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 Perspecti

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 envir

mental policies has led to claims of a “jobs versus the environment” trade-off by both busin

and labor leaders. The present research examines this claim at the industry level for four he

ily polluting industries: pulp and paper mills, plastic manufacturers, petroleum refiners, 

iron and steel mills. Combining a unique plant-level data set with industry-level demand inf

mation, we find that increased environmental spending generally does not cause a signific

change in employment. Our average across all four industries is a net gain of 1.5 jobs per 

million in additional environmental spending, with a standard error of 2.2 jobs—an econo

cally and statistically insignificant effect. There are statistically significant and positive effe

in two industries, but total number of affected jobs remains quite small. These small posi

effects can be linked to labor-using factor shifts and relatively inelastic estimated dema

 2001 Elsevier Science (USA)

Key Words: jobs-environment trade-off; distribution of environmental costs; translog c

function.

1. INTRODUCTION

Environmental polices involve economic costs that are unevenly borne b

viduals and industries across the economy. The possibility that workers co

adversely affected in heavily regulated industries has led to claims of a “jobs 

the environment” trade-off, a mantra echoed by both business and labor l

At a minimum, the visibility and emotion associated with potential job loss 

it a crucial issue in ongoing policy debates. Interest groups now routinely d

Congressional district-level estimates of job losses associated with propose

islation [1]. Not surprisingly, a third of the respondents to a 1990 poll t

it somewhat or very likely that their own job was threatened by environ

regulation [2].

Accepting the notion that potential job loss due to regulation is an imp

phenomenon to understand, one of the challenges for researchers in this 

how best to measure job loss. An individual separated from an existing job b

of an environmental regulation has clearly suffered a loss. Yet, pollution aba

activities themselves require labor input. Thus, environmental regulations m

1 To whom correspondence should be addressed. Quality of the Environment Division, R

for the Future. We acknowledge helpful comments from Hirschel Kasper, Raymond Kopp, 

anonymous reviewers of an earlier draft. The research in this paper was conducted at the Ce

Economic Studies, U.S. Bureau of the Census. Mary L. Streitwieser, Gordon M. Phillips, and 

P. Reznek have provided considerable assistance for which the authors are greatly appreciative. R

results are those of the authors and do not necessarily indicate concurrence by the Bureau of th

or the Center for Economic Studies.

412

0095-0696/01 $35.00

© 2001 Elsevier Science (USA)

All rights reserved.
jobs versus the environment 

create jobs—sometimes in the same industry, or even in the same firm. In ad

environmental regulation may cause firms in a particular industry to shift p

tion and jobs from areas not attaining federal air quality standards to th

attainment. Job loss in one area is then accompanied by job creation in ano

Key stakeholders, such as labor unions and trade groups, typically focus o

job changes and the cost of rearranging workers within an industry. Howev

job loss within an industry—which recognizes that all intraindustry emplo

changes associated with environmental regulation is also a relevant measu

ongoing policy debates. Such a measure recognizes that many firms endea

relocate displaced employees in other units of the same company, and that r

production or shutdowns at some plants are often accompanied by expanded 

at other plants, thereby offsetting at least some of the initial job losses. Con

tion of these net employment impacts has figured prominently in a number of 

environmental decisions.2

Most attempts to measure these net employment effects have depended on 

gate industry data or anecdotal evidence. Such approaches make it difficult 

entangle the effect of environmental regulation from other differences tha

across industries and changes that occur over time. This paper improves o

vious efforts by using detailed Census data. With a unique six-year panel o

on plant-level prices, inputs (including labor), outputs, and environmental 

ditures, we can estimate the structure of production and its dependence o

ronmental expenditures while controlling for any differences that are fixed 

across plants or over time (using plant and time dummy variables). In order t

tify the aggregate effects of new regulation, we make particular assumptions 

both industry structure and the distribution of new environmental expenditur

plants. While this approach does not eliminate the estimation problems asso

with observational studies, nor does it escape specific assumptions about the 

try’s competitive structure, it does bring a rich, new dataset to bear on the qu

of how employment changes in response to environmental regulation.

Our modeling and estimation decompose the labor consequences of inc

environmental spending into three components: increases in all factor inputs

ing output and factor shares constant (cost effect); changes in factor inte

(factor-shift effect); and changes in the quantity of output demanded (d

effect). Estimates of the production structure from detailed Census data al

to assess the first two components. We then use aggregate time-series data 

mate demand elasticities and compute the third component as well as the 

change in employment. In order to compute aggregate effects of new industr

regulation, we assume monopolistic competition with new regulatory expen

imposed in proportion to total costs.

We separately analyze four heavily polluting industries: pulp and paper, p

petroleum, and steel. In plastics and petroleum, we find that increased reg

raises employment by a small but statistically significant amount: 6.9 and 2

per million dollars of regulatory expense, respectively. In pulp and paper an

the estimates are even smaller and insignificantly different from zero; the a

2Examples include the 1990 Clean Air Act Amendments, Title IV (acid rain), vis-`a-vis 

impacts on coal miner jobs; the 1982 Iron and Steel Effluent Guideline issued by the U.S. Enviro

Protection Agency; the 1995 Spotted Owl decision under the Endangered Species Act vis-`a-vis 

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 jo

and 5.9 jobs gained per million dollars of regulatory expense. Given the $4.9 

increase in regulatory expense and 632,000 job decrease in manufacturing e

ment from 1984 to 1994, these estimates suggest that, at most, environment

ulation accounted for 2% of the observed decline in employment over this 1

period.

2. PREVIOUS STUDIES OF EMPLOYMENT AND

ENVIRONMENTAL REGULATION

A wide range of research efforts have been used to address the conn

between environmental regulation and employment, including aggregate 

modeling (macroeconomic and general equilibrium), economy-wide microeco

studies, industry-specific studies, and analyses of plant location and growth

mates of the economy-wide job impacts of environmental regulations tradit

are based on simulations of large macroeconomic and general equilibrium m

In a review of macroeconomic modeling efforts published in journals, 

publications, and by the U.S. EPA, Goodstein [3] found that seven of th

studies showed increases in employment, one showed a decrease, and on

mixed. He concludes, “on balance, the available studies indicate that enviro

tal spending has probably led to a net increase in the number of jobs in th

economy (although) if it exists, this effect is not large.”

General equilibrium assessments of environmental regulation, such as 

and Kopp [4] and Jorgenson and Wilcoxen [5], typically assume full emplo

specifically, the real wage adjusts so that labor demand equals labor supply

the real wage falls with increased environmental regulation due to reduct

productivity, employment will likely decline.3 Thus, environmental regulatio

to job loss in these models because individuals decide to work less in respon

lower relative price of leisure. However, such labor–leisure choices are unli

be the object of concern voiced by labor leaders or respondents to public o

polls.

At the individual firm or plant level, business and labor experts typically arg

environmental regulation increases a company’s production costs and puts u

pressure on prices. Price increases, in turn, result in a loss of sales and a

some reduction in plant-level employment. However, employer responses 

veys by the U.S. Department of Labor [7] indicate that environmental sp

accounts for only about 650 job losses per year, or less than one-tenth of on

cent of all mass layoffs in the United States. These surveys may understate po

job losses because they ignore the effects on smaller firms (under-sampled 

survey) as well as the possibility that environmental regulation may be an imp

secondary factor in plant closure decisions. Conversely, these estimates may 

state the net job impacts by failing to account for employment increases asso

with environmental regulation, such as control activities and/or shifts in emplo

to other plants.

3Of course, employment could rise as the real wage falls, depending on whether the uncomp

labor supply curve is upward sloping or backward bending. See Hausman [6].
jobs versus the environment 

Studies of specific industries are less common than economy-wide analyses

research on the electric power industry by Gollup and Roberts [8] found 

icant job loss associated with increased environmental regulations. More 

work by Berman and Bui [9] compares petroleum refineries in the Los A

area to all other U.S refineries. The authors find no evidence that enviro

tal regulation decreased labor demand, even when allowing for induced pla

and dissuaded plant entry. “If anything,” they note, “air quality regulation pr

increased employment slightly.”

An area of related work has focused on the possible influence of environ

regulation on plant location, capturing the notion that heavily regulated and 

polluted areas may suffer a relative penalty. Although new environmental 

tions may not cause relocation of existing plants, firms have considerable fle

in making decisions about the siting of new facilities. Studies by Bartik [10

and Yeats [11], and Crandall [12] suggest that firms are sensitive, in general 

to cost variations among states when deciding where to locate new facilities

ever, there is less evidence that plant location choices are related to stringe

environmental regulation. In an analysis that includes measures of environ

stringency, Bartik found that neither measures of expenditures nor emissio

dards had significant effects on plant location decisions. These results are sim

those of Levinson [13] and McConnell and Schwab [14], although Levinson d

that the locations of new branch plants of large multiplant companies in pol

intensive industries were somewhat sensitive to differences in regulations. A 

study by Gray [15] also finds that states with more stringent regulation (me

by a variety of state-specific measures) have fewer plant openings.

Finally, several studies have compared rates of manufacturing emplo

growth—not just new plants—in attainment areas versus nonattainment 

Papers by Henderson [16] and Kahn [17] found relatively lower growth rates i

ufacturing employment in nonattainment counties compared to those that a

the air quality standard. Becker and Henderson [18] found that environment

ulation reduced plant births and increased plant deaths in nonattainment 

shifting polluting activity to cleaner areas. With a similar approach, Greensto

estimates an annual loss of about 8,000 jobs over the period 1972–1987. 

tantly, his estimates assume that employment growth at polluting plants 

regulated areas is an appropriate control group from which to infer the 

change in employment in the absence of regulation.5

Overall, the existing evidence on the possible jobs versus the environment 

off presents a bit of a puzzle. Many find little to no effect, especially tho

consider the choice of plant location. However, recent work with microda

gests that employment growth rates do vary according to attainment statu

attainment areas growing faster than nonattainment areas. Whether such 

indicate either a net decline in employment, a spatial reallocation of product

even an employment increase in cleaner areas, is unclear.

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

5Alternatively, one could postulate that polluting plants in more regulated areas are the app

control and that environmental regulation has actually created 8,000 jobs per year in the less r

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 coup

structural model of production, estimated with detailed plant-level data, w

aggregate model of demand, estimated from industry-level time-series dat

detailed plant-level data allow us to control for many potential confoundin

ables, including any that are fixed either at a particular plant or in a par

year. The structural model allows us to ask questions about whether environ

expenditures are more labor-intensive than spending on conventional prod

using within-plant variation in environmental expenditures and employment t

tify the relation. These gains come with a trade-off: within-plant variation i

prone to measurement error than between-plant variation, and our cost fu

model is necessarily more complex than a reduced form relationship. Howev

work complements previous efforts by both providing an alternative approa

making use of a rich, new, detailed dataset.

Our model is based on the recognition that when environmental regulatio

tightened, employment will adjust to both a rearrangement of production ac

as well as a potential output contraction. Rhetoric surrounding the jobs ver

environment debate focuses on the output contraction: increased regulation 

production costs, reduces demand, and eventually costs jobs. This reasoning i

the fact that employment could rise if demand is less than unit elastic or if p

tion becomes more labor intensive. For that reason, it is useful to closely ex

how increased regulation translates into changes in employment.

On the production side, there are two arguments for increased employmen

environmental regulation raises production costs. As production costs rise

input, including labor, are used to produce the same amount of output. We r

this as the cost effect. Second, environmental activities may be more labor in

than conventional production. For example, cleaner operations may involve 

inspection and maintenance activities, or reduced use of fuel and materials. I

examples, the amount of labor per dollar of output will rise. This argumen

ously can go the other way: cleaner operations could involve automation a

employment.6 We refer to this effect as the factor-shift effect.

The more traditional concern is that as production costs rise in respo

increased environmental regulation, output prices will rise, quantity demand

fall, and plants will reduce employment levels. The extent of this effect depe

the cost increase passed on to consumers as well as the demand elasticity of in

output. These two features may not be independent: industries facing elast

put demand due to stiff competition may prove more adept at lowering the 

environmental compliance. Less competitive industries with inelastic deman

be less concerned about cost increases associated with regulation. We refer 

as the demand effect.

6Porter and van der Linde [20] have pointed out that regulation may stimulate techn

innovation, suggesting that environmental regulation may lead to less labor intensive productio
jobs versus the environment 

3.1. Production Effects

We consider the effect of increased regulation in three distinct steps. Fi

examine how changes in regulation affect employment at the plant level, h

output constant. Second, we consider how these effects will affect market pr

a particular industry. An important element of this analysis will be our assum

about the competitive structure of the industry as well as how new regula

likely to affect each plant differently. Finally, we consider the aggregate d

response to industry-level price changes and how they relate back to ind

plant-level employment.

To compute the effects of regulation at the plant level, we rearrange the defi

of plant-level employment in a convenient form. Specifically,

L = 1

Pl

νl · TC 

where L is employment, Pl is the annual wage, νl is the share of labor i

costs, and TC are total costs (including both conventional production and 

tory costs). With this rearrangement, the derivative of plant-level employme

respect to regulation can be written

∂L

∂RC









Y=Y



= TC

Pl

∂νl

∂RC   factor shift

+ νl

Pl

∂TC

∂RC   cost effect

 

where RC is a dollar measure of regulatory burden and Y = Y

 indicates ex

the constant output assumption.

Expressing the derivative in this way allows us to identify the cost and facto

effects. The first term on the right-hand side of (2) represents factor shift. C

in the share of labor translate directly into changes in employment as prod

becomes more or less labor intensive. The second term represents the cost ef

total costs rise with higher regulation. Higher costs, holding input shares co

yield larger expenditures on labor. Note that higher regulatory costs, as me

by reported expenditures on environmental activities, do not necessarily affe

costs one-for-one. There may be uncounted burdens and benefits associate

these environmental expenditures.7

3.2. Aggregating Plant Level Effects

Expression (2) reveals the change in employment associated with incre

regulation for a single plant. In order to compute an industry-wide effec

holding output constant—we need to make assumptions about how different 

7Our allowance for uncounted costs and benefits does not completely solve the problem 

regulatory expenditures as a proxy for regulation—since there may be other costs associated w

lation that are completely uncorrelated with the reported expenditures RC. This is, however, a 

approach [4, 5, 21].
418 morgenstern, pizer, and shih

are affected by the same regulation and then add these effects across plants. T

∂Lagg

∂RCagg









Y=Y



= 

I

i=1

∂Li

∂RCagg

= 

I

i=1

∂Li

∂RCi

∂RCi

∂RCagg

= 

I

i=1

TCi

Pl i

∂νL i

∂RCi

∂RCi

∂RCagg

+ 

I

i=1

νl i

Pl i

∂TCi

∂RCi

∂RCi

∂RCagg

 

where Lagg is the aggregate employment level, RCagg is the aggregate expen

on environmental regulation, and i subscripts indicate plant-level values fo

variable, in particular, the specific regulatory burden RCi of plant i.

At this point, we are forced to make an assumption about how regulatio

ferentially affect plants. As noted in Section 1, environmental regulations ty

focus on polluting industries, not individual plants, so the problem is specify

likely distribution of burden. For simplicity, we assume that regulations affect 

in proportion to their total costs.8 That is, we assume that an extra dollar of 

gate regulatory burden affects plant i by an amount equal to plant i’s total co

share of the industry-wide total costs, ∂RCi/∂RCagg = TCi/TCagg. Assuming t

relation between costs and regulatory burden, ∂TC/∂RC, is the same for all 

(which is true in our production model below), expression (3) becomes

∂Lagg

∂RCagg









Y=Y



= 1

TCagg



I

i=1

TC2

i

Pl i

∂νl i

∂RCi

+

∂TC

∂RC  1TCagg



I

i=1

νl iTCi

Pl i

= 1

TCagg



I

i=1

TC2

i

Pl i

∂νl i

∂RCi

+

∂TC

∂RC  Lagg

TCagg

 

We must also consider how plant-level cost effects translate into industr

price effects in order to address demand consequences at the industry level. 

this, we assume that there is monopolistic competition within the industry. S

cally, suppose that output from different plants can be aggregated into a com

good that is demanded by other agents in the economy. This composite g

defined by

qagg =

	ωiq

ρ−1/ρ

i


ρ/ρ−1

[e.g., 22], where qi is the output of plant i qagg is the aggregated output, and 

ρ are aggregation parameters. This aggregation formula recognizes that even 

4-digit Standard Industrial Classification code level, there can still be hetero

in output. The elasticity of substitution ρ among the output of different plan

tures this heterogeneity and leads to a fixed mark-up of equilibrium price ove

8That is, larger plants face larger costs in response to new regulations. This specification als

fies the question of aggregation discussed below. In earlier work, we assumed the burden of a

regulation was proportional to the level of existing regulation RCi/

j RCagg, with little impac

results. A significantly different assumption about the distribution of costs, such as assuming n

lation occurs in inverse proportion to existing regulation, could obviously influence our conclus

9The important practical assumption for our results is that prices rise in proportion to co

remains true in the limit of perfect competition as ρ tends to infinity as well as for more 

specifications.
jobs versus the environment 

This assumption is consistent with different costs among plants and noncomp

pricing behavior due to market concentration.

Our assumptions about market structure allow us to explicitly determin

changes in production costs at individual plants will affect market pric

demand, as well as how changes in demand will, in turn, affect individual pla

particular, if costs at each plant rise by the same proportion, the market pr

each plant’s output will rise by that proportion. Further, if aggregate deman

by some fraction, output demand at each plant will fall by that same fractio

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 d

for the composite industry output good qagg exhibits a constant elasticity σd. 

environmental regulations are tightened and total costs rise based on this a

tion, each plant faces a proportional rise in costs, say θ. Based on the in

model (5), this leads to a proportional rise θ in the price of each plant’s ou

well as the price of the composite good qagg. Demand for the composite goo

falls by σdθ · qagg.

We can therefore write the effect of increased regulatory expense on outp

∂qagg

∂RCagg

= −σd

∂TC/∂RC

TCagg

× qagg 

where σd is the industry-level elasticity of demand, qagg is output of the co

ite good, and ∂TC/∂RC1/TCagg is the fractional rise in cost at each 

This contraction in aggregate demand leads to a proportional output cont

σd∂TC/∂RC1/TCagg at each plant and, in turn, a proportional reduc

employment. Adding the affects across plants, we have the aggregate demand 

on labor:

∂Lagg

∂RCagg







 demand

effect

= −σd

∂TC/∂RC

TCagg

× Lagg 

That is, the demand effect per dollar of additional regulatory burden equals th

tional change in total costs ∂TC/∂RC/TCagg, scaled by the elasticity of dem

and the aggregate employment level Lagg.

3.4. Total Employment Effect

Combining the demand effect (7) with the previous production effects (4) 

an expression for the entire employment effect

∂Lagg

∂RCagg

= 1

TCagg



I

i=1

TC2

i

Pl i

∂νL i

∂RCi

+ 1 − σd

∂TC

∂RC  LaggTCagg

 

Equation (8) allows us to look at each piece of the employment effect sep

and assess its economic and statistical significance. In particular, we can isol

demand effect that frequently motivates concern among labor and industry 
420 morgenstern, pizer, and shih

and compare it to the overall effect. If the overall effect is positive, but the d

effect is large and negative, it suggests that these groups may be correct i

concerns about competitiveness, but wrong in their focus on employment.

Evaluation of this expression requires estimates of a structural model o

duction costs along with an industry-level demand elasticity. We now addres

issues.

4. ESTIMATION OF PRODUCTION TECHNOLOGY AND

DEMAND ELASTICITY

With the exception of the elasticity of demand, the parameters that descr

relation between employment and environmental regulation are determined b

duction technology. In recent work [23], we have developed a flexible appro

estimating production and pollution abatement technology, which we use t

mate these parameters and their standard errors. We now describe that 

along with our approach to estimating aggregate demand elasticities, and 

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 o

and environmental activities are distinct and described by separate cost fun

Specifically, PC = GY P i t describes the cost (PC) of producing nonen

mental output Y based on input price vector P at plant i at time t. Simila

RC = HR P i t describe the cost (RC) of producing environmental “out

similarly based on input price vector P at plant i at time t. Inputs include c

labor, energy, and materials.

We then allow for the possibility that these two activities are not, in fact, d

by rewriting PC = GY P i tf RCαr , where f RC is an increasing fu

of regulatory expenditure. The parameter αr describes the degree of inter

negative values indicate cost savings and positive values indicate additional bu

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

ln PC = αi + αt + i p ln P + αy ln Y + 1

2 ln P	

pp ln P + 1

2βy ln Y

2

× t p ln P + 	

yp ln Y ln P + βytt · Y + αr

RC

PC  

ln RC = γ + γr ln R + 	

p ln P + 1

2 ln P	

pp ln P + γtt + 	

pt ln P · t 

where P is a vector of input prices (capital, labor, energy, and materials), P

costs related to nonenvironmental output YRC are costs related to environ

output R, and t is time. The parameters have the following interpretations: 

plant-specific, Hicks-neutral productivity effects; αt are time dummies, cap

aggregate Hicks-neutral productivity trends; i p are vectors of plant-specifi

share parameters; pp is a matrix of share elasticities; αy and βy captur

economies; t p are year-specific factor biases; yp reflects biases of scale; a

captures any aggregate time trend in scale economies. All of these parameter

to nonenvironmental production. The environmental production parameter

the following interpretations: γr describes scale effects, p is a vector of agg
jobs versus the environment 

cost share parameters; pp is a matrix of share elasticities; γt describes the 

neutral productivity trend; and pt captures factor trends. Finally, αr describ

interaction between environmental and nonenvironmental activities.

Equation (10) implies the absence of any scale biases in environmental cost

is, there is no “	

rp ln P ln R” term. This term would normally reflect the depe

of the environmental factor shares (the derivative of the logged cost functio

respect to logged prices) on the scale of environmental output R. That is, as 

produce more and more environmental output R—all other things equal—

parameters would indicate any changes in factor inputs. When we turn to th

mation of aggregate (environmental + nonenvironmental) factor shares in th

section, these parameters would leave the model unidentified because we 

have any measure of environmental output R—necessitating our assumption

ever, the question of whether factor biases do in fact exist in the produc

environmental activities would be an interesting question for further researc

4.2. Cost Function Estimation

The standard approach to estimate models such as (9) and (10) is to specify 

tem of cost shares based on the first derivatives with respect to log prices. Sto

disturbances are appended to each equation and the system is estimated sim

ously (with cross-equation restrictions) in order to improve efficiency. The p

with this approach in the current context is that factor inputs used for enviro

tal activities cannot be distinguished from those used for conventional prod

so the distinct cost shares associated with Eqs. (9) and (10) are not observe

ther, since we have no direct measure of R, Eq. (10) cannot be estimated.

We work around these problems by noting that our assumption, excluding 

of scale in environmental costs H·, allows us to write the environment

shares solely as a function of input prices and time (and not R):

νx r = γx + 	

x ln P + δxtt 

for x ∈ k l e m, representing shares associated with capital, labor, energ

material costs, respectively. Coupled with nonenvironmental cost shares d

from (9),

νx y = αi x + 	

x ln P + βyx ln Y + βt x 

we can write the observed total cost shares as

νx = RC

RC + PC νx r +



1 − RC

RC + PC 

νx y  

These aggregate cost shares are both observable themselves and defined in 

of other observable variables (prices, output, time, and regulation as a sh

total costs). The set of equations in (13) can therefore be estimated alongs

production cost function (9) by treating each as a stochastic relation and 

random disturbances.

Because the endogenous variable PC appears on the right-hand side of th

duction cost function and aggregate share equations, we use a two-step app

We first estimate the system of equations setting RC = 0 (which elimates PC 

right-hand side as well as the regulatory cost share parameters  and ). W
422 morgenstern, pizer, and shih

these parameter estimates to construct exogenous predicted values PC to r

the actual values PC on the right-hand side of (9) and (13). These predicted 

are then used to re-estimate the system without the endogeneity problem. A

estimation stages, we impose symmetry (βij = βij and δij = δji) and homo

of degree one in prices (which allows us to arbitrarily drop a share equatio

use a maximum likelihood estimator that iterates on the covariance matrix es

until it converges.10

4.3. Distinguishing Features

Two key features of this approach are its distinction between environmen

nonenvironmental production activities and the extensive use of fixed effe

both the cost function and share equations). The distinction between environ

and nonenvironmental activities allows us to consider the possibility that the

in fact, distinct. This hypothesis—that αr = 0—is easy to test and, further, n

values of αr can be interpreted as the dollar-for-dollar offset in productio

associated with an increase in environmental expenditures [23]. That is, ∂PC/

αr/1 + αrRC/PC which is roughly equal to αr for small values of αr and 

RC 
 PC (empirically below 5%; see Table I).

This distinction also allows us to estimate differences between pollution 

technology and normal production technology. The identification of these 

ences hinges on variation in RC/RC + PC in the data. Plants with higher va

this ratio are more tilted towards pollution control; plants with lower values t

normal production. When we look at the estimation results, we will see that 

tion control is often labor-intensive, leading to higher employment as environ

regulation increases.

The extensive use of fixed effects is important in the context of correctly id

ing differences between pollution control technology and conventional prod

Productivity and differences in factor usage may vary among plants due to 

served or unquantifiable plant differences and/or differences in the outpu

Since these differences are potentially correlated with environmental activitie

are not caused by them—failing to control for plant differences may bias the 

10With the exception of our use of fixed effects, the modification of the share equations (

instrumenting for PC, Chapter 9.4 of Berndt [24] describes our methodology in detail. Caves e

discusses the use of fixed effects in the cost function.

TABLE I

Industry Statistics

PACE as a share Labor as a share Price 

of total costs of total costs ($00

Industry RC/TC νl = PlL/TC 

Pulp and paper 0.028 0.201 3

Plastic material 0.020 0.085 3

Petroleum 0.011 0.019 3

Steel 0.022 0.230 3

Note: PACE refers to expenditures on environmental regulation as measured by the Pollutio

ment Costs and Expenditures survey.
jobs versus the environment 

For example, plants with older capital vintages or poor management might u

ferent and/or less efficient combinations of inputs. If these same plants als

higher environmental costs, these differences would falsely be attributed t

ronmental activities. Our earlier work focusing on the parameter αr demon

this is indeed the case: inclusion of fixed effects suggests that αr is, if anythin

ative. However, a pooled model ignoring plant-level differences indicates t

is significantly positive.11 A test of the pooled model restriction is overwhel

rejected.

The benefits of the fixed-effects model come at a price. In similar work

and Shadbegian [26] emphasize the potential problems with measurement e

fixed-effects models and advocate pooled estimates to determine the added b

of environmental regulation. More generally, Griliches [27], Chamberlain [2

Hsiao [29], all point out that fixed-effect estimation exacerbates the bias 

zero when measurement error is primarily within units rather than among 

However, we have no alternative to control for the plant-level differences t

believe introduce significant bias. Further, we have no direct evidence tha

surement error is primarily within units rather than between units: long-diff

estimates, where possible, reveal similar estimates with larger standard error

4.4. General Results

Parameter estimates for the model are provided in Table V in the Appendix 

with additional detail concerning the data. Here, we briefly discuss those 

Roughly half the estimated parameters are significant at the 5% level. How

is difficult to systematically simplify the model. Restrictions on the fixed effect

share equations and cost function) are rejected in all industries.13 Tests of 

Douglas restrictions on the quadratic terms pp and pp are similarly reject

Theory tells us that the cost function should be monotone and concave.15 E

ing these conditions at each data point, we find our estimates to be locally mo

more than 99% of the time. However, concavity (as captured by the sign of th

price elasticities) is frequently violated with capital demand sloping upward 

than half the time in two of the four industries. Unfortunately, this is a well-

consequence of using translog cost functions when elasticities deviate signifi

away from unity [32, 33]. The specific problem with capital demand may a

a consequence of imprecise measures of the price of capital based on th

11Estimates of αr based on a pooled model ignoring fixed effects are −008 025 −01

156 055, and 1.98 (0.39), in the pulp and paper, plastics, petroleum, and steel industries, resp

The fixed-effect estimates in Table V are uniformly lower. Morgenstern, Pizer, and Shih [23] dis

point in greater detail. 12Griliches and Hausman [30] suggest long-difference estimators as a way to reduce meas

error bias. 13Since the pooled model (when fixed effects are restricted) provides an unbiased, though less 

estimate of the random effects model, this is evidence against the random effects model. As note

the pooled estimates of αr are both statistically and economically different than the fixed effect e

14The LR statistics are 69.98, 49.56, 127.36, and 28.62 for pulp and paper, plastics, petrole

steel testing the Cobb–Douglas restrictions on the production cost function, and 16.70, 17.84, 28

25.56 for the same industries, respectively, testing the environmental cost functions. With six de

freedom, the 1% critical value is 16.81. 15See Jorgenson [31].
424 morgenstern, pizer, and shih

and Jorgenson [34] approach. A Cobb–Douglas version of the model—which 

concavity by restricting the pp and pp terms to be zero—leads to only min

ferences in αr and γl, the key parameters in our calculation of the labor 

below.16 While we check the sensitivity of our main results to this assumpti

continue to focus on the general translog results since our interest is estimati

the Cobb—Douglas model is rejected by the data.

A key parameter in our analysis of the labor effects is the estimated lab

share associated with environmental activities. From Table V, we find est

for γl of 0.15, 0.36, 0.07, and 0.16 for pulp and paper, plastics, petroleum

steel, respectively. Only steel is not significant at the 5% level. Comparing 

estimates to the observed total cost shares in Table I (0.20, 0.08, 0.02, an

respectively), we see higher environmental labor shares in plastics and pet

and lower environmental labor shares in pulp and paper and steel. Note tha

the higher environmental labor shares in plastics and petroleum are signifi

higher in a statistical sense, the lower environmental labor shares in pulp and 

and steel are insignificantly lower.

A few simple calculations are possible using the aggregate summary st

in Table I and the estimates of γl. For example, switching a million dollar

production to regulatory expenditures would lower employment expenditu

roughly $50,000—(0.15–0.20) × $1 million—in the pulp and paper industry. 

on an average salary of $35,000, this would mean 1.4 fewer jobs. The n

would be +80 −14, and −16 jobs in plastics, petroleum, and steel, respe

We return to these kinds of calculations in more detail after discussing d

elasticities.

4.5. Estimating Aggregate Demand Elasticities

In addition to our estimate of supply-side cost functions, we also require 

gate demand elasticities to compute Eq. (8). We estimate these elasticitie

historical data on aggregate output and industry productivity. Changes in in

productivity represent an exogenous shift in the cost of supply and therefore 

us to identify the demand elasticity. As long as the industry in question is sm

ative to the entire economy, it is reasonable to assume that this change in in

productivity will not shift the demand schedule.

We use publicly available data developed by Jorgenson and his associates [

for both our industry-level output and productivity measures.17 Annual produ

growth is computed as the logarithmic difference between the annual cha

input price and the annual change in output price, where input price is com

as a divisia index of capital, labor, energy, and material prices [see 37]. Th

is the portion of any price change that is not captured by changes in input p

We then perform a simple regression of the annual change in log output on 

16Based on the Cobb–Douglas model, the estimates of αr are −056, 0.37, 0.60, and −005 

and paper, plastics, petroleum, and steel, respectively. The corresponding Cobb–Douglas estima

are 0.23, 0.25, 0.04, and 0.16. These can be compared to the translog parameter estimates in Ta

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

18While the typical assumption in a productivity calculation is competitive output markets, a 

markup of price over costs would not interfere.
jobs versus the environment 

TABLE II

Demand Elasticity Estimates

Sector Demand ela

Industry (IGEM : BLS) (standard 

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

$ln outputt = σd$ln prodt + εt 

where outputt is the industry level output in period t prodt is the change 

ductivity level measured by

$ln prodt =

vt + vt−1

2

	




ln Pt − ln Pt−1



− 
ln POt − ln POt−1



 

where εt is a random disturbance and σd is the demand elasticity being esti

The variable Pt is a vector of input prices (capital, labor, energy, and material

is output price, and vt is a vector of input shares. The results of these regr

are shown in Table II.19

The elasticity estimates are significant in three of the four industries, r

from slightly inelastic (0.40) to slightly elastic (1.86). Compared to industr

demand elasticities used in simulation models [5, 38], ranging from 1.10 t

these estimates are uniformly smaller. In part, this difference may be due 

use of translog cost functions on the demand side in those models, which freq

results in more elastic behavior. When we discuss our results in the next secti

can consider the impact of assuming larger elasticities than reported in Tabl

5. THE EFFECT OF REGULATION ON INDUSTRY EMPLOYMENT

We now combine the estimation results of the preceding section with Eq

order to compute the effect of environmental regulation on industry e

ment. There are two terms in (8) that cannot be computed directly 

the data: ∂νL i/∂RCi and ∂TC/∂RC. We first discuss the relationship b

these expressions and the parameters estimated in (9) and (13), then prese

results.

19We also considered models with lagged productivity changes on the right-hand side as wel

trols for aggregate productivity changes. With the exception of pulp and paper, these variat

to uniformly less elastic demand so that our results, if anything, overstate the adverse consequ

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 αr captures the potential nonzero ef

expenditures on environmental protection on conventional production cost

∂PC/∂RC = αr/1 + αrRC/PC. The expression PC is conventional prod

cost and RC is environmental expenditures. From Table I we know that RC

on the order of 1%–3% in the aggregate, and therefore ∂PC/∂TC ≈ αr. F

since TC = PC + RC, we have ∂TC/∂RC = 1 + αr for all plants.

The expression for ∂νl i/∂RCi is only slightly more complex. From (13) w

νl = νl r +

 PC

RC + PC 

νl y − νl r

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

∂νl i

∂RCi

= − PCi

RCi + PCi2 νl i y − νl i r

= − PCi

RCi − PCi2




αi l + 	

l ln P + βyl ln Y + βt l − γl − 	l ln P − δltt



We use (16) to compute ∂νl i/∂RCi for each observation and then use (8) to 

gate the effects.

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

∂Lagg

∂RCagg

= FPC/Pl TC · Gσd αr γl

where

F =













PC/Pl

TC  t · PC/PlTC  ln Pk · PC/PlTC  ln Pl · PC/PlTC  ln Pe · PC/PlTC 

ln YPC/Pl

TC  t80PC/PlTC  t81PC/PlTC  t85PC/PlTC  t88PC/PlTC 

t91PC/Pl

TC  d1PC/PlTC  d2PC/PlTC  dIPC/PlTC  L

TC















G =

 γl δlt δlk − βlk δll − βll δle − βle −βyl −β80 l −β81 l

−β85 l −β88 l −β91 l −α1 l −α2 l −α1 l 1 + αr1 − σd

F is a vector of sample means based directly on the data and G is a ve

estimated parameters from Eqs. (9) and (13) and reported in Table V and Ta

Overbars (x ̄) in F indicate means computed over the entire sample of pla

observations, with t80, etc. indicating time dummies in the noted year and d

indicating plant dummies for the particular plant (where I is the total num

plants). As before, TC is total cost, PC is production cost, Pl is the price of 

Pk is the price of capital, Pe is the price of energy, Y is the output level, an

the employment level. To compute standard errors, we ignore sampling varia

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

20 Note 

but the last element of F · G explains any factor shift, while the last term in

both the cost and demand effects.

20We assume the estimated demand elasticity is uncorrelated with the other parameters. Th

sible since the demand elasticity is based on aggregate time series data and the remaining pa

are estimated from a relatively short panel of cross-sectional data.
jobs versus the environment 

5.2. Estimated Effects

Table III displays our principal results, expressed as a change in emplo

associated with an additional $1 million ($1987) in environmental spendi

each of the four heavily regulated manufacturing industries under review 

and paper, plastics, petroleum, and steel). Results are presented separately 

cost, factor shift, and demand effects, as well as the net effect, which is th

of the components in Eq. (8). The grand total combines the four industr

estimates, weighting by each industry’s share of environmental expenditure

test the sensitivity of these results to the noted violations of cost function be

we estimated a Cobb-Douglas version of the model and found similar result

Line 1 displays the employment changes per million-dollar increase in en

mental spending caused by the overall increase in costs, holding output const

expected, these values are positive in all industries, increasing employment by 

where between one-half and five and one-half jobs per million dollars of spe

The variation among industries is explained primarily by the differences in 

cost shares νl and, to a lesser extent, by estimated differences in indirec

captured by the parameter αr in the model. For example, labor accounts f

than 2% of total costs in the petroleum industry, versus 10%–20% for the 

industries (see Table I). Therefore, $1 million in increased total costs tra

into roughly $20,000 in labor costs—or about one-half of one job. On the 

hand, when αr is significantly negative, as it is in the pulp and paper indust

indicates uncounted savings associated with environmental expenditures. Th

$1 million increase in environmental expenditures reflects less than $1 mil

increased total costs. These uncounted savings reduce the cost effect and le

21Average industry level expenditures measured by the PACE survey as a share of total, c

over all years in the sample. 22Estimates of the total employment effect are 0.91 (2.8), 6.05 (2.9), 1.21 (0.7), and −4

respectively, for pulp and paper, plastics, petroleum, and steel, based on the Cobb–Dougla

(standard errors are in parentheses).

TABLE III

Change in Full-Time Jobs per Million Dollars of Environmental Expenditure (Standard Err

in Parentheses; Asterisks Indicate Significance at the 5% Level)

Pulp and paper Plastics Petroleum Steel 

Cost 2.18 3.23∗ 0.65∗ 5.52∗ 

(1.58) (1.63) (0.24) (2.78) 

Factor shift −037 5.25∗ 1.77∗ 5.28 

(2.05) (2.46) (0.81) (4.41) 

Demand −294 −158 −026 −1027 

(2.51) (1.74) (0.22) (7.10) 

Total −113 6.90∗ 2.17∗ 0.53 

(2.72) (3.21) (0.88) (7.68) 

Weight 0.15 0.07 0.50 0.28

As noted in Section 3.2, additional environmental expenditures are allocated to individual obse

in the sample in proportion to their share of aggregate total costs (TC). Weights reflect the m

of industrywide environmental expenditures.
428 morgenstern, pizer, and shih

lower estimated employment consequence—hence, it is not surprising to see 

atively low estimated cost effect for pulp and paper. The average across a

industries is 2.4 jobs per million dollars of increase in environmental expend

Line 2 displays the employment changes attributable to the factor shift asso

with relatively more environmental and less production cost. Overall, it a

that environmental spending increases employment through factor shifts, refl

the fact that environmental activities are estimated, on average, to be more 

intensive than conventional production. This pattern of signs and significan

responds to the rough estimates in Section 4.4 (except steel), with the we

average across all four industries indicating 2.7 jobs gained per million dol

spending.

In some industries, such as petroleum, the explanation for the factor shift i

obvious. Materials are such a large (>90%) share of production costs that it 

be virtually impossible for environmental activities to be any less labor int

A similar, though not so extreme story holds for plastics, where labor is st

than 10% of production costs. This contrasts with steel and pulp and paper 

labor accounts for about 20% of total expenditures. In those industries, 

ambiguous labor effects as both the environmental and production-cost sha

large.

Line 3 displays the employment changes per million-dollar increase in en

mental spending associated with a drop in demand for final products as pric

As expected, these values are negative in all industries, reflecting the com

effects of increased costs, a constant markup of price over cost, and dow

sloping demand curves. Averaged across industries, reductions in demand c

loss of around 3–4 jobs per million dollars of spending. This loss conceals wid

ation within industries: in steel, the estimated loss is nearly ten jobs per milli

lars of spending, while in petroleum the job losses are negligible. These diffe

are consistent with the results in Table II, indicating a much higher demand 

ity for steel. Our results are also consistent with the findings of Berman and 

that labor demand at petroleum refineries may increase with regulation. N

the estimated demand effects are significant, reflecting the combined unce

about the increase in total costs and the demand elasticity.

Combining these three effects in line 4, we find significant job gains in p

and petroleum with insignificant effects in pulp and paper and steel. These 

follow from the fact that both plastics and petroleum had significantly positive 

shifts coupled with relatively small demand elasticities. The average across a

industries works out to be an insignificant gain of one and one-half jobs per 

dollars of additional environmental expenditures. Even doubling the demand 

as suggested by the higher elasticities found in simulation models, the overal

would remain statistically insignificant.

5.3. Conclusions

Economists traditionally focus on welfare and other well-grounded measu

social cost when evaluating public policy. Job loss is not a real social cost 

extent that a job lost in one area or industry is quickly replaced in another. 

the recent debate on trade with China suggests, discussions of job loss, espec

the industry level, are often central to the policy process [39].
jobs versus the environment 

Our study of environmental regulation in the pulp and paper, plastics, petr

and steel sectors suggests that a million dollars of additional environmental 

diture is associated with an insignificant change in employment, with 

confidence interval ranging from −28 to +59 jobs. To put these numbers 

text, it is useful to think about them in light of actual changes in employme

environmental spending observed in recent years. Between 1984 and 1994

environmental expenditures as measured by the Pollution Abatement Co

Expenditure (PACE) survey in all manufacturing industries rose by $4.9 

($1987). During that same period, total production employment in those 

tries declined by almost 632,000 jobs. Applying the most adverse estim

our 95% confidence interval—a loss of 2.8 jobs per million dollars of en

mental expenditures—we see that environmental spending may have acc

for the loss of at most 14,000 jobs, or about two percent of the jobs lost ov

period. On the other hand, environmental spending may have created as m

29,000 jobs.

Our structural model allows us to peer inside these results and unde

why the conventional wisdom might be wrong. Most importantly, there are 

positive employment effects in industries where environmental activities ar

tively labor intensive and where demand is relatively inelastic, such as plast

petroleum. In others, where labor already represents a large share of prod

costs and where demand is more elastic, such as steel and pulp and paper, t

little evidence of a significant employment consequence either way.

If our model results do not support the notion of a jobs versus the enviro

trade-off, why does this theme remain so steadfast in the business and labo

munities? Obviously, it is a politically and emotionally charged topic that a

attention. Beyond posturing in a public debate, however, it is possible tha

employers and employees honestly overestimate the potential for job loss asso

with environmental regulation by confusing the product demand schedule fa

the plant with the schedule faced by the entire industry. The former is ce

more price elastic due to competition from other firms.23 Or, firms may fe

they will be less adept in responding to new regulation than their compet

although this would fail to explain why all firms in an industry would oppo

regulation. If all firms and plants in an industry are faced with similar cost-inc

regulatory changes—which we believe is typically the case—most plants shou

be worried about losing business to other plants facing the same regulation.

A similar story might apply regarding the job-creating aspects of enviro

tal spending. Neither employers nor employees may be completely familia

the nature or type of changes in production technologies associated with en

mental protection. In that case, there might be a tendency to underestima

employment increases associated with the new environmental spending it

with changes in the mix of factor inputs. Our analysis suggests that these in

can more than offset the loss of jobs associated with rising prices and dep

demand. Ignoring these aspects of environmental regulation, it is possible th

employers and employees may overstate the job destructive aspects of enviro

tal regulation and understate its job-creation potential.

23That is, we expect the elasticity of substitution among output in the same industry, ρ in Eq

be larger than the elasticity of demand for the industry as a whole, σd.
430 morgenstern, pizer, and shih

Another explanation is that our estimates may mismeasure employment 

We might overestimate job loss by using industry-level demand elasticities 

in fact, regulation affects the entire economy—suggesting fewer opportunit

substitution and even smaller demand effects. Alternatively, we might underes

job loss for exactly the opposite reason: if regulatory consequences are re

concentrated in certain areas and at certain plants, the industry-level elasticit

likely too small. Further, our focus on industry-level employment misses an

that might be associated with shifting jobs among or within plants while in

level employment remains unchanged.

Notwithstanding these caveats, our results question the idea of a jobs ver

environment trade-off at the industry level. While environmental spending 

has consequences for business and labor, the hypothesis that such spendi

nificantly reduces employment in heavily polluting industries is not suppor

the data.

APPENDIX: DATA

The central data source for this research is the Longitudinal Research Da

collected by the U.S. Census Bureau. Environmental spending is measured 

Pollution Abatement Cost and Expenditure Survey. Energy expenditures are d

from the Manufacturing Energy Consumption Survey and the Annual Sur

Manufactures. We normalize all variables by dividing each by the sample me

• The Longitudinal Research Database (LRD) is a pooled, cross-s

time series comprised of the establishment responses to the Annual Sur

Manufacture (ASM) and the quinquennial Census of Manufactures (CM) fo

50,000 establishments in each year. The LRD contains information on cos

puts, and inputs at the plant level. Detailed quantity and expenditure infor

for energy consumption are only available up to 1981.

• The Manufacturing Energy Consumption Survey (MECS) is a triennial 

beginning in 1985 and conducted by the U.S. Department of Energy. The 

contains detailed fuel consumption and expenditure data by establishment, re

the discontinued energy data contained in the LRD prior to 1981.

• The Pollution Abatement Cost and Expenditure (PACE) survey contai

lution abatement investment spending and operating expenditures at the 

lishment level. The Census Bureau conducted this survey annually betwee

and 1991, except 1983 and 1987.

Based on the availability of detailed energy and environmental expenditur

our analysis includes the years 1979, 1980, 1981, 1985, 1988, and 1991. Samp

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 

are contained in the LRD. We construct a divisia index [40, 41] of output price 

on the corresponding producer prices of different products obtained from th

Department of Labor’s Bureau of Labor Statistics. The quantity index is ob

by dividing total value of shipments, adjusted for inventory, by this aggregate 

price index.
jobs versus the environment 

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 c

the plant level are from the annual PACE Survey. Operating expenses for po

abatement include depreciation on the pollution-abatement capital. Real 

tory expenditures are computed by deflating nominal pollution-abatement op

costs by the GDP deflator.

• Capital Stock. The gross book value of the capital stock at the beginn

the year and new capital expenditures each year are reported in the LRD. 

book value is used to compute the capital stock in 1979. A perpetual inv

method [42] is then used to generate a real capital stock series covering the 

1980–1991 based on the formula

kt = 1 − δkt−1 + q0

qt

It

where kt is the period t capital stock, and It is new capital expenditure measu

current dollars. The industry-specific economic depreciation rate (δ) is from 

and Wykoff [43]. The capital stock price indices (qt for various industries are 

from a dataset developed by Bartelsman and Gray [44].

• Service Price of Capital. The service price of capital is calculated usi

Hall and Jorgenson [34] procedure. The service price of capital is given by

pkt = qt−1rt + δqt − qt − qt−1 + qtCt

1 − utzt − kt

1 − ut



where

pkt = service price of capital,

qt = price index of new capital equipment,

rt = after tax rate of return on capital (opportunity cost),

δ = rate of economic depreciation,

Ct = effective property tax rate,

ut = effective corporate income tax rate,

zt = present value of allowed depreciation tax deductions

on a dollar’s investment over the life time of an asset,

kt = investment tax credit, and

t = year.

We use the average yield on Moody’s “Baa” bonds for the after-tax rate of 

on capital. The data on the tax-policy variables are from Jorgenson and Yu

and Jorgenson and Landau [46].

• Capital Costs. The capital costs were constructed as the product of the 

price of capital and the stock of capital.
432 morgenstern, pizer, and shih TABLE V Parameter Estimates Pulp Pulp and paper Plastics Petroleum Steel and paper Plastics Petroleum Steel

αy 0.7161∗ 0.8314∗ 0.7433∗ 0.7136∗ γk 0.1276

−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

−00726 γl 0.1531∗ 03621∗ 0.0748∗ 0.1565

(0.2746) (0.6958) (0.5905) (0.4671) (0.0770) (0.0914) (0.0292) (0.1610)

βkk 0.1095∗ 0.0029 0.0070∗ 0.0664∗ γe 0.1967∗ 02930

−00225

−07126∗

(0.0379) (0.0190) (0.0019) (0.0172) (0.0846) (0.2028) (0.0387) (0.1952)

βll 0.1120∗ 0.0668∗ 0.0133∗ 0.0491∗ δkt 0.0013 00627∗ −00043

−00122

(0.0114) (0.0094) (0.0016) (0.0201) (0.0171) (0.0186) (0.0110) (0.0160)

βee 0.0579∗ −00027 0.0128∗ 0.0211 δkk 0.9811∗ 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∗ −00035 δke

−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)

βky 0.0100

−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 0.0156 δle −02485 0.1503 0.0026

−08998∗

(0.0065) (0.0068) (0.0010) (0.0138) (0.1375) (0.2211) (0.0398) (0.3833)

Continued
jobs versus the environment 

TABLE V—Continued

Pulp Pulp

and paper Plastics Petroleum Steel and paper Plastics Petroleum Steel

βly −00446∗ −00302∗ −00078∗ 0.0066 δet −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∗ δee 0.4141∗ −03730

−01483∗ −01428

(0.0053) (0.0092) (0.0015) (0.0087) (0.1679) (0.5119) (0.0690) (0.5534)

βyt 0.0041 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 0.017Labor 0.002 0.006

Energy 0.002 0.012 0.010Materials

Fraction 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 0.011

Note. 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 w

The cost of labor includes production worker wages plus supplemental lab

(which accrued to both production workers and nonproduction workers) ad

to reflect the production-worker share. The price of labor is defined as the 

production workers divided by the number of production workers.

• Price of Materials. Expenditure data on individual materials are collec

a five-year cycle by the CM. We derive a divisia index of the price of ma

for each plant for the years 1977, 1982, 1987, and 1992. We linearly inte

estimates for intervening years.

• Cost of Materials. We use reported total expenditures on materials an

in the LRD to calculate material costs.

• Price of Energy. Detailed data on total quantities consumed and total 

ditures on various fuels were collected in LRD (through 1981) and subseq

in MECS (1985, 1988, 1991). These data are used to calculate the prices o

vidual fuels ($/million Btu) paid by each plant. The individual fuels includ

natural gas, distillate fuel oil, residual fuel oil, liquefied petroleum gases, an

tricity. These fuels typically account for about 90% of total energy cost. Th

of energy is computed as a divisia index of these fuels.

• Cost of Energy. The cost of energy is the summation of expenditures 

six individual fuels.

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