metadata: authors: 0: author: Do,Quy-Toan
1: author: Joshi,Shareen
2: author: Stolper,Samuel
count: India
docna: 0: docna: Can environmental policy reduce infant
            mortality ? evidence from the Ganga pollution cases
docty: Policy Research Working Paper
subtopic: Law and Justice Institutions,Hydrology,Health Care Services Industry,Global Environment,Pollution Management & Control,Brown Issues and Health
teratopic: Industry,Water Resources,Environment,Law and Development
abstracts: cdata!: In many developing countries,
            environmental quality remains low and policies to improve it
            have been inconsistently effective. This paper conducts a
            case study of environmental policy in India, focusing on
            unprecedented Supreme Court rulings that targeted industrial
            pollution in the Ganga River. In a difference-in-differences
            framework, the rulings are found to have precipitated
            reductions in river pollution and one-month infant
            mortality, both of which persist for more than a decade. The
            authors then estimate a pollution-mortality dose-response
            function across twenty-nine rivers in the Ganga Basin,
            instrumenting for pollution with its upstream counterpart.
            The estimation reveals a significant external health burden
            of river pollution, not just in the district of measurement,
            but also on downstream communities. It further provides
            suggestive evidence that reducing pollution was an important
            driver behind declines in infant mortality observed after
            the rulings.
geo_reg_key: 517198,517212,517191
repnme: repnme: Can environmental policy reduce infant
            mortality ? evidence from the Ganga pollution cases
display_title: Can environmental policy reduce
            infant mortality evidence from the Ganga pollution cases
listing_relative_url: /research/2016/08/26711804/can-environmental-policy-reduce-infant-mortality-evidence-ganga-pollution-cases
topicv3: Water m1326522 137,Environment, Natural Resources and Blue
            Economy m1327391 566,Water Pollution m1327598 1798,Mortality m1504365 3661,Health m1504661 3480
publishtoextweb_dt: 2016-08-19T00:00:00Z
historic_topic: Industry,Water Resources,Environment,Law and Development
no_of_pages: 50
author_keys: 0: author_key: 433894
1: author_key: 606405
2: author_key: 1217655
model_extractions: WPS7799 Policy Research Working Paper 7799 Can Environmental Policy Reduce Infant Mortality? Evidence from the Ganga Pollution Cases _Quy-Toan Do Shareen Joshi Samuel Stolper_ Development Research Group Poverty and Inequality Team August 2016
Policy Research Working Paper 7799

# **Abstract**

In many developing countries, environmental quality remains low and policies to improve it have been inconsistently effective. This paper conducts a case study of environmental policy in India, focusing on unprecedented Supreme Court rulings that targeted industrial pollution in the Ganga River. In a difference-in-differences framework, the rulings are found to have precipitated reductions in river pollution and one-month infant mortality, both of which persist for more than a decade. The authors then estimate a pollution-mortality dose-response function across twenty-nine rivers in the Ganga Basin, instrumenting for pollution with its _upstream_ counterpart. The estimation reveals a significant external health burden of river pollution, not just in the district of measurement, but also on downstream communities. It further provides suggestive evidence that reducing pollution was an important driver behind declines in infant mortality observed after the rulings.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at Abonfield@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Can Environmental Policy Reduce Infant Mortality? Evidence from the Ganga Pollution Cases<sup>_∗_</sup> Quy-Toan Do Shareen Joshi Samuel Stolper World Bank Georgetown University University of Michigan _Keywords:_ environmental policy, development, pollution, infant mortality. _JEL Codes:_ Q53, Q56, C36

> _∗_ We are grateful to Prashant Bharadwaj, Jishnu Das, Ram Fishman, Garance Genicot, Mark Giordano, Susan Godlonton, Rema Hanna, Hanan Jacoby, Remi Jedwab, Guido Kuersteiner, Shomik Lall, Arik Levinson, Rohini Pande, Martin Rama, Martin Ravallion, John Rust, Simone Schaner, George Shambaugh, Shinsuke Tanaka, and Jennifer Tobin for useful comments. Many thanks to Mr. Jairam Ramesh and Mr. M.C. Mehta for helpful discussions. Thank you as well to Michael Greenstone and Rema Hanna for sharing their pollution data, to Sam Asher and Paul Novosad for sharing their weather data, and to Shreya Shah for valuable qualitative research. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent.
# **Introduction**

Environmental economics and development economics are unified by the persistent puzzle of poor air and water quality in developing countries (Greenstone and Jack 2015). A growing literature provides evidence that pollution imposes a significant health burden (e.g. Jayachandran 2008, Ebenstein 2012, and Brainerd and Menon 2014), yet corresponding estimates of willingness-to-pay for environmental quality are surprisingly low (Kremer et al. 2011) and policies aimed at improving environmental quality have not reliably done so (Greenstone and Hanna 2014; Field, Glennerster, and Hussam 2011). While high levels of air and water pollution may be due in part to high marginal utility of consumption (Hanna and Oliva 2015) and high marginal costs of pollution abatement (Davis 2008), they are also likely driven by rent-seeking behavior (Duflo et al. 2013) and market failures (Jalan and Somanathan 2008), hence defining the scope of government regulation.

India provides a compelling setting in which to study developing-country environmental health and policy. The World Health Organization estimates that over three in every 1,000 Indian children under five years old in 2004 died because of water pollution (WHO 2004). India’s Central Pollution Control Board reports that sewage treatment capacity amounts to only 21 percent of the estimated daily sewage load in Indian cities (CPCB 2017a), and the gap between load and treatment capacity is expanding ( _Daily Mail_ 2015). At the same time, the flagship policy for addressing water pollution in India **–** the National River Conservation Plan (NRCP), established in 1986 and still active today **–** has failed to improve water quality (Greenstone and Hanna 2014).

We investigate a landmark decision in India’s environmental regulatory history: the Supreme Court case _M.C. Mehta vs. Union of India_ , subsequently bifurcated and known as the “Ganga Pollution Cases.” These cases represent India’s first-ever environmental public-interest litigation, and their unprecedented rulings **–** which mandated pollution cleanup by the tanning industry concentrated along the Ganga River in Kanpur, Uttar Pradesh **–** marked the rise of environmental activism by the Indian judiciary (Mehta 2009). Exploiting the quasi-random incidence of the litigation, we find that the ruling is strongly associated with reductions in both river pollution (as measured by biochemical oxygen demand [BOD]) and neonatal (one-month) mortality.

Our positive finding is an important data point because, in developing countries, there is very little evidence of environmental policies other than piped water provision being successful (Ravallion and Jalan 2003; Gamper-Rabindran, Khan, and Timmins 2010). Our reduced-form evidence highlights a rare instance of successful policy: judicial-led mandates primarily targeting industrial pollution. Our results suggest that the rulings improved several measures of water quality – BOD as well as calcium, sulfur, and chloride concentrations – and that mortality impacts occur primarily in the first month of life. Furthermore, the rulings’ impacts on pollution and health persist over time for at least ten years 2
as well as downstream into neighboring districts.

In the second phase of our analysis, we quantify the external infant health costs of river pollution in general and investigate the mechanisms of the Ganga Pollution Cases’ impacts. To do so, we estimate a pollution-health dose-response function in the Ganga River Basin. Since pollution is potentially endogenous, we instrument for it using its _upstream_ counterpart. This identification strategy has a strong logic: the decision to pollute upstream is orthogonal to downstream health inputs, but pollution reliably flows downstream nonetheless. The persistence of downstream impacts of the Supreme Court rulings, as well as recent research leveraging the upstream-downstream geographic relationship (Garg et al. 2017), provide further support for our instrumental variables (IV) strategy.

We first find that exceedance of India’s water quality standards raises the risk of neonatal mortality by 10-14 percentage points, on average, in the IV framework; the analogous ordinary least squares (OLS) regression yields a statistical zero. Since our IV point estimates are identified by variation in upstream water quality, they also show that the mortality burden of river pollution persists in downstream districts. Reduced-form regression of downstream neonatal mortality on upstream water quality indicate that the downstream mortality effect is approximately twenty percent of the main, in-district effect. Prior research documents the flow of pollution into downstream water sources (e.g., Lipscomb and Mobarak 2017), but we believe we are the first to connect such spillovers to economicallymeaningful health impacts. Our work thus underscores the large social costs of continually poor environmental quality, both at the point of measurement and in nearby areas linked by surface water Next, we leverage the IV strategy to investigate the mechanisms at work. In general, pollution policy may affect health directly through improved environmental quality, but it may also do so through increased awareness and avoidance (Kremer et al. 2011; Graff-Zivin, Neidell, and Schlenker 2011). Reduced-form estimates of policy impacts aggregate the effects of all such channels. Our finding of simultaneous reductions in river pollution and neonatal mortality in the Kanpur region is thus a necessary, but not sufficient, condition for pollution being the primary channel of the verdicts’ health impact.

Intuition suggests that if reduced water contamination is the _only_ channel of mortality reductions, then two statements should hold empirically: there is no residual effect of policy on mortality after appropriately controlling for pollution; and the policy is a valid instrument for pollution. Testing the degree to which these statements hold is equivalent to testing the null hypothesis that the policy’s mortality impacts are fully explained by its pollution impacts. Empirically, we formulate this as an overidentification test with two instruments for pollution: upstream pollution and the policy itself. This strategy is representative of a more general method of understanding policy impacts in which outcomes 3
of a policy change are compared to the outcomes of orthogonal changes in structural parameters (e.g., Angelucci and Attanasio 2013).

Armed with a plausibly exogenous source of pollution variation, we confirm that the two aforementioned statements hold empirically. First, the policy variable is no longer a significant predictor of mortality once pollution is included alongside it as an explanatory variable. Second, our overidentification tests fail to reject the null hypothesis that the rulings are a valid instrument for river pollution – i.e., that water quality improvements are the only channel through which the verdicts affected neonatal mortality.

The remainder of this paper is organized as follows. Section 1 describes the Ganga Pollution Cases amidst the more general context of pollution and related policy in India. Section 2 describes the various sources of data, while sections 3 and 4 present our empirical strategy and results, respectively. Section 5 places our contribution within the environmental policy literature, especially with regard to the puzzle of persistently poor environmental quality in the face of large social costs of pollution. Section 6 concludes.

# **1 The Ganga Pollution Cases**

In the aftermath of several decades of population and industrial growth, India’s rivers are heavily polluted **–** particularly in urban areas (Murty and Kumar 2011). Monitoring of biochemical oxygen demand (BOD) — a broad-based measure of organic water pollution— carried out by India’s Central Pollution Control Board (CPCB) shows that, as of 2011, average readings at approximately 37 percent of all sampling stations did not meet the government’s standard of acceptability for bathing set at 3 mg/l (CPCB 2017b). Waste from domestic consumption, industry, and agriculture all contribute to widespread water quality problems in surface and groundwater resources (Murty and Kumar 2011).

There have been efforts to improve Indian water quality, but there is little evidence that they have been successful. The most salient government effort to reduce river pollution is the National River Conservation Plan (NRCP), a federal top-down program targeting domestic pollution into India’s surface waters. NRCP began in 1985 as the Ganga Action Plan but has expanded over 30 years to now cover 190 towns along 41 rivers across India. Its goal since 1987 has been to restore the Ganga River to the standard for outdoor bathing, as defined by India’s “Designated Best Use” classification system.<sup>1</sup> The primary lever for achieving this goal has been the “interception, diversion, and treatment” of sewage (Madhya Pradesh Pollution Control Board 2017). To that end, 4,704 million-liters per day of sewage treatment capacity have been created since its inception (Ministry of Environment and

> 1India’s official water quality criteria have five tiers (A-E), each of which includes an acceptable range for multiple water quality measures, including BOD, dissolved oxygen, total coliforms, and pH. Outdoor bathing is tier B; tier A corresponds to untreated (but disinfected) drinking water.

4
Forests 2013). Nonetheless, popular media and non-governmental organizations have panned NRCP for reasons such as poor inter-agency cooperation, funding imbalances across sites, and an inability to keep pace with growing sewage loads (Suresh et al. 2007). Confirming public belief, Greenstone and Hanna (2014) find no discernible impact of NRCP on water quality levels.

The executive branch, however, is not the only source of environmental regulation in India; the Indian judiciary has, through the years, developed a reputation for environmental activism (Singh 2014). Article 21 of the Indian Constitution provides citizens with the “Right to Life”, and much jurisprudence in recent years has centered on the protection of this constitutional right. This paper examines the first instance of Supreme Court involvement in issues of river pollution in India.

The story begins in the pilgrimage city of Haridwar along the Ganga River. The Ganga is India’s longest river; in the Hindu religion, it is revered as a goddess. Upwards of 100,000 industrial operations (Mehta 2009) and 330 million people (CPCB 2009) reside in its basin (see Figure 1). In 1984, a matchstick tossed into the river by a smoker in Haridwar resulted in the river catching on fire for more than 30 hours, due to a toxic layer of chemicals produced by a pharmaceutical firm (Mehta 2009). In response to this event, environmental lawyer and social activist M.C. Mehta filed a writ petition in the Supreme Court of India charging that government authorities had not taken effective steps to prevent environmental pollution in the Ganga’s waters. The scale of the case – the whole 2,500-km stretch of the river – proved to be intractable. The court requested that Mr. Mehta narrow his focus; he chose the city of Kanpur (Supreme Court of India 1985).

Kanpur is a city of 2.9 million people lying directly on the Ganga in the State of Uttar Pradesh). For more than 100 years, it has been a major center for India’s tannery industry. Of the 400 tanneries currently located in Kanpur ( _The Hindu_ 2016), most are concentrated in the neighborhood of Jajmau, which lay directly on the southern bank of the Ganga River. Leather processing is a highly polluting industry; the procedures for washing, liming, fleshing, tanning, splitting, and finishing involve a large number of chemicals (Cheremisinoff 2001). Tannery effluent is characterized by large amounts of organic material – which, when deposited in rivers, depletes dissolved oxygen levels and thus the overall health of the watershed – and heavy metals like chromium, which is a documented carcinogen (Stout et al. 2009). It is also known for its conspicuous reddish-brown color (Durai and Rajasimman 2011).

Mehta selected Kanpur despite not having been born in or lived in Kanpur. In interviews granted to our research team in June 2014, he explained that “[Kanpur] was in the middle of the Ganga Basin, the reddish color of the pollution made the pollution highly salient, and the city seemed representative of many other cities in the Ganga Basin.” The court subsequently split the petition into two parts. The first dealt with the tanneries of Kanpur and the second with the city government. These are now 5
respectively called _Mehta I_ and _Mehta II_ in legislative digests, and are together known as the “Ganga Pollution Cases” **–** the foundational water pollution litigation in the Indian court system. In October 1987, the Court invoked the Water Act and Environment (Protection) Act as well as Article 21 of the Indian Constitution to rule in Mr. Mehta’s favor and order the tanneries of Jajmau to clean their wastewater within six months or shut down entirely. This was followed by a January 1988 judgment that required the Kanpur local municipal bodies to take several immediate measures to control water pollution: the relocation of 80,000 cattle housed in dairies or the safe removal of animal waste from these locations; the cleaning of the city’s sewers; the building of larger sewer systems; the construction of public latrines; and an immediate ban on the disposal of corpses into the river. The court also required all schools to devote one hour each week to environmental education and awareness.

Of the 87 tanneries named in Mr. Mehta’s petition, approximately 20 were shut down and at least 60 established primary treatment plants (PTPs). Moreover, several initiatives were undertaken in 1987 and 1988 to clean drains, expand the number of handpumps, and build latrines to improve sanitation systems in Jajmau (Alley 2002). Subsequent litigation in the Supreme Court over the past 25 years, and indeed many academic researchers of pollution in Kanpur, have argued that these projects were a failure and that newly established technologies were not appropriately maintained or used (Alley 2002; Singh 2007).

# **2 Data**

To assess the relationship between policy, water quality, and health in Kanpur, we collect and combine three types of data: infant mortality, river pollution, and other variables to be used as controls. In most of our analysis, we restrict our sample geographic area to the Ganga Basin, which is defined as the area that drains the Ganga and all of its tributaries. We make this restriction because of the singularity of this area in the context of our analysis. The Ganga Basin is not only a much more densely populated region than anywhere else in India, but also a region in which water issues have received special government attention: the National River Conservation Plan (NRCP) focused exclusively on the Ganga Basin (including the Ganga, Yamuna, Damodar, Gomti, and Mahananda Rivers) throughout its first 10 years (1986-1995). Extending the analysis beyond this region might confound the effect of the Supreme Court ruling with the effect of broad scrutiny in the Ganga Basin during this time period.

6
# **2.1 Health data**

We choose infant mortality as our primary health outcome. This choice conveys at least two significant statistical advantages. First, as noted by Chay and Greenstone (2003), infant survival is a shortterm measure; other measures of health stock, captured later in life, necessarily reflect the effect of many more health shocks experienced in an individual’s lifetime. Second, complete birth histories are available in certain Indian demographic surveys, so we can construct long pseudo-panels of infant survival to maximize statistical power. In contrast, morbidity variables such as diarrhea incidence and low birth weight are only available cross-sectionally from the time of survey; too few of such surveys have been completed for us to construct a meaningful panel. Panel variation in infant mortality allows us to include detailed temporal and cross-sectional fixed effects in regression analyses, which account for time-varying shocks to national infant health and time-invariant characteristics of specific areas, respectively.

We focus primarily on deaths within the first month of life, i.e., neonatal mortality, although we also test for a policy impact on one- _year_ mortality. The first month is the most vulnerable period of an infant’s life (United Nations Children’s Fund 2015). Globally, neonatal mortality accounts for over half of all infant deaths (World Development Indicators 2017); in our own data from India, its share of under-one mortality is 70 percent. Furthermore, several existing studies of pollution – in both water and air – and child health show that the largest impacts occur within one month of birth (Chay and Greenstone 2003; Gluckman et al. 2008; Currie and Almond 2011; and Brainerd and Menon 2014). While neonatal mortality is not a complete measure of the health costs imposed by water pollution, it nonetheless represents a very large loss of life, especially in the Indian context.

Our infant health data come from the Reproductive and Child Health II (RCH-2) module of the District-Level Household Survey II (DLHS-2), a national demographic survey conducted in two phases from 2002 to 2005. In the RCH-2 module, mothers report age and survival for all of their children; from these birth histories, we create a panel of child-month mortality in the first year. The full national dataset spans the years 1967-2004. The first four years of this range includes 2, 0, 0, and 6 reported births nationally; starting in 1971, the reported birth count rises roughly monotonically from 42 towards its peak of 80,961 in 1998. Because of the lack of data from 1967-1970, we drop these years from the sample. This leaves us with with 1,393,330 birth observations from the years 1971-2004, 647,865 of which are to mothers living within the Ganga Basin (240 districts) at the time of survey.

# **2.2 Pollution data**

To measure water quality, we use river pollution data collected under the auspices of India’s National Water Quality Monitoring Program (NWMP). These data were originally gathered by Greenstone and 7
Hanna (2014), from a combination of CPCB online and print records. The dataset spans the whole of India and runs from 1986-2004; an observation is a pollution monitor-month.

The national dataset contains information from 470 monitors situated along 162 rivers in 28 states. Excluding all areas outside the Ganga Basin leaves us with 101 unique pollution monitors along 29 rivers, in 62 districts representing 10 states; these are mapped in Figure 2. Over our 19-year period of observation, as many as 46 different measures of water quality are recorded at these monitoring stations, but only a few measures are consistently recorded over the entire sample time frame. The data are noisy, suggesting significant measurement error, and the panel is imbalanced. To mitigate these problems, we collapse monitor-level observations to the district level and construct moving averages of the data over a four-month window. These steps produce 8,725 district-month observations in the Ganga Basin with at least one non-missing value from our pollution measures of interest.

Our main indicator of river quality is BOD. This common, broad-based indicator of water pollution captures the amount of dissolved oxygen needed by water-borne, aerobic organisms to break down organic material present at a certain temperature (usually 20 degrees Celsius) and over a specific time period (usually five days). Its units are milligrams of oxygen consumed per liter (mg/l). Reduction of BOD is the primary goal of waste treatment plants in general (Brown and Caldwell 2001), but BOD is a particularly good choice for pollution measurement in the setting of Kanpur. Pollution from the tanning process primarily comes from two sources: the animal hides themselves, and the chemicals used to tan them. Both of these sources contain large amounts of organic matter, resulting in abnormally high BOD levels in tannery effluent. According to the United Nations Industrial Development Organization (UNIDO), effluent discharge into surface water typically is required to have BOD below 30-40 mg/l, while the typical BOD in _raw_ tannery effluent is approximately 2,000 (UNIDO 2011).

Use of a broad-based indicator of pollution raises the issue that it is likely affected by a variety of factors, not just the activities of tanneries. During the Supreme Court hearings, lawyers representing the tanneries argued that other firms, a negligent Kanpur municipality, and other negligent municipalities were contributing to the pollution around the tanneries (Mehta 2009: 78–82). This argument, however, was eventually dismissed in light of a report from the CPCB which demonstrated that the specific pollutant readings in the river were not even theoretically capable of being generated by any entity other than the cluster of tanneries (Mehta 2009: 83–84). Field reports of Jajmau report no other major industry in the area (Alley 2002).

In most of our analysis, we parameterize BOD as a dummy variable equaling one if average BOD in a district-month exceeds the national standard of 3 mg/l for bathing in surface water.<sup>2</sup> This 3-mg/l threshold is also a stated goal of the National River Conservation Plan, and is consistent with the crit-

> 2In addition to this BOD standard, suitability for outdoor bathing requires a total coliforms concentration of less than 5 per ml, pH between 6.5 and 8.5, and dissolved oxygen levels exceeding 5 mg/l.

8
ical values used by regulatory agencies in other countries (APHA 1992, ISO 1989). It is also consistent with the standards of proper measurement. According to the International Standards Organization (2003), the limit of detection for BOD is 3 mg/l; that is, BOD readings below three cannot be statistically distinguished from zero. These and other water quality standards in general highlight the nonlinear nature of the pollution–health relationship that has been documented in previous economics research on pollution and health (Chay and Greenstone 2003, Ebenstein 2012). For comparison, we also use the natural logarithm of BOD in certain analyses.

We also consider four other pollutants that shed additional light on the impacts of the Kanpur Supreme Court verdicts: calcium, sulfates, chlorides, and fecal coliforms (FCOLI). Calcium is the key component of lime, which is a standard ingredient used in the removal of hair and flesh and the splitting of the hide into its two primary layers. Sulfate and chloride ions, meanwhile, are the main components of the total dissolved solids (TDS) produced in tanning.<sup>3</sup> All three of these pollutants are measured in milligrams per liter (mg/l). Fecal coliforms (FCOLI) are an oft-used measure of domestic (as opposed to industrial) pollution, which was a major focus of the second verdict in the Ganga Pollution Cases. It is measured as the “most probable number” of coliform organisms per 100 milliliters of water (MPN/100 ml, reported in thousands). Other theoretically-relevant pollutants in our context are not recorded consistently in our time period.<sup>4</sup>

# **2.2.1 Assignment of upstream pollution**

Part of our analytical strategy relies on the measurement of pollution _upstream_ of a given location. Such measurement requires information on the precise location of pollution monitors. Unfortunately, latitude and longitude of monitors are incomplete and unreliable in our dataset. To circumvent this problem, we manually map each monitor according to the administrative descriptors provided (state, town, river) and an accompanying string description of location (e.g., “Sabarmati at Ahmedabad at V.N. Bridge”). With our monitors mapped, we trace the path of all rivers in our sample, from origin to last monitor downstream, and measure distances along the river between all pairs of neighboring monitors.

How far upstream should upstream pollution be measured? Two issues arise when answering this question. First, many water quality monitors in our dataset have more than one possible upstream counterpart. Second, there is no single “correct” distance at which to measure upstream pollution.

> 3Chloride ions are highly soluble in water, so their concentration is insensitive to standard tannery waste treatment (UNIDO 2011). However, surface-water chloride levels may respond to other policy impacts on the tanning industry, such as reduction or cessation of tannery operations.

> 4Total suspended solids (TSS) and total dissolved solids (TDS) are potential alternatives to BOD in our analysis, but the first of these is not recorded in large numbers in our data, and the second is not recorded in Kanpur prior to the verdicts. Chromium, perhaps the highest-profile pollutant in the tanning process, was not widely measured by the CPCB as of 2004.

9
The larger this distance, the weaker the upstream instrument will be as a result of pollution decay. On the other hand, monitors that are closer together are more likely to be subject to common shocks such as rainfall, which would create spurious correlation.

We thus adopt a variety of definitions of “upstream”, and check the robustness of our results to the definition used. To assign an upstream counterpart to a given pollution monitor, we use the following protocol: first, we follow the river upstream until it reaches a new district; then, we locate the nearest monitor along the river that falls within a distance range (in km) of [ _X, Y_ ] from the original monitor, where _X ∈{_ 0 _,_ 20 _,_ 50 _,_ 75 _,_ 100 _}_ and _Y ∈{_ 200 _,_ 300 _}_ . When a river splits upstream of a given monitor, so that there is an upstream monitor on each of two tributaries, we take the unweighted average pollution reading of these monitors as our upstream measure.<sup>5</sup> When there is no upstream monitor of any kind to be found, we use the river’s origin as an upstream location (subject to the distance-range requirement) and assign the sample-wide minimum value of pollution as our upstream measure.

## **2.3 Other data**

We include several types of variables as controls in many of our regression analyses. The main body of these controls consists of cross-sectional birth, mother, and child characteristics taken from RCH-2. A pair of climate controls are created using monthly, gridded rainfall totals from the University of Delaware and air temperature averages from the Indian Meteorological Institute; we use these gridded averages to interpolate monthly rainfall and temperature values at each district (in mortality-only regressions) or monitor (in regressions involving pollution). Finally, we add time-varying measures of common effluent treatment plant (CETP) capacity and the incidence of the NRCP, which capture the non-uniform intensity of environmental cleanup and policy efforts within the Ganga Basin.<sup>6</sup>

# **3 Empirical Strategy**

Our aim is to determine whether or not the 1987 Supreme Court decision affected environmental quality or health outcomes. Our analysis has two broad parts. In the first part, we use difference-in-differences (DD) **–** comparing outcomes in the affected area to those elsewhere in the Ganga Basin, before and after the verdict **–** to estimate the impacts of _Mehta vs. Union of India_ on infant mortality and river pollution. In the second, we employ instrumental variables (IV) to identify the direct relationship between river pollution and neonatal mortality. The IV strategy serves two purposes: first, it quantifies an important component of the external damages of river pollution; and second, it sheds some light

> 5We are unable to weight tributaries by their contribution to downstream water flow, due to a lack of data on volumetric flow rates at specific locations.

> 6We do not have any other information about the placement, utilization or other operational details of this CETP, or others like it, in India. All the results in this paper however, are robust to the exclusion of these policy controls.

10
on the mechanisms through which environmental regulation affects health.

# **3.1**

We specify the reduced-form impact of the Supreme Court verdict on neonatal mortality as follows: where _Mortalityidt_ is a dummy variable indicating whether a child _i_ , born in district _d_ , in year-month _t_ , died within the first month of life. _Tdt_ captures the incidence of policy **–** the _Mehta vs. Union of India_ court decisions in our case **–** and takes a value of one in affected — or “treated”— districts after October 1987 and zero otherwise. _Xidt_ is a vector of individual, location-by-time characteristics, which includes district and year-month fixed effects. The verdict’s impact on river pollution is specified analogously: where _Pollutiondt_ is a dummy for BOD greater than 3 mg/l in district _d_ and year-month _t_ .

From the pool of all Ganga Basin districts, we define our “treatment group” to comprise four districts: Kanpur Nagar, Unnao, Fatehpur, and Rae Bareli. This definition is justified by two facts. First, these are the four districts whose water quality is most likely to be affected by the verdict. Kanpur Nagar is the physical locus of the verdict; Unnao shares the same stretch of the Ganga River with Kanpur Nagar; and Fatehpur and Rae Bareli are the first districts downstream of these two, with borders approximately 28 and 44 kilometers from the Jajmau tannery cluster, respectively (in fact, the Jajmau tanneries are very near to Kanpur Nagar district’s downstream border, so that the majority of Kanpur’s population is not directly exposed to tannery pollution). Second, Unnao city contains its own cluster of tanneries, so inclusion of this district in the treatment group yields impacts that are net of any tanning output “leakage”. Nonetheless, we also test the robustness of our results to the exclusion of one or more non-Kanpur districts from the treatment group.<sup>7</sup> The crux of our identification strategy is the assumption that _Cov_ ( _Tdt, εidt_ ) = 0 – i.e., that the policy variable is uncorrelated with all unobserved predictors of neonatal mortality (and of pollution). We argue that this is a plausible assumption because environmental public interest litigation had no prior precedent in India, and because M.C. Mehta’s choice of Kanpur was motivated primarily by its central location and the salience of pollutants coming from its tanneries (M.C. Mehta, personal communication, 12/16/2014). Thus, Kanpur was not _a priori_ chosen on the basis of temporal trends

> 7There is no pollution monitor in Fatehpur district, so the treatment group contains only three districts in regressions involving pollution data.

11
in pollution, health, or citizen involvement. In fact, there is no evidence of any local movement to reduce pollution in the city in the mid-1980s (Jaiswal 2007).

To visually inspect the credibility of our identification assumption, we plot annual averages of both neonatal mortality and exceedance of the BOD threshold in the treatment and control groups over time; the annual averages smooth out otherwise noisy monthly time series. We then fit equations (1) and (2) to produce point estimates of policy-induced mean-shifts in these outcomes. In all cases, mortality regressions are run with child-month observations and pollution regressions are run with district-month observations.

# **3.2 Instrumental variables**

Estimation of equation (1) yields the reduced-form impact of policy on neonatal mortality, but it provides no information on the mechanisms of that impact. Theoretically, the Supreme Court decision could have had an impact on mortality through three channels: (i) reduced water contamination, (ii) reduced _exposure_ to contaminated water, and (iii) increased ability to cope with the consequences of pollution. In our context, it is likely that, in addition to reducing water contamination, the Supreme Court verdicts also drew attention to the river pollution problem; this could have driven households to avoid exposure through water-source switching or filtration, and to seek information on how to treat the consequences of contamination. Policy research in the environment-development literature frequently does not attempt to disentangle the relative importance of the reduced-contamination channel from that of other possible channels (e.g., Greenstone and Hanna 2014 and Watson 2006).

In principle, knowledge of river pollution’s direct impact on mortality can improve policy design. First, in quantifying the health burden imposed by pollution, we gain a sense of the potential for health improvements through policies that reduce pollution. Second, we can use this “dose-response function” to translate estimated policy impacts on pollution (from equation 2) into a prediction of policy impacts on mortality _exclusively_ through the reduced-contamination channel. If that prediction differs significantly from our reduced-form estimates of policy impacts on mortality (from equation 1), it is evidence that the policy operates at least in part through other channels. Note, moreover, that the reduced-contamination channel captures both the direct impact of the Supreme Court decision and any follow-on contamination-reducing policies communities could have undertaken in the aftermath of the verdicts.

Our primary obstacle in estimating a pollution-mortality dose-response function is the endogeneity of pollution. In general, pollution is correlated with other factors affecting mortality, such as urbanization (which brings access to health care facilities and education) and economic productivity (which raises incomes). To solve this problem, we instrument for pollution in a given location-time with its 12
_upstream_ counterpart, as defined in Section 2. The logic of this instrument leverages the unidirectional flow of rivers: upstream pollution reliably flows downstream (subject to some decay function of distance) to impart a negative health impact, and yet the determinants of this upstream pollution are plausibly orthogonal to downstream determinants of health. Our implicit assumption is that river flow is distinct from movements in economic, political and demographic variables, which likely take longer to diffuse from upstream to downstream.

We start by estimating the reduced-form analog of our IV strategy: where _Pollution_<sup>_u_</sup> _dt_<sup>isthepollutionreadingupstreamofdistrict</sup><sup>_d_inyear</sup><sup>_t_.Theprimaryparameter</sup> of interest in equation (3) is _γ_ 3, the impact of upstream pollution on downstream mortality. The point estimate of _γ_ 3 gives us a first check on whether our IV strategy will successfully identify the dose-response function; it also provides a causal estimate of the downstream external damages created by pollution, hence extending the work of Lipscomb and Mobarak (2017) and Sigman (2002, 2005) on the presence of pollution spillovers. Moreover, we can test the credibility of our instrument with a falsification test: for every district in our pollution sample, we replace our first-choice “upstream district” with the nearest off-river district available in our data and re-estimate equation (3). Statistical insignificance of _γ_ 3 in the falsification check is a necessary condition of instrument validity.

We then estimate our full two stage least squares (2SLS) model using, as a first stage, and, as a second stage, Coefficient _γ_ 3 in equation (5) now provides the estimated impact of river pollution on neonatal mortality in the average district. This point estimate provides a reference point for the magnitude of pollution’s burden on infant health. Combining this with the spillover mortality impact in the next district downstream identified from equation (3), we have a summary estimate of the external infant mortality cost imposed by river pollution.

Equations (4) and (5) also provide the basis for testing the mechanisms at work. We compare the estimated mortality burden of pollution with a single instrument (upstream pollution) to the estimated burden with two instruments (adding the Supreme Court verdict). If the policy affects health through 13
non-pollution channels, then an overidentification test should reject the null hypothesis that the policy instrument is a valid instrument for pollution. This null will also be rejected if the effect of pollution on mortality is heterogeneous across (e.g.) locations or pollutants, so that each of the two instruments measures a different local average treatment effect. _Failure_ to reject the null, on the other hand, provides suggestive evidence that pollution is the primary channel of policy-induced reductions in neonatal mortality. The overidentification test uses a C-statistic (see, e.g., Eichenbaum, Hansen, and Singleton 1988), also known as a difference-in-Sargan test statistic. It is equal to the difference of the two Sargan-Hansen J-statistics obtained from the regression using both _Tdt_ and _Pollution_<sup>_u_</sup> _dt_<sup>as</sup> instruments on the one hand and the regression using only _Pollution_<sup>_u_</sup> _dt_<sup>ontheotherhand.8</sup>

# **4 Results**

We report summary statistics for each analysis variable in Table 1, comparing the treatment group to the control group. The top panel tabulates statistics from variables observed at the child level. The bottom panel, meanwhile, describes those variables that we measure at the district level.

We highlight several noteworthy observations from Table 1. District-level neonatal and infant mortality rates over our observed time period are somewhat larger (8 percent and 11 percent, respectively) in the treatment group than in the control group (6 percent and 9 percent, respectively). These Ganga Basin rates are, in turn, larger than the analogous national rates of 5.3 percent and 7.6 percent in our data, respectively.<sup>9</sup> Meanwhile, average log-BOD is higher in the treatment group than in the control group (1.07 vs. 0.94), but the relationship flips when we summarize the frequency of exceeding the national standard of BOD<3 mg/l for “bathing class” water (0.34 in the treatment group vs. 0.39 in the control group). Moreover, there is no consistent relationship between treatment and control across all pollutants considered: high sulfate and chloride levels are more frequently observed in the Kanpur region, but high calcium levels are _less_ frequently observed there, and high FCOLI levels are about equally likely to occur in each group. While Table 1 reveals several cross-sectional differences between the treatment and control, the inclusion of district fixed-effects controls for these differences.

## **4.1 Difference-in-differences: a graphical representation**

We begin our analysis by graphically inspecting our difference-in-differences (DD) assumption of parallel trends in the treatment and control group. The results are depicted in Figures 3 and 4. In both figures, data points are annual averages of monthly observations in either the treatment group or the

> 8In Appendix B, we formally derive this test from a sparse model of pollution, policy, and health.

> 9According to the World Development Indicators (2017), the national infant mortality rate was 14.6 in 1971 and 5.78 in 2004; our measured sample-wide rate of 7.6 is closer to the latter number because births recorded in DLHS-2 are predominantly from more recent years.

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control group. Pre-ruling summary statistics for treatment and control are provided in Appendix Table A1.

Figure 3 contains two panels; the top panel shows neonatal mortality averages from the full period, while the bottom panel uses data within a smaller, symmetric 15-year window (1980-1994) surrounding the Supreme Court verdicts. In both panels, the vertical distance between the treatment and control group means is smaller after the verdicts than before them. The smaller scale of Panel B allows for a closer look, which shows parallel decreasing pre-trends, a roughly-constant slope in the control group over the full period, and a drop in treatment-group mortality in the aftermath of the verdicts.

Figure 4 provides analogous results for river pollution, using a dummy for exceedance of the bathing quality standard (BOD>3) as the dependent variable. Since this specification requires pollution data, the full time period available to us is 1986-2004. The top panel utilizes the full period, while the bottom panel restricts the estimation sample to 1986-1994 – a “short-run” post-period. Here, the outcome variable is somewhat noisy in the treatment group. In the short pre-period available to us for pollution, however, treatment- and control-group trends are parallel. Meanwhile, the likelihood of BOD exceeding the 3-mg/l cutoff drops significantly for the treatment group in the post-period, indicating a large DD estimate of the impact of the ruling on water quality.

While in principle it is possible to compare our treatment group to a single nearby (but not downstream) control district, inherent noise in our infant mortality and pollution data makes such a comparison imprecise. We illustrate this in Appendix Figure 1 by plotting average mortality (Panel A) and pollution (Panel B) in the treatment group and in a single district (Kannauj) just upstream of Kanpur Nagar. 10These graphs document a large time-series variance in outcomes of interest. Using the entirety of the Ganga Basin in our control group allows us to smooth our counterfactual significantly.<sup>11</sup>

# **4.2 The impact of** **_Mehta vs. Union of India_ on infant health**

To obtain a point estimate of the Supreme Court verdicts’ impact on infant health, we estimate equation (1). Table 2 provides our first set of results from this estimation. In columns 1-3, we use the full 1971-2004 period, while in columns 4-6, we use the shorter 1980-1994 period. Within each of these triplets, the first column uses one-year (infant) mortality as the dependent variable, the second uses one-month (neonatal) mortality, and the third uses one-year mortality _conditional on survival past the first month_ . In all columns, we multiply RCH-2 sampling weights by the inverse size of the relevant age

> 10The district of Kannauj is immediately upstream of Kanpur Nagar. The two Kannauj pollution monitors in our data lay approximately 93 kilometers upstream of the tanneries.

> 11We also test the robustness of the graphical DD strategy to the definition of the treatment group, by graphing treatment- and control-group average outcomes after redefining the treatment group to only include Kanpur Nagar district itself. The results are depicted in Appendix Figure A2; they do not change in any qualitatively meaningful way.

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cohort and use these as weights – for example, all observations from children at an age of one month are assigned a weight equal to (1/# of observations with age = one month). This weighting makes possible the translation of our point estimates, which come from monthly observations, into aggregate changes in mortality risk across the whole full first year of an infant’s life.<sup>12</sup> The remainder of the columns in Table 2 separate out these one-year mortality results into impacts in the first month of life vs. in months two through twelve. Given a baseline neonatal mortality risk of 12.1 percent in the treatment group prior to the verdicts, columns 2 and 5 indicate large impacts, with statistically significant point estimates of -0.024 (or 19.5 percent) and -0.020 (or 16.2 percent), respectively. Meanwhile, the estimated mortality impact in months two through twelve is a much smaller and statistically insignificant -0.001 in both column 3 and column 6. Together, Tables 1 and 2 reveal that most infant mortality events are neonatal and that the environmental policy in question here is associated with large-magnitude reductions in neonatal mortality – on the order of 16-20 percentage points. The adverse effect of pollution beyond the first month is more likely to be captured by morbidity, which we do not observe over time. We thus focus exclusively on neonatal mortality for the remainder of this paper, touching on possible mechanisms of exposure in Section 5.

We explore the timing and geography of the verdicts’ impacts on neonatal health in Table 3, using the same child-month mortality panel as in Table 2. Columns 1-3 show results from neonatal mortality regressions using post-periods of different lengths, while column 4 shows results from simultaneous estimation of short-, medium-, and long-run policy impacts. Column 5 tests the robustness of the main effect to the use of all of India as a control group. All estimates are statistically significant at the five-percent level or lower. Magnitudes range from 2 to 3.5 percentage points in these columns, which imply a 16-29 percent drop in neonatal mortality rates as a result of the rulings. Pairwise F-tests reject the null hypothesis that the 1995-1999 (medium-run) coefficient is equal to either of the other two period-specific coefficients (p-value _<_ 0.01).

In column 6, we investigate the extent to which the mortality reduction is driven by reduced _in utero_ exposure to pollution. To do so, we categorize treatment-group births by their degree of _in utero_ exposure. _All_ children born after the Supreme Court verdicts experience lower post-natal exposure. However, children born in the first nine months after the Supreme Court verdicts (November 1987 to July 1988) experience only partial _in utero_ reduction in exposure to contaminated water, while children born after July 1988 fully benefit from the effect of the verdicts. We define dummies for these two groups of children in the treatment region and estimate impacts for each group simultaneously. The predictive effect of fully benefiting from the verdicts while _in utero_ is a statistically-significant 2.9

> 12For example, column 1 indicates that, after the verdicts, the probability of infant death in a given month drops by 0.005 in the Kanpur region. This translates to a 28.9 percent reduction in the aggregate risk of infant mortality. The significance of point estimates in Table 2 is unaffected by choice of weights.

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percentage-point drop in neonatal mortality risk. Meanwhile, for children only partially “treated” by the Supreme Court rulings while _in utero_ , we find a positive though statistically _in_ significant effect on mortality risk. This result is consistent with much existing evidence that links mother’s health to the risks of neonatal mortality (Gluckman et al. 2008; Currie and Almond 2011). We note, however, that the difference between being partially and fully affected by the policy _in utero_ and other temporal factors cannot be disentangled. That is, other changes in the treatment region after July 1988 could also explain the result – for instance, relatively weaker policy effectiveness in the initial year following the verdicts.

In column 7, we test whether neonatal mortality impacts of regulation persist downstream of our defined treatment group. We define three downstream groups. ‘Near Downstream’ denotes the nearest group downstream and includes Pratapgarh, Kaushambi, and Allahabad districts. ‘Intermediate Downstream’ is the next nearest group and consists of Sant Ravidas Nagar, Mirzapur, and Varanasi districts. ‘Far Downstream’ collects the remaining 11 Indian districts through which the Ganga River travels beyond Varanasi. We estimate coefficients on the interaction between dummies for each of these groups and the post-ruling dummy. Only the ‘Near Downstream’ coefficient is statistically significant. In fact, it is significantly larger than the main treatment-group coefficient (-0.32 vs. -0.20), which may be explained by the location of Kanpur’s tanneries very near to the downstream district border. Meanwhile, the lack of impacts beyond Allahabad is not surprising, because pollution decay and other inputs to water quality should attenuate the effect the rulings as we move our focus downstream.

# **4.3 The impact of** **_Mehta vs. Union of India_ on river pollution**

The Ganga Pollution Cases therefore seem to have had a beneficial effect on infant health. Did they do so by reducing river pollution? One necessary condition for this explanation to hold is that the verdict is associated with reduced pollution levels. We test this condition in our reduced-form DD framework, estimating equation 2. Panel A of Table 4 reports regression results with our primary pollutant, BOD, while Panel B shows results for other relevant pollutants.

The BOD results displayed in columns 1-6 uniformly imply a significant drop in pollution. Our baseline estimate, shown in column 1 and corresponding to the full time-period of available pollution data, implies a 53.4 percentage-point drop in the likelihood of exceeding the bathing class BOD standard after the Supreme Court rulings. Prior to the ruling, the likelihood of exceedance in treated districts was 100 percent. The drop in exceedance is consistently observed throughout the post-verdict period, as shown in columns 2-4. It is also robust to an expansion of the control group to include all of India (column 5). Meanwhile, downstream impacts in column 6 mirror those from Table 3, column 7: the nearest group of districts downstream of the treatment group is associated with a large and 17
significant average drop in exceedance of the water quality standard, and the point estimate monotonically drops in the next two groups downstream. The evidence thus implies that the rulings’ impacts on both water quality and neonatal mortality persist some distance downstream of the cases’ focus.

In columns 7 and 8, we use the logarithm of the BOD level as our outcome variable instead of the bathing class dummy. With this specification of BOD, and over the full time period (column 7), the rulings are associated with a marginally significant drop in log-BOD, but the association is smaller and insignificant in the short run. To investigate the divergent results between a dummy variable and the continuous logarithm form, we graphically examine the distribution of the logarithm of BOD by time and treatment status. Figure 5 provides kernel densities of the residuals from regressing log- BOD on weather controls and district and year-month fixed effects. Panel A suggests that the verdicts are associated with significant, yet non-uniform, drops in BOD levels; the central peak shifts downwards, but the left tail is relatively unchanged. In contrast, Panel B illustrates a symmetric tightening of the log-BOD distribution around the mean. Put together, the two kernel density plots paint a picture of treatment-group reductions in BOD that are relatively concentrated in the middle to upper half of the distribution.

Panel B of Table 4 provides estimates of the impact of other pollutants that are relevant to the tanning industry and the Supreme Court verdicts. Calcium, sulfate, and chloride ions are all byproducts of the tanning process; all drop significantly in the aftermath of the rulings (columns 1, 2, and 3). FCOLI, meanwhile, is a strong indicator of domestic pollution and thus should be observed to drop if the second of the two Supreme Court verdicts was successful. We find no evidence of such a drop (column 4).

# **4.4 Mechanisms of policy transmission**

Having identified a strong link between the Ganga Pollution Cases and the local health of both infants (Tables 2 and 3) and rivers (Table 4), we now turn to the questions of how water pollution affects infant health and what that means for Indian environmental policy. Our strategy for answering these questions relies on an estimation of a dose-response function for water pollution and neonatal mortality. We use the merged sample of water pollution and neonatal mortality in this phase of analysis, which limits us to the 1986-2004 time period. To utilize our IV, we must further restrict our geographic focus to only those districts with non-missing upstream pollution measurements. Appendix Table A2 shows how the pool of available pollution monitors and their distance from upstream counterparts varies with our definition of upstream (which is detailed in Section 2). Appendix Table A3 summarizes the merged sample using our preferred upstream definition of [75 km, 200 km].<sup>13</sup> Finally, Appendix Table

> 13The 75-km lower bound minimizes the risk of off-river spatial correlation, while the 200-km upper bound excludes locations that are so far upstream as to not physically affect downstream pollution levels. We test the robustness of the

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A4 reproduces the main results of our DiD analysis with the merged sample.

Table 5 establishes a causal relationship between pollution and infant mortality. Column 1 shows the OLS relationship between BOD pollution (i.e., exceedance of the bathing-class standard) and neonatal mortality. It is a statistical zero, which may reflect the upward bias that would result from positive correlations between pollution, urbanization, and economic activity. Columns 2-5 replace the key independent variable with its upstream counterpart – the reduced-form analog of 2SLS with upstream BOD as the instrument. The specifications in these columns vary only by either sample time period or the definition of upstream. In stark contrast to the OLS result, the coefficient on upstream water quality is positive and significant across all four specifications. The interpretation of these coefficients is that exceeding the BOD standard for bathing-class water quality upstream increases the downstream likelihood of neonatal mortality by 2-3 percentage points – a significant spatial externality.

Our empirical strategy hinges on the assumption that upstream river pollution is exogenous once we condition on weather, policy, and district and year-month fixed effects. Implicit in this assumption is that these controls fully capture the endogenous location of households. To bolster this argument, we employ a placebo test that provides additional evidence supporting our exclusion restriction. In columns 6 and 7, each upstream district is replaced with the nearest off-river neighbor with nonmissing pollution data. If the resulting ‘falsified’ upstream pollution variable remains a significant predictor of downstream mortality, then we are likely capturing spurious, spatial correlation between a given district and its neighbors due to the regional nature of many health and environmental shocks. This, however, is not the case: the falsified upstream pollution variable loses both its magnitude and significance, thereby suggesting that our true upstream variable is isolating quasi-random variation in pollution that originates upstream and yet flows downstream to other districts.

The next step is to estimate the dose-response function and use it to test the importance of the pollution mechanism. The results of this step are documented in Table 6, which details the first and second stages of our 2SLS estimation and the associated over-identification tests. In columns 1-4, we employ a single instrument – upstream pollution, as before. Once again, the first three columns pertain to short-, medium-, and long-run periods of observation, while the fourth uses the full time period but parameterizes the policy variable as three period-specific dummies. The first-stage is strong in all four columns.<sup>14</sup> In the second stage, all specifications yield statistically significant point estimates of the pollution coefficient (in Panel B), which is approximately four to six times larger in magnitude than its upstream counterpart in Table 5. The implication is that surpassing the BOD standard of 3 mg/l raises the risk of neonatal mortality by 10-15 percentage points.

> lower bound in columns 4 and 5 of Table 5 as well as in columns 1-3 and 5-7 of Table A5.

> 14The first stage is also very strong in IV models that replace BOD with sulfates, chlorides, or calcium. These results are available upon request.

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The first four columns of Table 6 also provide some initial evidence on policy mechanisms. In these columns, the point estimates of the treatment coefficient in Panel B reveal whether the verdicts remain predictive of mortality impacts once pollution enters into the equation directly. In all cases, these point estimates are statistically insignificant, which strongly suggests that the verdicts acted on health primarily through the pollution channel.<sup>15</sup> We further test for the role of pollution in policy impacts by re-estimating the 2SLS model in columns 5-8 with the same parameters as in columns 1-4, respectively, except with two instruments instead of one. A rejection of the null (i.e., that the policy instrument is valid) is evidence of either (a) multiple channels of policy impact or (b) different local average treatment effects being captured by the two instruments. A failure to reject, on the other hand, does not formally rule out the existence of other mechanisms, but it does support the notion that reductions in pollution were central to the observed declines in mortality.

The results of the over-identification tests are tabulated as _p_ -values for the associated _C_ -statistic in columns 5-8. These _p_ -values are statistical measures of the difference between the dose-response coefficients from 1-IV and 2-IV specifications. The naked-eye version of the test is therefore a pairwise comparison of coefficients on 1[BOD > 3] between columns 1 and 5, 2 and 6, 3 and 7, and 4 and 8. In all cases, the 2-IV coefficient is smaller, but some of the deviation is a natural result of statistical noise.<sup>16</sup> The _p_ -value measures the significance of the deviation, and in columns 5-8 it runs from 0.348 to 0.634. That is, in all cases we fail to reject the null hypothesis that pollution fully explains the verdicts’ health impact. Appendix Table A5 shows that this finding is robust to the use of different definitions of upstream pollution and extending the control group to the entire remainder of India.

# **5 Discussion**

Our estimates of the Ganga Pollution Cases’ sizable environmental and health impacts are important because they represent the first documented success in India’s regulation of water quality. In addition, our findings are differentiated by the type of pollution studied. We provide the first quasi-experimental evidence of the association between biochemical oxygen demand and health. Prior work has focused on pollution from either domestic (Field, Glennerster, and Hussam 2011) or agricultural (Brainerd and Menon 2014) sources. Our own work links mortality to industrial pollution for the first time.<sup>17</sup>

> 15The three period-specific treatment coefficients are omitted from Panel B, column 4 for table conciseness but are similarly small and insignificant.

> 16One back-of-the-envelope method of interpreting our results is to compare the product of our ‘policy-on-pollution’ and ‘pollution-on-health’ point estimates with our ‘policy-on-health’ point estimate. The relevant results must hold constant the time-period (1986-2004), geography (Ganga Basin) and upstream range (here, we choose [75,200]). The reduced form results for this specific sample are in Table A3 (column 1 and column 4) and Table A4 (column 3) respectively. The product of the two coefficients from the reduced form coefficients is 0.111 * (-0.381) = -0.042. This is larger than, but still in the range of, the policy-mortality coefficient of -0.034. We stress, however, that the overidentification test is the statistically correct way to interpret our policy mechanism results.

> 17Our identified BOD impacts could, in principle, capture reductions in domestic pollution, but the absence of impacts on FCOLI – a more direct measure of domestic pollution – makes that explanation unlikely.

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In addition, we have described and implemented a novel, replicable methodology for producing an unbiased estimate of the water pollution-health dose-response function leveraging the flow of rivers.

Our reduced-form estimate of the relationship between environmental quality and health mirrors those of Ebenstein (2012) and Brainerd and Menon (2014) and indicates that the costs of river pollution are significant.<sup>18</sup> The latter authors’ findings are particularly consistent with our own results, in that they, too, specifically show neonatal mortality to be affected by water pollution in India. Given widespread breastfeeding practices in India, these results suggest that alternative modes of contamination other than drinking river water – such as bathing and person-to-person transmission – are at work (Cifuentes et al. 2000). Alternatively, pollution exposure _in utero_ may explain our neonatal mortality estimates. While we have limited ability to investigate the timing of pollution exposure, our policy analysis suggests that infants whose gestation periods fall fully after the verdicts have a better probability of survival.

In spite of the harms of pollution, environmental quality remains low in many developing countries. Air quality in Delhi, India, for example, averaged 128 _μ_ g/m<sup>3</sup> of fine particulate matter (PM2.5) in 2015 **–** ten times more than in Washington D.C. ( _Washington Post_ 2016) and significantly _worse_ than in 2010. In that same five-year period, the number of classified ’polluted river stretches’ in India doubled from 150 to 302, and the gap between sewage load and sewage treatment capacity expanded ( _Daily Mail_ 2015). Why, if pollution is so costly, does it persist at high levels?

One explanation is a high marginal utility of consumption (see Greenstone and Jack 2015), which could make the private opportunity cost of pollution reduction greater than the private benefit. In support of this hypothesis, Hanna and Oliva (2015a) show that when Indian households are randomly made wealthier (through a cash-and-livestock transfer), they choose to consume more energy but not cleaner energy. Relatedly, Greenstone and Hanna (2014) argue that, in India, air pollution policy has been more effective than water pollution policy because of relatively greater demand for air quality than for water quality. In our context, Kanpur Nagar district had BOD levels that placed it in the 63rd percentile of Ganga Basin water quality prior to the Supreme Court rulings. Higher pollution levels may, in principle, be associated with larger willingness-to-pay for environmental quality, but our qualitative investigations suggest that demand for water quality was quite low in Kanpur in the 1980s: there was no local movement to improve water quality at the time of Mehta’s writ petition, but there _was_ significant concern about the economic impacts of regulation in a city that relied heavily on the tanning industry for jobs.<sup>19</sup>

> 18Ebenstein (2012) shows that decreases in Chinese river water quality are associated with rises in adult deaths due to stomach cancer: a one-grade deterioration (from a six-grade scale) predicts a 10 percent rise in stomach-cancer mortality. Brainerd and Menon (2014), meanwhile, find that a 10 percent rise in agricultural pollution into India’s rivers is associated with a 6 percent increase in neonatal mortality.

> 19We conducted a qualitative research study in Kanpur in the winter of 2014. The study consisted of interviews of a large number of stakeholders and informants in the Ganga Pollution Cases. These included government officials,

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High marginal costs of abatement and political economic distortions are two other possible explanations for poor environmental quality in the developing world. Davis (2008) and Field, Glennerster, and Hussam (2011), for instance, identify environmental regulations whose designs had unintended consequences for air quality in Mexico and infant health in Bangladesh, respectively. Sigman (2002, 2005) and Lipscomb and Mobarak (2017), meanwhile, document greater water pollution upstream of administrative borders, where even a public agent may not fully internalize the social cost of poor water quality. Our IV analysis underscores this problem by documenting the existence of spatial spillovers in river pollution. It also connects this spatial externality directly to health outcomes for the first time, by showing that upstream pollution imparts a mortality burden on downstream communities. River pollution is thus a collective action problem requiring central government regulation and/or inter-jurisdictional bargaining.

Why, then, did the Ganga Pollution Cases succeed where other Indian government action – such as the ambitious National River Conservation Plan (NRCP) – failed? One feature that distinguishes the verdicts from the NRCP is that the environmental policy produced by the former emanated from the judicial branch rather than the executive. Article 21 of the Indian Constitution provides citizens with the “Right to Life”; _Mehta vs. Union of India_ is a watershed moment in Indian legal and environmental history because it was the first instance in which this constitutional ‘lever’ was used to drive environmental policy through the Indian Judiciary. It is plausible that decisions from the judiciary differ from those made by the executive in that they mandate agents to take specific – and verifiable – actions, rather than designing complex investment and incentive schemes that are arguably more difficult to implement (Davis 2008). Furthermore, the set of stakeholders empowered to monitor the execution of a judicial decision, which includes citizens, might reduce the scope for firms to cheat the government’s monitoring system (Duflo et al. 2013). Further understanding of what made the Ganga Pollution Cases successful might therefore shed light on what types of institutional arrangements lead to successful policy implementation.

# **6 Conclusion**

This paper provides empirical evidence that the Supreme Court decisions in _Mehta vs. Union of India_ , which primarily targeted the tanning industry in Kanpur district, induced a drop in both river pollution and neonatal mortality. Our investigation of the mechanisms of policy indicates that the observed drop in pollution may have played an important role in explaining the identified mortality effect. In deriving

> owners of tanneries, two non-governmental organizations, several journalists, three professors at the Indian Institute of Technology, an operator of a Common Effluent Treatment Plant (CETP), elderly rickshaw pullers who worked in Jajmau in the 1980s, and taxi drivers. We examined official documents from the time that the Ganga Action Plan was implemented and progress reports in later years. For background on citizen activism, we are grateful to the Ecofriends organization, which was established in Kanpur in 1993.

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and conducting our tests of this pollution channel, we show how information about different potential mechanisms can be backed out from analysis even when data on all possible mechanisms are not available.

We believe our analysis represents an important contribution to the broader puzzle of continually poor environmental quality in developing countries. First, we have identified a precedent for successful water pollution policy in India. While one data point cannot identify the keys to broadly effective environmental policy, the attributes of the Ganga Pollution Cases and their context – including (but not limited to) judicial backing, industrial focus, and water quality improvements rather than behavior change – may inform the design of successful policy in an area where it has previously proven elusive.

Second, we have demonstrated that river pollution has a real, adverse impact on infant health in India. This is important because existing research predominantly focuses on air quality rather than water quality, and demand for the latter appears correspondingly lower than demand for the former. Our results highlight the susceptibility of newborns in their first month of life, the harms of industrial pollution, and the association between mortality and the broad-based measure of biochemical oxygen demand. Methodologically, we show how the unidirectional flow of rivers can be leveraged for unbiased estimation of the water pollution-health dose-response function.

Finally, we have shown that the ultimate incidence of the costs of pollution is not limited to the origin of that pollution. Rather, water pollution flows downstream to other communities living along rivers, reducing not just water quality but also the likelihood of infant survival. This finding highlights the spatial externality inherent to pollution and underscores the need for inter-jurisdictional bargaining. All in all, we provide several pieces of insight into the challenges of designing effective environmental policy in the context of a developing country.

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