Federal Environmental Regulation Impacts on Local Economic Growth and Stability

Abstract

Federal environmental regulations under the Clean Air Act affect employment as local economies respond to a changing regulatory environment. We analyze the net employment growth and employment stability effects of Clean Air Act regulations, characterizing responses in these key indicators of local economic development. Using nationwide longitudinal county data from 1980 through 2005, we find that enhanced employment stability is associated with nonattainment areas for total suspended particulates (TSP) and 1-hour ozone; negative employment growth is found in TSP and carbon monoxide nonattainment areas, whereas positive employment growth for counties is found in 1-hour ozone and sulfur dioxide nonattainment areas. Also, regulatory effects persist after attainment status has been regained for TSP and transitory persistence is seen after regaining attainment status for 1-hour ozone.

Keywords

Clean Air Act environmental policy economic growth economic stability industrial diversification

Introduction

On September 2, 2011, President Obama revoked the new 8-hour ozone standard proposed by the U.S. Environmental Protection Agency (EPA) as a way to reduce regulatory burden in the path of economic recovery. The EPA’s proposed rules for ground-level ozone would increase the standard of 0.75 parts per million issued under the Bush Administration in 2008. This action became part of the president’s plans to boost hiring and economic growth by addressing regulations on business. It caused national debates over the actual effects of environmental regulations, particularly those under the Clean Air Act, on the economy and job creation. Some believe these regulations are more costly than the current administration estimated, whereas others see the regulations as imposing no harm or even having a net positive impact on the economy.

The Clean Air Act governs a significant aspect of federal environmental regulation. Ambient air quality standards are established for a set of criteria pollutants under the Clean Air Act, and localities failing to meet these standards are subject to being declared in nonattainment for a particular pollutant. Additional pollution abatement regulations are then applied in nonattainment areas, with the goal of restoring air quality to the ambient air quality standards. While much scholarly attention has been given to the industry-related effects of federal air quality regulations in the past, less is understood about the regulatory effects on local economic development. In particular, the effects of nonattainment status for the criteria pollutants on local economic growth and stability are largely unexplored.

Both economic growth and stability are important indicators of local economic performance, and they have significant subsequent impacts on the fiscal capacity of a local jurisdiction in that economic performance is a crucial determinant of local tax base or tax capacity. A strong tax base helps a local government generate sufficient resources to pay for public services and infrastructure, which in turn can further enhance local economic performance. As such, promoting economic growth and stability defines most of the current efforts in local economic development. Understanding the relationship between the Clean Air Act and local economic performance will enhance our understanding of federal policy impacts. Furthermore, the Clean Air Act provides a valuable context to assess the dynamic relationship between local economic growth and stability, important indicators of local economic health whose relationship has been in debate for decades.

In this article, we use regulatory variation between attainment and nonattainment counties for each criteria pollutant under the Clean Air Act in a national sample of counties to evaluate the local economic impacts of these air quality regulations. We also examine whether these impacts persist after attainment status has been regained. The findings suggest nonattainment regulations for total suspended particulates (TSP) and 1-hour ozone have contributed to greater employment stability. With respect to employment growth, we observe a significant and negative impact for TSP and carbon monoxide (CO), whereas we find a small and limited positive impact for 1-hour ozone and sulfur dioxide (SO₂). In addition, some of these effects persist after regaining attainment status.

The article is structured as follows: The next section provides a general research framework that demonstrates the connection between prior research on the industry-related impacts of the Clean Air Act and current research. This is followed by a discussion on the relationship between industrial diversification and economic stability and the trade-off between economic stability and growth. We specify the model and discuss the measurement for our variables in the fourth section. The fifth section summarizes the empirical findings. Conclusions and policy implications are offered in the final section.

Theoretical Framework

Federal regulations are designed under the Clean Air Act to improve air conditions in localities with poor air quality. To this end, ambient air quality standards are established for a number of criteria pollutants; during the years covered by this study, 1980 to 2005, the following pollutants were regulated: carbon monoxide (CO), 1-hour and 8-hour standards for ground-level ozone, TSP, particulate matter smaller than 10 µm (PM₁₀) and smaller than 2.5 µm (PM_2.5), sulfur dioxide (SO₂), lead, and nitrogen dioxide (NO₂).¹ Areas failing to meet the ambient air quality standard for a criteria pollutant are subject to being classified as a nonattainment area for that particular pollutant. Additional pollution abatement regulations are applied in nonattainment areas with the purpose of improving air quality in those areas.

Nonattainment regulations have been shown to affect polluting firms in several ways. Stricter air quality regulations result in additional pollution abatement costs for polluting facilities located in nonattainment areas. This creates an incentive for polluting firms to locate outside of these areas. As a result, employment and production in polluting industries is reduced in nonattainment areas as firms respond to nonattainment regulations (Greenstone, 2002). Firm location decisions have been shown to take attainment status into consideration (Henderson, 1996; List & McHone, 2000); this is the case both for polluting firms that are selecting a location for a new facility (Becker & Henderson, 2000) and for those considering relocating an existing facility (List, McHone, & Millimet, 2003). When polluting firms leave a nonattainment area, local capital in the form of infrastructure and labor are made available. Because of the disincentive facing polluting firms in nonattainment areas, we expect nonpolluting firms to bid for the available capital and labor resources. The infusion of new business sectors can change the fundamental economic structure of localities.

While Clean Air Act requirements have real impacts on polluting firms, the regulatory effects on local economies are less understood. Carr and Yan (2012) establish this branch of the literature by examining the extent to which air quality regulations alter local industrial diversity by decreasing specialization in polluting sectors of the economy. That study found some significant evidence suggesting that air quality regulations affect the local economic base through reducing the degree of industrial specialization in nonattainment areas, and such effects continue even after the areas have regained attainment status. This is expected to occur by altering (re)location decisions for polluting facilities, levels of production, and employment in polluting industries, and it can also be a result of the infusion of new sectors driven by local economic shifts in response to nonattainment regulations as discussed above. The extent of this impact in a given county would depend on the significance of regulated polluting industries to the local economy.

Given this clear connection between environmental regulations under the Clean Air Act and local industrial diversity, it is our intention to extend this branch of the literature and investigate whether these regulations will further influence the local employment of the affected localities. In particular, we are interested in the repercussion on the stability and rate of growth of local employment, which serve as appropriate indicators of the economic development effect. Furthermore, the existence of a trade-off between economic growth and stability has been debated for decades in the economic literature. We hope this article can serve as a new venue for investigation.

When local industrial diversity is altered by Clean Air Act regulations as identified by Carr and Yan (2012), the change in economic diversification will presumably in turn affect local employment. Beyond the above-mentioned indirect effect on employment through changing the level of local industrial diversity, regulatory requirements can also directly alter employment in nonattainment localities.

Figure 1 depicts the theoretical framework for the overall impact of the Clean Air Act effects on employment growth and stability. The Clean Air Act has a direct effect on employment and an indirect effect through changes in industrial composition. The intention of this study is to capture the entire effect of Clean Air Act regulations on local employment growth and stability.

Figure 1.

Conceptual framework.

Local Economic Growth and Stability

Industrial Diversification and Economic Stability

The causal relationship between economic diversity and economic cyclical fluctuations has been through considerable debate for several decades. The existing literature has generally viewed that industrial structure plays an important role in explaining regional cyclical differences. That is, economic diversity enhances economic stability in that a larger variety of economic sectors in a jurisdiction reduces its employment fluctuations. Nowadays, fostering economic diversity has become an important trend or objective of local economic development planning (Dissart, 2003).

Diversification is a process that helps disperse the regional employment among a variety of industries so that a region is less likely to suffer from severe economic downturns (Kort, 1981). “As a rule, since no two businesses have exactly the same seasonal and cyclical swings, the more types of production and trade are represented, the more stable will be that community’s business” (McLaughlin, 1930, p. 133). Businesses that are less vulnerable to downturns will be able to reemploy displaced workers from businesses that are affected by a cyclical swing.

Many empirical studies have identified a positive correlation between economic diversity and stability. For example, multiple empirical studies, including those from Kort (1981), Wundt (1992), Malizia and Ke (1993), and Wagner and Deller (1998), found that greater industrial diversity lowers economic instability. Kort also argued that there tends to be a wider variation of instability for small jurisdictions than larger ones. Similarly, Brewer and Moomaw (1985) found that equal distribution of employment enhances stability in the manufacturing sector. Smith and Gibson (1988) looked at unemployment rates and reached similar conclusions.

In all, the empirical research on the impact of economic diversity mostly focuses on the relationship between economic diversity and employment stability. A majority of these studies examine metropolitan areas and U.S. states, and scant attention has been paid to counties and rural areas (Dissart, 2003). This article addresses this gap in the literature through county-level analysis.

Although the positive causal relationship between industrial diversification and the stability of the regional economic base has been more or less agreed on, the definition and measurement of industrial diversification suffer from a lack of theoretical and statistical consistency (Jackson, 1984). Many studies do not support the assumed diversity–stability relationship (Attaran, 1986; Jackson, 1984). Furthermore, the positive relationship between industrial diversification and stability of a regional economy has been challenged both from theoretical and empirical perspectives in that there are a few regional economies that have high levels of specialization and that have historically experienced little fluctuation relative to the national economy. Two classic examples are small college towns and state capitals. These local economies are highly specialized but stable over time (Kort, 1981). The basic reasons for the existence of the anomalies is that economic diversification measures the mix and balance of the industry structure, but it fails to consider the fact that industries have varying stabilizing effects, and some industries have much stronger stabilizing effects than others (Hastie, 1972).

This article provides empirical analysis that will address the hypothesized diversity–stability relationship. Specifically, we evaluate the direct relationship between industrial diversification excluding the Clean Air Act regulatory impact and employment stability using county-level data spanning several decades. The findings of this analysis speak to the questions raised in this literature.

Stability and Growth Trade-Off

The relationship between industrial diversity and employment growth is less conclusive; however, there is a general perception that there is a trade-off between economic growth and stability. That is, higher employment growth usually brings about higher fluctuations in employment. Empirical evidence is found in several studies (Lande, 1994; Malizia & Ke, 1993). However, according to Borts (1960), economic growth and cyclical fluctuations do not always go together; it is the types of manufacturing industries present that affect the magnitude of cyclical fluctuations. Conroy (1975) also found little correlation between employment instability and growth rates. If the trade-off between growth and stability does exist, and diversification promotes stability, presumably diversification should lead to slower growth of the economy to some extent. There are, however, also some studies arguing that economic diversity has little to do with employment growth (Attaran, 1986; Lynch, 1979).

The Clean Air Act provides a valuable context for understanding the relationship between employment growth and stability. By examining the employment growth and stability effects stemming from a specific driving cause, we are able to gain meaningful insight into whether such a trade-off is inherent in local economic responses to regulatory pressures.

Empirical Analysis

Measurement of Employment Growth and Stability

Employment growth has been used as a measure of economic growth when evaluating the effects of economic development policies (Feiock, 1991), fiscal policies (Mark, McGuire, & Papke, 2000; Mofidi & Stone, 1990), and regional characteristics (Deller, Tsai, Marcouiller, & English, 2001). We continue in this vein using employment as an indicator of local economic performance, and we use the first difference of logged county employment as adopted by the previous economic development literature (Glaeser, Kallal, Scheinkman, & Shleifer, 1992; Glaeser, Scheinkman, & Shleifer, 1995; Mofidi & Stone, 1990) to capture to the employment growth.

While stability can be measured in several different ways, the regional science literature tends to measure economic stability with a variance-based statistic using employment data over time (Brewer, 1985; Dissart, 2003). The drawback of this measure is that it will generate a static measure over the observation period without being able to capture year by year fluctuation in employment. In public finance research, scholars have perceived the stability of revenue as a short-term phenomenon that should be measured as the extent to which actual revenue differs from projected revenue. Greater variation around the expected growth trend indicates greater revenue instability (Gentry & Ladd, 1994; White, 1983; Williams, Anderson, Froehle, & Lamb, 1973).

To allow for variation across observation units and over time, which is a required condition for panel data analysis (Carroll & Goodman, 2011), employment volatility in this study is measured by the absolute percent deviation of actual employment from expected employment as described below. Carroll and Goodman (2011) and Wang and Hou (2009) have adopted a similar measure to capture revenue and expenditure volatility. To construct this measure, we first model an employment growth trend with the ordinary least squares (OLS) estimator as defined in Equation (1), assuming random distribution of employment around the trend line.²

E m p_{i t}^{*} = δ_{i} + β_{i} T_{i} + ε_{i}

In this Equation, Emp_it* is the predicted employment level for county i in year t, δ_i is the constant for county i, β_i is the linear trend parameter for county i, and T_t is the time period year t. From this regression estimation, we calculate the deviation of actual employment for county i in year t (Emp_it*) from the projected employment for county i in year t as a percentage of that predicted employment as defined in Equation (2).

E S_{i t} = | \frac{E m p_{i t} - E m p_{i t}^{*}}{E m p_{i t}^{*}} |

Note that smaller values of ES_it indicate greater employment stability.

Model Specification

Given that the Clean Air Act classifies counties into two categories, nonattainment and attainment, we intend to explore if the more stringent regulations under nonattainment status affect county employment growth and stability. Attainment status is designated separately for each criteria pollutant. Nonattainment designations are reported annually in the Code of Federal Regulations (Protection of the Environment, 1978-2005). Designations are reported by county, although nonattainment areas do not always follow county boundaries. We are careful to only include counties in this analysis that are either entirely in attainment or entirely out of attainment; this is important because classifying counties containing both attainment and nonattainment areas as being in nonattainment would incorrectly also include employment data from attainment areas.

Table 1 reports the frequency of county observations in the data set for each pollutant regulatory category. Because of the small number of observations for particulate matter smaller than 10 µm (PM₁₀), lead, and nitrogen dioxide (NO₂), subsequent analysis will only report findings for CO, 1-hour ozone, TSP, and SO₂. While findings for the other three pollutants are not reported, they were included in the models for econometric purposes.

Table 1.

County Observations by Pollutant.

Pollutant	County observations
Carbon monoxide
Nonattainment	313
Gained attainment	267
Reference group	64,412
One-hour ozone
Nonattainment	3,402
Gained attainment	5,264
Reference group	56,326
Total suspended particulates
Nonattainment	291
Gained attainment	1,210
Reference group	63,491
Particulate matter smaller than 10 µm (PM10)
Nonattainment	30
Gained attainment	6
Reference group	64,956
Sulfur dioxide
Nonattainment	72
Gained attainment	92
Reference group	64,828
Lead
Nonattainment	—
Gained attainment	—
Reference group	64,992
Nitrogen dioxide
Nonattainment	—
Gained attainment	3
Reference group	64,989

Source. Authors’ calculations.

We use the following model to test the impact of air quality regulations on employment growth and stability:

E m p l o y m e n t M e a s u r e = α + β_{p} (n o n a t t_{p}) + δ_{p} (n o n a t t_{p} \cdot T N_{p}) + γ_{p} (g a i n a t t_{p}) + φ_{p} (g a i n a t t_{p} \cdot T G_{p}) + λ (X) + ε

where nonatt_p and gainatt_p, respectively, indicate whether a county is in nonattainment or has regained attainment status for pollutant p, TN_p and TG_p, respectively, give the number of consecutive years of nonattainment status or since regaining attainment status for pollutant p, and X is a vector of control variables.

The model is estimated separately for each employment measure: employment growth (annual change in the log of employment) and employment stability (ES_it), which was defined in Equation (2). Here we model the effects of attainment status and its associated regulatory requirements on county employment. County nonattainment status for pollutant p is given by the dummy variable nonatt_p, which takes a value of one to indicate a county observation when the county is classified as nonattainment for pollutant p. This variable is then interacted with TN_p, which indicates the number of consecutive years a county has had that nonattainment status. In addition to an immediate response in local employment caused by nonattainment classification, the regulatory effect is expected to change over time as an area continues in nonattainment; employment responses changing with time are captured by this interaction. Whereas our data series begins in 1980, TN_p is calculated based on attainment designations beginning in 1978, which was the first year that county-level attainment designations were made.

It is important that the model designate attainment status separately for each criteria pollutant. Different mixes of sources are responsible for emissions of each criteria pollutant, and so regulatory impacts will vary across regulated criteria pollutants. Detailed analysis of the implications of differences between criteria pollutant sources is reserved for discussion of the empirical results. Furthermore, it is necessary that all criteria pollutants be included simultaneously in the model. Excluding attainment designations for a criteria pollutant from the model would incorrectly restrict the attainment status effects to zero (Greenstone, 2002).

Counties that have regained attainment status after a period of nonattainment are identified by the dummy variable gainatt_p. This variable is interacted with TG_p, the number of years since regaining attainment status for pollutant p to capture the time variant effect of regaining attainment status. With these two dummy variables in place, the reference category consists of counties that have always been in attainment status.

X is a vector of socioeconomic and demographic control variables. Here we use the log of per capita income to measure the income level of our county observations, because income usually reflects the quality of life and is considered a factor in residential and commercial location decisions (Mark et al., 2000). Glaeser et al. (1992) also suggested that the wage level may affect the location decision of firms and employment, so we control for the wage level by the log of county total wages. In addition, we include the log of population, percentage of the population that is aged 15 to 64 and over 64, as well as the population proportion that is non-White. Finally, we incorporate county and year fixed effects.

As former research suggests, there exists a causal relationship between industrial diversification and employment stability, and there can be a potential trade-off between employment growth and stability; the factor of industrial diversification is also included in X. A typical measure of industrial diversification, according to Carr and Yan (2012), is the coefficient of specialization (CDIV).³ The CDIV compares the concentration of local employment across industries to the national average. However, as that study suggests that the air quality regulations under the Clean Air Act have a significant impact on the degree of industrial diversification, the regulatory variables of concern (nonatt_p, nonatt_p · TN_p, gainatt_p, and gainatt_p · TG_p) will be underestimated because they are collinear with CDIV.⁴ To capture the entire effect of air quality regulations on employment growth and stability, we follow the procedure adopted by Kau and Rubin (1979) and Ross (2011) and construct a revised CDIV measure: the residuals from the regression of CDIV on the four variables of interest. The residuals of the regression represent the degree of industrial diversification unexplained by and orthogonal to the variables that define the air quality standards.⁵ It should be noted that all control variables are lagged 1 year to eliminate the potential concern of endogeneity.

Data Source and Method

This study uses annual nationwide county data covering 1980 to 2005. The employment and wage data from 1986 to 2005 are provided by U.S. Census Bureau County Business Patterns; historical employment data covering 1980 to 1985 were obtained from the U.S. Census Bureau (U.S. Department of Commerce, Bureau of the Census, 1982, 1984a, 1984b, 1985, 1986, 1987). Per capita income is obtained from the Bureau of Economic Analysis (BEA). Annual estimates for population and demographic data are from the Census Bureau.

The attainment status information is provided by the EPA and is reported in the Code of Federal Regulations. While attainment designations are reported according to counties, not all counties containing nonattainment areas are entirely contained within the nonattainment area. Treating counties that only partially consist of nonattainment areas as being in nonattainment would incorrectly assign all employment data in that county to nonattainment areas. This is especially problematic in geographically large counties, where polluting firms would likely favor locations in the county that are not subject to the nonattainment regulations. Such intracounty employment shifts would mask nonattainment effects if these counties were incorrectly coded as being entirely in nonattainment. To address this problem, we only include counties in this analysis that are entirely in attainment or entirely in nonattainment. Whereas excluding counties from the data set that only partially contain nonattainment areas results in an unbalanced panel that is roughly 8% smaller than if all county-year observations were included, it correctly separates employment between attainment and nonattainment areas. This is necessary to ensure that the empirical model is accurately testing the effects of nonattainment status. We are still left with just fewer than 65,000 observations for this analysis.

Applying Equation (3), we use two-way fixed effects regression with robust standard errors to estimate the effects of air quality regulations on both the growth and stability of county-level employment. The standard errors are clustered on the variable identifying each county to control for intragroup correlation. The study includes both county fixed effects and year fixed effects to control for time-invariant unobservable influences and nationwide unobservable changes.

Table 2 reports descriptive statistics for the continuous variables included in the model.⁶

Table 2.

Descriptive Statistics.

Variable	N	Mean	SD	Min	Max
Change in ln(Employment)	64,992	0.015	0.10	−2.24	2.20
Employment Volatility	64,991	0.081	0.11	0.00	8.81
Real per capita income_{t − 1}^a	64,992	19,346	5,027	4,759	85,799
Population_{t − 1} ^a	64,992	51,415	130,954	91	5,912,192
% population_{t − 1} 15 to 64	64,992	0.567	0.13	0.25	0.84
% population_{t − 1} over 64	64,992	0.134	0.05	0.01	0.35
% Non-White_{t − 1}	64,992	0.115	0.16	0.00	0.95
Revised CDIV_{t − 1}	64,992	−0.001	0.09	−0.21	0.59
Real total wages_{t − 1} (thousands)^a	64,992	464	2,859	0.036	218,788

The log transformation is used for these variables in the model.

Source. Authors’ calculations.

Discussion

Nonattainment designations are made separately for each criteria pollutant. Because different sources are responsible for each criteria pollutant and nonattainment regulations are designed to address pollution sources, nonattainment regulatory impacts will vary across criteria pollutants. As a result, differences in estimated coefficients across the criteria pollutants are expected. Our discussion centers on the effects of nonattainment regulations for the criteria pollutants CO, 1-hour ozone, TSP, and SO₂, and their extended impact after jurisdictions regain attainment status.⁷ Estimated coefficients for Equation (3) are reported in Table 3.

Table 3.

Estimated Direct Employment Effects (1980-2005).

	Change in ln(Employment)			Employment volatility
	Coeff.	Robust SE	p	Coeff.	Robust SE	p
Nonattainment CO	−0.0821*	0.0475	.084	0.2073	0.1440	.150
Nonattainment CO * TN	0.0028	0.0030	.343	−0.0106	0.0112	.345
Gain Attainment CO	−0.0029	0.0299	.924	0.0274	0.0498	.582
Gain Attainment CO * TG	−0.0011	0.0020	.590	−0.0029	0.0021	.171
Nonattainment Ozone	0.0121*	0.0068	.075	−0.0395	0.0269	.142
Nonattainment Ozone * TN	0.0003	0.0002	.126	−0.0005	0.0004	.302
Gain Attainment Ozone	0.0205***	0.0070	.004	−0.0387	0.0246	.115
Gain Attainment Ozone * TG	−0.0005***	0.0002	.002	0.0000	0.0003	.902
Nonattainment TSP	−0.0533***	0.0119	<.001	−0.1352***	0.0146	<.001
Nonattainment TSP * TN	0.0025**	0.0011	.019	0.0016	0.0016	.313
Gain Attainment TSP	−0.0343***	0.0044	<.001	−0.1201***	0.0046	<.001
Gain Attainment TSP * TG	−0.0002	0.0004	.666	0.0006	0.0004	.164
Nonattainment SO₂	0.0662	0.0416	.111	−0.0485	0.0856	.571
Nonattainment SO₂ * TN	−0.0026	0.0029	.376	0.0054	0.0060	.371
Gain Attainment SO₂ * TG	−0.0004	0.0010	.671	0.0020	0.0016	.232
ln(per capita income_{t − 1})	0.0650***	0.0086	<.001	0.0369***	0.0122	.002
ln(population_{t − 1})	0.0739***	0.0088	<.001	−0.0257	0.0160	.109
% population_{t − 1} 15 to 64	0.2651***	0.0541	<.001	−0.4829***	0.0808	<.001
% population_{t − 1} over 64	0.2554***	0.0477	<.001	−0.3942***	0.0642	<.001
% Non-White_{t − 1}	−0.0173	0.0376	.646	0.0527	0.0505	.297
Revised CDIV_{t − 1}	−0.0254**	0.0114	.026	0.0168	0.0177	.340
ln(total wages_{t − 1})	−0.1084***	0.0059	<.001	−0.0308***	0.0117	.008
Constant	−1.0242***	0.1249	<.001	0.3078	0.2350	.190
N	64,992			64,991
R² (within; overall)	.0943; .0046			.0312; .0661
Number of groups	2,681			2,681

Source. Authors’ calculations.

***

p < .01. **p < .05. *p < .1.

Given that our model is structured in a way to understand the effects of air quality regulations on county employment conditional on how long a county has had a given attainment status, the coefficients of both the constitutive and interaction terms should be interpreted jointly rather than in isolation (Brambor, Clark, & Golder, 2006). To understand the dynamics of the overall regulatory impacts, we derive the marginal effects of a given attainment status across the range of years that jurisdictions are in that particular attainment status. For example, the expected effect of 5 years of CO nonattainment is given by β_CO + δ_CO · 5. Tables 4 and 5 report these marginal effects and their corresponding statistical significance (F statistics) according to percentile values of TN_p and TG_p.

Table 4.

Marginal Effects for Employment Growth Under Nonattainment.

Percentile	TN	F statistic	p	Marginal effect
CO
5	1	3.158	.076	−0.0793*
10	2	3.352	.067	−0.0765*
25	4	3.841	.050	−0.0708*
50	8	5.527	.019	−0.0595**
75	15	9.661	.002	−0.0397***
90	20	1.821	.177	−0.0255
95	22	0.703	.402	−0.0198
One-hour ozone
5	1	3.372	.066	0.0125*
10	2	3.578	.059	0.0128*
25	4	3.995	.046	0.0134**
50	10	5.187	.023	0.0153**
75	17	6.245	.013	0.0175**
90	23	6.752	.009	0.0193***
95	25	6.844	.009	0.0200***
TSP
5	1	21.737	<.001	−0.0508***
10	1	21.737	<.001	−0.0508***
25	2	23.506	<.001	−0.0484***
50	5	30.672	<.001	−0.0410***
75	9	37.327	<.001	−0.0311***
90	12	20.447	<.001	−0.0237***
95	13	13.920	<.001	−0.0213***
SO₂
5	1	2.655	.103	0.0636
10	1	2.655	.103	0.0636
25	3	2.925	.087	0.0583*
50	8.5	3.594	.058	0.0440*
75	15	1.528	.217	0.0270
90	19	0.341	.560	0.0166
95	20	0.209	.648	0.0140

Source. Authors’ calculations.

***

p < .01. **p < .05. *p < .1.

Table 5.

Marginal Effects for Employment Volatility Under Nonattainment.

Percentile	TN	F statistic	p	Marginal effect
CO
5	1	2.187	.139	0.1967
10	2	2.323	.128	0.1861
25	4	2.692	.101	0.1649
50	8	4.290	.038	0.1226**
75	15	1.451	.229	0.0484
90	20	0.003	.959	−0.0045
95	22	0.055	.815	−0.0257
One-hour ozone
5	1	2.225	.136	−0.0400
10	2	2.288	.130	−0.0404
25	4	2.413	.120	−0.0414
50	10	2.777	.096	−0.0441*
75	17	3.160	.076	−0.0473*
90	23	3.430	.064	−0.0501*
95	25	3.506	.061	−0.0510*
TSP
5	1	103.623	<.001	−0.1337***
10	1	103.623	<.001	−0.1337***
25	2	127.150	<.001	−0.1321***
50	5	267.663	<.001	−0.1274***
75	9	563.886	<.001	−0.1211***
90	12	267.331	<.001	−0.1164***
95	13	192.362	<.001	−0.1149***
SO₂
5	1	0.292	.589	−0.0431
10	1	0.292	.589	−0.0431
25	3	0.225	.635	−0.0324
50	8.5	0.006	.939	−0.0029
75	15	2.306	.129	0.0319
90	19	2.125	.145	0.0534
95	20	1.977	.160	0.0587

Source. Authors’ calculations.

***

p < .01. **p < .05. *p < .1.

A broad range of industries are responsible for TSP emissions, and motor vehicles are not a primary emitter of TSP; most particulate emissions originate from industrial processes and from stationary fuel combustion such as diesel generators (U.S. EPA, 1982). Thus, the strongest employment effects are expected for TSP nonattainment designations. This is shown in Table 4: TSP nonattainment decreases employment. One year of TSP nonattainment on average reduces employment by about 5.1%; after 5 years of TSP nonattainment, employment will be suppressed by 4.1%. The negative impact on employment associated with nonattainment diminishes with time as displaced jobs are absorbed into other industries, but job losses are not fully absorbed by other sectors of the economy as time progresses. After 13 years of TSP nonattainment, employment is still reduced by 2.1%. In all, when regulatory effects have some bearing on a broad range of polluting industries, as is the case for TSP nonattainment, significant net job losses occur.

While employment growth is reduced, employment stability is substantially improved, although this smoothing effect weakens over time. This is seen in Table 5, where 1 year of TSP nonattainment on average reduces employment fluctuations by 13.4%. After 5 years of nonattainment, local employment volatility is decreased by 12.7%. After 13 years of nonattainment, local employment instability is lowered by 11.5%.

It is important to note the trade-off observed between employment stability and growth under TSP nonattainment. That is, the relevant regulations governing TSP suppress local employment but enable the localities to gain with improved employment stability. These findings are consistent with the prior literature and serve as another piece of empirical evidence that supports this trade-off relationship between growth and stability.

CO emissions are largely from vehicle exhaust (U.S. EPA, 2006), and thus, nonattainment regulations targeting sources of CO pollution are expected to have more limited impact on local employment than TSP nonattainment regulations. Marginal effects for CO nonattainment are reported in Tables 4 and 5.⁸ We find some evidence of transitory increased employment volatility; a statistically significant effect is only found for counties that have been in nonattainment between 5 and 14 years. This effect diminishes with time, ranging from a 15.4% increase in volatility after 5 years of nonattainment to a 5.9% increase in volatility after 14 years of nonattainment. We also observe some suppressed employment due to the CO nonattainment regulations, seen in Table 4. The negative impact on local employment growth weakens as a county continues in nonattainment and only shows statistical significance at the 10% level up to the 19th year in nonattainment status. For example, employment is reduced by 7.9% on average in the first year of CO nonattainment, and this effect reduces to about 4% when nonattainment extends to the 15th year.

While motor vehicles are responsible for most CO emissions, they are only responsible for about half of nitrogen oxide emissions and a third of volatile organic compound emissions, which are the pollutants that produce ground-level ozone (U.S. EPA, 2006). Nonattainment regulations for ozone will target industrial sources responsible for pollutant emissions that are precursors to ground-level ozone, including electricity generation facilities and other industrial sources of fossil fuel combustion, creating some potential for local employment effects. While we expect to see some employment effects from ozone nonattainment regulations, these impacts are not expected to be as strong as those associated with TSP nonattainment because ozone nonattainment regulations are designed to target a narrower range of industries and motor vehicles. We do observe some employment growth in counties in nonattainment for ozone; after 10 years of nonattainment, county employment experienced a 1.5% growth. This suggests that the restructuring of the local industry mix caused by the ozone nonattainment regulations does not incur a significant job loss; rather, some employment growth likely occurs because such restructuring adds to the local labor demand rather than causing labor attrition. We also see a decrease in employment volatility due to the regulations. This effect is enhanced over time and is statistically significant at the 10% level after 9 years of nonattainment; employment volatility is reduced by at least 4.4% on average for a county remaining in 1-hour ozone nonattainment status for 10 years or longer. With this evidence in place, we find a complementary rather than a trade-off relationship between employment stability and growth.

Note that the reductions in employment volatility are much larger for TSP nonattainment than for ozone nonattainment. This suggests that a stronger employment smoothing effect through industrial restructuring occurs under TSP nonattainment compared with ozone nonattainment. This is consistent with expectations because TSP nonattainment regulations will target a broader range of industries than will 1-hour ozone nonattainment regulations. Also, there is not a statistically significant effect of ozone nonattainment on employment volatility for the first 8 years of ozone nonattainment.⁹ This is consistent with List, Millimet, and McHone (2004), in which firms in ozone nonattainment areas delayed investment decisions because of the New Source Review requirement in the Clean Air Act that an entire facility meet stricter emissions standards for new sources if a portion of the facility is modified. Some of the employment fluctuations resulting from responses to ozone nonattainment are expected to be delayed from the beginning of the nonattainment period, and this is what we find.

Also unlike TSP, SO₂ is emitted by a much narrower set of industries. SO₂ is primarily emitted by burning fossil fuels at industrial facilities, largely at electric utilities for power generation (U.S. EPA, 2010). Because SO₂ emissions regulations will be applied to a relatively small number of point sources of emissions, any economic impacts will be concentrated in a few firms in a jurisdiction. Due to the lack of additional regulations for most firms in a jurisdiction under SO₂ nonattainment, we do not expect to see significant employment effects overall in a jurisdiction. This expectation is largely confirmed in our empirical analysis. These more narrowly targeted nonattainment regulations do not affect employment stability. Positive employment growth is seen during the early years of SO₂ nonattainment, but this growth is moderated as a county continues in nonattainment. There is no result reported for regaining attainment status for SO₂ in Table 3 because it is perfectly collinear with nonattainment for SO₂ and the county fixed effects.

Given our expectation that some of the impacts of nonattainment regulations will persist after a county regains attainment status, Tables 6 and 7 report marginal effects of regaining attainment status based on the percentile values of TG_p. Statistically significant effects are only seen for TSP and for the initial years after regaining ozone attainment status. The TSP regulatory effects largely persist even after attainment status has been regained; for both employment growth and instability, the coefficients for regaining TSP attainment status are similar in magnitude to those for TSP nonattainment. This suggests a long-term local economic impact resulting from temporary TSP regulations. Although TSP nonattainment areas in some cases came under the new PM₁₀ nonattainment requirements because PM₁₀ replaced TSP as a more narrowly defined criteria pollutant after 1987, this is controlled for in the model. PM₁₀ nonattainment is included in the model, so the effects for regaining TSP attainment status are interpreted holding PM₁₀ attainments status constant.¹⁰ With respect to 1-hour ozone, we find that the regulatory impact from nonattainment on employment growth extended through the 16th year after regaining attainment status, and the magnitude of the effect is similar to that under ozone nonattainment. However, regaining attainment status for ozone has no discernible impact on employment volatility. In general, we observe persistence of regulatory impacts after regaining attainment status for TSP and ozone, which are the criteria pollutants that target a broader base of industries.

Table 6.

Marginal Effects for Employment Growth After Regaining Attainment Status.

Percentile	TG	F statistic	p	Marginal effect
CO
5	1	0.020	.888	−0.0040
10	2	0.037	.848	−0.0050
25	3	0.062	.803	−0.0061
50	6	0.228	.633	−0.0094
75	12	1.432	.231	−0.0160
90	18	1.930	.165	−0.0225
95	21	1.600	.206	−0.0258
One-hour ozone
5	1	8.102	.004	0.0200***
10	2	7.709	.006	0.0195***
25	5	6.544	.011	0.0180**
50	10	4.720	.030	0.0155**
75	17	2.609	.106	0.0119
90	22	1.503	.220	0.0094
95	24	1.157	.282	0.0084
TSP
5	2	86.021	<.001	−0.0347***
10	3	103.470	<.001	−0.0348***
25	6	186.961	<.001	−0.0353***
50	11	336.470	<.001	−0.0361***
75	17	140.163	<.001	−0.0371***
90	22	63.831	<.001	−0.0379***
95	24	49.445	<.001	−0.0382***
SO₂
5	1	0.180	.671	−0.0004
10	2	0.180	.671	−0.0008
25	4	0.180	.671	−0.0016
50	7	0.180	.671	−0.0029
75	13	0.180	.671	−0.0053
90	19	0.180	.671	−0.0078
95	22	0.180	.671	−0.0090

Source. Authors’ calculations.

***

p < .01. **p < .05. *p < .1.

Table 7.

Marginal Effects for Employment Volatility After Regaining Attainment Status.

Percentile	TG	F statistic	p	Marginal effect
CO
5	1	0.231	.631	0.0245
10	2	0.171	.679	0.0217
25	3	0.122	.726	0.0188
50	6	0.031	.861	0.0102
75	12	0.011	.917	−0.0071
90	18	0.097	.756	−0.0243
95	21	0.155	.694	−0.0329
One-hour ozone
5	1	2.486	.115	−0.0387
10	2	2.489	.115	−0.0388
25	5	2.495	.114	−0.0389
50	10	2.491	.115	−0.0391
75	17	2.457	.117	−0.0393
90	22	2.414	.120	−0.0395
95	24	2.393	.122	−0.0396
TSP
5	2	922.512	<.001	−0.1190***
10	3	1089.773	<.001	−0.1184***
25	6	1854.171	<.001	−0.1167***
50	11	2867.009	<.001	−0.1140***
75	17	1049.647	<.001	−0.1106***
90	22	441.206	<.001	−0.1078***
95	24	330.476	<.001	−0.1067***
SO₂
5	1	1.430	.232	0.0020
10	2	1.430	.232	0.0039
25	4	1.430	.232	0.0079
50	7	1.430	.232	0.0138
75	13	1.430	.232	0.0255
90	19	1.430	.232	0.0373
95	22	1.430	.232	0.0432

Source. Authors’ calculations.

***

p < .01. **p < .05. *p < .1.

In addition to the regulatory effects estimated by Equation (3), we also account for the control variables in X. Greater employment growth and volatility are seen in wealthier areas. Jurisdiction population positively affects the employment growth but does not affect employment stability. The share of the adult population enhances both the growth and stability of local employment. Higher local wages negatively affect local employment growth but help to improve employment stability.

Because higher values of CDIV indicate greater industrial specialization, Table 3 indicates that the factor of industrial specialization, excluding the impacts from attainment status designation, decreases employment growth but does not have a tangible impact on employment volatility. Our findings do not lend support to a complementary diversity–stability causal relationship, contributing to this debate in the literature.

Conclusions and Policy Implications

The U.S. national economy is currently making stumbling steps forward after the Great Recession. To stimulate the economy, national attention has been focused on how to create a better environment for business investment and employment. Thus, the regulatory effects on employment are at the center of environmental policy debates. In this article, we explore whether the implementation of air quality regulations weakens local economies. Our findings address this urgent question.

Using longitudinal county employment data from 1980 through 2005, we disentangle the impact of nonattainment status regulations on local employment stability and growth. The most significant impact on local employment originates from the nonattainment regulations for TSP. These regulations are seen to suppress employment growth while decreasing employment volatility both during nonattainment and after regaining attainment status. This supports a trade-off between growth and stability; however, we find employment growth in 1-hour ozone nonattainment areas and in areas that have recently regained attainment status for 1-hour ozone. This suggests that local industrial shifts in response to these regulations more than absorb employment reductions in polluting industries. The employment growth is accompanied by an increase in employment stability after a prolonged period of nonattainment, which suggests a complementary rather than a trade-off relationship between growth and stability. Some evidence of reduced employment in CO nonattainment areas is seen, along with transitory impacts on employment volatility in CO nonattainment areas and employment growth in SO₂ nonattainment areas.

Some effects persist after attainment status is regained. This is seen for TSP and 1-hour ozone and may be due to the nature of the nonattainment regulations, which can cause a permanent shift in the local economic and employment structure. Also, this persistence may result from the air quality maintenance regulations that replaced the former stringent nonattainment requirements. When an area regains attainment status, air quality maintenance regulations replace the nonattainment regulations. It would be informative for future analysis to identify whether persistence of regulatory effects after regaining attainment status results from the structure of the nonattainment regulations or from the regulations that replace the nonattainment requirements.

The observed variation in effects across criteria pollutants is as expected—air quality regulations for each criteria pollutant address different sources of pollutants and thus have varying impacts on local employment. Overall, we do see greater effects coming from regulations for criteria pollutants originating from a broad range of industries and smaller or no effects when a few specific industries are the primary source of pollutants.

Our findings address the current debate regarding the economic impacts of the Clean Air Act, as they suggest that air quality regulations exert nonuniform repercussions on local job growth and employment stability; the specific effect varies depending on the type of criteria pollutant and duration of the classification status. For instance, suppressed local employment is associated with TSP nonattainment, but a positive impact on employment growth is observed for nonattainment under the 1-hour ozone standard. It appears that local economies in 1-hour ozone nonattainment cannot only quickly absorb some regulatory burden with no significant net job losses but also create extra labor demand during the process of industrial restructuring. As data become available, future research should compare these employment effects with those of the current 8-hour ozone standard. This current standard was implemented in 2004 as a replacement for the 1-hour standard.

In addition, we observe both a trade-off and a complementary relationship between employment growth and stability. The specific relationship hinges on the regulated criteria pollutant. Our study lends scant support for the complementary diversity–stability causal relationship suggested by some prior studies.

As employment growth and stability are important indicators for local economic development and performance, our findings carry significant implications for local public administrators who pursue sustainable economic development and strong fiscal health. Not only will changing employment patterns directly affect residents by altering their job options, but the industrial effects of Clean Air Act regulations also extend to local government revenues and expenditures (Carr, 2011a, 2011b). If polluting industries are major sources of local employment, local officials should be prepared for the subsequent impact of nonattainment regulations. As Carr and Yan (2012) suggest, diversifying the local employment structure is important during the implementation of stringent air quality standards. Local officials should take advantage of the unique opportunity of industry reshuffling and create new focal points for growth while putting forward strategies to minimize the negative external costs associated with unemployment.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Wenli Yan is an assistant professor in the Wilder School of Government and Public Affairs at Virginia Commonwealth University. Her recent publications include articles in National Tax Journal, Public Money & Management, Regional Studies, Municipal Finance Journal, Journal of Public Budgeting, Accounting & Financial Management, Public Finance Review, and Public Budgeting and Finance.

Douglas A. Carr is an assistant professor at Oakland University. His research focuses on federal environmental policy and the public finance implications of federal policies. His recent publications include articles in Public Finance Review, Regional Studies, and Public Finance and Management.

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