qWork sponsored by the University of Chicago/Argonne National

Laboratory Collaborative Grant Program and the US Environmental

Protection Agency * O$ce of Atmospheric Programs under contract

No. W-31-109-ENG-38.

* Corresponding author. Tel.: 001-630-252-5393; fax: 001-630-252-

4498.

E-mail address: gboyd@anl.gov (G.A. Boyd)

Energy Policy 28 (2000) 289}296

Estimating the linkage between energy e$ciency

and productivityq

Gale A. Boyd!,*, Joseph X. Pang"

!Policy and Economic Analysis Group, Argonne National Laboratory, DIS-900 Rm, 1-14, 9700 C. Cass Avenue, Argonne IL, 60439-4815, USA

"Economics Department, University of Chicago, IL 60637, USA

Received 18 August 1999

Abstract

Many analyses have referred to industrial productivity bene"ts associated with energy e$ciency that are at least as great or larger

than the energy bene"ts. If plants with higher energy intensity also tend to have lower productivity, then energy policy needs to

consider this. This study examines this issue for two segments of the glass industry, using plant level data from the Census Bureau

Productivity is de"ned by the di!erence in &best practice' production e$ciency, as measured by data envelopment analysis (DEA)

This study uses regression analysis to estimate how di!erences in plant level electricity and fossil fuel intensity, i.e. energy use per unit

of production, are attributable to di!erences in plant level productivity and other economic variables, like energy prices and

cumulative production. In every case, productivity di!erences between plants are statistically signi"cant in explaining di!erences in

plant energy intensity. The coe$cient that links productivity to energy e$ciency yields a less than proportional impact for only one

industry and fuel type. For others the relationship is implies that a 1% increase in productivity increase energy e$ciency by more than

1%. This e!ect is statistically signi"cant for #at glass, but not container glass. The estimates of the price and learning-by-doing

coe$cients seem reasonable, but without imposing the assumption that all plants in the industry are equally productive. This lends

further credence to a signi"cant relationship between productivity and energy e$ciency. ( 2000 Elsevier Science Ltd. All rights

reserved.

JEL classixcation: L61; O30; Q20

Keywords: Productivity; Energy e$ciency; Frontier production function; Learning by doing

1. Introduction

Advocates of energy e$ciency have described productivity bene"ts associated with energy e$ciency projects that are as least as great or larger than the energy

bene"ts (Elliott et al., 1997; Porter and van der Linde,

1995). This typically is interpreted to mean that output

per unit of non-energy input(s) increase because of a project identi"ed to increase output per unit of energy. The

association of non-energy bene"ts from any project may

depend upon the original intent of the project. Improvements in the overall productivity of a process are likely

to reduce energy use, hence productivity is associated

with energy e$ciency. On the other hand, the search for

energy e$ciency improvements may identify a project

that improves the productivity of other input factors

as well.

Porter and van der Linde (1995) maintains that

a search for energy e$ciency that leads to a productivity

improvement is a common occurrence. Other analysts

have agreed that productivity improvements can reduce

energy and other inputs, but have questioned the anecdotal nature of case studies of energy e$ciency and

associated bene"ts as to their widespread validity

(Palmer et al., 1995). This paper o!ers a test of how

widespread this occurrence may be, by providing a rigorous measure of inter-plant productivity di!erences and

estimating the statistical linkage between that productivity measure and energy intensity. If plants that have

higher energy intensities are signi"cantly related to

0301-4215/00/$ - see front matter ( 2000 Elsevier Science Ltd. All rights reserved.

PII: S 0 3 0 1 - 4 2 1 5 ( 0 0 ) 0 0 0 1 6 - 1
plants with low productivity, then there is support for the

notion that energy (in)e$ciency may be a guidepost for

productivity improvements.

If higher energy intensities are signi"cantly related to

plants with low productivity, the implications are that

policies that are focused on energy e$ciency, e.g.

greenhouse gas policy, may not cost as much once the

non-energy bene"ts are included. In addition, energy

technology R&D policy might pro"tably focus on

broader productivity issues, rather than narrowly focusing on improving the energy use of speci"c types of

equipment.

Given the di$culty in determining a causal linkage,

the empirical validation of how di!erences in energy

e$ciency are correlated with di!erences in productivity is

still important. This empirical validation needs to be as

general as possible, since there are important policy implications. However, it is reasonable to expect that any

link between energy e$ciency and productivity will be as

industry-speci"c as is the energy use in those sectors. This

paper proposes an empirical approach to the question

and implements it for two segments of the glass industry,

#at and container. The glass industry is an energy-intensive manufacturing sector, which has undergone signi"-

cant technological changes in recent years. The Oxy-fuel

furnace has replaced the traditional furnace capacity in

many plants. While the empirical results for these segments of the glass industry may not generalize to other

industries, the approach can be extended to other industries that produce a homogeneous output.

The link between energy e$ciency and productivity is

estimated by relating inter-plant energy and production

e$ciency, holding other economic variables constant.

The industry segments for empirical study are chosen to

be rather narrow, so that the underlying assumption that

technical opportunities are similar can be best supported.

We do not examine speci"c energy e$ciency projects, but

instead compare di!erences in energy intensity across

plant, within each of the two industry segments. A regression analysis is used to estimate how plant level energy

intensity (energy use per unit of production) is attributable to plant level productivity and other &traditional'

economic variables, like energy prices. This study uses

plant level data from the Census Bureau. Without plant

level data these relationship could not be meaningfully

estimated.

This paper uses a rigorous production function

approach to measuring inter-plant productivity. Productivity is measured against the &best practice' production

function, or production frontier, via Shephard's distance

function (1970). The empirical implementation of the

distance function is a solution to a linear programming

(LP) problem. This approach, which is also called data

envelopment analysis (DEA), assigns an index to each

plant, which represents the amount by which all production inputs could be reduced, without changing output

levels. A plant that has an index of 1.0 is &best practice'

The study computes the DEA-based production e$ciency for the two glass industry sectors and uses it to

estimate the linkage between energy e$ciency and productivity.

The paper is organized as follows. First, we present the

measurement of the &best practice' production and e$-

ciency. Then, we discuss the con"dential data used in the

analysis and the constraints placed on the study by using

that data. Finally, we present the results from the regression model used to estimate the relationship between in

energy e$ciency and inter-plant productivity and discuss

the implications.

2. Frontier production function approach

Measuring energy `e$ciencya is typically handled by

comparing measures of energy intensity, the energy/output ratio. Although the best measure of energy e$ciency

has been the subject of some discussion (Freeman et al.,

1997) it is much simpler than measuring productivity

This paper uses the frontier production function to provide a measure of productivity, analogous to comparing

energy intensities across plants.

The production function is de"ned as, y"f (X) where

y is the maximum level of output that that can be produced, given a vector, X, of inputs into the production

process. The input vector, X, can be as richly described as

the data allow. This functional relationship allows us to

represent the technical ability to substitute one factor of

production holding the level of output constant.

When combined with the behavioral assumption that

economic agents have complete knowledge of the prices

of inputs and can use the complete knowledge of this

function, f (X), to minimize costs for a given level of

production many powerful economic concepts and results are obtained. However, these are strong assumptions and they should not, or need not, be imposed on an

empirical analysis. In this paper, we take an approach

that does not impose assumptions about optimal

behavior.

The minimal assumption of optimal behavior has two

implications for this analysis. The "rst is that maximal

output of the production function is not assumed to hold

for all observations, i.e. a plant may be ine$cient relative

to some other, better performing plant. This allows us to

de"ne a productivity index that is part of the study. The

second is that when cost minimization is not assumed

optimal factor demand equations do not exist. This does not

mean that prices have no impact in economic decisions

but that the theory of duality does not provide the

speci"cation of factor demands, including energy. For

this reason, a simple regression model for the in#uence of

productivity and other economic variables on the energy

} output ratio is used.

290 G.A. Boyd / Energy Policy 28 (2000) 289}296
Fig. 1. DEA Representation of &Best practice' production set,  ̧(y).

Our measure of relative productivity, or production

e$ciency, is based on non-parametric techniques (see

FaKre et al., 1985) The input set  ̧(y) is de"ned as

 ̧(y)"MX:y*f (X)N. Our e$ciency measure is based on

the distance function, D(y, X)"minMj:xj3 ̧(y)N

(Shephard, 1970). The set  ̧(y) can be de"ned by linear

programming using observed data. These linear programming (LP) methods for implementing the distance

function, also called data envelopment analysis (DEA),

use k observed input and output combinations as the

basis of the production technology by solving the following LP:

D(y

0

, x

0

)"min j

S6"+%#5 50

,

z>*y

0

, zX)jx

0

, z3Rk

`, j, y3R`,

X3RN

`.

To understand intuitively how this approach works,

consider Fig. 1. For, discussion, each point on Fig. 1 represents a combination of inputs that all produce the same

output level. Plants represented by points B and C are

technically e$cient, i.e. since they are on the isoquant

they cannot reduce inputs and maintain the given output

level y. However, plants A and D is ine$cient (less

productive). These plants could reduce inputs by the

amount indicated by the arrow and still remain in the

input set  ̧(y).

We now ask the question, &Is energy any di!erent from

other inputs?'. The answer from the perspective of the

proposed productivity measurement is &No.' All inputs

are treated the same. Our proposed measure of productivity computes the amount that energy, and all other

inputs, could be reduced without reducing output levels

In the case of plant A, and the other plants represented

by the solid dots, it appears that the low productivity is

related to very high-energy use. However, plant D and

those others represented by the clear dots have lower

energy intensity and lower amounts of production

(in)e$ciency as well. If the data are such that plants that

are highly ine$cient tend to lie in the region of production space that has higher energy input, then there is

support for the hypothesis that energy e$ciency and

productivity are statistical related. If ine$cient plants

were more randomly distributed in production space

then we would not "nd a statistical link between productivity and energy e$ciency.

To compute the amount of production e$ciency for

input into the empirical analysis, the LP speci"ed above

using the KLEFM input speci"cation, was run for each

observation in the data set. A plant is e$cient, i.e. on the

isoquant, if the LP assigns it an index of j"1.0. When

j(1.0, the plant is not e$cient. The average level of

production e$ciency was 76 and 83% for #at and container glass, respectively.

3. Plant level data

We derive our data from the Longitudinal Research

Database (LRD). The LRD is the plant level data which

forms the basis of much empirical economic analysis of

manufacturing. The LRD was developed by the Center

for Economic Studies (CES) at the Bureau of the Census

CES has constructed a panel of plant level data from the

Annual Survey of Manufactures and the Census of

Manufacturers. The LRD includes economic activity

G.A. Boyd / Energy Policy 28 (2000) 289}296 291
1 Our linear programming approach would automatically treat

a plant as e$cient if there were no other plants with su$ciently similar

production technologies, as represented by the input vector.

Table 1

Summary statistics for 1987}1995 balanced panel data for #at glass

Variable Number of observations Mean Standard deviation

Electric-output ratio 126 0.83 0.37

Fuel-output ratio 126 0.023 0.011

Cumulative output (Million $) 126 0.541 0.462

Capital-output ratio 126 1.78 1.03

Electric price ($/kwh) 126 0.046 0.016

Natural gas price ($/Mbtu) 126 2.84 1.34

LP e$ciency measure 126 0.77 0.17

2 A single year for container glass was used since we construct

a dummy variable for plants that include electricity based melting

furnaces in their production process. The data on furnace design did not

include the year the electric furnace was installed, only that it was

present in 1995. This restricted our ability to construct a panel for this

segment.

e.g. total shipment of output, labor cost, energy, plant

and equipment, materials, for a sample of plants during

the survey years and complete coverage during the years

of the economic census. Under Title 13 of the US Code,

this data is con"dential. However, academic and government researchers may access this micro-data under the

CES research associate program. Con"dentiality restrictions prevent the disclosure of any information which

would allow for the identi"cation of a speci"c plant or

"rm's activities. Sample statistics or statistical coe$-

cients, which do not reveal the identity of individual

establishments and "rms, can be released after review at

CES. The ability to access the plant level data, rather

than aggregate data signi"cantly enhances the information that can be obtained about microeconomic performance, particularly when examining the microeconomic

issue of e$ciency.

This paper analyzes a set of manufacturing plants in

the glass industry. The paper examines two major industry sub-segments, container glass and #at glass. These

sub-segments were chosen because they meet several

criteria: homogeneous production technology, energy

intensity, and data availability. Homogeneous production technology is very important in order for the

production function approach to provide reasonable

comparison among plants.1

Economic variables in our data set include:

f > * output (value of shipments),

f K * capital stock,

f  ̧ * labor (total man-hours with adjustment for nonproduction workers),

f E * consumption of electricity in kWh,

f F * cost of combustible fuels, and

f M * cost of materials (less energy).

All costs were de#ated using industry speci"c price

de#ators. The data for #at glass was a panel data from

1987}1995. For container glass a single year, 1995, was

used.2 Plant level electric prices are also derived from the

LRD. Since the plant level fossil fuel prices could not be

similarly derived, state level natural gas prices were

matched to the LRD based on plant location.

4. Results

Linear regression is used to estimate the in#uence of

the computed DEA production e$ciency and other variables on energy intensities (energy/output ratios). Electricity (kWh) and fossil fuel (de#ated expenditures) were

examined separately. The explanatory variables included

electricity and natural gas prices, the log of cumulative

output, capacity utilization, time trend (for #at glass) and

a dummy variable for electric furnaces (for container

glass).

Summary statistics are shown in Tables 1 and 2 for the

#at and container glass industries, respectively. The #at

glass segment includes 14 plants over nine years. The

container glass segment is a much larger component of

the glass industry, making the panel data less of an issue.

Even though a theoretical system of optimal factor

demands do not exist under conditions of ine$ciency

energy prices are still expected to send signals to decision

makers. The plant level price of electricity could be computed from the LRD, but not the fuel price. The statelevel industrial price for natural gas was used, since

nearly all fossil fuel used in glass plants is natural gas

The log of cumulative output of the plant is included to

represent plant speci"c learning by doing (LBD) regarding energy e$ciency. For process industries, the level of

utilization appears to strongly e!ect energy intensity

(Boyd et al., 1992, 1991). Typically, when output decreases energy use decreases less than proportionally

292 G.A. Boyd / Energy Policy 28 (2000) 289}296
Table 2

Summary statistics for 1995 data for container glass

Variable Number of observations Mean Standard deviation

Electric-output ratio 45 1.08 0.25

Fuel-output ratio 45 0.018 0.0088

Cumulative output (Million $) 45 0.561 0.221

Capital-output ratio 45 5.31 1.88

Electric price ($/kwh) 45 0.041 0.015

Natural gas price ($/Mbtu) 45 2.62 0.67

LP e$ciency measure 45 0.86 0.11

Table 3

Regression results for #at glass (t-ratios shown under the estimates)

Intercept Year Log cumulative

production

Capacity utilization Electric price Natural gas

price

Productivity Adjusted R2

Electric 4.07 0.08 !0.22 !0.18 !11.24 0.02 * 0.30

6.91 4.72 !5.03 !5.13 !4.69 0.78

4.58 0.07 !0.21 !0.08 !11.50 0.02 !0.97 0.42

8.40 4.29 !5.30 !2.14 !5.27 1.14 !5.08

Fuels 0.094 0.002 !0.005 !0.004 0.042 !0.004 0.32

5.536 3.539 !3.904 !3.588 0.604 !5.566

0.116 0.001 !0.005 0.001 0.031 !0.004 !0.042 0.58

8.581 3.032 !4.609 0.707 0.566 !6.615 !8.800

Table 4

Regression results for container glass (t-ratios shown under the estimates)

Intercept Electric furnace

dummy

Log cumulative

production

Capacity

utilization

Electric price Natural gas

price

Productivity Adjusted R2

Electric 3.44 0.83 !0.18 !0.01 !2.80 0.03 0.35

2.33 4.97 !1.69 !0.44 !1.21 0.59

4.08 0.85 !0.18 0.00 !4.33 0.05 !0.70 0.41

2.84 5.35 !1.84 0.25 !1.88 0.99 !2.25

Fules 0.009 !0.008 0.003 !0.001 0.040 !0.010 0.60

0.218 !1.866 1.081 !2.560 0.637 !6.869

0.029 !0.008 0.003 !0.001 !0.009 !0.009 !0.022 0.66

0.766 !1.850 1.085 !1.800 !0.150 !6.853 !2.728

hence intensities rise. To capture industry wide e!ects of

technology, a time trend is included in the #at glass

sector. The regressions were run "rst without the

measure of technical e$ciency and then with.

Table 3 shows the results for the #at glass industry.

Including production e$ciency in the regression improves the #at glass results. When production e$ciency

is included the magnitude of the utilization variable

drops, becoming insigni"cant in the fossil fuel equation.

Cumulative production, capacity utilization, energy

prices, and productivity all have the expected signs. Only

the cross-equation energy prices are not signi"cant.

Table 4 show that including productivity in the energy

intensity equation improves container glass regression

results in a similar fashion. The capacity utilization variable declines in size and become insigni"cant for the

electric equation. The &own price' coe$cient on electricity

increases in size and is &marginally' signi"cant. As before

all coe$cients have the expected sign, except for the

coe$cient on log cumulative production in the fuels

G.A. Boyd / Energy Policy 28 (2000) 289}296 293
Table 6

Price elasticities and learning ratio (evaluation at data mean)

Fuel Electric

Flat glass Electric price 0.06! !0.63"

N. gas price !0.43" 0.08!

Learning ratio 86%" 82%"

Container glass Electric price !0.02! !0.19"

N. gas price !1.11" 0.13!

Learning ratio 110%# 87%"

!not signi"cantly di!erent from 0.

"signi"cantly di!erent from 0 at 0.01 con"dence level.

#not signi"cantly di!erent from 1.0.

Table 5

Average and frontier energy intensity and productivity elasticity

Fuel Electric

Flat glass Average intensity 0.023 0.830

Frontier intensity 0.014 0.601

Elasticity !3.07! !1.61!

Container glass Average intensity 0.020 0.945

Frontier intensity 0.016 0.825

Elasticity !1.39" !0.85"

!signi"cantly di!erent from 1.0 at 0.01 con"dence level.

"not signi"cantly di!erent from 1.0.

equation. All coe$cients are signi"cant, except for the

cross price terms and log cumulative production in the

fuels equation.

Since the productivity coe$cients are signi"cant, there

is support for a link between intensity and productivity.

While the signs and signi"cance of the coe$cients are

easily interpreted, the magnitudes are not. One way to

understand the magnitude of the productivity coe$cient

is to compute the di!erence between the sample mean

energy intensity and the energy intensity for an e$cient

plant. Since, a linear regression predicts the sample mean

intensity when evaluated at the mean inputs, we subtract

one minus the sample mean productivity for each industry, times the appropriate productivity coe$cient, from

the sample mean intensity. This is shown in Table 5. The

energy intensity of &best practice' plants are substantially

lower than the average. The best practice energy intensity

ranges from 13 to 42% lower than the average.

This established that production e$ciency, as observed empirically, is not so randomly distributed in

production input space as to be statistically unrelated to

the level of energy intensity. The coe$cients are signi"-

cantly di!erent from zero, but we also wish to see

whether they depart from the proportional construction

of the productivity measure. Since energy is &just another

input' in the production process, it is true by construction

of the productivity measure that less productive plants

should be able to use less energy. Since the productivity

measure is proportional in all inputs, we would like to

know if the statistical link between energy and productivity is more or less than proportional.

To see this we recomputed the productivity coe$-

cients, interpreted as elasticities. The productivity coe$-

cients are evaluated at the frontier value of energy

intensity, since we wish to know how energy e$ciency

and productivity are related relative to best practice

plants. These elasticities are also shown in Table 5. Three

of the four elasticities are greater than one. However, the

container glass elasticities are not signi"cantly di!erent

from unity, while elasticities for #at glass are. The fuel

elasticity for #at glass is quite large, suggesting a 3%

increase in fuel intensity associated with each 1% decline

in productivity. This is particularly important, since fossil

fuel use is the primary energy used in #at glass, with

electricity being used for ancillary manufacturing functions. In both of the glass making sectors, the elasticity of

fuel use with respect to productivity is larger than the

elasticity of electricity use with respect to productivity.

Even though economic optimality is not imposed on

the entire estimation, the model estimates signi"cant

impacts of energy prices and learning by doing

Table 6 presents the price coe$cients interpreted as elasticities. The price variables are evaluated at the sample

means rather than the frontier, since we would like to see

294 G.A. Boyd / Energy Policy 28 (2000) 289}296
3Future study might include an equation for equipment choice, but

our available data would not support such an addition.

what types of signals the energy prices provide to the

glass plants in general. The own price elasticities for #at

glass seem quite reasonable. The cross price elasticities

are not signi"cant, indicating no substantial substitution.

The own price elasticities for container glass show a wide

range of variation; near zero for electric and above unity

for natural gas. These results need not be viewed as

unreasonable. The dummy variable for electric furnaces

may make it di$cult to estimate a price response in

a single equation. It may be that given the choice of

electric furnace (or not) there is little (additional) response to electric prices, but substantial #exibility in gas

use.3

The model also investigates whether there are plant

speci"c LBD that e!ects energy intensities. This is summarized by the learning ratio (see Table 6). The learning

ratio is de"ned as the percent of unit cost remaining when

cumulative production doubles. In this case, it is the

percent of energy consumption per unit of output. LBD

has a signi"cant e!ect on electric intensities and on fuel

intensities for #at glass. The learning ratio is not signi"-

cantly di!erent from 100% for fuel intensity in container

glass. The learning ratios are quite reasonable, albeit

higher than the `typicala 80% learning ratio found in the

review paper by (Dutton and Thomas, 1984). In a more

recent study, Bahk and Gort (1993) "nd higher learning

ratios, implying less LBD.

5. Conclusion

This paper proposes an approach to examine the link

between productivity and energy e$ciency, and tests this

link empirically using plant level micro data. Productivity is measuring using a frontier production function

approach to de"ne a benchmark of best practice. Linear

programming is used to compute this inter-plant

measure of productivity. Two energy intensive sub-sectors of the glass industry were chosen to provide speci"c

examples. The choice of a narrowly de"ned industry

sector allows one to assume that the production processes and opportunities are comparable between plants

in that sector. Since the assumption of production e$-

ciency or cost minimization is not imposed, optimal

factor demand systems do not exist. However, energy

prices are still expected to provide information about

energy intensity decisions, so a simple regression model is

used to hold energy prices, as well as capacity utilization

and LBD constant.

The regression models con"rm that di!erences between plants in productivity in#uence energy intensity.

All coe$cients that links productivity to energy e$ciency

are signi"cant. Since the productivity measure is constructed to be proportional in all inputs we want to know

if the elasticity of energy to productivity is greater than

unity. If it is then there is support for a stronger link

between energy intensity and productivity. A more that

proportional impact is estimated for both fuels in the #at

glass sector and for fossil fuel in container glass. In other

words, a 1% di!erence in overall productivity results in

more than a 1% di!erence in energy e$ciency, except for

electricity use in container glass. The results for #at glass

are signi"cantly greater than unity, but the results for

container glass are not. The results demonstrate that

productivity di!erences are important determinants of

energy e$ciency, in some cases, substantially so. It also

appears that the e!ects are industry speci"c.

Other economic variables, like energy prices and learning by doing also are found to be important and consistent with basic theory. Estimated price elasticities range

from !0.2 to !1.1, depending on the energy type and

sector. Learning ratios that were signi"cantly di!erent

from unity range from 82 to 87%. The coe$cient on

capacity utilization is not signi"cant when productivity is

entered in the model, suggesting that utilization is subsumed within the productivity measure.

This paper presents a method to empirically assess the

claims of widespread linkage between energy e$ciency

and productivity. The results show that best practice

plants are systematically more energy e$cient, holding

prices and learning constant. In every case, productivity

di!erences between plants account for at least a proportional di!erence in energy intensity. In the #at glass

sector, the paper supports the notion that higher energy

intensity may well be a indicator of lower plant productivity, since the estimated elasticities are signi"cantly

greater than unity.

Whether these results are at odds with the claims that

production bene"ts are greater than energy bene"ts depends on the interpretation of bene"ts. There is support

that energy intensity and productivity have at least a proportional link. Since energy costs are a small component

of total costs, any productivity bene"t that is associated

with an energy intensity improvement is likely to have

a large non-energy cost component. Therefore, a search

for energy e$ciency that reveals a productivity improvement will have much larger &non-energy' bene"ts.

The results lend statistical support to the notion that

there is a linkage, in one case a very strong one, between

energy intensity and productivity. The notion that there

are non-energy bene"ts, which reduce the overall costs of

policies to improve energy intensity, is also supported by

this work. For example, the most e!ective greenhouse gas

reduction policies would be those that best exploit this

linkage. R & D Policy that promotes productivity enhancing technology in the energy using sectors is consistent with these empirical results. The widespread policy

G.A. Boyd / Energy Policy 28 (2000) 289}296 295
implications for these speci"c empirical results depend

upon whether they can be generalized to other industry

sectors, or replicated.

Acknowledgements

I would like to thank George Tolley of the University

of Chicago Economics Department for his suggestions

and technical support in this collaborative research project. I would also like to thank Joe Roop, Skip Laitner,

Stephan DeCanio, Richard Howard and an anonymous

referee for their comments on an earlier draft of this

paper. The usual disclaimer applies.

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