Does Plant Ownership Affect The Level Of Pollution
Abatement Expenditure?
Alan Collins
Department of Economics, University of Portsmouth, Locksway Road, Milton, Southsea, Hampshire,
PO4 8JF, U.K.
Email: alan.collins@port.ac.uk
Richard I.D.Harris
Department of Economics and Finance
University of Durham, UK
Abstract
This paper considers a number of hypotheses. Primarily amongst them is the notion that foreign owned
plants spend more on pollution abatement than domestically owned plants after controlling for
productive efficiency and cognisant of the prevailing regulatory regime. The evidence drawn upon in
the first econometric assessment of this contention is plant level data from the UK metal manufacturing
industry. In essence, this study directly estimates the influence of ownership and efficiency
characteristics in firms’ decisions regarding whether to spend or not on pollution control and how much
to spend. To explore these themes a two stage econometric exercise was undertaken on a hitherto
unused source of environmental data, namely the UK Annual Business Inquiry Respondents Database
(or ARD). A Heckman-type sample selection model was estimated to examine the probability of
abatement expenditure being made or not and also to explain how much was spent on each of the
principal means of pollution control. The results suggest that older plants were more likely not to incur
any expenditure on process or post-production pollution capital expenditure. Plants that were non-EU
foreign owned were generally more likely to spend on pollution abatement than UK plants. Likewise,
in the main, the more efficient firms and the more capital-intensive firms were also more likely to
spend on pollution control than UK-owned firms. The significance or otherwise of a wide range of
other factors was also explored and reported on.
JEL-L
JEL-O JEL-D
Keywords
Pollution abatement
Efficiency
UK manufacturing
a
Support from the Economic and Social Research Council (reference no. R000222602) is gratefully
acknowledged, as is permission from the Office for National Statistics at Newport, South Wales, to use
the Annual Business Inquiry Respondents Database.
1
I
Introduction
An extensive body of theoretical studies continues to evolve and shed light on firms’ likely responses
to alternative environmental regulatory instruments and regimes 1. An interesting recent strand of this
work has begun to examine in particular firms’ technology choice with respect to the linkages between
profit maximisation and the imposition of specific pollution abatement instruments (Kort et al. 1991).
This is an important policy focus for regulators and the business community in the context of
increasingly aggressive environmental policy objectives, where the scope for policy substitution has
been raised. This relates to the idea that environmental policy objectives may be attained by (i) pure
productive efficiency enhancing means and (ii) direct pollution abatement enhancing means. It may be,
however, as in the U.K and elsewhere, that environmental regulatory bodies do not have as yet the
human capital resources to contemplate the environmental potential of the productive efficiency
enhancing means. In the context of the USA Gray and Shadbegian (1995) found a negative relationship
between a plant’s pollution abatement costs and its total factor productivity. Yet, it is reasonable to
posit that more efficient plants may have less need to engage in pollution abatement expenditure
(PAE), since, by virtue of their greater efficiency with regard to the use of resource inputs, they
intrinsically generate lower levels of pollution. Perhaps contrarily, it is also reasonable to think that
efficient plants will be amongst the most keen to seize worthwhile resource input minimizing
opportunities when they arise. Clearly, this is likely to be associated with higher levels of some
particular types of PAE, such as process-based capital expenditure, as opposed to arguably cruder endof-pipe solutions.
Despite the extensive theoretical activity in this area, relatively little econometric work has
taken place that moves us towards injecting more of an empirical dimension in support of this
developing firm-environment literature. The non-case study based empirical work that has taken place
has primarily focused on evidence drawn from the USA. Such work has usefully exploited the annual
Pollution Abatement Costs and Expenditures Survey (Barbera and McConnell 1986). Whilst metals
have recently been the subject of macro-scale environment-economy interaction modelling (Guinee et
al. 1999), this particular study contributes to the corpus of firm-environment research at the industry
level. This work empirically examines the linkages between plant ownership, productive efficiency and
the decision to engage in pollution abatement. More specifically, we address the following question.
2
After controlling for differences in productive efficiency, do domestic or foreign-owned plants spend
more on pollution abatement?
Evidence is drawn from the specific context of the UK metal manufacturing industry over the
period 1991-1994. Other UK industries over this time period would have presented equally satisfactory
sources of evidence to explore the hypotheses we set out, operating as they did under the same
environmental regulatory regime. Using this evidence, for those plants found to be engaging in
pollution abatement, this study also presents the first econometric study to consider what determines
their actual level of PAE in the four main categories of pollution control. These are, namely, process
based capital expenditure, post-production capital expenditure (end-of-pipe solutions), current
expenditure (using the firm’s own staff), and through payments to others (i.e. contracting out some
pollution control functions). Yet, as we have already emphasised, plausible reasoning could readily be
devised to articulate both positive and negative influences of productive efficiency on the undertaking
of PAE.
This paper extends the firm-environment literature in two key aspects. First, we believe our
study provides the first non-US econometric study of PAE relevant to a major pollution intensive
industry. This is furnished by exploitation, for the first time by economists, of the environmental data
contained within the Annual Business Inquiry Respondents Database (ARD) of the UK Annual Census
of Production (now known as the Annual Business Inquiry). Second, this study is the first to explicitly
consider the hypothesis that foreign plant ownership raises the probability that a plant will engage in
pollution abatement activity. Many studies have investigated or hypothesised how national or cultural
differences may influence firm profitability, productivity, and levels of research and development
amongst other things2. Hence, there might also reasonably be expected to feature some environmental
performance implications arising from such differences. For example, given that the stringency of
environmental regulatory regimes varies significantly in different countries one might expect there to
be environmental benefits aspects to the positive externalities generally said to arise from the presence
of foreign -owned (FO) plants in a particular host country (Blomstrom and Kokko 1998). Indeed there
1
See, for example Cornwell and Costanza (1994), Laffont and Tirole (1994, 1996), Damania (1996),
Jung et al. (1996), Fredriksson (1998), Goulder et al. (1999), Schwabe (1999), Baudry (2000).
2
See, for example, Dunning (1958, 1977), Vernon (1966, 1979), Kindleberger (1969), Caves (1974),
Johnson (1970,1975) Hymer (1976), Buckley (1983).
3
is a large body of work3 that has sought to suggest, identify and help explain a wide range of positive
effects on industry-wide or general manufacturing sector productivity, as a direct consequence of such
increasing FO firm penetration.
The principal findings of this particular study are that increasing efficiency levels leads to a
small increase in the probability of making expenditures on pollution abatement investment in the
production process but very substantially increases the probability of zero expenditure on direct staff
and operating costs relating to pollution control. It is also found that US-owned plants are more likely
to incur some spending on post-production assets but are significantly less likely to spend on other
forms of pollution control. EU-owned plants, however, are more likely to spend on pollution abatement
than UK plants. Plants owned by enterprises from Australia, New Zealand, South Africa and Canada
have a higher probability of incurring some expenditure on pollution abatement. Furthermore, and
probably reflecting actual or perceived variations in regulatory enforcement effort, plants in less
populated areas are far more likely not to engage in any PAEs.
The paper is organized in the following way. In Section II theoretical issues are considered
relevant to the linkages between ownership status, efficiency and the regulatory regime in the time
period under study, and our main hypotheses are posited. Section III presents some background
contextual information relating to the UK metal manufacturing industry over this period.
This
descriptive analysis suggests a number of hypotheses that are tested in the subsequent econometric
phase of the study. Section IV sketches an outline of the modelling strategy and econometric model
employed to examine the influence of productive efficiency and ownership status on PAE. Section V
presents the results with brief concluding remarks offered in Section VI.
II Ownership, Efficiency and Capital Intensity with Technology-Forcing Environmental
Standards
There are a number of theoretical reasons why one might expect differences between domestically and
foreign owned plants with respect to their level of efficiency and accordingly their environmental
performance. These relate to mainstream productive efficiency reasons and more specific resource
productivity based explanations. These are considered in turn.
3
See, for example, Globerman (1979), Krugman (1991ab), Grossman and Helpman (1991), Venables
(1994), Edwards (1998), Aghion and Howitt (1998)
4
Irrespective of the fact that FO-plants are more likely to be younger, explanations for higher
FO plant productivity (as in Figure 1) relates to a combination of two sources, namely, labour
productivity and superior technology that will tend to be more environmentally benign. Labour
productivity in the FO plant may, however, also be higher because more output-per-employee emerges
due to the use of a superior technology. The superior technology explanation can embrace more than
simply a swifter rate of engineering or scientific advance in FO firms. It may also incorporate “soft
technology” aspects with superior managerial and production organisation practices (e.g. Total Quality
Control). Thus superior technology enables firms with the same level of capital-per-worker, to produce
more output per unit-of-labour (position FO1 in Figure 1). The other extreme is that higher labour
productivity may simply arise because a plant uses more capital-per-worker (position FO2), so while
labour productivity is higher, capital productivity is lower. The evidence (at least for the UK) suggests
that the superior technology explanation is likely to predominate (see Harris, 1999ab).
Yet in the face of uniform environmental regulations across plants and a competitive market,
variations in PAE should in large part be explained by differences in plant efficiency. Contingent on
their overall stringency, more efficient plants should require less PAE to satisfy any given
environmental regulations. However, in terms of the dynamics underlying the competitive process,
efficient firms may also be the most likely to lead in the adoption of resource minimizing and hence
cost-saving production techniques. The latter view implies a greater tendency towards voluntary
overcompliance (Arora and Gangopadhyay 1995) by efficient firms with respect to environmental
regulations. This effect has been observed in a developing country context. For example, Eskeland and
Harrison (1997) present evidence that foreign-owned plants in four developing countries were
significantly less polluting than comparable domestic plants.
This overcompliance effect could be inferred from systematically greater PAE in efficient
firms than in inefficient plants. Overcompliance has been explained in terms of its possible role as an
element in a non-price competitive strategy (Kirchhoff 2000). In this sense it can be exploited in some
consumer markets as a source of competitive advantage on the grounds of quality differentials. Indeed
when such markets are also characterized by highly asymmetric information between firms and
consumers, there also arises the potential for “greenwash” i.e. where firms lie about their
environmental performance (Kirchhoff 2000). However, in the context of intermediate goods markets
such as metal manufacturing, this is a less convincing explanation for overcompliance. More likely is
5
an explanation based on a process of passive evolution linked principally to the notion of a sunk cost or
path dependency argument
Turning to Figure 2, it is likely that each technology choice will be associated with differing
levels of resource productivity (Y/R). Resource productivity can be increased by recovering more of
the potential residuals discharge from the production process to serve as output. In general, Technology
2 in Figure 2 could offer greater resource productivity than Technology 1 based on a number of
processes including wholly in-plant recycling of raw materials, the use of generated heat from
production as an energy source, and re-use of waste materials as another product line. If a technology
forcing environmental regulation was introduced to try to induce a higher level of resource
productivity, then this could require a shift to a level of technology superior to Technology 1. (Note,
Figure 2 also suggests that greater resource productivity is also associated in heavy industry with
greater capital intensity.)
In the context of the metal manufacturing industry over the relevant data time period, the
prevailing regulatory instrument across all plants was indeed a technology-forcing standard determined
by reference to an ambiguous and inefficient guiding doctrine – BATNEEC – Best Available
Technology Not Entailing Excessive Cost. This was formally introduced in UK statute within the 1990
Environmental Protection Act. On this basis there was greater regulatory pressure to install a capital
stock within newer plants that would be closer in function to the “best available technology” (BAT D)
level in the domestic market. However, under this guiding principle, environmental regulators could be
minded to tolerate some level of departure from this BAT D standard in older established plants, where
it might generate an excessive “corporate burden” (Pearce and Brisson 1993). By implication this
involved the environmental regulators forming an implicit view as to an acceptable rate of return for
the firm. The older established domestic firm could thus meet the requirement for greater resource
productivity whilst retaining most existing capital. This could be undertaken by augmenting the
existing capital stock with additional discrete pollution abatement orientated capital. Alternatively, the
firm could re-assign existing staff or hire new staff to engage exclusively in pollution abatement related
tasks. This would inevitably reduce labour productivity. Whichever option or combination is applied,
let this departure from the domestic level of BAT D be represented by Technology S in Figure 2.
Accordingly, it is likely that FO firms have a systematic tendency to overcomply (Y/R FO2 >
Y/RUK-owned) and ‘overspend’ on pollution abatement. This can arise from a combination of (i) higher
6
mainstream production capital intensity (K/LFO1 > K/LUK-owned) which will generally support greater
residual recovery in heavy industry, or, (ii) because the transplanted production technology intrinsically
embodies a given higher level of resource productivity. This would accord with the notion that FO
firms may have experience of stricter environmental regulation in their home country (say where
BATDomestic < BATForeign). Hence, for this reason they are, at least in the short run, locked into an
environmentally superior technology. Even in the long run such firms would have to make a judgement
concerning the extent to which they would meet expected future levels of stringency of environmental
regulations and set that against any benefits from relaxing resource productivity (reducing
overcompliance). Given that one would generally expect environmental regulations to be increasingly
stringent over time, then overcompliance, especially by FO firms, seems likely to persist even in the
long run.
Thus, the arguments presented here suggest that factors such as foreign-ownership, capitalintensity, efficiency, and the age of the plant are likely to be important in determining PAE 4. More
explicitly, distilled from the above discussion, premised largely on the overcompliance explanation, the
main hypotheses that are tested in the subsequent econometric phase are:
(i)
FO plants engage in greater PAE than domestically owned firms.
(ii)
More capital intensive plants engage in greater PAE.
(iii)
More efficient firms engage in greater PAE.
(iv)
Older plants engage in greater end-of-pipe (capital augmenting) PAE.
In addressing a uniform technology-forcing standard, PAE decisions can also reasonably be viewed as
a sequential decision process. First, firms decide whether they need to spend or not on pollution
control. They also need to decide what level and what type of pollution abatement expenditures they
wish to make. Given that certain types of pollution abatement expenditures are intrinsically more
expensive than others, then it seems reasonable to suppose that the level and type of expenditure
decision could be considered jointly. To test whether different forms of pollution abatement
expenditure are complements or substitutes to each other would require the estimation of a
simultaneous model. However, we do not have the econometric tools to estimate a simultaneous
Heckman model (see below for details), and furthermore we lack prior information that would allow us
7
to impose some structure on such a model (i.e., which variables should enter which equation in the 2stage Heckman approach in order to identify the system). Thus, as a first attempt we have resorted in
the econometric phase to estimating a reduced-form version of such a structural system. Yet to inform
such model development it is first necessary to broadly appreciate the significance and scale of the
focus of this study – the UK metal manufacturing industry.
III UK Metal Manufacturing Industry 1991-4: Background
Metal manufacture and use is vital to the social and economic prosperity of the entire globe, and most
nations participate to some degree in its manufacture (Roberts 1996). In the UK the metal
manufacturing industry comprises a presence in both iron and steel (ferrous metal) and non-ferrous
metal manufacture. Of the former, this includes manufacture of basic products such as steel tubes and
steel wire as well as drawing, cold rolling and forming of steel to be used in the manufacture of other
products. In terms of non-ferrous metals this primarily comprises the manufacture of aluminium and
aluminium alloys, copper, brass and other copper alloys. There are also some plants manufacturing
some other non-ferrous metals and their alloys. The source, nature and method of assembly of the
metal industry panel data available from the ARD used in this and the next section are described in the
Appendix.
By way of critical assessment of the data it should be noted that it was collected as part of the UK
government's Annual Business Inquiry that forms the basis of the 'official' statistics used to measure
output and costs in each industry. The government use a stratified sampling procedure to ensure that
the data collected achieve good coverage of each industry, and since employment information is
available for each plant (whether included in the annual inquiry or not) it is possible to weight the data
to obtain nationally representative figures. As such, the data we use is likely to be both accurate (in
terms of point estimates of pollution expenditures) and contain sufficient coverage of the industry to
make its use statistically robust when testing the types of hypotheses suggested in the previous section.
Metal manufacturing has long been a significant source of environmental pollution
(Braennvall et al. 1999). It poses considerable health risks to both workers (Comba et al. 1992, De et
4
Another potential influence is the location of the plant - this is discussed in section 4 when the model
8
al. 1995, Maynard et al. 1997, Moulin et al. 1998), and the public (Baxter et al. 1996, Guinee et al.
1999). Its role in diminishing the quality of the physical environment has also been the subject of much
scrutiny (Tremmel 1992, Dudka and Adriano 1997, Guinee et al. 1999). In contrast with most other
industrial sectors the waste residuals produced in metal manufacturing are largely non-dissipative (i.e.
not immediately or gradually dispersed into air, water or soil in the course of their normal use) (Kneese
et al. 1970).
The residuals comprise bulky solids (e.g. slag), much particulate matter, gaseous
emissions from energy conversion, and much liquid waste resulting from cleaning or “pickling” the
metal during fabrication to reduce oxide scales when the metal has contact with air. The principal
pickling agent for steel is sulphuric acid, but other acids such as hydrochloric, nitric and hydrofluoric
are also used. Slag may be re-used in road construction aggregates and in concrete manufacture. A
substantial volume of the particulates produced in the foundries as “flue dust” can be recovered “by
wet scrubbing” and other precipitation processes due to their relatively high metal contents. Other
particulates such as soot from coke and coal burning (used in reheating furnaces and rolling mills) can
also be captured by various forms of carbon filters and scrubbers. Most of the liquid waste acids can be
neutralized with lime but recovery has been considered problematic (Marquardt and Nagel 1992). That
said, from the liquid wastes in most plants it has been possible to generate commercial grade ferrous
chloride solution for use in flocculation processes in water treatment plants. These processes also apply
in some non-ferrous metal manufacture (copper and brass mills) but in addition with regard to copper,
lead and zinc, some very concentrated and highly toxic sulphur dioxide fumes are also generated. Some
of this sulphur may now, however, be recovered as commercial grade sulphuric acid.
Thus, it can be seen that environmental pollution is a major ‘output’ of the metals industries.
Before considering PAE in this section and by way of context, it is instructive to look at the pattern of
PAE across the UK manufacturing sector as a whole. In this way any distinctive features of metal
manufacturing can be drawn out. As a consequence of the heavy pollution potential of this industry
considered above, the declared expenditure per plant on managing waste residuals is relatively high,
only exceeded on average by three other sectors (see Figure 3). In the manufacturing sector as a whole,
of those plants that spend on pollution control, payments to others to manage and dispose of their waste
dominates over this time period as the prime means of dealing with waste residuals (Figure 4).
Recasting this picture in terms of plant ownership categories, UK manufacturing plants seem to lag
for estimation is presented.
9
behind in PAE with regard to all means of pollution control except payments to others (Figure 5).
Turning now to the metal industry specifically (Figure 6), over the period 1991-4 average annual
spending by plant on current staff for pollution control seems to have risen significantly from just over
£10,000 to approaching £35,000 (although there is some evidence to suggest this may have been offset
by a decline in process-based capital expenditure). Nevertheless, the figures for this and the other
means of pollution control (such as process capital expenditure) still seem remarkably small given the
scale and nature of the production processes being undertaken. The small magnitudes of these declared
levels of PAE (in the context of this pollution intensive industry) lend some weight to the view that
pollution control is inextricably bound up with the mainstream production process. In essence then it is
possible to view plant efficiency, in addition to regulatory stringency, as a key driver of the level of
PAE.
Viewing this pattern of expenditure by plant ownership category (see Figure 7) shows some interesting
deviations from the national picture. Of those plants that do spend, those plants from the
Commonwealth block (Australia, Canada and South Africa) tend to dominate in terms of overall PAE.
These are countries where strong vertical relations amongst firms can be expected since they are
countries where much of the metal ore deposits are extracted. Hence, there may be some PAE spillover
arising from linkages with the metalliferous ore extraction industry.
In contrast to the wider
manufacturing sector, of those plants that do spend, UK plants no longer lag behind all other ownership
groups in that US owned plants declare less PAE. The rest of Europe still dominates the UK in overall
term in PAE with the specific and surprising exception of process based capital expenditure, where the
UK even exceeds average European expenditure. This descriptive analysis does not, however, take
account of plant variations in productive efficiency, and perhaps underplays the fact that there are a
large number of firms that do not spend directly on PAE at all over the period 1991-4.
IV Econometric Model
In order to discover the strength of some of the relationships between PAE (by type) and factors such
as efficiency, foreign ownership and capital intensity, we posit a simple 2-stage model that assumes the
variables in wit (below) determine whether a plant spends/does not spend on pollution control. This
comprises:
wit = (ln EFF, ln GVA, ln AGE, ln KL, EU, US, AUS)it
+ ln DENt + 4-digit SIC industry dummies
[1]
10
where5 EFF is a measure of plant level technical efficiency;
GVA is real gross-value-added;
AGE is the age of the plant;
KL is the capital-to-labour ratio;
EU, US, AUS are dummy variables coded 1 if the plant is owned by an EU, US or
Australasian/Canadian/South African enterprise; and
DEN is population density of the Local Authority District in which the plant is located.
The DEN variable proxies for variations in regulatory stringency and the perceived extent of pollution
hazard. Strictly speaking, this departs from the approach used by Gray and Shadbegian (1998) in the
USA (though they too have previously considered the use of population density for this purpose in that
geographical context). This departure relates to different institutional environmental regulatory
frameworks. Within the USA, the federal Environmental Protection Agency (EPA) takes the lead role
in environmental regulation, however, state agencies are also strongly involved in the setting and
enforcement of environmental standards. Accordingly, differences by state have been found in
environmental regulatory stringency. By using electorally based proxies as a measure of regulatory
stringency, Gray and Shadbegian (1998) examined (in one industry) how this impacts on firms’
investment decisions. In the specific context of the United Kingdom, however, sub-federal agencies do
not have as heavy a role. Accordingly, such electoral proxies are not really valid in this particular
national context. This is not to say, however, that the UK Environment Agency does not over time and
spatially, vary in its level of regulatory stringency. It may, for example, concentrate its enforcement
efforts in more heavily populated areas, where they may quite reasonably perceive the risks of
environmental pollution on public health to be higher than in less populated areas.
The variables in the second-stage model that determine the amount spent (if PAE>0)
comprise:
xit = (ln GVA, EU, US, AUS)it + t + 4-digit SIC industry dummies
[2]
where t is a time trend.
In essence, by this approach, it is assumed that various plant level characteristics determine
whether it is in the interests of the plant to actually spend anything on pollution abatement. If the
answer is 'yes' then the scale of output (and thus presumably pollution), ownership, time and industry
5
Variables are formally defined in Table 1
11
effects determine the volume of spending. The dependent variables comprise zit = 1 if the plant spent
anything on pollution control in time t; and yit is real6 expenditure on pollution abatement by plant i in
time t.
The approach used is based on the standard Heckman (1979) sample selection model where
the selection mechanism comprises:
zit* = wit + uit, zit = 1 if zit* > 0 and 0 otherwise, where prob(zit = 1) = (wit)
[3]
where  is the density function and the regression model comprises:
yit = xit + it observed only if zit = 1, and (uit, it) ~ bivariate normal [0, 0, 1, , ].
[4]
Thus we wish to estimate the following model that is based on an efficient and unbiased estimator of 
when yit is observed only when zit = 1, i.e.,
E[yit | zit = 1] = xit + (wit)
[5]
where  is the correlation between (u, ), and (wit) = (wit)/[1(wit)] is the inverse Mills ratio
which is obtained from estimating the selection model 7 (where  is the probability density function
associated with the first stage of the Heckman model, i.e. equation [3]).
The variable names, definitions, and basic descriptive statistics are set out in Table 1. In this
study, the measure of plant level technical efficiency (or more accurately technical inefficiency) is
measured via a stochastic frontier production function that allows each plant to have different levels of
efficiency in different years. Full details are presented in Harris (1999b). Regarding the relevance of
our approach, we are clearly using existing (and econometrically appropriate) methods in order to test
specific hypotheses not usually tested because of lack of data. To devise new methods for testing, while
clearly an advance, is beyond the scope of the present paper.
6
Actually expenditure was deflated by the 4-digit producer price index for the industry to which the
plant belonged.
7
Note, the impact of sample selection is therefore obtained via the two estimated coefficients .
Typically, this is reported in most studies (and in most econometrics packages) via a single term,
usually denoted as  (=). This is also reported below, as well as the separate terms that comprise it.
12
V. Results
The results discussed below are based on an exploratory econometric analysis of the ARD data and as
such its is important to be aware of the limitations of the models estimated. Ideally one would wish to
consider the determinants of the different types of PAE simultaneously. Given limited information
about the fuller structural relationships and the fact that the econometric tools to estimate the full
structural model are currently unavailable, this study has resorted to estimating reduced-form models. It
is suggested in this study, however, that this first cut approach is not inconsistent with a profit
maximising model where firms maximize profit subject to producing goods and ‘bads’ (environmental
pollution) (see, for example, Kneese et al. 1970, Hernandez-Sancho et al 2000).
The results presented in Table 2 enable us to make some comments concerning PAE and in
respect of the four types of pollution control undertaken. First, the results suggest that by increasing
efficiency levels by one standard deviation – see Table 2b (and the results referring to spend/not spend)
for details – this would increase the probability of incurring expenditure on assets used in the
production process to minimise residuals by over 3 per cent and decrease the probability of spending
anything on payments to others by 4.8 per cent (Table 2d). Further, it would also decrease the
probability of spending on direct staff, material and operating costs relating to pollution control by 6.8
per cent (Table 2c). These results are generally supportive of the characterization of efficient firms as
being among the most keen to introduce resource input minimizing techniques, but (because they are
more efficient) they are less in need of undertaking current expenditures on pollution abatement.
With respect to the results on whether to spend or not spend in Table 2, increasing production
increases the probability of spending on all forms of pollution abatement. For example, a standard
deviation increase in real GVA increases the probability of incurring expenditure on payment to others
relating to pollution control by over 3 per cent. As to how much is spent (cf. the second block of results
in Table 2), the elasticity of pollution control spending with respect to the amount produced ranges
from 0.8 to 0.97, implying that as output increases the amount spent on pollution abatement increases
in a similar proportion. It is also possible to observe that older plants are more likely to spend nothing
on pollution abatement. A standard deviation increase in the age of plants increases the probability of
incurring no process or post-production capital expenditure by approximately 6-8 per cent depending
on PAE type.
13
In accordance with the simple model set out earlier, those plants with greater capital-intensity
are more likely to spend on pollution control. By illustration, a standard deviation increase in the K/L
ratio decreases the probability of incurring no production process capital expenditure by nearly 7 per
cent.
Perhaps indicative of greater stringency and enforcement of environmental regulations nearer
or in urban settlements, plants located in less populated areas are more likely not to spend on pollution
control. These results show that a standard deviation increase in the population density increases the
probability of incurring no expenditure on direct staff, material and operating costs relating to pollution
control in the metal industry by some 3.8 per cent
Turning now to the effects of plant ownership status, the results show (ceteris paribus.) no
statistically significant effect for EU-owned plant ownership with respect to spending on pollution
abatement (vis a vis UK-owned plants). This is despite the seemingly stronger profile given to the issue
of environmental quality in the politico-legal framework of continental Europe. For example, Krol and
Steil (1997) noted that in relation to some German non-ferrous metal plants, they had to address at the
Federal level alone 233 laws, 549 directives and 498 administrative regulations with environmentally
relevant rules. In addition, there were at the time 330 EU regulations and several thousands of state and
municipal regulations. Alternatively, this result may be indicative of success in increasing
harmonization of important aspects of EU environmental policy given that the UK is a member of the
EU bloc. Plants owned by enterprises from Australia, New Zealand, South Africa and Canada have a
higher probability of incurring some expenditure on pollution abatement. In particular, they are over 12
per cent more likely to spend on assets for post-production pollution control and waste management,
when compared to UK-owned plants, and between 14 and 27 per cent more likely to spend on
payments to others and expenditure on direct staff, material and operating costs, respectively
Against a backdrop of a regulatory regime driven by adherence to the BATNEEC principle,
this evidence is suggestive of systematically greater incidences of voluntary overcompliance being
more likely in Commonwealth FO-plants than UK-owned plants, but show rather mixed results for the
US with respect to PAE type. Whilst US-owned plants are 11.8 per cent more likely to incur some
spending on post-production assets, they are, ceteris paribus, significantly less likely to spend on other
forms of control (e.g., nearly 30 per cent less likely to make payments to others). This seems to
14
characterize US plants as being more likely to favour “techno-fix” and in-house solutions when
addressing environmental problems.
For those plants that have positive PAE spending, EU plants spend 95%-106% more than UK
owned plants on post-production expenditure and payments to others, respectively, but between 20 and
77% less than UK-owned plants on process capital expenditure and direct expenditure, respectively. It
is not entirely clear why this should be so, though it may simply reflect an historical regulatory
emphasis given to these types of pollution abatement methods in the UK. Commonwealth plants have
some 95-100% higher expenditure than UK-owned plants with respect to current expenditure. As with
the results for the probability of spending anything, US-owned plants are likely to spend more on postproduction pollution capital expenditure but less on other forms of pollution abatement. Hence, any
arguments concerning plant ownership and pollution control really needs to take into account the
nature of the PAE. Otherwise one would risk masking distinctive patterns, with respect to different
emphasises by ownership category, on particular approaches to pollution control.
There are some major differences across plants in different sectors of the metal industry. For
example, in terms of whether any expenditure takes place, copper, brass and brass alloy production is
associated with particularly high probabilities of incurring any PAE when compared to the reference
group (iron and steel manufacturing). In large part, this can be related to the need to control the more
toxic fumes (such as high concentration sulphur dioxide) associated with this sub-sector of the metal
industry. Non-ferrous metals also generally have a higher probability of PAE (and to a lesser extent
steel tubes and aluminium and alloys). In contrast, steel wire and other drawing, cold rolling and cold
forming of steel often have lower probabilities of incurring expenditure vis a vis the iron and steel
reference group.
Finally, in terms of how much is spent, the time dummy is negative in most cases, implying
that plants were finding other ways to meet pollution control targets other than through paying for such
control, and probably also linked to the impact of the 1991-93 recession which was particularly severe.
VI. Concluding Remarks and Summary
This paper has explored the determinants of the amount directly spent in metal manufacturing plants
on pollution control. In particular, the influence of ownership and efficiency characteristics were
15
considered as key determinants of the decision whether to spend or not on pollution control and how
much to spend. To explore these themes a two stage econometric exercise was undertaken on a hitherto
unused source of environmental data, namely the UK Annual Business Inquiry Respondents Database
(or ARD). A Heckman (1979) sample selection model was estimated to examine the probability of
abatement expenditure being made or not and also to explain how much was spent on each of the
principal means of pollution control.
Operating within the prevailing environmental regulatory regime of the time – a period which
followed the highly ambiguous BATNEEC doctrine - the results suggest that over the period 1991-4,
older plants were more likely not to incur any expenditure on process or post-production pollution
capital expenditure.
In accordance with our primary hypothesis, plants that were non-EU foreign
owned were indeed generally more likely to spend on pollution abatement than UK plants. 8 Should
pollution abatement objectives feature more highly on the Government policy agenda then this finding
would point to addressing some policy domain overlaps. In particular, it would suggest environmental
regulators should be actively supportive of greater levels of foreign direct investment (through
acquisitions), from particular country blocs. Likewise, in the main, the more efficient firms and the
more capital-intensive firms were also more likely to spend on pollution control than domesticallyowned firms. Accordingly, given the significance of efficiency in this environmental policy arena, a
key policy implication that warrants attention is for industrial and environmental regulators to explore
the potential benefits from policy integration in the framing of environmental objectives. The evidence
presented in this paper suggests that industrial and environmental policy could be more usefully
considered as complementary, especially with regard to pollution abatement current expenditures. The
significance or otherwise of a wide range of other factors was also explored and reported on including
the PAE dampening effects of lower perceived regulatory enforcement levels.
8
While the raw data shows that EU-owned plants spent more on PAE, after controlling for such factors
as age and capital-intensity, the econometric model estimated shows that EU-owned plants are no more
likely to incur any expenditure than are UK-owned plants.
16
References
Aghion, P. and P. Howitt (1998) Endogenous Growth Theory, MIT Press, Cambridge.Arora, S., and
Gangopadhyay, S. (1995) Toward a theoretical model of voluntary overcompliance. Journal
of Economic Behavior & Organization 28(3) pp.289-309.
Barbera, A.J. and McConnell (1986) Effects of Pollution Control on Industry Productivity: a Factor
Demand Approach. Journal of Industrial Economics XXXV(2) 161-172.
Baudry, M. (2000) Joint Management of Emission Abatement and Technological Innovation for Stock
Externalities, Environmental & Resource Economics, 16(2), 161-183.
Baxter, M.S., Mackenzie, A.B., East, B.W., and Scott, E.M. (1996) Natural Decay Series
Radionuclides in and Around a Large Metal Refinery Journal Of Environmental Radioactivity
32(1-2) 115-133.
Blomstrom, M. and Kokko, A. (1998) Multinational Corporations and Spillovers, Journal of Economic
Surveys, 12, 247-278.
Braennvall, M.L., Bindler, R., Renberg, I., Emteryd, O., Bartnicki, J. and Billstroem, K. (1999) The
Medieval Metal Industry was the Cradle of Modern Large-scale Atmospheric Lead Pollution
in Northern Europe. Environmental Science & Technology 33(24) 4391-4395.
Buckley, P.J. (1983) New Theories of International Business: Some Unresolved Issues, In: M. Casson
(ed.) The Growth of International Business, George Allen & Unwin, London.
Caves, R.E. (1974) Multinational Firms, Competition, and productivity in Host-Country Markets,
Economica, 41, 176-93.
Comba, P., Barbieri, P.G., Battista, G., Belli, S., Ponterio, F., Zanetti, D., and Axelson, O. (1992)
Cancer of the Nose and Paranasal Sinuses in the Metal Industry: a Case-Control Study. British
Journal of Industrial Medicine 49(3) 193-196.
Cornwell, L. and Costanza, R. (1994) An Experimental-Analysis of the Effectiveness of an
Environmental Assurance Bonding System on Player Behavior in a Simulated Firm.
Ecological Economics 11(3) 213-226.
Damania, D. (1996) Pollution Taxes and Pollution Abatement in an Oligopoly Supergame, Journal of
Environmental Economics and Management, 1996, 30(3)323-336.
Dudka, S. and Adriano, D.C. (1997) Environmental Impacts of Metal Ore Mining and Processing: a
Review. Journal of Environmental Quality 26(3) 590-602.
Dunning J. (1958) American Investment in British Manufacturing Industry George Allen & Unwin,
London
Dunning, J. (1977) Trade, Location of Economic Activity and Multinational Enterprise: a Search for an
Eclectic Approach, In: B. Ohlin, P.O. Hesselborn and P.M. Wijkman (eds.) The International
Allocation of Economic Activity, London, McMillan.
Edwards, S. (1998) Openness, Productivity, and Growth: What Do We Really Know? Economic
Journal 108(447) 383-98.
Eskeland, G.S. and Harrison, A.E. (1997) Moving to Greener Pastures? Multinationals and the
Pollution–haven Hypothesis, Policy Research Working Paper 1744, Policy Research
Department, Public Economics Division, The World Bank, Washington D.C.
Fredriksson, P.G. (1998) Environmental policy choice: Pollution abatement subsidies Resource and
Energy Economics 20(1)51-63.
Globerman, S. (1979) Foreign Direct Investment and Spillover Efficiency Benefits in Canadian
Manufacturing Industries, Canadian Journal of Economics, 12 42-56.
Goulder, L.H., Parry, I.W.H., Williams, R.C,, and Burtraw, D. (1999) The Cost-Effectiveness of
Alternative Instruments for Environmental Protection in a Second-Best Setting. Journal of
Public Economics 72(3) 329-360.
Gray, W.B., and Shadbegian R.J. (1995) Pollution Abatement Costs, Regulation and Plant-Level
Productivity, National Bureau of Economic Research Working paper 4994 NBER:
Cambridge, MA.
17
Gray, W.B., and Shadbegian R.J. (1998) Environmental Regulation, Investment Timing and
Technology Choice Journal of Industrial Economics XLVI(2) 235-256.
Grossman, G.M. and E. Helpman (1991) Trade, Knowledge Spillovers, and Growth, European
Economic Review, 35, 517-26.
Guinee, J.B., van den Bergh, J.C.J.M., Boelens, J., Fraanje, P.J., Huppes, G., Kandelaars, P.P.A.A.H.,
Lexmond, T.M., Moolenaar, S.W., Olsthoorn, A.A., De Haes, H.A.U., Verkuijlen, E., and van
der Voet, E. (1999) Evaluation of risks of metal flows and accumulation in economy and
environment. Ecological Economics 30(1), 47-66.
Harris, R.I.D. (1999a) Using the ARD Establishment Level Data to Look at Foreign Ownership and
Productivity in the United Kingdom - A Comment mimeograph (available at
http://www.pbs.port.ac.uk/~harrisr/griffith.pdf)
Harris, R.I.D. (1999b) Efficiency in UK Manufacturing 1974-1994, mimeograph (available at
http://www.pbs.port.ac.uk/~harrisr/ukeff.pdf)
Harris, R.I.D. and S. Drinkwater (2000) UK Plant and Machinery Capital Stocks and Plant Closures,
Oxford Bulletin of Economics and Statistics, 62, 239-261
Hernandez-Sancho, F., A. Picazo-Tadeo and E. Reig-Martinez (2000) Efficiency and Environmental
Regulation: An Application to Spanish Wooden Goods and Furnishings Industry.
Environmental & Resource Economics 15, 365-378.
Hymer, S.H. (1976) The International Operations of National Firms, Lexington Books, Lexington,
Mass.
Johnson, H.G. (1970) The Efficiency and Welfare Implications of the International Corporation, in C.P.
Kindleberger (ed.) The International Corporation, MIT Press, Mass.
Johnson, H.G. (1975) Technology and Economic Interdependence, Macmillan, London.
Jung, C.H. Krutilla, K. Boyd, R. (1996) Incentives for advanced pollution abatement technology at the
industry level: An evaluation of policy alternatives. Journal of Environmental Economics and
Management 30(1) 95-111.
Kirchhoff, S. (2000) Green business and blue angels - A Model of Voluntary Overcompliance with
Asymmetric Information. Environmental & Resource Economics 15(4), 403-420.
Kindleberger, C.P. (1969) American Business Abroad, Yale University Press, New Haven.
Kneese, A.V., Ayres, R.U., and d’Arge, R.C. (1970) Economics and the Environment : A Materials
Balance Approach Resources for the Future: Washington D.C.
Kort, P.M., van Loon, P.J.J.M. and Luptacik, M. (1991) Optimal Dynamic Environmental Policies of a
Profit Maximizing Firm Journal of Economics 54(3), 195-225.
Krol, W. and Steil, H.U. (1997) International Activities in Environmental Protection and the Effects on
the German Nonferrous Metal Industry: a Review. Metall 51(3), 126-136. [in German]
Krugman, P.R. (1991a) Increasing Returns and Economic Geography, Journal of Political Economy,
99, 483-499
Krugman, P.R. (1991b) Geography and Trade, MIT Press, London.
Laffont, J.J. and Tirole, J. (1994) Environmental Policy, Compliance and Innovation European
Economic Review, 38(3-4) 555-562.
Laffont, J.J. and Tirole, J. (1996) Pollution Permits and Compliance Strategies Journal of Public
Economics, 1996 62(1-2)85-125.
Marquardt, K. and Nagel, R. (1992) Treatment of Wastewater from the Metal-Working Industry.
Chemieingenieurtechnik 64(1), 1-5. [in German]
Maynard, A.D, Northage, C., Hemingway, M., and Bradley, S.D. (1997) Measurement of Short-Term
Exposure to Airborne Soluble Platinum in the Platinum Industry. Annals of Occupational
Hygiene, 41(1) 77-94.
18
Moulin, J.J., Wild, P., Romazini, S., Lasfargues, G., Peltier, A., Bozec, C., Deguerry, P., Pellet, F. and
Perdrix, A. (1998) Lung Cancer Risk in Hard-Metal Workers American Journal of
Epidemiology 48(3), 241-248.
Pearce, D. and Brisson, I. (1993) BATNEEC: The Economics of Technology-Based Environmental
Standards with a U.K. Case Illustration. Oxford Review of Economic Policy 9(4), 24-39.
Roberts, M.C. (1996) Metal Use and the World Economy. Resources Policy 22(3) 183-196.
Schwabe, K.A., (1999) The effects of separability on incentive-based instrument performance.
Economics Letters 63(3), 377-380.
Tremmel, G. (1992) Soil Contamination and Rehabilitation of Polluted Abandoned Sites of the Metal
Industry. Metall 46(9) 947-949. [in German]
Venables, A. (1994) Trade Policy under Imperfect Competition: a Numerical Assessment In: P.
Krugman and A. Smith. (eds) Empirical Studies of Strategic Trade Policy, Chicago University
Press, Chicago.
Vernon, R. (1966) International Investment and International Trade in the Product Cycle, Quarterly
Journal of Economics, 80 190-207.
Vernon, R. (1979) The Product Life Cycle Hypothesis in a New International Environment, Oxford
Bulletin of Economics and Statistics 41 255-67.
19
Appendix
The Data
The UK metal industry panel data used is comprised of the individual records of the Annual Census of
Production (ACOP) (now the Annual Business Inquiry). They are available from the UK Office for
National Statistics (ONS) branch located in Newport, South Wales. For each year there are two files
that can be merged to produce plant level data. One file covers the sample of establishments 9, known as
the ‘selected’ file, who were asked questions about financial matters (e.g. amounts spent on capital
expenditure, including any pre-production expenditure). The other file contains information (such as
employment and ownership structure) on ‘non-selected’ establishments (the remainder of the
population). Establishment level data can be ‘spread back’ to plants using employment shares and the
unique reference number allocated to each plant.
Using plant-level estimates of capital expenditure (on plant and machinery) based on acquisitions less
disposals and including pre-production expenditure, it is possible to estimate the capital stock for each
plant. Further, it is possible to do this using the same methods (and length-of-life assumptions) as those
used by the ONS when they calculate the ‘official’ estimates for the UK. Plant and machinery price
deflators, supplied by the ONS were applied to the data, to produce real gross investment in plant and
machinery by industry (see Harris and Drinkwater, 2000, for a discussion).
Estimates of gross value-added, were converted to real prices using 4-digit indices of producer prices
(inputs and outputs) provided by the ONS (i.e., we double-deflated using gross output and intermediate
outputs to obtain real gross value-added).
Regarding employment data, this was extracted from the individual records of the ACOP. These
estimates (together with the estimates for capital expenditure and output) were aggregated to the
industry level and compared to the published estimates in the various annual reports of the ACOP.
Establishments are either single plants or they make a return that covers several plants –details and
definitions are provided in the introductory notes for each annual census.
9
20
Typically, the margin of difference between the two estimates for ALL manufacturing industry was in
the region of 1%. Where differences did occur, this is likely to be due to the fact that the individual
returns database can have additional records added after the ACOP summary tables are compiled. We
used a more detailed procedure to obtain population weights (based on industries at the 4-digit level
sub-divided into size bands where this was possible) and in addition some errors (such as duplicate
cases) were discovered in the ACOP database.
21
Table 1: Definitions of variables used and (weighted) mean and standard deviation values
Variables
Proportion spending on:
Post-production capex
Definition
Assets used for post-production pollution
control and waste management
Assets used in production which through
improved technology reduce pollution
Spending on direct staff, material and
operating costs for pollution control,
treatment and monitoring and waste
reduction and management
Payments to others for treatment and
disposal of liquid and solid waste
Any of the above
Mean
S.D.
0.096
0.295
0.080
0.271
0.132
0.338
0.244
0.429
0.326
0.469
1.283
2.233
1.297
2.430
0.906
2.483
1.038
2.108
1.619
2.316
Frontier production function estimate of
technical efficiency (see Harris, 1999b)
Real gross-value-added in £m 1990 prices
the age of the plant (i.e. t minus year opened
+ 1, with all plants opening <=1970 coded
as 1970)
-0.622
0.642
-0.887
2.024
1.827
1.076
ln KL
capital-to-labour ratio (source of capital
stocks: Harris and Drinkwater, 2000)
-4.894
1.524
ln DEN
population density of the Local Authority
District in which the plant is located (people
per hectare)
Dummy coded 1 if EU-owned
Dummy coded 1 if US-owned
Dummy coded 1 if owned by Australasia,
Canada or South Africa
Dummy coded 1 if steel tubes
Dummy coded 1 if steel wire & products
Dummy coded 1 if other drawing, cold
rolling and cold forming of steel
Dummy coded 1 if aluminium and alloys
Dummy coded 1 if copper, brass & alloys
Dummy coded 1 if other non-ferrous
2.416
1.227
0.043
0.057
0.041
0.204
0.233
0.199
0.149
0.247
0.086
0.357
0.432
0.281
0.128
0.101
0.119
0.335
0.301
0.324
Process capex
Current (own staff)
Payments to others
Total
Amount (log of £’000) spent on:
Assets used for post-production pollution
Post-production capex
Process capex
Current (own staff)
Payments to others
Total
Independent variables
ln EFF
ln GVA
ln AGE
EU
US
AUS
SIC 2220
SIC 2234
SIC 2235
SIC 2245
SIC 2246
SIC 2247
control and waste management
Assets used in production which through
improved technology reduce pollution
Spending on direct staff, material and
operating costs for pollution control,
treatment and monitoring and waste
reduction and management
Payments to others for treatment and
disposal of liquid and solid waste
Any of the above
22
Table 2: Weighted FIML estimates of Heckman sample selection model: metal manufacturing 1991-94
Variable
Spend/not spend
ˆ
Amount spent > 0
z-value
(a) Post-production capital expenditure
ln EFF
0.031
0.39
ln GVA
0.102
3.77
ln AGE
-0.447
-7.61
ln KL
0.303
7.29
ln DEN
-0.090
-3.01
t


EU
0.131
0.77
US
0.523
3.67
AUS
0.526
3.30
SIC 2220
0.498
3.48
SIC 2234
-0.076
-0.54
SIC 2235
-0.576
-2.15
SIC 2245
0.542
4.16
SIC 2246
0.998
6.14
SIC 2247
0.806
5.82
Constant
1.040
3.36









Log L
p̂
Cont…
-1061.355
0.132
pˆ
x100
x i
0.58
1.90
-8.28
5.62
-1.68

2.58
11.84
12.11
10.80
-1.37
-8.16
11.98
25.24
19.45




N
Censored N
ˆ
z-value

0.798



-0.134
0.950
0.554
-0.305
0.474
0.118
1.373
0.804
0.733
1.202
2.334
-0.556
1.405
-0.781

19.68



-1.64
2.63
1.66
-0.96
1.40
0.31
1.80
2.59
2.14
3.77
4.39
-2.88
3.58
-2.80
1912
1608
23
(b) Process capital expenditure
ln EFF
0.165
ln GVA
0.055
ln AGE
-0.451
ln KL
0.363
ln DEN
-0.149
t

EU
0.113
US
-0.800
AUS
0.246
SIC 2220
0.139
SIC 2234
-0.722
SIC 2235
0.308
SIC 2245
-0.092
SIC 2246
0.236
SIC 2247
0.137
Constant
2.021






Log L
p̂
-1044.655
0.132
1.89
1.94
-7.31
8.30
-4.59

0.67
-3.74
1.48
0.94
-4.60
1.84
-0.65
1.28
0.88
6.30



N
Censored N
(c) Current expenditure on staff, etc.
ln EFF
-0.277
ln GVA
0.110
ln AGE
-0.282
ln KL
0.192
ln DEN
-0.152
t

EU
0.200
US
-0.632
AUS
0.885
SIC 2220
0.045
SIC 2234
-0.701
SIC 2235
-0.557
SIC 2245
0.051
SIC 2246
1.017
SIC 2247
0.549
Constant
0.934






Log L
p̂
Cont.
-1502.077
0.214
-4.19
4.31
-5.30
5.34
-5.38

1.33
-3.72
5.80
0.34
-5.41
-2.96
0.42
6.51
3.95
3.40



n
Censored N
3.07
1.03
-8.38
6.74
-2.77

2.20
-10.33
5.08
2.70
-10.67
6.40
-1.65
4.77
2.68





0.892



-0.203
-0.196
-1.558
0.109
0.753
1.666
0.919
1.758
2.094
1.850
2.024
-0.505
1.330
-0.671

23.65



-2.76
-0.55
-2.43
0.35
2.53
3.13
3.07
5.43
6.29
6.83
4.16
-1.92
2.67
-1.90

0.908



-0.067
-0.773
-0.457
0.954
-0.909
1.018
0.586
0.394
-0.692
0.825
2.374
-0.553
1.374
-0.760

27.48



1.13
-2.90
-1.08
4.86
-3.65
2.84
1.26
1.78
-3.44
4.03
7.42
-3.02
4.05
-3.02
1912
1627
-6.84
2.72
-6.97
4.74
-3.77

5.24
-12.73
26.66
1.11
-15.25
-11.60
1.26
30.79
15.33




1912
1430
24
( d ) Payments to others
ln EFF
-0.147
ln GVA
0.104
ln AGE
-0.195
ln KL
0.142
ln DEN
-0.038
t

EU
0.371
US
-1.125
AUS
0.420
SIC 2220
0.639
SIC 2234
-0.478
SIC 2235
0.060
SIC 2245
0.700
SIC 2246
0.639
SIC 2247
0.500
Constant
0.440






Log L
p̂
-2198.781
0.375
-2.39
4.46
-4.18
4.49
-1.55

0.25
-6.08
2.83
5.20
-4.15
0.43
6.08
4.29
3.80
1.91



N
Censored N
-4.77
3.40
-6.37
4.62
-1.23

1.21
-29.05
14.20
21.72
-15.67
1.99
24.17
21.63
16.92





0.967



-0.157
1.058
-0.896
1.008
0.464
0.195
0.395
0.479
0.981
0.449
0.344
-0.624
1.289
-0.805

34.77



-3.88
5.58
-2.26
5.14
2.67
0.91
1.85
2.77
5.17
2.49
1.44
-5.52
14.72
4.08
1912
1082
25
Figure 1; Technology Choice, Ownership and Productivity
Figure 2: Ownership, Resource Productivity and Capital Intensity
26
Figure 3: Average (real) pollution control expenditure per plant*, 1991-1994, by industry: all
manufacturing
* includes only plants with positive expenditure
Data weighted by population weights
Note, the (population weighted) estimate of total spending in all industries was £738.1 million p.a. for
the 1991-94 period.
27
Figure 4: Average (real) expenditure p.a. on pollution expenditure control* (and percentage of plants
with positive expenditure), 1991-1994: all manufacturing
* includes only plants with positive expenditure
Data weighted by population weights
28
Figure 5: Average (real) expenditure per plant*, 1991-1994, by type and ownership group: all
manufacturing
* includes only plants with positive expenditure
Data weighted by population weights
29
Figure 6: Average (real) expenditure p.a. on pollution expenditure control* (and percentage of plants
with positive expenditure), 1991-1994: metal industry
* includes only plants with positive expenditure
Data weighted by population weights
30
Figure 7: Average (real) expenditure per plant*, 1991-1994, by type and ownership group: metal
industry
* includes only plants with positive expenditure
Data weighted by population weights
31