Keywords Assigned input model · Pollution abatement · Productivity growth JEL Classification D24 · Q52

C. A. Pasurka Jr. ( B )

Office of Policy, Economics and Innovation, U.S. Environmental Protection Agency (1809T), 1200 Pennsylvania Ave., NW, Washington, DC 20460, USA e-mail: PASURKA.CARL@EPA.GOV

12 D. V. Aiken et al.

1 Introduction

With the passage of environmental legislation, came concerns about the international com- petitiveness effects of implementing regulations resulting from legislation ( Jaffe et al. 1995 ; Pasurka 2008 ). One concern is increased production costs associated with pollution abatement might reduce the competitiveness of industries in countries that implement environmental regulations. This perspective argues that the unilateral imposition of environmental regula- tions places firms in that nation at a competitive disadvantage relative to firms in other nations.

A related concern is whether these more stringent pollution control measures might push an industry out of developed nations (the “industrial flight” hypothesis) and that developing nations will compete with developed nations by minimizing mandated pollution abatement activities (the “pollution haven” hypothesis).

Pollution abatement requires firms to employ additional inputs to maintain the same level of good output production and therefore, by definition, productivity must decline. If the output of abatement activities (i.e., reduced levels of the undesirable byproducts of the intended output of production activities) is valued, then the measure of productivity must be adjusted. While it can be argued that the output of abatement activities should be incorporated into all productivity analyses, their effect on the traditional measure of productivity, in which we ignore the output of abatement activities, is also worthy of investigation. In contrast to most previous studies that focused on industries in one country, our study measures the associa- tion between pollution abatement and productivity growth for manufacturing industries in

Germany, Japan, the Netherlands, and the United States. 1 These calculations provide infor- mation about variation in the relative burden of pollution abatement across countries and how the burden is distributed across industries within each country.

For this paper we do not have data on undesirable outputs, but we have data on pollution abatement capital as well as capital used for the production of good (i.e., marketed) outputs. Thus, we will follow Pyrwes ( 1984 ) and compare the regulated outcome, i.e., when only “good” capital is used for production of the good output to the unregulated outcome, i.e., when both “good” and abatement capital are used to produce the good output. Because this technique assumes we can identify which inputs are assigned to good output production and which inputs are assigned to abatement activities, we refer to this as the “assigned input”

model. The remainder of this study is organized as follows. Section 2 surveys previous studies of the association between pollution abatement and productivity, while Sect. 3 describes the assigned input model we use to investigate the association between abatement activities and productivity. Section 4 discusses data sources and presents the results. We find that pollution abatement capital expenditures are not associated with a substantial decline in productivity. Finally, Sect. 5 summarizes our study and outlines conclusions that can be drawn from its results.

2 Previous Studies

We are aware of only three cross-country studies of the productivity effects of pollution abatement activities. The U.S. Congressional Budget Office ( 1985 ) investigated the effect of abatement activities on the manufacturing sectors of Germany, Japan, and the United States,

1 In fact, many researchers are unaware of the existence of pollution abatement cost data for countries other than the United States. For example, van Soest et al. ( 2006 , p. 1151) assert “Empirical tests of the relationship

between international competitiveness and the severity of environmental regulations are hampered by the lack of pollution abatement cost data for non-U.S. countries.”

Pollution Abatement and Productivity Growth 13

and found pollution abatement was associated with reduced output. Conrad and Morrison ( 1989 ) studied the association between pollution abatement and productivity growth of the manufacturing sectors of Canada, Germany, and the United States. Finally, Valentini ( 2003 ) undertook an econometric investigation of the effect of abatement capital expenditures on TFP growth of the food, textile, paper, basic metals, and transport equipment industries in Germany and the United States for 1971–1991.

The impact of pollution abatement on traditional measures of productivity (i.e., those models focusing solely on good output production) results from a reallocation of inputs from good output production to abatement activities. In a given technology and input vector, pollu- tion abatement is associated with reduced good output production, which is the opportunity cost of abatement activities. Färe et al. ( 2007 ) calculated the effect of pollution abatement on productivity growth by modeling the joint production of good and bad (i.e., the undesirable byproducts of good output production) outputs. This allowed them to calculate the associ- ation between abatement activities and productivity without resorting to survey estimates of inputs assigned to abatement activities. The model assumes inputs assigned to pollution abatement “crowd-out” inputs assigned to good output production on a one-for-one basis. Hence, reduced good output production associated with the reallocation of inputs from pro- ducing the good output to abatement activities represents the opportunity cost of abatement activities.

In the absence of information about bad outputs, most studies of the effect of pollution abatement on productivity rely on survey estimates of input costs associated with abatement activities. Studies employing an opportunity cost model adjust inputs by the quantity of inputs assigned to abatement activities and recalculate productivity. Denison ( 1978 ) initial use of the opportunity cost model was subsequently modified by Pyrwes ( 1984 ) and Conrad and Wastl ( 1995 ).

In order to assess the association between pollution abatement and labor productivity in the U.S. chemical industry, Pyrwes ( 1984 ) estimated a CES production function using a pooled time series with observations from 1971–1976 for 4-digit standard industrial classi- fication (SIC) chemical industries in which he assigned capital stock to either good output production or abatement activities. He calculated the decline in labor productivity associated with pollution abatement via a two-step process. First, he used the fitted value of good output production to estimate labor productivity with non-abatement capital:

ˆ F (K G , L , E , M)/L

where K G is the capital stock assigned to good output production, L is labor, E is energy, and M the material inputs. He then calculated labor productivity using the estimated production function to calculate fitted values of good output when the capital stock assigned to abatement activities was available for good output production:

F (K G +K A , L , E , M)/L

where K A is the capital stock assigned to pollution abatement. The difference between fitted good output production by the regulated and unregulated production functions represents the opportunity cost of abatement activities.

Using data on manufacturing industries in Germany from 1975 to 1991, Conrad and Wastl ( 1995 ) estimated a cost function to assess the association between pollution and total fac- tor productivity (TFP) by comparing cost diminution with and without capital expenditures and material input costs assigned to pollution abatement. When they subtracted the costs associated with pollution abatement from the rate of cost diminution, they found higher TFP growth rates in all industries.

14 D. V. Aiken et al.

Both Pyrwes ( 1984 ) and Conrad and Wastl ( 1995 ) found pollution abatement reduces good output production. 2 Because Pyrwes ( 1984 ) and Conrad and Wastl ( 1995 ) calculate the changes in good output production when inputs assigned to pollution abatement are reas- signed to good output production, their opportunity cost calculations are comparable to our perspective. In the next section, we specify our assigned input methodology.

3 Productivity

To estimate the productivity for the four countries, we apply a Malmquist productivity index. Here, we follow Färe et al. ( 1994 ) and use the geometric mean formulation of the output

oriented index. 3 It is defined as

where x τ ∈ℜ N denotes inputs and y τ ∈ℜ M + + outputs τ = t, t + 1. Shephard ( 1970 ) output oriented distance function is defined on the technology T τ as:

(2) In the case of a single output, this function becomes

D τ (x τ ,y τ )= min {θ : (x τ ,y τ o /θ ) ∈ T τ )

(3) where F (x) is a production function. Thus, for this case we may rewrite the index as

(4) Note that if the production function can be written with a Solow residual

F t+ 1 (x t+ 1 )

F t (x t+ 1 )

(5) then the Malmquist index takes the form

(6) where the residuals A(t) and A(t + 1) capture the state of the technology in periods t and

t+ F (x 1 )

A(t )

t+ 1. To estimate the index we use an activity analysis or DEA approach. Assume we have k=

1, . . ., K observations of inputs and outputs for t = 1, . . ., T periods. The τ period technology for observation k ′ is given by:

2 Numerous studies using U.S. data have found pollution abatement has an adverse effect on productivity. Among recent studies, both Gray and Shadbegian ( 2003 ), which used plant-level data for pulp and paper plants,

and Millimet and Osang ( 2003 ), which used 3-digit SIC manufacturing data, found pollution abatement was associated with declines in productivity. In addition, Shadbegian and Gray ( 2006 ) found pollution abatement is associated with lower levels of technical efficiency.

3 This index was introduced by Caves et al. ( 1982 ).

Pollution Abatement and Productivity Growth 15

F τ (x τ k ′ )= max

k= 1

(7) s.t.

In this paper, we choose the “good’ production model as the reference technology, i.e., we use x τG kn on the left hand side above. We also apply a meta-production function. “A meta- production function is defined as a common underlying production function that can be used to represent the input–output relationship of a given industry, e.g., agriculture in all coun- tries…” ( Lau and Yotopoulos 1989 , p. 242). Therefore, the production technology for each industry consists of observations from all countries in our sample. It follows that observation k ′ represents information for a specific year for a country in our sample.

Hence, the models we will estimate are of the form:

(8) s.t.

where x τG represents inputs assigned to good output production. The unregulated production function is specified as:

where x τA represents inputs that were assigned to pollution abatement by the regulated tech- nology.

The assigned input model specifies different input constraints when modeling the regulated (Eq. 8 ) and unregulated (Eq. 9 ) production functions. First, we assign inputs to either good output production or abatement activities. The regulated production function (Eq. 8 ) assumes only inputs associated with good output production are used to produce good outputs, while the unregulated production function (Eq. 9 ) assumes inputs assigned to abatement activities are also available for good output production. Hence, the assigned input model assumes cap- ital assigned to abatement activities results in an identical reduction in capital assigned to good output production (i.e., complete crowding out). The difference in good output produc- tion associated with the regulated and unregulated production technologies represents the opportunity cost of abatement activities.

The Malmquist productivity index (Eq. 4 ) is our measure of productivity change. For the regulated technology, production change (PRODR) is calculated by substituting the pro- duction function specified in Eq. 8 into Eq. 4 , while productivity change for the unregulated technology (PRODUR) is calculated by substituting the production function specified in Eq. 9 into Eq. 4 . If x t =x t+ 1 and y t =y t+ 1 (i.e., no changes in inputs or output), there is no change in productivity for the regulated technology, i.e., PRODR(•) = 1. Improved productivity is signaled by PRODR(•) > 1, while declining productivity is indicated by PRODR(•) < 1. Simi- lar interpretations exist for PRODUR. Färe et al. ( 1994 ) demonstrated PRODR and PRODUR

16 D. V. Aiken et al.

can be decomposed into technical change (i.e., a shift in the frontier) and changes in technical efficiency (i.e., a change in distance between an observation and the frontier). Because the same processes are used to construct the regulated and unregulated frontiers by the assigned input model, differences between PRODR and PRODUR are due solely to changes in the share of inputs assigned to pollution abatement by the regulated technology from one period to the next. As a result, we do not decompose PRODR and PRODUR into technical change and changes in technical efficiency.

The ratio PRODUR to PRODR yields the pollution abatement index (PAI), which measures the association between pollution abatement and productivity change:

PRODR While pollution abatement typically reduces good output production, its effect on produc-

tivity depends on relative growth rates of the unregulated and regulated production frontiers. If good output production associated with the unregulated and regulated frontiers changes by the same percentage, PAI equals unity and changes in abatement activity have no effect on PRODR. If good output production associated with the unregulated frontier increases by

a larger (smaller) percentage than its production associated with the regulated frontier, PAI exceeds (is less than) unity. Therefore, PAI > 1 indicates pollution abatement is a growing share of total capital spending, so that PRODR is growing more slowly than PRODUR, while PAI < 1 indicates pollution abatement is a declining share of total capital spending and PRODR is growing more rapidly than PRODUR. Hence, PAI > 1 indicates pollution abatement is associated with reduced productivity growth, while PAI < 1 indicates pollution abatement is associated with increased productivity growth.

With the assigned input model, good output production by the unregulated technology equals or exceeds good output production by the regulated technology. Therefore, care must

be exercised when interpreting PAI values. For example, the decline in the share of capital expenditures assigned to pollution abatement, which is reflected by PAI < 1, can result from either a reduction in regulatory stringency or improved technology for reducing bad output production. When data on bad output production are unavailable, it is not possible to make definitive statements about the source of the decline in the share of capital expenditures assigned to pollution abatement.

After regulations are implemented, PRODR is the observed change in productivity, while

PRODUR is unobserved. Equation 10 reveals that PAI > 1 can be associated with PRODR(•) >

1 or PRODR(•) < 1, while PAI < 1 can be associated with PRODR(•) > 1 or PRODR(•) < 1. Therefore, no definitive statement can be made about the association between PAI and the observed rate of productivity growth—PRODR. In the next section, we discuss the data and results generated by our assigned input model.

4 Data and Results

Implementing the model presented in the previous section, requires information on capital stock assigned to pollution abatement, capital stock assigned to good output production, employment, and good output production for the manufacturing sector (ISIC 15–36) in each country. In order to observe variations across industries, we also assemble data for the food and tobacco (ISIC 15–16), textiles and leather (ISIC 17–19), wood (ISIC 20), paper products (ISIC 21–22), chemical, rubber, and plastics (ISIC 23–25), non-metallic mineral products (ISIC 26), basic and fabricated metals (ISIC 27–28), and machinery and equipment (ISIC

Pollution Abatement and Productivity Growth 17

29–35) industries. 4 Developing estimates of capital stock assigned to pollution abatement and good output production first requires data on the share of capital expenditures assigned to air, water, and solid waste abatement activities for manufacturing and its associated industries

in each country. 5 We will now outline sources of these data. 6 The Federal Republic of Germany ( 1978–2003 , 1980,2000,2004–2005 ) started collect- ing data on abatement capital expenditures in 1975. From 1996 until 2002, its survey only collected estimates of expenditures for end-of-pipe abatement activities. Starting in 2002, the survey once again solicited estimates of expenditures associated with both end-of-pipe and integrated abatement technologies. The United States (U.S. Census Bureau, 1978–1996 ) collected abatement capital expenditure data between 1973 and 1994, excluding 1987 whose values we interpolate. In addition, is necessary to interpolate pollution abatement capital expenditures for tobacco products (ISIC 16) for 1978–1981 and 1985, leather and leather products (ISIC 19) for 1978–1981, and miscellaneous manufacturing (ISIC 36) for 1978– 1981 and 1985.

Japan’s Ministry of International Trade and Industry (MITI) ( 1977–2001 ) collected abate- ment investment expenditures data from large firms for 1965 to 2001, while the Ministry of Economy, Trade, and Industry ( 2002–2004 ) collected these data starting in 2002. 7 Because expenditures by media were not reported between 1980 and 1984 and in 1991, we interpolate the share of capital expenditures assigned to pollution abatement for these years. In addition, it is necessary to interpolate petroleum industry (part of ISIC 23–25) pollution abatement capital expenditures for 1991–1992.

While the Netherlands’ ( 1982–present ) first survey of abatement capital expenditures collected data for 1979, it also reported estimates of abatement capital expenditures for 1975–1978. Through 1997, it is only necessary to interpolate 1987–1988 expenditures for the chemicals (ISIC 24) and basic metals (ISIC 27) industries. After 1997, confidentiality considerations result in more data being withheld. The interpolations required to estimate the missing observations are discussed in the Supplementary material.

In addition to the aforementioned data problems, calculating the relative intensity of abatement activities is further complicated by variations in the composition of production activities within an industry across countries. This is especially true for Japan where no abate- ment expenditure data are collected for food and tobacco (ISIC 15–16), leather (ISIC 19), wood (ISIC 20), printing and publishing (ISIC 22), rubber and plastics (ISIC 25), fabricated metal products (ISIC 28), and several categories of machinery. Our aggregation procedure assumes the missing industries exhibit abatement intensities that are identical to the industries

4 Industries are defined in terms of international standard industrial classification (ISIC), Rev. 3 codes. Manufacturing includes Furniture; Manufacturing, N.E.C. (ISIC 36), but excludes Recycling (ISIC 37). The

Supplementary material, which is available from the corresponding author upon request, contains a detailed discussion of the data.

5 When calculating the share of capital expenditures assigned to abatement activities, we use values for total (abatement plus non-abatement) capital expenditures that appear in or are used by reports in which the abate-

ment data are published. However, we were unable to locate industry-level total capital expenditure data for the Netherlands that allowed us to replicate the aggregate shares of capital expenditure assigned to pollution abatement in 2000–2004 as reported by Statistics Netherlands. Nevertheless, the Statistics Netherlands data we use to derive 2000–2004 industry-level shares yield values for the manufacturing sector that are relatively close to published values.

6 While the surveys for Germany and the United States would permit more disaggregated industries, our industry selection is limited by the aggregated industry data for Japan and the Netherlands. In addition, the

perpetual inventory method precludes developing a pollution abatement capital stock time series for other countries.

7 Uno ( 1987 , 1995 ) discusses pollution abatement expenditures by industries in Japan.

18 D. V. Aiken et al.

with which they are aggregated. This will bias our results according to whether the actual abatement intensity of these industries is lower or higher than the industries with which they are aggregated. It follows that these biases may also affect our estimates for the entire manufacturing sector.

Next, we establish concordances between the national industrial classification systems used to report abatement capital expenditures and the ISIC (Rev. 3) system. 8 Table 1 reports the percent of capital expenditures assigned to abatement activities for selected years. Aside from sampling error, fluctuations in the share of capital expenditures assigned to pollution abatement can be explained by whether new regulations are being implemented.

Following Harrigan ( 1999 ), the OECD STructural ANalysis (STAN) Database for Indus- trial Analysis , which provides industry-level data for Organization for Economic Cooperation

and Development (OECD) countries from 1970 to the present, is our primary source of capi- tal expenditure data. 9 The product of the share of capital expenditures assigned to abatement activities and gross private fixed capital formation in current prices (GFCF) yields our esti- mate of capital expenditures assigned to abatement activities. Subtracting abatement capital expenditures from GFCF yields capital expenditures assigned to good output production.

Because the STAN Database does not provide capital stock estimates, we adopt the per- petual inventory method employed by Harrigan ( 1999 ) and use GFCF and the index of gross capital fixed formation (GFCFK) to derive real capital expenditures, i. Assuming capital has

a useful life of 10 years (T = 10) and an annual depreciation rate of 15% (δ = 0.15), allows us to derive the real capital stock for industry j in country c for year t:

k cj t =

( 1 − δ) n− 1 i cj,t −n

n= 1

In this distributed lag specification, the capital stock assigned to good output production and pollution abatement in period t consists of capital expenditures from period t − 1 to period t − 10. 10

The Groningen Growth and Development Centre ( 2006 ) is our source for total annual hours worked, which is our measure of labor. Finally, we use value added in current prices

8 The industry concordances developed by Bouman ( 1998 , p. 22) are the starting point for our concordances for Germany, the Netherlands, and the United States. We developed the concordance for industries in Japan. All

concordances are discussed in the Supplementary material. All GAMS programs, data, and the Supplementary material are available from the corresponding author upon request.

9 We employ data downloaded from the OECD STAN database in October 2004 and June 2005. Data in the STAN database are classified with ISIC, Rev. 3 codes. The Supplementary material explains how we derived

values the non-metallic minerals (ISIC 26) industry in the Netherlands. Because the downloaded OECD data contained no capital expenditure data for Japan, we used the OECD STAN Database for Industrial Analysis, 1974–1993 (OECD 1995) and the OECD STAN Database for Industrial Analysis, 1978–1997 (OECD 1999) to develop a consistent set of capital expenditure estimates for 1973–1994. We then use changes in 1994– 2003 capital expenditure values in the Japan Statistical Yearbook ( Japan, 1997–2005 ), to extrapolate the 1994 OECD values to 1995–2002.

10 If we wished to start our pollution abatement capital stock series in 1975, the perpetual inventory method requires we assume no pollution abatement capital exists before 1975. However, we have evidence that coun-

tries in our sample undertook pollution abatement capital expenditures prior to 1975. As a result, the time series of pollution abatement capital expenditures must be long enough to guarantee the retirement of all pollution abatement capital existing prior to 1975. Because we assume the service life of capital is 10 years, 1975–1984 investment flows are used to derive the pollution abatement capital stock series in which all pollu- tion abatement capital stock existing prior to 1975 to have been discarded. Therefore, we limit our analysis to those years (1985 and later) for which the pollution abatement capital stock has been derived in a consistent manner across countries.

Pollution Abatement and Productivity Growth 19

Table 1 Percent of capital expenditures assigned to air, water, and solid waste pollution abatement

1990 1994 1998 2002 Manufacturing (ISIC 15–36)

Germany 5.6 3.5 4.0 4.9 5.2 2.7 2.5 Japan

17.0 3.8 2.0 1.2 4.1 3.1 4.6 Netherlands

3.2 3.4 2.4 6.5 4.3 3.8 3.7 United States

9.8 5.0 3.4 5.9 6.7 Food and tobacco (ISIC 15–16) Germany

3.4 2.3 2.9 2.9 3.5 2.4 1.5 Japan Netherlands

3.3 5.8 1.7 6.9 3.1 3.8 3.1 United States

5.4 3.5 2.2 2.8 2.6 Textiles and leather (ISIC 17–19) Germany

1.9 1.7 1.7 2.5 3.5 1.7 1.4 Japan

19.2 2.2 5.2 3.7 3.4 3.1 7.7 Netherlands

1.1 0.3 0.8 1.2 9.3 0.0 3.4 United States

3.4 3.2 1.0 1.7 1.5 Wood (ISIC 20) Germany

3.3 5.3 3.2 5.4 7.0 2.7 2.1 Japan Netherlands

0.3 0.2 3.2 1.3 2.8 3.5 2.1 United States

5.9 4.0 2.1 5.6 5.1 Paper products (ISIC 21–22) Germany

3.4 3.4 3.1 4.9 5.5 3.1 1.7 Japan

21.8 3.9 3.2 6.0 8.0 8.5 6.6 Netherlands

1.2 0.2 1.4 1.7 0.7 1.5 1.7 United States

15.9 4.4 3.4 6.9 5.2 Chemicals, rubber, and plastics (ISIC 23–25) Germany

12.1 7.1 6.5 11.4 11.2 4.7 6.0 Japan

23.8 3.9 3.0 3.6 5.3 5.3 5.5 Netherlands

5.7 5.2 4.1 12.9 8.3 6.4 6.7 United States

13.8 9.2 7.0 12.1 16.9 Non-metallic mineral products (ISIC 26) Germany

4.3 5.6 5.3 5.4 4.8 4.3 3.0 Japan

12.7 3.0 3.3 3.4 4.4 1.5 26.0 Netherlands

1.1 1.6 1.5 1.2 7.1 4.7 3.3 United States

11.0 5.0 2.2 4.7 7.4 Basic and fabricated metals (ISIC 27–28) Germany

7.5 5.2 7.7 6.3 6.2 3.6 3.1 Japan

16.7 4.7 3.4 3.4 5.3 3.8 8.0 Netherlands

3.9 4.9 3.6 5.6 3.9 2.3 2.2 United States

14.6 8.7 4.1 6.4 4.6 Machinery and equipment (ISIC 29–35) Germany

2.2 1.4 2.1 2.2 2.3 1.3 1.3 Japan

4.6 0.9 0.8 0.6 3.2 1.9 3.5 Netherlands

0.7 0.2 0.2 1.5 0.7 2.3 0.9 United States

(VALU) and the index of value added (VALUK) from the OECD STAN Database to derive real value added.

Constructing four-country meta-frontiers requires converting real value-added and real gross fixed capital formation expenditures into a single monetary unit—U.S. dollars. We use unit value ratios (UVR), developed by the Groningen Growth and Development Centre ( GGDC 1997 ) and Inklaar et al. ( 2003 ), to convert industry real value added into U.S. dol- lars. The 1997 benchmark UVRs are available for ISIC (Rev. 3) manufacturing industries.

20 D. V. Aiken et al.

Table 2 Summary statistics: Average annual percent growth of inputs and output (1985–2002)

ISIC

Value

Capital stock

Capital stock Total hours

(total) worked Germany

(Rev. 3)

added

(good output )

0.86 1.73 1.71 0.15 Food and tobacco

Manufacturing

0.49 0.50 0.50 −0.40 Textiles and leather

20 1.97 1.87 1.99 −0.63 Paper products

0.03 3.77 3.83 −0.09 Chem., rubber, plastics

2.68 2.34 2.25 −0.94 Non-metallic minerals

26 1.05 0.83 0.85 −1.06 Basic & fabricated metals

1.56 1.43 1.33 −0.68 Machinery & equipment

0.77 2.02 2.05 −0.84 Japan Manufacturing

2.07 9.61 8.60 −1.68 Food and tobacco

Textiles and leather

Paper products

0.65 11.38 10.14 −0.68 Chem., rubber, plastics

2.25 10.82 9.61 −1.19 Non-metallic minerals

8.00 7.32 −2.17 Basic & fabricated metals

7.34 6.41 −1.60 Machinery & equipment

6.48 8.66 8.58 −1.45 Netherlands Manufacturing

2.49 3.40 3.43 −0.56 Food and tobacco

2.38 1.81 1.69 −1.04 Textiles and leather

20 3.50 4.29 4.49 −0.35 Paper products

3.74 5.48 5.40 −0.67 Chem., rubber, plastics

2.81 3.11 3.27 −0.55 Non-metallic minerals

26 2.09 0.68 0.90 −0.23 Basic & fabricated metals

1.68 3.73 3.52 −0.06 Machinery & equipment

2.87 4.97 5.03 −0.49 United States a Manufacturing

2.61 3.64 3.52 −0.39 Food and tobacco

3.49 3.16 0.27 Textiles and leather

−1.82 1.41 Paper products

0.45 6.79 6.54 0.55 Chem., rubber, plastics

4.01 4.08 3.95 0.76 Non-metallic minerals

−0.64 −0.29 Basic & fabricated metals

−0.38 −0.66 Machinery & equipment

3.61 4.66 4.68 −1.19 a United States annual changes are for 1985–1994

Value added for each industry in domestic currencies (i.e., Euros and yen) is multiplied by its respective 1997 UVR. Like Harrigan ( 1999 ), the Penn World Table (PWT) Version 6.2 (see Heston et al. 2006 ) is our source of purchasing power parity data for investment expenditures. Therefore, we convert investment expenditures to U.S. dollars using a single PPP for each country for each year.

Table 2 presents summary statistics of growth rates of value added, employment, and cap- ital stock assigned to good output production and abatement activities. When deriving value added and capital stock estimates for a meta-frontier, these growth rates can differ with their values for a single-country frontier.

Pollution Abatement and Productivity Growth 21

We calculate our meta-frontiers using a windows technology. 11 For example, the produc- tion technology of period t + 1 consists of observations from periods t + 1, t, and t − 1, while the production technology of period t consists of observations from periods t, t − 1, and t − 2. For each 2-year pair, four LP problems are solved for both the regulated and unregulated technologies. Because we use “windows” to model the production technology, our sample generates results for 2-year pairs starting in 1986–1987 and continuing through 1992–1993 for the United States, and 2000–2001 for Germany, Japan, and the Netherlands.

Table 3 presents the geometric means of annual changes in PRODUR, PRODR, and PAI for the manufacturing sector and its associated industries. 12 While the manufacturing sector of United States with a PAI of 1.0011 is most adversely affected by pollution abatement, regulated productivity growth in the manufacturing sector of Germany is higher than unreg- ulated productivity growth (PAI = 0.9976). In addition, it can be seen that there is substantial variation across industries. For the food industry (ISIC 15–16), the PAI ranges from 0.9998 for Germany to 1.0045 for the Netherlands. For the textile industry (ISIC 17–19), the range of the PAI is from 1.0000 for Germany to 1.0037 for the Netherlands. For the wood industry (ISIC 20), PAI ranges from 0.9991 for the United States to 1.0037 for Germany. For the paper industry (ISIC 21–22), the range of PAI is from 1.0000 for Germany to 1.0085 for Japan. For the chemical industry (ISIC 23–25), the PAI ranges from 0.9989 for the Netherlands to 1.0016 for Japan. For the non-metals mineral industry (ISIC 26), the range of PAI is from 0.9984 for the United States to 1.0044 for Japan. For the basic and fabricated metals industry (ISIC 27–28), the PAI ranges from 0.9994 for the Netherlands to 1.0007 for the United States. For the machinery and equipment industry (ISIC 29–35), the range of PAI is from 0.9997 for the Netherlands to 1.0009 for Japan. Overall, the largest decline in productivity change associated with pollution abatement involves the paper industry of Japan, while the largest increase in productivity associated with pollution abatement involves the non-metallic min- erals industry in the United States. While it is difficult to make causality statements about observed changes in productivity, the PAI values indicate assigning capital expenditures to pollution abatement did not have a substantial affect on productivity growth. In addition, our results reveal the importance of assessing the effect of pollution abatement using industry instead of economy-wide data on pollution abatement costs. For example, during the 1987– 2001 period Germany has a low PAI for the entire manufacturing sector, while it has a high PAI for the wood (ISIC 20) industry. Finally, out results indicate substantial variation in productivity effects across time periods.

For industries where PRODR and PAI assume values greater than unity (e.g., the non- metallic minerals industry in Germany), increased rates of productivity growth occurred simultaneously with increased levels of pollution abatement. In addition, industries where PRODR and PAI are less than unity (e.g., the chemical industry of the United States) simulta- neously experienced decreased rates of productivity growth with decreased levels of pollution abatement. For industries where PRODR exceeds unity while PAI is less than unity (e.g., the chemical industry in the Netherlands), increased productivity growth is associated with decreased levels of pollution abatement. Finally, PRODR less than unity and PAI greater than unity (e.g., the paper industry in Japan), signifies a case where decreased productivity growth is associated with increased levels of pollution abatement.

11 Shestalova ( 2003 ) compared changes in productivity and efficiency calculated using DEA with contempo- raneous and sequential frontiers, while Asmild et al. ( 2004 ) investigated the consequences of using ‘windows’

instead of contemporaneous frontiers. 12 Subtracting unity from the values reported in Table 3 and multiplying by 100 provides the average annual

percentage increase or decrease.

22 D. V. Aiken et al.

Table 3 Pollution abatement index, 1987–2001 (geometric means of 2-year pair values)

PRODR PAI Germany

ISIC (Rev. 3)

PRODUR

Manufacturing

1.0156 0.9976 Food and tobacco

1.0127 0.9998 Textiles and leather

1.0209 1.0037 Paper products

0.9993 1.0000 Chem., rubber, plastics

1.0191 1.0000 Non-metallic minerals

1.0157 1.0036 Basic & fabricated metals

1.0261 1.0000 Machinery & equipment

1.0175 0.9998 Japan Manufacturing

0.9852 1.0003 Food and tobacco

Textiles and leather

0.9827 1.0085 Chem., rubber, plastics

Paper products

1.0230 1.0016 Non-metallic minerals

0.9565 1.0044 Basic & fabricated metals

1.0181 1.0002 Machinery & equipment

1.0576 1.0009 Netherlands Manufacturing

1.0078 1.0000 Food and tobacco

1.0298 1.0045 Textiles and leather

1.0200 1.0011 Paper products

1.0232 1.0008 Chem., rubber, plastics

1.0381 0.9989 Non-metallic minerals

1.0171 1.0043 Basic & fabricated metals

1.0222 0.9994 Machinery & equipment

1.0023 0.9997 United States a Manufacturing

1.0010 1.0011 Food and tobacco

0.9896 1.0014 Textiles and leather

0.9784 0.9991 Paper products

0.9909 1.0010 Chem., rubber, plastics

0.9962 0.9998 Non-metallic minerals

1.0400 0.9984 Basic & fabricated metals

1.0142 1.0007 Machinery & equipment

1.0152 1.0004 a Results for United States are for 1987–1993

In order to view variations in annual changes that are obscured by the averages reported

in Table 3 , we plot PRODUR, PRODR, and PAI for the manufacturing sectors of Germany, Japan, the Netherlands, and the United States in Figs. 1 , 2 , 3 and 4 . Viewing these figures allows us to make two observations. Only the PAI of Germany shows substantial variation on

a year-to-year basis. Hence, only in Germany is there a notable divergence between PRODUR and PRODR. Manufacturing PAI in Germany ranges from 0.9590 in 1994–1995 to 1.0146 in 1988–1989. For Japan, the range of PAI is from 0.9965 in 1999–2000 to 1.0047 in 1986– 1987. For the Netherlands, PAI ranges from 0.9970 in 1997–1998 to 1.0044 in 1999–2000. For the United States, the range of PAI is from 0.9976 in 1989–1990 to 1.0035 in 1991–1992.

In order to provide a comparison with the USA, we calculate the PAI of Germany, Japan, and the Netherlands for the same 1987–1993 period as the USA. We find the 1987–1993 PAI

Pollution Abatement and Productivity Growth 23

Fig. 1 Trends in PRODUR, PRODR, and PAI for manufacturing in Germany (1987–2001)

Fig. 2 Trends in PRODUR, PRODR, and PAI for manufacturing in Japan (1987–2001)

of the manufacturing sectors of Germany (1.0081) and Japan (1.0021) are much higher than those reported in Table 3 for 1987–2001. As a result, only the PAI of the Nether- lands manufacturing (1.0008) is less than the PAI of USA manufacturing during 1987– 1993. Similar changes are also observed when comparing the PAI of individual industries for 1987–1993 relative to 1987–2001. While the PAI of the wood (ISIC 20) industry in Germany decreases from 1.0037 to 1.0019, the higher manufacturing PAI for Germany from 1987–1993 is driven by increased PAI of non-metallic mineral products (ISIC 26) from 1.0036 to 1.0071 and machinery and equipment (ISIC 29–35) from 0.9998 to 1.0028. The higher manufacturing PAI of Japan is driven by the increase in the PAI of chemicals,

24 D. V. Aiken et al.

Fig. 3 Trends in PRODUR, PRODR, and PAI for manufacturing in the Netherlands (1987–2001)

Fig. 4 Trends in PRODUR, PRODR, and PAI for manufacturing in the United States (1987–1993)

rubber, and plastics (ISIC 23–25) from 1.0016 to 1.0033. Even for the Netherlands, substan- tial changes emerge for individual manufacturing industries when we focus on the 1987–1993 period relative to 1987–2001. For example, the PAI of the textiles and leather (ISIC 17–19) and non-metallic mineral products (ISIC 26) industries decrease from 1.0037 to 1.0001 and from 1.0043 to 1.0008, respectively. On the other hand, the PAI of chemicals, rubber, and plas- tics (ISIC 23–25) increases from 0.9989 to 1.0088, while basic and fabricated metals (ISIC 27–28) increases from 0.9994 to 1.0023. Hence, limiting our analysis to years for which we have information for all four countries leads us to conclude that pollution abatement did not adversely affect the manufacturing sector of the USA relative to Germany, Japan, and the Netherlands.

Pollution Abatement and Productivity Growth 25

5 Conclusions

We investigated the association between pollution abatement capital expenditures and pro- ductivity growth for manufacturing industries in Germany, Japan, the Netherlands, and the United States from the 1987 through 2001. We believe this study represents the first appli- cation of pollution abatement capital expenditure data from these four countries being used in the same study.

Because releasing capital from pollution abatement results in the unregulated technology producing more of the good output than the regulated technology, the traditional view is that pollution abatement is associated with reduced rates of productivity growth. In our study, PAI measures productivity changes associated with assigning capital to pollution abatement.

The primary finding of our study is that since the mid-1980s there are relatively small dif- ferences in productivity growth for the regulated and unregulated technologies. In our model, this can be seen in PAI values close to unity. This is the consequence of similar growth rates for capital stock assigned to good output production and capital stock assigned to pollution abatement. If data existed back to the onset of assigning capital expenditures to pollution abatement, we suspect more substantial differences between PRODUR and PRODR would be observed. As a result of this evidence, our primary conclusion is pollution abatement capital expenditures are not associated with a substantial decline in manufacturing productivity.

As was the case for previous cross-country productivity studies, we confronted several problems in the course of developing data for our meta-frontier analysis. One problem is the reunification of Germany and the conversion of data from West Germany to the re-uni-

fied Germany. 13 In addition, the most recent STAN databases do not provide gross capital formation data for Japan. This forced us to use different sources for the capital expenditure estimates for Japan. Finally, the standard caveats associated with converting value added and gross capital formation data to U.S. dollars are compounded by the conversion of pre-Euro value added and capital formation data for Germany and the Netherlands into Euros via their January 1, 1999 irrevocable conversion rates (see Schreyer and Suyker 2002 , p. 7).

Both the model we specified and the data employed to implement it assume assigning capital expenditures to either good output production or pollution abatement is a feasible undertaking. Although widely used, several concerns have been raised about these surveys (see Becker and Shadbegian 2007 ; Gallaher et al. 2008 ). The primary concern involves the dif- ficulty associated with estimating “change in production process” capital expenditures (e.g., convert plant to consume fuels that generate fewer emissions). As the share of pollution abatement capital expenditures associated with change-in-process techniques increases— and the relative importance of end-of-pipe abatement expenditures declines—it becomes increasingly difficult to determine which capital expenditures are associated with abatement

activities. 14 In spite of these concerns, survey estimates of pollution abatement capital expen- ditures remain the best data currently available to compare changes in the absolute and relative burden of abatement costs incurred by the four countries in our sample.

Information on air and water pollution abatement capital expenditures assigned to change- in-process (CIP) and end-of-pipe (EOP) abatement strategies in Germany and the United

13 The 1995 issue (p. 704) of Statistisches Jahrbuch ( Federal Republic of Germany ) reports shares of capital expenditures assigned to pollution abatement by manufacturing and non-manufacturing plants in the former

West Germany of 5.0 and 4.8% for 1991 and 1992, respectively. Comparable values for the unified Germany were 5.3 and 5.6%.

14 Between 1973 and 1994, the share of manufacturing air pollution abatement capital expenditures rep- resented by “change in production process” techniques increased from 17.4 to 48.3% (U.S. Department of

Commerce 1976, p. 47; 1996, p. 25).

26 D. V. Aiken et al.

States provides some additional insights. The share of pollution abatement capital expendi- tures categorized as CIP expenditures increased from 15.5% in 1973 to 41.7 in 1994 (i.e., a 170% share increase) for the USA. Interestingly, the 2003 CIP share for the manufacturing sector in Germany was only 32%. There is substantial variation both in trends and shares for the last year for which data are available. Among pollution-intensive industries in Germany, only the chemical industry (ISIC 24) has an above average share of pollution abatement capital expenditures categorized as CIP. On the other hand, petroleum refining (ISIC 23) and rubber and plastics (ISIC 25) both report below average shares. For the USA, petroleum (ISIC 23) and chemicals (ISIC 24) report above average shares in 1994, while rubber and plastics (ISIC 25) report below average shares. While the share of pollution abatement capital expenditures categorized as CIP exhibits a substantial increase in USA manufacturing, there is substantial variation across industries. For example, petroleum and coal products (ISIC 23) and fabricated metals (ISIC 28) report share increases of 350% in CIP abatement techniques between 1973 and 1994, while non-metallic minerals (ISIC 26) increased only 50%, wood (ISIC 20) increased 70%, and primary metals (ISIC 27) increased 80%.

Our inability to account for employment and intermediate inputs—such as energy— assigned to pollution abatement represents a shortcoming of the empirical analysis presented in this study. A review of trends in the ratio of annual pollution abatement current account costs for air, water, and solid waste abatement to industry output reveals this ratio doubled between 1973 and 1994 for USA manufacturing ( U.S. Department of Commerce, 1978– 1996 ). Interestingly, more pollution intensive-industries experienced smaller than average in- creases, while less pollution-intensive industries experienced higher than average increases. Although this ratio increased by 50% between 1984 and 2003 for manufacturing in the Netherlands, the 2003 ratio for the Netherlands, it is still less than the 1994 USA value. Finally, while this ratio decreased by 20% between 1996 and 2002 for manufacturing in Germany, its 2002 value is higher than the 1994 USA value.

While it is difficult to make an exact assessment of the bias introduced by using only capital expenditure data, a simple assessment would be this approach introduces no biases if other inputs are assigned to pollution abatement in the same proportion as capital. It follows that our results understate the association between pollution abatement and productivity if the share of other inputs assigned to pollution abatement is higher than the share for capital; while our results overstate the association between pollution abatement and productivity if the share of other inputs assigned to pollution abatement is less than the share for capital. One approach to determine the importance of this exclusion involves using Bureau of the Census of the United States (1973–1994) data on labor and material, supplies, services, and equipment leasing costs assigned to pollution abatement. This would permit a comparison of our “capital stock only” results with results that calculate the association between abatement activities and productivity for U.S. manufacturing industries using the data on capital, labor, and aggregate intermediate inputs assigned to pollution abatement.

Finally, it would be useful to undertake a more thorough investigation of the associa- tion between the assigned input and joint production models. This investigation involves modeling a network technology that requires information not only on good and bad output production, but also data on inputs assigned to good output production and inputs assigned to abatement activities. This would permit an investigation into discrepancies in the productivity results produced by the joint production and assigned input models, and the consequences of adopting EOP and CIP abatement strategies.

Acknowledgements The authors wish to thank David Kauper and an anonymous referee for helpful com- ments and suggestions on earlier drafts of this paper. We also wish to thank Thomas Grundmann, Dieter

Pollution Abatement and Productivity Growth 27

Schäfer, Carsten Stahmer, and Kimio Uno for assistance with the pollution abatement expenditure data. Some of the research for this paper was conducted at the Library of Congress and was facilitated by the staff of the Asian Reading Room.

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