Journal of Environmental Economics and Management 43, 412

– 436 (2002) doi:10.1006/jeem.2001.1191, available online at http://www.idealibrary.com on



### Jobs Versus the Environment: An Industry-Level Perspective

Richard D. Morgenstern, 1 William A. Pizer, and Jhih-Shyang Shih

1616 P Street, NW, Washington, DC 20036

Received January 29, 2001; revised June 7, 2001; published online October 3, 2001

The possibility that workers could be adversely affected by increasingly stringent environmental policies has led to claims of a “jobs versus the environment” trade-off by both businessand labor leaders. The present research examines this claim at the industry level for four heavily polluting industries: pulp and paper mills, plastic manufacturers, petroleum refiners, andiron and steel mills. Combining a unique plant-level data set with industry-level demand information, we find that increased environmental spending generally does not cause a significant change in employment. Our average across all four industries is a net gain of 1.5 jobs per $1million in additional environmental spending, with a standard error of 2.2 jobs—an economically and statistically insignificant effect. There are statistically significant and positive effects in two industries, but total number of affected jobs remains quite small. These small positive effects can be linked to labor-using factor shifts and relatively inelastic estimated demand.

 2001 Elsevier Science (USA)

Key Words: jobs-environment trade-off; distribution of environmental costs; translog cost
function.

1. INTRODUCTION

Environmental polices involve economic costs that are unevenly borne by individuals and industries across the economy. The possibility that workers could be adversely affected in heavily regulated industries has led to claims of a “jobs versus the environment” trade-off, a mantra echoed by both business and labor leaders. At a minimum, the visibility and emotion associated with potential job loss make it a crucial issue in ongoing policy debates. Interest groups now routinely developCongressional district-level estimates of job losses associated with proposed legislation [1]. Not surprisingly, a third of the respondents to a 1990 poll thought it somewhat or very likely that their own job was threatened by environmental regulation [2].

Accepting the notion that potential job loss due to regulation is an important phenomenon to understand, one of the challenges for researchers in this field is how best to measure job loss. An individual separated from an existing job because of an environmental regulation has clearly suffered a loss. Yet, pollution abatement activities themselves require labor input. Thus, environmental regulations may also

1 To whom correspondence should be addressed. Quality of the Environment Division, Resources for the Future. We acknowledge helpful comments from Hirschel Kasper, Raymond Kopp, and the anonymous reviewers of an earlier draft. The research in this paper was conducted at the Center for Economic Studies, U.S. Bureau of the Census. Mary L. Streitwieser, Gordon M. Phillips, and Arnold P. Reznek have provided considerable assistance for which the authors are greatly appreciative. Research results are those of the authors and do not necessarily indicate concurrence by the Bureau of the Census
or the Center for Economic Studies.

412 0095-0696/01 $35.00
© 2001 Elsevier Science (USA) All rights reserved.jobs versus the environment 413

create jobs—sometimes in the same industry, or even in the same firm. In addition,environmental regulation may cause firms in a particular industry to shift production and jobs from areas not attaining federal air quality standards to those in attainment. Job loss in one area is then accompanied by job creation in another.

Key stakeholders, such as labor unions and trade groups, typically focus on gross job changes and the cost of rearranging workers within an industry. However, net job loss within an industry—which recognizes that all intraindustry employment changes associated with environmental regulation is also a relevant measure for ongoing policy debates. Such a measure recognizes that many firms endeavor to relocate displaced employees in other units of the same company, and that reduced production or shutdowns at some plants are often accompanied by expanded outputat other plants, thereby offsetting at least some of the initial job losses. Consideration of these net employment impacts has figured prominently in a number of major
environmental decisions. 2

Most attempts to measure these net employment effects have depended on aggregate industry data or anecdotal evidence. Such approaches make it difficult to disentangle the effect of environmental regulation from other differences that existacross industries and changes that occur over time. This paper improves on previous efforts by using detailed Census data. With a unique six-year panel of dataon plant-level prices, inputs (including labor), outputs, and environmental expenditures, we can estimate the structure of production and its dependence on environmental expenditures while controlling for any differences that are fixed eitheracross plants or over time (using plant and time dummy variables). In order to identify the aggregate effects of new regulation, we make particular assumptions about both industry structure and the distribution of new environmental expenditures over plants. While this approach does not eliminate the estimation problems associatedwith observational studies, nor does it escape specific assumptions about the industry’s competitive structure, it does bring a rich, new dataset to bear on the question of how employment changes in response to environmental regulation. Our modeling and estimation decompose the labor consequences of increasedenvironmental spending into three components: increases in all factor inputs, holding output and factor shares constant (cost effect); changes in factor intensities (factor-shift effect); and changes in the quantity of output demanded (demand effect). Estimates of the production structure from detailed Census data allow usto assess the first two components. We then use aggregate time-series data to estimate demand elasticities and compute the third component as well as the overall change in employment. In order to compute aggregate effects of new industry-wide regulation, we assume monopolistic competition with new regulatory expenditures imposed in proportion to total costs.

We separately analyze four heavily polluting industries: pulp and paper, plastics, petroleum, and steel. In plastics and petroleum, we find that increased regulation raises employment by a small but statistically significant amount: 6.9 and 2.2 jobs per million dollars of regulatory expense, respectively. In pulp and paper and steel, the estimates are even smaller and insignificantly different from zero; the average

2 Examples include the 1990 Clean Air Act Amendments, Title IV (acid rain), vis- ` a-vis potential impacts on coal miner jobs; the 1982 Iron and Steel Effluent Guideline issued by the U.S. Environmental Protection Agency; the 1995 Spotted Owl decision under the Endangered Species Act vis- ` a-vis potential impacts on loggers; and various regulatory decisions carried out under the Clean Water Act.414 morgenstern, pizer, and shih

across all four industries has a 95% confidence interval of between 2.8 jobs lost and 5.9 jobs gained per million dollars of regulatory expense. Given the $4.9 billionincrease in regulatory expense and 632,000 job decrease in manufacturing employment from 1984 to 1994, these estimates suggest that, at most, environmental regulation accounted for 2% of the observed decline in employment over this 10 year period.

2. PREVIOUS STUDIES OF EMPLOYMENT AND ENVIRONMENTAL REGULATION

A wide range of research efforts have been used to address the connection between environmental regulation and employment, including aggregate policy modeling (macroeconomic and general equilibrium), economy-wide microeconomicstudies, industry-specific studies, and analyses of plant location and growth. Estimates of the economy-wide job impacts of environmental regulations traditionally are based on simulations of large macroeconomic and general equilibrium models. In a review of macroeconomic modeling efforts published in journals, OECD publications, and by the U.S. EPA, Goodstein [3] found that seven of the nine studies showed increases in employment, one showed a decrease, and one wasmixed. He concludes, “on balance, the available studies indicate that environmental spending

 has probably led to a net increase in the number of jobs in the U.S.

economy

 (although) if it exists, this effect is not large.” General equilibrium assessments of environmental regulation, such as Hazilla and Kopp [4] and Jorgenson and Wilcoxen [5], typically assume full employment; specifically, the real wage adjusts so that labor demand equals labor supply. Since the real wage falls with increased environmental regulation due to reductions in productivity, employment will likely decline. 3 Thus, environmental regulation leads to job loss in these models because individuals decide to work less in response to a lower relative price of leisure. However, such labor–leisure choices are unlikely to be the object of concern voiced by labor leaders or respondents to public opinion polls.

At the individual firm or plant level, business and labor experts typically argue that environmental regulation increases a company’s production costs and puts upward pressure on prices. Price increases, in turn, result in a loss of sales and at leastsome reduction in plant-level employment. However, employer responses to surveys by the U.S. Department of Labor [7] indicate that environmental spendingaccounts for only about 650 job losses per year, or less than one-tenth of one percent of all mass layoffs in the United States. These surveys may understate potential job losses because they ignore the effects on smaller firms (under-sampled in the survey) as well as the possibility that environmental regulation may be an importantsecondary factor in plant closure decisions. Conversely, these estimates may overstate the net job impacts by failing to account for employment increases associated with environmental regulation, such as control activities and/or shifts in employment to other plants.

3 Of course, employment could rise as the real wage falls, depending on whether the uncompensated labor supply curve is upward sloping or backward bending. See Hausman [6].jobs versus the environment 415

Studies of specific industries are less common than economy-wide analyses. Earlyresearch on the electric power industry by Gollup and Roberts [8] found significant job loss associated with increased environmental regulations. More recent work by Berman and Bui [9] compares petroleum refineries in the Los Angeles
area to all other U.S refineries. The authors find no evidence that environmental regulation decreased labor demand, even when allowing for induced plant exit and dissuaded plant entry. “If anything,” they note, “air quality regulation probably increased employment slightly.”

An area of related work has focused on the possible influence of environmental regulation on plant location, capturing the notion that heavily regulated and more polluted areas may suffer a relative penalty. Although new environmental regulations may not cause relocation of existing plants, firms have considerable flexibility in making decisions about the siting of new facilities. Studies by Bartik [10], Low and Yeats [11], and Crandall [12] suggest that firms are sensitive, in general terms,to cost variations among states when deciding where to locate new facilities. However, there is less evidence that plant location choices are related to stringency of environmental regulation. In an analysis that includes measures of environmentalstringency, Bartik found that neither measures of expenditures nor emission standards had significant effects on plant location decisions. These results are similar to those of Levinson [13] and McConnell and Schwab [14], although Levinson did findthat the locations of new branch plants of large multiplant companies in pollutionintensive industries were somewhat sensitive to differences in regulations. A recent study by Gray [15] also finds that states with more stringent regulation (measured by a variety of state-specific measures) have fewer plant openings. Finally, several studies have compared rates of manufacturing employment growth—not just new plants—in attainment areas versus nonattainment areas. 4Papers by Henderson [16] and Kahn [17] found relatively lower growth rates in manufacturing employment in nonattainment counties compared to those that attainedthe air quality standard. Becker and Henderson [18] found that environmental regulation reduced plant births and increased plant deaths in nonattainment areas, shifting polluting activity to cleaner areas. With a similar approach, Greenstone [19]estimates an annual loss of about 8,000 jobs over the period 1972–1987. Importantly, his estimates assume that employment growth at polluting plants in less regulated areas is an appropriate control group from which to infer the likely change in employment in the absence of regulation. 5Overall, the existing evidence on the possible jobs versus the environment tradeoff presents a bit of a puzzle. Many find little to no effect, especially those thatconsider the choice of plant location. However, recent work with microdata suggests that employment growth rates do vary according to attainment status, with attainment areas growing faster than nonattainment areas. Whether such results indicate either a net decline in employment, a spatial reallocation of production, or even an employment increase in cleaner areas, is unclear.

4 Attainment status refers to whether a county meets federal air quality standards.

5 Alternatively, one could postulate that polluting plants in more regulated areas are the appropriate control and that environmental regulation has actually created 8,000 jobs per year in the less regulated

areas.416 morgenstern, pizer, and shih

3. DECOMPOSING THE EFFECT OF ENVIRONMENTAL REGULATION ON EMPLOYMENT

Our approach differs from previous empirical work in this area by coupling a structural model of production, estimated with detailed plant-level data, with an aggregate model of demand, estimated from industry-level time-series data. Thedetailed plant-level data allow us to control for many potential confounding variables, including any that are fixed either at a particular plant or in a particular year. The structural model allows us to ask questions about whether environmental expenditures are more labor-intensive than spending on conventional production,using within-plant variation in environmental expenditures and employment to identify the relation. These gains come with a trade-off: within-plant variation is more prone to measurement error than between-plant variation, and our cost function model is necessarily more complex than a reduced form relationship. However, this work complements previous efforts by both providing an alternative approach and making use of a rich, new, detailed dataset. Our model is based on the recognition that when environmental regulations are tightened, employment will adjust to both a rearrangement of production activities as well as a potential output contraction. Rhetoric surrounding the jobs versus the environment debate focuses on the output contraction: increased regulation raises production costs, reduces demand, and eventually costs jobs. This reasoning ignoresthe fact that employment could rise if demand is less than unit elastic or if production becomes more labor intensive. For that reason, it is useful to closely examine how increased regulation translates into changes in employment. On the production side, there are two arguments for increased employment. First, environmental regulation raises production costs. As production costs rise, more input, including labor, are used to produce the same amount of output. We refer to this as the cost effect. Second, environmental activities may be more labor intensive than conventional production. For example, cleaner operations may involve more inspection and maintenance activities, or reduced use of fuel and materials. In bothexamples, the amount of labor per dollar of output will rise. This argument obviously can go the other way: cleaner operations could involve automation and less employment. 6 We refer to this effect as the factor-shift effect.

The more traditional concern is that as production costs rise in response to increased environmental regulation, output prices will rise, quantity demanded will fall, and plants will reduce employment levels. The extent of this effect depends on the cost increase passed on to consumers as well as the demand elasticity of industryoutput. These two features may not be independent: industries facing elastic output demand due to stiff competition may prove more adept at lowering the cost of environmental compliance. Less competitive industries with inelastic demand may be less concerned about cost increases associated with regulation. We refer to this
as the demand effect.

6 Porter and van der Linde [20] have pointed out that regulation may stimulate technological innovation, suggesting that environmental regulation may lead to less labor intensive production.jobs versus the environment 417

3.1. Production Effects

We consider the effect of increased regulation in three distinct steps. First, we examine how changes in regulation affect employment at the plant level, holding output constant. Second, we consider how these effects will affect market prices in a particular industry. An important element of this analysis will be our assumptions about the competitive structure of the industry as well as how new regulation is likely to affect each plant differently. Finally, we consider the aggregate demand response to industry-level price changes and how they relate back to individual plant-level employment. To compute the effects of regulation at the plant level, we rearrange the definition of plant-level employment in a convenient form. Specifically,

L = 1

P

l ν l · TC

 (1)

where L is employment, P l is the annual wage, ν l is the share of labor in totalcosts, and TC are total costs (including both conventional production and regulatory costs). With this rearrangement, the derivative of plant-level employment with respect to regulation can be written

∂L

∂

RC     Y =  Y = TC

P

l ∂ν l

∂

RC

  factor shift + ν l P

l ∂ TC

∂

RC

  cost effect  (2)

where RC is a dollar measure of regulatory burden and Y =  Y indicates explicitly the constant output assumption.

Expressing the derivative in this way allows us to identify the cost and factor-shift effects. The first term on the right-hand side of (2) represents factor shift. Changes in the share of labor translate directly into changes in employment as production becomes more or less labor intensive. The second term represents the cost effect as total costs rise with higher regulation. Higher costs, holding input shares constant, yield larger expenditures on labor. Note that higher regulatory costs, as measured by reported expenditures on environmental activities, do not necessarily affect total costs one-for-one. There may be uncounted burdens and benefits associated with these environmental expenditures. 7

3.2. Aggregating Plant Level Effects

Expression (2) reveals the change in employment associated with increases in regulation for a single plant. In order to compute an industry-wide effect—still holding output constant—we need to make assumptions about how different plants

7 Our allowance for uncounted costs and benefits does not completely solve the problem of usingregulatory expenditures as a proxy for regulation—since there may be other costs associated with regulation that are completely uncorrelated with the reported expenditures RC . This is, however, a common approach [4, 5, 21].418 morgenstern, pizer, and shih

are affected by the same regulation and then add these effects across plants. That is,

∂L agg ∂

RC agg     Y =  Y = I 

i = 1 ∂L i
∂

RC agg = I 

i = 1 ∂L i
∂

RC i ∂ RC i
∂

RC agg

= I 

i = 1 TC i P

l i ∂ν L i
∂

RC i ∂ RC i
∂

RC agg + I 

i = 1 ν l i P

l i ∂ TC i
∂

RC i ∂ RC i
∂

RC agg  (3)

where L agg is the aggregate employment level, RC agg is the aggregate expenditure on environmental regulation, and i subscripts indicate plant-level values for each variable, in particular, the specific regulatory burden RC i of plant i

.At this point, we are forced to make an assumption about how regulations differentially affect plants. As noted in Section 1, environmental regulations typically focus on polluting industries, not individual plants, so the problem is specifying the likely distribution of burden. For simplicity, we assume that regulations affect plants in proportion to their total costs. 8 That is, we assume that an extra dollar of aggregate regulatory burden affects plant i by an amount equal to plant i ’s total costs as a share of the industry-wide total costs, ∂ RC i /∂ RC agg = TC i / TC agg . Assuming that the relation between costs and regulatory burden, ∂ TC /∂ RC , is the same for all plants (which is true in our production model below), expression (3) becomes

∂L agg ∂

RC agg     Y =  Y = 1

TC agg I 

i = 1 TC 2 i P

l i ∂ν l i
∂

RC i +  ∂ TC

∂

RC  1

TC agg I 

i = 1 ν l i TC i P

l i

= 1

TC agg I 

i = 1 TC 2 i P

l i ∂ν l i
∂

RC i +  ∂ TC

∂

RC  L agg TC agg  (4)

We must also consider how plant-level cost effects translate into industry-level price effects in order to address demand consequences at the industry level. To dothis, we assume that there is monopolistic competition within the industry. Specifically, suppose that output from different plants can be aggregated into a composite good that is demanded by other agents in the economy. This composite good is defined by

q agg =  ω i q  ρ

− 1 /ρ 

i  ρ/ρ

− 1  (5)

[e.g., 22], where q i is the output of plant i q agg is the aggregated output, and ω i and

ρ are aggregation parameters. This aggregation formula recognizes that even at the 4-digit Standard Industrial Classification code level, there can still be heterogeneity in output. The elasticity of substitution ρ among the output of different plants captures this heterogeneity and leads to a fixed mark-up of equilibrium price over cost. 9

8 That is, larger plants face larger costs in response to new regulations. This specification also simplifies the question of aggregation discussed below. In earlier work, we assumed the burden of additional regulation was proportional to the level of existing regulation RC i / j RC agg , with little impact on ourresults. A significantly different assumption about the distribution of costs, such as assuming new regulation occurs in inverse proportion to existing regulation, could obviously influence our conclusions.

9 The important practical assumption for our results is that prices rise in proportion to costs. This remains true in the limit of perfect competition as ρ tends to infinity as well as for more general specifications.jobs versus the environment 419

This assumption is consistent with different costs among plants and noncompetitive pricing behavior due to market concentration. Our assumptions about market structure allow us to explicitly determine how changes in production costs at individual plants will affect market prices and demand, as well as how changes in demand will, in turn, affect individual plants. In particular, if costs at each plant rise by the same proportion, the market price for each plant’s output will rise by that proportion. Further, if aggregate demand falls by some fraction, output demand at each plant will fall by that same fraction. We use this result when we compute our demand effect below.

3.3. Demand Effect

Our measurement of the demand effect is based on the assumption that demand for the composite industry output good q agg exhibits a constant elasticity σ d . Whenenvironmental regulations are tightened and total costs rise based on this assumption, each plant faces a proportional rise in costs, say θ . Based on the industry model (5), this leads to a proportional rise θ in the price of each plant’s output as well as the price of the composite good q agg . Demand for the composite good then falls by σ d θ

· q agg . We can therefore write the effect of increased regulatory expense on output as

∂q agg ∂

RC agg = − σ d  ∂ TC /∂ RC 

TC agg × q agg  (6)

where σ d is the industry-level elasticity of demand, q agg is output of the composite good, and  ∂ TC /∂ RC  1 / TC agg  is the fractional rise in cost at each plant. This contraction in aggregate demand leads to a proportional output contraction

σ

d  ∂ TC /∂ RC  1 / TC agg  at each plant and, in turn, a proportional reduction in employment. Adding the affects across plants, we have the aggregate demand effect
on labor:

∂L agg ∂

RC agg     demand

effect = − σ d  ∂ TC /∂ RC 

TC agg × L agg  (7)

That is, the demand effect per dollar of additional regulatory burden equals the fractional change in total costs  ∂ TC /∂ RC  / TC agg , scaled by the elasticity of demand σ d and the aggregate employment level L agg .

3.4. Total Employment Effect

Combining the demand effect (7) with the previous production effects (4) yields an expression for the entire employment effect

∂L agg ∂

RC agg = 1

TC agg I 

i = 1 TC 2 i P

l i ∂ν L i
∂

RC i +  1

− σ d   ∂ TC

∂

RC  L agg TC agg  (8)

Equation (8) allows us to look at each piece of the employment effect separately and assess its economic and statistical significance. In particular, we can isolate the demand effect that frequently motivates concern among labor and industry groups420 morgenstern, pizer, and shih

and compare it to the overall effect. If the overall effect is positive, but the demand effect is large and negative, it suggests that these groups may be correct in their concerns about competitiveness, but wrong in their focus on employment.

Evaluation of this expression requires estimates of a structural model of production costs along with an industry-level demand elasticity. We now address these
issues.

4. ESTIMATION OF PRODUCTION TECHNOLOGY AND DEMAND ELASTICITY

With the exception of the elasticity of demand, the parameters that describe therelation between employment and environmental regulation are determined by production technology. In recent work [23], we have developed a flexible approach toestimating production and pollution abatement technology, which we use to estimate these parameters and their standard errors. We now describe that model, along with our approach to estimating aggregate demand elasticities, and explain how all of these results can be combined to compute each term in Eq. (8).

4.1. Cost Model

We begin with the assumption that the production of nonenvironmental outputs and environmental activities are distinct and described by separate cost functions. Specifically, PC = G  Y P  i t  describes the cost ( PC ) of producing nonenvironmental output Y based on input price vector P at plant i at time t . Similarly, let
RC = H  R P  i t  describe the cost ( RC ) of producing environmental “output” R similarly based on input price vector P at plant i at time t . Inputs include capital, labor, energy, and materials.

We then allow for the possibility that these two activities are not, in fact, distinct by rewriting PC = G  Y P  i t  f  RC  α r , where f  RC  is an increasing function of regulatory expenditure. The parameter α r describes the degree of interaction: negative values indicate cost savings and positive values indicate additional burdens.

We choose the following translog parametrization for G · and H ·

ln PC = α i + α t +  i p ln P + α y ln Y + 1 2 ln P pp ln P + 1 2 β y  ln Y  2

× t p ln P + yp ln Y ln P + β yt t

· Y + α r RC

PC  (9)

ln RC = γ + γ r ln R +  p ln P + 1 2 ln P  pp ln P + γ t t +  pt ln P

· t (10)

where P is a vector of input prices (capital, labor, energy, and materials), PC are costs related to nonenvironmental output Y RC are costs related to environmental output R , and t is time. The parameters have the following interpretations: α i are plant-specific, Hicks-neutral productivity effects; α t are time dummies, capturing aggregate Hicks-neutral productivity trends;  i p are vectors of plant-specific, costshare parameters; pp is a matrix of share elasticities; α y and β y capture scale economies; t p are year-specific factor biases; yp reflects biases of scale; and β yt captures any aggregate time trend in scale economies. All of these parameters refer to nonenvironmental production. The environmental production parameters have the following interpretations: γ r describes scale effects,  p is a vector of aggregatejobs versus the environment 421

cost share parameters;  pp is a matrix of share elasticities; γ t describes the Hicksneutral productivity trend; and  pt captures factor trends. Finally, α r describes any
interaction between environmental and nonenvironmental activities.

Equation (10) implies the absence of any scale biases in environmental costs. That is, there is no “  rp ln P ln R ” term. This term would normally reflect the dependency of the environmental factor shares (the derivative of the logged cost function with respect to logged prices) on the scale of environmental output R . That is, as plants produce more and more environmental output R —all other things equal—theseparameters would indicate any changes in factor inputs. When we turn to the estimation of aggregate (environmental + nonenvironmental) factor shares in the next section, these parameters would leave the model unidentified because we do not have any measure of environmental output R —necessitating our assumption. However, the question of whether factor biases do in fact exist in the production of environmental activities would be an interesting question for further research.

4.2. Cost Function Estimation

The standard approach to estimate models such as (9) and (10) is to specify a system of cost shares based on the first derivatives with respect to log prices. Stochasticdisturbances are appended to each equation and the system is estimated simultaneously (with cross-equation restrictions) in order to improve efficiency. The problemwith this approach in the current context is that factor inputs used for environmental activities cannot be distinguished from those used for conventional production,so the distinct cost shares associated with Eqs. (9) and (10) are not observed. Further, since we have no direct measure of R , Eq. (10) cannot be estimated.

We work around these problems by noting that our assumption, excluding biases
of scale in environmental costs H · , allows us to write the environmental cost shares solely as a function of input prices and time (and not R ):

ν x r = γ x + 

x ln P + δ xt t (11)

for x ∈ k l e m , representing shares associated with capital, labor, energy, and material costs, respectively. Coupled with nonenvironmental cost shares derived from (9),

ν x y = α i x + x ln P + β yx ln Y + β t x (12)

we can write the observed total cost shares as

ν x = RC

RC + PC ν x r +  1

− RC

RC + PC 

ν x y  (13)

These aggregate cost shares are both observable themselves and defined in terms of other observable variables (prices, output, time, and regulation as a share of total costs). The set of equations in (13) can therefore be estimated alongside the production cost function (9) by treating each as a stochastic relation and adding
random disturbances.

Because the endogenous variable PC appears on the right-hand side of the production cost function and aggregate share equations, we use a two-step approach. We first estimate the system of equations setting RC = 0 (which elimates PC on the right-hand side as well as the regulatory cost share parameters  and  ). We use422 morgenstern, pizer, and shih

these parameter estimates to construct exogenous predicted values PC to replace
the actual values PC on the right-hand side of (9) and (13). These predicted values are then used to re-estimate the system without the endogeneity problem. At both estimation stages, we impose symmetry ( β ij = β ij and δ ij = δ ji ) and homogeniety of degree one in prices (which allows us to arbitrarily drop a share equation). We
use a maximum likelihood estimator that iterates on the covariance matrix estimate until it converges. 10

4.3. Distinguishing Features

Two key features of this approach are its distinction between environmental and nonenvironmental production activities and the extensive use of fixed effects (in both the cost function and share equations). The distinction between environmental and nonenvironmental activities allows us to consider the possibility that these are, in fact, distinct. This hypothesis—that α r = 0—is easy to test and, further, nonzero
values of α r can be interpreted as the dollar-for-dollar offset in production costs associated with an increase in environmental expenditures [23]. That is, ∂PC/∂ RC =

α r /  1 + α r RC /PC  which is roughly equal to α r for small values of α r and when
RC PC (empirically below 5%; see Table I). This distinction also allows us to estimate differences between pollution controltechnology and normal production technology. The identification of these differences hinges on variation in RC /  RC + PC  in the data. Plants with higher values of this ratio are more tilted towards pollution control; plants with lower values towardsnormal production. When we look at the estimation results, we will see that pollution control is often labor-intensive, leading to higher employment as environmental regulation increases.

The extensive use of fixed effects is important in the context of correctly identifying differences between pollution control technology and conventional production.Productivity and differences in factor usage may vary among plants due to unobserved or unquantifiable plant differences and/or differences in the output mix. Since these differences are potentially correlated with environmental activities—but are not caused by them—failing to control for plant differences may bias the results.

10 With the exception of our use of fixed effects, the modification of the share equations (13), and instrumenting for PC , Chapter 9.4 of Berndt [24] describes our methodology in detail. Caves et al. [25]
discusses the use of fixed effects in the cost function.

TABLE I Industry Statistics

PACE as a share Labor as a share Price of labor

of total costs of total costs ($000/Year) Industry  RC / TC   ν l = P l L/ TC   P l 

Pulp and paper 0.028 0.201 34.8
Plastic material 0.020 0.085 35.0
Petroleum 0.011 0.019 36.7
Steel 0.022 0.230 38.2

Note : PACE refers to expenditures on environmental regulation as measured by the Pollution Abatement Costs and Expenditures survey.jobs versus the environment 423

For example, plants with older capital vintages or poor management might use different and/or less efficient combinations of inputs. If these same plants also havehigher environmental costs, these differences would falsely be attributed to environmental activities. Our earlier work focusing on the parameter α r demonstrated this is indeed the case: inclusion of fixed effects suggests that α r is, if anything, negative. However, a pooled model ignoring plant-level differences indicates that α r is significantly positive. 11 A test of the pooled model restriction is overwhelmingly rejected.

The benefits of the fixed-effects model come at a price. In similar work, Gray and Shadbegian [26] emphasize the potential problems with measurement error in fixed-effects models and advocate pooled estimates to determine the added burden of environmental regulation. More generally, Griliches [27], Chamberlain [28], and Hsiao [29], all point out that fixed-effect estimation exacerbates the bias toward zero when measurement error is primarily within units rather than among units. However, we have no alternative to control for the plant-level differences that webelieve introduce significant bias. Further, we have no direct evidence that measurement error is primarily within units rather than between units: long-difference estimates, where possible, reveal similar estimates with larger standard errors. 12

4.4. General Results

Parameter estimates for the model are provided in Table V in the Appendix along with additional detail concerning the data. Here, we briefly discuss those results. Roughly half the estimated parameters are significant at the 5% level. However, it is difficult to systematically simplify the model. Restrictions on the fixed effects (both share equations and cost function) are rejected in all industries. 13 Tests of Cobb– Douglas restrictions on the quadratic terms  pp and pp are similarly rejected. 14

Theory tells us that the cost function should be monotone and concave. 15 Evaluating these conditions at each data point, we find our estimates to be locally monotonemore than 99% of the time. However, concavity (as captured by the sign of the ownprice elasticities) is frequently violated with capital demand sloping upward more than half the time in two of the four industries. Unfortunately, this is a well-known consequence of using translog cost functions when elasticities deviate significantly away from unity [32, 33]. The specific problem with capital demand may also be a consequence of imprecise measures of the price of capital based on the Hall

11 Estimates of α r based on a pooled model ignoring fixed effects are

− 0

 08  0

 25 

 − 0

 13  0

 57 

, 1

 56  0

 55  , and 1.98 (0.39), in the pulp and paper, plastics, petroleum, and steel industries, respectively. The fixed-effect estimates in Table V are uniformly lower. Morgenstern, Pizer, and Shih [23] discuss this point in greater detail.

12 Griliches and Hausman [30] suggest long-difference estimators as a way to reduce measurement
error bias.

13 Since the pooled model (when fixed effects are restricted) provides an unbiased, though less efficient, estimate of the random effects model, this is evidence against the random effects model. As noted above, the pooled estimates of α r are both statistically and economically different than the fixed effect estimates.

14 The LR statistics are 69.98, 49.56, 127.36, and 28.62 for pulp and paper, plastics, petroleum, and steel testing the Cobb–Douglas restrictions on the production cost function, and 16.70, 17.84, 28.98, and 25.56 for the same industries, respectively, testing the environmental cost functions. With six degrees of freedom, the 1% critical value is 16.81.

15 See Jorgenson [31].424 morgenstern, pizer, and shih

and Jorgenson [34] approach. A Cobb–Douglas version of the model—which forces concavity by restricting the  pp and pp terms to be zero—leads to only minor differences in α r and γ l , the key parameters in our calculation of the labor effects
below. 16 While we check the sensitivity of our main results to this assumption, we continue to focus on the general translog results since our interest is estimation and the Cobb—Douglas model is rejected by the data.

A key parameter in our analysis of the labor effects is the estimated labor cost share associated with environmental activities. From Table V, we find estimates
for γ l of 0.15, 0.36, 0.07, and 0.16 for pulp and paper, plastics, petroleum, and steel, respectively. Only steel is not significant at the 5% level. Comparing those estimates to the observed total cost shares in Table I (0.20, 0.08, 0.02, and 0.23, respectively), we see higher environmental labor shares in plastics and petroleum and lower environmental labor shares in pulp and paper and steel. Note that while the higher environmental labor shares in plastics and petroleum are significantly higher in a statistical sense, the lower environmental labor shares in pulp and paper and steel are insignificantly lower.

A few simple calculations are possible using the aggregate summary statistics
in Table I and the estimates of γ l . For example, switching a million dollars from production to regulatory expenditures would lower employment expenditures by roughly $50,000—(0.15–0.20) × $1 million—in the pulp and paper industry. Based on an average salary of $35,000, this would mean 1.4 fewer jobs. The number
would be + 8

 0

 − 1

 4, and

− 1

 6 jobs in plastics, petroleum, and steel, respectively. We return to these kinds of calculations in more detail after discussing demand
elasticities.

4.5. Estimating Aggregate Demand Elasticities

In addition to our estimate of supply-side cost functions, we also require aggregate demand elasticities to compute Eq. (8). We estimate these elasticities using historical data on aggregate output and industry productivity. Changes in industry productivity represent an exogenous shift in the cost of supply and therefore allowus to identify the demand elasticity. As long as the industry in question is small relative to the entire economy, it is reasonable to assume that this change in industry productivity will not shift the demand schedule.

We use publicly available data developed by Jorgenson and his associates [35, 36] for both our industry-level output and productivity measures. 17 Annual productivity growth is computed as the logarithmic difference between the annual change in input price and the annual change in output price, where input price is computed as a divisia index of capital, labor, energy, and material prices [see 37]. That is, it is the portion of any price change that is not captured by changes in input prices. 18 We then perform a simple regression of the annual change in log output on annual

16 Based on the Cobb–Douglas model, the estimates of α r are

− 0

 56, 0.37, 0.60, and

− 0

 05 for pulp and paper, plastics, petroleum, and steel, respectively. The corresponding Cobb–Douglas estimates of γ l are 0.23, 0.25, 0.04, and 0.16. These can be compared to the translog parameter estimates in Table V.

17 These data are available at http://www.economics.harvard.edu/faculty/jorgenson/data/35klem.html.

18 While the typical assumption in a productivity calculation is competitive output markets, a constant markup of price over costs would not interfere.jobs versus the environment 425

TABLE II Demand Elasticity Estimates

Sector Demand elasticity σ Industry (IGEM : BLS) (standard error)

Pulp and paper Paper and allied 1.34 (13 : 123–125) (0.17)
Plastic material Rubber and plastic 0.49 (17 : 135, 143–145) (0.29)
Petroleum Petroleum refining 0.40 (16 : 141, 142) (0.19)
Steel Primary metals 1.86 (20 : 42–52, 58) (0.35)

Note that positive values of σ reflect an increase in demand as prices fall. See (14).

productivity growth

$ ln output t = σ d $ ln prod t + ε t (14)

where output t is the industry level output in period t prod t is the change in productivity level measured by

$ ln prod t =  v t + v t

− 1 2  ln P t − ln P t

− 1 

− ln PO t − ln PO t

− 1 

 (15)

where ε t is a random disturbance and σ d is the demand elasticity being estimated.
The variable P t is a vector of input prices (capital, labor, energy, and materials), PO t is output price, and v t is a vector of input shares. The results of these regressions
are shown in Table II. 19

The elasticity estimates are significant in three of the four industries, ranging from slightly inelastic (0.40) to slightly elastic (1.86). Compared to industry-level demand elasticities used in simulation models [5, 38], ranging from 1.10 to 2.55, these estimates are uniformly smaller. In part, this difference may be due to the use of translog cost functions on the demand side in those models, which frequently results in more elastic behavior. When we discuss our results in the next section, we can consider the impact of assuming larger elasticities than reported in Table II.

5. THE EFFECT OF REGULATION ON INDUSTRY EMPLOYMENT

We now combine the estimation results of the preceding section with Eq. (8) inorder to compute the effect of environmental regulation on industry employment. There are two terms in (8) that cannot be computed directly from
the data: ∂ν L i /∂ RC i and ∂ TC /∂ RC . We first discuss the relationship between these expressions and the parameters estimated in (9) and (13), then present our
results.

19 We also considered models with lagged productivity changes on the right-hand side as well as controls for aggregate productivity changes. With the exception of pulp and paper, these variations led to uniformly less elastic demand so that our results, if anything, overstate the adverse consequences of environmental regulation. In the pulp and paper industry, the elasticity estimate rose to 1.99.426 morgenstern, pizer, and shih

5.1. Relation to Structural Cost Model

As noted earlier, the parameter α r captures the potential nonzero effect of expenditures on environmental protection on conventional production costs, with ∂PC/∂ RC = α r /  1 + α r RC /PC  . The expression PC is conventional production
cost and RC is environmental expenditures. From Table I we know that RC / PC is on the order of 1%–3% in the aggregate, and therefore ∂PC/∂ TC ≈ α r . Further,
since TC = PC + RC , we have ∂ TC /∂ RC = 1 + α r for all plants. The expression for ∂ν l i /∂RC i is only slightly more complex. From (13) we have

ν l = ν l r +  PC

RC + PC   ν l y − ν l r 

where ν l y and ν l r , from (11) and (12) do not depend on RC . Therefore,

∂ν l i ∂RC i = − PC i  RC i + PC i  2  ν l i y − ν l i r 

= − PC i  RC i − PC i  2 α i l + l ln P + β yl ln Y + β t l − γ l −  l ln P

− δ lt t 

 (16)

We use (16) to compute ∂ν l i /∂ RC i for each observation and then use (8) to aggregate the effects.

We can now write (8) in terms of estimated parameters and sample statistics as

∂L agg ∂

RC agg = F  PC /P l  TC

  · G  σ d  α r  γ l  



where

F = 

      PC /P l 

TC   t

· PC /P l 

TC   ln P k · PC /P l 

TC   ln P l · PC /P l 

TC   ln P
e · PC /P l 

TC 

 ln Y PC /P l 

TC   t 80 PC /P l 

TC   t 81 PC /P l 

TC   t 85 PC /P l 

TC   t 88 PC /P l 

TC 

 t

91 PC /P l 

TC   d 1 PC /P l 

TC   d 2 PC /P l 

TC   d I PC /P l 

TC   L TC 

     

G =  γ l  δ lt  δ lk − β lk  δ ll − β ll  δ le − β le  − β yl  − β 80  l  − β 81  l 

− β 85  l  − β 88  l  − β 91  l  − α 1  l  − α 2  l  − α 1  l   1 + α r  1

− σ d  



F is a vector of sample means based directly on the data and G is a vector of estimated parameters from Eqs. (9) and (13) and reported in Table V and Table II. Overbars (  ̄ x ) in F indicate means computed over the entire sample of plant-year observations, with t 80 , etc. indicating time dummies in the noted year and d 1 , etc. indicating plant dummies for the particular plant (where I is the total number of plants). As before, TC is total cost, PC is production cost, P l is the price of labor,
P

k is the price of capital, P
e is the price of energy, Y is the output level, and L is the employment level. To compute standard errors, we ignore sampling variation in

F , which is relatively small, and focus on the covariance matrix of G

. 20 Note that all
but the last element of F

· G explains any factor shift, while the last term includes
both the cost and demand effects.

20 We assume the estimated demand elasticity is uncorrelated with the other parameters. This is sensible since the demand elasticity is based on aggregate time series data and the remaining parameters are estimated from a relatively short panel of cross-sectional data.jobs versus the environment 427

5.2. Estimated Effects

Table III displays our principal results, expressed as a change in employment associated with an additional $1 million ($1987) in environmental spending, for each of the four heavily regulated manufacturing industries under review (pulp and paper, plastics, petroleum, and steel). Results are presented separately for the cost, factor shift, and demand effects, as well as the net effect, which is the sum of the components in Eq. (8). The grand total combines the four industry-wide estimates, weighting by each industry’s share of environmental expenditures. 21 To test the sensitivity of these results to the noted violations of cost function behavior, we estimated a Cobb-Douglas version of the model and found similar results. 22Line 1 displays the employment changes per million-dollar increase in environmental spending caused by the overall increase in costs, holding output constant. Asexpected, these values are positive in all industries, increasing employment by somewhere between one-half and five and one-half jobs per million dollars of spending. The variation among industries is explained primarily by the differences in labor
cost shares ν l and, to a lesser extent, by estimated differences in indirect costs captured by the parameter α r in the model. For example, labor accounts for less than 2% of total costs in the petroleum industry, versus 10%–20% for the other industries (see Table I). Therefore, $1 million in increased total costs translates into roughly $20,000 in labor costs—or about one-half of one job. On the other hand, when α r is significantly negative, as it is in the pulp and paper industry, this indicates uncounted savings associated with environmental expenditures. That is, a $1 million increase in environmental expenditures reflects less than $1 million in increased total costs. These uncounted savings reduce the cost effect and lead to a

21 Average industry level expenditures measured by the PACE survey as a share of total, computed over all years in the sample.

22 Estimates of the total employment effect are 0.91 (2.8), 6.05 (2.9), 1.21 (0.7), and

− 4

 41  6

 2 

, respectively, for pulp and paper, plastics, petroleum, and steel, based on the Cobb–Douglas model (standard errors are in parentheses).

TABLE III Change in Full-Time Jobs per Million Dollars of Environmental Expenditure (Standard Errors in Parentheses; Asterisks Indicate Significance at the 5% Level)

Pulp and paper Plastics Petroleum Steel Total

Cost 2.18 3.23 ∗ 0.65 ∗ 5.52 ∗ 2.42 ∗ (1.58) (1.63) (0.24) (2.78) (0.83)

Factor shift

− 0

 37 5.25 ∗ 1.77 ∗ 5.28 2.68 ∗ (2.05) (2.46) (0.81) (4.41) (1.35)

Demand

− 2

 94

− 1

 58

− 0

 26

− 10

 27

− 3

 56 (2.51) (1.74) (0.22) (7.10) (2.03)

Total

− 1

 13 6.90 ∗ 2.17 ∗ 0.53 1.55 (2.72) (3.21) (0.88) (7.68) (2.24)
Weight 0.15 0.07 0.50 0.28

As noted in Section 3.2, additional environmental expenditures are allocated to individual observations in the sample in proportion to their share of aggregate total costs (TC). Weights reflect the magnitude of industrywide environmental expenditures.428 morgenstern, pizer, and shih

lower estimated employment consequence—hence, it is not surprising to see a relatively low estimated cost effect for pulp and paper. The average across all four industries is 2.4 jobs per million dollars of increase in environmental expenditures.

Line 2 displays the employment changes attributable to the factor shift associated with relatively more environmental and less production cost. Overall, it appears that environmental spending increases employment through factor shifts, reflecting the fact that environmental activities are estimated, on average, to be more laborintensive than conventional production. This pattern of signs and significance corresponds to the rough estimates in Section 4.4 (except steel), with the weighted average across all four industries indicating 2.7 jobs gained per million dollars of spending.

In some industries, such as petroleum, the explanation for the factor shift is fairly obvious. Materials are such a large ( > 90%) share of production costs that it would be virtually impossible for environmental activities to be any less labor intensive. A similar, though not so extreme story holds for plastics, where labor is still less than 10% of production costs. This contrasts with steel and pulp and paper where labor accounts for about 20% of total expenditures. In those industries, we see ambiguous labor effects as both the environmental and production-cost shares are large.Line 3 displays the employment changes per million-dollar increase in environmental spending associated with a drop in demand for final products as prices rise. As expected, these values are negative in all industries, reflecting the combinedeffects of increased costs, a constant markup of price over cost, and downwardsloping demand curves. Averaged across industries, reductions in demand cause aloss of around 3–4 jobs per million dollars of spending. This loss conceals wide variation within industries: in steel, the estimated loss is nearly ten jobs per million dollars of spending, while in petroleum the job losses are negligible. These differencesare consistent with the results in Table II, indicating a much higher demand elasticity for steel. Our results are also consistent with the findings of Berman and Bui [9] that labor demand at petroleum refineries may increase with regulation. None of the estimated demand effects are significant, reflecting the combined uncertainty about the increase in total costs and the demand elasticity.

Combining these three effects in line 4, we find significant job gains in plastics and petroleum with insignificant effects in pulp and paper and steel. These results follow from the fact that both plastics and petroleum had significantly positive factor shifts coupled with relatively small demand elasticities. The average across all four industries works out to be an insignificant gain of one and one-half jobs per million dollars of additional environmental expenditures. Even doubling the demand effect, as suggested by the higher elasticities found in simulation models, the overall effect would remain statistically insignificant.

5.3. Conclusions

Economists traditionally focus on welfare and other well-grounded measures of social cost when evaluating public policy. Job loss is not a real social cost to the extent that a job lost in one area or industry is quickly replaced in another. Yet, as the recent debate on trade with China suggests, discussions of job loss, especially at the industry level, are often central to the policy process [39].jobs versus the environment 429

Our study of environmental regulation in the pulp and paper, plastics, petroleum,and steel sectors suggests that a million dollars of additional environmental expenditure is associated with an insignificant change in employment, with a 95% confidence interval ranging from

− 2

 8 to + 5

 9 jobs. To put these numbers in context, it is useful to think about them in light of actual changes in employment and environmental spending observed in recent years. Between 1984 and 1994, total environmental expenditures as measured by the Pollution Abatement Cost and Expenditure (PACE) survey in all manufacturing industries rose by $4.9 billion($1987). During that same period, total production employment in those industries declined by almost 632,000 jobs. Applying the most adverse estimate inour 95% confidence interval—a loss of 2.8 jobs per million dollars of environmental expenditures—we see that environmental spending may have accounted for the loss of at most 14,000 jobs, or about two percent of the jobs lost over the period. On the other hand, environmental spending may have created as many as 29,000 jobs. Our structural model allows us to peer inside these results and understand why the conventional wisdom might be wrong. Most importantly, there are strongpositive employment effects in industries where environmental activities are relatively labor intensive and where demand is relatively inelastic, such as plastics and petroleum. In others, where labor already represents a large share of production costs and where demand is more elastic, such as steel and pulp and paper, there is little evidence of a significant employment consequence either way. If our model results do not support the notion of a jobs versus the environmenttrade-off, why does this theme remain so steadfast in the business and labor communities? Obviously, it is a politically and emotionally charged topic that attracts attention. Beyond posturing in a public debate, however, it is possible that both employers and employees honestly overestimate the potential for job loss associated with environmental regulation by confusing the product demand schedule faced by the plant with the schedule faced by the entire industry. The former is certainly more price elastic due to competition from other firms. 23 Or, firms may fear that they will be less adept in responding to new regulation than their competitors— although this would fail to explain why all firms in an industry would oppose new regulation. If all firms and plants in an industry are faced with similar cost-increasing regulatory changes—which we believe is typically the case—most plants should not be worried about losing business to other plants facing the same regulation.

A similar story might apply regarding the job-creating aspects of environmental spending. Neither employers nor employees may be completely familiar withthe nature or type of changes in production technologies associated with environmental protection. In that case, there might be a tendency to underestimate the employment increases associated with the new environmental spending itself or with changes in the mix of factor inputs. Our analysis suggests that these increases can more than offset the loss of jobs associated with rising prices and depressed demand. Ignoring these aspects of environmental regulation, it is possible that bothemployers and employees may overstate the job destructive aspects of environmental regulation and understate its job-creation potential.

23 That is, we expect the elasticity of substitution among output in the same industry, ρ in Eq. (5), to be larger than the elasticity of demand for the industry as a whole, σ d .430 morgenstern, pizer, and shih

Another explanation is that our estimates may mismeasure employment effects. We might overestimate job loss by using industry-level demand elasticities when, in fact, regulation affects the entire economy—suggesting fewer opportunities for substitution and even smaller demand effects. Alternatively, we might underestimate job loss for exactly the opposite reason: if regulatory consequences are relatively concentrated in certain areas and at certain plants, the industry-level elasticities are likely too small. Further, our focus on industry-level employment misses any costs that might be associated with shifting jobs among or within plants while industry level employment remains unchanged.

Notwithstanding these caveats, our results question the idea of a jobs versus the environment trade-off at the industry level. While environmental spending clearlyhas consequences for business and labor, the hypothesis that such spending significantly reduces employment in heavily polluting industries is not supported by
the data.

APPENDIX: DATA

The central data source for this research is the Longitudinal Research Database collected by the U.S. Census Bureau. Environmental spending is measured by the Pollution Abatement Cost and Expenditure Survey. Energy expenditures are derived from the Manufacturing Energy Consumption Survey and the Annual Survey of Manufactures. We normalize all variables by dividing each by the sample mean.

• The Longitudinal Research Database (LRD) is a pooled, cross-section, time series comprised of the establishment responses to the Annual Survey of Manufacture (ASM) and the quinquennial Census of Manufactures (CM) for over50,000 establishments in each year. The LRD contains information on cost, outputs, and inputs at the plant level. Detailed quantity and expenditure information for energy consumption are only available up to 1981.

• The Manufacturing Energy Consumption Survey (MECS) is a triennial survey beginning in 1985 and conducted by the U.S. Department of Energy. The survey contains detailed fuel consumption and expenditure data by establishment, replacing the discontinued energy data contained in the LRD prior to 1981.

• The Pollution Abatement Cost and Expenditure (PACE) survey contains pollution abatement investment spending and operating expenditures at the establishment level. The Census Bureau conducted this survey annually between 1979 and 1991, except 1983 and 1987.

Based on the availability of detailed energy and environmental expenditure data, our analysis includes the years 1979, 1980, 1981, 1985, 1988, and 1991. Sample sizes
are shown in Table IV.

Data on input and output quantities and prices are constructed as follows:

• Output . Data on the total value of shipments, by individual product codes, are contained in the LRD. We construct a divisia index [40, 41] of output price based on the corresponding producer prices of different products obtained from the U.S. Department of Labor’s Bureau of Labor Statistics. The quantity index is obtained by dividing total value of shipments, adjusted for inventory, by this aggregate output price index.jobs versus the environment 431

TABLE IV Sample Size by Industry

Industry Plants Observations

Pulp and paper 142 615
Plastic material 107 404
Petroleum 165 717
Steel 128 536

• Regulation . Data on (nominal) annual pollution abatement operating costs at the plant level are from the annual PACE Survey. Operating expenses for pollutionabatement include depreciation on the pollution-abatement capital. Real regulatory expenditures are computed by deflating nominal pollution-abatement operating costs by the GDP deflator.

• Capital Stock . The gross book value of the capital stock at the beginning of the year and new capital expenditures each year are reported in the LRD. Gross book value is used to compute the capital stock in 1979. A perpetual inventory method [42] is then used to generate a real capital stock series covering the period
1980–1991 based on the formula

k t =  1

− δ  k t

− 1 + q 0
q t I t 

where k t is the period t capital stock, and I t is new capital expenditure measured in current dollars. The industry-specific economic depreciation rate ( δ ) is from Hulten and Wykoff [43]. The capital stock price indices ( q t  for various industries are drawn from a dataset developed by Bartelsman and Gray [44].

• Service Price of Capital . The service price of capital is calculated using the Hall and Jorgenson [34] procedure. The service price of capital is given by

p k  t  =  q t

− 1 r t + δq t −  q t − q t

− 1  + q t C t  1

− u t z t − k t 1

− u t 

where

p k  t  = service price of capital,

q t = price index of new capital equipment,

r t = after tax rate of return on capital (opportunity cost),
δ = rate of economic depreciation,
C t = effective property tax rate,

u t = effective corporate income tax rate,

z t = present value of allowed depreciation tax deductions on a dollar’s investment over the life time of an asset,
k t = investment tax credit, and

t = year. We use the average yield on Moody’s “Baa” bonds for the after-tax rate of return on capital. The data on the tax-policy variables are from Jorgenson and Yun [45] and Jorgenson and Landau [46].

• Capital Costs . The capital costs were constructed as the product of the service price of capital and the stock of capital.432 morgenstern, pizer, and shihTABLE VParameter EstimatesPulpPulpand paper Plastics Petroleum Steel and paper Plastics Petroleum Steelαy0.7161∗0.8314∗0.7433∗0.7136∗γk0.1276−00510 0.0532 0.1460∗(0.0273) (0.0362) (0.0281) (0.0304) (0.1085) (0.1053) (0.0545) (0.0715)αr−06221∗0.3774 0.5900−00726γl0.1531∗03621∗0.0748∗0.1565(0.2746) (0.6958) (0.5905) (0.4671) (0.0770) (0.0914) (0.0292) (0.1610)βkk0.1095∗0.0029 0.0070∗0.0664∗γe0.1967∗02930−00225−07126∗(0.0379) (0.0190) (0.0019) (0.0172) (0.0846) (0.2028) (0.0387) (0.1952)βll0.1120∗0.0668∗0.0133∗0.0491∗δkt0.0013 00627∗−00043−00122(0.0114) (0.0094) (0.0016) (0.0201) (0.0171) (0.0186) (0.0110) (0.0160)βee0.0579∗−00027 0.0128∗0.0211δkk0.9811∗0.4311 03400∗05153∗(0.0090) (0.0163) (0.0018) (0.0209) (0.4718) (0.4545) (0.0964) (0.2243)βyy−00336−00408 0.0039 0.0389∗δkl−00764 0.0251 0.0078−01398(0.0316) (0.0328) (0.0184) (0.0191) (0.2554) (0.2843) (0.0394) (0.2375)βkl−00347∗−00030−00017∗−00035δke−01040 0.1902 01260∗04567∗(0.0128) (0.0092) (0.0008) (0.0088) (0.1996) (0.2624) (0.0554) (0.1942)βke−00116 0.0103 0.0005−00264∗δlt−00154 00516∗0.0097−00935∗(0.0112) (0.0086) (0.0012) (0.0073) (0.0126) (0.0181) (0.0067) (0.0355)βky0.0100−00365∗−00132∗−00383∗δll−03011−11840∗−02801∗14959∗(0.0071) (0.0049) (0.0021) (0.0031) (0.3037) (0.3458) (0.0986) (0.5676)βle−00114−00016 0.0003 0.0156δle−02485 0.1503 0.0026−08998∗(0.0065) (0.0068) (0.0010) (0.0138) (0.1375) (0.2211) (0.0398) (0.3833)Continuedjobs versus the environment 433TABLE V—ContinuedPulpPulpand paper Plastics Petroleum Steel and paper Plastics Petroleum Steelβly−00446∗−00302∗−00078∗0.0066δet−00232∗−00426−00115 0.0501(0.0052) (0.0041) (0.0010) (0.0072) (0.0107) (0.0290) (0.0076) (0.0346)βey−00041∗0.0085−00104∗−00177∗δee0.4141∗−03730−01483∗−01428(0.0053) (0.0092) (0.0015) (0.0087) (0.1679) (0.5119) (0.0690) (0.5534)βyt0.0041 0.0110∗−00028∗−00004(0.0014) (0.0020) (0.0013) (0.0014)Number of observations 615 404 717 536Number of plants 142 107 165 128Fraction of observations with negative estimated share values(zeros are omitted)Capital 0.003 0.054 0.017Labor0.002 0.006Energy 0.002 0.012 0.010MaterialsFraction of observations with positive own-price elasticities(zeros are omitted)Capital 0.763 0.216 0.666Labor 0.055 0.522 0.353 0.002Energy 0.104 0.010 0.285 0.011Materials 0.006 0.084 0.187 0.011Note. Plant and time dummies are not reported due to confidential requirements.434 morgenstern, pizer, and shih

• Labor . The quantity of labor is defined as the number of production workers. The cost of labor includes production worker wages plus supplemental labor cost (which accrued to both production workers and nonproduction workers) adjusted to reflect the production-worker share. The price of labor is defined as the cost of production workers divided by the number of production workers.

• Price of Materials . Expenditure data on individual materials are collected on a five-year cycle by the CM. We derive a divisia index of the price of materials for each plant for the years 1977, 1982, 1987, and 1992. We linearly interpolate estimates for intervening years.

• Cost of Materials . We use reported total expenditures on materials and parts
in the LRD to calculate material costs.

• Price of Energy . Detailed data on total quantities consumed and total expenditures on various fuels were collected in LRD (through 1981) and subsequentlyin MECS (1985, 1988, 1991). These data are used to calculate the prices of individual fuels ($/million Btu) paid by each plant. The individual fuels include coal,natural gas, distillate fuel oil, residual fuel oil, liquefied petroleum gases, and electricity. These fuels typically account for about 90% of total energy cost. The price of energy is computed as a divisia index of these fuels.

• Cost of Energy . The cost of energy is the summation of expenditures for the
six individual fuels.

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