Political turnover and firm pollution discharges: An empirical study

Journal Pre-proof Political turnover and firm pollution discharges: An empirical study

Yuping Deng, Yanrui Wu, Helian Xu PII:

S1043-951X(19)30124-5

DOI:

https://doi.org/10.1016/j.chieco.2019.101363

Reference:

CHIECO 101363

To appear in:

China Economic Review

Received date:

16 December 2018

Revised date:

3 October 2019

Accepted date:

7 October 2019

Please cite this article as: Y. Deng, Y. Wu and H. Xu, Political turnover and firm pollution discharges: An empirical study, China Economic Review(2018), https://doi.org/10.1016/ j.chieco.2019.101363

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Political Turnover and Firm Pollution Discharges: An Empirical Study Yuping Denga,b [email protected], Yanrui Wub,* [email protected], Helian Xua [email protected] a

School of Economics and Trade, Hunan University, P.R. China

b

Department of Economics, Business School, University of Western Australia, Australia

*

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Corresponding author.

Abstract

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This paper aims to examine the relationship between political turnover and pollution discharges by listed firms in China. The empirical results show that political turnover is associated with more firm pollution discharges, particularly if the newly appointed officials are promoted locally or normally transferred. Furthermore, higher frequency of political turnover is linked with more pollution discharges. Lastly, our extended analysis illustrates that political connection is positively associated with firm pollution discharges and plays a moderating role in the relationship between political turnover and environmental performance.

1. Introduction

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JEL codes: P16; D22; O12

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Keywords: Political turnover; pollution discharges; political connection; personnel management; China.

China achieved remarkable economic growth during the last four decades. Local government officials have played active roles in this growth. From the perspective of political promotion tournaments the existing literature has investigated why local government officials are so keen to build local infrastructure and support local business at the price of excessive resource usage and malignant pollution (Li & Zhou, 2005; Wang et al., 2009; Wu et al., 2014). A main observation is that political promotion tournaments incentivize local officials to transform their “helping hands” into 1

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“grabbing hands”, and lead to an abnormal development pattern. Thus, the environmental problem in China is the consequence of economic development, while the drive for economic development comes from local government officials (Ghanem & Zhang, 2014; Zheng et al., 2014; Man, 2016). Political turnover is an important and dynamic factor for the understanding and study of local government behaviour and a new frontier of research on the role of local officials in promoting

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regional economic growth, too. Different officials have different policy preferences, and their

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incentives and restraint mechanisms vary according to the individual situation. Thus, political turnover

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will lead to changes in economic performance (Caporale & Leirer, 2010; Eaton & Kostka, 2014;

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Uddin et al., 2017; Li & Zhang, 2018). Do new officials improve environmental quality in order to

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meet environmental performance assessments or pursue short-term economic growth at the expense of the environment? How do firm pollution discharges change during the period of political turnover?

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Answers to these questions would be helpful for the understanding of China’s environmental problem

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and have implications for deepening the reform of the system of cadre management. The existing literature has focused on the relationship between political turnover and regional economic performance. Research shows that political turnover has a significant and negative effect on economic growth, but its effect however varies greatly according to individual factors, such as ages, turnover frequency and so on (Caporale & Leirer, 2010; Cheng & Leung, 2016; Luo et al., 2017). Additionally, political turnover may help terminate relationship network which is established on the basis of geographic advantage (Persson & Zhuravskaya, 2016; Charron et al., 2016), and hence eliminate corruption consequently (Choi & Woo, 2010; Sidorkin & Vorobyev, 2018). In recent years, an increasing number of scholars investigated the effects of political turnover on business operations 2

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from a micro perspective. Researchers reveal that political turnover has a significantly negative effect on firm investment (Cohen et al., 2011; Kang et al., 2014; Julio & Yook, 2016; Jens, 2017) and R&D expenditures (Ang & Longstaff, 2013; Bhattacharya et al., 2017). Furthermore, firms increase their rent-seeking costs in order to establish new connections after political turnover (Faccio, 2010; Duchin & Sosyura, 2012), and hence their dividend and bonus are reduced accordingly (Bhattacharya et al.,

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2017). It is shown that political turnover also significantly influences financial markets (Diebold &

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Yilmaz, 2009; Goodell & Vahamaa, 2013; Bernal et al., 2016; Liu et al., 2017). These influences are

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more obvious during economic recession (Pastor & Veronesi, 2013; Kelly et al., 2016).

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A large number of studies on environmental pollution generally analyse the problem from

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economic points of view, such as economic growth, industrial structure, foreign direct investment (FDI) and international trade, and attempt to verify the environmental Kuznets curve hypothesis,

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pollution haven hypothesis and so on (Cole et al., 2013; Roy, 2017). However, many scholars have

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criticised these methods which ignore the role of local governments. The existing literature focuses on whether government behavior leads to a “race to the bottom” or “race to the top” in environmental standards (Fredriksson & Wollscheid, 2014; Millimet & Roy, 2015). Some documents show that local governments pursue the “race to the bottom” strategies when they use weak environmental standards to attract or retain economic activities in their jurisdictions (Manderson &Kneller, 2012; Bu & Wagner, 2016). But if local governments think improvement in environmental quality should be conducive to a higher standard of living, they tend to raise environmental regulatory stringency in their locations to attract clean industries. By doing so, the new standard creates a “race to the top” in environmental quality (Dong et al., 2012; Stavropoulos et al., 2018). China with the largest 3

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transitional economy provides an ideal institutional environment for the study of the relationship between local government behavior and environmental quality. It is argued that Chinese decentralization, a combination of political centralization and fiscal decentralization, is the institutional background for understanding the insufficient supply of environmental public goods (Qian & Roland, 1996; Cai & Treisman, 2006; Wang & Chen, 2014; He, 2015). A growing body of

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literature reveals that local government would be reluctant to increase environmental policy stringency

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as high regulatory costs would hurt the growth of the local economy (Zhang et al., 2017). Research

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also shows that government officials have incentives to support local firms through tolerating their

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heavy pollution and even protecting them from being penalized for producing excessive pollution (Wu

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et al., 2013; Ghanem and Zhang, 2014; Jiang et al., 2014). Therefore, fiscal decentralisation and GDP growth assessment imply that local governments lack enthusiasm in environmental protection.

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However, the existing literature mainly focuses on the effects of political turnover on economic

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growth and business operations. It fails to analyse the role of government officials who are the decision makers. Local government officials have control over a lot of resources and their decision-making has far-reaching influence on economic growth and resource allocation. In addition, previous studies emphasise how political incentives caused by fiscal decentralisation affect government performance in environmental regulations at the macro level. They pay little attention to the officials’ behavior associated with political exchange, tenure system and so on. In this paper, we manually collect the biographical records of 643 mayors and 612 Party secretaries during 2007-2015, and combine firm-level information with official turnover data to examine how political turnover affects pollution discharges of Chinese listed firms. To deal with 4

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possible endogeneity problems, we choose workplace connection and birthplace connection as the instrumental variables for political turnover and adopt the two-stage least-squares estimation method. We find that political turnover is positively linked with the intensity of firm pollution discharges and this result is still valid after a series of robustness checks. We also distinguish different types of political turnover, namely local promotion vs external appointment, normal turnover vs abnormal

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turnover, and high-frequency turnover vs low-frequency turnover. We find that pollution discharge

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increases if: (a) the newly appointed official is locally promoted, (b) the newly appointed official is

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normally transferred, and (c) local officials are transferred at higher frequency. Additionally, the

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negative effects of political turnover are more serious for firms located in the central and western

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regions owing to weak institutional environment there. Similarly, firms in pollution-intensive industries increase their pollution discharge following political turnover. Finally, we examine the

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effectiveness and consequences of building political connections to seek protection for pollution or

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pollution shelter during political turnover. We argue that firms build political connections with local governments and expect the return of favors in the future. Our extended analysis illustrates that politically connected firms reduce their pollution discharge during the first year of a new official’s appointment. With the growth of appointed officials’ tenure as well as expansion in firm social networks, pollution discharges of political connected firms in pre-turnover years and after-turnover years are higher than those in turnover years. Our study makes several contributions. First, this paper extends the literature in the field by investigating the relationship between political turnover and environmental pollution. Although extant work indicates that environmental pollution may be affected by fiscal decentralization and 5

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government regulation (Manderson &Kneller, 2012; He, 2015; Bu & Wagner, 2016), there is very little information about how political turnover influences corporate environmental performance. The existing literature tested the role of political leaders in determining government priorities in environmental protection. For example, List & Sturm (2006) find that environmental policies proposed by US governors change when they face binding term limits. Wu et al. (2014) document the

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role of prefecture-level leaders in affecting environmental treatment. Their findings suggest that

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public investment in environmental improvement rises in a prefecture if the environmental quality is

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explicitly linked to the prefecture leader’s chance of promotion. Yu et al. (2019) investigate the effects

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of political leaders’ characteristics on environmental pollution using data of 230 prefecture-level cities

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in 2002-2014. All these studies provide insight into environmental pollution in China according to the theory of the new political economy. Two papers investigated the relationship between political

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exchange and environmental pollution, which are most closely related to our work. The first paper by

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Zheng et al. (2014) uses a panel dataset of 86 Chinese cities from 2004 to 2009 to test whether there is an association between environmental quality and an urban leader’s probability of being promoted. Their empirical results suggest that local leaders are more likely to be promoted if their cities experience environmental progress. The second paper by Guo & Shi (2017) uses daily air quality index data and major air pollution indexes from December 2013 to June 2016 to study the short-term effect of municipal party secretary turnover on air pollution. Their results show that the concentrations of SO2 decrease significantly one month before and one month after municipal party secretary turnover, while other indexes show no obvious changes during turnover period. Our paper is distinguished from the above two works by identifying the causal relationship between political 6

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turnover and environmental pollution at the firm level. We adopt the instrumental variable approach to address the potential endogeneity in the model. Using these empirical strategies for identification, we find that political turnover is positively associated with firms’ pollution discharges. Second, this paper also extends the literature on political connection by highlighting how political connection moderates the effects of political turnover on firms’ environmental performance.

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To the best of our knowledge, several papers examined the relationship between political connections

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and environmental performance. For instance, Maung et al. (2016) investigate how political

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connections in non-listed entrepreneurial firms affect pollution fees levied by national and provincial

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governments in China. They find that politically connected firms pay lower environmental levies than

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non-politically connected ones. Zhang (2017) uses comprehensive environmental information disclosure data of China’s listed firms to study the relationship between political connections and

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corporate environmental responsibility. Their empirical results show that political connections have

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significant and positive impacts on corporate environmental responsibility. Using survey data from family-owned firms in 31 Chinese provinces in 2008, Du (2015) provides strong evidence to show that political connection is significantly and positively associated with corporate environmental misconduct. Jia (2017) explores how political connections between local governors and key officials in the center affect environment pollution and finds that political connections strengthen the career concerns of politicians and increase the marginal payoff of pollution. Our paper shows a similar phenomenon and further reports the additional possibility of establishing political connections once local officials are transferred. The findings of our study present a dynamic view of political connections when firms face political turnover. 7

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Finally, this paper contributes to the recent literature investigating whether and how political turnover affects firm performance. While the existing literature concludes that political turnover may affect corporate activities, such as investment (Cohen et al., 2011; Julio & Yook, 2012; An et al., 2016) and innovation (Ang & Longstaff, 2013; Bhattacharya et al., 2017), it is still unclear how political turnover influences firms’ environmental performance. China has the right environment to study the

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relationship between political turnover and environmental pollution for two reasons. On the one hand,

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unlike other countries, China has undergone frequent political turnover because local officials are

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often transferred every several years to prevent them from establishing too many political connections

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and hence becoming corrupted (Xu et al., 2016). On the other hand, corporate environmental

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responsibility is increasingly viewed as an important business strategy by researcher and investors (Etzion, 2007; Lopez et al., 2017; Zhang, 2017). Once local officials are transferred, the political

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connections between ex-officials and polluting firms become uncertain or even disappear. Naturally, a

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firm’s pollution decision will be adjusted accordingly. Thus, we propose a mechanism-based theoretical framework to explore possible negative effects of political turnover on firms’ environmental performance and investigate the channels through which political turnover may crowd out firms’ investment in R&D and weaken the enforcement of environmental regulations, resulting in an increase in firm pollution discharges. Thus, our study provides important additions to the existing literature on political turnover. The remainder of this paper is structured as follows. Section 2 presents the theoretical analysis of the relationship between political turnover and pollution. Section 3 demonstrates the model specification and describes the data and variables. Section 4 discusses the empirical results followed 8

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by robustness analysis in Section 5. Finally, Section 6 presents the conclusions and policy recommendations.

2. Research Hypotheses 2.1. The institutional background of political turnover in China Political turnover stems from the cadre exchange system. The Central Committee of the Chinese

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Communist Party ultimately controls the mobility of government officials, and regular carder exchange

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is under the control by the superior Party Committee. Since the launch of economic reforms and

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open-door policy, the central government has accelerated the establishment of the cadre exchange

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system. In 1980, the central government enacted a retirement system for senior cadres and ended the

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virtually lifelong tenure of leading cadres. Under the new regime, ministerial and provincial leaders are

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required to retire at the age of 65 if they are not promoted to higher positions, and bureau chiefs are required to retire at the age of 60 under the same condition. Later, in 1990, the rules for the regular

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exchange of cadres in the Party and state organs were introduced. The original intention of setting up this policy is to curb corruption due to the devolution of central power, and the ministerial and provincial leaders are required to be regularly exchanged or transferred. Thus, a leader should be transferred if he or she holds the same position for ten years. After years of practice and reform, “the Provisional Regulations on the Transfer of the Party and State Leading Cadres” was issued in 1999. For the first time government officials at the county level were also required to follow these regulations. Since then, cadre rotation has increasingly been regarded as a means of reducing regional disparities and bridging administrative gaps (Eaton & Kostka, 2014). To support the country’s Western Development Strategy, leaders in eastern and western areas have been exchanged regularly. A large number of 9

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leaders in western areas were have been selected to take up temporary posts in central government institutions and economically-developed eastern provinces. More recently, the revised “Regulations on the Transfer of the Party and State Leading Cadres” in 2006 re-emphasised the relationship between tenure and political turnover, and standardised the tenure system and the management of the Party and government leaders. According to the new policies, officials who hold posts in the government, judicial

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and Party organs at the county level or above for 10 years must be transferred to new official posts in

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other locations. The same role applies to other officials who hold posts at the same level in the same

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locality for a period of 15 years. These rules indicate that local officials who served for 5 years after

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lateral moves and are not promoted will be urged to leave their offices. The above-mentioned policies

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make political turnover a normal state of affairs in China.

2.2. The relationship between political turnover and pollution discharges

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Along with fiscal decentralisation and accelerated marketisation, local governments have more

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authority in allocating economic resources. The hierarchical structure of China’s administrative system makes each political leader’s performance distinguishable and comparable and thereby allows a sensible link between political turnover and economic performance (Qian & Xu, 1993; Li & Zhou, 2005). Municipal governments, the third level of China’s political hierarchy, have huge and flexible discretion in implementing macroeconomic policies and intervening in micro-economic activities (Wang & Hui, 2017; Yu et al., 2019). As officials have different capabilities, preferences and incentive restrictions, political turnover brings huge uncertainties in policy implementation, personnel transfers and assignment of responsibilities (Krueger & Walker, 2008; Yee et al., 2016). These uncertainties have

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significant effects not only on economic growth (Jones & Olken, 2005; Eaton & Kostka, 2014), but also on business activities (Julio & Yook, 2012; Luo et al., 2017). Relevant literature indicates that political turnover may strengthen promotion tournaments among the local officials and reduce firms’ incentives to reduce emissions (Li & Zhou, 2005). A GDP-led promotion system may drive transferred officials to turn a blind eye on environmental standards and

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pollution discharge for two reasons. On the one hand, the transferred officials need to show more

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outstanding achievement during their tenure, and their incentives for GDP growth promotion are

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strengthened by the intergovernmental competition (Zhou, 2007; Wang et al., 2009). Subsequently,

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the transferred officials may allocate limited resources in a manner to maximise their promotion

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prospects and underinvest in environmental protection. Wang & Wheeler (2005) and Lopez et al. (2011) argued that excessive focus on GDP growth may also cause environmental degradation as the

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transferred officials may compete for more resources by relaxing environmental standards and

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reducing public expenditures on environmental protection. On the other hand, frequent turnover fosters the short-sighted behavior of the transferred officials as well as speculative activities by the polluting firms. In order to demonstrate outstanding achievement as soon as possible, the transferred officials tend to adopt short-sighted policies, such as excessive borrowing and investment to stimulate economic growth in a short time. As a result, governments may only concentrate on investment in infrastructure facilities rather than in the public services (Eaton & Kostka, 2013; Azzimonti, 2015). Some officials even pursue economic growth at the price of excessive resource usage and malignant pollution, and hence offer “grabbing hands” rather than “helping hands” in the provision of public services (Wu et al., 2014; van der Kamp et al., 11

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2017). In addition, neighbouring governments often imitate each other (Oates & Portney, 2003; Madsen, 2009). Thus, frequent turnover increases officials’ short-sighted behaviour that is destructive to the environment (Yu et al., 2019). Given the above discussion, we propose the following hypothesis: Hypothesis H1: Political turnover leads to a significant increase in firm pollution discharges.

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2.3. Theoretical mechanism of political turnover influencing the environment

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Political turnover may influence the environment in two ways, namely crowding-out

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innovation and weakening the enforcement of environmental regulations. Due to vertical

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administrative centralization and GDP growth-oriented promotion assessment in China, local leaders

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have considerable authority in their jurisdictions including the rights of resource allocation (Zhou,

uncertainty (

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2007; Feng et al., 2019). Political turnover leads to policy changes and an increase in policy stor & Veronesi, 2013). In order to reduce uncertainty, firms are inclined to adopt two

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measures. The first approach is to build political connections which help them access reliable information or innovative resources (Fan et al., 2008). However, the costs of building and maintaining political connections may drain vital resources away from innovation when firms face frequent political turnover, thereby weakening their innovation abilities (Cheung et al., 2010). In addition, the existing literature suggests that firms commit resources to lobbying activities during the phase of political uncertainty (Lopez et al., 2017). An empirical research of Chinese firms also shows that, when local leaders change frequently, entrepreneurs need to constantly cultivate new connections with officials and therefore corruption hinders businesses (Zhu & Zhang, 2017). Corruption increases

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transaction costs through bribery payments and efforts to build connections with bureaucrats, which limit the scope of investment in innovative activities (Anokhin and Schulze, 2009; Paunov, 2016). Another approach is to be reluctant to allocate resources in venture investment (Julio & Yook, 2012). Innovation activities require irreversible investment since a large fraction of the cost correspond to equipment procurement and salaries of R&D staff, which cannot be recovered if the

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project fails (Bhattacharya et al., 2017). Increased uncertainty might therefore induce companies to

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avoid R&D investment (Gulen & Ion, 2016). Additionally, the newly appointed officials will intervene

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in business activities in order to boost short-term growth due to their limited tenure (Chen et al., 2011;

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An et al., 2016). To cater for local officials’ interests, firms become accustomed to carrying excessive

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investment in projects which have special characters like short-term and quick effects, and their investment in innovation has to be reduced accordingly. Thus, political turnover has the crowding-out

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effects on innovation. Lack of innovation or motivation to innovate is not only detrimental to resource

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use efficiency but also disadvantageous to the absorption of clean production technology, which increases the burden on regional environment. Hence, the following hypothesis is proposed: Hypothesis H2a. Political turnover through political connection crowds out firm investment in R&D that can subsequently increases firm pollution discharges. Political turnover also encourages local officials to relax environmental regulations and even indulge in pollution, thus deteriorating the environment (Kahn et al., 2015; Liu et al, 2018). In order to have an outstanding record, newly appointed officials are motivated to inadequately implement or distort environmental policies. Fredriksson & Svensson (2003), for instance, develop a theory of environmental policy formation, taking into consideration of the degree of corruptibility and political 13

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turbulence. Their theoretical analysis suggests that political instability induced by government failure to remain in power has a negative effect on the stringency of environmental regulations. According to Eaton & Kostka (2014), incentives of the cadre promotion system and typically short tenure of local leaders predispose officials to select the path with least resistance to the implementation of environmental initiatives. Thus, firms may bribe local officials to get away with environmental

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regulations and penalties. This is especially the case in China, where local officials have been

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evaluated on the basis of economic growth since 1978 (Li & Zhou, 2005; Zhang et al., 2017; Yu et al.,

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2019). It can be inferred accordingly that the existing institutions distort the behaviors of the

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transferred officials, and cause them to acquiesce to or even indulge firms in permitting their pollution

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discharges (Jia, 2017).

In addition, China’s central government relies heavily on local governments for implementing

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environmental policies, but local government priorities may be very different from those of their

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superior governments (Liang & Langbein, 2015). Meanwhile, the subordinate departments often lag behind their superior policymaking bodies creating a situation in which environmental policy may be formally in place but there are no clear guidelines from local jurisdictions. Such a disjunction between superior policymaking bodies and subordinate departments not only weakens the enforcement of environmental regulations, but also triggers speculative behaviour in pollution by the firms. Thus, during the first year of a new official in office, a firm may discharge more pollutants due to relaxed environmental regulation (Kahn et al., 2015; Liu et al, 2018). Given the above analysis, we propose the following hypothesis:

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Hypothesis H2b: Political turnover weakens the enforcement of environmental regulations and is consequently associated with increased firm pollution discharges.

2.4 Political Turnover and Politically Connected Firms China’s local governments control local land supply, finance (through state banks) and so on. Given this background, many firms choose to establish political connections with local governments

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and hence obtain key resources such as bank loans (Faccio, 2010; Duchin & Sosyura, 2012), tax

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privileges (Adhikari et al., 2006; Kim & Zhang, 2016) and debt relief (Boubakri et al., 2008; Bliss &

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Gul, 2012; Keefe, 2019). This government-firm relationship would in turn boost firm’s excessive

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investment in unprofitable but politically favored projects (Bliss and Gul, 2012; Boubakri et al., 2012;

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Piotroski & Zhang, 2014), resulting in insufficient investment in environmental protection and more pollution discharges. The existing literature also reveals that politically connected firms may bribe

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local government officials to lower environmental standards (Lyon & Maxwell, 2008; Wu et al.,

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2014). Thus, political connection may act as an umbrella of protection that helps firms avoid environmental responsibility (Zhang, 2017). These firms, which maintain good relationship with ex-officials will lose their political advantages when the newly appointed leaders take office (Kato & Long, 2006; Earle & Gehlbach, 2015). Thus, political turnover will inevitably have negative effects on business activities. However, political turnover also means redistribution of political resources. In order to maintain previous interest alignment or establish new relations, local firms will actively seek political connection with the new officials (Chow et al., 2012). In addition, although political turnover may not break political connection between local firms and ex-officials, it will have a deterrent effect on polluting firms in the short term (Guo & Shi, 2017). When local officials are transferred, a 15

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politically sensitive period causes politically connected firms to reduce their illegal pollutant discharge and emissions (Piotroski et al., 2015). Finally, the newly appointed officials are not very familiar with local firms. So politically connected firms are more likely to receive government attention than unconnected firms (Zhang, 2017). In line with the above arguments, the following hypothesis is proposed:

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Hypothesis H3: During the first year of a newly appointed official in office, politically

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connected firms reduce their pollution discharges.

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3. Research Design

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3.1 Empirical Specifications

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To test Hypothesis H1, we use the following determinants of environmental performance

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framework proposed by Wang & Jin (2007):

(1)

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where the subscripts denote firm i and time period t. Pollution is an indicator of environmental pollution, Turnover is the index of political turnover and Control denotes other control variables that affect pollution.

and

are firm fixed effects and year fixed effects; and

is the error term.

To test Hypotheses H2a and H2b, the mediating effect models proposed by Baron & Kenny(1986) are introduced. (2) (3)

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where Mediator denotes the channels through which political turnover influences the environment. These channels include innovation (Innovation) and environmental regulation (Regulation). To test Hypotheses H3, the following panel data model is considered:

where PC is the index of political connection.

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However, the proposed models may be affected by endogeneity. On the one hand, political

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turnover may be endogenous in the environmental equation in spite of controlling for firm and

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city-specific characteristics to alleviate the omitted variable bias. The main reason is that political

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turnover may be determined simultaneously with environmental outcomes, as argued by Zheng et al.

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(2014). On the other hand, omitted variables may also lead to endogeneity. To address the potential endogeneity, we adopt the instrumental variable (IV) approach proposed by Tse et al. (2017). The IV

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approach is widely used in the existing literature because it can overcome endogenous problems

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originated from measurement error or omitted variables as well as sample selection bias and bidirectional causality (Chen & Kung, 2016; Bascle & France, 2017). Following Shih et al. (2012) and Opper et al. (2015), we choose two dummy variables as our IVs in the two stage least square (2SLS) regression. Our first IV for political turnover is “workplace connection” (Workplace) which is proxied by a dummy variable indicating whether a city’s mayor previously worked with his/her immediate supervisors. We assume that whether local officials previously worked with their current supervisors affects the likelihood of their political turnover. If a prefecture mayor previously worked with his/her provincial governor (or a mayor of municipalities previously worked with one of the members of the 17

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Political Bureau’s Standing Committee), then the major and governor are considered to have a factional tie and the instrumental variable Workplace takes the value of one and zero otherwise. Our second instrument for political turnover is “birthplace connection” (Birthplace). Similar to workplace connection, birthplace connection is also a dummy variable which indicates whether a city’s mayor was born in the same city as his/her immediate supervisors. If a prefecture mayor has the

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same birthplace as the provincial governor (or a municipal mayor has the same birthplace as one of

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the members of the Political Bureau’s Standing Committee), the instrumental variable Birthplace

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takes the value of one and zero otherwise. The underlying assumption for the selection of this IV is

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that the birthplace link may enhance the prefecture mayor’s potential turnover but it does not affect

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firm pollution discharges.

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The first-stage specification of the 2SLS setup is given as follows: (5)

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where Workplace denotes workplace connection between local officials and their immediate supervisors, and Birthplace denotes birthplace connection between local officials and their immediate supervisors.

The second stage regression is based on the following specification: (6) ̅̅̅̅̅̅̅̅̅̅̅̅̅

(7)

̅̅̅̅̅̅̅̅̅̅̅̅̅

(8) (9)

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These models are derived, respectively, by replacing the Turnover variable in models (1)-(4) with which is the predicted value of political turnover.

is the error term.

3.2 Data Issues Our data are drawn from several sources. First, we used the database of the Institute of Public and Environmental Affairs (IPEA) as the primary source for firm pollution indicators. Next, we

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obtained firm-level accounting data and other information from the CSMAR database. The database

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contains personal information about top managers (such as birthplaces, age and educational

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background), sector and ownership of listed companies, and other firm-level financial indicators.

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According to the names and registered addresses of listed firms, the IPEA and CSMAR databases

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were matched.

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We manually obtained the personal information of the mayors and party secretaries of the cities where the listed firms are located. The information is sourced from major websites such as

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people.com, xinhuanet.com and Baidu encyclopedia. It contains detailed information about local politicians’ birthplaces, age, educational background, work experience, tenure and professional qualifications. After collecting the officials’ personal data, we then merged the personal data with firm data and obtained 1255 prefecture leaders (including 643 mayors and 612 party secretaries) who served in 233 cities during the period of 2007-2015. To clean the data, we exclude the following observations: (a) firms without pollution information, (b) firms with invalid information on fixed assets and total assets, (cd) firms without their directors’ bio information, and (d) firms with less than two years of data available in the database. Our final sample comprises 3,988 firms with 15,238 firm-year observations covering the years of 2007-2015. 19

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Relevant variables are deflated by using the price index of investment in fixed assets and other price indexes with the year 2000 as the reference.

3.3 Variable Definitions 3.3.1 Dependent Variables The dependent variable or intensity of pollutant discharge (Pollution) is calculated as the

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physical units of a given pollutant by a firm over the value of the firm’s annual sales. In this study we

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mainly focus on sulfur dioxide intensity (SO2 for short) as a measure of environmental pollution.

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There are several reasons for this decision. First, SO2 is considered the most threatening

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environmental pollution generated by China’s manufacturing sector, and it has been widely used as a

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major indicator to measure environmental pollution (He et al, 2012; Jiang et al., 2014; Zeng et al.,

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2018). At present, China is the world’s largest producer and consumer of coal, also being the world’s biggest emitter of SO2. About 90% of SO2 emissions come from coal burning (Yang et al, 2016).

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Second, SO2 is responsible for acid rain and sulfuric fog, and harmful to human health. Thus, the discharge of SO2 is strictly monitored by national governments. Finally, the information for SO2 provided by IPEA is much more complete than that for other pollutant types in the dataset, and can effectively ensure data consistency and hence may help obtain better results. 3.3.2 Explanatory Variables Political turnover. Political turnover may influence firm pollution discharge. On the one hand, official turnover creates a politically sensitive period during which firms’ collusion with local government and illegal discharges are likely to be reduced or stopped (Guo & Shi, 2017). On the other hand, political turnover strengthens promotion tournaments among the local officials and hence reduce 20

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firms’ incentives to control emissions (Li & Zhou, 2005). The variable, Turnover, takes a value of one if the mayor is newly appointed and zero otherwise. We focus on mayor turnover because Party secretaries are mainly responsible for Party affairs in the Chinese administration system while the mayors are responsible for economic policies and other affairs. Thus, the mayors are more closely related with firms.

of

For the exact dates of the newly appointed mayors taking office, we follow Li & Zhou (2005) to

ro

construct the Turnover variable. Specifically, if a mayor takes office between January 1 and June 30,

-p

then we define the current year as the mayor’s first year and Turnover takes the value of one. If a

re

mayor takes office between July 1 and December 31, then we define the following year as the mayor’s

lP

first year and Turnover takes the value of one. Additionally, as a robustness test, we also construct a dummy variable of Turnover_secretary to examine to what extent the turnover of Party secretaries

na

influences firm pollution discharges. Specifically, Turnover_secretary is equal to 1 if the Party

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secretary is replaced in a certain year and 0 otherwise. Political Connection. Political Connection (PC) is another explanatory variable in our analysis. The existing studies show that political connection affects the choice between clean and dirty technology by the firms, lowers the barrier to entry for heavy polluters and reduces firm willingness for emission reductions (Maung et al., 2016; Jia, 2017). Thus political connection is the institutional origin which causes firms to adopt strategic pollution strategies. Following the existing literature, we construct several variables to measure political connection. First, political connection is defined by using firm donation information (Claessens et al., 2008; Cooper et al., 2010; Titl & Geys, 2019). If the annual financial statement of a listed firm shows 21

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evidence of charitable donation, then the Donation variable as a proxy for political connection takes the value of one and zero otherwise. Second, we focus on political connection through the social network of birthplaces (Zhu & Chung, 2014; Chen & Kung, 2016). That is, if the director of a listed firm has the same birthplace as the mayor of the city where the firm is located, then the Hometown variable as a proxy for political connection takes the value of one and zero otherwise. Finally, political

of

connection is defined to reflect the political background (Background) of a firm’s top manager. If the

ro

firm’s top manager is currently serving or has formerly served in the government, or as a National

-p

eople’s Congress (N C) delegate or a Chinese eople’s olitical Consultative Conference (CPPCC)

re

member, the Background variable takes the value of one and zero otherwise. The similar concept was

3.3.3 Control Variables

lP

adopted by Fan et al. (2008) and Cai & Sevilir (2012).

na

Firm-specific Control Variables. Following the existing literature (Jiang et al., 2014), we include

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some firm-specific control variables such as the liability ratios (Liability), profitability ratios (Profitability), firm scale (Scale) and property rights (Ownership) in the regression models. First, high liability significantly reduces firms’ credit capacity and leads to insufficient investment in pollution treatment. We use the ratio of total debts over total assets as a measure of the Liability ratio. Second, firms with higher profitability are inclined to spend more in introducing environmentally friendly facilities and technologies. In this paper, Profitability is measured by the ratio of net profits over net assets. Third, we consider firm scale effects (Scale). Large firms may pay greater attention to their social image and long-term development, and hence undertake more initiatives to fulfil their environmental responsibility. The scale variable is measured as the natural logarithm of total assets. 22

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Finally, we include Ownership in the regression models. State-owned enterprises (SOEs) keep close contact with local governments and have great bargaining power in negotiating pollution discharges (Wang and Jin, 2007). We adopt the popular classification of ownership in the literature on Chinese listed firms (Donelli et al., 2013; Su et al., 2018). Official-specific Control Variables. In line with Persson & Zhuravskaya (2016) and Jia (2017),

of

we use officials’ age (Age), the length of their tenure (Tenure) and their educational background

ro

(Education) to control for individual characteristics. According to China’s Civil Service Retirement

-p

regulation, the age for promotion opportunity is capped at 55 for prefecture officials and 60 for

re

municipal officials. Thus, officials’ age affects their promotion opportunity and hence their attitude

lP

towards environmental pollution. Furthermore, as the newly appointed officials anticipate a new round of promotional assessment at the beginning of their tenure, they are more likely to relax environmental

na

regulations and ignore pollution treatment. Along with the growth of their tenures, a decrease in

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promotion incentives and hence more efforts in environmental protection may lead firms to reduce pollution discharges (Yu et al., 2019). To capture the potential nonlinear effects of mayors’ tenure on firm pollution discharges, we incorporate both the tenure as well as its square into the models. Finally, officials with higher educational background may have better professional skills and knowledge which help them pay more attention to environmental quality, while poorly-educated officials have less awareness of environmental protection. We construct a dummy variable Education to reflect the effect of educational background. Specifically, Education takes the value of one for officials with a bachelor degree or below and zero otherwise.

23

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We also investigate the channels through which political turnover affects firm pollution discharges. Our theoretical framework shows that political turnover may crow out the innovation inputs (Innovation) and weaken environmental regulation (Regulation), which would in turn increase firm pollution discharges. On the one hand, a general survey of relevant documents reveals that most scholars use investment in R&D and patent applications to measure innovation (Leenders & Wierenga,

of

2008; Knut & Jungmittag, 2008). We choose the ratio of R&D spending over total sales revenues as

ro

the proxy variable for Innovation. On the other hand, pollution treatment expenditure and inspection

-p

times for pollution discharge are used to measure environmental regulation in the literature (Lanoie et

re

al., 2008; Cole et al., 2010; Manderson & Kneller, 2012; Brunel & Levinson, 2016). However

lP

micro-level data of Chinese firms are either unavailable or incomplete. For this reason, following Freeman & Kolstad (2006) and Liao & Shi (2018), the removal ratio of industrial sulphur dioxide (the

na

ratio of the removal amount of industrial sulphur dioxide to its total amount) is used as a measure of

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environmental regulation. Therefore, the higher this ratio, the stricter the environmental regulation and control is. All variables are as defined in Appendix A1.

3.4 Descriptive Statistics Table 1 reports the summary statistics of the sample used in this paper. The number of observations is 1,693 per year on average, with the largest (2,016) in 2014, and the smallest (972) in 2007. In addition, the average turnover is 28.93% for mayors and 36.15% for Party secretaries. It is also shown that the officials are transferred at the highest frequency two years after the 17th and 18th National Congress of the Communist Party of China (2009 and 2014, respectively). 24

Journal Pre-proof Table 1 Summary Statistics of the Sample Secretary Turnover 29.93%

204

27

25.82%

37.31%

2,021

213

27

29.54%

41.24%

2010

1,989

215

27

24.43%

37.77%

2011

1,877

219

27

28.40%

35.90%

2012

1,640

212

27

22.99%

31.16%

2013

1,913

215

27

27.44%

30.95%

2014

2,016

218

27

33.79%

41.07%

2015

1,505

224

27

32.69%

38.57%

Cities

Industries

2007

972

190

2008

1,305

2009

ro

Observations

of

26

Mayor Turnover 20.47%

Year

-p

Table 2 (Panel A) reports the descriptive statistics of the variables used in this study. It is clear

re

that there are significant differences in firm pollution discharges. Among the 15,238 firm-year

lP

observations, 28.93% experience a mayor turnover while 36.15% experience a Party secretary turnover during the sample period. The statistics of the control variables show that the average

na

liability is 4.53%. The average profitability is 43.07%, with a standard deviation of 23.31%. 28.43%

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of the sample firms are SOEs, and the remaining 71.57% are non-SOEs. The mayors are 39-60 years old and have the average tenure of 3.70 years. The Party secretaries have the average tenure of 4.49 years and are 37-65 years old.

Table 2 Descriptive Statistics of the Variables and Univariate Analysis Panel A: Descriptive Statistics for the Major Variables Minimum

Maximum

0.4618

Std. Deviation 2.0143

0.0050

14.9859

15,238

0.2893

0.4534

0

1

Turnover_secretary

15,238

0.3615

0.4047

0

1

Donation

15,238

0.1458

0.3970

0

1

Hometown

15,238

0.0266

0.1608

0

1

Variables

Observations

Mean

SO2

15,238

Turnover

25

Background

15,238

0.2086

0.4063

0

1

Liability

15,238

0.0453

0.1634

4.22e-5

2.6676

Profitability

15,238

0.4307

0.2331

3.56e-5

1.4227

Scale

15,238

22.5217

1.6958

6.7218

28.5510

Ownership

15,238

0.2843

0.4511

0

1

Innovation

15,238

0.0525

2.0021

0

0.7275

Regulation

15,238

0.4943

0.2437

0.0138

0.6990

Age

15,238

49.9609

4.0255

39

60

Tenure

15,238

3.7006

1.6844

1

10

Education

15,238

0.2015

0

1

Age_secretary

15,238

51.2485

3.9595

37

65

Tenure_secretary

15,238

4.4850

1.8290

1

10

Education_secretary

15,238

0.3026

0.4594

0

1

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Mean

Differences

T-statistic

-0.0703**

-1.9534

-0.1263***

-3.7724

Panel B: Univariate Analysis Turnover=0

ro

10,830

0.5414

4,408

0.6117

Turnover_secretary=0

9,352

0.5130

Turnover_secretary=1

5,886

0.6394

Turnover=1

na

SO2

Observations

lP

Political Turnover SO2

0.3587

-p

Secretary attributes

of

Mayor attributes

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Note: ** and *** indicate significance at the 5% and 1% levels, respectively.

Table 2 (Panel B) compares the average pollution discharge in years with political turnover versus the years without political turnover. It is shown that the average pollution discharge is much higher when the mayor is replaced than that when no change occurs. The difference is significant at the 5% level. The results are similar when the Party secretaries are considered. Thus, the univariate analysis demonstrates that firm pollution discharge increases in the years when governmental officials are transferred. The analysis of the correlation between the variables is reported in Appendix A2. In general, the correlation between the variables is not high. In addition, the values of the variance

26

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inflation factor (VIF) of the variables in the regressions are less than 10. Thus, multicollinearity is not a problem.

4. Empirical Analysis and Results 4.1 Baseline Regression Analysis Table 3 presents our estimation results by using Equations (5)-(6). The results from the first-stage

of

procedure show that both Workplace and Birthplace are significantly correlated with political turnover.

ro

In the second-stage regression, the estimated coefficient of the predicated variable of political turnover ) is positive and significant at the 5% level, indicating political turnover is associated with

-p

(

re

more pollution discharges in listed firms. We also perform under-identification test and

lP

weak-identification tests to verify the validity of the instrumental variable. The values of both

na

Cragg-Donald Wald F-statistic and Kleibergen-Paap rk Wald F-statistic are greater than the Stock-Yogo weak ID test critical value at the 10% level and hence confirm the validity of the IVs used.

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In addition, the p-value of the over-identification fails to pass the significance test at the 10% level. Thus, our instrumental variables are reasonable. Overall, we can draw the conclusion that political turnover leads to an increase in firm pollution discharges and hence hypothesis H1 is confirmed.

Table 3 2SLS Results of the Baseline Model Variables

Workplace Birthplace

Turnover First Stage (1) 0.0768*** (0.0127) 0.1125*** (0.0264)

(2) 0.0867*** (0.0127) 0.1160*** (0.0265) 27

SO2 Second Stage (3)

(4)

Journal Pre-proof 1.1734** (0.5859) 0.0101 (0.0435) -0.0015 (0.0056) 0.0009 (0.0023) -0.0092 (0.0189) -0.0055*** (0.0013) -0.0036 (0.0129) 0.0001 (0.0015) -0.3641*** (0.0312) Yes Yes 15,238

Liability Profitability Scale Ownership Age Tenure

of

Tenure_square Education

re

-p

ro

Yes Yes 15,238

lP

Year fixed effects Firm fixed effects Observations Centered R2 Underidentification test Cragg-Donald F-statistic Kleibergen-Paap rk F-statistic F-statistics P-value of over-identification test

Yes Yes 15,238 0.173 53.787*** 27.636 28.093 0.575

1.2221** (0.5420) 0.1625 (0.1428) -0.4982** (0.1985) -0.0328*** (0.0108) 0.0153 (0.0670) 0.0032 (0.0060) 0.1281*** (0.0493) -0.0179*** (0.0057) 0.5186** (0.2449) Yes Yes 15,238 0.232 64.898*** 32.971 34.421 0.425

Note: Regressions control for both year and firm fixed-effects. Robust standard errors corrected for clustering at the firm

na

level are shown in parentheses. *, ** and *** indicate ssignificance at the 10%, 5% and 1% levels, respectively.

The results from the second-stage procedure also demonstrate that the coefficients of Liability are

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positive but fail to pass the significance test at the 10% level. Therefore, a rise in liability has no significant effect on firm pollution discharge. By contrast, the coefficients of Profitability are significant and negative, suggesting that an improvement in firm profitability is associated with lower pollution discharge. In addition, an expansion in firm scale is linked with reduction in firm pollution intensity. This implies that a firm can obtain positive externalities from economies of scale. Additionally, the results show that the coefficients of Ownership are positive but fail to pass the 10% significance test. Thus, there is no significant difference in pollution intensity between SOEs and non-SOEs. We also investigate the effects of personal characteristics. The coefficient of Age is positive but fails to pass the 10% significance test, suggesting no link between officials’ age and firm 28

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pollution discharge. Our estimated results also show that the coefficient of Tenure is significantly positive while that of its squared term is significantly negative. This indicates an inverted U-shaped relationship between mayors’ tenure and firm pollution discharge. Additionally, the coefficient of Education is positive and passes the 1% significance test, which implies that less educated officials are associated with more pollution discharges.

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4.2 Mediating Effects

ro

We also investigate the channels through which political connections influence the environment

-p

and present the results in Table 4. The estimated results in Columns (4) and (5) show that the

re

coefficient of the predicted value of political turnover is significantly positive, indicating that our key

lP

variable significantly influences the two proposed mediators (innovation and environmental regulation). Furthermore, the results in Column (6) show that the coefficient of Innovation is

na

significantly negative, demonstrating that investment in R&D is beneficial to emission reduction.

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Meanwhile, the coefficient of Regulation is significantly negative, suggesting that environmental regulation can remarkably reduce pollution discharge. Thus, the proposed mediators affect firms’ pollution intensities after the control of the influence of the key variable. We further adopt the technique by Sobel (1982) to test the mediating effects. The testing results are shown in Part B, Table 4. It is shown that the Z-value of the two mediators are statistically significant. Thus, the results of the Sobel test provide further support for Hypotheses H2a and H2b. Table 4 2SLS Results of the Mediating Effects and Sobel (1982) Tests Part A: 2SLS Results of the Moderating Effects

Variables

(1)

First Stage Turnover (2) 29

(3)

Innovation (4)

Second Stage Regulation (5)

SO2 (6)

Journal Pre-proof 0.0869*** (0.0127) 0.1146*** (0.0264)

Workplace Birthplace

0.0869*** (0.0127) 0.1146*** (0.0264)

0.0847*** (0.0135) 0.1138*** (0.0263) 1.1785** (0.4830)

0.6537*** (0.0626)

1.1588** (0.5792) -0.2653*** (0.1001) -0.0265** (0.0129)

0.0035 (0.0031) 0.0234** (0.0104)

Innovation Regulation

test

Yes

Yes

Yes

Yes

Yes

Yes Yes 15,238

Yes Yes 15,238

Yes Yes 15,238

Yes Yes 15,238 0.216 64.683*** 32.713 34.312 0.729

Yes Yes 15,238 0.237 64.683*** 32.716 34.312 0.333

Yes Yes 15,238 0.191 57.834*** 29.605 30.536 0.494

of

Yes

ro

Control variables Year fixed effects Firm fixed effects Observations Centered R2 Underidentification test Cragg-Donald F-statistic Kleibergen-Paap rk F-statistic P-value of over-identification statistic

-p

Part B: Sobel (1982) Test for Mediator Variables c

a

Innovation Regulation

1.2221 1.2221

1.1785 0.6537

re

Mediators

b

0.4830 0.0626

-0.2653 -0.0265

Z 0.1001 0.0129

-1.7951** -2.0156**

level are shown in parentheses.

⁄√

lP

Note: Regressions control for both year and firm fixed-effects. Robust standard errors corrected for clustering at the firm , where a is the estimated effect of political turnover on each and

are corresponding standard errors. These are

na

mediator; b is the estimated effect of each mediator on pollution,

drawn from Part A of this table. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.

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4.3 Different Types of Political Turnover

In this section, we consider the effects of different types of political turnover. We add External as a dummy variable which has a value of one if the new official is externally transferred, and zero for local promotion. The estimation results in Columns (5) and (7), Table 5, show that the cross term between the predicted value of political turnover and the dummy variable of external appointment (

) is negative and significant at the 5% level, indicating that the intensity of

pollution discharges induced by political turnover of externally-appointed officials tends to be smaller than that of locally promoted officials. Thus, the negative effect of political turnover is more pronounced if the newly appointed official is locally transferred. On the one hand, it may be easier for 30

Y

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locally promoted officials are to build political connections with polluting firms and relax environmental regulations. On the other hand, external appointments can break the well-connected networks between ex-officials and local firms and enhance the effectiveness of environmental regulations, thereby curbing speculative activities for polluting firms to some extent. Political turnover can be normal or abnormal. For normal turnover, firms can forecast officials’

of

promotion more accurately and respond accordingly in advance. In contrast, abnormal turnover can

ro

create a highly politically sensitive situation in which the unexpected transfer of local officials has a

-p

deterrent effect on firms’ illegal pollution behavior. Thus firms may reduce their emissions. The

re

categorisation of normal versus abnormal turnover here follows Li & Zhou (2005). Normal turnover is

lP

associated with promotion, lateral moves and step-down (to make way for younger people). Abnormal turnover occurs if the ex-mayors are dead, dismissed or resigned. Thereby, the dummy variable

na

Abnormal takes the value of one if officials are abnormally transferred and zero otherwise. The

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estimated results in Columns (6) and (7) show that the coefficients of the interaction term between the predicted value of political turnover and Abnormal are negative and significant at the 5% level, indicating that abnormal turnover leads to less increase in firm pollution discharges than normal turnover.

Table 5 2SLS Results Based on Different Types of Political Turnover Turnover First Stage

Variables Workplace Birthplace

(1) -0.2284*** (0.0127) -0.0908*** (0.0186)

(2) -0.2118*** (0.0127) -0.0154*** (0.0050)

(3) -0.2306*** (0.0128) -0.1041*** (0.0189)

31

(4) 0.0037 (0.0086) 0.1304*** (0.0314)

(5)

SO2 Second Stage (6) (7)

(8)

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Workplace×External

0.2766*** (0.0152)

0.2782*** (0.0153)

Birthplace×External

0.3307*** (0.0249)

Workplace×Abnormal

0.4765*** (0.0595)

0.3315*** (0.0248) 0.0940* (0.0480)

Birthplace×Abnormal

0.5034*** (0.0663)

0.1409** (0.0576) 1.4395** (0.6644)

1.4165** (0.6429)

-0.7164** (0.3585)

-0.6568** (0.3217) -0.7002** (0.3515)

-0.6626** (0.3340)

×External

Control variables

Yes

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

Yes

Firm fixed effects

Yes

Yes

Yes

15,238

15,238

15,238

Underidentification test Kleibergen-Paap F-statistic

P-value of over-identification over-identification test

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

15,238

15,238

15,238

15,238

15,238

0.223

0.220

0.227

0.179

57.621***

63.625***

62.905***

46.272***

15.589

16.080

11.447

15.878

15.372

16.964

11.348

18.598

0.590

0.603

0.611

0.346

ro

Yes

re

Cragg-Donald F-statistic

1.4392* (0.8578)

Yes

-p

Centered R2

of

×Abnormal

Observations

1.1300** (0.5670)

lP

Notes: Regressions control for both year and firm fixed-effects. Robust standard errors corrected for clustering at the firm level are shown in parentheses. *, ** and *** indicate significance at the10%, 5% and 1% levels, respectively.

na

Turnover frequency affects the length of officials’ tenure and their political incentives. Among

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the sampled firms, 15.17% of them experience political turnover twice, 64.21% three times, 18.89% four times and 1.73% five times. To capture the effect of turnover frequency, a frequency-specific variable is included in the regressions. The dummy variable Frequency is defined as the number of changes in government officials that a firm experiences during the sample period. The estimation result in Column (8) of Table 5 shows that the coefficient of

is positive and passes the

significance test at the 10% level. Thus, the environment is more negatively affected by political turnover with higher frequency.

4.4 Estimated Results by Region and Industry

32

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To take regional uneven development into consideration, we divide our sample into two groups according to firm locations, namely the eastern regions and the central and western regions. A dummy variable East identifying eastern regions is introduced into the models. The estimation results are shown in Columns (4) and (6), Table 6. The coefficient of the interaction term between the predicted value of political turnover and East is significantly negative. Thus, political turnover in the eastern

of

areas is linked with less increase in pollutions than that in the central and western areas. This could be

ro

due to better institutional environment in the eastern regions than that in the central and western

-p

regions. A better institutional environment restrains the short-sighted behavior of the transferred

re

officials as well as speculative activities by the polluting firms to some extent.

lP

We also divide our sample into dirty and clean industries on the basis of average emission intensity following the existing literature (Dellachiesa and Myint, 2016). The estimated results in

na

Columns (5) and (6), Table 6, reveal that the coefficients for the interaction term between the

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predicted value of political turnover and the dummy variable Clean of clean industries ( ) is negative and significant. This implies that there is less increase in pollution discharge after political turnover in the clean industries than those in the dirty industries. One possible explanation is that firms in dirty industries are larger taxpayers. The newly appointed officials may relax environmental regulation or even provide protection for the polluters in order to pursue the short-term economic growth goal. Another explanation is that heavily polluting firms are more likely to discharge more pollutants if environmental policy has not been fully worked out owing to political turnover. Table 6 2SLS Results Based on Different Regions and Industries Variables

Turnover

SO2 33

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Workplace Birthplace Workplace×East Birthplace×East

First Stage (2) -0.2109*** (0.0198) -0.2317*** (0.0362)

(1) -0.2116*** (0.0203) -0.2702* (0.0420) 0.0670*** (0.0161) 0.1422*** (0.0343)

Workplace×Clean

0.1410*** (0.0195) 0.3629*** (0.0333)

Birthplace×Clean

（(4)

(3) -0.2306*** (0.0128) -0.2041*** (0.0389) 0.2782*** (0.0153) 0.3315*** (0.0248) 0.0940* (0.0480) 0.1409** (0.0576)

of

2.0558** (0.8744) -1.8970* (1.0771)

ro

×East ×Clean

Second Stage (5)

1.3554*** (0.4284)

-1.3785** (0.5680) yes Yes Yes 15,238 0.319 64.558*** 16.434 17.106 0.230

(6)

1.6833*** (0.5553) -0.6121*** (0.1585) -1.3696*** (0.3334) yes Yes Yes 15,238 0.327 38.171*** 20.832 16.577 0.198

na

lP

re

-p

Control variables Yes yes yes Yes Year fixed effects Yes Yes Yes Yes Firm fixed effects Yes Yes Yes Yes Observations 15,238 15,238 15,238 15,238 Centered R2 0.263 Underidentification test 38.514*** Cragg-Donald F-statistic 16.006 Kleibergen-Paap rk F-statistic 11.496 P-value of over-identification 0.451 test Notes: Regressions control for both year and firm fixed-effects. Robust standard errors corrected for clustering at the firm

Jo ur

level are shown in parentheses. *, ** and *** indicate significance at the10%, 5% and 1% levels, respectively.

4.5 The Effects of Political Connection on Pollution Discharge Political turnover can terminate the usual relationship between local governments and firms (Piotroski & Zhang, 2014). Re-building political connection with the newly appointed officials may provide firms with economic benefits and protection for excessive pollution. In order to examine how polluting firms establish new political connection with newly appointed officials and hence whether they discharge more, we incorporate the interaction terms between the political turnover and political connections into the empirical models. According to the estimated results in Columns (1), (2) and (3) of Table 7, the coefficients of the predicted value of political turnover ( 34

are positive and

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significant at the 10% level. Political turnover hence leads to an increase in firm pollution discharge. This result is consistent with the baseline regression finding. The coefficients of political connection PC in Columns (1), (2) and (3) are significantly positive, indicating that political connection is positively linked with firms’ SO2 emission. There are several explanations. Charity donation is a more disguised and hidden strategy than other rent-seeking activities (Shleifer & Vishny, 1994; Ma &

of

Parish, 2006; Cooper et al., 2010). It helps firms form cooperative alliances with newly appointed

ro

officials (Khwaja & Mian, 2005; Claessens et al., 2008). Thus, polluting firms may use charitable

-p

donation to disguise environmental misconduct and divert officials’ attention away from their

re

environmental performance (Du, 2015). In addition, personal relationship is prominent in Chinese

lP

culture. The “same birthplace” network plays a critical role in political connection (Yen & Abosag, 2016) and may hence provide protection for the polluters. Finally, firms’ top managers with political

na

background maintain close relationship with local officials and are much easier to be protection for

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pollution.

The coefficients of the cross term between the predicted value of political turnover and political connection (

are significant and negative in Columns (1), (2) and (3). Thus,

political connection leads firms to discharge less pollutants during the first year in office of the newly appointed officials. The possible explanation for this is that political turnover can break the well-connected networks between ex-officials and local firms and new networks may not be ready during the first year of appointment. In addition, political turnover exerts a deterrent effect on politically connected firms and thereby their pollution discharges decrease accordingly. Thus, our third hypothesis H3 is supported. 35

Journal Pre-proof Table 7 2SLS Results Based on Different Types of Political Connections Dependent variable: SO2

Variables

PC ×PC

(1)

(2)

(3)

1.2462* (0.6558) 0.3451* (0.1841) -1.1570* (0.6164)

1.0595** (0.5670) 0.5334** (0.2212) -1.2310** (0.6003)

1.3916* (0.7675) 0.4190* (0.2183) -1.4168* (0.7146)

×PC

(4) 0.9889* (0.5114) 0.4058* (0.2085) -1.0121* (0.5571) 0.3364 (0.4432) -0.3411 (0.4128) Yes Yes Yes 15,238 0.243 56.147*** 14.899 14.637 0.321

(5) 0.9137** (0.4605) 0.6135** (0.2440) -1.2418** (0.6300) 0.2688 (0.3983) -0.3885 (0.4575) Yes Yes Yes 15,238 0.319 44.558*** 16.434 13.632 0.374

(6) 1.1932** (0.6216) 0.3928* (0.2292) -1.0625* (0.6532) 0.2933 (0.4930) -0.1050 (0.4536) Yes Yes Yes 15,238 0.265 44.511*** 11.762 11.426 0.331

re

-p

ro

of

Control variables Yes Yes Yes Year fixed effects Yes Yes Yes Firm fixed effects Yes Yes Yes Observations 15,238 15,238 15,238 Centered R2 0.242 0.264 0.238 Underidentification test 60.018*** 50.051*** 57.438*** Cragg-Donald F-statistic 16.140 15.060 14.766 Kleibergen-Paap rk F-statistic 15.155 13.386 15.053 P-value of over-identification 0.127 0.615 0.413 test Note: This table only reports the second-stage estimation results based on different types of political connection. The dependent

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variable is listed in the first row. Standard errors clustered at city level are shown in parentheses. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. Political connection is measured by charitable donation in Columns (1)

na

and (4), hometown network in Columns (2) and (5) and political background in Columns (3) and (6).

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While there is much uncertainty surrounding the re-establishment of political connection, political turnover further strengthens this uncertainty and causes firms to adopt strategic pollution behaviour. To test for changes in the firm pollution dynamics across the turnover cycle, the interaction term between turnover dummy and political connection is included in the regressions. We choose the period after the turnover year as the reference period and use the pre-turnover period and the turnover year to illustrate how firm pollution discharges change. Given that the average tenure of the mayors is 3.7 years and most firms experience political turnover three times, we define Turnover_pre as the one-year period before political turnover takes place. Specifically, as the Turnover dummy is defined as the calendar year when the mayor turnover occurs, so Turnover_pre is equal to 1 if the firm-year-official observation falls in the period of one year immediately before the turnover year. 36

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The coefficients of

in Columns (4)-, (5) and (6) of Table 7 are positive and pass the

10% significance test, which implies that firm pollution discharge in the turnover year is higher than that in the year after political turnover occurs . In addition, the coefficients of

fail to

pass the 10% significance test, implying there are no significant differences in the pollution discharges in the pre-turnover period and post-turnover period. This finding highlights that political turnover

of

influences firm’s pollution behavior in a short time. The estimated results also show that the

ro

coefficients of the cross term between the predicted value of political turnover and political

-p

connection are insignificant in pre-turnover period, indicating there are no significant differences in

re

the pollution discharges in pre-turnover period and those in post-turnover period. Table 6 also are significantly negative in

lP

demonstrates that the coefficients of the cross term

Columns (4), (5) and (6). Thus, the effects on pollution discharges by political connection change

na

significantly between the turnover year and post-turnover years. The reason might be that with the

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increase of new officials’ tenure, the top managers of listed firms can re-establish new political connection with local officials. Thus firm pollution discharge shows a significant change after the new connection is established.

5 Robustness Checks In this section, we conduct several sensitivity analyses to check the robustness of our main findings. 5.1 Alternative measures of environmental pollution and political turnover We consider two optional pollution indexes, namely the intensity of chemical oxygen demand emission (COD) and the intensity of industrial wastewater emission (Water), as alternative measures 37

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of environmental pollution. The main estimation results of the baseline model are presented in Columns (1) and (2), Table 8 (with more detailed results reported in Appendices A3-A5). In line with An et al. (2016) and Xu et al. (2016), we also construct a dummy variable, Turnover_secretary, to measure political turnover. The estimated results in Columns (1), (2) and (3), Table 8, show that the coefficients of political turnover are positive and significant at the 5% level, suggesting that political

of

turnover is positively correlated with firm pollution discharge. This result is consistent with those in

ro

Table 3 and hence hypothesis H1 still holds. We also find that the magnitude of the coefficient of

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Turnover_secretary is smaller than that of Turnover. This may imply smaller effects of the turnover of

Table 8 2SLS Results for Robustness Checks COD (1) 1.2587** (0.7084)

Water (2) 0.1718** (0.0831)

Yes Yes Yes 14,951 0.204 63.337*** 32.523 33.641 0.840

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Control variables Year fixed effects Firm fixed effects Observations Centered R2 Underidentification test Cragg-Donald F-statistic Kleibergen-Paap rk F-statistic P-value of over-identification

na

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Variables

re

the Party secretaries on firm pollution discharges than those caused by mayor turnover.

Yes Yes Yes 14,833 0.187 64.898*** 32.971 34.421 0.299

SO2 (3)

(4) 1.9847** (0.5186)

(5) 0.9936* (0.5427)

(6) 1.1953** (0.4955)

15,238 0.334 64.906*** 32.972 34.425 0.381

14,830 0.326 64.738*** 33.480 34.287 0.428

10,904 0.261 62.396*** 31.312 33.580 0.360

0.6476** (0.2872) Yes Yes Yes 15,238 0.227 75.967*** 73.440 41.232 0.355

Note: This table reports over-identification testthe second-stage estimation results for robust checks. The dependent variables are listed in the first row. Standard errors clustered at city level are shown in parentheses. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.

5.2. Outliers and firms of special categories First, all continuous variables are winsorized at the top and bottom one percent to remove the effect of outliers following the literature (Crinò and Ogliari, 2017). The results listed in Column (4) of 38

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Table 8 imply the same set of inferences from the baseline specification. That is, the estimated coefficients have the expected sign and pass the significance tests. Second, we also exclude firms that are registered in four municipalities, namely Beijing, Shanghai, Tianjin and Chongqing, as these municipalities directly report to the central government. The estimated results in Column (5) of Table 8 show that political turnover is still associated with more firm pollution discharges. Finally, we focus

of

on firms which exist before and after political turnover. We are interested in whether firms discharge

ro

more pollutants after they experience political turnover. The results are shown in Column (6) of Table

re

5.3 Further consideration of endogeneity problems

-p

8 and clearly consistent with the findings so far in this paper.

lP

To deal with endogeneity further, we adopt two different approaches, namely Heckman two-stage procedure and propensity score matching (PSM) method. The two-stage procedure by

na

Heckman (1979) is used to correct endogenous problems caused by sample self-selection bias. We

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delete non-polluting firms and focus on the relationship between political turnover and firm pollution discharge. The first stage of Heckman’s procedure involves a probit model which regresses the political turnover dummy against the control variables used for the OLS regression. We incorporate the officials’ age (Age), length of tenure (Tenure) and educational background (Education) into the probit model following the existing literature (Li & Zhou, 2005; Xu et al., 2013),. Given that the likelihood of political turnover is affected by economic performance in various jurisdication, we choose the lagged growth rate of per capita GDP (gdp_lag), fixed-asset investment (invest_lag) and employment rates (employ_lag) as the macro control variables. The estimated results of the two-stage procedure are provided in Table 9. The inverse Mills ratio (imr) can be obtained from the first-stage 39

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estimation result and incorporated into the pollution equations (in the second stage regression) as a control variable. The second-stage estimation results show that the coefficient of political turnover is still positive and significant at the 5% significant level after consideration of sample self-selection bias. Thus, the main conclusions still hold. That is, political turnover is associated with more pollution discharges by the firms. Table 9 Estimation Results: Heckman Procedure and PSM

of

ables

Dependent variable: SO2 Heckman’s Two-stage Procedure 1st stage

2nd stage 0.1716** (0.0791)

Liability

0.1934 (0.1458)

PSM 2nd stage 0.1706** (0.0790)

-1.1325*** (0.3655)

0.1936 (0.1456)

-0.5302*** (0.1941)

0.7224** (0.3123)

-0.5321*** (0.1938)

Scale

-0.0321*** (0.0110)

0.0196** (0.0080)

-0.0322*** (0.0110)

-0.0091 (0.0662)

0.1654*** (0.0393)

-0.0097 (0.0662)

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Profitability

re

-p

Turnover

1st stage

ro

Vari

Tenure Tenure_square Education

-0.0517*** (0.0050)

-0.0125 (0.0106)

-0.0063 (0.0062)

0.0452 (0.0372)

0.1188** (0.0493)

0.1130** (0.0486)

-0.0058* (0.0033)

-0.0182*** (0.0057)

-0.0175*** (0.0056)

-0.9047*** (0.1081)

0.4840*** (0.1636)

0.4867*** (0.1368)

Jo ur

Age

na

Ownership

gdp_lag

0.0885* (0.0478)

employ_lag

0.0779 (0.1310)

invest_lag

0.0995* (0.0529) 0.0158** (0.0076)

pgdp 0.1956 (0.2546)

imr Control variables

Yes

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

Yes

City fixed effects

Yes 40

Journal Pre-proof Firm fixed effects Pseudo R2 Adj_R2 Observations

Yes

Yes

Yes

0.2119

——

0.0021

——

——

0.3420

——

0.3523

14,250

15,238

15,238

15,238

It is noticed that the estimated coefficient of the turnover variable is much smaller than those derived from the IV method in Section 4. There is no precise explanation for this discrepancy. Jiang

of

(2017) presented a survey of 255 papers published in top finance journals that rely on the instrumental

ro

variable (IV) approach for identifying causal effects. The author observed that the magnitude of the IV

-p

estimates is on average nine times of that of their corresponding uninstrumented estimates. Jiang

re

(2017) offered several explanations. One possible cause is due to the use of weak IVs. Angrist and

lP

Pischke (2009) argued that weak instruments yield IV estimates that are biased toward their

na

corresponding OLS estimates. For this reason, we performed weak identification tests to verify the validity of the instrumental variable in all regressions (Tables 3-8). Both Cragg-Donald Wald

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F-statistic and Kleibergen-Paap rk Wald F-statistic are higher than the Stock-Yogo weak ID test critical value at the 10% level, suggesting that our IVs are not weak. We also use a PSM model to address endogenous problems originated from sample selection bias. We first estimate firm propensity to experience political turnover based on regional economic development and firm-specific factors including per capita GDP (pgdp), Liability, Profitability, Scale and Ownership. Then each firm in the treatment group (which experienced political turnover) is matched with a control firm (which did not experience political turnover) with the closest propensity score. We calculate the difference and its t-statistic between the treatment and control groups in terms of each of the above variables, and find that the biases of all variables are less than 10% after matching and t-statistics pass the 5% significant test, indicating that PSM performs well in our model. 41

Journal Pre-proof After matching, we run a second-stage regression that examines the relationship between political turnover and the environment impact. The estimated results presented in Table 9 show that the coefficient of political turnover is positive and significant at the 5 % level. Thus, our findings in Section 4.1 are confirmed after sample selection bias is taken into consideration by using the PSM method. It is interesting to note that the magnitude of the estimated coefficient of the turnover variable in both Heckman and PSM models is very close.

of

6. Conclusions and Policy Recommendations

ro

This paper examines the relationship between political turnover and firm pollution discharges.

-p

The empirical results show that the first year in office of a new city government official is associated

re

with an increase in firm pollution discharges. This result is robust to alternative measures of

lP

environmental pollution and political turnover and various sample selections. Our additional analysis

na

suggests that a firm discharges more pollutants if the newly appointed official is locally promoted. In comparison with normal turnover, abnormal turnover has a more pronounced effect on firm

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environmental performance. Furthermore, we find that high frequency of political turnover is associated with more pollution discharges. We also investigate how polluting firms establish new political connection with newly appointed officials. The results show that politically connected firms reduce their pollution discharges during the first year of a new official’s appointment. With the growth of appointed officials’ tenure as well as expansion in firm social networks, pollution discharges of political connected firms in the post-turnover year are higher than those in the turnover year. Our findings not only contribute to the understanding of the relationship between political turnover and firm pollution behaviours, but also have important policy implications for cadre 42

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management and firm governance reforms in China. By improving the cadre performance evaluation system and monitoring environmental responsibility, the short-sighted behavior of government officials may be controlled to some extent. Moreover, local governments should make greater effort to establish new government-business relations so that environmental policies can be effectively and continuously implemented. A reliable pollution levy system and an emission trading system are

of

urgently needed to improve firm environmental performance. As for firm governance, the

ro

environmental information disclosure system of listed firms should be strengthened and corporate

-p

environmental responsibility should be strictly monitored.

re

Funding

lP

This work was funded by the National Natural Science Foundation of China [grant number 71703035]

Conflict of Interest

na

and China Scholarship Council (File No.201706135020).

References

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All three authors have no conflict of interest.

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Journal Pre-proof Appendix A1 Variable Definitions Definition

SO2

Amount of discharged SO2(ton)/ sales (10 thousand RMB)

Turnover

A dummy variable, equals to one if the city where the firm’s headquarters are located experiences a mayor turnover and zero otherwise.

Turnover_secretary

A dummy variable, equals to one if the city where the firm’s headquarters are located experiences a turnover of Party secretary and zero otherwise.

Donation

A dummy variable, equals to one if the annual financial statement of a listed firm discloses its charitable donation and zero otherwise.

Hometown

A dummy variable, equals to the mayor has the same birthplace as one of the top managers of a listed firm located in the official’s jurisdiction and zero otherwise.

Background

A dummy variable, equals to one if one of the top managers of a listed firm is currently serving or has formerly served in the government, or as a National eople’s Congress delegate or a Chinese eople’s olitical Consultative Conference member.

Innovation

The ratio of R&D spending over total sales revenues

Regulation

The removal of industrial sulphur dioxide /(the removal of industrial SO2+the amount of industrial SO2 discharged) in each city

Liability

The ratio of total debts over total assets

Profitability

The ratio of net profits over net assets

Scale

The natural logarithm of total assets

Ownership

A dummy variable, equals to one if the firm whose ultimate controllers are the state and zero otherwise

Age

The age of the major in office

Tenure

The period of time when a mayor takes office

Education

A dummy variable, equals to one for local officials with bachelor degree or below and zero

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Variables

otherwise.

Appendix A2 Pearson Correlation Matrix of Variables Variables

SO2

Turnover

0.0158** 0.051

Liability

0.0185** 0.023

Turnover

Liability

Profitability

-0.0239*** 0.003 56

Scale

Ownership

Age

Tenure

Journal Pre-proof Profitability

-0.0240*** 0.003

-0.0200** 0.014

0.696*** 0.001

Scale

-0.0396*** 0.001

0.0217*** 0.007

-0.2614*** 0.001

-0.5017*** 0.001

Ownership

-0.0424*** 0.001

0.0374*** 0.001

-0.0550*** 0.001

-0.0791*** 0.001

0.0966*** 0.001

Age

0.0094 0.247

-0.0071 0.378

0.0248*** 0.002

0.0168** 0.038

-0.0060 0.456

0.0190** 0.019

Tenure

0.0078 0.335

0.005 0.534

-0.0180** 0.027

-0.0179** 0.036

0.0412*** 0.001

0.0398*** 0.001

-0.1242*** 0.001

Education

0.0178** 0.028

0.0097 0.232

0.0167** 0.040

-0.0170** 0.036

-0.0015 0.854

0.0151* 0.063

0.1572*** 0.005

0.1102*** 0.005

Note:This table reports the Pearson correlation matrix of the variables. P-values are presented in the parentheses below the

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correlation coefficients. *, ** and *** indicate significance at the10%, 5% and 1% levels, respectively.

Appendix A3 2SLS Results for Robustness Checks Using COD as an Alternative measure for Environmental Pollution Dependent variable: COD (2)

1.2587** (0.7084)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

1.5050**

1.8598**

1.0045

1.4344**

1.4156**

1.4984***

1.3809***

1.1453**

(0.7305)

(0.9396)

(0.7657)

(0.7352)

(0.7073)

(0.5783)

(0.5316)

(0.5910)

0.7687***

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(1)

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Variables

-0.7294*

×External

(0.4012)

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-0.7335*

×Abnormal

(

(0.4204)

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1.0510 (1.2627)

×East

-0.2393

(0.4099)

×Clean

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-0.7843* (0.4333)

×PC

×PC

Control variables

Yes

Year fixed effects Firm fixed effects Observations Centered R2

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PC

0.4763

0.8838**

1.2989**

0.7160***

0.6783***

(0.4556)

(0.3731)

(0.5766)

(0.2602)

(0.2800)

(0.2857)

-1.2438

-2.5812*

-2.7210*

-1.7160***

-1.1984*

-1.3001**

(1.4659)

(1.5083)

(1.6216)

(0.6142)

(0.6200)

(0.5470)

0.9490*

0.7660

(0.5856) 0.7309 (0.7016)

(0.5484)

(0.4828)

-1.0113*

-0.6827

0.9663

(0.5155)

(0.5147)

(0.7333)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

14,951

14,951

14,951

14,951

14,951

14,951

14,951

14,951

14,951

14,951

0.204

0.219

0.216

0.208

0.192

0.268

0.207

0.196

0.283

0.215

Underidentification test

63.337***

59.920***

15.046***

35.614***

57.767***

47.253***

40.170***

30.908***

42.418***

39.810***

Cragg-Donald F-statistic

32.523

11.825

48.485

12.209

15.727

14.794

12.087

15.399

27.028

17.047

Kleibergen-Paap F-statistic

33.641

12.955

17.928

10.072

15.157

12.399

11.086

11.096

20.651

16.650

P-value of over-identification

0.840

0.406

0.159

0.138

0.819

0.832

0.184

0.864

0.522

0.923

Note: ①This over-identification test

table reports the second-stage estimation results for robust checks using COD as an alternative measure for

environmental pollution.② Political connections in Columns (5) and (8) are measured by charitable donation and those in Columns (6) and (9) according to birthplace identity and those in Columns (7) and (10) according to political background. 57

Journal Pre-proof ③Standard errors clustered at city level are shown in parentheses. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.

Appendix A4 2SLS Results for Robustness Checks Using Water as an Alternative measure for Environmental Pollution Dependent variable: Water

Variables (1)

(2)

0.1718** (0.0831)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

0.2125**

0.2203**

0.1465*

0.1634*

0.1154*

0.1423**

0.1288**

0.1562**

(0.0926)

(0.1143)

(0.0910)

(0.0855)

(0. 0676)

(0.0610)

(0.0530)

(0.0657)

0.0694**

-0.0701*

×External

(0.0391) -0.0655

×Abnormal

(0.0397) 0.2829**

×East

-0.0906**

×Clean

-0.0552*

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(0.1400)

(0.0371)

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(0.0282) PC

0.1838**

0.0563

0.0482*

(0.0780)

(0.0303)

(0.0292)

(0.0333)

-0.1198

-0.2016

-0.2848*

-0.1278**

-0.1398**

-0.1505**

(0.1616)

(0.1494)

(0.1554)

(0.0579)

(0.0561)

(0.0611)

0.0418

0.0383

0.0479

(0.0650)

(0.0600)

(0.0482)

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×PC

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

Firm fixed effects

Yes

Yes

Yes

Observations

14,833

14,833

14,833

Centered R2

0.187

0.189

Underidentification test

64.898***

59.869***

Cragg-Donald F-statistic

32.971

12.957

Kleibergen-Paap F-statistic

34.421

10.855

P-value of over-identification

0.299

0.449

-0.0054

-0.0278

-0.0679

(0.0689)

(0.0687)

(0.0816)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

14,833

14,833

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Control variables

14,833

14,833

14,833

14,833

0.196

0.204

0.219

0.216

0.208

0.229

0.224

65.046***

35.654***

57.955***

47.127***

34.240***

40.824***

45.244***

48.558***

48.485

20.110

15.796

14.749

12.123

20.352

26.732

26.945

29.928

12.201

15.216

14.749

9.120

18.103

20.655

23.288

0.283

0.656

0.458

0.290

0.125

0.729

0.699

0.404

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14,833

0.201

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Note: ①This over-identification testtable

0.1270*

(0.0681)

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×PC

0.0370

(0.0509)

reports the second-stage estimation results for robust checks using Water as an alternative measure for

environmental pollution. ②Political connections in Columns (5) and (8) are measured by charitable donation and those in Columns (6) and (9) according to birthplace identity and those in Columns (7) and (10) according to political background. ③Standard errors clustered at city level are shown in parentheses. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.

Appendix A5 2SLS Results of the Moderating Effects Using COD and Water as Alternative measures for Environmental Pollution

Variables

Part A: 2SLS Results of the Moderating Effects Second Stage Innovation Regulation COD Innovation (1) (2) (3) (4) 58

Second Stage Regulation (5)

water (6)

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Innovation Regulation

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Mediators Innovation Regulation

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Mediators Innovation Regulation

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test

1.3278* 1.0307*** 0.3429*** (0.7528) (0.3578) (0.0630) -0.2328 (0.1583) -0.0212** (0.0104) Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 14,833 14,833 14,951 14,951 14,951 0.221 0.208 0.219 0.234 0.220 63.132*** 63.132*** 56.676 *** 63.073*** 63.073*** 32.269 32.272 29.329 32.259 32.262 33.538 33.538 29.971 33.514 33.514 0.637 0.269 0.807 0.620 0.204 Part B: Sobel (1982) Test for Mediator Variables Dependent variable: COD c a b 1.2587 1.0792 0.4628 -0.2328 0.1583 1.2587 0.4486 0.0635 -0.0212 0.0104 Dependent variable: Water c a b 0.1718 1.0307 0.3578 -0.0515 0.0185 0.1718 0.3429 0.0630 -0.0032 0.0015

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Control variables Year fixed effects Firm fixed effects Observations Centered R2 Underidentification test Cragg-Donald F-statistic Kleibergen-Paap rk F-statistic P-value of over-identification statistic

0.4486*** (0.0635)

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1.0792** (0.4628)

0.1883** (0.0881) -0.0515*** (0.0185) -0.0032** (0.0015) Yes Yes Yes 14,833 0.235 56.879*** 29.436 30.090 0.366

Z -1.2439 -1.9586** Z -2.0018** -1.9862**

Note: ①This table reports the second-stage estimation results for robust checks using COD and Water as alternative measures for environmental pollution. ②Regressions control for both year and firm fixed-effects. Robust standard errors

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corrected for clustering at the firm level are shown in parentheses. ③

⁄√

political turnover on each mediator; b is the effect of each mediator on pollution,

, where a is the effect of and

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errors. ④ *, ** and *** indicate ssignificance at the 10%, 5% and 1% levels, respectively.

59

are corresponding standard

Y

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Political Turnover and Firm Pollution Discharges: An Empirical Study

 Political turnover is associated with significantly more firm pollution discharges

 Higher frequency of political turnover is linked with more pollution discharge  Political connection plays a moderating role in the relationship between political turnover and environmental performance

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 Politically connected firms reduce their pollution discharges during the first year of a new

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official’s appointment

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