and Bui’s

(1998, 2001) list of regulated industries is not readily comparable with this paper’s list

for at least two reasons. First, their list is not pollutant-specific. Second, their papers

examine local regulations in the South Coast Air Basin that are over and above federal

and state regulations, so their set of regulated industries is likely to be broader than those

scrutinized by the federal EPA. Nevertheless, there is substantial overlap between their list

of industries targeted in the South Coast and the industries that are classified as emitters

of at least one pollutant by this paper’s assignment rule.
1182 journal of political economy

uation perspective. It also briefly discusses some of the sources of this

variation and why they may reinforce the credibility of the subsequent

analysis.

The first dimension of variation is that at any point in time the pollutant-specific nonattainment designations are reserved for counties

whose pollution concentrations exceed the federal standards. This crosssectional variation allows for the separate identification of industry-specific shocks and the regulation effects. This may be especially important

in the 1967–87 period, because there were dramatic shocks (e.g., oil

crises, recessions, and increases in foreign competition) that affected

industries differentially.

The second dimension of variation is that a county’s attainment/

nonattainment designations vary over time as its air quality changes.

Consequently, individual plants might be subject to regulations in one

period but not in a different one. This longitudinal variation allows for

the inclusion of plant fixed effects in equations for plant-level growth.

Consequently, the paper presents estimated regulation effects that are

derived from within-plant comparisons under the attainment and nonattainment regulation regimes.

The third dimension of variation is that within nonattainment counties, only plants that emit the relevant pollutant are subject to the regulations. This intracounty variation allows for estimation of models that

include unrestricted county by period effects so that time-varying factors

common to all plants within a county are not confounded with the effects

of regulation. For example, the 1980–82 recession caused polluting and

nonpolluting manufacturers in Allegheny County, Pennsylvania (i.e.,

Pittsburgh), to reduce their operations. Since Allegheny County was

designated nonattainment for all four pollutants at this time, this decline

would be falsely attributed to the regulations if the intracounty variation

in emitting status were unavailable.

Some of the sources of variation in nonattainment status reinforce

the credibility of an evaluation based on the CAAAs. Specifically, the

county-level nonattainment designations are federally mandated and

therefore may be unrelated to differences in tastes, characteristics, or

underlying economic conditions across counties. Moreover, the nonattainment designations depend on whether local pollution levels exceed the federal standards. And while pollution levels are not randomly

assigned, scientific evidence suggests that during the years under study,

many counties were designated nonattainment because of pollution that

was related to weather patterns—a factor that is unlikely to be related

to local manufacturing sector activity.11

11 Cleveland et al. (1976) and Cleveland and Graedel (1979) document that wind patterns often cause air pollution to travel hundreds of miles and that the concentration of
environmental regulations 1183

III. Data Sources and Summary Statistics

This section comprises four subsections. The subsequent analysis is

based on the most comprehensive data available on manufacturing activity and clean air regulations, and subsection A describes the sources

and structure of these data. Subsection B documents the scope of the

regulatory program both geographically and within the manufacturing

sector. Subsection C examines whether nonattainment status is orthogonal to observable determinants of plant growth. Subsection D explores

whether nonattainment status covaries with county shocks that affect

emitters and nonemitters.

A. Data Sources and Structure

The manufacturing data come from the micro data underlying the five

quinquennial Censuses of Manufactures from 1967 to 1987. In each

census a plant observation contains information on employment, capital

stock, total value of shipments, age, whether it is part of a multiunit

firm, and whether the observation is due to a survey response or derived

from an administrative record. The four-digit SIC code and county of

location allow the data on which pollutants are emitted and nonattainment designations to be merged. Importantly, the censuses contain a

unique plant identifier, making it possible to follow individual plants

over time.12

I linked consecutive Censuses of Manufactures to create four periods:

1967–72, 1972–77, 1977–82, and 1982–87. A plant observation in an

individual period includes information from the censuses at the beginning and end of the period.13 Plants that appear in the first census of

a period but not in the last are considered “deaths”; analogously, plants

that appear in the last but not in the first are designated “births.” Plants

that appear in both censuses of a period are labeled “stayers.”14 There

are 1,737,753 plant observations in these four periods.

O3 in the air entering the New York region in the 1970s often exceeded the federal

standards. Figure 2 below graphically depicts the counties that were designated nonattainment for O3 and reveals that virtually the entire Northeast, even counties without

substantial local production of O3, is O3 nonattainment for at least one period. It is evident

that this region’s nonattainment designations partially reflect its location downwind from

heavy O3 emitters in the Ohio Valley. 12 See the appendix in Davis, Haltiwanger, and Schuh (1996) for a more thorough

description of these data. 13 Approximately 0.5 percent of plants change SIC codes in a period. Plants are equally

likely to switch into and out of emitting industries, so it does not appear that they alter

their SIC code to evade regulation. 14 The permanent plant identifier and the criteria specified by Davis et al. (1996) are

used to determine whether a period-specific plant observation qualifies as a birth, death,

or stayer. The distribution of plants across these categories is 29 percent births, 27 percent

deaths, and 44 percent stayers.
1184 journal of political economy

Each of the 3,070 counties is assigned four pollutant-specific attainment/nonattainment designations in every period. A county’s pollutantspecific designation in a given period is based on its attainment/nonattainment status in the first year of that period (e.g., 1982 determines

the regulatory status for the 1982–87 period). All counties are attainment for the four pollutants in the 1967–72 period because the CAAAs

were not in force until the end of this period. The attainment/nonattainment designations for the 1977–82 and 1982–87 periods are obtained from the list of nonattainment counties published in the Code of

Federal Regulations (CFR) in the first year of those periods.15 The CFR

does not list the identity of the nonattainment counties in the early

1970s, and the EPA does not maintain a historical record of them.

Consequently, I filed a Freedom of Information Act request and obtained data from the EPA’s national pollution monitoring network for

these years. For the 1972–77 period, I consider a county nonattainment

for a pollutant if it had a pollution monitor reading that exceeded the

relevant federal standard in 1972. The Data Appendix provides more

details on the determination of nonattainment/attainment status.

There are at least two reasons that this definition of the regulation

variables is preferable to alternatives based on nonattainment status later

in a period. First, it is unlikely that plants can quickly change their

production processes in response to regulation. Second, Berman and

Bui (1998, 2001) document that the plant-level regulations associated

with nonattainment status often set compliance dates a number of years

in advance.16

B. The Incidence and Geographic Scope of the Nonattainment Designations

Table 1 reports summary information on the incidence of the pollutantspecific nonattainment designations. Column 1 lists the number of counties designated nonattainment for each pollutant, period by period. It

is apparent that the regulatory programs for O3 and TSPs are the most

pervasive.

Column 2 details the number of counties that switch from attainment

to nonattainment between periods, and column 3 enumerates the

15 The publication of nonattainment counties in the CFR begins in 1978, so this year

determines the designations for the 1977–82 period. 16 The determination of nonattainment status from a single year might cause measurement error in the regulation variables, leading to attenuation bias in the estimated effects

of regulation. In order to explore this possibility, I experimented with designating a county

nonattainment if it received this designation in the first or second year of a period or the

year before a period begins. (In the case of the 1982–87 period, this is 1981, 1982, or

1983.) I also used as a measure of regulation the total number of years during the period

in which the county is designated nonattainment. The paper’s findings are unchanged

when nonattainment status is assigned in these alternative ways.
environmental regulations 1185

TABLE 1

Incidence and Changes in Nonattainment Status

Nonattainment

Period t

(1)

Attainment Period t1

and Nonattainment Period t

(2)

Nonattainment Period t1

and Attainment Period t

(3)

A. Carbon Monoxide (CO)

1967–72 0 0 0

1972–77 81 81 0

1977–82 144 90 27

1982–87 137 15 22

B. Ozone (O3)

1967–72 0 0 0

1972–77 32 32 0

1977–82 626 595 1

1982–87 560 104 170

C. Sulfur Dioxide (SO2)

1967–72 0 0 0

1972–77 34 34 0

1977–82 87 75 22

1982–87 60 7 34

D. Total Suspended Particulates (TSPs)

1967–72 0 0 0

1972–77 296 296 0

1977–82 235 108 169

1982–87 176 24 83

Note.—There are 3,070 counties in the Census of Manufactures data files. See the Data Appendix for a description

of how the pollutant-specific nonattainment designations are assigned.

changes from nonattainment to attainment. It is evident that there is

substantial movement into and out of nonattainment status between

periods. For example, of the 945 counties that are designated nonattainment for at least one of the pollutants, only 21 retain the same

designations for all four pollutants throughout the three periods in

which the CAAAs are in force. These changes in regulatory status reflect

a number of factors, including the EPA’s increasing awareness of which

counties exceeded the federal standards (e.g., the large increase in the

number of nonattainment counties between 1972–77 and 1977–82, particularly in the case of ozone), air quality improvement in nonattainment

counties, and deterioration in attainment ones. This intercounty variation in nonattainment status is important for identification purposes

because it allows for the inclusion of county or plant fixed effects in

the econometric models.

Figures 1–4 graphically summarize the incidence of the four nonattainment designations. The shading indicates the number of periods a

county is designated nonattainment for the relevant pollutant: white for

zero, light gray for one, gray for two, and black for three. By moving
1186

Fig. 1.—Incidence of nonattainment for carbon monoxide by county (1972–77, 1977–82, and 1982–87). Source: EPA Air Quality Subsystem 

Code of Federal Regulations (various issues).

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All use subject to JSTOR Terms and Conditions
1187

Fig. 2.—Incidence of nonattainment for ozone by county (1972–77, 1977–82, and 1982–87). Source: EPA Air Quality Subsystem Datab

Federal Regulations (various issues).

This content downloaded from 152.5.254.73 on Fri, 7 Nov 2014 15:44:08 PM

All use subject to JSTOR Terms and Conditions
1188

Fig. 3.—Incidence of nonattainment for sulfur dioxide by county (1972–77, 1977–82, and 1982–87). Source: EPA Air Quality Subsystem 

Code of Federal Regulations (various issues).

This content downloaded from 152.5.254.73 on Fri, 7 Nov 2014 15:44:08 PM

All use subject to JSTOR Terms and Conditions
1189

Fig. 4.—Incidence of nonattainment for total suspended particulates by county (1972–77, 1977–82, and 1982–87). Source: EPA Air Quality 

Database, Code of Federal Regulations (various issues).

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All use subject to JSTOR Terms and Conditions
1190 journal of political economy

TABLE 2

Manufacturing Employment, by Pollutant Emitted and Pollutant-Specific

Attainment Status

1967–72

(1)

1972–77

(2)

1977–82

(3)

1982–87

(4)

CO-emitting plants 1,111,534 1,040,563 951,515 744,061

CO attainment 1,111,534 839,456 648,526 517,767

CO nonattainment ... 201,108 302,989 226,294

O3-emitting plants 5,453,418 5,581,151 5,542,548 5,412,151

O3 attainment 5,453,418 5,108,078 1,294,500 1,492,627

O3 nonattainment ... 473,073 4,248,048 3,919,524

SO2-emitting plants 1,783,243 1,717,904 1,598,742 1,358,083

SO2 attainment 1,783,243 1,468,781 1,233,592 1,170,479

SO2 nonattainment ... 249,123 365,150 187,604

TSPs-emitting plants 2,101,561 2,071,924 1,899,173 1,697,843

TSPs attainment 2,101,561 1,303,442 1,114,749 1,160,430

TSPs nonattainment ... 768,482 784,424 537,413

Total manufacturing sector 17,438,187 17,350,726 17,521,355 17,100,413

Note.—See the note to table 1. Employment is the mean of total employment in the first and last years of each fiveyear period covered by the 1967–87 Censuses of Manufacturers.

back and forth between the maps, one can see that many counties were

regulated for more than one pollutant (e.g., parts of southern California, Arizona, and the Rust Belt). The national scope of the regulatory

programs is also evident: all 48 continental states have at least one

nonattainment county.17

Table 2 presents the levels of employment for emitters of each of the

pollutants and the entire manufacturing sector in the four periods.18

The level is calculated as the mean of the levels in the first and last

years of a period. The table also separately lists employment in nonattainment and attainment counties within the four categories of emitters

by period.

The portion of the manufacturing sector that is an emitter varies

across the pollutants. For instance, O3 emitters account for the largest

share (roughly 31.7 percent) of total manufacturing employment. The

shares for the other polluting industries are 11.2 percent for TSPs, 9.3

percent for SO2, and 5.5 percent for CO. Although they are not shown

in table 2, the ranges for capital stock and shipments are 19.9 percent

(TSPs emitters) to 46.2 percent (O3 emitters) and 10.7 percent (TSPs

emitters) to 37.9 percent (O3 emitters), respectively. Regardless of the

measure, it is apparent that the emitting industries account for a substantial proportion of the manufacturing sector.

17 Alaska and Hawaii are excluded from the analysis. 18 Many plants emit multiple pollutants, so the pollutant-specific rows (e.g., CO-emitting

plants) of table 2 are not mutually exclusive. Consequently, summing across the rows

within a single period overstates employment in plants that emit any pollutant in that

period.
environmental regulations 1191

Table 2 also documents that within the four sets of emitting plants,

a meaningful share of employment is located in both attainment and

nonattainment counties. Consequently, it may be possible to obtain precise estimates of the effects of the pollutant-specific nonattainment designations. Finally, the level of employment in emitting industries located

in nonattainment counties is a summary measure of the size of the group

that was potentially affected by these designations.

C. Is Nonattainment Status Orthogonal to Observable Determinants of

Plant Growth?

In the ideal case, nonattainment status would be orthogonal to all determinants of plant growth. The regulation effects could then be calculated by a simple comparison of mean growth rates in the two sets

of counties.

While it is impossible to make statements about unobserved covariates,

it is instructive to compare observable ones in nonattainment and attainment counties. If the observable covariates are balanced across the

two sets of counties, then the unobservables may be more likely to be

balanced (Altonji, Elder, and Taber 2000). Further, consistent inference

does not rely on functional form assumptions about the relationship

between the observables and plant growth when the observable determinants are balanced. To the extent that the observables are unbalanced, these comparisons will identify likely sources of bias and inform

the choice of statistical model.

Table 3 displays the means of determinants of plant growth within

three categories of counties. These categories comprise counties that

are attainment for CO in the 1972–77 period (col. 1a), attainment for

CO in 1972–77 but CO nonattainment in a later period (col. 1b), and

CO nonattainment in 1972–77 (col. 2). Panel A of the table presents

means of county-level covariates, and panel B documents means of the

characteristics of CO-emitting plants. The comparison of 1972–77 CO

nonattainment and attainment counties is only one of the comparisons

that underlie the subsequent analysis, but it captures many of the themes

that are present in comparisons of nonattainment and attainment counties in different periods and for different pollutants.

Inspection of columns 1a and 2 provides a comparison of all CO

attainment counties with CO nonattainment counties in the 1972–77

period. It is evident that both the county-level and plant-level characteristics differ with nonattainment status. In particular, nonattainment

counties have higher population densities, rates of urbanization, average

education levels, per capita income, and per capita government revenues. Moreover, a smaller fraction of their jobs are in the manufacturing

sector, and they have lower poverty rates. Importantly, the average num1192 journal of political economy

TABLE 3

Means of County and Plant Characteristics by 1972–77 CO Nonattainment

Status

CO Attainment,

1972–77

(1a)

CO Attainment,

1972–77, and CO

Nonattainment,

1977–82 or 1982–87

(1b)

CO Nonattainment, 1972–77

(2)

A. County Characteristics in 1970

Number of counties 2,989 100 81

Population 47,157 395,376 620,654

Population density 1,826 6,354 4,868

% urban .65 .90 .94

% ≥12 years of education .50 .55 .57

% ≥16 years of education .10 .11 .13

% employment in

manufacturing .262 .266 .242

Unemployment rate .044 .045 .046

Poverty rate .119 .082 .081

Income per capita

(1982–84 dollars) 7,456 8,712 9,414

Per capita government

revenues 248 296 403

B. CO-Emitting Plant Characteristics in 1972

Number of CO-emitting

plants 1.0 6.8 14.2

Average employment 269 362 175

% operating at least 10

years 55.2 59.3 51.3

% part of multiunit firm 34.6 40.7 40.1

Note.—See the note to table 1. All entries are averages across counties in the relevant category. The data on county

characteristics are derived from the 1970 Census. The 1972 Census of Manufactures is used to determine the means

of CO-emitting plant characteristics. The entries in col. 1a are calculated from the 2,989 counties that are designated

CO attainment in the1972–77 period, and the sample in col. 2 comprises the 81 counties that are CO nonattainment

in the same period. Col. 1b is the subset of the col. 1a counties that are CO attainment in 1972–77 and CO nonattainment

in at least one of the 1977–82 and 1982–87 periods.

ber of CO-emitting plants is substantially higher in nonattainment counties (14.2) than in attainment counties (1.0). Further, CO-emitting

plants in nonattainment counties are younger, more likely to be part

of a multiestablishment firm, and smaller (as measured by employment).

An alternative to forming the “counterfactual” from all CO attainment

counties is to restrict this group to counties that are CO attainment in

1972 but CO nonattainment in later periods. A statistical model that

includes county fixed effects effectively refines the counterfactual group

in this way. Columns 1b and 2 permit an exploration of the similarity

of these two sets of counties. It is evident that this subset of 1972–77

CO attainment counties is more similar to the nonattainment counties

than the unrestricted set of attainment counties was. For example, the

means of the population density, level of education, income per capita,
environmental regulations 1193

and poverty rate in column 1b are all closer to the means of these

variables in nonattainment counties. However, the average number of

CO-emitting plants and the mean characteristics of these plants differ

across these columns.

It is apparent that nonattainment status is not orthogonal to observable county- or plant-level determinants of plant growth in either set of

attainment counties. Moreover, it is plausible that the same is true for

unobservable characteristics. It will be necessary to estimate statistical

models that attempt to control for these differences to obtain consistent

estimates of the regulation effects.

D. Do Countywide Shocks Covary with Nonattainment Status?

This subsection explores the validity of the assumption that nonattainment status is orthogonal to county-specific determinants of growth that

are common to polluters and nonpolluters. This identifying assumption

is pervasive in the previous literature (e.g., Bartik 1985; Barbera and

McConnell 1986; McConnell and Schwab 1990; Henderson 1996; Levinson 1996; Berman and Bui 1998, 2001; Becker and Henderson 2000,

2001). For brevity I focus on the case in which the dependent variable

is the percentage growth in plant employment, but the findings are

similar for capital stock and shipments.19

Table 4 presents two estimates of the effect of the regulation of each

pollutant on employment growth. The first estimate is derived from a

sample that is limited to plants that emit the relevant pollutant and is

contained in column 1. The column 2 estimate is obtained from all

1,620,942 plant observations with nonmissing employment growth. In

both cases the reported parameter is taken from an indicator that is

equal to one if the county is nonattainment for the specified pollutant

and the plant is an emitter of that pollutant.

The regressions control for a number of plant-level variables that the

next section describes in greater detail. Additionally, the two specifications include county fixed effects and industry by period indicators.

For the column 1 specification’s estimated regulation effect to be unbiased, it is necessary to assume that the regulation of that pollutant is

the only county-level determinant of employment growth that differs

between nonattainment and attainment counties. In contrast, the column 2 specification controls for unobserved, permanent county-level

determinants of growth common to emitters and nonemitters.

A comparison of the estimates in columns 1 and 2 provides an in19 The percentage growth is calculated as the change in plant employment between t

and divided by the mean of the t  5, t and levels. Section IV provides more details t  5

about this measure of percentage change.
TABLE 4

Estimated Regression Models for the Percentage Change in Employment with One Regulation Effect per Regressio

Carbon Monoxide Ozone Sulfur Dioxide

Total Suspended

Particulates

CO Emitters

(Np14,456)

(1)

All Plants

(Np1,620,942)

(2)

O3 Emitters

(Np543,121)

(1)

All Plants

(Np1,620,942)

(2)

SO2 Emitters

(Np99,854)

(1)

All Plants

(Np1,620,942)

(2)

TSPs Emitters

(Np257,135)

(1)

All Plants

(Np1,620,94

(2)

CO regulation effect .041

(.040)

.074

(.031)

O3 regulation effect .068

(.011)

.025

(.009)

SO2 regulation effect .049

(.030)

.040

(.027)

TSPs regulation effect .021

(.017)

.016

(.014)

2 R .127 .100 .112 .100 .095 .100 .121 .100

Note.—The entries are taken from regressions in which the dependent variable is the change in plant employment between t and divided by the mean of the t  5, t a

t  5 levels. The equations are weighted by the denominator of the dependent variable. All specifications include county fixed effects and industry by period indicatoHeteroskedastic-consistent standard errors are reported in parentheses.

This content downloaded from 152.5.254.73 on Fri, 7 Nov 2014 15:44:08 PM

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environmental regulations 1195

formal test of this assumption. The estimates will differ if nonemitters’

growth rate covaries with nonattainment status. The regulation effects

for SO2 and TSPs are similar in the two columns. However, the regulation

effects for CO and O3 in column 1 appear to be biased upward. Most

dramatically, the column 1 O3 estimate suggests that nonattainment

status at the beginning of a period is associated with a 6.8 percent

increase in employment in O3-emitting industries five years later. Since

pollution can be modeled as an input and regulation as a tax on pollution, standard neoclassical models predict an ambiguous effect on

demand for other inputs (e.g., labor). Nevertheless, such a large, positive

effect is surprising. In column 2, the estimated regulation effect for O3

shrinks to 2.5 percent, demonstrating the importance of allowing for

county-specific factors common to emitters and nonemitters.

It is evident that in the case of CO and O3, nonattainment status is

not orthogonal to county-level shocks to growth. The next section describes the preferred statistical models and explains how they try to

purge the likely sources of bias.

IV. Identification Strategy

In order to explore more rigorously the effects of the nonattainment

designations on the growth of manufacturers’ activity, the plant-level

data are fit to the following equation:

Ept pt  E 5

%DEpt p (Ept pt E 5)/2

p b1Xpt5 2 b t i ind b3t ct nonattain 5

b41(emit CO p 1 & nonattain CO p 1)cit5

b53 3 1(emit O p 1 & nonattain O p 1)cit5

b62 2 1(emit SO p 1 & nonattain SO p 1)cit5

b 1(emit TSPs p 1 & nonattain TSPs p 1) De , 7 cit5 pt

where Here De p a g Du . p indexes a plant, c references county, pt p ct pt

i indexes industry, and t and index the last and first years of a t  5

period, respectively. The term %DEpt is the dependent variable (i.e.,

employment, capital stock, and the value of shipments) and is measured
1196 journal of political economy

as the percentage change between t and 20 The term Dept t  5. is the

stochastic error term. Equation (1) is weighted by the denominator of

the dependent variable to account for differences in cell size.

The term is a vector of variables, calculated at so that they Xpt5 t  5

are “pretreatment.” There are indicators for four categories of plant

size based on shipments (i.e., smaller than the median, between the

median and the seventy-fifth percentile, between the seventy-fifth percentile and the mean, and greater than the mean); whether the plant

has operated for at least 10 years; ownership by a firm with multiple

establishments; and whether the observation is a response to the Census

Bureau questionnaire or is derived from federal administrative records.

Previous research shows that these variables are important determinants

of plant-level growth (Dunne, Roberts, and Samuelson 1989a, 1989b;

Davis and Haltiwanger 1992). The vector also contains the average Xpt5

industry-specific wage in the plant’s county as a measure of labor costs

and the number of employees at other plants in the same industry within

the same county to adjust for agglomeration effects (Krugman 1991).

The term indi is a vector of industry indicator variables whose effects

are allowed to vary by period. In most of the subsequent analysis, there

are 13 industry indicators: one for each of the 12 industries that are

classified as an emitter of at least one of the four regulated pollutants

and one for the remaining “clean” industries. These variables nonparametrically absorb all time-varying industry-level unobservables at the

level at which the regulations are applied. Further, the nonattainct5

vector contains a separate dummy variable for each of the four pollutantspecific nonattainment designations. These dummies control for unobserved factors that equally affect polluting and nonpolluting plants in

nonattainment counties. Their effect is also allowed to vary by period.

The parameters b4–b7 capture the variation in the dependent variables

specific to polluting plants (relative to nonpolluters) in nonattainment

counties (relative to attainment ones). These parameters provide estimates of the mean effect of the pollutant-specific regulations on the

plants that are directly targeted by them. Henceforth, they are referred

to as the “regulation effects.” An attractive feature of this specification

is that, in contrast to the previous literature, each of the estimated

20 This measure of percentage change is an alternative to the difference of the natural

logarithms of the year t and levels. It is a second-order approximation to the ln t  5

difference measure, ranges from 2.0 to 2.0, and portrays expansion and contraction

symmetrically (Davis et al. 1996). Importantly, it allows the sample to contain observations

on “births” and “deaths,” i.e., plants that do not operate in either the first or last year of

a period. A comparison of the results from a sample of “stayers” reveals that the estimated

regulation effects are nearly identical when the dependent variable is calculated as the ln

difference.
environmental regulations 1197

regulation effects is obtained while holding the others constant.21 This

is relevant because many plants were subject to more than one of the

nonattainment designations.22

Prior research indicates that there are important permanent and transitory regional determinants of manufacturing activity.23 There are a

number of ways to model these factors with the available data. One

possibility is to include county fixed effects so that counties that were

never designated nonattainment for a particular pollutant do not help

identify the parameters of interest. In this case, the pollutant-specific

regulation effects are estimated from 189 (CO), 730 (O3), 134 (SO2),

and 436 (TSPs) counties.

As the specification of Dept indicates, another possibility is to include

a full set of fixed effects for the more than 735,000 plants in the sample

and county by period indicators. The plant fixed effects greatly reduce

the degrees of freedom, but they control for differences in permanent

plant growth rates that might be correlated with nonattainment status.

Such a correlation might occur if nonattainment counties provide the

conditions necessary for emitting plants or industries to flourish (e.g.,

easy access to the interstate highway system, a workforce that suits their

technology, or proximity to a natural resource). In this specification,

the regulation effects are identified from within-plant comparisons of

growth rates under the nonattainment and attainment regimes. The

county by period indicators nonparametrically adjust for time-varying

shocks to growth common to emitters and nonemitters within the same

county.

In the subsequent tables, heteroskedastic-consistent standard errors

of the regression parameters are reported (White 1980). Since the data

are taken from censuses, the standard errors’ interpretation is not

straightforward. On the one hand, the sample includes all the members

of a finite population, so the standard errors need not be calculated.

On the other hand, the observed finite population can be considered

21 I also experimented with including the 12 “cross-pollutant” interactions (e.g., 1(emit

O3 p 1 & nonattain CO p 1) in the specification. Across the dependent variables and

specifications, the hypothesis that they are jointly equal to zero is generally not rejected

by a test at standard confidence levels. Moreover, in these plant-level regressions, their 2 x

inclusion does not substantially alter the estimates of the four regulation effects. Notably,

the cross-pollutant interactions are more important in grouped regressions and with

stricter definitions of emitter status, as in Greenstone (1998). 22 McConnell and Schwab (1990), Henderson (1996), and Becker and Henderson (2000,

2001) use the equivalent of the O3 nonattainment designation but restrict the effect of

the other pollutant-specific designations to equal zero. The remainder of the literature

uses regulatory measures that do not account for the pollutant-specific nature of the

CAAAs. 23 Bartik (1985) and Holmes (1998) show that a number of local factors including

unionization density, tax rates, the provision of public services, and right-to-work laws

affect firms’ investment decisions. Moreover, Blanchard and Katz (1992) demonstrate that

shocks to regions’ growth rates can persist for as long as a decade.
1198 journal of political economy

a member of an unobserved superpopulation; thus the standard errors

associated with regression parameters have their usual interpretation.

In summary, the estimated regulation effects are purged of many likely

sources of bias. For example, the specification that includes plant fixed

effects, county by period indicators, and industry by period dummies is

robust to all unobserved permanent determinants of plant growth, all

unobserved transitory factors common to polluting and nonpolluting

plants within a county, and all unobserved industry-specific shocks to

growth. However, the estimated regulation effects are not robust to

transitory determinants of growth specific to emitting industries (or

plants) located in counties that are nonattainment for the emitted pollutant(s). In other words, county by industry and county by plant shocks

to growth are potential sources of bias.

V. The Amendments’ Impact on Manufacturing Sector Activity

This section is divided into three subsections. Subsection A presents the

estimated effects of the regulations on the growth rates of employment,

shipments, and capital from fitting the preferred specifications discussed

in Section IV. Subsection B tests for heterogeneity in the regulation

effects across industries. Subsection C probes the robustness of the

results.

A. The Effects of the CAAAs on Manufacturing Activity

In a standard neoclassical model in which pollution, labor, and capital

are inputs in the production process, the predicted effect of regulation,

which increases the price of pollution, on labor and capital demand is

ambiguous. The theoretical prediction on output is unambiguously negative. This subsection tests these predictions.

Total Employment

Table 5 presents the employment results from the estimation of equation

(1), using data from all plant observations over the four periods. The

columns correspond to specifications that include additional sets of

controls as one reads from left to right; the exact controls are noted at

the bottom of the table. The mean five-year growth rate of total employment is 1.4 percent.

The specification in column 1 includes industry by period fixed effects

and allows the effect of nonattainment status to vary by period. Here,

the estimated regulation effects are derived from comparisons between

all attainment and nonattainment counties.

The results in column 1 suggest that nonattainment status modestly
environmental regulations 1199

TABLE 5

Estimated Regression Models for the Percentage Change in Employment

(1) (2) (3) (4)

CO regulation effect (b4) .084

(.032)

.075

(.031)

.086

(.030)

.163

(.045)

O3 regulation effect (b5) .001

(.011)

.022

(.010)

.011

(.010)

.049

(.015)

SO2 regulation effect (b6) .004

(.029)

.016

(.028)

.003

(.029)

.001

(.036)

TSPs regulation effect

(b7)

.024

(.014)

.010

(.013)

.020

(.013)

.024

(.024) 2 R .109 .119 .144 .504

Industry by period fixed

effects yes yes yes yes

Nonattainment by period

fixed effects yes yes no no

County fixed effects no yes no no

County by period fixed

effects no no yes yes

Plant fixed effects no no no yes

Note.—See the note to table 4. In all specifications, the sample includes the 1,620,942 plant observations with

nonmissing and nonnegative employment levels. The mean five-year growth rate of employment in the sample is 1.4

percent.

retards the growth of employment. The estimates indicate that a CO

nonattainment designation at the beginning of a period is associated

with an 8.4 percent reduction in employment levels in CO-emitting

plants five years later. This estimate would be judged statistically significant at conventional levels. The regulation effect for TSPs is 2.4 percent and would be considered significant at the 10 percent level but

not by stricter criteria. In contrast, O3 and SO2 nonattainment statuses

are basically uncorrelated with the respective growth of emitters of those

pollutants. Interestingly, the estimated regulation effects for O3 and SO2

differ from the estimates that did not account for the effects of the

other nonattainment designations as in table 4.

Columns 2 and 3 report the results from adding county fixed effects

and county by period effects to the specification, respectively. In both

cases, F-tests easily reject the null that the additional parameters are

jointly equal to zero. As discussed above, the regulation effects from the

specification in column 2 are due to comparisons of counties that experience a change in attainment status over the course of the sample.

The estimates in column 3 are based on comparisons between emitters

and nonemitters within nonattainment counties. In light of the differences in these first three specifications, it is striking that the estimated

regulation effects are essentially the same across the columns.

The specification that requires the least restrictive assumptions for

unbiasedness of the regulation effects is the one in column 4, which

includes a full set of plant fixed effects. All permanent differences in
1200 journal of political economy

plant growth rates are controlled for here. As evidenced by the marked

increase in the statistic (.504 compared to .144), the “fit” of the 2 R

regression is substantially greater. However, an F-test fails to reject the

null that the plant fixed effects are jointly equal to zero. This “overparameterization” explains the increased standard errors of the four

regulation effects.

The intent in estimating this model is to probe the robustness of the

estimated regulation effects from columns 1–3. In this specification, two

of the regulation effects imply a larger negative effect on employment

and two are essentially unchanged relative to the other specifications.

In particular, CO nonattainment status at the beginning of a period is

associated with a 16.3 percent decline in employment in CO-emitting

plants by the end of the period. The magnitude of the regulation effect

for O3 is larger, and the estimate is now 0.049; moreover, it would be

judged statistically significant at standard levels. The increased magnitude of these two regulation effects is consistent with the notion that

CO and O3 nonattainment counties offer competitive advantages to

emitters of these pollutants. In contrast, the regulation effects for SO2

and TSPs are essentially unchanged from the other specifications.24

Capital Stock

The last subsection documented a robust negative correlation between

nonattainment status and employment growth. Here, I explore whether

nonattainment status is associated with the capital stock growth rate.

Investment may be particularly sensitive to regulation because it reflects

plants’ conjectures about future profitability. Although it is difficult for

plants to adjust their capital stock in the short run, the length of time

between observations (five years) means that any impact of regulation

should be apparent. In particular, it is likely that five years is enough

time for establishments to bring new investments “on line,” to substantially reduce their capital stock through depreciation,25 to open new

plants, or to cease operations. Interestingly, the previous literature finds

24 It is thought that environmental regulations weaken polluters’ competitive position

by causing them to hire additional nonproduction workers (e.g., engineers or environmental compliance officers) that aid in ensuring adherence to the regulations but do not

directly contribute to the production of the firm’s output. I examined this hypothesis and

found that the regulations’ effects were approximately equal across production and nonproduction workers. In other words, these data do not support this hypothesis. 25 Dixit and Pindyck (1994) show that the sunk cost nature of many investments combined with uncertainty about the future may make it more profitable for a firm to respond

to a large negative shock by allowing its capital stock to depreciate, rather than by ceasing

operations.
environmental regulations 1201

that environmental regulations are not a significant deterrent to new

investment in plants and equipment.26

There are at least three limitations to the Census of Manufactures

data on capital stock. First, the censuses’ measure of capital stock comprises productive capital and potentially “nonproductive” pollution

abatement equipment that is mandated by the regulations. This combined measure may cause the estimated regulation effects to be biased

upward, relative to the preferred measure of productive investment.27

Second, the book value method is used to measure capital stock, which

likely overstates the importance of recent investment relative to a perpetual inventory measure.28 Third, the capital stock measure does not

allow for a test of whether the regulations cause plants to change the

rate of new investment or affect the value of existing capital. A measure

of capital stock that separates new investment from the depreciation/

retirement of existing capital would allow for a more nuanced analysis.

Panel A of table 6 presents estimates of the impact of the nonattainment designations on capital stock accumulation. The mean five-year

growth rate of capital stock when the book value method is used is 36.5

percent. The columns correspond to specifications that include additional sets of controls as in table 5.

Across the specifications, the capital stock estimates suggest that nonattainment status retards investment, but the evidence is less decisive

than in the employment regressions. Similarly to the employment results, the estimated regulation effects are roughly constant across the

first three specifications. The commonality of these estimates is especially apparent in the context of the standard errors. The estimates

indicate that the effect of the nonattainment designations on capital

stock ranged from small and positive (TSPs) to somewhat large and

negative (CO and SO2). However, the regulation effect for CO in column

3 is the only one that would be judged statistically different from zero.

The addition of plant fixed effects in column 4 greatly increases the

26 A review article concludes that “environmental regulations do not deter investment

to any statistically or economically significant degree” (Levinson 1996). 27 The “lumping” of these two types of investment together introduces a positive, mechanical relationship between regulation and observed investment. A preferred measure

of capital stock would exclude the investments in pollution abatement equipment that

were mandated by the amendments. The 1986 Pollution Abatement Costs and Expenditures Survey provides some indirect evidence on the magnitude of this bias. It shows that

the heaviest-polluting industries devote approximately 4–10 percent of total investment

to abatement equipment. This share is likely to be larger in nonattainment counties and

indicates that the upward bias may not be insignificant (see Becker 2001). 28 A book value system permanently records the value of an investment at its purchase

price. This value is never updated to reflect inflation or changes in the good’s market

value. Therefore, the relative contribution of recent investment, which is entered in current dollars, is overstated. A perpetual inventory measure of capital stock accounts for

these changes but is not feasible with the Census of Manufactures questionnaire.
1202 journal of political economy

TABLE 6

Estimated Regression Models for the Percentage Change in Capital Stock and

Shipments

(1) (2) (3) (4)

A. Capital Stock (Np1,607,332)

CO regulation effect (b4) .047

(.043)

.047

(.042)

.097

(.043)

.092

(.062)

O3 regulation effect (b5) .009

(.022)

.016

(.021)

.001

(.021)

.041

(.029)

SO2 regulation effect (b6) .024

(.047)

.048

(.049)

.057

(.055)

.063

(.048)

TSPs regulation effect

(b7)

.026

(.027)

.042

(.025)

.010

(.024)

.043

(.039) 2 R .074 .109 .155 .462

B. Shipments (Np1,737,753)

CO regulation effect (b4) .058

(.029)

.036

(.029)

.072

(.029)

.146

(.046)

O3 regulation effect (b5) .022

(.018)

.048

(.018)

.019

(.016)

.032

(.024)

SO2 regulation effect (b6) .007

(.033)

.026

(.030)

.027

(.030)

.010

(.039)

TSPs regulation effect

(b7)

.014

(.019)

.002

(.018)

.010

(.018)

.032

(.034) 2 R .127 .142 .185 .516

Industry by period fixed

effects yes yes yes yes

Nonattainment by period

fixed effects yes yes no no

County fixed effects no yes no no

County by period fixed

effects no no yes yes

Plant fixed effects no no no yes

Note.—See the note to table 5. The mean five-year growth rates of capital stock and shipments are 36.5 percent and

10.0 percent, respectively.

statistic. But the null that these extra parameters are jointly equal 2 R

to zero is not rejected at conventional significance levels. As in the

employment regressions, this specification indicates that the nonattainment designations have a larger negative impact on growth. In particular, the estimated regulation effects from this specification are 0.092

for CO, 0.041 for O3, 0.063 for SO2, and 0.043 for TSPs. The loss

of the more than 700,000 degrees of freedom causes three of the four

standard errors to increase so that the null hypothesis of zero is not

rejected for any one of them.

Shipments

Panel B of table 6 reports estimation results for the growth in constantdollar shipments. The mean five-year growth rate of shipments is 10.0
environmental regulations 1203

percent.29 In columns 1–3, the regulation effect for CO is negative and

statistically distinguishable from zero in two of the three specifications.

These estimates indicate that CO nonattainment status is associated with

a 3.6–7.2 percent decrease in shipments by CO emitters. The regulation

effect for O3 is small and positive, and those for SO2 and TSPs are small

and negative.

As with the employment and capital stock regressions, controlling for

plant fixed effects in column 4 causes the estimated negative effects of

nonattainment status to have a greater magnitude. In this specification,

the estimated regulation effects are 0.146 for CO, 0.032 for O3,

0.010 for SO2, and 0.032 for TSPs. Again the interpretation of the

standard errors is not obvious, but the regulation effect for CO is the

only one that is statistically significant at conventional levels. Overall,

these results imply that nonattainment status, particularly CO nonattainment status, is associated with a reduction in shipments by polluting

manufacturers.

A Comparison of the Estimates across the Dependent Variables

A comparison of the estimates across the three dependent variables

within and across specifications provides a crude view into the “black

box” of how firms respond to environmental regulations. For example,

consider the regulation effects for CO. In the specifications in columns

1–3, they range from 0.075 to 0.086 for employment, 0.047 to

0.097 for capital stock, and 0.036 to 0.072 for shipments. The

estimates from the specification in column 4 are 0.163, 0.092, and

0.146, respectively. Within these two divisions of the specifications, the

estimates are approximately equivalent across the dependent variables,

particularly in the context of the associated standard errors. The same

pattern is evident in the effects of the other nonattainment designations,

although they are not as large either economically or statistically. Overall, the estimates suggest that the nonattainment designations cause the

growth of employment, capital stock, and shipments to decline by

roughly equivalent proportions.30

B. Is There Heterogeneity in the Regulation Effects across Industries?

This subsection explores whether the regulation effects vary by industry.

This is informative for at least two reasons. First, it serves as an internal

29 Four-digit industry deflators from the Bartelsman and Gray (1994) NBER Productivity

Database are used to express the total value of shipments in 1987 dollars. 30 It would be informative to have plant-level data on pollution emissions. These data

would allow for the calculation of the marginal rate of technical substitution between

pollution and labor or capital. These measures of the ease of substitution are important

policy parameters and are left for future research.
1204 journal of political economy

validity check on the results above. If the negative effects are concentrated in a small subset of industries, it may be reasonable to assume

that the overall regulation effects are due to an unobserved factor that

is unrelated to regulation. As an example, union activism might differ

over time and the union activity in a particular industry might be more

heavily concentrated in nonattainment counties (e.g., in the Rust Belt).

Further, such an unobserved factor could interact with the dramatic

reductions in demand experienced by some industries during the periods under consideration; for instance, employment of production

workers in primary metal industries (SIC code 33) declined from

1,059,000 in 1967 to only 538,000 in 1987. Second, it provides an opportunity to measure the effects of these regulations across industries.

This could be useful in evaluating the claims that particular industries

are especially harmed by the CAAAs.

Table 7 presents the industry-specific regulation effects from the estimation of equation (1) for employment. The results for capital stock

and shipments are qualitatively similar but are not presented here because of space considerations. The estimated specification includes plant

fixed effects, county by period effects, and industry by period effects,

as in column 4 of table 5. The regulation effects are allowed to vary

across the industries that emit the relevant pollutant, so there are a

total of 23 estimated regulation effects. Columns 1–4 report the industryspecific regulation effects and heteroskedastic-consistent standard errors

(in parentheses). Each row pertains to an industry so that by reading

down a column, one can compare the pollutant-specific regulation effects in each of the relevant industries. The final row lists the statistic 2 x

and associated p-value (in parentheses) from tests that the pollutantspecific regulation effects are equal across industries.

A number of points emerge from the table. First, it is apparent that

the estimation of industry-specific regulation effects demands a lot from

the data. For example, the standard errors are substantially larger than

they were in table 5. Notably, the positive estimates tend to be especially

poorly determined.

Second, the four tests fail to reject the null hypothesis that the 2 x

pollutant-specific regulation effects are equal across industries. This is

certainly related to the imprecision of the estimates, but an “eyeball”

test does reveal striking similarities in the parameters within a column

(see especially the CO and TSPs effects).

Third, almost all the emitting industries are negatively affected by the

nonattainment designations. Only five of the 23 estimated industry-specific regulation effects are greater than zero. Of these five, four occur

in industries that emit other pollutants for which the associated regulation effect is negative; thus the overall effect of the CAAAs on these

industries may still be negative. I conclude that the estimated regulation
environmental regulations 1205

TABLE 7

Do the Employment Regulation Effects Vary by Industry?

Industry Name (SIC Code)

CO

Regulation

Effects

(1)

O3

Regulation

Effects

(2)

SO2

Regulation

Effects

(3)

TSPs

Regulation

Effects

(4)

Lumber and wood (24) .006

(.034)

Pulp and paper (2611–31) .080

(.077)

.110

(.056)

.105

(.074)

.006

(.064)

Iron and steel (3312–13,

3321–25)

.177

(.061)

.104

(.068)

.038

(.059)

.012

(.050)

Printing (2711–89) .072

(.027)

Organic chemicals

(2961–69)

.071

(.151)

Rubber and plastic (30) .093

(.046)

Fabricated metals (34) .013

(.026)

Motor vehicles (371) .026

(.057)

Inorganic chemicals

(2812–19)

.089

(.113)

Petroleum refining (2911) .133

(.092)

.172

(.101)

.180

(.109)

Stone, clay, and glass (32) .072

(.039)

.039

(.062)

.063

(.039)

Nonferrous metals (333–34) .169

(.163)

.063

(.147)

x2 statistic of equality 1.03

(.79)

11.67

(.17)

5.82

(.32)

1.57

(.67)

Note.—See the note to table 5. All the entries are taken from a single regression in which the dependent variable

is the change in plant employment between t and divided by the mean of the t  5 t and levels. The specification t  5

includes plant fixed effects, county by period effects, and industry by period effects, as in col. 4 of table 5. The regulation

effects are allowed to vary across the industries that emit the relevant pollutant. Cols. 1–4 report the industry-specific

regulation effects and heteroskedastic-consistent standard errors (in parentheses). The last row lists the x2 statistic and

associated p-value (in parentheses) from tests that the pollutant regulation effects are equal across industries that emit

the relevant pollutant.

effects in table 5 do not reflect the experiences of a small subset of

emitting industries.

Fourth, the total effect of the regulations is particularly harsh on

industries that emit multiple pollutants in counties that are nonattainment for those pollutants. For example, a literal interpretation of the

coefficients suggests that pulp and paper plants located in counties that

are nonattainment for all four pollutants at the beginning of a period

experience an employment decline of almost 29 percent over five years.

Similar calculations suggest that employment declines by 14.1 percent

in a period at petroleum-refining plants in counties that are nonattainment for CO, O3, and SO2.
1206 journal of political economy

C. Robustness Checks

This paper has used variation in regulation across counties, industries,

and time in an effort to estimate the causal effect of regulation on

industrial activity. However, as is always the case with a nonexperimental

design, there is a form of unobserved heterogeneity that can explain

the findings without a causal interpretation. In addition to the efforts

presented above, I probed the robustness of the estimates in a number

of other ways but found little evidence that undermines the basic

conclusions.

Table 8 reports the results of some of these robustness checks in

columns 1–3. The entries are the estimated regulation effects and heteroskedastic standard errors (in parentheses). The results for the three

dependent variables are in separate panels. Each column represents a

different specification or sample. All specifications include county by

period fixed effects and industry by period indicators. The results are

qualitatively similar when the specification with plant fixed effects is fit,

but the standard errors increase substantially because two of the robustness checks significantly cut the sample size. The entries in column

0 are taken from column 3 of tables 5 (employment) and 6 (capital

stock and shipments) and should be compared to the entries in the

other columns.

One potential source of bias arises from the manner in which nonattainment status is determined and dynamics in the growth of manufacturing activity. Recall that a county’s nonattainment designations are

determined by its pollution concentrations, which are increasing in manufacturing activity. Thus nonattainment status in the first year of a period

is likely an increasing function of previous growth. This may induce a

mechanical correlation between the regulation variables and the unobserved components of the dependent variable in equation (1) if manufacturing growth follows a dynamic process. When the process is meanreverting, this correlation is likely to bias the estimated regulation effects

downward.31

To determine whether the results above are due to dynamics, column

1 presents results from the estimation of an equation that includes as

controls the lagged value of the dependent variable (i.e., the percentage

change between and ) and interactions of the lag with the t  5 t  10

four pollutant-emitted indicators. The parameters from the lagged de31 To understand the direction of bias, consider the case in which there is “above-average”

growth among emitters of a pollutant in a county in the period between and t  5 t 

10. This growth might cause the county to be designated nonattainment in If the t  5.

dependent variable follows a mean-reverting process, these polluters are likely to have

smaller growth in the period between t and This slower growth would have occurred t  5.

even in the absence of regulation, yet the regression would attribute this decline to

regulation.
environmental regulations 1207

TABLE 8

Probing the Robustness of the Regulation Effects

Base

Specification

(0)

Dynamic

Model

(1)

Limit Sample

to “Stayers”

(2)

4.5%

Emission Rule

(3)

A. Total Employment

CO regulation effect (b4) .086

(.030)

.094

(.028)

.059

(.023)

.097

(.028)

O3 regulation effect (b5) .011

(.010)

.007

(.010)

.019

(.008)

.016

(.010)

SO2 regulation effect (b6) .003

(.029)

.005

(.027)

.010

(.021)

.006

(.028)

TSPs regulation effect (b7) .020

(.013)

.013

(.014)

.022

(.011)

.013

(.013)

B. Capital Stock

CO regulation effect (b4) .097

(.043)

.134

(.041)

.110

(.033)

.115

(.040)

O3 regulation effect (b5) .001

(.021)

.007

(.021)

.021

(.016)

.009

(.020)

SO2 regulation effect (b6) .057

(.055)

.085

(.045)

.032

(.036)

.006

(.052)

TSPs regulation effect (b7) .010

(.024)

.002

(.024)

.038

(.021)

.010

(.033)

C. Shipments

CO regulation effect (b4) .072

(.029)

.092

(.027)

.048

(.024)

.075

(.027)

O3 regulation effect (b5) .019

(.016)

.019

(.016)

.000

(.015)

.016

(.016)

SO2 regulation effect (b6) .027

(.030)

.054

(.025)

.023

(.025)

.020

(.030)

TSPs regulation effect (b7) .010

(.018)

.054

(.016)

.037

(.015)

.008

(.020)

Note.—See the notes to tables 5 and 6. The entries are the estimated regulation effects and heteroskedastic standard

errors (in parentheses) from separate regressions for the three dependent variables. The dependent variable is identified

in the panel heading. Each column represents a different specification or sample. All specifications include county by

period effects and industry by period effects. The entries in col. 0 are taken from col. 3 of tables 5 (employment) and

6 (capital stock and shipments) and should be compared to the other columns. In col. 1, the lagged dependent variable

is included as a regressor, and its effect is allowed to vary by the pollutant emitted. The sample size is 884,812 for

employment, 921,403 for capital stock, and 944,596 for shipments. In col. 2, the sample is limited to stayer plants, and

the respective sample sizes are 762,513, 764,115, and 768,096. In col. 3, industries that account for at least 4.5 percent

of industrial sector emissions of a pollutant are classified as an emitter of that pollutant (see App. table A2).

pendent variables are not reported in the table but provide evidence

of dynamic patterns of growth in manufacturing activity. However, the

commonality of the estimates in columns 0 and 1 implies that the regulation effects are not due to these dynamics.32

It is frequently assumed that environmental regulations primarily affect the location decisions of new plants (e.g., Bartik 1985; McConnell

and Schwab 1990) because “grandfather” clauses and political lobbying

32 The estimates are virtually identical when the col. 0 sample is limited to plants with

nonmissing lagged dependent variables.
1208 journal of political economy

protect incumbent plants. In column 2 of table 8, the sample is limited

to “stayers,” that is, plants that are operating in both the first and last

years of a period. The estimated regulation effects in this column are

remarkably similar to those from the base specification, indicating that

the regulations also restrict the growth of stayers. The negative coefficients from the capital stock regression are noteworthy because the

regulations frequently require stayers to install “end of the line” pollution abatement equipment that increases measured investment (but

not productive investment).33

I also examine the sensitivity of the estimated regulation effects to

the definitions of emitting status. For example, column 3 of table 8

presents the results from regressions in which the group of emitters is

expanded such that industries that account for more than 4.5 percent

of the industrial sector’s emissions of a pollutant are classified as an

emitter of that pollutant (see App. table A2). The estimated regulation

effects are generally unchanged by this expansion of the list of emitters.

Further, I tested whether the effects differed when an industry is required to account for at least 9 percent of industrial sector emissions

to qualify as a polluter. The estimated regulation effects are also qualitatively similar in this case.34

Another possible source of bias is that plants located in a county that

is currently nonattainment but is “expected” to become attainment in

the near future might delay investments until the regulation designation

is changed. In the presence of this type of temporal shifting, the estimated regulation effects would be negative; but over longer periods,

regulation would have no effect on manufacturing activity. In order to

explore this possibility, I restricted the sample so that plant observations

from counties that are nonattainment for a particular pollutant in a

given period but attainment for the same pollutant in the next period

are dropped. This sample restriction is implemented four separate

times, once for each of the pollutant-specific nonattainment designations. The estimated regulation effects from these restricted samples

are statistically indistinguishable from estimates based on the full sample. Consequently, it is unlikely that this form of temporal shifting of

investment is the source of the estimated regulation effects.

A further potential source of bias comes from unobserved regional

shocks to industries. I estimated a model that included industry by period by region fixed effects, where industry is defined as one of the 13

industries described above and regions are the nine Census Bureau

regions of the United States. The estimated regulation effects from this

33 Greenstone (1998) provides evidence on the regulations’ effect on plant location and

exit decisions. 34 These results and the other results discussed in the remainder of this subsection are

available from the author.
environmental regulations 1209

specification are also similar to those presented in tables 5 and 6. Additionally, I fit a model that allows the industry shocks to vary at the

state level rather than the census region level. In a further specification,

I disaggregated industry and estimated an equation that includes SIC

three-digit industry by period by census region fixed effects. Neither of

these alternatives changes the estimated regulation effects by a meaningful amount. Overall, there is little evidence that the regulation effects

are due to regional industry shocks.

Finally, owing to the coincidence of the implementation of these

regulations and the decline in manufacturing activity in “Rust Belt”

states, it is sometimes thought that the regulations caused this decline

(e.g., Kahn 1999). To examine this possibility, I separately estimated the

regulation effects on samples from the Rust Belt and non–Rust Belt

states.35 Across the three measures of manufacturing activity, the estimates indicate that the regulations retard the growth of polluting manufacturers in both sets of states.

VI. The Magnitude of the Regulation Effects and Their

Interpretation

The analysis above indicates that the CAAAs reduced the relative growth

of pollution-intensive manufacturing activity in nonattainment counties.

This section provides answers to three important questions about the

estimated regulation effects. How large are they? Can they be used to

assess claims that the CAAAs cause manufacturers to shift production

(and jobs) abroad? Further, do they provide estimates of the costs of

the nonattainment designations that can be compared with estimates

of their benefits?

A. The Magnitude of the Regulation Effects

Table 9 develops two measures of the magnitude of the regulation effects. Notice that there are three panels, one for each of the measures

of manufacturing activity. Column 1 presents the estimated regulationinduced change in the measures of activity. This is calculated by multiplying the sum of the activity in targeted plants (recall table 2) by the

relevant estimated regulation effects from the specification that includes

plant fixed effects (i.e., col. 4 of tables 5 and 6). The estimated regulation-induced changes are presented separately by pollutant, and their

sum is listed in the “all manufacturers” row. Column 2 lists the 95 percent

confidence interval of these estimates. Column 3 reports the change in

35 The Rust Belt is defined to include Illinois, Indiana, Michigan, New York, Ohio, and

Pennsylvania.
1210 journal of political economy

TABLE 9

Two Measures of the Magnitude of the Regulation Effects

Estimated Regulation-Induced

Change, 1972–77 to 1982–87

Change

1972–77

to

1982–87

(3)

Mean of

1972–77

and

1982–87

Levels

(4)

Ratio

of Col.

1 to

Col. 3

(5)

Ratio

of

Col.

1 to

Col.

4

(6)

Mean

(1)

95% Confidence Interval

(2)

A. Total Employment

CO emitters 119,100 [54,600, 183,500] 296,502 892,312 .402 .133

O3 emitters 423,400 [169,400, 677,400] 169,000 5,496,651 2.505 .077

SO2 emitters 800 [57,400, 55,800] 359,821 1,537,994 .002 .001

TSPs emitters 50,200 [48,200, 148,500] 374,081 1,884,883 .134 .027

All manufacturers 591,900 [118,400, 1,065,200] 250,183 17,215,016 2.366 .034

B. Capital Stock (Millions of Dollars)

CO emitters 7,500 [2,400, 17,500] 65,977 110,639 .114 .068

O3 emitters 18,600 [7,200, 44,300] 175,235 258,645 .106 .072

SO2 emitters 4,800 [2,400, 11,900] 85,092 144,078 .056 .033

TSPs emitters 5,700 [4,500, 15,900] 56,635 108,261 .101 .053

All manufacturers 36,600 [16,400, 89,600] 409,687 565,888 .089 .065

C. Shipments (Millions of 1987 Dollars)

CO emitters 25,700 [9,800, 41,500] 25,601 235,616 1.003 .109

O3 emitters 40,500 [19,000, 100,000] 2,281 773,443 17.751 .052

SO2 emitters 1,500 [10,000, 13,000] 29,806 310,140 .050 .005

TSPs emitters 7,600 [8,200, 23,500] 24,581 211,875 .310 .036

All manufacturers 75,300 [27,400, 178,000] 227,673 2,051,492 .331 .037

Note.—The entries in col. 1 are calculated by multiplying the parameter estimates from col. 4 of tables 5 (employment)

and 6 (capital stock and shipments) and the level of the outcomes in emitters in nonattainment counties; table 2

presents the employment levels. For instance, the effect of CO regulation on employment in CO-emitting industries is

calculated by multiplying the estimated effect of CO nonattainment (.163) by the sum of the levels of employment

in CO-emitting plants located in CO nonattainment counties for 1972–77 (201,108), 1977–82 (302,989), and 1982–87

(226,294), which yields an estimated change of 119,100 jobs. Col. 2 presents the 95 percent confidence interval of

this estimate based on the heteroskedastic-consistent standard errors. The entries in col. 3 are the difference between

the 1972–77 and 1982–87 levels of the outcome variables, and the entries in col. 4 are the means of these two values.

The shipments measures were converted to 1987 dollars using the Bartelsman and Gray (1994) NBER Productivity

Database four-digit deflators.

the measure of activity between the period in which the CAAAs were

first in force and the last period (i.e., 1972–77 and 1982–87), separately

for emitters of each of the pollutants and the entire manufacturing

sector. Finally, column 4 lists the mean of the levels from these two

periods for the same categories of plants.

The entries in columns 1, 3, and 4 are used to calculate the two

measures of the magnitude of the regulation effects. Column 5 reports

the ratio of the entries in columns 1 and 3, and column 6 lists the ratio

of columns 1 and 4. Thus these columns normalize the regulationinduced changes by the total change in and mean of the measures of

activity, respectively.

Panel A reports these calculations for employment. For example, they

indicate that employment in CO-emitting industries located in CO nonattainment counties declined by 119,100 jobs (relative to CO emitters

in CO attainment counties) in the first 15 years in which the CAAAs
environmental regulations 1211

were in force.36 The 95 percent confidence interval of this estimate is

[54,600, 183,500]. Analogous calculations indicate that the cumulative regulation-induced change (95 percent confidence interval) in

employment in nonattainment counties is 423,400 [169,400,

677,400] for O3, 800 [57,400, 55,800] for SO2, and 50,200 [48,200,

148,500] for TSPs. The large decline in O3 employment reflects the

high levels of employment in O3-emitting industries. The sum of the

regulation-induced changes is 591,900 [118,400, 1,065,200].

Column 5 reports that the total regulation-induced change in employment is almost 2.4 times as large as the decline in manufacturing

sector employment (roughly 250,000 jobs). This ratio is large, but manufacturing sector employment was essentially flat in these periods. The

second measure reveals that the regulation-induced change in employment in nonattainment counties was a more modest 3.4 percent of total

manufacturing sector employment.

Panels B and C present the analogous calculations for capital stock

and shipments, respectively. The cumulative regulation-induced changes

in capital stock and shipments across all four regulations are $36.6

billion [$16.4 billion, $89.6 billion] and $75.3 billion (1987 dollars)

[$27.4 billion, $178.0 billion], respectively. These changes are 8.9 percent and 33.1 percent of the total change in these measures of manufacturing activity. When they are normalized by the mean levels of capital

stock and shipments, they are 6.5 percent and 3.7 percent, respectively.

Overall, these two measures indicate that during the first 15 years in

which the CAAAs were in force, the cumulative regulation-induced

changes in manufacturing activity in nonattainment counties were not

insignificant relative to either changes in or the level of total manufacturing sector activity. It is important to bear in mind, however, that the

legislation also specified regulations for attainment counties. Consequently, it is likely that the total effect of the CAAAs is even larger than

indicated in table 9.

B. Interpretation

It would be informative if the estimated regulation effects could be used

to determine how much production (and employment) was shifted

abroad as a result of the nonattainment designations.37 This would pro36 This is calculated by multiplying the estimated effect of CO nonattainment status

(0.163) by the sum of the levels of employment in CO-emitting plants located in nonattainment counties for 1972–77 (201,108), 1977–82 (302,989), and 1982–87 (226,294). 37 A related question is whether environmental regulations alter the international location decisions of polluters. An extrapolation of this paper’s findings to this question

suggests that international differences in the stringency of environmental regulation will

tend to shift polluters’ production to countries with relatively lax environmental standards.
1212 journal of political economy

vide one measure of the national costs of these regulations. Unfortunately, such a calculation is not possible because it cannot be determined

whether the lost activity in nonattainment counties moved to foreign

countries or attainment counties. Since it is likely that the regulation

effects partially reflect some shifting of manufacturing activity within

the United States, they probably overstate the national loss of activity due

to the nonattainment designations. Moreover, the possibility of intracountry shifting means that the regulation effects are also likely to overstate losses in nonattainment counties. The reason is that the identification strategy relies on comparisons between nonattainment and

attainment counties, which leads to “double counting” when production

is moved from a nonattainment county to an attainment one.38

There are at least two reasons to doubt that the regulation effects

entirely reflect a movement of plants from nonattainment to attainment

counties. First, counties frequently move into and out of nonattainment

status. Thus firms may consider it unlikely that they can remain in the

United States and escape future regulation. Second, production in many

of the regulated industries (e.g., iron and steel and pulp and paper)

requires substantial “sunk” costs that make it costly to shift locations.

The estimated regulation effects have an additional limitation as a

measure of the costs of regulation. They are calculated in terms of

employment, investment, and shipments, but these measures are not

readily comparable to standard measures of the benefits of regulation.

The conversion of these measures into a monetary unit would have

great practical importance. For instance, it would then be possible to

compare the costs of the regulations with hedonic housing market estimates of the monetary gains to homeowners from regulation-induced

pollution reductions.

A full monetizing of the regulation-induced losses is left to future

research, but it is worth noting that this task is tractable. In a freely

functioning market economy, jobs and capital are not lost or made

obsolete. In response to a shock such as the imposition of environmental

regulations, these factors of production generally become employed in

another capacity. Thus the losses due to regulation are the adjustment

costs associated with the shifting of resources to new sectors. It is evident

that monetized estimates of the costs of the CAAAs require reliable

estimates of the magnitude of these frictions.

Recent research indicates that these frictions may be quite substantial

and can persist for as long as a decade (Blanchard and Katz 1992).

Jacobson, LaLonde, and Sullivan (1993) document that displaced work38 In the extreme, the estimated regulation effects entirely reflect a movement of manufacturing activity from nonattainment to attainment counties. In this scenario there is

no loss of production (and jobs) to foreign countries, and the regulation effects overstate

the lost production in nonattainment counties by a factor of two.
environmental regulations 1213

ers endure substantial wage losses. Further, Goolsbee and Gross (2000)

and Ramey and Shapiro (2001) show that it is costly for firms to adjust

their capital stock in response to demand shocks. Consequently, workers

and firms that were affected by the CAAAs may have suffered substantial

losses.

VII. Conclusions

This paper provides new evidence that environmental regulations restrict industrial activity. I find that in the first 15 years after the CAAAs

became law (1972–87), nonattainment counties (relative to attainment

ones) lost approximately 590,000 jobs, $37 billion in capital stock, and

$75 billion (1987 dollars) of output in polluting industries. Although

these estimates are not derived from a randomized experiment and

therefore cannot meet a strict definition of causality, they provide robust

evidence that these regulations deter the growth of polluters. In the

first place, the findings are derived from the most comprehensive data

available on clean air regulations and manufacturing activity. Second,

the preferred statistical model for plant-level growth controls for all

permanent plant characteristics, unrestricted industry shocks, and unrestricted county shocks. Third, the effects are robust across a variety

of specifications. Finally, the regulation effects are evident across three

different measures of manufacturing activity and a wide range of polluting industries.

The federal standards for ozone and particulates were tightened recently, causing a substantial increase in the number of nonattainment

counties.39 The balance of evidence from this paper suggests that the

new nonattainment counties will experience reductions in employment,

investment, and shipments in polluting industries. To gain a clearer

understanding of whether it is worthwhile to incur the costs associated

with these reductions, it is crucial to understand the regulations’ effectiveness at cleaning the air and the benefits of cleaner air. Recent research finds that these policies are effective at reducing concentrations

of air pollution and that cleaner air, particularly reductions in TSPs,

provides substantial monetary benefits to homeowners and reduced infant mortality rates (Smith and Huang 1995; Henderson 1996; Chay

and Greenstone 2000, 2002a, 2002b). Regardless of whether these policies pass or fail a cost-benefit test, this paper’s findings undermine the

contention that environmental regulations are costless or even beneficial for the regulated.

39 Although legal wrangling over this policy change is not concluded, the Supreme

Court’s Whitman v. American Trucking Associations decision appears to uphold the EPA’s

decision to tighten these standards.
1214 journal of political economy

TABLE A1

Selected National Ambient Air Quality Standards

Maximum Allowable Concentration

(Primary Standard)

Carbon monoxide:

Maximum 8-hour concentration 9 parts per million

Maximum 1-hour concentration 35 parts per million

Nitrogen dioxide:

Annual arithmetic mean .053 parts per million

Ozone:

Maximum 1-hour concentration .12 parts per million (after 1979)

.08 parts per million (through 1979)

Sulfur dioxide:

Annual arithmetic mean .03 parts per million

Maximum 24-hour concentration .14 parts per million

Total suspended particulates:

Annual geometric mean 75 micrograms per cubic meter

Maximum 24-hour concentration 260 micrograms per cubic meter

Note.—A county is in violation of one of the hourly based standards (i.e., one-hour, eight-hour, or 24-hour) if it

exceeds the standard more than once in a year. In 1987 the EPA switched its focus from the regulation of all particulates

(i.e., TSPs) to small particulates (i.e., PM10s). In 1997 the ozone standard was revised, and the particulates standard

was further modified to regulate even smaller particulates (i.e., PM2.5s).

Data Appendix

A. Determining the County-Level, Pollutant-Specific Regulation Designations

The centerpiece of the Clean Air Act Amendments is the annual county-level

assignment of nonattainment and attainment status for CO, O3, SO2, and TSPs.

The legislation specifies that the pollutant-specific designations be based on

whether a county’s ambient pollution concentration exceeds the relevant federal

air quality standard. Table A1 lists the standards. This section describes how

these designations are determined for each of the four periods (i.e., 1967–72,

1972–77, 1977–82, and 1982–87) examined in this paper.

Although the 1970 amendment passed before the 1967–72 period ended, the

associated enforcement activities did not commence until late 1972 (Liroff

1986). Consequently, every county is designated attainment for all four pollutants

in the 1967–72 period.

The determination of the nonattainment designations in the 1977–82 and

1982–87 periods is relatively straightforward. In 1978 the EPA began to publish

annually a list of nonattainment counties in the Code of Federal Regulations.40 For

each of the regulated pollutants, the CFR lists every county as “does not meet

primary standards,” “does not meet secondary standards,” “cannot be classified,”

“better than national standards,” or “cannot be classified or better than national

standards.” Further, the CFR occasionally indicates that a part of a county did

not meet the primary standards. For the 1977–82 (1982–87) period, a county

is assigned to the pollutant-specific nonattainment category if all or part of it

failed to meet the pollutant-specific “primary standards” in 1978 (1982); otherwise, it is assigned to the pollutant-specific attainment category. These annual

county-level, pollutant-specific designations were hand entered for the 3,070

U.S. counties.

40 Vernon Henderson and Randy Becker generously allowed me to photocopy the relevant sections of the CFR.
environmental regulations 1215

The determination of the identities of the nonattainment counties in the

1972–77 period is more complicated. The EPA did not publish them in the early

years of regulation, and I was told that records from that period “no longer

exist.” Consequently, I filed a Freedom of Information Act request and obtained

the EPA’s “Quick Look Report” data file, which contains annual summary information on the readings from each EPA pollution monitor.41 This file is used

to replicate the EPA’s statutory selection rule; counties with monitor readings

exceeding the pollutant-specific national standard in 1972 are assigned to the

pollutant-specific nonattainment category for the 1972–77 period. All other

county by pollutant combinations are designated attainment.42

B. Determining Which Plants Were Subject to the Regulations

An important part of the analysis is the determination of which manufacturing

plants (or industries) were not targeted by the regulations in the examined

period. A historical list of regulated plants or industries is unavailable from the

EPA. Consequently, I devised a system to divide the manufacturing sector into

emitters and nonemitters that attempts to mimic the EPA’s focus on the dirtiest

plants and industries in the initial years of regulation.

The EPA’s estimates of industrial emissions are used to determine the pollutants emitted by each industry. These estimates are reproduced in table A2.

The table lists the estimated annual emissions of each of the regulated pollutants

by industry, as well as each industry’s share of total industrial sector emissions.

Industries that are excluded from the table either produce negligible levels of

the regulated pollutants or had escaped the EPA’s attention as late as the early

1990s. Communications with EPA officials indicate that it is unlikely that the

excluded industries were subject to significant regulatory oversight in the 1970s

and 1980s.

In the assignment of polluter status to industries, one possibility is to assume

that the industries listed in table A2 are regulated for all the pollutants. Since

some industries are major polluters of a particular pollutant but not of another,

it is evident that this is not a sensible approach. Consequently, I label all industries that account for at least 7 percent of the industrial sector emissions of

a pollutant to be an emitter of that pollutant; excluded industries and those

whose emissions fall below the 7 percent threshold are considered nonemitters

of that pollutant. An industry is designated an O3 emitter if it exceeds the 7

percent threshold for either nitrogen dioxide or volatile organic compounds,

both of which are precursors of ozone. The results are insensitive to other

“reasonable” definitions of emitter status. These results are discussed in Section

V.

41 This date file comes from the EPA’s Air Quality Subsystem database and contains

annual statistics on the readings from all state and national pollution monitors for the

four criteria pollutants. 42 I tested whether the results were sensitive to the choice of a pollution monitor–based

definition of which county/pollutant combinations were heavily regulated for this period

(i.e., 1972–77). The paper’s conclusions are insensitive to dropping the 1972–77 period

from the sample.
TABLE A2

Annual Industrial Sector Pollutant Releases by Industry

Industry (SIC Code)

Carbon

Monoxide

Nitrogen

Dioxide

Volatile Organic

Compounds Sulfur Dioxide

Total Suspended

Particulates

Emitter St

(3)

Emissions

(1)

Share

(2)

Emissions

(1)

Share

(2)

Emissions

(1)

Share

(2)

Emissions

(1)

Share

(2)

Emissions

(1)

Share

(2)

Metal mining (10) 5,391 .2% 28,583 1.6% 1,283 .1% 84,222 3.5% 140,052 15.4% *Nonmetal mining (14) 4,525 .1% 28,804 1.6% 1,736 .1% 24,129 1.0% 167,948 18.5% *Lumber and wood products (24) 123,756 3.5% 42,658 2.4% 41,423 3.0% 9,149 .4% 63,761 7.0% TSPsWood furniture and fixtures (parts of

25)† 2,069 .1% 2,981 .2% 59,426 4.4% 1,606 .1% 3,178 .3% Clean

Pulp and paper (2611–31) 624,291 17.5% 394,448 21.7% 96,875 7.1% 341,002 14.0% 113,571 12.5% CO/O3/SO2

Printing (2711–89) 8,463 .2% 4,915 .3% 101,537 7.5% 1,728 .1% 1,031 .1% O3

Inorganic chemicals (2812–19) 166,147 4.7% 108,575 6.0% 52,091 3.8% 182,189 7.5% 39,082 4.3% SO2Organic chemicals (2861–69) 146,947 4.1% 236,826 13.0% 201,888 14.8% 132,459 5.4% 44,860 4.9% O3Petroleum refining (2911) 419,311 11.8% 380,641 21.0% 309,058 22.7% 648,153 26.6% 36,877 4.1% CO/O3/SO2Rubber and miscellaneous plastic

products (30) 2,090 .1% 11,914 .7% 140,741 10.3% 29,364 1.2% 5,355 .6% O3

Stone, clay, glass, and concrete (32) 58,043 1.6% 338,482 18.6% 30,262 2.2% 339,216 13.9% 171,853 18.9% O3/SO2/TSP

Iron and steel (3312–33, 3321–25) 1,518,642 42.6% 138,985 7.7% 82,292 6.0% 238,268 9.8% 83,017 9.1% CO/O3/SO2

Nonferrous metals (333–34) 448,758 12.6% 55,658 3.1% 27,375 2.0% 373,007 15.3% 22,490 2.5% CO/SO2

Fabricated metals (34) 3,851 .1% 16,424 .9% 102,186 7.5% 4,019 .2% 3,136 .3% O3Electronics (36) 367 .0% 1,129 .1% 4,854 .4% 453 .0% 293 .0% CleanMotor vehicles, bodies, and parts

(371) 35,303 1.0% 23,725 1.3% 101,275 7.4% 25,462 1.0% 12,853 1.4% O3

Dry cleaning (721) 101 .0% 179 .0% 7,310 .5% 152 .0% 28 .0% *Industrial sector total 3,568,055 1,814,927 1,361,612 2,434,578 909,385

Source.—EPA Sector Notebook Project (1995).Note.—For each pollutant, emissions in col. 1 lists the number of short tons emitted per year. Share in col. 2 reports the fraction of industrial sector emissions. The paper’s 

designates an industry an emitter of a pollutant if it accounts for at least 7 percent of industrial sector emissions. Each industry’s emitter status is summarized in col. 3. Nitrogen and volatile organic compounds are the primary ingredients of ozone (O3). If an industry emitted more than 7 percent of either of these pollutants, it is designated an O3 emitt

remainder of the manufacturing sector is designated nonemitters of all criteria pollutants and labeled clean.

* Metal mining, nonmetal mining, and dry cleaning are outside of the manufacturing sector.

† Wood furniture and fixtures comprises the following SIC codes: 2511, 2512, 2517, 2519, 2521, 2531, and 2541.

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environmental regulations 1217

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