Environment firms productivity downturn, decline in competition and

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Environment firms productivity downturn, decline in competition and

Environment and economic activity have been commonly thought as notions with
complicated trade-off between each other. Their interaction over decades was a subject
of the heated debates, thus no surprise this reflected in a various theoretical
and empirical studies. Especially they have escalated after the appearance of so
called “Porter Hypothesis” (PH), by Porter
(1991) and Porter and van der Linde
(1995), suggesting that stringent environmental regulation induce innovations
which in turn increase the overall competitiveness at the industry level
through superior productivity. Authors advocate that accurately constructed
environmental policies can bring benefits through process balancing –
substituting input resources, reducing production disruptions, usage of less
costly materials or better utilization of them, and product balancing – through
improvement in its performance or average quality, reducing costs by eliminating
expensive materials and shrinking in disposal costs. The central message is
that strict environmental policies can enhance productivity by triggering
innovations.

Figure 1. Schematic
representation of the Porter Hypothesis

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Source: Ambec et al. (2013) after Porter (1991)

 

As it confronted heavily the traditional view that environmental regulations
are those instruments that negatively affect productivity (Jaffe et al. (1995), Gray (1987), Barbera and McConnell (1990)) it
is also criticized in sense of undermining competitive abilities of the firms
to seek their profit-maximizing behaviour, thus to act rationally (Palmer et al. (1995)). The main argument
against is summarized in the economist’s well-known saying: “There are no free
lunches” (Sinclair-Desgagné 1991,
2). Means that, innovation itself are financially demanding, and while
computing opportunity costs with including stronger environmental regulations
on firms, it obviously leads to rise in production costs, which prevail their
revenue (eg. Sinclair-Desgagné (1991), Ambec et al. (2013)). To these
conclusions considerably contributed US industrial slowdown in 1970s. For
instance, Jaffe et al. (1995) suggested
that it is large abatement costs responsible for US firms productivity downturn, decline in competition and also in
charge for pushing companies to reallocate their manufacturing processes to
another countries. On the other hand, as stated by Ko?luk and Zipperer (2014), Porter
and van der Linde (1995) these older studies have considerable
identification issues, as are concentrated mainly on domestic effects and
undervalued environmentally friendly innovations, neglecting industry and
country-specific effects.

This literature review concentrates mainly on more recent empirical
works which are grouped according to the regions or industries covered, as results
are very controversial. For instance, the assumption that environmental
stringency might increase productivity received a lot of critics from
neoclassical viewers as it is difficult to assimilate all the relevant factors of
its arguments in the theoretical models (Broberg
et al., 2013). According to Smith
and Walsh (2000) there is a problem of methods used to identify the forces
to productivity adjustment, thus in practice it is complicated to reject Porter
Hypothesis. Suggesting that there are “no painless environmental policies” Smith and Walsh (2000,74) claim that
productivity is rather harmed by costly environmental regulations.

Prevailing empirical studies to some extent
are concentrated on one of the three variants of Porter Hypothesis defined
by Jaffe and Palmer, 1997: the “narrow”, “weak” or “strong”. The
“narrow” version suggests, that under specific types of environmental policies
innovation and productivity benefit. “Weak” says that regulations will trigger
a certain type of innovation as firms likely to invest in other activities
compared to those they could have done without new policy constraints. While
“strong” states that with a “kick” of a new tighter regulation, companies broaden
and optimize their decisions and processes what leads to increase in productivity.
The authors own empirical results go along with a “weak” form, also stating
that coefficients greatly differ across industries.

While analysing most studies in the field, Ko?luk and Zipperer (2014), found that consequences vary a lot,
however, with traditional measures at the plant-level, among regulated and
non-regulated plants, outcome is negative, but not very robust. The effect
depends significantly on the particular plant characteristics. The results from
the industry level show that early studies commonly view environmental
stringency policies as a negative burden for productivity, while more recent
ones have evidence of no or positive linkage between them.

As a cross-country analysis suffers from the lack of reliable and
comparable method of estimation, Botta
and Ko?luk (2014) constructed the proxy – environmental policy stringency
index (EPS), that includes aggregated and scored instruments, related to
climate and air pollution and allows for quantitative and qualitative measure
of environmental regulation tightness over a long-time period. They assume
stringency as the “cost” on an activity, which is damaging the environment. It
rises the opportunity costs of polluting, therefore, providing an incentive for
environment-friendly activity. The index ranges from 0 (not stringent) to 6
(the highest degree of stringency) and covers most OECD countries from 1990 to
2012. The indicators are divided into market-based (e.g. taxes on pollutants or
other environmentally harmful activities) and non-market-based instruments
(e.g. subsidies for environmental favourable activities). According to the
results, indicators showed positive and significant correlation with GDP as
well as with Environmental Performance Index (EPI) and negatively associated
with  emissions per unit of GDP in nominal and PPP
values. Besides, market-based instruments are significantly correlated with
Green Patent Index, what suggests that this type of instruments positively
affect the “green innovations”.

With a help of the same EPS index, Albrizio
et al. (2017) empirically tested the “strong” version of Porter Hypothesis –
if the stringent environmental policy induces productivity, using a panel of
OECD countries on multi-factor productivity (MFP) growth. For the estimation, authors
combined the industry and firm-level impacts and policy instruments, according
to their price mechanisms – market-based and non-market ones. The panel includes
11 OECD countries and 22 manufacturing sectors over the time period 2000-2009. To
track the effect, a three-year moving average has been selected for lagging EPS
in time for both levels – industry and firm. This moving average is also used as
an interaction term with the distance to the global frontier. At the industry
level, findings report that tightening in environmental policy have a positive
short-term effect on productivity growth in countries, where industries are
technologically developed. This impact fading with the increasing distance to
the global frontier and becomes not significant far from it. The overall
estimated marginal effect from the industry analysis depends on the
technological stage of the country-industry pair and the global frontier. The
firm analysis results only partially confirm these positive findings – only
one-fifth of the firms benefit, while the least productive in the sample
experience a negative effect. The authors suggest that this difference between
firm and industry level may be due to the sample composition in terms of
countries or years included or to the entry-exit dynamics of the firms, in
which the least efficient firms will exit the market, what will increase
overall productivity of the industry. However, the empirical result explains only 16,5% of overall
variation, yet the most results are significant at 1% level. Lanoie et. al (2011) argue that their
study is the first to empirically detect the impact of all three channels of
Porter Hypothesis. As for its “strong” variation, the proxy for business
performance and environmental policy instruments – command-and-control
regulation, environmental related taxes were used. The database consists of
4,200 facilities in 7 OECD countries – USA, Canada, Japan, Germany, France,
Hungary, and Norway. Output presents that the negative impact of direct
stringency on productivity (-0.078) is detected. However,
indirect effect (through environmental R) is positive.

Jaraite and Maria (2012) studied the effect of environmental policy in terms of
“enhancing performance of the European Union’s  Emissions Trading Scheme” on efficiency and
productivity of power generation across EU states over the time period from
1996 till 2007. It also contains  as unwilling output and consists of emissions,
generated from public electricity, combined public heat, power generation and
public heat plants. Types of inputs such as labor, fuel inputs and net
installed electrical capacity are included to the estimation. The key result
shows that the price of emissions positively effects the efficiency, however,
there are no significant effect observable for productivity. Similar results
received Rubashkina et al. (2015), analysing policy stringency on the productivity
growth in 17 European countries from 1997 till 2009, with a focus on
manufacturing sectors by the instruments variable approach. Pollution abatement
and control expenditures (PACE) were used as a proxy for the environmental
regulation stringency, while total factor productivity (TFP) for the sectoral
economic performance. Productivity equations were estimated in both – level and
growth rates. The results showed any significant effect of policy stringency on
the factor productivity, across different specifications and regardless
controls used. Interestingly, the model outcome represents that higher R
investments do not contribute to the productivity of the certain country-sector,
even more – additional patents can even decline its productivity. However, Franco and Marin (2017) checked
for the environmental tax stringency on innovation and productivity for 13
manufacturing sectors with a panel of 8 European countries between 2001 and
2007 not only in within-sectors but also in upstream and downstream sectors.
The main results state that downstream
policy stringency is the most relevant to productivity and innovation growth, however,
within-sector regulations have positive the impact only on productivity.
Besides, upstream regulations are negatively correlated with productivity. The
possible reason might be that higher taxes imposed on downstream sectors forces
their connecting upstream sectors to innovate and generate new technologies
which boost the performance of downstreamers.

Aiken et.al
(2009) specified regulated
and unregulated production boundaries to determine relation between pollution
abatement expenditures and productivity changes across manufacturing sectors in
Germany, Japan, Netherlands and United States from 1987 through 2001 by
“assigned input” model. The evidence display that pollution abatement expenses
does not have significant negative influence on productivity growth. The survey by Rexhäuser
and Rammer (2014) attempts to track the effect for Germany. More
precisely, how is the profitability affected when innovations are induced by voluntary
applied environment regulation. The research is based on the collected firm-level
information on environmental innovation in Germany for different pollutants and
whether the innovation was induced by governmental environment regulation or
not. The findings state that innovations that do not enhance resource
efficiency do not positively influence the productivity and vice versa. This
effect applies for both types of innovations – regulation-induced and
voluntarily implemented, with a larger effect for regulative ones. However, the
paper states that results represent only the resource efficiency but not the total
efficiency (productivity), so according to authors this is the argument against
the Porter Hypothesis. Lundgren,
Marklund (2015) analysed how the firms environmental performance affect the
economic performance (measured as profit efficiency) in Swedish manufacturing
industry over the period from 1990 till 2001, have found that if environmental
performance is the result of the environmental policy then “it is not a
determinant for the profit efficiency”. And vice versa, when it is voluntarily
implemented, then it affects positively and significant, so the Porter
hypothesis is not supported. At the same time, Manello (2017) examines this aspect at international level, via
firm-level data of Italian and
German firms operating in the chemical sector during the period 2004–2007. For
the estimation DDF (difference-in-difference) framework is used to neutralize
the potential difference between economies in order to test the “win-win”
opportunities, means if company is subjected to more stringent environmental
policy, its investment in innovation able to lift up the productivity and
simultaneously cut emission quantities. The result states that the average
distance to the frontier decreased over the years. This demonstrates that
plants which are implementing the best technologies available, overcome those, adopting
technologies with less strict environmental requirements. Generally, the
distance between the industries in two countries started to decrease after
initial shock of the European Pollution Release and Transfer Register (E-PRTR)
established in 2001. The overall result, estimated by Sequential
Malmquist–Luenberger indexes (SML) confirmed a support to the “win-win”
opportunities for both Italian and German firms and also demonstrated significant
correlation between policy stringency and TFP (Total factor productivity)
growth indexes. Chatzistamoulou et al.
(2017) estimates the changes of productivity output in Greek manufacturing
industries between 1993 and 2006 after the implementation of the Kyoto’s
protocol, devoted to balance the operating expenditures to provide pollution
abatement initiatives. The study uses industry-level balanced panel with 4700
plant’s abatement expenditures (pollution abatement index – PAI, following Aiken, (2009)) as a proxy for the
policy stringency. The empirical outcome shows only insignificant result on the
productivity growth with a considerable variation across industries.

On the other hand, Managi et al.
(2005) question the relationship between environmental regulations,
technological innovation and productivity growth in the offshore oil and gas
industry through a unique micro-level data set from the Gulf of Mexico. Analysis was done through implementing
standard statistical causality tests to detect relationship between different
productivity indexes and regulations with DEA (Data Envelopment Analysis) to
measure changes in productivity from 1968 to 1998. Authors argue that
particular model have been chosen as it helps to decompose productivity part
and gives a possibility to measure dynamics of different components over time.
Output shows that despite increase in stringency of regulations, productivity
in the market enlarged considerably. Survey on productivity growth and
environmental regulation in Mexican and U.S. food manufacturing industry conducted by Alpay et. al (2002), implies dual profit model assuming that profit
changes can be enforced by technological development, price adjustment or
equilibrium attaining. The data series on profits, prices, capital stocks, and
environmental regulatory activity in Mexico and the United States were used for
the estimating model from 1971 to 1994 for Mexico and from 1962 to 1994 for the
US. The results report that regulatory inspections (as proxy for environmental
policy stringency) in Mexico have increased in average 2.8% of primal
productivity growth, however no clear impact of pollution abatement regulation
on manufacturing productivity in the US. Berman and Bui (2001) investigated
the effect of air quality regulation on productivity of oil refiners.
Interestingly, they used a direct measure of a local pollution regulator on the
most heavily regulated oil refiner in in the US – Air Basin (South Coast), Los
Angeles and compared to other US regions in the second step. The total
productivity was obtained as a sum of the data on physical amount from detailed
products and materials in the Census of Manufacturers. The fixed-effect model
used in order to allow for the heterogeneity across plants and also to allow
for regulation differences that influence an abatement. Inputs, which are
limited by the policy are quasi-fixed: pollution abatement capital and
abatement operating costs (which include costs of labor, materials and
services). Labor, material and capital are the chosen variables. The final
estimated results cover the time of sharp stringency in regulations between
1979 and 1992 and demonstrate that productivity in Air Basin considerably
increased. Even during the period of the most stringent regulation – from 1987
to 1992, refineries in South Coast still experienced growth, compared to
falling production in other US regions covered by less strict policies. At the same time, Greenstone
et al. (2012) conducted the research on how the air quality regulations
influence the manufacturing plants productivity (TFP) levels in US. Using
detailed production data of around 1.2 observations from Annual Survey of
Manufacturers from 1973 till 1993, found that stringency in the policy leads to
the around 2.6 percent decline in TFP among surviving plants. Lanoie et al. (2008) in their empirical
analysis, using GLS model to track the impact of environmental regulation
stringency on the total factor productivity (TFP) of Quebeck manufacturing
sector, stated and tested three types of assumption: 1. Dynamic assumption of
the Porter hypothesis through the lagged variables; 2. Authors consider that
effect is more observable in the more polluting industries; 3. Impact is
greater in internationally competitive sectors. When all sample is used, results
support the hypothesis that more stringent environmental policy leads to
positive outcome for productivity, however only in dynamic case. This result is
greater for the industries that are more involved in international competition.
With regard to more polluting industries, opposite have been revealed.

Empirical research by Hamamoto (2006) devoted to study,
whether the stringency of environmental policy in five manufacturing industries
in Japan have an effect on R activity and therefore is responsible for
the productivity growth in 1960s and 1970s. The empirical result implies, that
relationship between pollution control and investment on innovations, using data
from 1996 to 1976, are significant and positive. Afterwards, findings
demonstrate that pollution control expenditures decrease average age of capital
stock and have a positive impact on modernization at 10% significance level.
Further, it is found that increase in R investment, induced by increased
policy regulations contribute to the total factor productivity growth in the
stated period. Similar results were received by Yang
et al. (2012) which examine
Taiwanese manufacturing industries between 1997 and 2003 to find if
environmental policies (pollution abatement costs as a proxy) induce R
and productivity. The findings show that stricter environmental protection have
positive correlation with R what in turn has a conclusive influence on
productivity growth.

Referring to the industries, Rassier and Earnhart (2010) test the
“strong” version of the Porter hypothesis by analysing the impact of the waste water
policy, measured by “waste water discharge limits” on the profits of the firms
(proxied by the return of scales) operating in the chemical manufacturing
industry. Authors used a panel data analysis with a sample of quarterly data,
which consists of 926 observations, including 59 chemical manufacturing firms and annual data with 337
observations, consisting of 73 firms over ten years (1993-2003). The model
includes several controls: sales
growth, capital intensity, age
of assets, size, market share, industry concentration. Empirical results demonstrate
that stringent clean water regulation serves as a negative factor for the firms
profitability, showing with 90% confidence, that 10% increase in tightening
discharge limit, declines return on sales by 1.7%. Nearly the same
results receive Gray and Shadbegian
(2003), assuming that higher pollution abatement costs have
a negative influence on productivity, analysing 116 pulp and paper mills
between 1979 to 1990, with a substantial difference in integrated and
non-integrated ones.

However, Sadeghzadeh (2014),
developed a model to look over not only on productivity, but also on competitiveness.
He suggests, that environmental regulation contributes to the productivity
growth by reallocating input from less productive to more productive firms,
making former to leave the market. Thus, market becomes more productive
compared to that it was before, however less competitive. This enables
remaining firms to increase the prices what in turn harms welfare. The main
findings also indicate that tighter environmental regulations deliver strong
incentives to adopt cleaner abatement technologies, thus more stringent policy
leads to increases in average productivity and environmental quality or a
“win-win” situation. This conclusion is partly supported by Xepapadeas and de Zeeuw (1999) results, which indicate that
stringency in the environmental rules will not provide a “win-win” situation in
sense of reducing emissions and increasing profitability, but productivity is
supposed to increase due to induced modernization of the capital stock by the
stricter policy.

One possible reason
for such a mixed evidence in the literature might be, according to Ambec et al.(2013), that the prevailing
number of previous studies have reckoned only the static dimension, where
technology, processes, products and consumer preferences are fixed. While the
actual dynamic competition reflects the reality with changing technological opportunities
combined with incomplete information and complications in adjusting individual,
group or corporate incentives. This means, if policies trigger innovations,
which in turn reduce inefficiency, costs and stimulate technology development
and therefore growth, they simply need time to occur by adapting optimally to
the new regulations. However, the most number of works regress proxied
stringency of regulation at time 0 on productivity at the same time 0, eventually
it says nothing. For instance, the studies which used the lags of three or four
years between regulation stringency and changes in productivity allows for
dynamic effect, as in Lanoie et al. (2008)
or Managi et al. (2005) lead to the
positive outcome.

The second misunderstanding
that not all policies, but only well-designed ones (eg. stringency,
flexibility, predictability and competition friendliness) can contribute to
productivity growth (Ko?luk and
Zipperer, 2014. Ambec et al. (2013)).
Besides, market-based and
flexible instruments like tradable allowances or performance standards are more
favourable for innovation than technological ones as they give more variation
to find the best suited technological solutions to minimise the expenditures
due to compliance. For example, well-defined property rights for innovations
and R activities can benefit innovating firms and slow down diffusion.
This view is also supported by
Sadegzadeh (2014), stating that
If the encouraged technological change is a principal source of productivity enforcement,
then the environmental regulations lead to explained by Porter, Pareto
improvement or a “win-win” situation by not only protecting the environment,
but also stimulating aggregate competitiveness and productivity. 

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