Oversized solutions to big problems: The political economy of partnerships and environmental cleanup in India

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Environmental Development journal homepage: www.elsevier.com/locate/envdev

Oversized solutions to big problems: The political economy of partnerships and environmental cleanup in India Shareen Joshia, George Shambaughb,

⁎

a

Edmund Walsh School of Foreign Service, Georgetown University, 3700 O Street, NW, Washington, DC 20057, United States Edmund Walsh School of Foreign Service and Department of Government, Georgeto wn University, 3700 O Street, NW, Washington, DC 20057, United States b

A R T IC LE I N F O

ABS TRA CT

JEL codes: Q52 Q53 Q55 L67

Managing polluted industrial waste from small firms in poor communities is a challenge in many developing countries. In recent years, partnerships among national governments, private sector actors, investors, and foreign aid donors have become popular solutions to address the problem. We argue, however, some partnerships may exhibit a boom and bust pattern that potentially leaves communities worse off in the long run. We test this argument by analyzing variations in the effectiveness of Common Effluent Treatment Plants (CETPs) built in India. We first examine the boom-bust cycle in a public-private partnership CETP in the city of Kanpur. We then extend the analysis to all 88 CETPs in India between 1986 and 2004. We use difference-in-difference techniques to evaluate the effectiveness of CETPs that are fully public, fully private, and those that augmented capacity via partnerships among governments, private sector actors and foreign donors. Our findings suggest that these public-private partnerships tend to follow distinct patterns of success and failure over time.

Keywords: India Public private partnership PPP Partnerships Environment Pollution Water Common effluent treatment plant Industrial waste Leather industry

1. Introduction Many rapidly developing countries are facing the deterioration of water quality due to industrial pollution.1 Though industrial waste accounts for a small volume of total waste thrown into water bodies, it has a higher level of toxicity and higher health costs (Do et al., 2018). Managing industrial runoff in developing countries is fraught with political and economic problems. Much of the industrial waste comes from clusters of small-firms such as brick kilns, leather tanneries and textile units (Mead and Leidholm, 1998; Dasgupta, 2000; Blackman, 2000). Small firms in these clusters are often major sources of employment and income and are, thus, politically sensitive (Ayyagari et al., 2008). Moreover, clean technologies are often expensive and require significant economies of scale to be sustainable (Mead and Leidholm, 1998). Cash-strapped governments often lack the necessary financial and technological resources and/or face competing demands to address this issue (Brandon and Ramankutty, 1993). Private sector actors, in turn, are generally unwilling to bear the risks of building these facilities in highly polluted or poor areas (Misra, 2002). The potential for collaboration and collective ⁎

Corresponding author. E-mail addresses: [email protected] (S. Joshi), [email protected] (G. Shambaugh). 1 According to estimates from UN Water, 70 percent of industrial waste in developing countries is dumped untreated into water ways, where they pollute the usable water supply (UN Water, 2013). https://doi.org/10.1016/j.envdev.2018.09.004 Received 13 August 2017; Received in revised form 21 September 2018; Accepted 23 September 2018 2211-4645/ © 2018 Elsevier B.V. All rights reserved.

Please cite this article as: Joshi, S., Environmental Development, https://doi.org/10.1016/j.envdev.2018.09.004

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action in managing these risks is often undermined further by weak political institutions, limited enforcement of regulations, and corruption (Duflo et al., 2013; Hallward-Driemeier and Pritchett, 2015).2 Consequently, clusters of small firms in highly polluted and relatively poor areas tend to be underserved (Brandon and Ramankutty, 1993; de Oliveira, 2008). In the past two decades, partnerships among governments, private sector actors, and aid donors have become popular solutions to these problems (Kindornay et al., 2014). The United Nations actively promotes such partnerships; more than 3800 partnerships are now listed in a global registry (United Nations, 2018).3 The World Bank Group actively tracks these partnerships to bring greater efficiency and stabilization to infrastructure development (World Bank, 2018). It is also exploring new financing models that are designed to include the private sector in development projects (Kim 2017). Among the many types of multi-sector and multi-actor partnerships, partnerships that include at least one public actor (e.g., national government, local government, or intergovernmental donor) and at least one private actor (e.g., non-governmental owner, operator, investor or participant) have received the most attention. The literature broadly refers to these arrangements as “Public Private Partnerships” (PPPs). We accept this inclusive definition with the recognition that public sector actors in developing countries may partner with a range of other governmental and non-governmental actors, including international development organizations and non-governmental organizations, when pursuing large infrastructure projects. Despite their popularity, the evidence suggests that public-private partnerships do not always work as expected (International Civil Society Center, 2014). A recent review argues that successful projects appear to share some common characteristics: strong leadership, partners with overlapping interests, clear goals, dedicated funding, strong management systems, strong surveillance, a system of meta-governance, a clear sense of problem-structure and the ability to adapt to the socio-political context. In this study, we expand the theoretical work on public-private partnerships by developing a stylized model that explains the how the interests and motivations of the partners in change over time, thus generating many of the challenges that these prescriptions are attempting to manage. Using ideas from political economy, we argue that certain types of partnerships are prone to specific patterns of success and failure over time. We focus on public-sector infrastructure projects and illustrate our argument by analyzing industrial water treatment plants in India. This paper also contributes to the literature on the effectiveness of partnerships undertaken by the public sector in environmental projects. It also provides insights on the persistence of water pollution in India, which is currently in a state of crisis. More than half of its rivers are currently polluted. On the river Ganga for example, 302 of 445 stretches now fail to meet the official standard of bathing quality (CPCB, 2013). A plethora of legislation and legal rulings by the Indian Supreme Court led to the construction of a large number of Common Effluent Treatment Plants (CETPs) in industrial clusters. CETPs are “end-of-pipe” technologies that combat pollution by treating effluent from multiple sources prior to release into a river.4 Small firms are required by law to pre-treat and then route the remaining still-toxic waste to a CETP. Approximately 88 CETPs were constructed all over India in the 1990s, about a third of which involved partnerships. In all official documents, the Central Pollution Control Board (CPCB) describes these as public-private partnerships even though they often include a combination of government actors, private sector actors, foreign donors and/or the World Bank. CETPs are known to be ineffective (CPCB, 2007a, 2007b; World Bank, 2007, Greenstone and Hanna 2014).5 Their failure is commonly blamed on the technical characteristics, implementation gaps and the challenge of compliance by small firms (Tare et al., 2003). Closer analysis reveals, however, that failure has not been universal; rather, that success and failure vary over time in patterns that vary based on whether the CETPs are fully private, fully public or designed as partnerships that include a mix of governments, private actors, multinational financial institutions, investors or donors. We argue that the political economy of these partnerships generates a boom and bust pattern. In the short run, economic and political incentives align and encourage larger and riskier projects than the local government, private actors or other partners would do absent the partnership. The initial boom of activity has the potential to generate expectations of relatively high profits to partners and high quality services to the local community. As operations get underway and the initial surge of building and financial support subside, demands for governments to sustain high rents and/or provide subsidies increase. If governments are unwilling or unable to do so, shirking is likely to grow throughout the process. The underlying misalignment in incentives of the different partners can open up the possibility of many different types of corruption: outright rent-seeking, non-transparency in budgets and operations, arbitrary (and frequent) changes in policies, exclusion of the public from deliberation or decisions, etc. (Hallward-Driemeier and Pritchett, 2015). Given the relatively large scale of these partnerships, the long-term outcome will be worse than in simpler ownership structures. We recognize that the challenges associated with long-term budgeting, financial planning, risk management and sustainability apply to public and private projects as well as those established as partnerships. Partnerships differ in that they facilitate larger and often more challenging and riskier endeavors than the partners would likely take on by themselves.

2 Duflo et al. (2013) demonstrate that auditors systematically report plant emissions just below the standard, although true emissions were typically higher. Hallward-Driemeier and Pritchett (2015) argue that small firms in developing countries use many types of deal-making strategies to avoid regulatory checks and penalties. 3 Sustainable Development Goal 17 seeks to “Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development”. A special report recognizes that such partnerships are useful for mobilizing knowledge, expertise, technology and financial resources (United Nations, 2016). Data on these partnerships is available at: https://sustainabledevelopment.un.org/partnerships/ (Retrieved on April 24, 2018). 4 Performance Status of Common Effluent Treatment Plants in India. 2002–2005. Central Pollution Control Board. http://www.cpcb.nic.in/Water/CETPS.html 5 See: http://www.worldbanktribunal.org/docs/cetp.pdf

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We test this hypothesis with two types of analysis. First, we demonstrate this boom-bust cycle using a single case study a partnership CETP in the city of Kanpur, which is known for its cluster of small-scale leather tanneries. Second, we extend the analysis to all 88 CETPs in India between 1986 and 2004. We analyze readings from pollution monitors upstream and downstream from CETPs that are fully private, fully public and public-private partnerships. We find evidence that partnered projects follow a boom and bust cycle over time and are less effective in reducing industrial pollution in the long term than fully private or fully public operations. The remainder of the paper is organized as follows: Section 2 presents our argument about the efficacy of public-private partnerships. Section 3 provides an overview of our data. Section 4 analyzes changes in efficacy over time for a partnership in the city of Kanpur. Section 5 analyzes variations in efficacy based on ownership structure by evaluating all CETPs in India. 2. The argument We define public-private partnerships (PPPs) as voluntary collaborations among at least one public actor (e.g., national government, local government, or intergovernmental donor) and at least one private actor (e.g., non-governmental owner, operator, investor or participant).6 This is consistent with the broad definition of multiple stakeholder partnerships used by the United Nations (United Nations, 2016, International Civil Society Center, 2014).7 Such partnerships have become increasingly common (United Nations, 2016). A review of recent data reveals many different types of partnerships across the developing world – including multisector partnerships, cross-sector development partnerships, and public-private partnerships – that vary in size, scope, as well as the constellation of actors. Our definition of public-private partnerships is able to account for the range of actors who work on pollution mitigation technologies in India, such as CETPs, that involve public actors (e.g., the Indian government, local governments, foreign governments and intergovernmental donors) and private actors (e.g. non-governmental investors, private sector owners, managers and operators, and tanneries and other firms). Though there is mixed evidence of their performance, there is broad agreement that partnerships enable the partners to pool resources, expand the scale of their operations and focus on specific goals (International Civil Society Center, 2014: 4). In this sense, partnerships are capability multipliers – they enable actors to create larger and often more challenging and riskier projects than would otherwise be possible in the absence of a partnership. 2.1. The case for partnerships Partnerships are attractive to both private and public partners. Private sector actors, NGOs, donors and other interested parties enter into these partnerships for a variety of reasons including gaining access to new consumers and new resources (such as government contracts or support from aid donors), reducing risk or transferring risk to the government, or other positive externalities that are not part of the formal contracting arrangement such as humanitarian concerns, reputational gains, easier access to future government contracts, reduced regulatory or bureaucratic burdens, and other commercial benefits (Shambaugh and Matthew, 2015, 2016). Local governments, in turn, enter into partnerships to better provide goods or services that their constituents demand. Partnerships with the private sector or other entities may facilitate the provision of the desired goods and services, or it may generate revenues that enable the government to provide other benefits to its constituents (Chi et al., 2003). In addition to overcoming budget constraints and other cost/benefit motivations, local governments may be seeking access to specialized technical information and expertise (Hance, 2003; Feigenbaum and Henig, 1997). They may also pursue or avoid partnerships out of a tactical desire for shortterm gains associated with pleasing a particular constituency (Denisova et al., 2009) or for ideological reasons (Arin and Ulubaşoğlu, 2009; Henig, 1989). 2.2. Why partnerships may underperform As noted in the introduction, public-private partnerships don’t always deliver the intended results. Successful projects appear to have had strong leadership, partners with overlapping interests, clear goals, dedicated funding, strong management systems, strong surveillance, systems of meta-governance, a clear sense of problem-structure and the ability to adapt to the socio-political context (International Civil Society Centre, 2014). Here, we argue that most of these factors, and a partnership’s likelihood of success, ultimately depends on the interests and incentives of the partners themselves. Projects work well when all partners have common objectives over the long-run. In reality, however, partners from the public, private and non-profit sectors operate on different motivations and time-horizons. In some partnerships, especially those for which the price or quality of services provided cannot be sustained or increased over time, the incentives of all interested parties converge in the short run, but diverge over time (Shambaugh and Matthew, 2015, 2016). To illustrate the importance of this specific issue, we restrict our attention to a discussion of the literature on public-private partnerships and the challenge of sustainability. This literature discusses the features of partnerships between a public and a private actor, but we 6

This includes the possibility of a national government and a state government as public actors. The United Nations defines a multi-stakeholder partnership as an initiative “voluntarily undertaken by Governments, intergovernmental organizations, major groups, and other stakeholders, which efforts are contributing to the implementation of inter-governmentally agreed development goals and commitments” (United Nations, 2016:4). 7

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emphasize that the issues we focus on in this literature apply to any partnership among actors whose incentives and time-horizons can become misaligned over time in a predicable way based on their underlying motivations and preferences. Studies of PPPs suggest that the performance of a partnership can depend on the type of service it provides. Specifically, PPPs tend to perform better when providing services for which demand is elastic and the services can increase in quality and price (Vernon, 1977). In contrast, they tend to underperform when societal pressures put downward pressure on prices and upward pressure on the quality of services as is common with basic social services and environmental goods (Shambaugh and Matthew, 2015, 2016). Others emphasize that PPPs are difficult to sustain and renegotiate because of information and power asymmetries that favor either the government (Vernon, 1977) or the firm (Lonsdale, 2005); or because either firms are focused on short-term profits rather than longterm gains, or because governments are focused on short-term political or financial gains and willing to pass long-term costs onto their successors (Pint, 1990). Some scholars argue that PPPs provide efficiency gains, while others argue that such gains are ephemeral because of political considerations (Diggs and Roman, 2012), the monopolistic behavior of private sector partners in the absence of strong regulation (Bortolotti and Perotti, 2007), the lack of accountability (Berg and Berg, 1997; Morgan and England, 1988), transparency, and challenge of long-term risk sharing (Bloomfield, 2006). Temporal affects are generally conceptualized in terms of the difficulties of making accurate forecasts and potential asymmetries in information among the partners involving risk and uncertainty (Shambaugh and Matthew, 2015, 2016; Iossa and Martimort, 2012; Chi, 1998). Governments in democratic countries like India are presumed to have a perverse incentive to maximize the current value of their partnerships, and discounting future risks when the current policymakers will no longer be in office (Helm and Tindall, 2009).8 Firms are similarly expected to have an incentive to hide information about future costs and maximize current over future returns (Long, 2013). We argue that many of these outcomes are manifestations of a common set of underlying political and economic motivations that shape the behavior of stakeholders in partnerships. Moral hazard problems induce the actors to take greater risks, expend more resources and/or make more extensive short-term commitments than they would without the other’s involvement. This creates a problem of overshooting in the short run which, in turn, generates unrealistically high expectations regarding ongoing political and economic rents. The involvement of additional donors or financiers increases these tendencies by providing additional resources early on. While the incentives of private sector and government partners, their clients and constituents, and external donors all converge in the short term, pressure to maintain or increase rents over time generates divergence. To the extent that additional rents become costprohibitive or are perceived to be excessive over time, the clients, in turn, will likely demand subsidies from the government to compensate. If the government is unwilling or unable to enforce higher rents and/or subsidies, shirking and corruption are likely (Shambaugh and Matthew, 2016). This argument, as pertinent to CETPs, is illustrated in the stylized graph in Fig. 1. It depicts changes in the level of mitigation and expected rents to CETP operators over time based on whether they are fully private, fully public or partnerships. We posit that, all else equal, environmental impact is highly and positively correlated with CETP capacity, and that CETP capacity is highly and positively correlated with the size of the CETPs client base and the total rents that it receives from them. We posit that private CETPs are likely to be effective locally, but will tend to be small in scale and located among relatively wealthy industrial clusters. We expect public CETPs to larger and located in larger but poorer industrial clusters. We consider partnerships to be capability enhancers and expect them to be the largest in scale and located in areas that are underserved by fully private and fully public areas. These will likely be the most problematic or risky technically, politically or economically. Our expectations match the size and capacity of CETPs in India presented in Table 1. For clarity and simplicity, the level of mitigation and total rents are shown on the same axis in our stylized figure though we recognize that are not likely to be perfectly correlated. Time is divided into three periods: Phase 1 involves building of the CETP and infrastructure preparation, Phase 2 is the startup phase immediately after construction has been completed, Phase 3 involves ongoing and longer-term operations. Our moral hazard argument predicts that the efficacy of CETPs designed as partnerships will follow a predictable pattern:

2.2.1. Phase 1: building and infrastructure preparation Pollution mitigation efforts are expected to improve in the construction phase of all CETPs as a result of infrastructure improvements, increased public awareness, and political and legal attention given to pollution problems. To the extent that creating partnerships generates supplemental funding or access to technology, decreases the costs to governments, reduces the risks to private sector actors or provides other positive benefits to both, we expect the size and scope of partnerships to exceed those of fully public or fully private CETPs. Furthermore, to the extent that partnerships are created in locations that are otherwise underserved by political or private sector actors, the basic infrastructure improvements needed are expected to be high. Consequently, benefits of infrastructure development during the building phase of the CETP to be high and potentially greater for PPPs than for purely private or purely public CETPs. This is reflected the positive slope of the pollution mitigation lines during the first phase for all CETPs and the slightly steeper slope of the line for partnerships relative to other types (see Fig. 1).

8 This may be driven by personal considerations of a specific policy-maker rather than the type of regime in which they operate. A policy-maker who is closely associated with a large and salient project may gain recognition for the short-term achievement, which may translate into opportunities for advancement within the political system. This kind of problem is frequently found in foreign-aid projects and has often led to the misallocation of funding, away from long-term investment in developing countries that are heavily dependent on aid (Easterly, 2001).

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Fig. 1. Mitigation and expected rents. Table 1 Summary statistics of CETP types. Note: Throughout the sample period, the rupee-USD exchange rate is Rs. 50–70 = USD 1.

Average of Initial Construction/ Capital Costs (Rs. 100,000) Average of Capital Cost: Govt. Contribution (Rs. 100,000) Average of Capital Cost: Members Contribution (Rs. 100,000) Average of Capacity MLD Member contribution per unit of Capacity Initial cost per unit of capacity Number of CETPs in category

Private ownership

Fully public

Sub-contracting to private sector

Partnership with joint ownership

669.36

742.09

404.25

926.15

95.25

468.82

127.17

454.44

90.13

283.96

146.00

248.06

5.49 16.41 121.86 50

11.85 24.30 63.52 7

5.99 24.37 67.48 14

9.88 25.10 93.73 16

2.2.2. Phase 2: startup During initial operations, all CETPs are expected to reduce pollution levels in ways that approximate their design expectations. We depict this in a flattening of the pollution mitigation curve. Performance levels are expected to stabilize as long as the expected levels of rents from political and private sector actors are maintained. For private CETPs, revenues are generated from client rents. They are expected to be stable as long as expectations of the different partners remain aligned. For public CETPs, the revenues are generated primarily from a government support. This support is expected to be stable in the short term, but is also expected to plateau due to competing demands on public resources. Partnered projects are dependent on government, donor, and client support. The partners’ expected rate of return will likely reflect the new baseline set at the peak of government, investor and donor assistance at the beginning of Phase 1. As new construction is completed, government support plateaus and other startup money is exhausted, partnered project will become increasingly reliant on their clients. They are expected to remain effective as long as initial expectations of political support and the capacity and willingness of clients/constituents to provide expected rents continues. This becomes problematic to the extent that overshooting in Phase 1 generates a baseline of expected returns among partners that exceeds the ability to sustain rents. Extended financing from external donors can lengthen the duration of this phase by helping to sustain positive economic and political rates of return for CETP operations, but once alternate funding sources are exhausted, client-generated revenue is unlikely to be sufficient to sustain operations. Consequently, the level of mitigation peaks for partnerships in Phase 2. 2.2.3. Phase 3: ongoing operations Phase 3 reflects the ongoing operations after the initial surge of startup funding have been depleted and the firms self-reliant. We posit that CETP owners’ expectations regarding future rents will likely grow with their output and/or the rents they are currently able to secure. This implies that their expectations regarding future rents for ongoing operations (Phase 3) can be approximated by the rents received during the startup phase (Phase 2). If a preference for positive rates of return is assumed for private actors, then their expectations will be that future rents will be ever increasing from the baseline created during the startup phase. Consequently, to be sustainable, the rate of return for fully private and partnered CETPs must be positive. This is particularly problematic for partnerships that overshoot in Phase 1 and create CETPs that have the potential to generate higher rents than are politically or economically sustainable (are reflected in their respective mitigation lines in Fig. 1). The 5

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minimally sustainable level of rents for PPPs is reflected in the dotted line in Fig. 1. The ongoing rents that clients are willing or able to pay is expected to correspond to the output as reflected in the dash dot line. When the expectations of rents and willingness to pay rents diverge, the private partner is likely to call on the public partner to enforce existing rent payments or promote higher rent payments, subsidize the CETP partners or subsidize their clients to make up the shortfall. If the government is unable or unwilling to make up for the lost rents to its partners, the CETP operators may attempt to increase costs to their clients. To the extent CETP operators can make participation in the CETP conditional on paying higher rents, they can transform the CETP into club goods.9 Increasing exclusivity by restricting participation to those who can pay higher fees is a common solution to the problem of under-provision associated with collective goods. This solution may, however, be ineffective if participation by all parties is necessary to achieve a desired outcome. It is also counter-productive if the goals of building the CETP include increasing services to otherwise underserved constituents. If demands for higher rents exceed the willingness or ability of the leather tanneries to pay, they are unlikely to comply with pre-treatment obligations. The result would be a decline in effectiveness. Many types of corrupt practices can also emerge at this juncture – bribes to disguise non-compliance, non-transparent reporting, changes in policy that raise the burden of compliance, inadequate maintenance of the CETP technology itself, etc. In sum, we predict that the level of pollution mitigation to increase during the building and infrastructure preparation phase of all CETPs (Phase 1 or the “Building Phase”). We hypothesize that the efficacy of private CETPs will improve over time to the extent that the private owners can maintain increasing rates of return on their investment. The efficacy of public CETPs is expected to plateau given limits to political financing and time-inconsistency problems among politicians. The efficacy of partnerships is expected to peak above private and public CETPs, but then decline dramatically unless the government is willing and able to maintain high total rents for its partners and/or subsidies for its constituent. This framework is stylized and simplistic, but if confers important advantages in illustrating the challenge of CETPs run in the form of partnerships. The focus here is entirely on incentives of the partners, rather than the technical characteristics of CETPs, the specifics of the partnership arrangements, or the many different types of corruption that are frequently documented in such projects (Dasgupta, 2000; Huang and Gupta, 2014). The mechanisms outlined in our model suggest that these latter variables could be viewed as endogenous: technical failures and corruption in the relationships between governments and partners are likely to emerge from the misalignment of incentives between partners over time that we have documented here. The key idea here is that corruption emerges spontaneously from the misalignment of incentives between partners in the long-run. 3. Data This paper uses the same water quality data as Greenstone and Hanna (2014) and Do et al. (2018). These data were originally gathered from a combination of CPCB online and print records that are collected under India’s national monitoring program. Fig. 2 provides a map of pollution monitors in India. For each CETP, we analyze the pollution level indicated by the closest downstream river monitor while controlling variation in ambient river pollution levels as indicated by the closest upstream monitor. We control for the impact of all CETPs located in between the two monitors by weighting their readings in terms of the CEPTs capacity and its inverse distance to the downstream monitor. This requires precise measures of CETP and monitor locations. Unfortunately, the widely cited geographic locations of the river monitoring stations used in Greenstone and Hanna (2014) are not accurate.10 Consequently, we re-estimated the positions using data gathered from Do et al. (2018). The second map in Fig. 3 shows CETPs as stars and monitors as balloons. The balloons inside hexagons show the inaccurate locations of three monitors around Kanpur based on Greenstone and Hanna (2014), the balloons without hexagons show the corrected locations. Out of the 88 CETPs in India, 34 (38.6%) located in between part of a pair of upstream and downstream monitors that are no more than more than 65 km from the CETP (31 or 90% of them are located between monitors that are closer than 45 km). The first CETPs became operational 1989, the last became operational in 2004. We track pollution data in the five years preceding their operational status in order to establish a temporal and track changes in pollution levels during the construction phase of each project.11 The pollution monitors record as many as 46 different measures of water quality are recorded at these monitoring stations, but only a few measures are consistently recorded over our sample timeframe.12 Of those, Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) are most widely used for measuring industrial water pollution. While the monitors can provide data on both indicators, we use BOD rather than COD because BOD data are more widely and consistently available on over our time period of interest.13 We also analyze readings for fecal coliforms (FCOLI), which is primarily a measure of biological waste and domestic pollution. CETPs were designed to manage industrial waste, but there are some instances in which they treat a mixture of industrial and domestic affluent. Moreover, FCOLI levels may also go down in the initial stages of construction of CETPs due to investments in infrastructure such as the construction, maintenance and cleaning of drains. While these effects may indeed be in play 9

Club goods are defined as goods that excludible, but not rival in consumption among members. This is likely due to the historic reference point used in estimating the longitude and latitude of the data by the India Government. 11 Monitors record pollution at either the monthly or quarterly frequency. 12 These are: Fecal Coliforms; Total Coliforms; Biochemical Oxygen Demand; Chemical Oxygen Demand; Dissolved Solids; pH; Alkalinity; Conductivity; Hardness; Turbidity; Total Dissolved Solids; Calcium; Chlorine; Magnesium; Sodium; Sulfates; and Nitrogen. 13 Biochemical oxygen demand and chemical oxygen demand use oxygen content to estimate pollution levels. BOD measures the amount of oxygen that bacteria will consume while decomposing organic matter under aerobic conditions, COD measures the total quantity of oxygen required to oxidize organic material (Brown and Caldwell, 2001). Analyzing the variation in river pollution across other indices recorded by the river monitors is beyond the scope of this paper, but is a fruitful avenue for future research. 10

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Fig. 2. Pollution monitors for India. Note: Based on Greenstone and Hanna (2014), as refined by Do, Joshi, and Stolper (2018).

we expect the impact of CETPs to be greater on BOD. Our empirical work will highlight the difference between BOD and FCOLI readings to raise confidence in our inference about changes in industrial pollution as a result of CETP construction. We conducted a thorough review of the management documents in the public domain for all the CETPs mentioned in the Central Pollution Control Board’s annual reports to determine ownership and management information.14 Based on the Central Pollution Control Board (CPCB) reports, we operationalize the ownership structure of CETPs on a nominal three-point scale. The CPCB uses a four-point scale that includes: (1) Full public ownership in which the government finances construction and operates the plant. (2) Full private ownership in which a co-operative company of individual participants organized alone or through an industrial organization. (2a) Subcontracted operations in which a private outside agency is contracted to manage and operate the CETP. (3) Full Public-Private Partnerships that involve joint public and private sector ownership of a joint sector company where the government and individual participants or an industrial organization are members of a co-operative or shareholders in a company formed for the management of a CETP. For simplicity, we combine private sector ownership (2) and subcontracting operations to the private sector (2a) into a single category. See Table 1. 4. Variations in the efficacy of parnerships over time: the case of Kanpur We illustrate our argument about variations in the efficacy of partnerships over time by analyzing a partnership CETP in Kanpur. It is an excellent example of a partnership among two national governments (the governments of India and the Netherlands), a state government agency (the Kanpur Jal Nigam or water board, which supplies potable water to Kanpur city), and a cluster of privately owned tanneries. The collaboration intended to draw the technological capability of the Dutch government, the management and 14

We are grateful to Sarina Jain for outstanding research assistance in putting together this data in Jan 2016. 7

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Fig. 3. Pollution Monitors and CETPs in the Kanpur Area. Note: The balloons inside hexagons show the inaccurate locations of three monitors around Kanpur based on Greenstone and Hanna (2014). The balloons without hexagons show the corrected locations.

operational expertise of the water suppliers, and the needs of the tanneries. Initial funding was largely provided by the two international governments, though others, such as the World Bank, and the tanneries themselves, also participated at various stages. We review the history of this project to illustrate that the partnership was effective in the initial stages, but failed to achieve long-term sustainability. Kanpur is one of the leading producers of leather in India. More than 400 small-scale leather tanneries are clustered in the neighborhood of Jajmau, just downstream from the city center. The tanneries produce a large volume of toxic waste that was routinely discharged directly into the Ganga river, contaminating both river water and groundwater (Beg and Ali, 2008; Tewari et al., 2012). In 1986, a leading environmental lawyer, Mr. M.C. Mehta, filed a public interest case at the Supreme Court of India. He charged that government authorities had not taken effective steps to prevent environmental pollution in the Ganga’s waters.15 In October 1987, the Court ruled in Mr. Mehta’s favor and order the tanneries of Jajmau to clean their wastewater within six months or shut down entirely.16 Choosing how to comply with the ruling was difficult because the cluster of numerous but small tanneries in Kanpur lacked the capital and technological capacity to acquire pollution mitigating equipment. A unique opportunity emerged during the visit of an official delegation from the Netherlands who sought a partnership with the Indian government on issues related to water. The Dutch mission selected Jajmau (along with Mirzapur) for the “reasons of introducing Dutch developed technologies mainly for the reduction of the industrial/tannery pollution load (Government of India and Government of Netherlands, 1989: 6).” It was decided CETP was to be built in a rare partnership between the Government of India, the Government of the Netherlands and the private tanneries themselves (who were to contribute to the setup and operational costs). The Indo-Dutch partnership led to a great deal of preparatory activities in Jajmau. Between 1987 and 1988, many “crash programs” were implemented to clean drains, expand the number of hand pumps, build new hand pumps, and latrines to improve sanitation systems. In 1989, a 12-kilometer drain was built to transport the tannery waste to a common treatment plant. Since this was done specifically to prepare for the CETP, in our subsequent empirical analysis, we consider 1989 to be the year of establishment 15

We learned this in a personal interview with Mr. Mehta. He confirmed that he was not from the area and had never visited the area. 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. 16

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of the CETP.17 A pilot chrome-recovery plant for pre-treating waste was also built at the largest tannery at this time (Maheswari and Dubey, 2000: 23). The technology, and the first phase of the project more generally, were widely regarded as successes and generated considerable optimism for the CETP that was under construction (Government of India and Government of Netherlands, 1989). The Indian and Dutch governments intended to transfer control of the CETP to the Kanpur Nagar Nigam, who would then collect maintenance costs from the tanneries, once construction was completed. The issue of operating and maintenance costs became contentious, however, because by the time construction was completed, the CETP had gone over budget significantly, and the tanneries were already being asked to pay three times more for of the CETP than they had anticipated (Schjolden, 2000). In 1997, the Government of India stepped in and declared that 60% of the costs should be borne by the tanneries and 40% by the local water board (Ministry of Environment and Forests, 1997). The costs for each of the 300+ tanneries ware determined based on production capacity rather than actual production or revenue. The tanners reported paying approximately Rs. 2–3 per hide (Schjolden, 2000: 37). According to one report, the average cost for a tannery amounted to approximately Rs. 400,000 per month. Many tanneries reported difficulty in making payments (Schjolden, 2000). As a result of higher than promised costs and perceptions of free riding, many tanneries and CETP operators began shirking and bribing government pollution inspectors instead of complying with the system.18 According to one report, only 25% of the registered tanneries were actually paying their dues (Schjolden, 2000). Frequent power cuts, aging equipment that has been heavily corroded, and frequent breakdowns of machinery meant that the plant often to sit idle, while untreated waste continued to flow into the river (McBride 2014, Gallagher 2014). This created a perfect downward spiral. Instead of paying their dues, maintaining the equipment and making the effort of treating their waste and complying with the CETP codes, tannery owners could simply bribe government inspectors. Since inspectors and maintenance officials work for the same local monopoly, it is also in their incentives to allow the CETP to fail. This ensures a steady stream of rents as well as the freedom to shirk from maintenance. Alley (2002: 173–179) who documents the complex web of relationships between industrialists and government officials. He argues that “Their immediate interest lies in circulating money in such a way that benefits accrue to both parties, which leaves as little as possible for cleaning up the premises and the resources they use” (Alley, 2002: 179). These field observations, together with our theory, give us some clear hypotheses to test in our data. We now use statistical methods to explore the different phases of the CETP’s operation and examine whether the CETP was effective in the earliest stages. 5. Empirical analysis To explore our predicted temporal dynamics, we examine the performance of the CETP in Jajmau during different phases of its operation. We explore the CETP’s impact through a regression model with the following specification:

Pollutionmdt = α 0 + αJp Jajmau × CETP Phasep + αJ Jajmau + βXmdt + δt + θd + ϵmdt where Pollutionmdt is the pollutant reading at monitor m, located in district d in year-month t, Jajmau is a dummy variable that takes value 1 if the monitor is located in Jajmau (and 0 otherwise). The index j denotes the Jajmau monitor.19 CETP Phase p is a dummy variable that takes on the value of 1 based on the phase p of CETP operations. In this model, we refer to the phases of operations as Phase 1: Building (1989–1993), Phase 2: Startup (1994–1997) and Phase 3: Ongoing Operations (1998–2002).20 αJ andαJp are our coefficients of interest. αJ specifies the relative impact of the existence of the CETP at Jajmau, αJp specifies the relative impact of the CETP at Jajmau during each of the three phases relative to the period before 1989. Xmdt is a set of control variables that includes a dummy variable that takes value 1 for the one monitor in the sample that is located immediately upstream from Jajmau (and 0 otherwise) and another dummy variable that takes value 1 if the monitor is located immediately downstream from Jajmau (and 0 otherwise). δt is a year-month fixed-effect and θd is a location fixed-effect. Inclusion of year-month fixed effects allows us to control for monthly variations in river volume, precipitation, economic and political cycles, etc. that may affect all monitors. The monitorspecific fixed effect is a location-specific fixed-effect that controls for all observable and unobservable confounding factors that are specific to Jajmau and unvarying over time. Table 2 presents descriptive statistics. We present two sets of summary statistics: one for the entire sample of monitors in India, and the second for just the states that constitute the Ganga basin, which includes states through which all the tributaries and distributaries of this river flow. Note from this table that the mean of the Ganga Basin Indicator specifies than 24 percent of river monitors are in the Ganga basin – this is consistent with the observation that the Ganga is India’s largest and longest river, and as noted in the introduction, it is also one of the most polluted. The two samples allow us to examine whether the results are robust 17 1989 was the year that the Supreme Court of India passed its first ruling requiring specific tanneries to shut down unless cleaner technologies were immediately adopted. This ruling raised the level of scrutiny of environmental issues in Jajmau, led to immediate efforts such as the cleaning of drains, the closure of firms, and ultimately paved the way to the adoption of the CETP (Do et al., 2018). 18 Many factors are believed to have contributed to the sizeable increase in project costs. First, as in the case of all foreign grants, the money was routed from the Ministry of Environments and Forests in the central government of India to the state government of Uttar Pradesh. From there, the money was routed to the Kanpur Jal Nigam, who was expected to oversee construction, engineering and technical tasks. It ultimately took over the task of maintaining the CETP as well. On the ground, this state apparatus is largely a monopoly with no oversight (Alley, 2002: 170). Some of the grant money was indeed set aside to strengthen local institutions, but these projects largely failed to provide any effective checks on the activities of the Jal Nigam (Alley, 2002: 170). 19 Our fieldwork in Kanpur confirmed that the monitor in Jajmau is located very close downstream to the location where the drain carrying effluent from the CETP opens out onto the river. 20 2002 is the last year for which data are available for the CETP at Jajmau.

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Table 2 Summary statistics of the monthly pollution data. Note: (i) Monitor observations are recorded on a monthly basis; (ii) The Ganga Basin Indicator is a dummy variable that is equal to 1 for all monitors located in the Ganga Basin and 0 otherwise. Variable

Monitor Obs.

Panel (A): All-India Sample of pollution monitors Log (BOD) 45,098 Log (FCOLI) 35,640 Jajmau × Building phase (1989–1994) 46,179 Jajmau × Startup phase (1994-98) 46,179 Jajmau × Ongoing operations 46,179 Phase (1999-02) Upstream jajmau 46,179 Jajmau 46,179 Downstream jajmau 46,179 Building phase (1989–1994) 46,179 Startup phase (1994–1998) 46,179 Ongoing operations phase (1998–2002) 46,179 Ganga basin indicator 46,179 Upstream BOD 45,451 Upstream FCOLI 40,387

Mean

Std. Dev.

Min

Max

Monitor Obs.

0.746 0.568 0.001 0.001 0.001

0.931 0.282 0.025 0.032 0.036

−2.303 0 0 0 0

4.625 1.456 1 1 1

0.006 0.004 0.005 0.271 0.290 0.374 0.235 −0.588 3.663

0.079 0.060 0.074 0.444 0.454 0.484 0.424 2.013 3.400

0 0 0 0 0 0 0 −3.507 0

1 1 1 1 1 1 1 4.654 14.557

Mean

Min

Max

Panel (B) Ganga Basin Only 10,390 0.894 0.823 7,038 0.719 0.312 10,837 0.003 0.053 10,837 0.004 0.066 10,837 0.006 0.074

−2.303 0 0 0 0

4.205 1.456 1 1 1

10,837 10,837 10,837 10,837 10,837 10,837 10,837 10,692 9,415

0 0 0 0 0 0 1 −3.507 0

1 1 1 1 1 1 1 4.205 14.557

0.027 0.016 0.023 0.210 0.305 0.405 1 −0.615 4.192

Std. Dev.

0.162 0.124 0.151 0.408 0.460 0.491 0 2.115 4.255

relative to the reference pool. Table 3 presents regression results using the two samples. Note that in the sample for all monitors in India, we also include a control for whether the monitor is located in the Ganga basin. Columns 1 and 3 provide analyses for BOD, while columns 2 and 4 provide analysis for FCOLI. Our results presented in Table 3 confirm that pollution readings for BOD and FCOLI at Jajmau monitor are worse than the average for the Ganga Basin (in the BOD regression, αJ = 0.990*** and in the FCOLI regression, αJ = 0.293***), and all of India (in the BOD regression, αJ = 0.791*** and in the FCOLI regression αJ = 0.489***), when controlling for levels at the nearest upstream and downstream monitors. This is consistent with previous work that demonstrates that with respect to industrial pollution, the Ganga basin is a chronically polluted region, and Jajmau is a particularly polluted spot within this basin (CPCB, 2013). The model presented earlier predicted that the project would be most effective in the early stages of the project. In the earliest phase, greater scrutiny of the site, investments in infrastructure, and efforts such as cleaning of drains and raising of awareness are likely to lower pollution levels. Similarly, once the project is in the startup phase, both funding and operational support are likely to Table 3 Jajmau CETP Performance. Notes: (i) Monitor observations are recorded on a monthly basis. (ii) log(BOD) and log(COD) are the logarithm of BOD and COD respectively; these are monthly readings from pollution monitors; (iii) The numbers reported for each variable include its coefficient value with the corresponding standard error, clustered at the monitor level, in the parentheses below; (iv) * indicates significance at the, 10 level, ** indicates significance at the .05 level and *** indicates significance at the .01 level; (v) All regressions include monitor-level fixed effects and yearmonth fixed effects. Sample: ganga basin

Jajmau monitor × Building phase (αJ1) Jajmau monitor × Startup phase (αJ2) Jajmau monitor × Ongoing operations phase (αJ3) Jajmau monitor (αJ) Building phase (1989–1993) Startup phase (1994–1997) Ongoing operations (1998–2002)

Sample: all monitors in India

Log(BOD)

Log(FCOLI)

Log(BOD)

Log(FCOLI)

−0.266* (0.112) −0.374*** (0.108) −0.167 (0.115) 0.990*** (0.153) −0.055 (0.158) −0.349* (0.161) −0.559*** (0.158)

-0.014 (0.024) 0.027 (0.027) 0.174*** (0.036) 0.293** (0.093) −0.274*** (0.051) −0.036 (0.057) −0.115* (0.053)

−0.346*** (0.050) −0.417*** (0.051) −0.097 (0.053) 0.791*** (0.070) −0.461*** (0.104) −0.426*** (0.103) −0.562*** (0.110) 0.315*** (0.043) 0.260*** (0.031)

−0.034* (0.015) 0.030 (0.016) 0.134*** (0.018) 0.489*** (0.038) 0.059 (0.035) 0.041 (0.035) 0.009 (0.036) −0.257*** (0.026)

Ganga basin indicator Upstream monitor log(BOD)

0.194** (0.066)

Upstream monitor Log(FCOLI) R-Squared Monitor observations

0.052*** (0.008) 0.835 6658

0.737 10,324

10

0.736 44,592

0.059*** (0.003) 0.765 33,856

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be strong. Thus, α1 and α2 are expected to be negative. As ongoing operations proceed, we expect effectiveness to decline (with zero or positive value for α3 ). The results indicate that compared to the period before 1989, BOD levels in the sample that is restricted to the Ganga Basin and the same that includes all of India were lower in the periods 1989–1993 (for the Ganga basin, α1 = −0.05521 and for the entire country, α1= −0.461***), 1994–1997 (for the Ganga basin, α2= −0.349* and for the entire country α2= −0.426***), and 1998–2002 (for the Ganga basin, α3 = −0.559*** and for the entire country, α3= −0.526***), when controlling for a variety of factors that affect pollution and locational fixed-effects. BOD remained elevated in the Ganga basin in all periods, but FCOLI was lower in the Ganga Basin in the 1989–1993 and 1998–2002 periods for the Ganga basin (α1= −0.274*** and for the entire country, α3= −0.115,* respectively), than in the pre-1989 era. There was, unfortunately, no significant improvement in FCOLI levels across all of the river monitors in India. The interaction terms between the Jajmau Monitor and Phase of Operations support the predictions of our model: In the BOD models, αJ1 and that αJ 2 are negative and significant when compared to the Ganga Basin (αJ1 = −0.266*, αJ 2 = −0.374**) and all of India (αJ1 = −0.346**, αJ 2 = −0.417**), while αJ3 is not significant in either model. This matches our expectations that the management of industrial waste will improve during the building phase as infrastructure improvements are made, reach a maximum during the startup phase when the Indo-Dutch partnership at its most intense, then fail during ongoing operations as preferences diverge. The lack of significance of αJ3 in either model is particularly interesting. Unfortunately, as our argument predicts, the results suggest that the impact of the CETP is fleeting and, after a few years of operation, the pollution levels at Jajmau are statistically indistinguishable from the reference group. As predicted, a similar but more muted pattern exists with FCOLI. The Jajmau monitor did not register a significant difference from the Ganga Basin generally in either the building or startup phase of the Jajmau CETP, but it did register an expected worsening during ongoing operations (αJ3 = 0.174**). Further matching expectations when compared to all of India, pollutions levels improved in the first period (αJ1 = −0.034*) and worsened in the third (αJ3 = 0.134***). This matches our expected pattern. We caution however, that the changes in the levels of FCOLI in our sample may be driven by a variety of factors since this is a broad measure of domestic pollution. In sum, the CETP succeeded in decreasing pollution levels in the building and startup phases; its efficacy then slows down or declines as ongoing operations settle in. A possible limitation of this analysis is that these findings may be unique to the specific CETP built in Kanpur. We thus shift to a broader analysis of the impact of CETPs of different types across India. 6. CETPS in India: do partnerhsips behave differently? Next, we examine how the efficacy of CETPs with different organizational structures varies over time. Descriptive statistics for this sample are presented in Table 4. As mentioned above, Panel (A) of this table presents descriptive statistics for the sample of pollution monitors that have a CETP within 65 km. Panel (B) of Table 4 presents summary statistics of variables for all pollution monitors in India. We begin by exploring the issue of CETP placement. We now examine the performance of the complete sample of all 88 CETPs in India. Our review suggests that all of these are constructed in industrial or urban clusters. To confirm whether this means that CETPs are placed in highly polluted areas, we regress a dummy variable for CETP placement on the pollution levels recorded in the closest monitor upstream from the CETP (the dummy variable takes the value 1 if a monitor in our sample has any type of CETP within 65 km downstream from it, and 0 otherwise). As expected, we find a statistically significant and positive relationship between pollution levels and the placement of CETPs (see Table 5). Yet, when we run these regressions with the inclusion of monitor-specific fixedeffects, the relationship significantly diminishes, the level of FCOLI becomes insignificant, and the explanatory power of the estimate increases from R-squared 0.116 to 0.547. This simple analysis suggests the importance of locational factors that might strongly influence the readings. These could include economic, demographic, social, political and geographical characteristics of the area. This finding highlights the importance of using necessitates the use of monitor-level fixed-effects as well as time fixed-effects in all the regressions we perform with this sample. In subsequent analyses, we also evaluate the efficacy of CETPs relative to local and national pollution levels. We do this by analyzing two samples, one that contains monitors within 65 km of each CETP and one that contains monitors across the entire country (see Table 4). Our next step is to examine the effects of CETPs with different ownership types over time. Our key conjecture is that CETP performance is likely to be strong in the initial stages and then deteriorate over time. To examine the variation in CETP performance over time and across ownership types, we use the following specification:

Pollutionmdt = α 0 + αtype,

p Ownership

Type × CETP Phaseptype, p + βtype Ownership Type + γp CETP Phasep

+ δAny CETP + λm + θd + ϵmdt

Pollutionmdt is the pollutant reading at monitor m, located in district d in year-month t. Ownership Type (type) is operationalized as a vector of three dummy variables that take value 1 for each of three CETP ownership types: Public, Private and Partnerships. Any CETP is a dummy variable that takes value 1 if there is a CETP within 65 km of monitor m at time t. CETP Phase p is a dummy variable that takes on the value of 1 based on the phase p of the CETP. In this model, Phase1(Building ) is a dummy variable that takes value 1 if the reading is taken within two years before the CETP is operational,22 Phase2(Startup) is a dummy variable that takes value 1 if the 21

This coefficient is not statistically significant. 11

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Table 4 Summary statistics of sample monitors. Note: Monitor observations are recorded on a monthly basis. Panel (A): monitors within 65 km of CETPS Variable

Monitor Obs

Any CETP Nearest monitor log(BOD) Upstream monitor log(BOD) Nearest monitor log(FCOLI) Upstream monitor log(FCOLI) Private CETP × Building phase Private CETP × Startup phase Private CETP × Ongoing operations Private CETP Public CETP × Building phase Public CETP × Startup phase Public CETP × Ongoing operations Public CETP Partnership CETP × Building phase Partnership CETP × Startup phase Partnership CETP × Ongoing operations Partnership CETP Building phase Startup phase Ongoing operations Capacity (millions of liters per day)

3,654 3,521 3,438 2,740 2,920 3,654 3,654 3,654 3,654 3,654 3,654 3,654 3,654 3,654 3,654 3,654 3,654 3,654 3,654 3,654 3,654

Mean

Std Dev

1.000 2.003 1.276 8.782 7.252 0.105 0.086 0.155 0.672 0.014 0.013 0.030 0.095 0.048 0.025 0.003 0.207 0.167 0.125 0.213 7.216

0.000 1.387 1.404 4.061 3.402 0.307 0.281 0.362 0.469 0.117 0.113 0.169 0.293 0.215 0.158 0.055 0.405 0.373 0.330 0.410 8.784

Min 1 −2.303 −2.303 0.693 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1

Panel (B): all monitors in India Max 1 4.644 4.654 14.557 14.557 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 36

Monitor Obs

Mean

Std Dev

58,349 56,731 45,069 43,210 40,968 58,349 58,349 58,349 58,349 58,349 58,349 58,349 58,349 58,349 58,349 58,349 58,349 58,349 58,349 58,349 58,349

0.076 0.878 -0.726 5.882 3.582 0.007 0.007 0.935 0.975 0.001 0.001 0.002 0.006 0.003 0.002 0.003 0.017 0.011 0.010 0.942 0.644

0.266 1.080 2.077 3.112 3.529 0.085 0.083 0.246 0.156 0.030 0.028 0.043 0.079 0.056 0.045 0.055 0.129 0.106 0.098 0.234 4.099

Min

Max

0 −3.507 −3.507 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 4.654 4.654 14.557 14.557 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 55

Table 5 Are CETPs Located in Highly Polluted Areas? Notes: (i) The dependent variable takes value 1 if a particular monitor has a CETP of any type within 65 km downstream from it (and 0 otherwise); (ii) Monitor observations are recorded on a monthly basis. (iii) Standard errors, clustered at the monitor level, are in parentheses. * indicates significance at the 10% level, ** indicates significance at the 5% level and *** indicates significance at the 1% level. (1) (2) Sample: all monitors in India Dependent variable: placement of a CETP downstream Upstream monitor log(BOD) Upstream monitor log(FCOLI) Year-month fixed effects Locational fixed effects R-squared Monitor observations

0.050*** (0.001) 0.020*** (0.000) Yes No 0.116 41,053

0.009*** (0.001) 0.001 (0.000) Yes Yes 0.547 41,053

reading is taken within three years after the CETP becoming operational, and Phase3(Ongoing Operations ) is a dummy variable that takes value 1 if the reading is taken four and more years after the CETP becoming operational. The three ownership types are interacted with the phase dummies to analyze the performance of CETPs that are public, private, and owned as public-private partnerships in the different phases of operations. λm is a year fixed-effect and θd is a location fixed-effect. As explained earlier, these fixed-effects are important as they control for the unique characteristics of industrial clusters as well as the temporal variations that affect industrial pollution such as weather, economic business cycles, slow-downs due to festivals, etc. The key coefficient of interest to us is αtype, p. This is the incremental pollution (compared to the comparison group) at the monitor closest to a given CETP that can be identified by its ownership structure and its phase of operations (p). Our model predicts:

• Coefficients for partnerships for the building phase will be negative (e.g., lower pollution levels), the coefficients on the startup • •

phase will be smaller in magnitude and may approach zero, and the coefficients during the ongoing phase may be zero or become positive (e.g., higher pollution levels). Purely public operations are expected to have somewhat smaller effects during the startup phase. Their coefficients during the startup phase are expected to be negative, while their coefficients during the ongoing operations phase are expected to be positive. Purely private operations are expected to have smaller or insignificant effects during the building phase, but their coefficients are

22 The four-year gap reflects the time between the Supreme Court rulings and the date of the launch of the CETP. In the next section, we reduce this to a two-year gap as the process was much quicker because the supreme court was not involved.

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Table 6 CETP Performance by management type. Notes: (i) Sample in columns (1)–(2) includes only those pollution monitors that are close to CETPs (within 65 km upstream and downstream), Sample in columns (3)–(4) includes all pollution monitors on all rivers of India; (ii) ln(BOD) and ln(COD) are the logarithm of BOD and COD respectively, these are monthly readings from pollution monitors; (iii) The numbers reported for each variable include its coefficient value with the corresponding standard error, clustered at the monitor level, in the parentheses below; (iii) Monitor observations are recorded on a monthly basis, (iv) * indicates significance at the, 10 level, ** indicates significance at the .05 level and *** indicates significance at the .01 level.

Private CETP × Building phase (αprivate,1 ) Private CETP × Startup phase (αprivate,2 ) Private CETP× Ongoing operations phase (αprivate3 ) Private CETP (βprivate ) Public CETP × Building phase (αpublic1 ) Public CETP × Startup phase (αpublic2 ) Public CETP × Ongoing operations phase (αpublic2 ) Public CETP (βpublic ) Partnership CETP × Building phase (αpartnership1) Partnership CETP × Startup phase (αpartnership2 ) Partnership CETP × Ongoing operations (αpartnership3 ) Partnership CETP (βpartnership ) Upstream pollution monitor Monitor capacity weighted by inverse distance R-squared Monitor observations F-statistic of joint test: Public p-value F-statistic of joint test: Private p-value F-statistic of joint test: PPP p-value

(1) (2) Sample: monitors within 65 km

(3) (4) Sample: all monitors in India

Log(BOD) −0.032 (0.031) −0.392* (0.146) −0.494* (0.186) −0.133

Log(FCOLI) 0.113 (0.261) −0.257 (0.385) −0.624 (0.470) 0.116

Log(BOD) -0.024 (0.049) −0.117 (0.075) −0.036 (0.090) 0.165

Log(FCOLI) 0.255 (0.165) 0.271 (0.297) −0.065 (0.190) 0.085

(0.186) 0.007 (0.048) 0.154* (0.059) 0.048 (0.081) −0.027

(0.285) −0.193 (0.199) 0.987 (0.537) 0.106 (0.292) 0.013

(0.103) −0.032 (0.051) 0.241 (0.158) 0.449*** (0.102) −0.038

(0.159) −0.481*** (0.113) 0.755*** (0.087) 0.025 (0.211) −0.004

(0.029) 0.201 (0.115) 0.213* (0.105) 0.217* (0.108) −0.073

(0.057) 0.654 (0.400) 0.905 (0.574) 1.150 (0.866) −0.137

(0.079) 0.500*** (0.054) 0.532*** (0.089) 0.709*** (0.073) −0.183***

(0.074) 0.420*** (0.103) 0.214* (0.109) 0.436** (0.156) −0.139**

(0.055) 0.450*** (0.054) 0.000 (0.001) 0.847 3329 4.779 0.018 4.810 0.017 1.138 0.388

(0.123) 0.410*** (0.049) 0.007 (0.004) 0.889 2518 1.400 0.297 0.800 0.550 1.538 0.258

(0.044) 0.248*** (0.039) 0.002 (0.003) 0.657 43984 91.023 0.000 1.648 0.161 89.169 0.000

(0.043) 0.590*** (0.036) −0.001 (0.002) 0.722 32076 69.879 0.000 3.002 0.019 11.058 0.000

expected to remain negative (e.g., lower pollution levels) during the startup and operations phases.

• CETPs are expected more likely to have significant and stronger impacts on BOD rather than FCOLI, given their technical design to manage industrial pollution.

We run this regression on two samples: (1) All monitors along the rivers of India. This enables us to evaluate the performance of the CETP relative to pollution levels at an average monitor across all of India. (2) All monitors along the rivers of India that are part of upstream and downstream monitor pairs with a CETP that lies in between them within 65 km of each (90.5% of those selected are within 48 km of a CETP). Restricting our attention to the sample of monitors close to CETPs enables us to evaluate the performance of designated CETPs relative to others in similar local environments. This is useful because CETPs are often placed in highly polluted and industrial areas. Results from regression analyses on all these samples are presented in Table 6. Columns (1) and (2) present results relative to monitors that are located within 65 km of the CETPs. Columns (3) and (4) present results relative to all monitors in India. The results obtained here are in line with our predictions. First, note that the coefficients for is αtype, phase in the BOD regressions presented in columns (1) and (3), are more likely to be statistically significant than their comparable results in columns (2) and (4) which are the FCOLI regressions. Recall that BOD measures both industrial and domestic pollutants while FCOLI measures just domestic pollutants. FCOLI readings may very well decline in the startup phase of CETP construction due to the investments in establishing new drains or cleaning existing ones. In the long-run however, this effect is likely to dissipate. The results we observe for BOD suggest that there may have been more changes in

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industrial pollution than domestic pollution in the aftermath of CETP construction. We must however, interpret these effects cautiously, since FCOLI levels in river water may be driven by many other factors in the surrounding area. A second observation from Table 6 is that organizational type matters. F-tests for the joint significance of the organizational structure variables, presented for all regressions at the bottom of the table, confirm that in the BOD regressions, organizational structure does have a statistically significant effect on observed pollution levels. Given that we are controlling for both locational and time fixed-effects, as well as including controls for the CETP characteristics and river characteristics, this is a noteworthy result for it confirms that organizational type (private v. public v. partnership) affect the performance of CETPs independently. This result persists regardless of whether we use the restricted sample of monitors close to CETPs, or the wider sample of all pollution monitors in India. A third observation from Table 6 is that, in comparison to others, private CETPs are effective in the startup and operations phases. The coefficient for αprivate,2 , presented in column (1), is −0.392 and the coefficient for αprivate3 is −0.494. Both coefficients are statistically significant at the 10% level. In contrast, the public and partnered projects are not effective in their startup and operational phases. On the contrary, the startup phase for public and partnership CETPs are positive and statistically significant (αpublic2= 0.154* αpartnership2, = 0.213*), as is the operational phase of partnership CETPs (αpartnership3 = 0.217*), suggesting that these organizational types may have an incrementally damaging effect on levels of BOD in startup phases of the project. Partnered projects may continue to have a negative effect on pollution levels even during the operational phase. When compared to the sample of all monitors in India (column 3), these results are even more striking. Public and partnered projects continue to show negative and statistically significant coefficients in the startup phase. In summary, these results lend support to our model and suggest that organizational structure matters, with partnered projects performing less well after initial successes in the buildup phase. The results are strong regardless of how we define the comparison group, and the results are most clearly pertinent to BOD, rather than FCOLI, an indicator of domestic pollution. 7. Discussion and suggestiions for future research We argue that the ownership structures of environmental cleanup projects are associated with specific patterns of success and failure over time. Indian efforts to manage industrial waste and river pollution reveal that common effluent treatment plants that are built as partnerships, as opposed to fully public or fully private operations, face specific sustainability challenges. In the short run, economic and political incentives of all partners align and encourage pollution mitigation projects to grow beyond economically or politically sustainable levels. The initial surge of activity generates expectations of sustained profits and successful service. As operations get underway and initial building and financial support subside, demands for governments to sustain high rents and provide subsidies increase. If governments are unwilling or unable to comply, shirking and corruption are likely to grow, as documented in many case studies (Huang and Gupta, 2014). Given the relatively large scale of these partnerships, the long-term outcome will be worse than in fully private or fully public efforts. We first test the model by taking a close look at the history of CETPs in the city of Kanpur, the “leather capital” of India. Here a CETP was funded in a bilateral partnership between the Indian and Dutch governments, to support a cluster of small-scale tanneries. The plant was however, too big, went hugely over-budget and was expensive to maintain in the long run. We use data from pollution monitors to show that the project followed a predicted pattern of success and failure. When we expand our analysis to the case of India to compare partnerships with fully public and fully private operations, we find a similar pattern: partnerships perform relatively well in the initial stages and then become progressively worse. We recognize that relying on river pollution monitors is a second-best strategy for assessing the effectiveness of CETPs. We attempt to compensate for changes in river pollution due to other sources by using the pollution levels indicated at the nearest monitor upstream from the CETP as the baseline against which to judge variations pollution levels at the nearest monitor downstream of the CETP. We also control for variations in water quality across the Ganga basis and across all of India on a monthly basis. These strategies help to reduce, but cannot completely eliminate, changes in pollution levels due to rainfall and seasonal variations, shifts in industrial demand for leather, or India-wide anti-pollution policies. It would be optimal for researchers to repeat this analysis by measuring the quality of the effluent discharged by each CETP directly should sufficient reliable data become available. Despite the caveats of our data and methods, the analysis yields some interesting insights on the strategies for addressing pollution in India’s small-scale industrial clusters. Previous explanations for the failures of CETPs in such settings emphasize technical issues and the failure of firm owners to pre-treat the effluent from their units going into the system (Tare et al., 2003). The most common solutions thus involve technological fixes and the intensification of enforcement efforts. Instead of focusing on these outcomes, we focus our attention on understanding the behavior that led to them. If the failure is the result of a misalignment of incentives overtime as our findings suggest, then the failure is more likely to be avoided by modifying the scale of each project and the managing expectations of the partners on the front end. In short, successful partnerships require attention to issues of scale, cost and management. Rather than focusing on just the creation of end-of-pipe technology and large pollution mitigation plants, significant attention must be given to management structures and business models that focus on alignment of incentives among partners, investors, and users over time. In 2017, the World Bank’s Private Participation in Infrastructure Database recorded 132 partnerships in the energy, transport, and water sectors in low and middle income countries worth $36.7 billion (World Bank, 2017). Our argument suggests that these partnerships may generate significant short-run improvements, especially in areas that were underserved. Over time, however, demands by private partners for a positive rate of return on their investments may make their services increasingly expensive. In the cases of the India CETPs, small tanneries were often unwilling or unable to pay and the system collapsed. In circumstances where 14

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some have the will and capacity to pay, it is plausible that public-private partnerships could be sustained by making access to their services more exclusive. Sports stadiums created by public-private partnerships are, for example, generally more expensive than their public counterparts, but are generally accepted as club goods (Shambaugh and Matthew 2016). It would be fruitful to analyze how these dynamics affect the political sustainability of public-private partnerships used to provide basic services like drinking water, electricity, or transportation where inequality of access is likely to be more problematic. The subsidization and eventual renationalization public services in Argentina in the early 2000s are suggestive, but future research is needed to understand the conditions under which the transformation of basic collective services into club goods is politically sustainable. 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