The protected polluters: Empirical evidence from the national environmental information disclosure program in China

Journal Pre-proof The protected polluters: Empirical evidence from the national environmental information disclosure program in China Tuo Zhang, Li Xie PII:

S0959-6526(20)30390-5

DOI:

https://doi.org/10.1016/j.jclepro.2020.120343

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JCLP 120343

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Journal of Cleaner Production

Received Date: 12 June 2019 Revised Date:

23 January 2020

Accepted Date: 31 January 2020

Please cite this article as: Zhang T, Xie L, The protected polluters: Empirical evidence from the national environmental information disclosure program in China, Journal of Cleaner Production (2020), doi: https://doi.org/10.1016/j.jclepro.2020.120343. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

CRediT author statement Tuo Zhang: Conceptualization, Supervision, Methodology, Visualization, Software Data curation, Writing- Original draft, Writing- Reviewing and Editing Li Xie: Writing-Original draft, Visualization, Investigation, WritingReviewing and Editing, Funding acquisition

8696 Words

The Protected Polluters: Empirical Evidence from the National Environmental Information Disclosure Program in China Tuo Zhanga , Li Xieb* a.Center for East Asian Economic Studies, Graduate School of Economics, Kyoto University,606-8501,Kyoto,Japan b.School of Economics and Trade, Hunan University,410079,Changsha,Hunan,People's Republic of China

Highlights: We investigated the impacts of the Chinese Environmental Information Disclosure(EID) program EID exerts significant influences only on non-politically connected polluters, while, by contrast Politically connected firms are less susceptible The community should be empowered to deter the pollution shelter effects

*

Corresponding author. E-mail address:[email protected](Li Xie) -1-

Graphical Abstract:

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Abstract: As a bottom-up approach, the effectiveness of the environmental information transparency policy hinges on a broad societal ecosystem, including elements such as the active mass media and the robust civil society. However, due to the lack of public participation and accountability mechanisms, it is still doubtful whether the Chinese environmental transparency program promoted corporate pollution mitigation efforts. In this study, we investigated the impacts of the Environmental Information Disclosure (EID) program, an important Chinese environmental transparency program, on corporate mitigation investments, by using the 2012 Chinese Private Enterprise Survey. Our TobitIV model provides robust evidence that transparency policy exerts significant influences only on non-politically connected polluters, while, by contrast, politically connected firms are less susceptible to the EID program. We suggest that the community should be empowered to deter the shelter effects of local governors to the connected firms, which deteriorate the effectiveness of the transparency program. Keywords: Mandatory Environmental Information Disclosure Program; Corporate Mitigation investments; Pollution Shelter effect; Corporate Political Connections JEL Classification: P2, P3, Q52, Q53.

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1 Introduction A growing literature recognizes rent-seeking activities among local governments and polluters as cruxes of environmental degradation in China (Zhou, 2007; Yuan and Li, 2015; Nie, 2017). Local cadres ignore environmental protection in favor of economic development. Therefore, in the last decades, the central government initialized a handful of re-centralization measures, in order to monitor the implementation of national environmental regulations by local cadres (Kostka and Nahm, 2017). Launched in 2008, the mandatory environmental information disclosure (EID)program is one of these re-centralization measures. Despite the local governments' reluctance, the central government intended to build a unified EID framework nationwide, in order to setup a countervailing power against polluters, and thereby deter the collusions between local governments and polluters (Zhang et al., 2016). However, the majority of previous studies found that the Chinese EID program failed to improve the local environment. They attribute this failure to the lack of accountability in EID policy design (Johnson, 2011; Tan, 2014; Li, 2016; Peng et al., 2019). For example, Seligsohn et al. (2018)argue that in western economies the public can exercise ex-post accountability through the bottom-up system, while in contrast, the Chinese top-down EID program fails to provide effective public accountability. Therefore, former studies put forward the hypothesis that, due to the shelter effect from local governments, polluters are not accounted for their pollution even after their environmental violations are made public thanks to the EID program. Previous research provides insights into the failure of the EID program in China. However, due to limited data availability, these studies failed to provide evidence to support the assertions on accountability. Consequently, in this paper, we attempt to study the effectiveness of EID policy in China, by empirically investigating the heterogeneous responses of firms with or without political connections, to the EID program. In other words, the aim of this study is to investigate whether local protectionism ruins the effectiveness of the EID program. We employed the 2012 China Private Enterprise Survey to study the responses of private firms to the EID program. We used the Urban Pollution Information Transparency Index -4-

(PITI) to measure the stringency of the EID policy enforcement at the city level. The endogenous enforcement of national policies by local cadres(Wang and Wheeler, 2005; Tan, 2014) may threaten the results of the Ordinary Linear Model. Therefore, we used the largest firm’s dominance in the local labor market as an instrumental variable(IV)for the PITI scores(Lorentzen et al. ,2013). Furthermore, we explored the heterogeneous impacts of the EID policy on firms with or without political connections, measured by the congress membership of their owners, through the interaction term of political connections and PITI scores. Our findings show that, in general, the impacts of the EID program on corporate mitigation investments are not significant. Further, we explored the shelter effect of political connections on corporate pollution behaviors under the EID program. The empirical results show that firms without political connections significantly increased their pollution control investment intensity in response to the transparent policy. In contrast, EID had no impact on those firms with political connections. Thus, we show that politically connected firms are less susceptible to the EID program due to local protectionism. Based on the firm-level dataset developed, this study confirms for the first time that the failure of the EID program in China is caused by local protectionism. In other words, due to the lack of accountability, the EID program can not deter the collusions between local governments and firms. Therefore, this study offers additional insight into the role of rent-seeking activities in environmental policy in China. Another major contribution of this study is to recognize the endogenous enforcement of state environmental policies by local cadres, tackling endogeneity through an instrumental variable approach. Since the national EID program is implemented by local cadres, a simple OLS would lead to estimation biases due to the self-selection problem. According to Lorentzen et al. (2013), the higher the proportion of workers employed by the largest employer in a city, the more difficult for local cadres to enforce the national EID program due to the intensified lobbying activities. Therefore, we used the largest firm’s dominance in the local labor market as an instrumental variable for PITI scores. We also deliberately avoided using data from the year after the establishment of the EID policy, in order to avoid reverse causality. Thus, the empirical conclusions of this paper -5-

are more reliable. Section2 provides a brief policy and literature review, followed by the development of the hypotheses in Section 3. Section 4 explains the samples and empirical strategies. Section 5 presents the baseline results. Section 6 discusses the shelter effect on the effectiveness of the EID policy. Finally, Section 7draws the conclusions and policy implications.

2Policy background and literature review Mandatory EID programs paved their way across the world during the 1990s and early 2000s.In 1987, the US Federal Government launched the Toxics Release Inventory (TRI), one of the earliest mandatory EID programs. The TRI program achieved great success and represents a benchmark for its counterparts(García et al., 2007). In the 1990s, an increasing number of OECD countries carried out their own EID programs. For example, the National Release Inventory of Canada started in 1993, while in 1998 the UK Pollutant Inventory program and the National Pollutant Inventory of Australia were launched. Thanks to the rapid ICT developments, information transmission costs have been reduced significantly, providing technical feasibility for EID in low-income countries. Indonesia, Vietnam, and the Philippines joined the long queue of EID countries in the early 2000s. The Chinese central government also launched two pilot EID programs in the Jiangsu Province and in Inner Mongolia. Large-scale deployments of mandatory EID attract academic interest from both the normative and the empirical perspective. 2.1 Theories on environmental information disclosure Although social activists advocate the disclosure of environmental information as an inborn right-to-know for citizens, economists evaluate EID programs from a social welfare perspective. Theoretical analyses of EID started with the Coase’s framework. According to Coase(1960), on the premise of zero transaction costs and perfect information, victims and polluters can bargain with each other spontaneously, resulting in optimal social welfare. The equilibrium pollution does not hinge on property attributions, which is well known as the Coase’s Theorem. However, in the case of asymmetric information, laissez-faire equilibrium will lead to excessive pollution and economic outcomes in favor of the polluters who dominate information(Huber and Wirl, 1998). -6-

Cohen and Santhakumar (2007) proved that mandatory EID can help to solve asymmetric information in the Coase’s bargaining activities by providing information for the stakeholders. From this perspective, EID will substitute the traditional environmental policies by allowing effective Coase’s bargaining activities (Cohen, 2001). Beyond the Coase’s framework, mandatory EID will also complement traditional environmental instruments(Tietenberg and Wheeler, 2001). For example, incentive frameworks, such as the emission cap and trade, would be either inapplicable or lose their efficiency, if abatement efforts and emissions cannot be properly monitored due to information unavailability (Xepapadeas, 1991).In this respect, EID programs could provide regulators with the necessary information in order to facilitate incentive-based policies. 2.2 Empirical studies on former EID programs Most prior studies provided empirical evidence supporting the effectiveness of EID programs, not only in developed countries but also in developing economies(Foulon et al., 2002; Kathuria, 2009).For example, the US TRI program reduced the release of chemicals by 45% in the first seven years of implementation(US Environmental Protection Agency, 1995), a tremendous success compared with other environmental policy instruments. In developing countries, the public disclosure programs of pollution information effectively increased the share of compliant polluters(Kathuria, 2009). Meanwhile, several channels for the success of EID programs have been identified. Hamilton (1995)provided evidence that the investors in stock markets would react actively to media reports on TRI releases, thereby negatively affecting the market value of polluters. Fung and O'rourke (2000) further emphasized the importance of grassroots campaigns, which ruthlessly focus the maximum attention on minimum performers, and induce them to improve their environmental governance. Generally speaking, most prior studies attribute the success of EID programs to various channels of accountability.And empowering the community was always regarded as key for such accountability(Tietenberg and Wheeler, 2001). To summarize, most prior studies acknowledge the bottom-up feature of the EID policy. Compared with traditional top-down policy instruments designed and implemented by the -7-

regulators, bottom-up approaches hinge on community involvement(Mariam, 2001; Fraser et al., 2006). In this respect, the EID policy should not only entitle the public to the environmental right-to-know but also empower them with the right to exert their pressure on polluters(Fang et al., 2017). 2.3 Assessments of the EID program in China Recently, a handful of studies investigated the effectiveness of the Chinese EIDprogram (Johnson, 2011; Tan, 2014; Li, 2016; Peng et al., 2019). The majority of them found insignificant impacts of EID on urban environmental quality. For example, Seligsohn et al. (2018)proved that the EID program in China alone makes no significant impacts on pollution mitigation, other than information provision. Despite its general ineffectiveness, some researchers argue that EID programs surely allow the public to monitor polluters to some extent. For example, Tan (2014)found that, though a lack of accountability under the decentralized administrative structure as reduced its effectiveness, the EID program allows environmental Non-Governmental Organizations(NGOs) to proactively affect the environmental governance of polluters. Although former literature has provided insightful critiques to the Chinese EID program, these studies are affected by several flaws. Firstly, most studies acknowledge the lack of accountability as a major shortcoming in the design of the Chinese EID program, however, no statistical evidence has been provided so far. Secondly, empirical studies of the Chinese EID program mainly focused on city-level evidence. Therefore, their analysis is confined to city-level responses and does not allow to investigate the mitigation behaviors of polluting firms, which is of more concern. For instance, there is still a lack of knowledge on the channels of Chinese EID. Thirdly, like other state environmental policies, the environmental transparency policy is endogenously enforced by local governors. To this respect, Tan (2014)found that the enforcement of the EID program is eventually weakest in heavily polluted cities. The endogenous enforcement may result in inconsistencies in the estimations in former studies. Therefore, in this study, we propose to use firm-level samples to investigate their direct responses to the EID program, by employing a newly published national survey. To the best of our knowledge, our study is the first to investigate the impacts of the EID program

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on firms’ pollution mitigation in China. More into detail, the focus of this study is on the heterogeneous impacts of the EID policy, i.e., whether the political connections of polluters would protect them from public pressures and accountability by the local governors, while unconnected firms are more susceptible to the EID policy due to the lack of protection. Moreover, in order to avoid possible estimation biases due to endogenous enforcement, an instrumental variable approach has been employed. In Section 3the hypotheses to be tested are developed, while Section 4 elaborates the instrumental variable approach.

3Theory and hypotheses The point of departure of our study is the standard theory of externalities with asymmetric information, which originates from Coase (1960).In a laissez-faire economy without the EID program, allowing for mitigation through contracts will not make for social optimum with perfect information, and will benefit the agents with informational rents(Huber and Wirl, 1998). EID program overcomes information asymmetry between polluters and other stakeholders. In an economy with EID, informed stakeholders would bargain with the polluters for their environmental rights spontaneously, resulting in the optimal level of emissions and in the improvement of overall social welfare(Cohen and Santhakumar, 2007). 3.1 Heterogeneity of the impacts of EID in China: the role of political connections Figure 1 illustrates the multi-level environmental governance system in China. Each stakeholder has unique responsibilities. More into detail, the state council plays the roles of national target setting and policy designation, while local governors are entitled to the administrative power on local firms. On the one hand, local governors are the major implementers of environmental protection policies and the direct supervisors of polluting firms; on the other hand, local governors are also highly motivated to promote local economic development in order to gain advantages in terms of future promotions(Li and Zhou, 2005; Zhou, 2007). The state and the local Environmental Protection Administrations(EPA) are the professional divisions for the environment and report to their direct superiors. Therefore, the GDP-oriented local governors can form mutually -9-

beneficial coalitions with polluters, where the local EPA implements lax environmental standards and even indulges in pollution(Jia and Nie, 2015). Since the local government mainly reports to the central government, the public's environmental appeals are often ignored. In terms of EID policy, this is clearly reflected in the low response rate of local EPAs to public requests for pollution information(Wang, 2016). Therefore, the EID program fails to provide additional accountability by embowing the public in the supervision of pollution. Moreover, it is possible to anticipate the heterogeneous impacts of EID to polluters within and out of political coalitions, which we elaborate as follows: 3.1.1 The shelter effect of local government for politically connected polluters Under the current EID program, local governments would protect polluters within their coalitions through several possible channels, a phenomenon that is always referred to as ‘shelter effect’. This effect may manifest itself in several ways. Firstly, as in the nature of political collusion, local cadres conceal pollution behaviors, for example, by fabricating

C enter:State C ouncil State EPA V ertical Leaderships: Prom otionalTournam ents

V erticalG uidance

Local:ProvincialG overnm ents Environm ental Inform ation D isclosure H orizontal Leadership PoliticalC onnections & C ollusions

ProvincialEPA Polluters Figure 1 The multi-layered structure of environmental governance in China. pollution records, resulting in the falsification of environmental information (Ghanem and Zhang, 2014). Secondly, even when the public obtains access to honest information, local cadres will exercise their administrative power to protect polluting firms within the coalition, thus significantly relieving public environmental pressures. Thirdly, local governments do not respond to requests for disclosure or even respond falsely (Wang, 2017). -10-

Therefore, we hypothesized that: Hypothesis I: Ceteris paribus, the Chinese mandatory EID program fails to promote the pollution mitigation investments of politically connected firms, due to the pollution shelter provided by the local governments. 3.1.2 The impacts of the EID program on polluters without political connections Ina similar way as in developed countries, in China, the mandatory EID policy allows novel channels for diversified stakeholders to motivate polluters without political connections. For example, environmental public interest litigations emerge, in which the prosecutors use EID data as factorial evidence of pollution. Moreover, some unusual channels have been identified. NGOs, the new entrepreneurial actor in environmental governance in China, facilitate multinational companies (MNCs) to supervise mitigation behaviors of firms in their supply chain, through the re-transmission of environmental information on their websites (Tan, 2014). Therefore, we hypothesized that: Hypothesis II: Ceteris paribus, the Chinese mandatory EID program will stimulate firms without political connections to increase their investments in effective pollution control. 3.2The aggregated impacts of the Chinese mandatory EID program According to the collusion theory, large polluting firms are more likely to collude with local cadres compared to small and medium firms(Li et al., 2008).On the one hand, large firms are influential in local economic development, therefore, local cadres are motivated to promote economic growth by supporting them. On the other hand, large polluters are more active in the quest for pollution shelters in order to avoid serious legal punishments for their pollution. Thus, Chinese EID program malfunctions because of the local protectionism of large polluters. Therefore, we hypothesized that: Hypothesis III: In general, ceteris paribus, the Chinese mandatory EID program can not improve corporate environmental governance.

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4 Research design 4.1 The sample The firm-level sample employed in our study is derived from the2012 Chinese Private Enterprise Survey (CPES),which was jointly conducted by the All-China Industry and Commerce Federation, the China Society of Private Economy at the Chinese Academy of Social Sciences, and the United Front Work Department of the Central Committee of the Communist Party of China(Chen et al., 2019). They conducted a multi-stage sampling survey of private entrepreneurs across the country. A total of 5,073 private companies were surveyed, collecting a wide variety of information on firms and their business owners. More importantly, the questionnaire includes information about business owners’ participation in local political activities and corporate pro-environmental behaviors, which makes it one of the best datasets to study political connections and environmental governance of polluters in China. 4.2 Variables 4.2.1 Corporate pollution mitigation efforts We measured the impact of the EID program on corporate pollution mitigation efforts by their annual total investments in pollution mitigation facilities. Mitigation investments are direct measurements of corporate pollution control efforts(Milliman and Prince, 1989), and have been used widely in the evaluation of mitigation incentives for polluters in China((Wang and Chen, 1999; Miao et al., 2019). 4.2.2 Political connections Former studies measured corporate political connections either by using the connections of firms with the government, for example, the share of state ownership, or the personal connections of business owners with local governments, for example, the number of owners that are members of the legislatures or of the ruling parties(Faccio et al., 2006; Fan, 2016; Fan and Chen, 2017; Kung and Ma, 2018). All the firms investigated in this study are private companies without state interests; thus,

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we used the latter measurement of corporate political connections. More into detail, the corporate political connections were identified based on business owners’ responses to the following questions: “Are you a member of the National People's Congress (NPC)or of the Chinese People's Political Consultative Conference(CPPCC)? If you are an NPC member, what is the level of your membership? If you are a CPPCC Member, what is the level of your membership?". Simple statistics show that 50% of the entrepreneurs surveyed served either as NPC or CPPCC members. Therefore, we developed a binary variable for political connections, which takes the value of one if the owner is an NPC or CPPCC member at the county level and above, otherwise it takes the value of zero. 4.2.3 Stringency of the EID program We used the PITI score, jointly published by the Institute of Public & Environmental Affairs (IPE) and the Natural Resources Defense Council(NRDC), as the measure of environmental information transparency policy at the city level. This comprehensive index evaluates the stringency of environmental pollution information disclosure for more than 100 key cities nationwide in terms of records of enterprise violations, results of “enforcement campaigns” against polluting enterprises, clean production audit information, enterprise environmental performance ratings, disposition of verified petitions and complaints, environmental impact assessment (EIA) reports and project completion approvals, discharge fee data, and response to public information requests. Figure 2 depicts the geographical distribution of the PITI scoresfor2011. The cities evaluated are distributed throughout the country and are widely representative of the national situation. A considerable variation in PITI scores indicates that, even if confronted with similar pressures from the center, local cadres responded distinctly to the national EID policy. Moreover, coastal cities outperformed their western counterparts, which suggests that information openness policy is endogenously implemented in cities and that this endogenous selection may lead to biases in estimation if not handled with care. To the best of our knowledge, the PITI index is by far the best proxy for urban EID in China and has been widely used in prior studies (Li, 2016; Brehm and Svensson, 2017; Kung and Ma, 2018; Seligsohn et al., 2018; Peng et al., 2019). In addition, we compared

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the PITI scores to another EID measurement based on an entire-array-polygon method used by Kosajan et al. (2018). A simple linear regression reveals that these two EID measurements are significantly correlated with each other. Moreover, the R square is 85%, which indicates that the PITI score is a reliable measure of the extent of the EID level in cities.

Figure 2 The 2011 PITI scores. Data source: The Third Annual Assessment of Environmental Transparency in 113 Chinese Cities (The PITI Report 2011). 4.2.4 The control variables Our dataset provided us with comprehensive features of the surveyed firms, so that, based on existing literature, we could control a widerange of covariates influencing the corporate mitigation incentives in our regression equation. The control variables can be

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divided into three categories: (1) the stringency of environmental regulations, which is measured by the ratio of pollution charge to annual revenue(Jaffe et al., 1995; Jaffe and Palmer, 1997); (2) firms’ attributes, including the operating profit margin (%), tax burden rate (%)(Earnhart and Lizal, 2006), and firm age; and (3)firm owners’ attributes, including age, gender(Braun, 2010), and education level(Rivera and De Leon, 2005). 4.3 Empirical strategies 4.3.1Baseline model We employed the following model to identify the causal relationship: Mitigationijk =α +β PITI j + ΓFirmAttrijk + ΦOwnerAttrijk + η j + µ k + ε ijk

(1)

Where the subscript i , subscript j , and subscript k indicate the ith private enterprise in the jth province and the kth industry, respectively. The dependent variable Mitigationijk measures the environmental governance of private firms, that is, their investments in pollution mitigation. We used the ratio of pollution mitigation investments to the operating income as the dependent variable. The ratio form can not only remove the influence of the price level across cities but also control the size effects.

FirmAttrijk represents the firms’ attributes, including the operating profit margin (%), the tax burden rate (%), and the firm age. The tax burden rate was defined as the proportion of tax accrual to turnover. OwnerAttrijk represents the characteristics of entrepreneurs.

η j is the dummy variable for the province; µk is the industry dummy and ε ijk is the error term. For reasons of feasibility, we pretreated the samples through the following procedures: (1) only the samples that answered at least one question on the political connections were retained; (2) the outliers whose profit margin exceeded 100% were eliminated; (3) the samples with pollution mitigation investments greater than the annual turnover were also eliminated. The remaining sample size was 4,745. Nearly 62% of the samples had zero pollution mitigation investments during the sampling period. The OLS estimation of Equation(1) could falsely lead to a negative prediction of mitigation investments. Therefore, we performed the truncated Tobit regression, which -15-

can help to avoid the problem of negative prediction (Amemiya, 1984). Since the Tobit regression is nonlinear, we directly reported the marginal effects of all explanatory variables.

Table 1 Descriptive statistics of major variables Count

Mean

S. d.

Min

Max

4,654

41.725

16.642

13.200

83.700

Pollution Mitigation (%)

3,879

0.238

0.790

0.000

9.412

Political Connection (1=Y)

4,745

0.397

0.489

0.000

1.000

Pollution Levy (%)

3,836

0.116

0.545

0.000

10.000

Turnover (Log)

4,330

15.520

3.381

0.000

24.937

Profitability (%)

3,983

9.937

19.881

-99.438

100.000

Tax Burden (%)

4,071

7.080

10.259

0.000

100.000

Firm age (year)

4,444

7.626

5.128

0.000

22.000

Owner age (year)

4,674

45.057

9.035

15.000

86.000

College Education (1=Y)

4,745

0.902

0.297

0.000

1.000

Gender (1=Female)

4,717

0.167

0.373

0.000

1.000

PITIscore Firm Attributes

Owner Attributes

4.3.2 Tackling endogeneity The multiple variable regression for Equation (1) could lead to biases in estimations, due to endogenous problems connected to the following two reasons. First, because of the diversity of the EID frameworks among cities, the PITI scores could contain considerable measurement errors. Second, the transparency of urban environmental information could be an endogenous selection by local cadres. Tan (2014)found that the degree of information transparency is negatively associated with sulfur dioxide emissions per unit of GDP, which indicates that the national EID policy is enforced endogenously at city level. Therefore, the direct use of PITI as the explanatory variable could have been subject to selection problems, resulting in biased regression coefficients. The instrumental variable approach is always used to tackle endogenous problems. We used the single firm’s dominance in the local labor market, that is, the proportion of workers employed by the largest industrial firms in the city, as an instrumental variable -16-

for the PITI score. According to Lorentzen et al. (2013), the larger the dominance of the largest firm in the local labor market, the more difficult for local governors to implement the EID policy, due to the intensified lobbying activities. In order to ensure the exogeneity of IV, we deliberately used the data for 2007, i.e., one year before the transparency policy took effect, and did not use the data for 2008 and beyond to avoid reverse causality. More into detail, we used the 2007 Chinese Industrial Enterprise Database to calculate the largest firm dominance in the local labor market. This dataset provides employment information for all industrial firms across cities. We could conveniently obtain the headcount of the largest firm, as well as the aggregated employment in the city, and thereby calculate the corresponding ratio. In order to avoid highly skewed distributions, we used the logarithmic form of this ratio. Moreover, were moved the largest firm in terms of employment from our dataset, to avoid inverse causality.

4.4 Shelter effect identification In order to examine the shelter effect due to local protectionism, the following model was established:

Mitigationijk =β0 + β1PITI j + β2PoliticalConnetionijk + β3PITI j ×PoliticalConnectionijk +ΓControlsijk +εijk (2) Where

represents the political connections of individual private

firms. More importantly, the interaction term of

and

was

introduced into the model. From the interaction term of Model 2,we can obtain the following formula:

E(Mitigationijk PoliticalConnectionijk =1) − E(Mitigationijk PoliticalConnectionijk = 0) = β2 + β3PITI j (3) Equation (3) shows that the coefficient of the cross-term β3 reflects the shelter effect of political connections, that is, how political connections affect the relationship between EID and corporate pollution control investments. By estimating the model, we tested the following null hypothesis:

H 0 : β3 = 0 According to the collusion hypotheses, connected firms would be less susceptible to the EID program due to the shelter effect. Therefore, we anticipate that H 0 would be rejected, -17-

and that β3 was significantly below zero. Furthermore, we planned to analyze the magnitude of the shelter effect for connected firms, compared with the inducing effects of the EID program for the non-connected firms. To assess whether the impacts of the EID program can offset the local protection from political connections on pollution mitigation investments, we further verified the following hypothesis:

H 0' : β1 + β3 = 0 We anticipate that the null hypothesis cannot be rejected, which indicates that the shelter effect of political connections simply offsets the inducing impacts of the EID program. These two assumptions can be verified by directly estimating Equation (2). The selection of IV is similar as it is in Section 4.3.2. Specifically, we employed the largest firm’s dominance in the local labor market and its cross-terms with political connections as the instrumental variables for PITI j and PITI j × PoliticalConnectionijk .

5 Baseline results In this section we examine whether EID affects corporate mitigation investments, employing the Tobit model and the Tobit-IV approach for Equation (1). Table 2reportstheestimationresults. More into detail, the Tobit regression results are reported in Column 1.The first stage regression results for the Tobit IV approach are reported in Column2, while and the second stage results for Tobit IV approach are reported in Column3, 4 and 5, each with an alternative set of control variates respectively. Since the Tobit model is nonlinear, we reported the marginal effects for all regressors directly. The standard errors are reported in parenthesis and are clustered at the level of provinces. All the regressions control a complete set of provincial and industry dummies. The coefficient of PITI in the simple Tobit regression, as shown in Column 1 of Table 2, was not significant after controlling the regulation intensity, the firm attributes, and the owner attributes. The sign of the Tobit regression coefficient was reasonably positive. Since the coefficient of PITI reflects the correlation between EID and corporate mitigation investments, we conclude that the extent of city-level information disclosure is positively associated with corporate environmental governance, though insignificant. Before interpreting the Tobit-IV results in Column2 and Column3of Table 2, we ran a -18-

series of tests to ensure the validity of the IV approach. In the first stage, the coefficient of the IV was significantly negative, and the F-statistics for the first stage regression was 76.62, which indicates that the risk of a weak IV is pretty low(Stock and Watson, 2014). In the second stage, we ran the Wald test of exogeneity and obtained that Prob > χ 2 is larger than 10%, which provides no sufficient information in the sample to reject the null hypothesis of no endogeneity for the PITI score(Cameron and Trivedi, 2010). Therefore, we are confident to say that the estimation results of Tobit-IV are more reliable than the Tobit regression itself. The baseline results in Column 3 of Table 2 show that the EID program cannot significantly enhance motivations for corporate mitigation investments. The baseline results confirm the former findings and further provide novel pieces of evidence for the failure of the Chinese mandatory EID program.

Table 2 Tobit regressions examining the impacts of the EID program on corporate pollution mitigation(left-censored at zero) OLS

Estimation method

Tobit

First Stage

Tobit IV

Tobit IV

Tobit IV

Dependent Variable

Mitigation investments

PITI

Mitigation investments

Mitigation investments

Mitigation investments

(1)

(2)

(3)

(4)

(5)

0.001

0.011

0.007

0.015

(0.003)

(0.010)

(0.010)

(0.010)

PITI

Instrumental

-6.802***

Variable Pollution Levy （%）

(0.369) 0.817***

1.069**

0.851***

0.801***

(0.207)

(0.468)

(0.064)

(0.071)

0.174***

-0.273**

0.194***

0.181***

(0.031)

(0.137)

(0.025)

(0.028)

0.015***

-0.055***

0.013***

0.015***

(0.004)

(0.015)

(0.003)

(0.003)

0.006

0.011

0.009*

0.006

Firm Attributes Turnover (Log)

Profitability (%)

Tax Burden (%)

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(0.005)

(0.029)

0.006

-0.035

0.006

(0.011)

(0.058)

(0.010)

0.014***

-0.025

0.023***

0.014**

(0.005)

(0.032)

(0.005)

(0.006)

0.216

0.924

0.319*

0.202

(1=Y)

(0.141)

(0.932)

(0.173)

(0.175)

Gender (1=Male)

0.306**

0.626

0.229

0.310**

(0.154)

(0.725)

(0.143)

(0.144)

-5.000***

37.495***

-4.799***

-2.410***

-5.958***

(0.673)

(3.287)

(0.838)

(0.722)

(0.889)

Industry Dummy

Y

Y

Y

Y

Y

Province Dummy

Y

Y

Y

Y

Y

0.238

0.433

0.126

1,779

1,917

1,677

Firm age (year)

(0.005)

(0.005)

Owner Attributes Owner age (year)

College education

Constant

Exogeneity test (p-value) Samples

1,677

1,691

Note: The dependent variable is corporate pollution mitigation, which is defined as the pollution mitigation investments over the turnover. For all Tobit regressions, we directly reported the marginal effects and the standard errors in parentheses, clustered at the provincial level. * p< 0.10, ** p < 0.05, *** p < 0.01.

Former studies such as Tan (2014) show that the extent of EID is not correlated to urban environmental quality. The present study complements former findings from the firmlevel evidence. The baseline regressions show that, in general, the EID program has no significant influence on corporate environmental governance, thereby failing to improve urban environmental quality. As figured out by Seligsohn et al. (2018), the effectiveness of the EID program on environmental improvements hinges on its accountability. Although the Chinese central government intended to supervise the enforcement of environmental regulations by local governments through the EID program, local governors have no incentives to promote the environmental governance of private firms, due to the lack of corresponding accountability mechanisms. The difference between the simple Tobit regression and the Tobit-IV approach indicates that the relationship between the missing enterprise characteristics and corporate

-20-

mitigation investments are exactly the opposite of their relationship with the PITI score, thereby reducing the magnitude of the simple Tobit regression estimator. Therefore, we argue that the biases of the simple Tobit estimator originate from the fact that local officials may conduct discriminatory enforcement based on corporate characteristics. For example, a missing corporate identity may portray its connection with government officials. Private companies with high political connections may be less affected by EID policies, an aspect that will be discussed in detail in the next section.

From Column 5, it is possible to determine the impact of various variables on corporate pollution mitigation investments. The pollution charge is the most important factor. A 1% increase in corporate sewage charges would improve their pollution control investments by around 0.85%. Firms have significantly increased the level of pollution control investments driven by sewage charges (Wang and Jin, 2002). Second, other corporate financial characteristics affect its pollution control investments, including the size of the company (turnover) and the profit margin. These results are basically in line with intuition. For example, both the profit margin and the firm age are significantly positive in the regression, with higher profitability (as measured by net profit over revenue) and a higher age is associated with higher mitigation efforts. Among the human capital variables, age and education of the owner are significant and have a positive impact on environmental governance.

6 Discussion: the shelter effect of political connections In the baseline analysis, we found that EID failed to improve corporate pollution control investments significantly. In order to gain a nuanced understanding of the relationship among pollution information disclosure, political connections, and corporate pollution control behaviors, we investigated the causes of failure of the EID program and examined whether political connections provide protection for polluters. According to the collusion hypothesis, political connections lead local governments to carry out lax environmental law enforcement. Under pressure from the central government, local governments may prioritize transferring this pressure to private companies without political connections, and force them to increase environmental governance. In relation to firms with political connections, even in the case of EID, the -21-

local government will not exert pressure in order to maintain the coalition. Therefore, we anticipate that connected firms are less susceptible to EID programs.

6.1 Heterogeneous impacts of EID on firms with and without political connections In this subsection, we examine the heterogeneous impacts of EID on firms with and without political connections. We employed the Tobit-IV approach with alternative groups of control variables. Table 3reports the results for both the non-politically connected firms (marked as “ PC = 0 ”) and the politically connected firms (marked as “ PC = 1 ”). Only the PITI scores and the dummies were added in the regression equation, as shown in Columns1 and 2.Firms’ attributes were further added in Columns3 and 4, while the complete set of control variables are included in Columns5 and 6. It clearly emerges from Table 3that non-politically connected firms responded positively to EID policies, while the connected firms failed to do so. On average, EID policies prompted 3% of incremental mitigation investments to annual revenues of unconnected firms. The heterogeneity of the EID effects verifies Hypotheses I and II, for example, possible collusions between connected polluters and the authorities undermine the effectiveness of EID in China. According to the collusion theory, the connected firms can easily get sheltered by the local authorities. Therefore, though they are confronted with pollution information disclosure, they are reluctant to upgrade their pollution abatement facilities. On the contrary, unconnected firms are more susceptible to the information disclosure program, and they are stimulated to increase their investments in pollution mitigation in order to avoid possible penalties from the authorities, as well as the pressure from the public.

Table 3 Heterogeneity in the impacts of the EID program: the role of political connections (1)

(2)

(3)

(4)

(5)

(6)

PC=0

PC=1

PC=0

PC=1

PC=0

PC=1

Tobit IV

Tobit IV

Tobit IV

Tobit IV

Tobit IV

Tobit IV

-22-

PITI

0.038

-0.007

0.029***

-0.003

0.030***

-0.004

(0.029)

(0.006)

(0.008)

(0.006)

(0.011)

(0.006)

***

***

***

Pollution Levy

1.154

0.567

1.162

0.577***

（%）

(0.315)

(0.050)

(0.288)

(0.046)

0.251***

0.077***

0.245***

0.074**

(0.065)

(0.027)

(0.066)

(0.030)

Firm Attributes Turnover (Log)

***

Profitability (%)

0.012

0.010

***

0.013

0.010

(0.004)

(0.006)

(0.004)

(0.006)

0.013

-0.008

0.012

-0.009

(0.013)

(0.018)

(0.013)

(0.018)

0.017

-0.002

0.010

-0.005

(0.014)

(0.009)

(0.017)

(0.009)

0.010

0.009

(0.008)

(0.007)

College education

0.293*

0.208

(1=Y)

(0.158)

(0.204)

Gender (1=Male)

0.528**

0.024

(0.231)

(0.171)

0.000

0.000

(.)

(.)

Tax Burden (%)

Firm age (year)

Owner Attributes Owner age (year)

Owner age (year)

Constant

-2.954

***

-7.078

***

-6.681

-8.380

***

-7.598

-7.960***

(1.796)

(0.346)

(1.563)

(.)

(1.956)

(0.534)

Industry Dummy

Y

Y

Y

Y

Y

Y

Province Dummy

Y

Y

Y

Y

Y

Y

930

349

827

305

818

303

1,290

649

1,133

560

1,121

556

-5,774.8

-3,183.7

-4,915.7

-2,684.2

-4,858.2

-2,662.7

Left-censored Samples Log-likelihood

Note: The dependent variable is corporate mitigation investments, which is defined as the pollution mitigation investments over the turnover. “PC=1” refers to samples with political connections, and “PC=0” refers to samples without political connections. For all Tobit regressions, we directly reported the marginal effects and the standard errors in parentheses, clustered at the provincial level. * p< 0.10, ** p < 0.05, *** p < 0.01

Our results further confirm former findings, that politically connected firms are less -23-

susceptible to environmental policy shocks. In his research on the shock of the smog policy in 2013, Wang (2015) found that the listed firms whose board member was a former local bureaucrat, benefited from their political connections, as they may exploit their ties with local governments to shirk their responsibilities to abate their emissions. On the contrary, unconnected firms may undertake higher compliance costs.

6.2 Identifying the shelter effect In this subsection, we report the empirical results on whether political connections would weaken the inducing effects of the EID policy on corporate mitigation investments. We expect companies with political connections to be less susceptible to EID. Table 4reports the empirical results of Equation(2). Each column controls an alternative sets of variates as shown. And in Column 4we controlled all explanatory variables, therefore the results in Column (4) are the main estimation results. The bottom line in Table 4 is the Wald chisquare statistics for the null hypothesis H 0' . First, we focused on the coefficient estimates of the interaction term of

PITI j and PoliticalConnetionijk . The coefficient was significantly negative at the 1% level, rejecting the null hypothesis H 0 , thus supporting the shelter effect of political connections. In other words, for firms with political connections, the pollution control investments are, on average, 0.034% lower than that of firms with no political connections, indicating that political connections help private firms to reduce the pressure of EID program. The coefficient of PITI j is significantly positive, revealing that environmental information transparency significantly promotes the mitigation efforts of companies without political connections. This is consistent with a series of former studies, whereby politically connected polluters are sheltered by local protectionism (Nie and Li, 2013; Wang, 2015; Deng et al., 2019).By further verifying the null hypothesis, we found that the Wald chi-square statistics for hypothesis

is under its critical value. Therefore, the null

cannot be rejected, which indicates that, overall, the protective effect of

political connections offsets the positive impacts of EID on pollution control investments.

Table 4 Tobit Regressions examining the role of political connections on the baseline results, -24-

(left-censored at zero) (1)

(2)

(3)

(4)

Pollution Mitigation

Pollution Mitigation

Pollution Mitigation

Pollution Mitigation

Tobit IV

Tobit IV

Tobit IV

Tobit IV

0.032**

0.031**

0.030**

0.031**

(0.013)

(0.012)

(0.013)

(0.012)

Political

1.899***

1.584***

1.832***

1.572***

Connection (1=Y)

(0.377)

(0.386)

(0.377)

(0.387)

PITI

PITI ×Political Connection

***

-0.034

(0.008)

***

-0.033

(0.009)

***

-0.033

(0.008)

-0.033*** (0.009)

Firm Attributes Pollution Levy

0.837***

0.837***

（%）

(0.053)

(0.053)

***

0.171

0.165***

(0.019)

(0.020)

***

0.006

0.007***

(0.002)

(0.002)

***

0.015

0.014***

(0.004)

(0.004)

0.010

0.006

(0.007)

(0.007)

Turnover (Log)

Profitability (%)

Tax Burden (%)

Firm age (year)

Owner Attributes Owner age (year)

College education (1=Y) Gender (1=Male)

Constant

***

-2.195

***

-4.847

0.015***

0.009**

(0.004)

(0.004)

0.046

-0.004

(0.115)

(0.118)

**

0.229

0.269***

(0.100)

(0.103)

***

-2.939

-5.299***

(0.551)

(0.630)

(0.587)

(0.658)

Industry Dummy

Y

Y

Y

Y

Province Dummy

Y

Y

Y

Y

Left-censored

2,474

2,156

2,448

2,137

Samples

3,719

3,222

3,685

3,195

6.26

0.04

0.10

0.04

-25-

Note: The dependent variable is corporate pollution mitigations, which is defined as the pollution mitigation investments over the turnover. For all Tobit regressions, we directly reported the marginal effects and the standard errors in parentheses, clustered at the provincial level. * p< 0.10, ** p < 0.05, *** p < 0.01.

Our results verify the former findings, that the effectiveness of the EID program hinges on empowering the community (Tietenberg and Wheeler, 2001).Moreover, the transparency policy should be built upon a broader societal ecosystem, including the active media and robust civil society(Stromseth et al., 2017), or else it will lose its effectiveness. For example, Fung and O'rourke (2000) showed that grassroots campaigns against the minimum performers in pollution control would significantly induce their environmental governance under the US TRI program. However, no such societal ecosystem is well-founded in China. The media are always instructed to selectively report positive news, and bottom-up campaigns for environmental protection are always suppressed by the local governments. Therefore, the Chinese EID program only entitles the public with the right to know and, by contrast, no corresponding public accountability mechanism is established. Due to the collusions between local governments and firms, the Chinese national EID program failed to lay public pressure on the polluters with political connections. Since mega polluters are always politically connected, in general, the Chinese national EID program lost its effectiveness.

7 Conclusions and policy implications Environmental information disclosure (EID) has been acknowledged as an effective quasi-regulation in the environmental regime, which helps to improve the environment in the context of OECD countries. However, the effectiveness of the EID program in developing countries has not been rigorously investigated. In this study, we used the

2012Chinese Private Enterprise Survey to study the responses of polluters to the mandatory EID policy in China. We documented robust evidence on the following two aspects. In general, information disclosure programs cannot effectively improve corporate mitigation investments. Furthermore, firms without political connections responded to the EID policy by increasing their mitigation investments; on the contrary, the politically -26-

connected polluters have not been significantly affected by the EID program yet, due to the shelter effect by the local governments. Therefore, our study suggests that the EID program can induce mitigation investments only of polluters with proper accountability schemes. To the best of our knowledge, this is the first study to use firm-level samples to empirically assess the impacts of the Chinese EID program and to explore the reasons for its ineffectiveness. Moreover, our findings shed new light on the reason for the failure of the Chinese EID program, which lays in the fact that, with the lack of additional accountability, it can hardly deter the collusions between local governments and polluters. In relation to the policy implications, our results suggest that politicians should not take for granted that EID alone can promote corporate environmental governance. EID only secures its effectiveness with accountability mechanisms, otherwise, it becomes only a provision of information. Furthermore, the public should be empowered to supervise the polluters and to counter weight local protectionisms by taking advantage of the EID program. More grassroots campaigns should be encouraged, as only in this way can the external costs of pollution could be accounted to the polluters, and will the EID program work more effectively. We acknowledge that there are several limitations to our research. For example, due to the limited coverage of our sample, the scope of this study includes only Private-Owned polluters, while State-Owned Enterprises (SOEs) are not included. As SOEs are engaged in heavy pollution industries, it is worthwhile to investigate their responses to the EID program in future research.

Acknowledgments The authors gratefully acknowledge financial support from the National Natural Science Foundation of China under Grant No.71573074.We also thank Toru Morotomi, Steven Edward Ivings and the audience members at the 14th Seminar on Environmental Policy at Kyoto University, for their comments on prior versions of this paper. We acknowledge using the data based on the Chinese Private Enterprises Survey (CPES), which was conducted by the Privately Owned Enterprises Research Project Team (member -27-

organizations include the All-China Federation of Industry and Commerce, the State Administration for Market Regulation, the Chinese Academy of Social Sciences, the China Society of Private Economy, and the United Front Work Department of CCP). The Research Center for Private Enterprises at the Chinese Academy of Social Sciences (PCPE-CASS) is the authorized organization that manages and issues the survey data. We appreciate the data support from the abovementioned organizations. Any political issues caused by contexts are the sole responsibility of the authors.

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Conflict of Interest and Authorship Conformation Form Please check the following as appropriate: o √ All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. o

√ This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue.

o

The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript

o

√The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript:

Author’s name Zhang Tuo,

Affiliation Center for East Asian Economic Studies, Graduate School of Economics, Kyoto University, Kyoto, Japan

Xie Li(Corresponding Author)，

School of Economics and Trade, Hunan University, Changsha, People's Republic of China