modeling compliance to environmental regulation: evidence from manufacturing industries

NORTH- HOLLAND

Modeling Compliance to Environmental Regulation: Evidence From Manufacturing Industries

James L. Regens, Entergy and Spatial Research Laboratory, Tulane University Medical Center

Barry J. Seldon and Euel Elliott, School of Social Sciences, University of Texas at Dallas

We examine the demand for pollution control equipment from 1973 to 1991 by those U.S. manufacturing industries that are highly sensitive to environmental regulation. We also consider the political determinants of the U.S. Environmental Protection Agency (EPA) enforcement budget. Because, as we demonstrate, the EPA enforcement budget is an important determinant of the industries' investment in pollution control equipment, we are able to establish a relationship between political factors and economic decision making on the part of the industries. Thus, our analysis demonstrates that the demand for pollution control equipment is sensitive to both economic and political factors. © 1997 Society for Policy Modeling. Published by Elsevier Science Inc.

Key Words: Environmental regulations; Manufacturing industries; Political factors: Economic factors.

1. INTRODUCTION

The decades between Franklin Roosevelt's New Deal and Ron- aid Reagan's presidency produced a tremendous expansion in the scope and magnitude of governmental activity in the United States addressing such diverse concerns as employment conditions and wages, occupational health and safety risks, fraud and deception

Address correspondence to Dr. E. Elliott, School o f Social Sciences, University of Texas at Dallas, Dallas, TX 75083-0688.

The authors thank Wim P. M. Vijverberg for helpful suggestions and Daniel M. O'Brien and Oleg Fomin for research assistance and computer programming.

Final draft accepted Februrary 12, 1997.

Journal o f Policy Modeling 19(6):683-696 (1997) © 1997 Society for Policy Modeling Published by Elsevier Science Inc.

0161-8938/97/$17.00 PII S0161-8938(97)00001-X

684 J.L. Regens, B. J. Seldon, and E. Elliott

in market practices, pollution of the ambient environment, and consumer product safety. In fact, the evolving regulatory agenda did much to transform the nation's administrative framework as well as constitutional order.

While the nature and impact of these regulatory regimes have become a major source of controversy among policymakers and social scientists alike, perhaps none has generated more controversy than those governing the environment (see Regens and Elliott, 1992; Vogel, 1986; Pashigian, 1985; Maloney and McCor- mick, 1982; Mitnick, 1980). For instance, some evidence suggests that the slowdown in productivity growth throughout the 1970s was exacerbated by environmental policy (Jorgenson and Wil- coxen, 1990; Hazilla and Kopp, 1990; Portney, 1981; Haveman and Christiansen, 1981). This concern has produced a burgeoning literature, primarily in economics, investigating the benefits and costs of regulatory activity. Some authors stress the potential benefits gained from regulation (Graves, Murdoch, Thayer, and Wald- man, 1988; Kneese, 1984). Others stress potential industry-specific costs such as the creation of market entry barriers or other possible economic costs (Beladi and Samanta, 1988; Lichtenberg, Parker, and Zilberman, 1988; Wallace, Watson, and Yandle, 1988; Barbera and McConnell, 1986; Maloney and Yandle, 1984).

How sensitive, then, are the monetary costs, and by implication benefits, imposed upon private-sector economic actors to political and economic influences? We provide insights into the answer to that question by evaluating the relative importance of political and economic influences on private sector expenditures to comply with environmental regulation in the United States over the past two decades. Building upon previous work that focused on a single industry or the aggregate economy, our research delineates the micro- and macro-level determinants of compliance costs across several industries that are especially sensitive to the environmental regulatory regime. And, by concentrating on those manufacturing industries most susceptible to pollution control and abatement regulations, we are able to obtain a clearer picture of the political and economic forces at work.

2. THE M O D E L A N D D A T A

We examine annual capital investment in pollution control equipment by those manufacturing sectors that accounted for the

C O M P L I A N C E T O E N V I R O N M E N T A L R E G U L A T I O N 685

overwhelming majority of pollution control abatement expenditures in the United States from 1973 to 1991.1 The industries, with their Commerce Department two-digit Standard Industrial Classification (SIC) codes, are Forest Products (24); Paper and Allied Products (26); Chemicals and Allied Products (28); Petro- leum and Coal (29); Rubber and Plastics (30); Iron and Steel (33); and Fabricated Metals (34). 2 We focus on those sectors because they frequently attempt to influence the formulation and implementation of environmental regulations through political action. Moreover, governmental policy is itself very sensitive to them because their production processes are a major source of environmental residuals. The expenditures examined in this analysis are for the reduction of pollutants by preventing their generation, their recycling or their treatment prior to disposal by means ac- ceptable to federal, state, and local authorities. The data are lim- ited to spending on activities resulting from rules and regulations restricting the release of environmental pollutants into the public domain. Costs to firms for activities, such as plant closings or construction delays attributable to environmental regulation as well as voluntary nonmarket activities that may use productive resources (for example, nonmandatory litter removal), are excluded.

Expenditures in all seven industries, measured in constant 1982 dollars to adjust for inflation, rose initially before beginning a long-term decline in the mid-1970s. That slowdown in capital expenditures was interrupted by a slight increase beginning around 1984 and continuing through 1987 as industrial output for the U.S. economy became more robust during the mid-1980s. The initial increase reflects the pollution controls imposed immediately after the establishment of the U.S. Environmental Protection Agency (EPA) and its regulatory framework in the early 1970s. These manufacturing industries, being highly sensitive to changes in the regulatory framework for environmental protection, also experi- enced slight upward adjustments in capital expenditures for pollution abatement in the 1977-78 period, coinciding with passage and initial implementation of significant amendments to the Clean Air Act of 1970.

' The year when the industries we consider began significant investments in pollution control equipment was 1973; 1991 is the year for which all variables we use are available.

2 The electric utility industry is excluded from our analysis because data for nonmanufac- turing industries are not collected in the Census Bureau ' s Pollution Aba temen t Costs and Expendi tures Survey.


In estimating the regression equations from the panel data, we test (and, when necessary, correct for) serial correlation and heteroskedasticity. Economic determinants of investment in pollution control equipment include the cost of capital (by the law of demand), industry output (as output increases the industry re- quires more pollution abatement equipment to comply with environmental regulation), the stock of the industry's pollution control equipment (as this stock increases, the industry need not add so much in any given year), and the Environmental Protection Agency (EPA) enforcement budget (as this increases, firms may feel compelled to invest more in pollution control equipment than they would otherwise).

We also explore the impact of two political variables upon the EPA's enforcement budget. These variables are Democratic party strength in Congress (because Democrats have been more supportive of environmental regulation; see Regens, 1989) and the impact of the Reagan Presidency (because of that administration's focus on deregulation; see McCubbins and Page, 1985; Moe, 1985; and Regens and Elliott, 1992). These political variables, then, have an indirect effect upon the industries' investment in pollution control, which can be measured as the product of the derivative of investment with respect to EPA enforcement dollars times the derivative of EPA enforcement dollars with respect to the various political variables, as we note below.

Because industry output may also depend on pollution control expenditures, we use an instrumental variable approach, estimating an instrument for output that depends upon all the exogenous variables in the system as well as real wage and price levels for the particular industry.

The demand for pollution control equipment originates from the cost minimization problem of the firm where total cost (C) is:

C = WX + Vl + ~(K;E,Q)

where X is a vector of production inputs, W is a vector of input prices, U is the user cost of capital, I is investment in pollution control equipment, K is the stock of pollution control equipment which the firm already has in place, Q is the quantity of the firm's output, E is the EPA's enforcement budget, and d~(K;E,Q) is the expected fine for polluting. Because the cost function is separable into C 1 = WX and C2 = UI + ~(K;E,Q), we concentrate on Cz. The first order condition for this cost minimization problem yields

COMPLIANCE TO ENVIRONMENTAL REGULATION 687

the inverse derived demand equation for pollution control equipment, U = eO'(K;E,Q). Because K = I + (1-A)K1 where ( 1 - A)K_~ is the remaining stock of previously purchased equipment, we may write U = dp'(I + ( 1 - A ) K _ I ; E , Q ) . Rearranging terms and aggregating over all firms in industry i yields industry i's derived demand for pollution control equipment at time t,

I~,, = w(u~., ;E, ,Q,,, ,K~,,_~).

We further suppose that Et = ®(D,,R,) where D, is Democratic congressional strength and R, is a binary variable for the Reagan Presidency, both at time t. We will estimate this last equation separately from each industry's demand function, because the equation E t = O(D,,R,) is a macro "political environment" relation that, while it affects each industry, is the same for each industry. (And note that the right hand side variables of the Et equation are not endogenous, so it would be inappropriate to estimate it simultaneously with Ii,, using, for instance, two-stage least-squares). However, we can rid E, of its error term before entering it into each industry's demand function. We do this by treating E, as an instrumental variable (as we do with industry output), regressing it on all exogenous variables in the system including D, and Rt. This means that, for each industry's demand function, we will use instrumental variables for both E, and Qi,,.

Our approach will allow us to estimate the effects of both economic and political variables upon the demand for pollution control equipment as follows. The effects of the economic variables upon pollution control investment are measured as the derivative of L,, with respect to the relevant variable (for example, ,gL,,/OE,) while the effects of the political variables upon the EPA enforcement budget are measured as the derivative of E, with respect to the relevant political variable (for example, OEt/OD,). Then, by the chain rule, the effect of a political variable on pollution control investment is the product of the two relevant partial derivatives (for example, OlffODt = [OIi, t /OEt] [cOE,/ODt]).

3. THE DA T A, REGRESSION MODEL, A N D HYPOTHESES

The data and sources are as follows:

/,,t = Real capital expenditures on pollution control in the ith industry at time t, calculated by dividing nominal


Ui, t

Qi , t

Ki, t - 1 =

E t z

O t

R t

expenditures (in millions of dollars, from Bureau of the Census Current Industrial Reports) by the 1982- based deflator for machinery. This can be interpreted as quantity because we divide expenditures (nominal price times quantity) by the machinery deflator, which can be interpreted as the nominal price of the capital normalized to 1982 = 100. Real price of capital equipment (from the Economic Report of the President), a proxy for the price of pollution control equipment. Output index for the ith industry based on 1982 = 100 (Federal Reserve Board, Federal Reserve Bulletin). Stock of pollution control equipment which the industry has in place at the beginning of the period, This is estimated as the discounted sum of past real capital expenditures on pollution control in the ith industry, calculated using all past values of L This is possible because the Is for the industries we study date only from 1973. Thus, we have all the data necessary to calculate Ki.,-1 under varying assumptions concerning the depreciation rate A. We will assume, alternatively, that this depreciation rate is 5 percent and 10 percent for a sensitivity check. If there are great differences in the results, we will try still other rates of depreciation. We will also assume, alternatively, that depreciation is linear and that it is exponential. This is explained in more detail below. Real EPA enforcement budget at time t, calculated by dividing the nominal enforcement budget (in thousands of dollars, from the Office of Management and Budget's Budget of the United States), by the gross national product (GNP) deflator for government goods and services. This, like the other political variables, is the same for all industries at time t. (1/2) (D , + Ds) where DH is the percentage of democratic members in the House of Representatives and Ds is the percentage of Democrats in the Senate. We do not simply divide the total of Democrats in both houses by the total number of members in both houses because that would understate the importance of the Senate, a coequal branch of the federal government. 1 for years of the Reagan Presidency, 0 otherwise.


We will use an instrumental variable (IV) approach where we create an instrument for Q~,, by regressing it on the exogenous variables listed above and the two variables:

W~, = Real wage rate in the ith industry, calculated by dividing the nominal wage rate, obtained from the Bureau of Labor Statistics, by the price index for the ith industry.

P~,, = Real price for the ith industry, constructed by dividing the price index for the ith industry by the all commodi- ties producer price index.

We illustrate our construction of K~.t-1 for a 5 percent depreciation rate on pollution control equipment. The construction for a 10 percent depreciation rate is similar. First, we should note that, prior to 1973, the regulatory regime was not in place, and competi- tive forces did not encourage investment in pollution control (Re- gens and Rycroft, 1989). Because L.t = 0 for t <~ 1972 and because we have data for 1973-91, we calculate the linear and exponential versions of Ki,,_l as

and

K,., , = E',.=l? 72 (1 - 0 . 0 5 y ) l i . , ,

K, ,_ , = ~i=~? 72 (1 - 0 . 0 5 ) ' I , ,

for each t = 1973, 1974, . . . , 1991. Because we have the entire time series, we do not have to employ a transformation to impose a distributed lag as an approximation.

In what follows, Qw,i,t is the instrument constructed by regressing industry output on all exogenous variables as well as IV,.,, and Pi,,. As discussed above, we also create an instrument for E, on the right hand side of the demand equation, which we denote as Ew,,. All statistical tests for the demand equation will employ the IV-corrected covariance matrix. The linear regression equations will be

[i,t = (3/-0 q- (3/-181,i -1- 12282,i -t- . . . q- Ol.6Se,,i q- [31U,, t -1- [32Qlv . i , t

+ 133 K~.,_, + [34 Ew., + e/.~., (1 )

and

E, = % + "hD, + ~/2R, + % ; (2)

where &,i, S2,i, . . . , S6,i are dummy variables for six of the seven industries (one is excluded, as usual, to avoid perfect collinearity with the intercept term; and for any particular industry, Sji is the


same each period for j, i = 1, 2 . . . . ,6), and e~,i,t and eE,, are error terms. Adjustments will be made as necessary to correct for serial correlation and heteroskedasticity.

We have definite expectations concerning the slope coefficients of the linear regression and enforcement equations. [31 should be negative by the law of demand. [32 should be positive because higher levels of output would be accompanied by increased emis- sions; hence, the industries will need more pollution control equipment to comply with environmental regulations. [33 should be negative, because the firms would not need to spend so much if they had already invested substantially in pollution control equipment. [34 should be positive as long as increases in the EPA's enforcement budget increase the probability that the firms would have to pay fines for violations of environmental regulation. ~1 should be positive, because the EPA will be encouraged to expand its environmental policing efforts with increased Democratic strength in Con- gress, especially given the detailed congressional oversight of the EPA since the agency's inception. And ~/2 should be negative, because the Reagan Administration was less interested in environmental regulation enforcement. For example, from 1980 to 1982, reauthorization of the Federal Water Pollution Control Act was held up by disagreement between the Congress and the Reagan White House.

4. SOME DETAILS OF THE ESTIMATION P R O C E D U R E S

We estimated versions of Equations 1 and 2 assuming depreciation rates of 5 and 10 percent and both linear and exponential depreciation on the pollution control equipment. We excluded an industry dummy variable for SIC 24 to avoid perfect collinearity between the dummies and the intercept; the effect of the excluded dummy, of course, is captured in the intercept term.

The results for both depreciation rates were similar. Therefore, we will discuss our results in the following section using a 5 percent depreciation rate.

We ran the original regression Equations 1 and 2 and tested for serial correlation using Durbin's (1970) h statistic for Equation 1 (because Ki, t-1 contains the lagged endogenous variable) and the Durbin-Watson statistic for Equation 2. For Equation 1, this necessitated a separate test for each SIC code; therefore, we constructed an h statistic for each industry. We consistently found autocorrelation in the versions of Equation 1 we tried, so we corrected for autocorrelation using the Cochrane-Orcutt procedure


(Kmenta, 1986) to construct autocorrelation coefficients for each industry of Equation 1. The Durbin-Watson statistic for Equation 2 was in the inconclusive region; and, even when we adjusted for autocorrelation, there was little difference from the original model; so we used the original model for the tests discussed below.

Once the autocorrelation correction was completed, we then tested for heteroskedasticity using the White test (Kmenta, 1986). Once again, we found problems in Equation 1; so we corrected for heteroskedasticity by estimating the variance of the dependent variable and dividing the variables by this estimate, as suggested by Kmenta (1986, p. 291). The White test found no evidence of heteroskedasticity in Equation 2.

5. EMPIRICAL RESULTS

Table i presents estimation results for our regressions assuming a 5 percent depreciation rate on pollution control equipment assuming a linear depreciation scheme (column 1) and an exponential depreciation scheme (column 2). We believe that linear depreciation is more reasonable and better supported by our statistical results than exponential depreciation for three reasons. First, exponential depreciation implicitly assumes that some portion of any year's capital investment lasts forever, while the linear depreciation model allows capital to be scrapped after a sufficiently long period. Second, we note that the coefficient associated with pollution equipment already in place is quite different between the two models, as we would expect; and the only other qualitative difference is that the price coefficient is twice as large in the exponential depreciation model and becomes significant. We believe that these differences support the linear depreciation model over the exponential for two reasons: (1) it seems plausible that demand is relatively price-inelastic (and the magnitudes of the price coefficients suggest that the linear-depreciation demand is the more price-inelastic), and (2) the quantity of accumulated pollution equipment should be important (and this variable is significant at the 1-percent level in the linear-depreciation model while it is not significant at even the 10-percent level in the exponential-depreciation model). Third, the R 2 for the linear-depreciation model is slightly greater than that for the exponential-depreciation. For these reasons, therefore, we will discuss the linear- depreciation model in what follows, but we present the exponential-depreciation model to explicate the differences)

We also ran all regressions assuming a 10 percent depreciation rate, but the R2s were lower than the corresponding R2s assuming a 5 percent depreciation rate in all cases.

692 J . L . R e g e n s , B. J. Seldon, and E. Elliott

Table 1: Pollution Control Equipment Demand and EPA Enforcement Budget Regression Equation Estimates (standard errors in parentheses)

Linear Exponential depreciation depreciation

Demand equation: R 2 = 0.5731 R 2 = 0.5356 N = 126 N = 126

R P A C E intercept 0.06614** 0.04847* (0.03212) (0.03255)

SIC 26 dummy -0.05829** -0.04498* (0.03300) (0.03365)

SIC 28 dummy -0.05856** -0.04573* (0.03316) (0.03382)

SIC 29 dummy -0.05911"* -0.04604* (0.03304) (0.03376)

SIC 30 dummy -0.02836 0.05576 (0.11895) (0.11263)

SIC 33 dummy -0.05965** -0.04483* (0.03305) (0.03362)

SIC 34 dummy 0.05414*** 0.03528** (0.01617) (0.01585)

Enforcement budget 0.00008*** 0.00008*** (0.00003) (0.00003)

Industry output 0.60183*** 0.49724*** (0.11542) (0.12457)

Pollution equipment -0.15403*** -0.00316 (0.05970) (0.04399)

Price -51.511300 - 108.72425"* (54.62812) (54.04170)

Enforcement equation: R 2 = 0.3036 N = 20

R E P A intercept -779,561.86950" (421,612.69311)

Congressional Democrats (%) 22,690.93301"** 7,049.06276

Reagan dummy 31,354.02159 (59,382.61665)

* Significant at the 10 percent level in a one-tailed test.

** Significant at the 5 percent level in a one-tailed test.

*** Significant at the 1 percent level in a one-tailed test.

In the demand equation, the industry dummy variables of the linear-depreciation model are all significant at the 10 percent level except for SIC 30. All other variables in the linear-depreciation demand equation have the hypothesized signs, and all except price are significant at the 1 percent level.

COMPLIANCE TO ENVIRONMENTAL REGULATION 693

The estimated coefficients suggest the following changes in pollution control investment for unit changes in the dependent variables of the demand equation: OIJOE, = 0.00008 where I is in millions and E is in thousands, so a $1.00 increase in the EPA enforcement budget increases pollution control investment in each industry by $0.08 over what the industry would have invested, ceteris paribus, or a total of $0.56 for the seven industries. Because oLJoQi~ = 0.60183, a unit increase in the industry's output (where units are normalized to Qi,1982 = 100 for each industry) increases pollution control investment by $0.60 over what would have been invested. OL.,/OKi.,-1 = -0.15403 where both L.~ and Ki.,_l are in the same units, so a $1.00 increase in the value of the industry's stock of pollution control equipment will reduce pollution control investment by slightly more than $0.15, ceteris paribus. The insig- nificance of the price coefficient suggests that perhaps OL.,/OU~., = 0, so changes in the price of pollution control equipment are not important considerations, at least in the range of prices for our sample. This, of course, could change if the price of the equipment increased dramatically.

As we would expect, the elasticities differ for the different industries. The enforcement elasticity of demand ranged from 0.052 for SIC 28 (Chemicals and Allied Products) to 1.153 for SIC 30 (Rubber and Plastics). This is reasonable because the investment in SIC 28 was typically much higher than in SIC 30: real investments in millions of 1982 dollars in SIC 28 ranged from a low of $384.63 in 1983 to $1,630.70 in 1991, while real investments in SIC 30 ranged from $22.96 in 1983 to $76.32 in 1990. Because the elasticity is calculated as (OL.JOE,) (E,/Ii.t) = ~4 (E,/L.~), it is lower for the industry with higher investments. For similar reasons, the output elasticity of demand ranged from 0.046 for SIC 28 to 1.080 for SIC 30 while the stock-of-equipment elasticity of demand ranged from -0.598 for SIC 30 to -0.678 for SIC 33 (Iron and Steel).

The enforcement equation explores the effects of two political variables, Democratic congressional strength and the Reagan Ad- ministration influences, upon the EPA enforcement budget. These political variables, if statistically significant, then act indirectly upon pollution control investment through the mediating variable, which captures EPA enforcement efforts. The statistical support of our hypothesized relationships are mixed.

In the enforcement equation, the binary variable for the Reagan years is insignificant. We found this to be surprising, especially given the fact that real EPA investment budgets fell during the

694 J . L . Regens, B. J. Seldon, and E. Elliott

Reagan Presidency, so that casual empiricism would suggest that the binary variables should be important. Even when we defined the Reagan binary variable to be 1 for only the first 2 years of the Reagan Administration, which is consistent with Wood (1988), the Reagan dummy variable had no significant effect upon the EPA enforcement budget.

Democratic congressional strength, on the other hand, is positively signed as hypothesized and is significant at the 1 percent level. The magnitude of the coefficient suggests that a 1-percent increase in Democratic congressional strength leads to an increase of nearly $22,700,000 in 1982 dollars. 4 This is not unreasonable: The average EPA enforcement budget in 1982 dollars was $534,373,290 (23.5 times $22,700,000), while the maximum change in Democratic congressional strength was the drop of 9.795 from 1979-80 to 1981-82 with the election of Republicans to Congress when Reagan won the presidency. The Democratic-congressional- strength elasticity of the EPA enforcement (OEt/OD,) (D]OE~) is 1.146 (and is the same, of course, for all industries).

Democratic congressional strength, then, also affects pollution control investment indirectly. Our estimates suggest that a 1-percent increase in Democratic congressional strength increases pollution control equipment investment in an industry by nearly $1,816,000 in 1982 dollars ($0.08 for each of $22,700,000). This also is reasonable: $1,816,000 is fairly small when compared with real investment, which has a minimum of $22,960,000 for SIC 30 in 1983 and is often in the billions of 1982 dollars for other SIC codes. The Democratic-congressional-strength elasticity of demand (calculated as [OIi,,/OD,] [D]Ii, t] = [OlJOE~] [OE,/OD~] [D,/Ii,~] at the mean values of D, and/~., for the various industries) range from 0.059 for SIC 28 (Chemicals and Allied Products) to 1.322 for SIC 30 (Rubber and Plastics).

6. CONCLUSION

Capital expenditures by key U.S. manufacturing industries to comply with environmental regulations throughout the 1970s and 1980s were sensitive to a mix of economic and political factors. As economic theory would predict, pollution control investment

4 A linear approximation is appropriate because, while Democratic congressional strength can only range from 0 percent to 100 percent, it never approaches these limits; it ranges from 50.930 percent in 1981-82 to 64.065 percent in 1977-78.


is positively related to industry output, negatively related to the industry's stock of pollution control equipment, and nonpositively related to the cost of the equipment. We also find that pollution control invstment is positively related to the EPA enforcement budget, suggesting at the very least that industries believe that the EPA will increase its investigatory efforts as their budget increases.

Among the political factors, we find that the Reagan Adminis- tration did not significantly affect the EPA enforcement budget and, therefore, pollution control investment. The partisan compo- sition of Congress, however, is positively associated with the EPA enforcement budget and pollution abatement expenditures. To the extent that Democratic congressional strength fell with Reagan's coattails, then, the Reagan Administration did have an effect. In general, this positive relationship between democratic strength and the EPA enforcement budget supports the assumption that Democrats tend to be more supportive of command-and-control approaches to environmental concerns. It is also consistent with the view that, over the long run, political principals, most especially the Congress and the relevant oversight committees, play a domi- nant role in determining social regulatory policymaking by agen- cies such as the EPA.

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Graves, P., Murdoch, J.C., Thayer, M.A., and Waldman, D. (1988) The Robustness of Hedonic Prices: Urban Air Quality. Land Economics 64:220-233.

Haveman, R., and Christiansen, G.B. (1981) Environmental Regulations and Productivity Growth. In Environmental Regulation and the U.S. Economy (H. Peskin, Ed.). Baltimore, MD: Johns Hopkins University Press.

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modeling compliance to environmental regulation: evidence from manufacturing industries

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