Environmental enforcement and compliance in developing countries: Evidence from India

World Development 117 (2019) 313–327

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Environmental enforcement and compliance in developing countries: Evidence from India Shreekant Gupta a,b,⇑, Shalini Saksena c, Omer F. Baris d a

Delhi School of Economics, University of Delhi, Delhi 110007, India Lee Kuan Yew School of Public Policy, National University of Singapore, 469C Bukit Timah Road, Oei Tiong Ham Building, Singapore 259772 c Delhi College of Arts and Commerce, University of Delhi, Netaji Nagar, New Delhi 110023, India d Graduate School of Public Policy, Nazarbayev University, 53 Kabanbay Batyr, Block C3-4016, Astana 010000, Kazakhstan b

a r t i c l e

i n f o

Article history: Accepted 3 February 2019

JEL codes: L200 L510 Q13 Q530 Q580 Keywords: Pollution Environmental enforcement Inspection Monitoring Asia India

a b s t r a c t Effective implementation of environmental regulations is an important concern for emerging economies that face serious environmental degradation. In this paper we analyze compliance and enforcement of environmental regulations in India. In particular, we model: (i) plant-level compliance with water and air pollution control laws in the state of Punjab, and (ii) the decisions of the regulatory agency, namely, the Punjab Pollution Control Board to enforce these laws through inspections and other administrative actions. The two decisions are interrelated. For a sample of 117 large water polluting plants and 109 large air polluting plants the probability of inspection influences plant-level compliance and vice versa. We also find enforcement activity is targeted towards frequent violators. Plants that belong to dirty industries are more stringently monitored but those belonging to more profitable firms less so. Plants with high abatement costs and those that are new comply less frequently. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction The success of environmental regulation depends crucially on effective monitoring and enforcement. In the large and growing body of literature on monitoring and enforcement of environmen-

tal regulations, especially in the context of water and air pollution,1 empirical research focuses predominantly on the United States (US) and to a much lesser extent on Europe.2 Paucity of such research for developing countries—even those that are globally dominant such as China and India—is remarkable.3 In marked contrast to the United

⇑ Corresponding author at: Delhi School of Economics, University of Delhi, Delhi 110007, India. E-mail addresses: [email protected] (S. Gupta), [email protected] (S. Saksena), [email protected] (O.F. Baris). See, for example, recent surveys by Alm and Shimshack (2014), Shimshack (2014) and Stranlund (2013, 2017) which extend earlier contributions by Cohen (1999, 2000) and Gray and Shimshack (2011). Burgeoning research on this topic, for example, recent interesting papers by Muehlenbachs et al (2016) and Lim (2016) will necessitate yet another survey. 2 See the survey by Tosun (2012) and a handful of studies on specific countries such as Norway (Telle, 2013), Germany (Almer & Goeschl, 2010) and Belgium (Billiet & Rousseau, 2011; Rousseau, 2007). 3 The handful of studies on China are Dasgupta, Laplante, Mamingi, and Wang (2001), Wang, Mamingi, Laplante, and Dasgupta (2003) and Lo, Fryxell, and Rooij (2009). Similarly, for India Duflo, Greenstone, Pande, and Ryan (2013), Kathuria and Sterner (2006) and Pargal, Mani, and Huq (1997) are the only three studies we know of. Finally, studies by Dasgupta, Hettige, and Wheeler (2000) for Mexico, Féres and Reynaud (2011) for Brazil and Haque (2017) for Bangladesh round out this sparse list for developing countries. In their comprehensive survey Alm and Shimshack state ‘‘(E) nvironmental deterrence research in international, and especially in developing and transitional country, contexts is limited. There is still much work to be done.” (2014, p. 261) 1

https://doi.org/10.1016/j.worlddev.2019.02.001 0305-750X/Ó 2019 Elsevier Ltd. All rights reserved.

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Sates, this is largely due to lack of firm/plant-level data on emissions, enforcement and compliance.4 Nevertheless, such research is of vital importance in view of the serious air and water quality challenges that confront developing countries, China and India in particular.5 In this paper, we analyze enforcement of and compliance with air and water pollution regulations through a plant and firmlevel dataset for Punjab, India.6 We focus on air and water pollution by plants and examine enforcement of two key laws, namely, the Water (Prevention and Control of Pollution) Act of 1974 and the Air (Prevention and Control of Pollution) Act of 1981.7 In particular, we investigate the bidirectional causal relationship between the enforcement strategy of the regulator and compliance behavior by examining economic and financial factors that influence enforcement and compliance decisions.8 The enforcement strategy adopted by the regulator, namely, Punjab Pollution Control Board (henceforth the Board)9 and plant-level compliance by the firm are interrelated. Thus, grossly polluting and recalcitrant plants are likely to face a higher probability of inspections and stricter enforcement actions against such behavior, while rigorous enforcement by the Board is likely to result in greater compliance by firms. Apart from a plant’s compliance behavior, however, there are likely to be other factors that could determine the Board’s enforcement actions. These include the contribution by a firm to state employment and output, the size of the firm and its political backing. Similarly, factors other than those pertaining to the existing enforcement regime are likely to determine a plant’s compliance behavior. These could include both plant-level characteristics such as nature of plant’s production process, cost of compliance, plant’s age, it’s location, as well as firmlevel characteristics such as the financial stability of a firm, its capacity to lobby the regulator for favorable terms and its share in total output. We will address a number of interrelated questions by way of analyzing the relationship between stringency of enforcement by the Board and the compliance behavior of plants. Does the existing enforcement regime have any bearing on compliance behavior of plants and vice versa? Are enforcement actions targeted at larger firms because they are expected to be large polluters or ‘deeppocketed’? Which are the other plant and firm characteristics that impact compliance and enforcement decisions? Among them, which ones are more important than others? Are high cost onsite inspections more effective than administrative actions which also include relatively low cost enforcement actions such as serv-

4 The few studies on developing countries are even more salient. We found one study on environmental enforcement practices and functioning of regulatory agencies for a large number of countries ranging from those in North America and Europe to Africa and Asia (Farmer, 2007). It contains useful information of a descriptive nature. 5 For example, 14 cities In India ranked among world’s top 20 polluted cities (WHO, 2016). Official Indian data shows every third city in the country failed to meet national air quality standards (www.business-standard.com/article/current-affairs/ india-has-94-cities-with-toxic-air-half-of-them-in-just-four-states-118042300070_ 1.html). According to the State of Environment Report: India 2009, 70 percent of surface water resources and a growing percentage of groundwater reserves are contaminated by biological, toxic, organic and inorganic pollutants (Government of India, 2009). 6 Punjab is a prosperous state in North India with diversified industrial activity. It ranks 8th in per capita income among all major states of India. The share of manufacturing and secondary sector (manufacturing, utilities and construction) in state income is about 15 and 25 percent, respectively (esopb.gov.in/Static/PDF/GSDP/ Punjab/GSDP.pdf). Like the rest of India it faces significant environmental challenges. For instance, it is among the top four states that have failed to meet national air quality standards (www.tribuneindia.com/news/punjab/punjab-among-top-4-statesfailing-air-quality-standards/579301.html). 7 For a review of environmental legislation in India see Gupta (2014). 8 In India, implementation and enforcement of pollution control laws is the responsibility of State Pollution Control Boards (SPCBs) in this case Punjab Pollution Control Board (PPCB)–also refered to as the ‘Board’ in the paper. 9 See Section 2 for details on the activities of the Board.

ing notices of violation, show cause notices and administrative orders? In the rest of the paper we present an overview of environmental regulation in India, and in Punjab in particular in Section 2 and review the empirical literature in Section 3. In Section 4 we present our theoretical model of enforcement and compliance that underpins the empirical strategy in Section 5. We present our results in Section 6 and conclude.

2. Environmental regulation in India and in Punjab There are elaborate legislative provisions for environmental protection in India. An extensive network of central and state pollution control boards, covering all states in the country has been established. Actual enforcement of environmental regulations takes place at the state level since the state pollution control boards have been entrusted with this task. The Punjab Pollution Control Board (PPCB) was constituted in 1975 under the Water (Prevention and Control of Pollution) Act, 1974. The major functions of the Board are the prevention, control and abatement of water and air pollution (excluding vehicular pollution for which the implementing agency is the State Transport Department). The pollution control and assessment regulatory functions specifically include the following:
‘‘To inspect industrial plants and manufacturing process, sewage or trade effluents, works and plants for the treatment of sewage and trade effluent or any control equipment, to review plans, specifications or other data relating to plants set up for effluent treatment or air pollution control devices, in connection with the issue consents for installation and operation of industrial plant and to give, such directions to such persons as it may consider necessary to take steps for the prevention and control or abatement of water or air pollution.
To ensure that hazardous wastes generated by the industry are stored and disposed off without any detrimental effect to the environment.
To assess the quality of water of rivers, streams, wells and ambient air in the State and to plan a comprehensive programme for the prevention, control and abatement of pollution.
To lay down or modify annual effluent standards for the sewage and trade effluents and for the quality of receiving waters resulting from the discharge of effluents and for the emissions of air pollutants into the atmosphere from industrial plants and automobiles.”10 The Board collects, inter alia, a water cess under the provisions of Water Cess Act, 1977 and meets a part of its expenditure from this cess. The policies and decisions made by the Board are implemented through various cells/branches. All 22 districts in Punjab are covered through 7 zonal and 14 regional offices. The main sources of air and water pollution in the state are industries, vehicles, sewage and solid waste, road dust and nonpoint sources. Industry is one of the main sources of air and water pollution (surface and groundwater). For the purposes of granting ‘consent to establish’ and ‘consent to operate’ the Board has classified industries into three categories, namely, Red, Orange and Green in view of their pollution potential. In addition, a White category comprises industries that are exempt from consent.11 10

www.ppcb.gov.in/FunctionBoard.aspx, last accessed January 29, 2019. For a list of industries see www.ppcb.gov.in/Categorisation.aspx. For details on the consent procedure see below and www.ppcb.gov.in/policybaoard.aspx. 11

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2.1. The course of compliance and enforcement events and decisions A firm’s decision to comply with the provisions of the Water and Air Acts is at two levels: (a) initial compliance that entails installation of the required pollution control devices, and (b) continuing compliance that requires regular operation and maintenance of the pollution control devices and compliance with air and water pollution discharge standards. All prospective entrepreneurs are required to obtain the consent of the Board to establish an industrial plant in the form of a No Objection Certificate (NOC) under Section 25/26 of the Water Act and Section 21 of the Air Act. The construction of the plant and the installation of the pollution control device must be completed within the period of validity of the NOC which otherwise has to be revalidated for this purpose. The next step for a firm is to obtain the consent of the Board for operating an outlet for discharge of sewage/trade effluent under Section 25/26 of the Water Act and for operating an industrial plant under Section 21 of the Air Act. Consent to operate is granted for 5 years for units in Red and Orange categories and 10 years for those in the Green category or till such time the plant modifies its processes or pollution control device. A firm has to apply for renewal of consent two months prior to its lapse. It is an offense to operate without a valid consent. The firm is also required to operate the pollution control equipment regularly and to get air and water samples analyzed periodically at designated labs. Limits for water quality parameters for major pollutants are shown in Table 1. The Board issues NOCs to new units on submission of a scheme for pollution control. It also issues consents (to discharge) under the Water and Air Acts after a unit takes adequate pollution control measures (Fig. 1). In effect, the Board performs its regulatory function through the consent mechanism. As stated earlier, consents range from 1 to 5 years for the highly polluting industries and 1–10 years for the green category industries. The Board assesses compliance by a firm through on-site inspections. All large and medium firms and red category smallscale firms are inspected at least once every six months and once a year, respectively. The Board carries out two main types of inspections: (i) compliance evaluation inspections where a firm’s pollution control facilities, monitoring methods and records are examined. This amounts to verification of initial compliance by a firm, and (ii) compliance sampling inspections where air/water samples are collected on-site.

Table 1 Standards for discharge of trade effluent under the Water Act. Parameter

Maximum concentration (mg/litre) except for pH

pH Biochemical oxygen demand (5 day 20 °C)

5.5–9.0 30 (into inland surface water)

Suspended solids Chemical oxygen demand Total dissolved solids Chloride Sulfates Bioassay test

100 (on land for irrigation) 100 250 2100 1000 (into inland surface water) 600 (on land for irrigation) 1000 90% survival of fish after 96 h in 100% effluent

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Pollution control devices and testing procedures with the firm (if any) are also inspected. Such inspections check for continuing compliance by a firm. Violations by a firm can be in the form of: (i) (ii) (iii) (iv) (v)

operating without a valid NOC and/or consent to operate; absence/inadequacy of pollution control device; discontinuous operation of pollution control device; non-submission of Environmental Audit Statement, and exceeding effluent/emission standards.

These violations are discovered through routine/surprise inspections or through public complaints that in turn result in inspection of a firm. Violating firms are first sent letters or notices of violation. Reminders are sent to them if necessary to take measures for compliance. If a firm does not respond to these notices, the Board issues show cause notices and a hearing is fixed where the firm is given a chance to explain the steps it proposes to take to attain compliance.12 The Board can also file a criminal suit against a firm at any stage of noncompliance. Court cases, however, are usually lengthy and prosecution (if any) can take years. In light of this, the Water and Air Acts were amended in 1987 to give executive powers to the Chair of a State Pollution Control Board whereby s/he can issue directions to prohibit/regulate/close a violating firm. Thus, as a last resort the Board can issue an administrative order to a violating firm for its closure (Sections 31-A and 33-A of the Air Act and the Water Act, respectively).

3. Empirical literature on compliance and enforcement Environmental regulation and enforcement is a common area of study that has attracted many researchers from various disciplines. As expected there is extensive literature on this subject not only from an economic perspective but also from the lens of political science, public policy, political economy, sociology, management and law. For instance, early contributions by political scientists focused qualitatively on enforcement styles, namely, ‘enforced’ versus ‘negotiated’ compliance (Hunter & Waterman, 1992) or ‘centralized’ top-down versus ‘cooperative compliance’ (Burby & Paterson, 1993), the basic idea being whether a negotiated/flex ible/cooperative model of enforcement worked better than rigid top down enforcement. Even prior to this, sociologists had addressed the same question such as the seminal work of Hawkins (1984) where he conducted a field study of pollution control officers to identify two polar enforcement styles–a sanctioning style that was accusatory and adversarial vis-à-vis a conciliatory one that relied upon bargaining. This notion of some sort of discretionary/pragmatic (even conciliatory) enforcement vis-à-vis one that is rigid has been an enduring theme in the work of political scientists and sociologists.13 At the other end of the spectrum managerial literature has focused on issues such as determinants of monetary penalties for environmental violations and the role of environmental compliance in executive compensation (Habib & Bhuiyan, 2017). The qualitative and case study literature provides a rich and nuanced perspective that cannot be captured by data. It is thus a valuable complement to exercises such as this paper. Unfortunately this literature is as sparse for developing countries as that for quantitative studies.14 12 Show cause notices are served either in violation of provisions of Section 25/26 of the Water Act punishable under Section 43, 44, and/or in violation of provisions of Section 43 of the Air Act punishable under Section 37, 39 read with Section 40. 13 See for example an excellent compilation by Hunter and Waterman (1996). 14 A notable exception is a relatively recent study by Ma and Ortolano (2000).

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Fig. 1. Course of events and decisions. Note: An oval node preceded by a blank arrow joins the chain at the same node followed by a filled arrow. Thus, for instance when a firm does not apply for NOC, node ‘b’ leads from ‘Apply for NOC’ stage and rejoins the chain at the ‘Inspected’ stage.

In term of quantitative empirical studies on compliance and enforcement, Magat and Viscusi (1990) is one of the earliest by economists. They examine the impact of inspections on the level of biochemical oxygen demand (BOD) discharges by pulp and paper plants in the US and on whether or not a firm was in compliance in any given period. Pollution by a firm is assumed to depend on lagged pollution, inspections, capacity, location, nature of output and season. A key finding is inspections substantially reduced BOD discharges with a lag of about one quarter, and also had a permanent effect on reducing a firm’s future pollution levels. Laplante and Rilstone (1996) extend this work to measure the impact of inspections on self-reported emissions by pulp and paper plants in Quebec. Their basic model includes the number of actual inspections and the expected number of inspections as explanatory variables in different equations. Laplante and Rilstone, 1996 could test for the impact of inspections on the level of emissions relative to the standard, and thus measure the extent of violation whereas Magat and Viscusi, 1990 could only test if plants complied or not. The results strongly suggest that both the threat of an inspection as well as actual inspections influenced emissions. Gray and Deily (1996), as one of the most comprehensive studies in this area, inquire whether enforcement influences a firm’s compliance behavior, and in turn whether a firm’s compliance decision affects the level of enforcement at its plants. They find the expected interactions between decisions, namely, (i) at the plant level, greater enforcement led to greater compliance, (ii) greater compliance led to less enforcement. In an earlier study, the same authors also found that plants in a declining industry (steel) predicted to face relatively heavy enforcement were more likely to close (Deily & Gray, 1991). Gray and Shadbegian (2005) examine the determinants of compliance of pulp and paper mills in the US with air pollution regulations, including plant and firm level characteristics and various enforcement measures. They find that enforcement measures increase compliance in a heterogeneous manner. Certain firms are more responsive to inspections while others are more sensitive to other enforcement measures including notices of violation, phone calls and penalties.

The more recent literature has focused on traditional regulation versus non-mandatory voluntary programmes as the main motivation for plant level environmental compliance decisions (Thornton, Gunningham, & Kagan, 2005; Gray & Shadbegian, 2005 & Gray & Shimshack, 2011). Traditional regulatory structure with rigorous monitoring (inspections and emissions tests) and enforcement (administrative, judicial and penalties) result in deterrence impacts, both specific and general. The former refers to the impact of regulatory actions on subsequent violations at the inspected/sanctioned plant, while the latter includes the spillover impact on compliance behavior of other plants. Large legal sanctions send a ‘threat’ message to all in the industry and this encourages them to invest in compliance. In their review of empirical research Gray and Shimshack (2011) probe the potency of conventional regulation in generating both specific and general deterrence. The threat of legal sanctions increases the perceived risk of being caught and the cost of violation. This motivates firms to invest in compliance. They conclude that traditional regulatory actions generate (i) substantial specific and general deterrence and (ii) reduction in number and extent of violations. Thus, firms that are already in compliance are motivated to further reduce emissions (an explanation for overcompliance by certain firms). The non-mandatory voluntary compliance theories highlight the efficacy of informal social and economic sanctions vis-à-vis the fear of legal sanctions in motivating regulated business firms to comply with the law. Corporate officials are concerned about maintaining a reputation of a good environmental citizen and therefore are often found to opt for over-compliance as a good business strategy. Thornton et al. (2005) study the deterrence impact of legal sanctions on 233 firms in the US and find little evidence of general deterrence behavior among firms. For most firms, general deterrence messages are redundant and if at all, they serve as ‘reminder and reassurance’ messages by reinforcing their perception of the need to continue compliance activities. They however find support for the informal sanctions theory, whereby knowledge of enforcement actions leads to corporate environmental measures because of fear of informal sanctions for violations.

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For India, there is little empirical work on enforcement of and compliance with environmental regulations largely because there is no comprehensive database on emissions, inspections and compliance. In an unpublished paper Pargal et al. (1997) use plant-level survey data for eight Indian states to study the link between enforcement stringency and the level of compliance. They estimate plant-level BOD emissions as a function of factor prices, scale of operation, regulatory pressure as measured by inspections and shift factors such as plant age and other plant-level characteristics (e.g., whether they export or not, whether the company is publicly traded and the sector to which they belong). Inspections are modeled as a function of emissions, plant age and size, extent of manufacturing activity in a state, end use of water in the effluent stream and the level of development in a district (a measure of community pressure). They find inspections have no impact on emission levels and attribute this to the low probability of enforcement as well as low penalties for noncompliance and thus draw attention to shortcomings in the working of the formal regulatory system in India. The other two studies for India look at monitoring and enforcement via third parties and do not explicitly model the behavior of firms and regulators. Kathuria and Sterner (2006) look at a cluster of chemical firms in Gujarat that collectively regulate their emissions and the impact of fines/penalties imposed by the cluster on individual firms on ambient water quality. Duflo et al. (2013) is an experimental investigation that alters incentives to third party auditors such that they report more truthfully and consequently plants reduce their emissions. Broadly, the following stylized facts emerge from the empirical studies:
higher levels of enforcement activity result in lower levels of pollution in subsequent periods,
greater compliance results in less enforcement activity,
inspections are effective at inducing more frequent selfreporting,
plants which are not financially sound are more likely to be in noncompliance. Our paper is a micro-econometric study using plant and firmlevel data for 175 firms in Punjab across 22 districts. We model compliance and enforcement decisions as a simultaneous game and empirically investigate the interdependent decisions (recognizing the potential endogeneity between them) that depend not only on environmental variables but also on plant-level and firmspecific variables. To our knowledge, this is the only study for India that models enforcement and compliance using purpose collected primary plant and firm-level data, with the exception of Pargal et al. (1997). There is a more complete treatment of the factors affecting enforcement–in addition to formal inspections by the Board, we also gather data on other enforcement actions such as notices of violation, show cause notices and administrative orders issued to a firm. Furthermore, we analyze both air and water pollution whereas earlier studies look at either water or air pollution.

4. The model Following Becker (1968) and Stigler (1970), the theoretical research on environmental regulation and enforcement examines the firm’s compliance decision through a two-player simultaneous game. The players, namely the Firm and the Board, choose whether to comply with environmental regulations or not and whether to inspect or not, respectively. The Board internalizes the cost of pollution and environmental damage and decides by comparing it with the cost of inspection. Comparing the cost of compliance with

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the expected value of the penalty for violation, the firm responds to the enforcement mechanism simply by following the ‘marginal deterrence’ principle (Stigler, 1970). In this model, the cost of compliance and the penalty for violation are both exogenously determined and known to both players. The penalty for violation must be strictly greater than the cost of compliance in order to pose a sufficiently credible threat to the Firm. The game is shown in extended and normal forms in Fig. 2. This game has no pure strategy Nash Equilibrium (Holler, 1993). In the mixed strategy equilibrium, the players randomize their choices by assigning positive probabilities to all available pure strategies. Accordingly, the firm complies with probability 1  i=f and the Board carries out an inspection with probability c=f where i is the cost of inspection, f is the fine levied by the Board on the firm and c is the cost of compliance.15 Note in this result that the probability of the Firm’s compliance is decreasing with the cost of inspection but increasing with the fine. Similarly, the Board’s inspection frequency is increasing with the cost of compliance and decreasing with the fine. While these results are now ubiquitous in the literature, the model is rich enough to capture the effects of a multitude of variations in players’ strategies. In a continuous setting, the compliance rate increases (decreases) with an increase (decrease) in inspection rates or the monetary magnitude of the fine.16 Heyes (1994) introduces the concept of ‘uninspectability’, which takes all costly efforts conducted by a non-compliant Firm in order to avoid an inspection or a detection of violation upon an inspection. From the Firm’s perspective, we may infer that the cost of uninspectability must be less than that of compliance. By definition, the cost of compliance by the Firm must include all expenditures for pollution control equipment and for retrofitting current capital, operation and maintenance costs and any lost productivity of original capital due to pollution control efforts, as well as all expenses incurred in obtaining NOC, consent to operate, and sample testing procedures. Interestingly, there are instances in our data set where firms adopt some form of uninspectability by installing the pollution control device (PCD) in some plants, but not using them to control emissions or effluents optimally. Even more, some firms are perpetually found to be non-compliant although the requisite PCDs are installed in the plants. As a matter of fact, all plants that need a PCD (90% of them) have one and most of them (90% and above) have all the requisite consents to establish an industrial plant and operate outlets for discharges. However the compliance rate in our data set is only 57% in case of water pollution and 55% in case of air pollution. Clearly many firms opt for investing in uninspectability in order to avoid the penalty. These include the installation cost of PCDs and other costs related to obtaining the necessary permits and consents. Even firms that comply make these investments for their plants. On the other hand, the stringency of monitoring and the quality of inspections are at the Board’s discretion to decide. As we will discuss in more detail shortly, our data set contains information on the number of inspections, violation notices (served post detection of violation) and whether legal action was initiated against recalcitrant firms. Furthermore, we observe that penalties imposed on non-compliant plants were either practically non-existent or so low that they were inconsequential–a problem that still prevails. Therefore, the Board’s decision for the quality and thoroughness of inspection makes a crucial difference, since the likelihood of a penalty will ultimately depend on this choice. Obviously, the likelihood of detection when a firm does not comply increases with a

15 For an exact derivation of the mixed strategy equilibrium, see Germani, Scaramozzino, Morone, and Morone (2017). 16 For a more extended review of the earlier literature see Heyes (2000).

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Fig. 2. Strategic game between the Firm and the Board.

thorough inspection, which will be more costly than a cursory inspection. We have data on the number of total inspections carried out over a period of five years, although our data set does not allow for a clear-cut distinction between the quality of inspections and their technical details. While most of these inspections must be mandatory, in certain cases there may also be follow-up inspections when a plant is found to be grossly violating. These inspections are likely to be thorough. There are several cases of gross or recurrent violation, which call for not just mere follow-up inspections, but also notices of violation served to such plants. There are also rudimentary notices of violation that are served after a public complaint but not necessarily followed by a genuine inspection. We include all cases of inspections and notices to stand for an overall measure of enforcement (ACTIONS). Note that uninspectability investments by the Firm will reduce the possibility of detection, especially when the inspection by the Board is not carried out thoroughly. All in all, the likelihood of detection of a violation decreases if the Firm invests heavily in uninspectability and increases if the Board inspects more thoroughly. In both cases, we can still expect the likelihood of compliance to increase with the magnitude of the fine. There is a substitution effect between the probability of detection and the magnitude of the fine. When the fines are increased, we expect more compliance by the Firm, and less inspections will be necessary (Becker, 1968; Polinsky & Shavell, 2000 & Germani et al., 2017). We can also infer that when the relative cost of a thorough inspection increases, the Board will choose not to inspect and the Firm will respond by investing more in uninspectability. In what follows, we will empirically estimate the likelihood of compliance and the likelihood of inspection. It is necessary at this point to recall that the exogenous variables above are certainly related to the firm-specific characteristics. For example, we expect the plant-level compliance cost to be relatively lower for a large firm in comparison to a smaller size firm due to economies of scale. Similarly, larger firms are expected to have more resources available for uninspectabilty–including their political backing and its capacity to lobby the regulator for favorable terms. Bi-causality between enforcement and compliance arises since enforcement actions are plant specific. It is further expected that public complaints, political pressure and recurring noncompliance result in stricter monitoring of a plant by the Board. For a plant against which there are no public complaints or adverse lobbying,

the most important factor that could determine the stringency of enforcement would be its record of compliance. Thus, grossly polluting and recalcitrant plants are likely to be grouped together to face both a higher frequency of inspections and stricter action against noncompliance. Even if we assume that regulator’s main concern is the quality of environmental output, and it is not acting in a profit maximizing way as an institution, the discretionary power of a bureaucrat is the central component for inspection decisions. A corrupt regulator would like to inspect larger firms more often, for the obvious reason that the amount of the bribery to be collected as a side payment will be bigger. If that is the case, we expect to see that the likelihood of inspection will be higher for a large firm, but detection will be less likely. When the corruption is not an issue, we should expect to see these probabilities to move in the same direction as the size of the firm changes. From the Board’s perspective, if inspections are nothing more than some rudimentary checks on paperwork, the cost of inspection should not make any significant difference for small or large firms. The cost of a thorough inspection, however, is expected to be higher if a large firm is under scrutiny as the necessary workload will increase. At the same time, any political backing that would influence the Board’s decision, or any indirect pressure and lobbying by the firm directed at the Board’s decision will reduce the relative cost of a cursory inspection, but will increase the cost of a thorough inspection. This will increase the likelihood of uninspectability by the Firm and cursory inspections by the Board. The lobbying power of the firm can reduce the probability of detection either by decreasing the frequency of inspections or by lowering the quality of them. Similarly, larger firms are likely to be more powerful in contesting the regulator’s decisions, which lowers the frequency of inspections. Note that, the likelihood and extent of organic or inorganic connections between the firm and the politicians can very well be for legitimate reasons. The Board’s decision to penalize a firm that has a large share in overall employment and output will receive more public attention. Depending on the electoral business cycle, this can strengthen or weaken the Board’s hand. The Board’s decision can make the government more or less popular among the electorate. Politicians try to avoid higher unemployment if elections are near and punishing a big firm will leave many out of jobs. 5. Model specification and data Following Gray and Deily (1996) we estimate the relationship between two potentially endogenous decisions: enforcement and compliance. Regulatory boards working with limited regulatory budgets target plants that are most likely to be out of compliance. Plants on the other hand, decide optimal levels of compliance depending on the nature of existing monitoring and enforcement regime. As common in the literature, the probabilities of compliance 1  i=f and inspection c=f are associated with their respective frequencies. In our empirical analysis, we estimate the determinants of these frequencies. Specifically, we use a probit model for one specification of compliance equation, and for the rest, we use Poisson or negative-binomial estimation. 5.1. The compliance decision In its simplest version, a plant’s compliance decision is determined by the regulatory activity faced by it along with plant and firm level characteristics.

COMPLY ¼ f ðENF ACTION; CASE; UP ; UF Þ COMPLY is a plant’s observed compliance status measured as (i) COMP_NUM and (ii) COMP. Compliance status is determined from

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the files and inspection reports maintained by the Board. A firm is noncompliant if (a) it is operating without the consent to operate and/or (b) it has not installed adequate pollution control device (or is not operating the device properly) and/or (c) it exceeds the concentration limit with respect to any pollution parameter as reported in the air and water samples’ inspection reports. The dependent variable ‘COMP_NUM’ is the number of times a plant is found in compliance in the given time period, which in essence captures the duration of a plant’s compliance period. COMP_NUM is a count variable and hence it is estimated using poisson or negative binomial regression models. ‘COMP’ is a dummy that reflects a plant’s overall compliance status. COMP = 1 if a plant is found in compliance more often than not. The COMP equation is estimated using a probit model. ENF_ACTION and CASE represent the Board’s regulatory measures. The Board monitors and enforces the provisions of the Air and Water Acts through on-site inspections (INSPECT) and/or by serving violation notices and/or initiating legal action against recalcitrant plants. ENF_ACTION includes measures of the Board’s regulatory activities such as: (i) INSPECT: number of times a plant has been inspected, (ii) ACTIONS: number of inspections as well as number of general notices and show-cause notices served to a plant (ACTIONS = INSPECT + NOTICES). Medium and large scale plants are supposed to be inspected at least once every six months. Plants, however, are not necessarily inspected every year and sometimes not inspected for a number of years (see Appendix Tables A.1 and A.2 for details on inspections and compliance status of plants covered in this study). Thus, about 10% of the plants in the dataset were not inspected at all and about a quarter faced at most one inspection in five years. Also, in certain cases of grossly violating plants, inspections are carried out more often. Thus, the measure INSPECT includes inspections which are carried out as part of the mandatory requirement, and those which are follow-up inspections to check the compliance status of a plant that was previously found to be out of compliance. ACTIONS is a measure which includes number of inspections as well as number of violation notices served to a plant. Violation notices are served (i) when a plant is found to be non-compliant during an on-site inspection, (ii) if the plant is known to be operating without the valid consents and (iii) in response to public complaints against a plant. While on-site inspections are clearly costly, use of violation notices is a relatively inexpensive method of enforcement. As a last resort, the Board undertakes legal action against perpetual violators. We use a dummy variable CASE to capture such severe forms of enforcement, which takes the value 1 if legal action has ever been initiated against the inspected plant under the provisions of Water and Air Acts during the period of study. A priori, it is expected to have a positive coefficient since plants that are being prosecuted/have been prosecuted are already under close supervision by the Board and this must induce greater compliance. However, this variable also captures a plant’s compliance history. Plants that are known to be gross and recurrent violators in the past are more likely to remain out of compliance. Thus, CASE can also have a negative coefficient. Different regulatory enforcement measures are likely to impact a plant’s compliance behavior differently. INSPECT capturing the ‘inspection effect’ (Gray & Shadbegian, 2005) is likely to be more effective in achieving specific deterrence as opposed to ACTIONS and CASE which are likely to result in both specific and general deterrence. While violation notices (and hence ACTIONS) are likely to result in weaker incentive to return to compliance, INSPECT and CASE represent measures that are more along the traditional

319

command-and-control regulatory structure and are likely to be more effective in restoring/maintaining compliance. The vector UP includes specific characteristics of the plant such as COST, PCD, AGE and zonal dummies (Z i ). To model costs of compliance, an appropriate measure would be one that represents the expenditure required to bring a plant into full compliance and maintain it. Since the data includes different kinds of industries it is difficult to assess the total capital cost of bringing every kind of plant into compliance. The variable COST measures the expenditure on installing and maintaining the pollution control device (actual and expected), normalized by firm’s value of output. This, however, need not be the total expenditure required to attain full compliance. The sign of the coefficient cannot be predicted a priori. On one hand, a plant that incurs large expenditure on pollution control is likely to return to compliance if it violates in a particular time period unintentionally. On the other hand, plants could have large pollution abatement expenditures only because they are more pollution intensive, per se, and yet they may fall well short of the required expenditure for full compliance. PCD is a dummy variable that takes the value 1 if a plant is required to install a pollution control device (PCD).17 Not all plants are required to install a PCD. A plant that has adequate arrangements to recirculate all the trade effluent it generates does not need to install an effluent treatment plant (ETP). Similarly, plants that do not use rice husk in loose form as fuel, do not need to install a fluidized bed combustion system.18 For plants that do not require a PCD, compliance is determined on the basis of adequacy of alternative methods to control pollution (i.e. recirculation system and/or adequate stack height) and possession of a valid consent to operate. AGE is measured since the year of commissioning of the plant. Older plants using outdated abatement technology and dirtier production processes are ceteris paribus more likely to be in violation. Zonal dummies (Z i ) are included to capture regional variation in compliance behavior of plants. The sample covers 22 districts that come under the jurisdiction of 4 zones as defined by PPCB. UF is a vector of firm level characteristics that includes: (i) The rate of return (ROR) or profit after tax (PROFIT) as a measure of the financial health of a firm. Since compliance is expensive financially sound firms are more likely to invest in costly abatement technology and be in compliance. Such firms, however, could also spend money to avoid action by the pollution control board against them for noncompliance. Thus, a priori the sign of the variable is ambiguous; (ii) Allowing for economies of scale in pollution abatement technology, larger firms are expected to have higher compliance rates because of lower unit costs of abatement. Gray and Deily (1996) point out that such economies might arise ‘‘if there are fixed costs to learning about the regulations, or in researching their implementation.” Thus, TURNOVER measures the value of total output for sale or for internal consumption. However, larger firms are in a better position to lobby regulators on behalf of their plants and hence may have a higher probability of non-compliance. (iii) The OWNER dummy variable classifies firms as state owned or private on the presumption that the corporate culture and management systems of private firms are superior, and that this has a bearing on their compliance behavior.

17 An effluent treatment plant (ETP) for trade effluent and an air pollution control device (APCD) for emissions. 18 In fact, as long as generation of air and water pollution is within manageable limits, a recirculation system for trade effluent and adequate stack height for air pollutants are deemed adequate and there is no need for an ETP or APCD.

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(iv) LISTED is a dummy for firms that are listed on any of the stock exchanges (national or state level). A listed firm is more likely to be concerned about its image and reputation as an environmentally friendly firm, and consequently it may be more likely to comply. (v) PAPER, SUGAR and CHEMICAL are category dummy variables for firms belonging to pulp and paper, sugar and chemical industries respectively. These firms are relatively more pollution intensive and hence these variables are expected to have a negative sign. (vi) EXPORT is a dummy for firms that export. Such firms are expected to be more concerned about a clean/green image in the World market and thus the variable is expected to have a positive sign. (vii) ENERGY is a measure of a firm’s power and fuel expenses. Energy intensive firms are likely to be large polluters. It is noted here that abatement efforts are also energy intensive. However, as long as the energy intensive production effect outweighs the abatement effect (Pargal et al., 1997), this variable is expected to have a negative sign. 5.2. The enforcement decision The regression model estimates the enforcement equation as

ENF ACTION ¼ f ðCOMPLY; CASE; WP ; WF Þ The dependent variable ENF_ACTION is a measure of the Board’s regulatory activity. It is measured in terms of INSPECT or ACTIONS as defined above. Both equations are estimated using poisson or negative binomial regression models. COMPLY is a measure of plant’s compliance behavior (COMP_NUM and COMP). A priori, this variable is expected to have a negative sign since firms that are complying are likely to face a lower frequency of inspections. From the Board’s point of view, the dummy variable CASE captures the plant’s compliance history which is likely to impact the Board’s perception about the compliance behaviour of the plant. Harrington (1988) showed how regulators can increase the efficiency of regulatory activities by making future enforcement conditional on a plant’s past compliance history (targeting behavior). Though the variable CASE seems to capture the same effect as COMPLY for a plant, it is different in nature. While a plant may have a poor compliance record only because of marginal violation, only the very persistent and intentional violators are taken to court. Thus, the difference between CASE and COMPLY is one of the extent of violation as also the intention and attitude of a plant towards environmental problems. The vector WP includes specific characteristics of the plant such as AGE, PCD and plant location captured by zonal dummies. Older plants using outdated abatement technology and those that need a PCD are likely to be monitored closely. Vector WF includes firm level characteristics described earlier such as ROR, TURNOVER and OWNER, and also a measure of the firm’s total employment reflecting concern for the local economy. Thus, firms that are large employers may face more lenient enforcement by the Board. In the absence of employment data, however, we use wages (WAGES) as a proxy. The following econometric issues are addressed: (i) Bi-causality between enforcement and compliance arises due to potential endogeneity of these decisions which is tackled while estimating the following two structural equations.

COMPLY ¼ f ðENF ACTION; CASE; UP ; UF Þ ENF ACTION ¼ f ðCOMPLY; CASE; WP ; WF Þ

Perpetual violating plants are targeted by the regulating Board and inspected more frequently. Statistical associations between measures of enforcement and compliance often show a negative correlation between them, suggesting that such regulatory measures are counterproductive. Issues arising from reverse causality can be addressed either by using data on lagged values of enforcement and compliance measures (Magat & Viscusi, 1990), or by using the method of predicted probability. In the absence of data on lagged values of enforcement and compliance measures, we use the second method. When the compliance or enforcement decision is found to be endogenous based on Hausman’s endogeneity test, the predicted values of the endogenous variable are obtained from first stage reduced form regressions of the concerned variable on a set of instruments and then used in the structural specifications19 for second stage estimation. (ii) All specifications have dependent variables that are not continuous, and thus ordinary least squares (OLS) technique cannot be employed. Regression equation with the binary dependent variable COMP is estimated using a probit model. Regression equations with count-dependent variables such as COMP_NUM, INSPECT and ACTIONS are estimated using quasi maximum likelihood estimation techniques. (iii) Count data is modeled using Poisson regression. However, in fitting parametric models based on the poisson distribution, one has to check for over-dispersion, which is a common feature given that population is heterogeneous. In the presence of over-dispersion, the count data equation is estimated using negative binomial regression, after obtaining as estimate of the fixed variance parameter from first stage Poisson regression. (iv) All regression estimations are corrected for heteroscedasticity. Robust Huber White standard errors or generalised linear model (GLM) standard errors which also account of the over-dispersion or under-dispersion factor are estimated. 5.3. Data Primary data on environmental variables at plant level has been collected from the administrative database of PPCB in Patiala, Punjab for 1997–98 and years prior to it. The data in the files is confidential and was made available for this study on the request of the Central Pollution Control Board, New Delhi. The consent management cell at PPCB maintains files on each firm and all paperwork and correspondence with a plant such as regulatory notices and penalties, reports of inspections, pollution analysis etc., is filed here. These files provide information on plant characteristics, details of installation of abatement equipment and all communications between the Board and inspected plants. While regional offices of the Board maintain such files for plants belonging to small scale and medium scale industries, the files for plants belonging to the large and large-medium scale industries are maintained at the main PPCB office in Patiala. Since the study focuses on plants belonging to the large and large-medium scale industries, all the data required was available at the head office. The wholesale price index (WPI) for manufactured products was used to deflate turnover of industries while the total wage bill was deflated using the consumer price index (CPI) for industrial workers. While the WPI is available from the Indian central bank (Reserve Bank of India), CPI was obtained from the Monthly Review of the Indian Economy published by Centre for Monitoring the Indian Economy (CMIE), Mumbai. Firm level financial data was extracted from the CMIE corporate database PROWESS. This 19 As we note below, however, in most cases the null hypothesis of no endogeneity is not rejected and we do not have to use predicted values.

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database is regularly updated and contains data on over 6000 firms in India since 1988–89. These are firms that are registered under the Companies Act and are typically large and medium firms. The data is primarily gathered from profit and loss statements and balance sheets of companies. The database inter alia contains information on cash flows, products manufactured, raw materials consumed, changes in capital structure, stock price movements, financial returns and investment plans (see Table A.3 in the Appendix for a sample of data in PROWESS). For Punjab, PROWESS contained data on 196 firms for the year 1997–98. Appendix Table A.4 lists the variables for which data was extracted. Of these data for 175 firms was used for which plant level information was available. Table 2 describes the variables used in this paper. 6. Results We report results separately for water and air pollution below (Sections 6.1 and 6.2, respectively). In each subsection we first discuss the compliance equation and then the enforcement equation. 6.1. Water pollution The inspections and compliance equations for water pollution data are estimated using observations on 117 plants. Table 3 lists summary statistics for variables used in the analysis. 6.1.1. The compliance equation Regression results for the compliance equation are shown in Table 4 where COMP_NUM and COMP are regressed on variables representing enforcement activities (INSPECT or ACTIONS), measure of compliance history (CASE), firm level characteristics such as power and fuel consumption (ENERGY), stock market listing (LISTED), rate of return (ROR) as a proxy for the financial health of the firm, type of industry the firm belongs to (SUGAR, PAPER and CHEMICAL), whether the firm exports (EXPORT) and plant characteristics such as age of plant (AGE), abatement cost (COST), treatment plant requirement (PCD) and zonal dummies Z i . Since firm/plant characteristics such as SALES, TURNOVER, WAGES and ENERGY are highly correlated 20 separate compliance equations are estimated by including one correlated variable at a time, of which only the one with ENERGY is reported here. Other firm level variables were also tried such as net value added/gross value added but the results did not change significantly. Models A and B estimate the determinants of the frequency of compliance (COMP_NUM) of plants. The models are estimated as follows:
Models A(i) and B(i) where regulatory actions are the only independent variables.
Models A(ii) and B(ii) where regulatory actions as well as other firm and plant level variables are included as independent variables. INSPECT and ACTIONS are found to be exogenous and therefore the equations are estimated using the actual number of inspections or regulatory actions as regressors. INSPECT (models A(i) and A(ii)) and ACTIONS (models B(i) and B(ii)) have highly significant and positive coefficients, suggesting the intuitive result that more inspections and enforcement actions result in greater compliance. Within the regulatory measures, there is evidence for greater positive impact of on-site inspections (INSPECT) on compliance frequency of plants than that of all regulatory actions put together (ACTIONS). This result is expected as the latter measure also 20

The correlation matrix is available from the authors on request.

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includes other low-cost and indirect measures of enforcement such as serving general and show-cause notices of violation. The compliance equation with COMP as the dependent variable does fairly well, on an average predicting compliance status correctly 80% of the time (Table 4, model C). However, the coefficient of INSPECT has the incorrect sign, suggesting that regulatory activities21 decrease compliance. This result needs to be interpreted cautiously. To study the link between inspection frequency and compliance pattern, a measure of lagged inspections should have been used. As stated earlier, plants are not inspected every year and sometimes not inspected for a number of years. In our dataset, about 10% of the plants dataset were not inspected at all and about a quarter of the firms faced at most one inspection in five years. Compliance status (which is known only for the years when firms are inspected or notified through court summons or violation notices), is not known in many cases for the period following inspections. The data can thus be used only to obtain the compliance status, the number of inspections faced and the number of notices served during the same time period. In such a case COMP and enforcement measures such as INSPECT or ACTIONS may actually reflect reverse causality: firms that are more likely to be out of compliance face higher enforcement activities. While we could not reject the hypothesis of exogeneity of INSPECT or ACTIONS based on the Hausman test, it must be noted that even after trying to tackle reverse causality through the lagged variable or predicted variable approach, some endogeneity persists (Gray & Shadbegian, 2005). Thus, our results do not indicate that regulatory actions are counterproductive. They in fact highlight the targeting of regulatory actions towards non-complying plants. The coefficient of CASE in different specifications is found to be significant (models A and B) and the negative sign implies that this variable captures the impact of past history of gross noncompliance on the plant’s compliance frequency. Legal action is usually initiated as a last resort against recurrent violators. Such plants are more likely to have low compliance rates. Estimates of models A(ii), B(ii) and C show that some firm level and plant level variables are also important in explaining the variation in compliance frequency across plants. Plants belonging to the relatively more polluting pulp and paper industry (PAPER) have significantly lower compliance rates (model A(ii)). Firms which export their output (EXPORT) are more likely to have a lower frequency of compliance. The perverse sign of this significant determinant may be explained by the fact that firms that also produce for foreign markets are relatively larger firms and hence this variable is likely to represent the impact of size of the firm on compliance frequency. Larger firms have greater capacity to get around regulations. The relatively more financially sound plants (ROR) are likely to be out of compliance. Among plant-level determinants, older plants (AGE) are more likely to comply. Higher abatement costs (COST) are found to be significant deterrents and such firms have a high probability of being out of compliance. The zonal dummy variable Z 2 has a significant coefficient, indicating some difference in compliance pattern across zones. 6.1.2. The enforcement equation Estimates of enforcement equations are reported in Table 5 (models A to D). Variation in dependent variables INSPECT (models A and B) and ACTIONS (models C and D) are explained in terms of compliance measures COMP, COMP_NUM and CASE (representing compliance history of firms) and other firm and plant level characteristics. While COMP is found to be exogenous in all specifications, COMP_NUM is found to be endogenous in model B (and hence we 21 In a different specification not listed in Table 4, COMP was also regressed on the enforcement variable ACTIONS. The coefficient of ACTIONS was also found to be significant and negative.

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Table 2 Description and definition of variables used. Variable Name

Description

Definition

Units

Source

COMP

Compliance status of a plant

0/1

Primary

COMP_NUM

Number of times found in compliance Total number of inspections

0,1,2,3,. . .

Primary

0,1,2,3,. . .

Primary

0,1,2,3,. . .

Primary

–

Primary

0/1 0/1

Primary Primary

0/1 1980 rupees 1980 rupees 1980 rupees 1980 rupees – 0/1 Number 0/1 0/1 0/1 0/1 0/1

Primary Prowess

ZI ROR

Pollution control device Court case filed under provisions of Water/ Air Act. Zonal dummy Rate of return

PROFIT

Net profits

A dummy variable = 1 if the plant is complying. Compliance over the most recent 5 year period for which data is available, is determined using a simple majority rule. If a plant is out of compliance more often than not, then COMP = 0 or else 1. Number of times a plant is found to be in compliance in the last 5 years. This variable is not defined for plants which have not been inspected in 5 years. Number of times a plant was inspected during the most recent 5 year period. It includes general inspections for verification and for sample collection. Number of inspections and show cause notices served for violation of provisions under Section 31-A of Air Act or Section 33-A of Water Act during the last five years. Actual and expected installation and annual operation and maintenance cost of the pollution control device, normalized by value of output. A dummy = 1 if the plant requires an ETP/PCD as per PPCB guidelines. A dummy variable = 1 if a case was filed against the plant by the Board during the reference time period. 3 zonal dummies for 4 zones as defined by PPCB. Reference category is zone 4. Obtained as a ratio of net profits to total assets of the plant where total assets include fixed assets, investments and current assets. Excess of income over all expenses (after tax).

TURNOVER

Value of output

Value of total output for sale and intermediate consumption.

WAGES

Wages and salaries

Total expenditure incurred by an enterprise on all employees, including the management.

ENERGY LISTED AGE EXPORT OWNER PAPER SUGAR CHEMICAL

Power and fuel expenses Listing flag Age of plant Exporting or not Govt/Private Paper industry Sugar industry Chemical industry

Power and fuel expenses, normalized by value of output A dummy variable = 1 if company is listed on the stock exchange. Age = 1998 – Year of commissioning of the plant. Dummy = 1 if the firm exports. Dummy = 1 if plant does not belong to Government owned firm (Central, State or Co-operative). Dummy = 1 if the plant/firm belongs to the paper industry Dummy = 1 if the plant/firm belongs to the sugar industry Dummy = 1 if the plant/firm belongs to the chemical industry.

INSPECT ACTIONS COST PCD CASE

Total number of inspections and violation notices Abatement Cost

Prowess Prowess Prowess Prowess Prowess Primary Prowess Prowess Prowess Prowess Prowess

Table 3 Summary statistics of variables for water pollution. Variable

Unit

Mean

Median

Max

Min

Std. Dev.

Skewness

Jarque–Bera

No. of obs.

ACTIONS AGE CASE CHEMICAL COMP COMP_NUM COST ENERGY EXPORT INSPECT LISTED OWNER PAPER PCD ROR SUGAR TURNOVER WGS

0,1,2,3,4,. . . Years 0/1 0/1 0/1 0,1,2,3,4,. . . Rs. Lakhs 0/1 0,1,2,3,4,. . . 0/1 0/1 0/1 0/1 Rs. Lakhs 0/1 Rs. Lakhs Rs. Lakhs

6.63 16.71 0.13 0.20 0.57 1.84 1464 0.07 0.61 4.84 0.83 0.97 0.09 0.83 1.75 0.06 50.24 3.87

5 12 0 0 1 1 0 0.04 1 3 1 1 0 1 2.86 0 12.6 0.55

46 75 1 1 1 7 91520 0.51 1 34 1 1 1 1 25.73 1 1383.56 208.35

0 2 0 0 0 0 0 0 0 0 0 0 0 0 86.28 0 0 0

6.87 14.89 0.34 0.40 0.50 1.72 9150 0.08 0.49 5.18 0.38 0.18 0.28 0.38 10.88 0.24 141.05 19.56

2.76 1.75 2.22 1.53 0.29 1.17 9.05 2.34 0.44 2.81 1.75 5.13 2.97 1.75 4.51 3.71 7.66 9.93

739.00 99.22 138.81 46.00 19.54 27.16 34410 292.75 19.68 854.39 65.03 3387.75 396.46 65.03 6448.82 944.93 23328.88 51598.68

117 114 117 117 117 105 109 109 117 117 117 117 117 117 117 117 117 117

Note: Rs. lakhs = 100,000 Indian rupees.

use the predicted value of COMP_NUM obtained from the first stage regression). There are a few large counts, with about twothirds of the firms facing 0–5 inspections in 5 years (mean 4.5, standard deviation 5.1). Thus, the cross-section raw data is expectedly over-dispersed and the results are corrected for overdispersion where needed.22 Compliance variables COMP and COMP_NUM have significant coefficients with the expected signs. Greater compliance results 22 The model is tested for overdispersion. After confirming overdispersion and calculating the fixed variance parameter, the model is re-estimated using Negative Binomial Quasi Maximum Likelihood Estimation (NB-QMLE) technique. Results of tests for overdispersion are available from the authors on request.

in fewer regulatory actions against the plant confirming Harrington’s targeting hypothesis (Harrington, 1988). The variable CASE, interpreted as a measure of past compliance history of a plant, has a significant positive coefficient in models A and C. This suggests that the regulator sorts firms into high- and low-frequency monitoring groups based on compliance history and firms belonging to the former group are subjected to greater inspections a la Harrington (1988). Concentration of regulatory activities on plants that are more likely to be non-compliant is also evident from the significant coefficients of variables depicting greater pollution intensity (SUGAR, PAPER, CHEMICAL, PCD). On the latter, plants that are not large water polluters do not need an effluent treatment plant, i.e., PCD.

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S. Gupta et al. / World Development 117 (2019) 313–327 Table 4 Estimation of compliance equation (water pollution) Dependent Variable !

COMP_NUM Negative Binomial Count

Constant INSPECT

Model B (ii)

Model C

0.298⁄⁄⁄ (0.106) 0.072⁄⁄⁄ (0.014)

0.254 (0.329) 0.119⁄⁄⁄ (0.026)

0.384⁄⁄⁄ (0.128)

0.162 (0.375)

0.817 (0.761) 0.207⁄⁄⁄ (0.059)

0.041⁄⁄⁄ (0.013) 0.857⁄⁄ (0.361)

0.046⁄⁄ (0.020) 0.596⁄ (0.327)

0.652 (0.656)

0.276 (1.110) 0.173 (0.199) 0.011 (0.011) 0.363⁄⁄ (0.170) 0.222 (0.316) 0.803⁄⁄ (0.336) 0.068 (0.219)

0.053 (1.221) 0.018 (0.208) 0.010 (0.012) 0.305⁄ (0.178) 0.009 (0.332) 0.574 (0.408) 0.001 (0.230)

3.875⁄ (2.215) 0.914⁄ (0.482) 0.057⁄⁄⁄ (0.022) 0.230 (0.346) 0.103 (0.551) 0.035 (0.607) 0.296 (0.482)

0.012⁄⁄ (0.006) 0.0001⁄⁄ (0.00004) 0.156 (0.224) 0.164 (0.212) 0.363⁄ (0.207) 0.185 (0.331)

0.010⁄ (0.006) 0.0001 (0.00006) 0.460⁄ (0.266) 0.185 (0.242) 0.500⁄⁄ (0.231) 0.114 (0.332)

0.033⁄⁄⁄ (0.012) 0.0001⁄⁄⁄ (0.00004) 0.670 (0.513) 0.156 (0.375) 1.308⁄⁄⁄ (0.449) 0.750 (0.603)

95 0.269 1.710 3.569 3.999 3.743 40.021 0.000 1 1.053

106 0.369 0.493 1.153 1.555 1.316 52.893 0.000 – –

LISTED ROR EXPORT SUGAR PAPER CHEMICAL Plant-level Variables AGE COST PCD Z1 Z2 Z3

p < 0:01,

⁄⁄

p < 0:05,

⁄

Binary Probit

Model B (i)

Firm-level Variables ENERGY

⁄⁄⁄

Poisson Count

Model A (ii)

1.114⁄⁄⁄ (0.321)

Included observations R-squared S.D. dependent var Akaike Schwarz Hannan-Quinn LR statistic Prob(LR statistic) QML parameter used in estimation GLM adjusted covariance (variance factor)

COMP

Model A (i)

ACTIONS CASE

COMP_NUM

105 0.163 1.716 3.486 3.544 3.499 26.786 0.000 1

1.775⁄ (0.076)

95 0.314 1.710 3.890 4.320 4.064 27.363 0.026 1.074 0.277

105 0.088 1.716 3.576 3.652 3.607 15.461 0.000 1

p < 0:1. Standard errors in parentheses.

Among the firm-level determinants, ROR is significant across all specifications while LISTED has a negative and significant coefficient in models A and B, indicating that firms with higher rates of return (net profit as a percentage of total assets) and those that are listed at the stock exchange are likely to face fewer inspections. One (charitable) interpretation of a significant negative coefficient of ROR is that using ROR as a proxy for the financial health of a firm, the Board may not closely watch firms that are doing well since these firms are more likely to be in compliance than the firms that are not in a position to meet abatement expenses. On the other hand, it could be that firms with ‘deep pockets’ can buy off the regulators. OWNER and AGE have positive and significant coefficients indicating that private and older plants are likely to be inspected more frequently. These along with the significant coefficient on ROR indicate the possibility of lobbying by older, larger and influential firms for preferential treatment and/or of rent seeking by the inspectors.

6.2. Air pollution Compliance and monitoring/enforcement equations for air pollution are estimated using observations on 109 plants. Table 6 lists summary statistics for variables used in the analysis. 6.2.1. The compliance equation Estimation results for the compliance equation are given in Table 7. Models A (i and ii) and B (i and ii) explain variation in frequency of compliance (COMP_NUM) while model C has COMP as the dependent variable. Based on the Hausman’s test, all regulatory actions are found to be exogenous in compliance equations. The results for the compliance equations for air pollution are similar to the ones obtained for compliance equations for water pollution. Once again, INSPECT (models A(i) and A(ii)) and ACTIONS (models B(i) and B(ii)) have highly significant and positive coefficients, suggesting significant deterrence impact of regulatory activities on plants’ compliance

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Table 5 Estimation of enforcement equation (water pollution). Dependent Variable !

Constant

INSPECT Poisson Count Model A

INSPECT Negative Binomial Count Model B

ACTIONS Poisson Count Model C

ACTIONS Poisson Count Model D

1.969 (0.706)

1.284 (0.724)

0.853 (0.519)

0.311 (0.282)

Compliance Variables COMP

0.410⁄⁄⁄ (0.147)

COMP_NUM CASE Firm-level Variables ENERGY (total) LISTED ROR EXPORT SUGAR PAPER CHEMICAL OWNER Plant-level Variables AGE PCD Z1 Z2 Z3 Number of Observations R-squared Adjusted R-squared S.D. dependent var Akaike Schwarz Hannan-Quinn LR statistic Prob(LR statistic) QML parameter used in estimation ⁄⁄⁄

p < 0:01,

⁄⁄

p < 0:05,

⁄

0.536⁄⁄ (0.231) 0.021 (0.233)

0.504⁄⁄⁄ (0.132)

0.382⁄⁄⁄ (0.147)

0.559⁄⁄⁄ (0.184) 0.036 (0.205)

0.296⁄⁄ (0.144)

0.008 (0.015) 0.426⁄⁄⁄ (0.154) 0.033⁄⁄⁄ (0.011) 0.054 (0.148) 0.990⁄⁄⁄ (0.198) 0.264 (0.187) 0.196 (0.149) 2.849⁄⁄⁄ (0.686)

0.025⁄ (0.015) 0.359⁄⁄ (0.174) 0.020 (0.012) 0.118 (0.161) 0.763⁄⁄⁄ (0.204) 0.416⁄⁄⁄ (0.155) 0.231 (0.168) 2.146⁄⁄⁄ (0.657)

0.010 (0.012) 0.374⁄⁄⁄ (0.113) 0.035⁄⁄⁄ (0.008) 0.022 (0.100) 1.105⁄⁄⁄ (0.147) 0.311⁄ (0.167) 0.273⁄⁄⁄ (0.104) 2.223⁄⁄⁄ (0.521)

0.018⁄ (0.009) 0.222 (0.142) 0.021⁄⁄ (0.009) 0.112 (0.133) 0.750⁄⁄⁄ (0.136) 0.507⁄⁄⁄ (0.139) 0.292⁄⁄ (0.135) 1.592⁄⁄⁄ (0.183)

0.023⁄⁄ (0.011) 1.553⁄⁄⁄ (0.290) 0.046 (0.166) 0.315 (0.193) 0.035 (0.274)

0.002 (0.006) 0.936⁄⁄⁄ (0.206) 0.168 (0.172) 0.139 (0.166) 0.328 (0.274)

0.025⁄⁄⁄ (0.008) 1.262⁄⁄⁄ (0.229) 0.059 (0.125) 0.307⁄ (0.159) 0.0212 (0.164)

0.006 (0.005) 0.623⁄⁄ (0.198) 0.069 (0.143) 0.149 (0.145) 0.111 (0.217)

114 0.522 0.449 3.704 4.816 5.200 4.972 178.277 0.000 1

114 0.516 0.442 3.704 4.901 5.285 5.057 29.333 0.015 0.607989

114 0.599 0.538 5.106 5.479 5.863 5.635 242.419 0.000 1

114 0.648 0.594 5.106 5.318 5.703 5.474 260.757 0.000 1

p < 0:1. Standard errors in parentheses.

frequency. On-site inspections are relatively more effective in ensuring higher compliance rates. The compliance equation with COMP as the dependent variable does fairly well, on an average predicting compliance status correctly 74% of the time (model C). However, the coefficient of INSPECT is not significant.23 As a measure of plant’s compliance history, CASE has a negative and significant coefficient in all specifications, suggesting that plants against which the Board has had to resort to the extreme enforcement measure of filing a court case, are most likely to have low rates of compliance. Plant level determinants such as COST and PCD are found to be significant in some specifications. Abatement cost (COST) has the expected sign in model C, suggesting that higher abatement costs reduce the probability of compliance. Models A and B indicate greater compliance frequency of plants which need (and have) 23 In a different specification not listed in Table 7, COMP was also regressed on the enforcement variable ACTIONS. The coefficient of ACTIONS was also found to be positive and insignificant.

air pollution control devices (PCD). Most other firm and plant level characteristics are found to be insignificant. Compliance behavior is found to primarily depend upon the nature of regulatory and enforcement regime in place.

6.2.2. The enforcement equation Table 8 presents estimation results for the enforcement equations for air pollution. The results indicate that the regulatory Board’s enforcement activities with respect to air pollution are not significantly determined by plant’s compliance frequency or overall compliance status. This is not to suggest that enforcement activities are carried out as a matter of routine, irrespective of compliance status. There is evidence of targeting of inspections and other enforcement actions against plants more likely to be out of compliance, as captured by the significant positive coefficient of CASE. Also, plants which are large polluters and need pollution abatement devices (PCD) are found to face a higher frequency of inspections and other enforcement actions. The results suggest

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S. Gupta et al. / World Development 117 (2019) 313–327 Table 6 Summary statistics of variables for air pollution. Variable

Unit

Mean

Median

Max

Min

Std. Dev.

Skewness

Jarque-Bera

No. of obs.

AGE CASE CHEMICAL COMP COMP_NUM COST ENERGY EXPORT INSPECT LISTED NOTICES OWNER PCD ROR TURNOVER WAGES

Years 0/1 0/1 0/1 0,1,2,3,4,. . . Rs. Lakhs 0/1 0,1,2,3,4,. . . 0/1 0,1,2,3,4,. . . 0/1 0/1 Rs. Lakhs Rs. Lakhs Rs. Lakhs

15.55 0.11 0.19 0.55 1.87 21.36 0.05 0.58 3.36 0.81 2.17 0.96 0.74 3.32 31.47 1.52

12.00 0.00 0.00 1.00 2.00 0.70 0.03 1.00 3.00 1.00 1.00 1.00 1.00 2.62 12.97 0.52

74.00 1.00 1.00 1.00 7.00 1355.03 0.28 1.00 12.00 1.00 15.00 1.00 1.00 27.01 239.80 16.63

1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 14.78 0.00 0.00

13.79 0.31 0.40 0.50 1.45 135.52 0.06 0.50 2.77 0.40 2.29 0.19 0.44 6.48 44.45 2.48

1.87 2.49 1.56 0.20 0.92 9.42 1.75 0.32 0.86 1.56 2.03 4.93 1.11 0.35 2.55 3.05

132.26 193.15 44.97 18.17 17.22 36223 59.09 18.21 13.66 44.97 336.52 2697.35 25.13 23.36 338.01 875.48

109 109 109 109 94 103 85 109 109 109 109 109 109 108 108 108

Note: Rs. lakhs = 100,000 Indian rupees.

Table 7 Estimation of compliance equation (air pollution) Dependent Variable !

Constant INSPECT

COMP_NUM

COMP_NUM

COMP

Negative Binomial Count

Negative Binomial Count

Binary Probit

Model A (i)

Model A (ii)

Model B (i)

Model B (ii)

Model C

0.079 (0.125) 0.169⁄⁄⁄ (0.023)

0.370 (0.301) 0.193⁄⁄⁄ (0.031)

0.159 (0.141)

0.211 (0.347)

0.194 (0.665) 0.014 (0.082)

0.072⁄⁄⁄ (0.017) 0.183 (0.221)

0.078⁄⁄⁄ (0.023) 0.348 (0.259)

ACTIONS 0.386⁄⁄ (0.195)

CASE Firm-level Variables ENERGY

2.004⁄

LISTED ROR EXPORT

0.104

CHEMICAL Plant-level Variables AGE

PCD Z1 Z2 Z3 Included observations R-squared Adj. R-squared S.E. of regression Akaike Schwarz Hannan-Quinn LR statistic Prob(LR statistic) QML parameter used in estimation GLM adjusted covariance (variance factor) p < 0:01,

⁄⁄

p < 0:05,

1.142 (1.135) 0.125 (0.185) 0.004 (0.012) 0.050 (0.145) 0.207 (0.222)

0.012 (0.014) 0.034 (0.169)

0.007 (0.005) 0.007⁄ (0.004) 0.517⁄⁄ (0.219) 0.218 (0.195) 0.092 (0.196) 0.323 (0.289)

COST

⁄⁄⁄

0.530⁄⁄ (0.226)

⁄

94 0.326 0.311 1.207 3.083 3.164 3.116 36.264 0.000 1 0.683479

p < 0:1. Standard errors in parentheses.

72 0.373 0.232 1.290 3.884 4.327 4.060 4.260 0.988 0.832352 0.201638

94 0.129 0.111 1.371 3.299 3.381 3.332 15.886 0.000 1 0.92757

(1.322) 0.186 (0.214) 0.033 (0.027)

2.083⁄⁄ (0.656) 3.656 (2.862) 0.095 (0.442)

(0.329) 0.223 (0.255)

0.030 (0.526)

0.013⁄⁄ (0.006) 0.005 (0.003) 0.623⁄⁄ (0.253) 0.123 (0.224) 0.089 (0.230) 0.157 (0.336)

0.016 (0.013) 0.034⁄⁄ (0.015) 0.484 (0.465) 0.337 (0.439) 0.640 (0.527) 0.018 (0.808)

72 0.187 0.005 1.469 4.167 4.609 4.343 32.677 0.002 1.268375 0.207799

80 0.264 0.452 1.354 1.771 1.521 28.776 0.007

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S. Gupta et al. / World Development 117 (2019) 313–327

Table 8 Estimation of monitoring and enforcement equation (air pollution). Dependent Variable !

Constant

INSPECT Poisson Count Model A

INSPECT Negative Binomial Count Model B

ACTIONS Poisson Count Model C

ACTIONS Poisson Count Model D

1.087 (0.448)

0.811 (0.381)

1.068⁄⁄ (0.395)

1.439⁄⁄⁄ (0.303)

Compliance Variables COMP COMP_NUM CASE Firm-level Variables ENERGY LISTED ROR EXPORT CHEMICAL OWNER Plant-level Variables AGE PCD Z1 Z2 Z3 Included Observations R-squared S.E. of regression S.D. dependent var Akaike Schwarz Hannan-Quinn LR statistic Prob(LR statistic) QML parameter used in estimation ⁄⁄⁄

p < 0:01,

⁄⁄

p < 0:05,

⁄

0.166 (0.154)

0.064 (0.194) 0.057 (0.162) 0.389⁄⁄ (0.183)

0.471 (0.276)

0.217 (0.171) 0.339⁄⁄ (0.137)

0.221 (0.224)

0.022 (0.022) 0.269 (0.181) 0.013 (0.011) 0.244 (0.128) 0.369⁄ (0.192) 0.197 (0.377)

2.511⁄⁄ (1.187) 0.277 (0.217) 0.001 (0.014) 0.146 (0.163) 0.312 (0.228) 0.454 (0.377)

1.031 (0.906) 0.365⁄⁄⁄ (0.141) 0.001 (0.009) 0.064 (0.106) 0.272 (0.171) 0.015 (0.283)

0.597 (1.014) 0.316 (0.205) 0.007 (0.010) 0.071 (0.156) 0.294 (0.202) 0.169 (0.277)

0.010 (0.006) 0.976⁄⁄⁄ (0.300) 0.094 (0.196) 0.071 (0.193) 0.306 (0.239)

0.001 (0.006) 0.989⁄⁄⁄ (0.281) 0.003 (0.229) 0.008 (0.239) 0.250 (0.378)

0.004 (0.004) 0.500⁄ (0.293) 0.204 (0.158) 0.045 (0.151) 0.174 (0.187)

0.003 (0.005) 0.826⁄⁄⁄ (0.244) 0.189 (0.208) 0.061 (0.192) 0.132 (0.322)

80 0.289 2.544 2.757 4.662 5.079 4.829 53.469 0.000 1

85 0.200 2.655 2.728 5.005 5.407 5.167 30.043 0.000 1.158799

80 0.295 3.954 4.304 5.765 6.182 5.932 82.766 0.000 1

85 0.301 3.867 4.251 5.714 6.116 5.875 87.148 0.000 1

⁄

p < 0:1. Standard errors in parentheses.

the fact that the regulator uses the available information on past compliance history of a plant to target potential violators, as well as the possibility of plants opting for a strategic move in the form of installing a PCD but not complying. Most other firm level and plant level characteristics are found to be insignificant in explaining compliance behavior of plants. It is worth noting here that the Air (prevention and control of pollution) Act, 1981, is comparatively new, enforced in Punjab only in the year 1984. Around 15% of the firms in our sample were never inspected in five years, and around 75% of the firms in our sample were inspected less than once in five years. Thus data on long term compliance pattern of firms with respect to air-pollution is not sufficient. 7. Concluding remarks Our main hypothesis in this paper is that compliance and regulatory actions are interdependent. Plants that face more frequent regulatory actions have a higher probability of being detected and hence, are likely to be more compliant with pollution control regulations. Similarly, greater monitoring and enforcement actions are likely to be directed against recalcitrant plants. Empirical evidence presented in this paper suggests this is indeed the case.

We find compliance decisions depend significantly on regulatory actions–regulatory pressure built through inspections and/or violation notices is effective in ensuring greater compliance across plants. This underscores the need for strengthening pollution control boards through greater autonomy and more financial resources and manpower. On the whole firm and plant-level characteristics do not influence compliance.24 This is an important result from a policy perspective. It highlights the fact that compliance behavior is not significantly driven by plant/firm characteristics that are given but is more responsive to the frequency and thoroughness of inspections and other regulatory actions. Thus, the regulator has greater scope of improving compliance behavior through regulatory actions (Gray & Shadbegian, 2005). As hypothesized, evidence also indicates enforcement activity is directed more toward plants that are likely to be out of compliance. Thus plants with low compliance rates, history of non-compliance and/or those belonging to dirty industries are inspected more frequently. This is consistent with enforcement-cost minimizing 24 The only exception being for water polluting plants where we find those with high abatement costs and those that are relatively new comply less frequently; and for air polluting plants those with a pollution control device comply more.

S. Gupta et al. / World Development 117 (2019) 313–327

hypothesis and also with Harrington’s targeting model. Unlike compliance decisions, however, we find some evidence for enforcement being influenced by firm and plant level characteristics. For example, for water pollution, plants belonging to bigger firms (i.e., those listed on the stock exchange) and firms that are more profitable, face less stringent enforcement. To summarize, the empirical evidence in this paper suggests, other things equal, enforcement actions do target the worst offenders and can result in deterrence and improved environmental outcomes. This calls for greater allocation of resources and concerted efforts to improve the functioning and autonomy of pollution control boards. Conflict of interest None. Acknowledgements We thank Arun Agrawal and two anonymous reviewers for comments and suggestions that significantly improved the paper. Financial support by the Centre for Development Economics, Delhi School of Economics, for this study is gratefully acknowledged. We also thank the Central Pollution Control Board for facilitating the data collection, Punjab Pollution Control Board, Patiala for providing the data and Ms. Smriti for able research assistance. K.L. Krishna provided useful comments. This paper was completed while Shreekant Gupta was visiting the Graduate School of Public Policy at Nazarbayev University and Omer F. Baris was visiting the Lee Kuan Yew School of Public Policy at National University of Singapore. They thank both schools for their hospitality. The usual disclaimer applies. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.worlddev.2019.02. 001. References Alm, J., & Shimshack, J. (2014). Environmental enforcement and compliance: Lessons from pollution, safety, and tax settings. Foundations and TrendsÒ in Microeconomics, 10(4), 209–274. Almer, C., & Goeschl, T. (2010). Environmental crime and punishment: Empirical evidence from the german penal code. Land Economics, 86(4), 707–726. Becker, G. S. (1968). Crime and punishment: An economic approach. Journal of Political Economy, 76(2), 169–217. Billiet, C. M., & Rousseau, S. (2011). How real is the threat of imprisonment for environmental crime? European Journal of Law and Economics, 37(2), 183–198. Burby, R. J., & Paterson, R. G. (1993). Improving compliance with state environmental regulations. Journal of Policy Analysis and Management, 12(4), 753–772. Cohen, M. A. (1999). Monitoring and enforcement of environmental policy. In T. Tietenberg & H. Folmer (Eds.). International Yearbook of Environmental and Resource Economics (Vol. III, pp. 44–106). Edward Elgar publishers. Ch. 2. Dasgupta, S., Hettige, H., & Wheeler, D. (2000). What improves environmental compliance? Evidence from mexican industry. Journal of Environmental Economics and Management, 39(1), 39–66. Dasgupta, S., Laplante, B., Mamingi, N., & Wang, H. (2001). Inspections, pollution prices, and environmental performance: Evidence from China. Ecological Economics, 36(3), 487–498. Deily, M. E., & Gray, W. B. (1991). Enforcement of pollution regulations in a declining industry. Journal of Environmental Economics and Management, 21(3), 260–274. Duflo, E., Greenstone, M., Pande, R., & Ryan, N. (2013). Truth-telling by third-party auditors and the response of polluting firms: Experimental evidence from india. The Quarterly Journal of Economics, 128(4), 1499–1545. Farmer, A. (2007). Handbook of environmental protection and enforcement: Principles and practice. Taylor & Francis. Féres, J., & Reynaud, A. (2011). Assessing the impact of formal and informal regulations on environmental and economic performance of brazilian manufacturing firms. Environmental and Resource Economics, 52(1), 65–85.

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