Impact of Informal Regulation of Pollution on Water Quality
in Rivers in India
Bishwanath Goldar* and Nandini Banerjee**
Abstract
This paper presents an econometric analysis of determinants of water quality in Indian
rivers. Water quality (water class) data for 106 monitoring points on 10 important rivers
for five years, 1995 to 1999, are used for the analysis. To explain variations in water
quality, an Ordered Probit model is estimated, in which industrialization, urbanization,
irrigation and fertilizer use in agriculture, and poll percentage (a proxy for informal
regulation) are taken as the main explanatory variables. A significant negative
relationship is found between the level of industrialization of a district and the water
quality in rivers at monitoring point(s) falling in the district. Similarly, irrigation and
fertilizer consumption in agriculture are found to bear a significant negative relationship
with river water quality. A significant positive relationship is found between poll
percentage and water quality, and also between the proportion of people who have
completed school education in a state and the water quality in rivers flowing through the
state. These results point to a significant favorable effect of informal regulation of
pollution on water quality in rivers in India.
JEL: Q 25
[Paper presented at the Second World Congress of Environmental and Resource
Economists, Monterey, June 24-27, 2002]
________________
*
**
Institute of Economic Growth, University Enclave, Delhi – 110007, India
National Council of Applied Economic Research, New Delhi, India
Impact of Informal Regulation of Pollution on Water Quality in Rivers in India
Bishwanath Goldar and Nandini Banerjee
1. Introduction
Water pollution is a major environmental concern in India. The main sources of water
pollution are: (1) discharge of domestic sewage and industrial effluents, which contain
organic pollutants, chemicals and heavy metals, and (2) run-off from land-based activities
such as agriculture. Agricultural development is causing deterioration in water quality in
Indian rivers in two ways. First, with increasing use of fertilizers and pesticides in
agriculture, the run-off from irrigated lands is polluting the water bodies. Secondly,
because of the growing irrigation intensity and the high rates of abstraction of ground and
surface water for this purpose, rivers at many places do not have sufficient water for
dilution of industrial effluents/ domestic sewage1, aggravating thereby the problem of
water pollution.
It is known that the present formal environmental regulation system in India,
based on ‘command and control,’ has not performed well in controlling pollution of
Indian rivers. 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 (Pargal, Mani and Huq, 1997; Murty, 1999; Murty and Prashad, 1999).
Water quality monitoring data (discussed later) reveal that at many monitoring points on
India rivers, the water quality is poor. Also, there is a good deal of anecdotal evidence on
high levels of pollution of Indian rivers, especially in relatively more industrialized states
and in the vicinity of big cities.2
The object of this paper is to analyze variations in water quality across different
monitoring points on Indian rivers with a view to identifying determinants of water
quality. For this purpose, an econometric model is estimated. Since industrialization,
urbanization, and irrigation and fertilizer consumption in agriculture are expected to be
important factors affecting water quality, these variables are included in the model as
determinants of water quality. An important focus of the study is on the impact of
informal regulation of pollution on ambient water quality, motivated by a growing body
of literature on the role of informal regulation in controlling pollution. Accordingly,
some proxy variables for informal regulation (reflecting the strength of informal
regulation) are included in the econometric model for capturing the effect of informal
regulation on water quality.
1
Chopra and Goldar (2000); Central Pollution Control Board, Ministry of Environment and Forests,
Government of India, Annual Report, 2000-01.
2 See, for example, State of India’s Environment, Centre for Science and Environment, New Delhi.
1
The significance of informal regulation for achieving environmental goals is well
recognized in the literature.3 It is known that when formal regulation is weak or absent,
informal regulation through local community participation can force the polluter to abate
pollution. Informal regulation takes many forms, including demand for compensation by
community groups, social ostracism of the polluting firm’s employees, the threat of
physical violence, and efforts to monitor and publicize the firm’s emissions (Pargal,
Hettige, Singh and Wheeler, 1997). Two “formal” channels of informal regulation are
(1) to report violation of legal standards to the regulatory institutions (where such
standards and institutions exist), and (2) to put pressure on regulators (politicians and
administrators) to tighten their monitoring and enforcement.
Education, degree of political organization and environmental awareness are
considered to be important factors determining the strength of informal regulation, which
is influenced also by information, legal or political recourse, media coverage, the
presence of non-government organizations (NGOs), and the efficiency of existing formal
regulation. Many of these factors are correlated with the community income levels.
Therefore, in several studies (for example, Pargal, Hettige, Singh and Wheeler, 1997), the
mean community income level (or the development level) has been taken as a proxy
variable in econometric analysis to capture the effect of informal regulation.
There have been two earlier studies on informal regulation of water pollution in
India, one by Pargal, Mani and Huq (1997) and the other by Murty and Prashad (1999)
(discussed later). Both studies have considered the discharge of effluents by large and
medium scale industries and examined how this is influenced by the characteristics of
local communities. The present study is different from the studies of Pargal-Mani-Huq
and Murty-Prashad in that it is concerned with ambient water quality, not industrial
discharge of effluents. Hence, it has a more comprehensive coverage of the sources of
water pollution.
The rest of the paper is organized as follows. Sections 2 and 3 discuss the system
of formal regulation of water pollution in India and the existing water quality monitoring
network, providing thereby background information for the study. Section 4 quickly
reviews the two earlier studies undertaken in the Indian context on informal regulation of
industrial water pollution. Section 5 discusses the data and methodology used for this
study. Section 6 presents the econometric results. Section 7 summarizes and concludes.
2. Regulation of Water Pollution in India
The legal provisions that empower the Indian government to enforce environmental
regulations are the Water (Prevention and Control of Pollution) Act (1974) and the
Environment (Protection) Act (1986).4 The Water Act prescribes both general and
3
See, for example, Pargal and Wheeler (1996), Afsah, Laplante and Wheeler (1996), Pargal, Hettige,
Singh and Wheeler (1997) and Pargal, Mani and Huq (1997).
4 For discussion on formal regulation of water pollution in India, see Pargal, Mani and Huq (1997), Kuik
et al. (1997, Chapter 4), Murty (1999), Sankar (1999) and Goldar and Pandey (2001), among others.
2
industry specific standards for the discharge of wastewater into water bodies. Discharge
of wastewater, carrying pollutant concentrations beyond the specified standards, into
surface waters, public sewers, on land for irrigation and marine coastal waters is
prohibited. The Act lays down penalties for non-compliance. These standards uniformly
apply to all firms within an industry, or to all firms in general (where specific standards
do not exist). The standards differ according to the class of water bodies into which the
wastewater is discharged (for example, the standards are most strict for discharge into
surface water bodies and relatively less strict for disposal on land for irrigation). The
pollution standards are concentration based, i.e. they are specified as milligrams (mg) of
pollutant per liter of wastewater discharged. The Environment Act provides the Central
Government with greater powers to set and enforce environmental standards than what
was provided in the Water Act. However, the basic features pertaining to industrial
pollution abatement remain the same.
There is a basic division of power between the center and the states in India in
regard to environmental regulation, 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 (Pargal, Mani and Huq, 1997).
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, etc. Some of these inspections are also
programmed in response to public requests and litigation. There are penalties for noncompliance. Until 1988, the enforcement authority of the SPCBs was very weak. But,
now, the SPCBs have the power to close non-compliant factories or cut-off their water
and electricity by administrative orders.
Highly Polluting Industries
In 1992, the CPCB identified 1551 large and medium industrial units in 17
categories of highly polluting industries, contributing the major part of the industrial
pollution load. The industrial units were given a time schedule to install necessary
pollution control equipment to comply with the prescribed standards. In 1993, out of the
1551 identified industrial units, 540 were defaulters, i.e. these did not have pollution
control equipment to comply with the standards. Over time, more and more of the
defaulting industries have installed the necessary pollution control equipment or have
closed down. By December 2000, out of 1551 industrial units, 1350 had installed the
necessary pollution control facilities, 177 had been closed down and the remaining 24
industrial units were defaulting. A state-wise summary status of the pollution control in
the 17 categories of highly polluting industries is given in Annex I.
3
Industrial Pollution Control along the Rivers and Lakes
A program was initiated in 1993-94 to identify polluting industries along the
Indian rivers for priority action for control of industrial discharge into rivers. In July
1997, the National River Conservation Authority decided that the polluting industries
which were discharging their effluents into rivers and lakes should be directed to install
requisite effluent treatment systems within three months failing which closure notices
should be issued. A total of 851 industrial units were identified in 1997 which were
discharging 100 kg/day or more of BOD (Biological Oxygen Demand). In the course of
the next few years, most of these units installed the requisite effluent treatment systems.
The number of defaulters declined from 851 in August 1997, to 574 in June 1998, 514 in
March 1999 and 22 in December 2000. As of December 31, 2000, 233 out of the 851
identified polluting industrial units had been closed, 596 had installed the requisite
effluent treatment systems, and 22 remained defaulters. The state-wise break-up of
identified industrial units and defaulting units is shown in Annex II.
It is evident from the above that during the second half of the 1990s the pollution
control authorities at the central and state level had made most of the water polluting
large and medium scale industries install the requisite wastewater treatment facilities. It
is, however, difficult to say whether this has had a large effect in terms of reducing
discharge of pollutant by industries into the rivers. Doubts arise on this point because
even if an effluent treatment plant is installed, it may not be operated regularly. Indeed,
there is a perception that the wastewater treatment plants in many industrial units in India
are activated only at the time inspections are scheduled to occur (Pargal, Mani and Huq,
1997).
Urban Wastewater
It should be mentioned here that disposal of untreated wastewater from the cities
and towns is a major cause of water pollution of rivers in India. It is known that the cities
and town are generating large volumes of wastewater, of which only a small part is
treated. To give some facts, a study undertaken by the CPCB has brought out that in
1994-95 class-I cities in India (299 cities) generated 16,663 mld of wastewater of which
only about 24% was treated.5 6 The rest was disposed without treatment. A similar study
of the class-II towns (345 towns) done by the CPCB has brought out that in 1995 the
volume of wastewater generated in class-II towns was 1649 mld of which only about 4%
was treated.7
5
Status of water supply and wastewater generation, collection, treatment and disposal in class-I cities,
Central Pollution Control Board, Delhi, February 2000.
6 The proportion of wastewater treated was 20.5% in 1989-90.
7 Status of water supply and wastewater generation, collection, treatment and disposal in class-II towns,
Central Pollution Control Board, Delhi, March 2000.
4
The present legislation gives the CPCB sufficient power for control of water
pollution. For example, the CPCB is empowered to lay down and maintain ambient
water standards, to demand information regarding effluent emissions, to shut down
polluting activities and prevent new discharges of effluent and sewage. However, the
legislation has had limited success in checking water pollution arising from discharge of
untreated sewage. The rise of public opinion against polluting industries has found
expression in Public Interest Litigation (PIL). But, this has not been matched by a similar
challenge to municipal pollution. A few cases have been filed against municipalities for
their failure to keep the cities clean and maintain environmental standards (for example,
the Ratlam case). These cases, filed by citizen, manifest that there is some degree of
informal pressure on municipalities (at least on some of them) to perform their duties for
the control of pollution. The media has probably also played a role in making the
municipalities pay some attention to the environmental problems. It must be admitted,
however, that neither formal nor informal pressure has been very successful in making
the municipalities undertake collection and treatment of urban wastewater.
3. Water Quality Monitoring
Water quality monitoring program was started by the CPCB in 1976 with 18 stations on
river Yamuna. The program was gradually extended over time. In 1989, there were 324
monitoring stations. At present, there are 507 monitoring stations in the country spread
over all important water bodies. Out of 507 stations, 414 stations are on rivers, 25 on
ground water, 38 on lakes and 30 on canals, creeks, drains, ponds etc.
The quality of water is monitored for 25 physico-chemical and biological
parameters. The monitoring network covers 126 rivers (including the tributaries), wells,
lakes, creeks, ponds, tanks, drains and canals. Five classes are used for water quality, A
to E. A is the best quality, and E is the worst. At certain stations, the water quality is
found to be below E. The criteria for the water classes are shown in Table 1.
The distribution of river water quality at the monitoring stations (in terms of water
class) during 1999 is shown in Table 2. At 4.7% of the monitoring stations, the water
class was A. At about 60% of the monitoring stations, the water class was D, E or
“below E”.
On the basis of last 10 years’ water quality monitoring results, the CPCB has
estimated riverine length having different level of pollution. This is presented in Table 3.
The estimates indicate that about 14% of the length of river stretches are highly polluted
(BOD level above 6 mg/l), while another 19% are moderately polluted (BOD level in the
range of 3 to 6 mg/l). Thus, about two-thirds of the length of river stretches are relatively
clean (BOD level less than 3 mg/l).
5
Table 1: Primary water quality criteria for designated-best-use-classes
.-----------------------.----------.------------------------------.
| Designated-Best-Use
| Class of |
Criteria
|
|
| water
|
|
|-----------------------|----------|------------------------------|
| Drinking Water
|
A
| 1. Total Coliforms Organism |
| Source without
|
|
MPN/100ml shall be 50 or |
| conventional
|
|
less
|
| treatment but after
|
| 2. pH between 6.5 and 8.5
|
| disinfection
|
| 3. Dissolved Oxygen 6mg/l or |
|
|
|
more
|
|
|
| 4. Biochemical Oxygen Demand |
|
|
|
5 days 20oC 2mg/l or less |
|-----------------------|----------|------------------------------|
| Outdoor bathing
|
B
| 1. Total Coliforms Organism |
| (Organised)
|
|
MPN/100ml shall be 500 or |
|
|
|
less
|
|
|
| 2. pH between 6.5 and 8.5
|
|
|
| 3. Dissolved Oxygen 5mg/l or |
|
|
|
more
|
|
|
| 4. Biochemical Oxygen Demand |
|
|
|
5 days 20oC 3mg/l or less |
|-----------------------|----------|------------------------------|
| Drinking water
|
C
| 1. Total Coliforms Organism |
| source after
|
|
MPN/100ml shall be 5000
|
| conventional
|
|
or less
|
| treatment and
|
| 2. pH between 6 to 9
|
| disinfection
|
| 3. Dissolved Oxygen 4mg/l or |
|
|
|
more
|
|
|
| 4. Biochemical Oxygen Demand |
|
|
|
5 days 20oC 3mg/l or less |
|-----------------------|----------|------------------------------|
| Propagation of Wild
|
D
| 1. pH between 6.5 to 8.5
|
| life and Fisheries
|
| 2. Dissolved Oxygen 4mg/l or |
|
|
|
more
|
|
|
| 3. Free Ammonia (as N) 1.2
|
|
|
|
mg/l or less
|
|-----------------------|----------|------------------------------|
| Irrigation,
|
E
| 1. pH between 6.0 to 8.5
|
| Industrial Cooling,
|
| 2. Electrical Conductivity
|
| Controlled Waste
|
|
at 25oC micro mhos/cm Max.|
| disposal
|
|
2250
|
|
|
| 3. Sodium absorption Ratio
|
|
|
|
Max. 26
|
|
|
| 4. Boron Max. 2mg/l
|
'-----------------------'----------'------------------------------'
Source: Central Pollution Control Board, Ministry of Environment and Forests, Government of India
6
Table 2: Distribution of Monitoring Points according to Water Class, 1999
Class
A
B
C
D
E
Below E
All
% of monitoring points
4.7
14.1
19.7
54.6
4.7
2.2
100.0
Source: Computed from Water Quality data of the CPCB, available at their website.
Table 3: Riverine length according to pollution level
Pollution level
Definition used
High pollution
Moderate pollution
Relatively clean
All
BOD >6 mg/l
BOD= 3-6 mg/l
BOD<3 mg/l
length of river stretches
(km)
6086
8691
30242
45019
Per cent
14
19
67
100
Source: Prepared from data provided in the Annual Report of the CPCB, 2000-01.
The water quality monitoring results obtained in recent years indicate that the
critical source of pollution of Indian aquatic resources lies in organic and bacterial
contamination.8 This is attributable mainly to discharge of domestic wastewater in
untreated form from the urban centers, which may be explained by the fact that the
municipal corporations do not have adequate resources for treating the ever-increasing
load of municipal sewage. A related factor, aggravating the pollution problem, is that the
receiving water bodies do not have sufficient water for dilution. It goes without saying
that the problem of pollution of rivers caused by untreated urban sewage is compounded
by the discharge of industrial effluents.
4. Earlier Studies on Informal Regulation of Pollution in India
There have been two earlier studies on informal regulation of water pollution in India,
one by Pargal, Mani and Huq (1997) and the other by Murty and Prashad (1999). The
studies are briefly discussed below.
Pargal, Mani and Huq (1997) have used survey data for 250 industrial plants 9 to
examine regulatory inspections and water pollution emissions in India, and to check
whether the monitoring and enforcement efforts of provincial pollution control authorities
8
9
CPCB, Annual Report, 2000-01.
Data were collected in a survey of industrial plants in eight states of India, conducted in early 1996.
7
are affected by local community characteristics that act as proxies for political power.
They have also tested for informal pressure on plants that would result in negotiated
lower emissions.
Two main plant level variables used in the study are emissions and inspections.
The former is measured by the BOD load, and the latter by the total number of
inspections that each plant had been subject to between 1990 and 1994.
Pargal et al. note that in their 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 neighboring communities. This brings out the importance
of NGOs and community pressure in making industrial plant undertake control of
industrial pollution
An important finding of the study is that high levels of pollution elicit a formal
regulatory response in the form of inspections in India. However, no significant impact
of inspections is found on emissions, which is interpreted by the authors as reflective of
bureaucratic or other problems in following through.
As regards informal sources of pressure on industrial plants, the results of the
analysis indicate a positive relationship between district development index (taken as a
proxy for informal regulation) and the number of inspections, but no significant negative
relationship is found between district development index and BOD emissions. This,
according to the authors, could be due to community activism being unrelated to levels of
urbanization, income, and education so that dirty plants are targeted, irrespective of
where they are located, or these findings could mean that direct community pressure on
plants is not a major determinant of emissions reduction in the sample of Indian firms
covered in the study. Another inference drawn by the authors is that the community
pressure that exists is probably channeled through the formal mechanism rather than
through direct negotiation with plants. All in all, the econometric analysis undertaken by
Pargal, Mani and Huq provides no evidence of successful informal pressure on plants in
India, even though many firms covered in the survey said that they had undertaken
pollution abatement in response to NGO pressure or complaints of neighboring
communities.
Murty and Prashad (1999) have carried out for Indian industry an analysis similar
to that of Pragal, Mani and Huq (1997). They, however, find evidence of significant
informal pressure on firms to control pollution.
Murty and Prashad have used cross-section data for a sample of 100 factories
belonging to 11 highly water polluting industries in 13 states, collected in a survey done
in 1994/95. The ratio of BOD concentration in effluent to that in influent is taken as the
dependent variable, an index of the reduction in pollution due to efforts made by the
factory. In the regression equation estimated, they use a number of explanatory variables,
including community specific variables like district development index and the rate of
participation in the previous parliamentary elections.
8
The results of the regression analysis show a significant negative relationship
between the index of development and the BOD ratio, implying thereby that higher levels
of development are associated with greater abatement of pollution by factories.
Similarly, a significant negative relationship is found between political participation of
local communities as indicated by their rate of participation in recent elections and the
BOD ratio. The implication is that the more active the local people are politically, the
higher is the extent of pollution abatement done by the factories located in that area.
The main conclusion of the study of Murty and Prashad is that the affluence of
local communities and the level of political activity reflected in their rate of participation
in elections play important roles in determining the degree of compliance of industries
with water pollution standards. Needless to say, this is indicative of significant informal
pressure on firms to curb water pollution.
5. Data Sources and Methodology of the Present Study
As mentioned earlier, this study is concerned with ambient water quality in India.
For the analysis, data on water quality for 10 rivers 10 (106 monitoring points) for five
years, 1995 to 1999 are used. These data, collected under the water-quality monitoring
program of the government, have been taken from the website of the CPCB. The timeseries and cross-section data are pooled to estimate an econometric model for explaining
variations in water quality.
Since the available water quality data are in terms in water class at different
monitoring points, an ordinary regression analysis cannot be applied. An Ordered Probit
model has therefore been applied for the econometric analysis of water quality.
One would expect water quality in rivers to be affected by industrialization,
urbanization, irrigation and fertilizer use in agriculture. These variables have therefore
been included in the econometric model as explanatory variables. To obtain data on
these variables, the water quality monitoring points have been mapped into districts
(since the data on these explanatory variables could be obtained only at that level). Data
on industrialization, urbanization, irrigation and fertilizer use have been taken from a
publication of the CMIE (Center for Monitoring Indian Economy), entitled ‘District
Profile 2000’.11
An important explanatory variable used in the model is the poll percentage (as an
indicator of informal regulation). In this case, it has been necessary to map the water
10
The ten rivers (including tributaries) covered in the study are Brahmani, Cauvery, Chambal, Ganga,
Godavari, Mahanadi, Mahi, Sabarmati, Tapi and Yamuna.
11 The ratio of workers engaged in manufacturing to all workers (full-time workers or “main workers” as
defined by the population census) in a district is taken as an indicator of the level of industrialization.
These data relate to 1991 (not available for the mid-1990s). An indicator for the level of urbanization is
formed by taking the ratio of urban households to total households in a district. In this case, again, the data
relate to 1991. Indicators of irrigation intensity (gross irrigated area as % of gross cropped area) and
fertilizer use (kg of fertilizer consumption per hectare) are based on data for 1995.
9
quality monitoring points into the parliamentary constituencies. Poll percentage data for
different parliamentary constituencies of the 11th Loksabha (1996) have been collected
from Election Commission of India office.
In addition to the variables listed above, two other variables have been included in
the model for which data could be obtained only at the state level. Thus, in these cases,
the figure obtained for a state has been applied to all the observations belonging to the
state. These two variables are (1) Per capita gross domestic product (in 1997-98), data
taken from Economic Survey, Government of India, and (2) Percentage of people who
have completed school education (higher secondary) (among people aged six years or
above), data taken from National Family Health Survey II (1997-98).
6. Empirical Results
It would be useful to present first some summary statistics on water quality for the 106
monitoring stations covered in the study. Table 4 shows the distribution of these
monitoring points according to water quality (water class) for three years, 1995, 1997 and
1999. It is seen from the table that during the period 1995 to 1999, there has been, on
average, an improvement in river water quality. The shares of D and E class have
declined while the shares of A and B class have gone up between 1995 and 1999.
Table 4: Distribution of Monitoring Points according to Water Class
Water class
A
B
C
D
E or lower
All
Percentage of monitoring points falling in the water class
in the year:
1995
1997
1999
0
16
14
62
8
100
4
19
14
56
7
100
4
27
19
45
5
100
Note: The distribution shown is for the 106 monitoring points covered in the study.
From the popular writings on India’s environment one would get the impression
that the water quality in Indian rivers has been going down. The finding of an
improvement in average water quality for the sample of 106 monitoring points therefore
calls for a closer scrutiny. Accordingly, a more detailed examination of water quality
information was undertaken for 13 major rivers (including the ones covered in the study)
for the years 1995 (277 points) and 1999 (325 points). The comparison did not reveal
any significant improvement (or deterioration) in the average water quality between 1995
and 1999. It was realized, however, that the data for 1999 contain water quality
information in respect of many new monitoring points, and this may affect inter-temporal
10
comparability of the results. When the new points were excluded, the water quality
monitoring results indicated a slight improvement in average water quality in rivers
between 1995 and 1999.
Estimates of the Ordered Probit Model
Table 5 presents the estimates of the Ordered Probit model. Data for all the five
years are used for the estimation of the model. It should be pointed out here that the data
on each of the explanatory variables relate to a specific year. For example, the poll
percentage data relate to 1996, and the same set of figures is used for five years, 1995 to
1999. The implication is that the explanatory variables are able to explain only the crosssectional variation in water quality, not inter-temporal changes.
Table 5: Determinants of Water Quality in Indian Rivers:
Estimates of the Ordered Probit Model
Dependent Variable: Water Class
Period: 1995-1999
Explanatory
Estimates of the Model
variables
(1)
(2)
(3)
Poll percentage
0.0285 (6.3)***
0.0228 (5.4)***
0.0212 (5.2)***
Urbanization
0.0086 (2.5)**
-0.0000 (-0.01)
0.0087 (2.6)***
Industrialization
-0.0859 (-4.1)***
-0.0655 (-3.2)*** -0.0841 (-4.3)***
Per capita GDP in
-0.0997 (-5.1)***
-0.1418 (-6.9)***
the state
% population
0.2644 (4.6)***
0.2671 (4.9)***
completed school
education (higher
secondary)
Fertilizer use
-0.0036 (-2.6)***
-0.0028(-2.4)**
intensity
Irrigation intensity
-0.0136 (-5.1)***
Year dummy ‘96
Year dummy ‘97
Year dummy ‘98
Year dummy ‘99
-0.0931 (-0.6)
0.2113 (1.3)
0.4253 (2.7)***
0.5241 (3.3)***
-0.1173 (-0.8)
0.2018 (1.3)
0.3950 (2.6)***
0.4832 (3.1)***
-0.0913 (-0.6)
0.2067 (1.3)
0.4150 (2.7)***
0.5087 (3.3)***
LR chi-square (df)
No. of observations
97.0 (10)
511
129.1 (10)
521
69.2 (8)
511
Note: Figures in parentheses give t-ratios. Due to gaps in data, all the 530 observations could not
be used for the estimation of the model.
* statistically significant at ten per cent level of significance.
** statistically significant at five per cent level of significance.
*** statistically significant at one per cent level of significance.
11
Since water quality data for different years are pooled for the estimation of the
model, dummy variables for years have been included. The purpose is to capture the
effect of year-specific factors. This will also capture any trend effect on water quality.
It is seen from Table 5 that the coefficient of the poll percentage variable (relating
to the 1996 parliamentary elections) is positive and statistically significant at one per cent
level (see also Figure 1 which reveals a cross-district positive association between poll
percentage and water quality in rivers). This finding is in agreement with the results of
Murty and Prashad (1999). The finding of a significant positive relationship between poll
percentage and ambient water quality points to the existence of successful informal
regulation of pollution in India with a significant favorable effect on water quality in
rivers. This conclusion is reinforced by the positive relationship found between water
quality and school education – the coefficient of the education variable is positive and
statistically significant. It is needless to say that a high proportion of educated people in
the community should render informal regulation strong, and therefore a positive
relationship between water quality and education may be interpreted as a reflection of the
informal regulation of pollution.12
Fig. 1: Poll Percentage in Constituencies and Water
Quality at Corresponding Monitoring Points
100%
80%
A+B
60%
C
40%
D+E+Below E
20%
0%
low poll percentage high poll percentage
Note: A comparison of water quality is presented in the figure between the bottom one-third
and top one-third of the locations in terms of poll percentage. The distribution of monitoring
points according to water class is shown in the figure. The poll percentage data are for 1996.
The water quality observations are for five years, 1995 to 1999. See table 1 for the
definition of water classes.
12
It may be pointed out here that literacy was tried as an explanatory variable in the model. But, the results
were not found satisfactory.
12
Turning to other explanatory variables, a significant negative relationship is found
between water quality and industrialization (measured by the ratio of workers engaged in
manufacturing activities out of total workers in a district). Such a relationship is
expected. Given the weakness of the formal regulation system, a high level of
industrialization should result in higher pollution load and hence lower water quality in
rivers. Further, if industries give high employment benefits (reflected in the proportion of
workers engaged in industries), it would be difficult for the local community to put much
pressure on industries to curb pollution, a point already recognized in the literature.
The results in respect of the irrigation (gross irrigated area as % to gross cropped
area) and fertilizer use (kg per hectare) variables indicate that the higher the extent of
irrigation and fertilizer use in a district, the lower is the water quality in rivers (other
things remaining the same). This provides empirical support to a point made in the
introductory section of the paper that agricultural development is adding to water quality
problems in rivers in India, because of run-off from irrigated lands and because the water
available in rivers is reduced to levels insufficient for dilution of pollutants.
Since fertilizer use and irrigation are highly correlated, both cannot be included in
the same equation. This is the reason why in equation (2), fertilizer has been replaced by
irrigation.
The coefficient of GDP per capita is negative and statistically significant. This
may be interpreted as showing the adverse effect of economic development on
environment (in the initial stages). It may be mentioned here that Murty and Prashad
(1999) have used this variable to explain the extent of pollution abatement in Indian
factories. They found an inverse relationship between GDP per capita and the extent of
pollution abatement done.
As regards urbanization, the coefficient is found to be positive and statistically
significant in some of the equations estimated. Inasmuch as the discharge of urban
wastewater is a major cause of pollution of Indian rivers, one should expect a negative
relationship to arise between urbanization and water quality. It needs be noted, however,
that the income level or development level of communities is often taken as a variable to
represent informal regulation. Urbanization is obviously an important component of
development and is correlated with development. Accordingly, the finding of a positive
coefficient of urbanization in the estimated model may be treated as a reflection of the
favorable effect of informal regulation on water quality.
Turing to of the year dummies, the coefficients for 1998 and 1999 are found to be
positive and statistically significant (1995 is the excluded category). This probably
captures the effects of certain factors which contributed to improvement in water quality
in the late 1990s. This is obviously consistent with the water quality data reported in
Table 4.
13
Since the coefficients for the year dummies for 1998 and 1999 are found to be
statistically significant, the Ordered Probit model has been estimated separately for the
periods 1995-97 and 1998-99. The results are reported in Table 6.
In respect of poll percentage and the proportion of people having higher
secondary school education, the results reported in Table 6 are by and large similar to
those reported in Table 5. Thus, there is evidence of effective informal regulation of
pollution both in the period 1995-97 and in the period 1998-99.
It is interesting to note, however, that the coefficient of the industrialization
variable is statistically significant in the estimates for 1995-97 but not for 1998-99. On
the other hand, the coefficient of irrigation is higher in numerical value and statistical
significance in the estimates for 1998-99 as compared to the estimates for 1995-97. It
seems therefore that the adverse effect of industrialization on water quality in rivers
became weaker, while the adverse effect of irrigation became stronger, during the late
1990s as compared to the mid-1990s.
Table 6: Determinants of Water Quality in Indian Rivers:
Estimates of the Ordered Probit Model, 1995-97 and 1998-99
(Dependent Variable: Water Class)
Explanatory
variables
Poll percentage
Urbanization
Industrialization
Per capita GDP in
the state
% population
completed school
education (higher
secondary)
Irrigation intensity
Year dummy ‘96
Year dummy ‘97
Year dummy ‘99
Estimates of the Model
For 1995-97
0.0212 (3.8)***
-0.0000 (-0.01)
-0.1045 (-3.9)***
-0.1696 (-6.2)***
For 1998-99
0.0246 (3.8)***
-0.0003 (-0.06)
-0.0139 (-0.5)
-0.1224 (-3.8)***
0.2962 (4.2)***
0.2640 (3.1)***
-0.0083 (-2.4)**
-0.0213 (-5.0)***
-0.1314 (-0.8)
0.2172 (1.4)
0.0745 (0.5)
LR chi-square (df)
73.3 (8)
57.7 (7)
No. of observations
313
208
Note: Figures in parentheses give t-ratios.
** statistically significant at five per cent level of significance.
*** statistically significant at one per cent level of significance.
14
The above finding that the level of industrialization bore a significant negative
relationship with water quality in rivers in the mid-1990s, but not in the late 1990s finds
corroboration when trends in water quality are analyzed for the category of relatively
more industrialized districts. Figure 2 presents a comparison of water quality in such
districts in 1995/96 with that in 1998/99. The figure brings out clearly that there was a
marked improvement in water quality in these districts during the second half of the
1990s. This must have narrowed the gap in water quality between more industrialized
and less industrialized districts, and therefore made the inverse relationship between
industrialization and river water quality weaker.
Fig. 2: Distribution of Monitoring Points in terms of
Water quality (class) in relatively more
industrialised districts
100%
80%
B
C
D+E
60%
40%
20%
0%
1995/96
1998/99
Note: Distribution of monitoring points in 1995/96 and 1998/99 by water quality (class) is
shown in the graph for the relatively more industrialized districts (top 25% districts in terms
of the level of industrialization). For the definition of water classes, see Table 1.
What caused the marked improvement in water quality in relatively more
industrialized districts during the second half of the 1990s? It seems the credit should
partly go to the pollution control boards. It was noted in Section 2 above (see Annex I
and II) that during this period, the boards compelled most water polluting large and
medium scale industries to install requisite wastewater treatment facilities. Further,
informal pressure (particularly, public interest litigation) must have also played an
important role in making industries undertake pollution abatement.
15
7. Conclusion
The paper investigated determinants of water quality in rivers in India. Data on water
quality (water class) for 106 monitoring points for five years 1995 to 1999 were used for
the analysis. Given the nature of the water quality data, an ordinary regression analysis
could not be applied. The econometric analysis was therefore undertaken by using an
Ordered Probit model. A significant negative relationship was found between the level of
industrialization, irrigation and fertilizer consumption intensity in a district and the water
quality in the monitoring points falling in the district. A significant positive relationship
was been found between poll percentage and water quality. The results also indicated a
positive relationship between the proportion of population having completed school
education (higher secondary) in a state and water quality in rivers flowing through the
state. These results may be taken as indicative of a significant favorable effect of
informal regulation on water quality in rivers in India.
Econometric analysis brought out that the adverse effect of industrialization on
water quality in rivers declined in the late 1990s compared to the mid-1990s, while the
adverse effect of irrigation on water quality in rivers intensified in this period. The
lessening of the adverse effect of industrialization on river water quality is perhaps a
consequence of the increased efforts of the pollution control authorities directed at
making industries undertake pollution abatement. Informal pressure on industrial firms
must have also played an important role.
16
References
Afsah, S., B. Laplante and D. Wheeler (1996), “Controlling Industrial Pollution: A New
Paradigm”, Policy Research Working Paper #1672, Policy Research Department,
Environment, Infrastructure and Agriculture Division, World Bank, Washington, DC.
Chopra, K. and B. Goldar (2000), ‘Sustainable Development Framework for India: The
Case of Water Resources’, Report Submitted to the United Nations University, Tokyo, as
a part of the UN University Project on ‘Sustainable Development Framework for India’,
October.
Goldar, B. and R. Pandey (2001), “Water Pricing and Abatement of Industrial Water
Pollution: A Study of Distilleries in India”, Environmental Economics and Policy
Studies, 4(2).
Kuik, O.J., M.V. Nadkarni, F.H. Oosterhuis, G.S. Sastry and A.E. Akkerman (1997),
Pollution Control in the South and North: A comparative assessment of environment
policy approaches in India and the Netherlands, Indo-Dutch Studies on Development
Alternatives – 21, New Delhi: Sage Publications.
Murty, M.N. (1999), “Role of Government in Environmental Management”, in M.N.
Murty, A.J. James and Smita Misra (eds.), Economics of Industrial Pollution Abatement:
Theory and Empirical Evidence from the Indian Experience, Delhi: Oxford University
Press.
Murty, M.N. and U.R. Prashad (1999), “Emissions Reduction and Influence of Local
Communities in India”, in M.N. Murty, A.J. James and Smita Misra (eds.), Economics of
Industrial Pollution Abatement: Theory and Empirical Evidence from the Indian
Experience, Delhi: Oxford University Press.
Pargal, S., H. Hettige, M. Singh, and D. Wheeler (1997), “Formal and Informal
Regulation of Industrial Pollution: Comparative Evidence from Indonesia and the United
States”, World Bank Economic Review, 11(5): 433-50.
Pargal, S., M. Mani and M. Huq (1997), “Inspections and Emissions in India: Puzzling
Survey Evidence on Industrial Water Pollution”, PRD Working Paper #1810,
Development Research Group, World Bank, Washington, DC, August.
Pargal, S. and D. Wheeler (1996). “Informal Regulation of Industrial Pollution in
Developing Countries: Evidence From Indonesia”, Journal of Political Economy, 104(6):
1314-1327.
Sankar, U. (1999), “Laws and Institutions Relating to Environmental Protection in India”,
Occasional Paper no. 2, Madras School of Economics, Chennai.
17
Annex I: Status of Pollution Control in 17 Categories of Highly
Polluting Industries, India, 1995 and 2000
State/ Union
Number of
No. of units not having adequate
territory
units
facilities to comply with standards
identified
Andhra Pradesh
Assam
Bihar
Goa
Gujarat
Haryana
Himachal Pradesh
Jammu and Kashmir
Karnataka
Kerala
Madhya Pradesh
Maharashtra
Orissa
Punjab
Rajasthan
Tamil Nadu
Uttar Pradesh
West Bengal
Delhi
Pondicherry
Other states/UT
Total
Mar-95
Dec-2000
173
15
62
6
177
43
9
8
85
28
78
335
23
45
49
119
224
58
5
6
3
32
5
11
0
8
7
0
4
21
4
21
28
10
11
2
8
40
27
3
4
6
1
1
2
0
0
0
0
0
0
0
5
5
4
0
0
0
3
3
0
0
0
1551
252
24
Source: Central Pollution Control Board, Annual Report, 1994-95 and 2000-01
18
Annex II: Status of Defaulters under the Program of Industrial Pollution
Control Along the Rivers and Lakes, India, 1997 and 2000
State/Union
Territory
Number of
Closed
defaulters subsequently
in Aug. 97
Acquired
requisite
treatment/
disposal
facilities
Number of
defaulters in
Dec.2000
Andhra Pradesh
Assam
Bihar
Goa
Gujarat
Haryana
Himachal Pradesh
Jammu and Kashmir
Karnataka
Kerala
Madhya Pradesh
Maharashtra
Orissa
Punjab
Rajasthan
Tamil Nadu
Uttar Pradesh
West Bengal
Pondicherry
60
7
14
0
17
21
0
0
20
36
2
6
9
18
0
366
241
30
4
17
5
4
0
3
8
0
0
2
4
1
3
1
1
0
118
59
7
0
37
0
10
0
14
12
0
0
17
32
0
3
4
16
0
248
176
23
4
6
2
0
0
0
1
0
0
1
0
1
0
4
1
0
0
6
0
0
Total
851
233
596
22
Source: Central Pollution Control Board.
19