WK:b IZ51Q
POLICY RESEARCH WORKING PAPER   1810
Inspections and Emissions                                       a~peo
p'' nts in .. A.     direct A
in  India                                                     rtMun-  y pesreQ pn
maorp ro~i red anrg.
Puzzling Survey Evidence about                                 eison    rdofolrmak
Industrial Pollution                                           nptIrposbibeae
fw    t . ow pro ,b,,ty Of
enforc.ehnt and the low
Sheoli Pargal
Muthukumara Mani
Mainul Huq
The World Bank
Development Research Group
August 1997                                                           U



POLICY RESEARCH WORKING PAPER 1810
Summary findings
Industrial plants face pressure to abate water pollution       They find that high levels of pollution in India elicit a
from many sources, national and local, through formal        formal regulatory response: inspections. But inspections
government regulation and through more informal              are ineffective in bringing about changes in behavior,
pressure from consumer groups and concern for the            probably because of bureaucratic or other problems in
firm's reputation.                                           follow-through. Moreover, poorly paid inspectors with
Formal regulation tends to reflect the bargaining          low morale may be susceptible to "rent-seeking."
power of local communities and is not as uniform or            They find little evidence to support the hypothesis that
blind as the law would imply. Regulators are not immune    better-educated and higher-income communities are
to the pulls and pushes of powerful community interests.    better able to pressure plants to reduce emissions than
Studies of enforcement in the U.S. steel industry, for       are poorer communities, although there are significantly
example, find that it is weaker at plants that are major     more inspections in more developed districts. In India,
employers in the local labor market.                         whatever community pressure exists is probably
Using survey data from India, Pargal, Mani, and Huq        channeled through formal regulatory mechanisms.
examine whether the monitoring and enforcement                  Larger plants in India, as in the rest of the world, tend
efforts of provincial pollution control authorities are      to be "cleaner" than smaller plants. Indian policymakers
affected by local community characteristics (which serve     and regulators may want to explicitly recognize the
as proxies for political power). They also test for          tradeoff in environmental quality of the existing
evidence that informal pressure on plants results in         regulatory bias toward the small- and medium-scale
negotiated reductions in emissions.                          sector.
This paper - a product of the Development Research Group - is part of a larger effort in the group to study environmental
regulation in developing countries. The study was funded by the Bank's Research Support Budget under the research project
"The Economics of Industrial Pollution Control in Developing Countries" (RPO 680-20) and by a trust fund under the
research project "Social and Environmental Consequences of Growth-Oriented Policies." Copies of the paper are available
free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Eric Krapf, room N10-039x,
telephone 202-458-0513, fax 202-522-3230, Internet address ekrapf@worldbank.org. August 1997. (21 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the
countries they represent.
Produced by the Policy Research Dissemination Center



INSPECTIONS AND EMISSIONS IN INDIA
PUZZLING SURVEY EVIDENCE ON INDUSTRIAL WATER POLLUTION
by
Sheoli Pargal
Muthukumara Mani
and
Mainul Huq
The World Bank
Policy Research Department
1818 H Street, NW
Washington D.C. 20433
We thank Ken Chomitz and Zmarak Shalizi for valuable comments; David Wheeler for generous access
to the plant data used for this paper; and Adriana Castro for excellent research assistance.



I.    Introduction
Industrial plants face pressure to abate pollution from many sources, working through different channels.
On the one hand there is regulation by local or central pollution control authorities, who can formally
impose fines, closures or other types of punishment for violating regulatory provisions. On the other
hand are communities, which seek compliance with locally acceptable norms or standards.
Many cases of direct negotiation and "Coasian" bargaining between plant managements and local
inhabitants have been documented around the world. These informal arrangements may rely upon
reputational concelns, direct threats or social pressure. Of course, reputational and other concerns can
play out through the market as well, e.g., through consumer pressure in export and domestic markets, or
through the impact on stock prices, bank lending rates, etc.2 This impact has sometimes been explicitly
leveraged by regulators via public information campaigns and various types of "Green" labeling
schemes.3
At the same time, it is recognized that formal regulation, especially the monitoring and enforcement of
standards, tends to reflect the bargaining power of local communities and is not quite as uniform or blind
as the law would imply. Regulators are not immune to the pulls and pushes of powerful interests at the
community level. Especially in a democratic polity, local political power cannot be ignored when
deciding where to target limited resources. Gray and Deily (1996) show that enforcement in the US steel
industry is lower at plants which are large employers in the local labor market. Dion et al. (1997) find
considerable local variation in the stringency of monitoring of compliance with uniform national
standards in the pulp and paper sector in Quebec. There may be no objection on economic efficiency
grounds if regulators end up focusing on areas where activism, political power, or incomes are high,
since these would be the very areas which would have a higher valuation of environmental damages.
While such outcomes tend to offend the sense of equity that is implicit in uniform national regulation,
they are problematic only if they result in a diversion of resources from "objectively" serious
I   For instances see Pargal and Wheeler (1996), Hettige, Huq, Pargal and Wheeler (1996).
2   See Hamilton (1993), Arora and Cason (1996).
3   For example: the TRI in the USA, PROPER in Indonesia.
2



environmental problems to more frivolous issues.4 As is well known, it is entirely possible that the
regulatory process might be "captured" by particular interest groups (Stigler 1971, Peltzman 1976).
Firms factor in local activism as well as the strength of likely regulatory pressures when making location,
production, or pollution abatement decisions. For instance, studies on the exposure of different
population subgroups to environmental pollution5 have noted that hazardous waste generators in the US
tend to be disproportionately located in disadvantaged areas, leading to questions of regulatory
discrimination on income or racial grounds.6 While discrimination has not been conclusively
demonstrated by any study, Hamilton (1993) has shown that commercial hazardous waste generating
firms do take explicit account of the ability of a community to organize politically, as proxied by voter
turnout rates, when making capacity expansion decisions. Informal arrangements between plants and
communities are, thus, likely to be complementary to existing formal regulation, and usually exist
alongside the latter.
In this paper we use survey data from India to examine whether the monitoring and enforcement efforts
of provincial pollution control authorities are affected by local community characteristics that act as
proxies for political power. At the same time we test for evidence of informal pressure on plants that
would result in negotiated lower emissions.
We follow the basic approach taken by Pargal and Wheeler (1996) in modeling pollution emissions as
the equilibrium outcome of a demand for emissions or "use of environmental services" originating from
industrial plants, and a supply of environmental services being provided by communities. The supply
price of pollution is determined by the community's valuation of the damage caused by pollution and by
its ability to extract recompense from the polluting plants, i.e., its bargaining power. We think that the
negotiating strength of communities is likely to be highly correlated with income level, and correlates of
income like literacy. In the absence of district level data on income, we have used an indicator of district
development which incorporates literacy, urbanization, and other factors to proxy for negotiating power.
4   See for instance Dasgupta and Wheeler (1997).
5   See Gould (1986), Greenberg and Anderson (1984), Brooks and Sethi (1997).
6   Been (1994) has shown that race and poverty may be important factors in siting decisions for landfills and
other undesirable entities. She also shows that the impact of such entities on disadvantaged communities is
often exacerbated by the functioning of property markets.
3



The demand for environmental services from plants is a derived demand akin to other factor demands.
Demand shift factors include plant characteristics, external pressure to abate and the prices of other
inputs - all of which would affect the marginal cost of abatement.
This paper is organized as follows. Section II presents an overview of the state of environmental
regulation in India. We present our model and econometric estimation framework in section III and
describe the data in section IV. Estimation results are presented in section V, with section VI
concluding.
H.    Environmental regulation in India
There is a basic division of power between the centre and the states in India, reflecting the federal nature
of the Indian Constitution. The mandate of the Central Pollution Control Board (CPCB) is to set
environmental standards for all plants in India, lay down ambient standards, and coordinate the activities
of the State Pollution Control Boards (SPCBs). The implementation of environmental laws and their
enforcement, however, are decentralized, and are the responsibility of the SPCBs. Anecdotal evidence
suggests wide variations in enforcement across the states. In fact it has been argued (Gupta 1996) that
although states cannot compete by lowering environmental standards in order to attract new investment,
they can get around this by lax enforcement.
The two main pollution control statutes in India are the Water (Prevention and Control of Pollution) Act
of 1974, and the Air (Prevention and Control of Pollution) Act which came into being in 1981.
Thereafter, Parliament passed the Environment (Protection) Act in 1986. This was designed to act as
umbrella legislation for the environment, with responsibility for administering the new legislation falling
on the Central and State Boards. The law prohibits the pollution of water bodies and requires that
generators of effluent/ discharges get the prior consent of the SPCBs. This consent to operate must be
renewed periodically.
SPCBs have the legal authority to conduct periodic inspections of plants to check whether they have the
appropriate consent to operate, whether they have effluent treatment plants, take samples for analysis,
4



etc.7 Some of these inspections are also programmed in response to public requests and litigation. The
penalty for non-compliance is fines and imprisonment, but until 1988 the enforcement authority of the
SPCBs was very weak. It was limited to criminal prosecution (with its attendant delays) and seeking
injunctions to restrain polluters. Now, however, SPCBs have the power to close non-compliant factories
or cut-off their water and electricity by administrative orders. The potential cost to the plants of non-
compliance is thus not trivial, so there should be an incentive for plants to comply with the law.
However, compliance depends on both monitoring and enforcement of the law by the SPCBs.
Since water pollution regulations have been on the books longest, and there are well known and
relatively inexpensive means of testing these emissions, we decided to focus on water pollution
monitoring in this paper. Also, we examine the impact of inspections on water pollution emissions to
assess just how successful the laws are in their implementation. It is often the case that organizations
measure "success" in achieving their policy goals in terms of an increase in spending or the number of
actions taken, rather than outcomes. For instance, assessing performance by counting the frequency or
absolute number of inspections rather than the resulting environmental quality would be valid if, indeed,
inspections have an impact on emissions. In the Indian context, despite a strong legal framework and the
existence of a large bureaucracy for dealing with environmental regulation, the public perception is that
implementation remains weak.
Given the penalties in force for non-compliance in India and keeping in mind the extent of the SPCBs'
powers, it should be emphasized that the impact of inspections on compliance will be only as strong as
the threat of enforcement and punishment faced by the plant. In an environment of corrupt local
inspectors or bureaucratic procedures that hamstring action against errant behavior, inspections alone are
unlikely to be effective. Also, the reality is that resource constraints at the state level mean that
environmental management often degenerates into crisis management.8 Inspections are undertaken at the
time that operating consent is granted, and thereafter usually only in response to complaints, accidents or
other emergencies.
7   "...to inspect sewage or trade effluents, works and plants for the treatmnent of sewage and trade effluents... or in
connection with the grant of any consent as required by this Act." Water (Prevention and Control of Pollution)
Act, 1974.
s   Conversation with Utpal Mukhopadhaya, former Environment Secretary in Maharashtra, India.
5



In our sample of 250 plants, 51 plants indicated that they had undertaken abatement in response to NGO
pressure and 102 said they had done so in response to complaints from neighbouring communities. This
led us to conjecture that there would be a limited plant level response to inspections alone.
m.   Model and estimation
We assume a competitive industrial structure where firms take output and factor prices as given.
Pollution emissions are modeled as another factor of production in an extended KLEM framework.
Firms face a pote,ntial penalty for polluting which is a function of regulatory and community pressure.
This penalty is increasing in emissions levels, Z; in the intensity of the direct regulatory monitoring and
enforcement effort, 1i; and in community bargaining power, which is proxied by the level of
development, d, of the district in which the plant is located. The expression +(Z,d,g), where fz>O, Pg0,
Od >O, is a reduced form representation of the total pollution penalty faced by the plant. Thus each firm
minimizes costs as described below:
Min PL*L+ PK.K+PM*M+PE*E+(Z,d,4l), s.t. y <f(L,K,E,M,Z; 0)
L,K,E,M,Z
where y is output, L, K, E and M refer to labor, capital, energy, and materials respectively. 0 captures
exogenous shift factors. From the Kuhn-Tucker conditions for cost minimization we obtain the usual
marginal conditions as well as the following implicit equation for optimal emissions at an interior
optimum: Xfx - Px = 0, for X=L, K, E, M and Xfz - 1z(Z,d, .t) = 0, where X is the Lagrange
multiplier.
Solving this system of equations, we get a reduced form expression for equilibrium emissions. At a
given level of output, y, emissions are Z =  (y, PL . PK I PE ,pM; 0, X, li, d) . Reflecting the fact that
regulatory effort is not uniform but varies in accordance with different priorities, including the perceived
magnitude of environmental damage, degree of political power etc., inspections or monitoring, pt, are
modeled separately as lt = y (Z,d;8) with 8 being a set of shift factors.
Thus we model emissions as a function of factor prices, scale of operation, indicators of informal
pressure, regulatory pressure as measured by inspections, and shift factors (plant age, firm characteristics
like whether they export or not, and whether or not they are publicly traded, and sector). Plant
inspections, in turn, are modeled as dependent upon expected emissions levels, the use to which the
6



water in the effluent stream is put, plant age and size, state per capita GDP and extent of manufacturing
activity as measured by the number of plants, and the district's level of development. The latter is our
proxy for envirommental awareness and activism in the plant's vicinity that might lead to increased
inspections - in response to complaints or other types of citizen action.9 Although inspections are
expected to be a function of expected emissions levels, since past pollution levels predict current
emission levels very well (Magat and Viscusi 1990), we have used current emission levels to proxy for
expected emissions in the inspection equation.
Our empirical specification is as follows.
(1)    BOD emissions:
Z = 1o +P1I Size + P3 PL + P3 PE + P4 PK + 15J + P6age + P3exporter +f38employment share + P1sector + u
(2)    Inspections:
L = aO+al Z+a2 state manuf. + a3 per capita gdp+ a4 district development+ a5 age + a6 size +
a7recipient water body used for bathing + a8 recipient water body used by downstream industry +co
Here, u and 0) are error termns, assumed to be iid normal and uncorrelated across the equations. Given
the possible endogeneity of inspections and emissions, OLS is expected to lead to biased results. We
have hence used two-stage least squares (2SLS) for our analysis.
We are agnostic about the coefficient signs on factor prices, since they are inputs into both the
production of the final output, and emissions abatement, and can therefore have opposing effects.
Organic water pollution is usually due to greater materials intensity, so to the extent that different factors
are substitutes or complements of materials in production we would get appropriate coefficient signs.
We suspect that labor is a large component of abatement in the Indian context and so wages would be
expected to have a positive coefficient. But, to the extent that labor and materials are complements in
production, a move away from materials intensive methods would lead to a decline in organic water
pollution, leading to a negative relationship. Our initial hypothesis is that the production effect is likely
Recent work (e.g. Hettige et al (1996)) has analyzed the role of community action in directly pressuring
polluters in several countries of South and South-East Asia, and we conjecture that literacy, urbanization, and
income levels may be valid indicators of the same phenomenon in our sample. These variables are subsumed
in the district development index used in our analysis.
7



to outweigh the abatement effect.'0 Abatement requires both capital and energy so price increases for
these factors would be expected to lead to a decline in abatement and a consequent increase in emissions.
If the relationship between materials and energy or capital is one of gross substitutability, this would also
lead to a move towards more materials intensive production, which would reinforce the positive impact
on emissions.II
Our prior hypothesis was that large plants are likely to be less pollution intensive than small primarily
because of scale economies in abatement, but also since smaller plants might lack technical information
and access to financial resources, especially if capital markets are imperfect.12 It appears likely that
more concentrated industries would be more resistant to regulation, but since we lack data on the market
concentration rates in these sectors we cannot include a measure of market power in our regressions.
Recent work using US data has found that multi-plant firms are more likely to be compliant than single
plant firms, partly because there may be spillovers of a non-compliant reputation across plants belonging
to the same firms leading to more stringent enforcement of regulations (Gray and Deily 1996). Thus we
expected multi-plant firms to be more likely to have lower emissions than single plant firms, ceteris
paribus. As mentioned earlier, we expected sensitivity to environmental news, and hence greater
compliance and lower overall emissions on the part of traded firms than non-traded. Finally, based on
anecdotal evidence, we expected plants with a greater exposure to foreign ideas and influence, like
exporters, to be more likely to be in compliance than domestically owned plants.
XV.   Data
As described above, the source for all the plant-level data was a survey of industrial plants in 8 states of
India conducted in early 1996. Its coverage was fairly good both in terms of sector and state-wise
distribution. Leaving out the plants in the cement sector, Table I lists the distribution of plants by state
and Table 2 the distribution of plants by sector. In addition, we used district and state level data on
'° The argument on production is more compelling for materials prices - which we do not have. Instead, we have
used sectoral dummies to capture the very different materials intensity of production in different sectors.
" For a discussion of these issues in greater detail see Pargal and Wheeler (1996).
12 The survey does not cover the huge informal small scale sector in India since those plants rarely have the
means to test effluent or install pollution control equipment, and therefore do not have an accurate idea of their
emissions levels or compliance status.
8



prices and socio-economic characteristics. Data sources are listed in Appendix Table IA, and summary
statistics on the variables used in our regressions appear in Appendix Table 2A.
Table 1.
State              Number of Plants
Andhra Pradesh      30
Delhi               12
Gujarat             45
Karnataka           24
Maharashtra         25
Tamil Nadu          36
Uttar Pradesh       58
West Bengal         20
Total              250
Table 2.
Sectors                   Number of Plants
Leather                   40
Oil and Petrochemicals    22
Paper                     68
Iron and Steel            55
Synthetic Fibre           11
Textiles                  54
Total                     250
The two main plant level variables used in the study are emissions and inspections. We have focused on
Biological Oxygen Demand (BOD), the most commonly used measure of organic water pollution in both
industrial and developing countries.13 It is usually measured by the amount (kg) of oxygen used over
five days to completely oxidize the organic pollutants in the effluent. The monthly average 5-day BOD
measure generated by each plant is used in the analysis. Our inspection measure is the total number of
inspections that each plant has been subject to between 1990 and 1994. Table 3 provides the mean
number of such inspections per plant in our sample in each state, along with the total number of large and
medium plants in the state.
13  BOD measures the oxygen used during the breakdown of organic pollutants by naturally-occuning micro-
organisms. This process removes dissolved oxygen from the water and can seriously damage some fish
species as well as accelerate the growth of undesirable algae.
9



We have used the number of years the plant has been in operation to measure the age of the plant. The
export variable is a dummy indicating whether the plant is an exporter or not.
Table 3.
State        Average number of inspections    Total number of
per plant between 1990 and 1994    plants in the state
Andhra Pradesh                 44                              15972
Delhi                          11                               3346
Gujarat                        27                               1104
Karnataka                      15                               5850
Maharashtra                    36                              15264
Tamil Nadu                     43                              15502
Uttar Pradesh                  15                              10124
West Bengal                    10                               5679
Since data on per capita income or consumption at the district level was not available, we have used
instead an index of district development which was created by the Centre for Monitoring the Indian
Economy (CMIE). This is a weighted average of the value of sectoral output, the level of bank credit to
each primary sector, labor force participation rates, savings rates, literacy and urbanization.'4 We expect
it to be highly correlated with income.
We use per capita state domestic product to measure the overall level of development of the state, which
is a proxy for state-level interest in environmental regulation. We expect more developed states to focus
more on environmental regulation. The number of large and medium manufacturing facilities that are
subject to regulation is used to indicate the size of the regulated manufacturing sector in the state. In the
absence of relevant district level data on labor and energy prices, we have used the state level
manufacturing wage and electricity tariff for industry. Given the substantial heterogeneity that exists
across states (and homogeneity within states) in factor prices, our variables appear to be good proxies.
The rate of interest is the state-wide average rate at which the firms can borrow.
14  The population density of the district as well as the population in the surrounding community were considered
as other possible proxies, but not used due to their high collinearity with the development index.
10



V.    Estimation results
We feel strongly that the inspection and emissions equations are not independent. We expect high
emissions levels to trigger inspections and a significant impact of inspections on emissions. Also, many
of the same variables are expected to determine inspections as emissions. We have hence used two stage
least squares methods to estimate both equations. We report both 2SLS and OLS estimation results in
Tables 4 and 5 below. From the similarity of coefficient estimates under both OLS and 2SLS for the
BOD equation, it appears that inspections are truly exogenous as determinants of BOD emissions levels.
On the other hand, this is not the case for the inspections equation. 5 First stage regression results are
reported in the Appendix (Tables 3A and 4A).
Table 4.     DEPENDENT VARIABLE: LOG NUMBER OF INSPECTIONS
Independent Variables    OLS                     2SLS
(in logs)
Coeff.      T-stat.     Coeff.      T-stat.
BOD load                      0.120      1.918*       0.235     2.116**
Output                        0.024       0.345       -0.042      -0.516
Age of Plant                  0.108       0.503        0.138       0.626
Development Index             0.669     4.299**       0.671      3.563**
Number of Plants in State      1.153     3.05**        1.027     2.697**
Per Capita State Domestic     0.361       0.718       0.571        1.039
Product
Water Used for Bathing        0.786     2.872**       0.616       1.900*
Water Used for Industrial     -1.146    -2.741**      -1.323    -3.089**
Purposes
Constant                    -16.802    -3.185**      -18.224    -3.253**
Number of Observations           75 l                    71
Ri                      l      0.37 l                   0.36
* * significant at 5% confidence levels
* significant at 10% confidence levels
The inspection equation indicates that inspections are focused on plants whose emissions levels are high,
and whose effluent flows into water bodies which are used for non-industrial purposes.16 Inspections
15  A Hausman test failed to reject the hypothesis of exogeneity for the two equations.
16  End-use of the water body receiving the effluent was classified among bathing, irrigation, drinking, fishing,
industry, recreation, and other. The use for bathing was highly correlated with fishing (0.62), irrigation (0.31)
and drinking (0.53), so we used a dummy for bathing in the analysis.
11



also occur significantly more frequently in more developed districts. They are strongly positively
associated with the size of the manufacturing sector as measured by the number of plants in the state and,
unsurprisingly, are not related to plant characteristics like age and size once total emissions have been
controlled for.
The large positive and significant elasticity of inspections with respect to the number of plants in the
state indicates that the latter may be a structural determinant of funding for monitoring and enforcement.
In other words, funding for inspectors and equipment appears to be allocated by state pollution control
boards in line with the perceived magnitude of the regulatory problem. The strong positive relationship
between inspection rates and district development is interesting, although not entirely surprising. The
economic efficiency argument for targeting monitoring resources to areas where the valuation of
damages and willingness-to-pay to avoid damage is relatively high has been mentioned earlier. In
addition, it may well be the case that these areas are perceived as being more likely to complain or make
use of political or other channels to pressure regulators to take action against polluters. If this were a
diversion of regulatory resources away from legitimate problems it would be a concern on both ethical
and economic grounds. However, from the results on load and end-use of effluent it appears that
inspections do take place in response to the correct signals on enviromnental damage.
In this regard, the BOD equation is somewhat surprising since it indicates that total BOD emissions are
unaffected by inspections.'7 This raises obvious questions about the nature of enforcement, including the
level of fines and probability of punishment, as well as the possibility of corrupt inspectors. At the same
time it supports the view of Indian analysts that abatement equipment is only activated when inspections
are scheduled to occur. 18
A particularly intriguing aspect is why inspections should be significantly higher in more developed
districts. If this is in response to expectations or actual exertion of community pressure, as has been
posited above, then, given the lack of a plant response to inspections, one wonders about the rationality
of such expectations or such pressure. A somewhat depressing alternative explanation is that richer
17 The contemporaneous nature of this dataset leaves us with no way of assessing whether there is, in fact, a
lagged impact with inspections affecting BOD emissions in the next period.
is Communication of Vandana Bhatnagar, TERI.
12



districts may be where opportunities for rent seeking by state inspectors are highest, resulting in perverse
incentives to inspect (to say the least).
Returning to the estimation results, we note that BOD load is increasing in output, but at a decreasing
rate, so that per unit output larger plants are cleaner than smaller ones. This is a very robust result and
corroborates evidence from other countries. Plant age is not a significant variable.19 The export dummy
is negative and close to significance, indicating that foreign influence may not be as big an influence on
clean production or abatement as has been hypothesized in the literature.20 Again this is consistent with
the findings of other research.
Table 5.      DEPENDENT VARIABLE: LOG BOD LOAD
Independent Variables (in      OLS                     2SLS
logs)
Coeff.     t-stat.      Coeff.    t-stat.
Number of Inspections              0.165         1.368      0.279        1.168
Output                             0.366      3.944**       0.368     3.898**
Age of Plant                       0.287        1.041       0.264       0.931
Export dummy                      -0.754       -1.600      -0.750      -1.598
Share of Employment                0.546      2.998**       0.523     2.683**
Development Index                  0.247        0.638       0.204       0.556
State Rate of Interest           -10.481     -3.854**    -10.476    -3.765**
State Electricity Price            7.940      3.802**       7.487 X     3.1 **
State Manufacturing Wage          -1.647    -2.200**       -1.465    -2.158**
Paper dummy                        1.848      2.319**       1.868     2.305**
Textile dummy                     -1.277      -1.948*      -1.229      1.793 *
Leather dummy                     -0.324       -0.443      -0.228       -0.292
Constant                          16.842       1.716*      17.080      1.685*
Number of Observations                71                      71 _
___________________________        0.599                    0.596
* * significant at 5% confidence levels
* significant at 10% confidence levels
19  A more accurate measure of age would be the vintage of capital. Although our data did include a measure of
the average age of capital stock, too few plants reported it for us to be able to use it in our regressions.
However, it was highly correlated (0.7) with plant age, so we may not be too far off in using it.
20  See, for instance, Eskeland and Harrison (1997).
13



The coefficient on the dummy for whether or not a plant is traded on the stock market is not significant,
implying that market reputation concerns have yet to be translated into stock price effects in India. Since
37% of the plants in our sample are traded on Indian bourses, this points to the fact that environment
related information is not used much by traders in India. This may also reflect the fact that
environmental liabilities are (correctly) perceived to be insignificant. The coefficient on multi-plant
status was not significant in the inspection equation. We hence dropped both the traded dummy and the
dummy for multiplant status from the reported regressions.
State manufacturijg wage is significantly negatively associated with BOD emissions indicating that
labor and materials are complements in production, and that this effect outweighs any effect of lower
wages on abatement effort. The elasticity of BOD emissions with respect to the statewide rate of interest
is highly negative, indicating complementarity of capital and materials in production in the sectors being
examined. The strong positive elasticity of BOD emissions with respect to electricity price implies a
relationship of gross substitution between materials and energy that would reinforce the impact of energy
prices on the running of abatement equipment.
Of the sectoral dummies included in the regression, paper is highly significant and positive, indicating
that this is much dirtier than the other sectors, while textiles (which here includes spinning units as well
as dying and finishing units) is relatively cleaner.
Coming to the location specific factors that might be expected to reflect informal pressure on plants, we
find that BOD emissions are not affected by whether or not the plant is located in an area with high
levels of development, as measured by the development index. These would be the very communities
we would expect to lobby hard and negotiate forcefully for lowered emissions.
At the same time, plants that are seen to be big employers in the district, as measured by their share of
total district employment, are significantly larger polluters than others. This supports the idea that these
plants are subjected to no (or little) pressure from district and local organizations because of the huge
leverage they have by virtue of their employment generating ability. As mentioned earlier, this is
consistent with the findings of Gray and Deily (1996) who find that enforcement effort is less likely to be
14



directed towards plants when the political costs in terms of unemployment are likely to be high.21 Local
employment share was not a significant determinant of the inspections equation, so we infer that in the
BOD equation this variable reflects the level of local pressure alone.
VI.   Conclusions
This paper shows that high levels of pollution elicit a formal regulatory response in the form of
inspections in India. However, there appears to be no impact of inspections on emissions, underlining
the institutional failures that Indian academics have been concerned about.22 In the most positive
interpretation, inspections are probably ineffective in bringing about desired changes in behavior because
of bureaucratic or other problems in following through - the probability of enforcement is low and the
penalty for non-compliance is not stringent enough to act as a deterrent. On a more pessimistic note,
inspections in an environment of poorly paid inspectors with poor morale may be opportunities for rent
seeking more than anything else.
We cannot come to a strong conclusion about the efficacy of inspections, however suggestive our initial
results, simply because our data do not allow us to look at actual compliance or non-compliance, which
are the relevant results of interest. In the absence of time series data we are also unable to infer a causal
relationship between inspections and reduced emissions. An important task for future research would be
to examine the impact of lagged inspections on emissions.
Our results on proxies for informal sources of pressure on industrial plants are ambiguous. Our work in
other countries has indicated that communities are often able to pressure plants to reduce emissions and
that better educated and higher income communities are better able to do so. However, we find little
evidence in support of this thesis in this analysis of Indian manufacturing plants. At the same time we
are bemused by the significantly higher level of inspections observed in more developed districts.
There are at least two possible explanations for why BOD emissions may be independent of the district
development index. On the optimistic side, this may be the result of community activism being
21 Pargal and Wheeler (1996) find the opposite result on emissions for Indonesian plants that are large employers.
Unlike this analysis, they do not control for formal regulation in that paper.
22  Comment of Professor Murali Patibandla, Indian Institute of Management, Ahmedabad.
15



unrelated to levels of urbanization, income, and education so that dirty plants are targeted, irrespective of
where they are located. Less sanguinely, this may be because direct community pressure on plants is not
a major determinant of emissions reduction in our sample of Indian firms. In support of the latter thesis,
we noted that pressure from neighbours and NGOs were not significant as predictors of lowered BOD
levels in India although the plants in our survey identified them as being important factors in inducing
abatement.
We have controlled for the impact of formal regulation on emissions, as measured by plant inspections.
Since there does not appear to be any residual variation in emissions by location in our sample, but there
is a large variation in the rate of inspection by location, we infer that the community pressure that exists
is probably channeled through the formal mechanism rather than through direct negotiation with plants.
It may well be that plants in India are less amenable to meeting with representatives of the community or
that the community has trouble organizing itself to negotiate with plants. Also, our sample consists of
large and medium sized enterprises which may be less susceptible to direct pressure than smaller entities.
All in all this analysis provides no evidence of successful informal pressure on plants in India. More
ominously, the lack of an impact of inspections on emissions points to severe problems in the working of
the formal regulatory system. Under these circumstances, the Indian public's recourse to Public Interest
Litigation seems perfectly rational.
Our other findings are interesting because they support the plant level regularities in pollution control
costs observed by other analysts.23 The evidence that larger plants are relatively "cleaner" than smaller
ones is consistent with findings in the rest of the world. Indian policy makers and regulators thus need to
explicitly recognize the trade-off in environmental quality of the existing regulatory bias towards the
small and medium scale sector.
23  See, for instance, Dasgupta et al. (1996).
16



References
Arora, Seema and Timothy N. Cason (1996). "Why do firms volunteer to exceed environmental
regulations? Understanding participation in EPA's 33/50 program." Land Economics 72(4), 413-32.
Been, Vicki (1994). "Locally undesirable land uses in minority neighbourhoods: disproportionate siting
or market dynamics?" Yale Law Journal 103, 1383-1422.
Brooks, Nancy and Rajiv Sethi (1997). "The Distribution of Pollution: Community Characteristics and
Exposure to Air Toxics. " Journal of Environmental Economics and Management: 32, 233-250.
Dasgupta, Sushmita, Mainul Huq, David Wheeler, and Chonghua Zhang (1996). "Water Pollution
Abatement by Chinese Industry: Cost Estimates and Policy Implications." World Bank Policy Research
Working Paper. Number 1630.
Dasgupta, Sushmita and David Wheeler (1997). "Citizen Complaints as Environmental Indicators:
Evidence From China." World Bank Policy Research Working Paper. Number 1704.
Dion, Catherine, Paul Lanoie and Benoit Laplante (1997). "Monitoring Environmental Standards: Do
Local Conditions Matter?" World Bank Policy Research Working Paper. Number 1701.
Eskeland, Gunnar S. and Ann E. Harrison (1997). "Moving to Greener Pastures? Multinationals and the
Pollution-haven Hypothesis." World Bank Policy Research Working Paper. Number 1744.
Gray, Wayne B. and Mary E. Deily (1996). "Compliance and Enforcement: Air Pollution Regulation in
the U.S. Steel Industry." Journal of Environmental Economics and Management, Vol. 31, 96- 111.
Gupta, Shreekant (1996). "Environmental Policy and Federalism in India." National Institute of
Public Finance and Policy. Mimeo.
Hamilton, James (1993). "Politics and Social Costs: Estimating the Impact of Collective Action on
Hazardous Waste Facilities." RAND Journal of Economics, Vol. 24, No. 1, Spring 1993.
Hettige, Hemamala, Mainul Huq, Sheoli Pargal and David Wheeler (1996). "Determinants of Pollution
Abatement in Developing Countries: Evidence from South and Southeast Asia." World Development,
24(12): 1891-1904.
Magat, Wesley and W. Kip Viscusi (1990). "Effectiveness of the EPA's Regulatory Enforcement: The
Case of Industrial Effluent Standards." The Journal of Law and Economics, 33, 331-360.
Pargal, Sheoli and David Wheeler (1996). "Informal Regulation of Industrial Pollution in
Developing Countries: Evidence From Indonesia." Journal of Political Economy, 104(6), 1314-
1327.
Peltzman, San (1976). "Towards a More General Theory of Regulation." The Journal of Law and
Economics 19, 211-240
Stigler, George J. (1971). "The Theory of Economic Regulation." The Bell Journal of Economics and
Management Science 2, 3-21.
17



Appendix
Table 1A.          Data Sources
Variable                               Source
Plant data
BOD load (mg per month)                PRDEI Industrial pollution survey, 1996
Output (Rs. 1000)                      PRDEI Industrial pollution survey, 1996
Total number of inspections (1990-94)  PRDEI Industrial pollution survey, 1996
Effluent use                           PRDEI Industrial pollution survey, 1996
Age of plant                           PRDEI Industrial pollution survey, 1996
Whether exporter,                      PRDEI Industrial pollution survey, 1996
District data  ':
District development index             CMIE: Profile of Districts
Plant employment share in districts    PRDEI Industrial pollution survey, 1996
manufacturing work force               and CMIE: Profile of Districts
State data
Total number of manufacturing plants in state  Annual survey of industries, 1992-93
State domestic product per capita (1992-93)   Economic Survey of India, 1994-95
State manufacturing wage (Rs/year) (1992-93) Annual survey of industries, 1992-93
State rate of interest (percent)       PRDEI Industrial pollution survey, 1996
State electricity price (paisa/kilowatt hour)    TERI: Energy Data and Directory
_Yearbook, 1995-96
18



Table 2A.           Summary Statistics
Variable                 Obs   Mean            Std. Dev.    Min       Max
Plant data
BOD load (mg per month)      124      I.OOE+09     7.52E+09    80000 8.10E+10
Output (Rs. 1000)            250      4.65E+08     2.27E+09         0 2.25E+10
Total number of inspections  243            27      30.48113        1       132
(1990-94)                 _
Water used for bathing       250         0.088      0.283863        0         1
purposes
Water used for industrial    250         0.132       0.33917        0         1
purposes
Age of plant                 240      20.19583      20.69138        0       121
Whether exporter             233      0.300429      0.459432        0         1
District data
District development index   250      4.843267      0.649739 3.688879 6.556778
Plant employment share in    244       0.00053      0.001348  7.09E-06  0.01331
district manufacturing work
force
State data
Total number of              250      11197.54       3940.71     3346    15972
manufacturing plants in state
State domestic product per   250      6512.528        1874.3     4280    11650
capita (1992-93)
State manufacturing wage     250      24489.75      5769.686  15832.35 38600.17
(Rs/year) (1992-93)
State rate of interest (percent)    250  18.22964   1.327959      14.7    20.02
State electricity price      250      211.6532      21.85969i   149.5    232.9
(paisa/kilowatt hour)
19



Table 3A.    First stage regression results
DEPENDENT VARIABLE: LOG BOD LOAD
Variable (in logs)                Coef.                 t stat
Output                                    0.499       3.58**
Age of Plant                              0.425        1.456
Export dummy                              -0.550       -1.165
Share of Employment                       0.391       1.723*
Development Index                         0.451         1.14
State Rate of Interest                   -8.196       -1.622
State Electricity Price                   8.334      3.638**
State Manufacturing Wage                  -1.605       -1.56
Paper dummy                                1.672     2.862**
Textile dummy                             -1.214      -1.838*
Leather dummy                             -0.110       -0.144
Number of Plants in State                 0.214        0.398
Per Capita State Domestic Product         -1.156      -0.991
Water Used for Bathing                     1.217      1.966*
Water Used for Industrial Purposes        0.160        0.281
Constant                                  10.849       0.797
Number of Observations                       72
0.5111
* * significant at 5% confidence levels
* significant at 10% confidence levels
20



Table 4A.    First stage regression results
DEPENDENT VARIABLE: LOG OF INSPECTIONS
Variable (in logs)                Coef.                t stat.
Output                                   -0.036      -0.559
Age of Plant                              0.195         1.52
Export dummy                              0.309        1.406
Share of Employment                       0.290     2.835**
Development Index                         0.454     2.494**
State Rate of Interest                    0.395       0.212
State Electricity Price                   2.475     2.548**
State Manufacturing Wage                 -1.907    -3.751 **
Paper dummy                               0.457       1.773*
Textile dummy                            -0.289       -1.093
Leather dummy                            -0.766    -2.333**
Number of Plants in State                 0.734     2.662**
Per Capita State Domestic Product         1.255     2.686**
Water Used for Bathing                    0.575       1.683*
Water Used for Industrial Purposes       -0.904    -2.811 **
Constant                                 -9.862       -1.362
Number of Observations                      161
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _  0.2 8 7
* * significant at 5% confidence levels
* significant at 10% confidence levels
21






Policy Research Working Paper Series
Contact
Title                           Author                  Date             for paper
WPS1787 Trading Arrangements and         Diego Puga              June 1997         J. Ngaine
Industrial Development          Anthony J. Venables                       37947
WPS1788 An Economic Analysis of Woodfuel  Kenneth M. Chomitz     June 1997         A. Maranon
Management in the Sahel: The Case Charles Griffiths                       39074
of Chad
WPS1789 Competition Law in Bulgaria After   Bernard Hoekman      June 1997         J. Ngaine
Central Planning                Simeon Djankov                            37947
WPS1790 Interpreting the Coefficient of  Barry R. Chiswick       June 1997         P. Singh
Schooling in the Human Capital                                            85631
Earnings Function
WPS1791 Toward Better Regulation of Private  Hemant Shah        June 1997          N. Johl
Pension Funds                                                             38613
WPS1792 Tradeoffs from Hedging: Oil Price    Sudhakar Satyanarayan   June 1997     E. Somensatto
Risk in Ecuador                                                           30128
WPS1793 Wage and Pension Pressure on the  Alain de Crombrugghe    June 1997        M. Jandu
Polish Budget                                                             33103
WPS1794 Ownership Structure, Corporate   Xiaonian Xu             June 1997         J. Chinsen
Governance, and Corporate       Yan Wang                                  34022
Performance: The Case of Chinese
Stock Companies
WPS1795 What Educational Production      Lant Pritchett          July 1997         S. Fallon
Functions Really Show: A Positive   Deon Filmer                           38009
Theory of Education Spending
WPS1796 Cents and Sociability: Household    Deepa Narayan        July 1997         S. Fallon
Income and Social Capital in Rural  Lant Pritchett                        38009
Tanzania
WPS1797 Formal and Informal Regulation   Sheoli Pargal           July 1997         E. de Castro
of Industrial Pollution:        Hemamala Hettige                          89121
Comparative Evidence from       Manjula Singh
Indonesia and the United States  David Wheeler
WPS1798 Poor Areas, Or Only Poor People?   Martin Ravallion      July 1997         P. Sader
Quentin Wodon                             33902
WPS1799 More for the Poor Is Less for the  Jonath B. Gelbach    July 1997          S. Fallon
Poor: The Politics of Targeting  Lant H. Pritchett                        38009
WPS1800 Single-Equation Estimation of the    John Baffes         August 1997       P. Kokila
Equilibrium Real Exchange Rate    Ibrahim A. Elbadawi                     33716
Stephen A. O'Connell



Policy Research Working Paper Series
Contact
Title                            Author                  Date              for paper
WPS1801 Regional Integration as Diplomacy   Maurice Schiff        August 1997        J. Ngaine
L. Alan Winters                            37947
WPSI 802 Are There Synergies Between      Harry Huizinga          August 1997        P. Sintim-Aboagye
World Bank Partial Credit                                                   38526
Guarantees and Private Lending?
WPSI 803 Fiscal Adjustments in Transition  Barbara Fakin          August 1997         M. Jandu
Economies: Social Transfers and the Alain de Crombrugghe                    33103
Efficiency of Public Spending: A
Comparison with OECD Countries
WPS1 804 Financial Sector Adjustment Lending: Robert J. Cull      August 1997         P. Sintim-Aboagye
A Mid-Course Analysis                                                       37644
WPS1805 Regional Economic Integration and  Junichi Goto           August 1997         G. llogon
Agricultural Trade                                                          33732
WPS1806 An Intemational Statistical Survey    Salvatore Schiavo-Campo August 1997     M. Gueverra
of Government Employment and     Giulio de Tommaso                          32959
Wages                            Amitabah Mukherjee
WPS1807 The Ghosts of Financing Gap:      William Easterly        August 1997        K. Labrie
How the Harrod-Domar Growth                                                 31001
Model Still Haunts Development
Economics
WPS1808 Economic Transition and the       Francisco H. G. Ferreira   August 1997      M. Geller
Distributions of Income and Wealth                                          31393
WPS1809 Institutions in Transition: Reliability   Aymo Brunetti   August 1997         M. Geller
of Rules and Economic Performance Gregory Kisunko                           31393
in Former Socialist Countries    Beatrice Weder