Does emissions trading affect labor demand? Evidence from the mining and manufacturing industries in China

Journal of Environmental Management 254 (2020) 109789

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Research article

Does emissions trading affect labor demand? Evidence from the mining and manufacturing industries in China Shenggang Ren, Donghua Liu *, Bo Li, Yangjie Wang, Xiaohong Chen School of Business, Central South University, Changsha, 410083, PR China

A R T I C L E I N F O

A B S T R A C T

Keywords: Emissions trading program Market-based regulation Labor demand Difference-in-differences China

We use “China’s sulfur dioxide (SO2) emissions trading program” as a quasi-natural experiment to identify the causal effect of this market-based environmental regulation on firm’s labor demand. Based on the difference-indifferences (DID) method and a series of robustness tests, we observe robust evidence that the emissions trading program significantly increases the labor demand of regulated firms, and that this positive employment effect is driven by the expansion of firm’s production scale. The observable evidence leads us to cautiously conclude that the market-based environmental regulations in even developing countries could achieve the double dividend of coexistence of environmental protection and employment growth.

1. Introduction The first 40 years of China’s reform and opening up has witnessed great economic growth with its low-cost labor and resource advantages. However, with the rapid economic growth, China is facing some of the most serious environmental challenges in the world (Li et al., 2018; Shi and Xu, 2018). Only 1% of China’s 560 million urban population can breathe air that the EU believes to be “safe” (World Bank, 2007) Ac­ cording to Environmental Performance Index report (Hsu et al., 2016), China became a disaster area of air pollution with the second worst air quality. To address the major air pollution issues, China’s government has implemented various environmental regulations and policies. For example, since 1980s, the Chinese government has issued the law on the prevention and control of air pollution, the environmental protection act, the environmental air quality standard, and integrated air pollutant discharge standard. Particularly, the emission standards on sulfur di­ oxide (SO2) emissions from fossil fuel aimed to reduce acid rain and improve human health. Besides the command-and-control regulatory approaches, the Chinese government introduced emissions trading program in 2007, to further reduce SO2 emissions. Evidence indicates that these policies have play significant roles in environmental protec­ tion in China (Yu et al., 2019), and industrial SO2 emissions show a relatively steady decline since 2006 (Liu et al., 2017). However, the cost of improving the ecological environment might be expensive. One of the costs imposed by environmental regulations is

their negative impacts on employment. In terms of output effect, firms might increase product prices in order to address the compliance cost of regulations, which results in lower market demand and then reduces labors for production (Berman and Bui, 2001; Morgenstern et al., 2002). It is projected that in China, 450 million workers in the following 20 years will migrate from rural to urban areas, posing tremendous burden on the government to continue keeping stable employment (Liu et al., 2017). If the mantra which is that environmental regulation “kills jobs” becomes true (Ferris et al., 2014), environmental regulations would hurt China’s social transformation and economic growth. Proponents of environmental regulation argue that, however, envi­ ronment and employment can both benefit from environmental regu­ lations (International Labour Organization (ILO), 2009). From the perspective of substitution effect, environmental regulations could drive firms to employ more workers to install and maintain pollution abate­ ment equipment, develop green technology, and engage in environ­ mental management activities (Morgenstern et al., 2002; Sheriff et al., 2019). In addition, the Porter Hypothesis postulates that strict but flexible environmental regulations may provide incentives for techno­ logical innovation that could substantially improve firm competitive­ ness (Porter and Van der Linde, 1995), thereby having greater incentives for scale expansion and then increasing labor demand. Consequently, which of these effects dominant is an empirical question. In recent years, a growing number of studies have investigated the impact of environmental regulation on employment (see the surveys of

* Corresponding author. No.932 South Lushan Road, Changsha, Hunan, 410083, PR China. E-mail addresses: [email protected] (S. Ren), [email protected] (D. Liu), [email protected] (B. Li), [email protected] (Y. Wang), [email protected] (X. Chen). https://doi.org/10.1016/j.jenvman.2019.109789 Received 27 March 2019; Received in revised form 10 August 2019; Accepted 26 October 2019 Available online 12 November 2019 0301-4797/© 2019 Published by Elsevier Ltd.

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Dechezlepr^etre and Sato, 2017). However, most of these studies focus on the impact of command-and-control environmental policies on labor demand. For instance, some studies examined the employment effect of the National Ambient Air Quality Standard (NAAQS) in the U.S. (Sheriff et al., 2019), the Cluster Rule in the U.S. (Gray et al., 2014), the biofuels policy in the EU (Neuwahl et al., 2008) and the wastewater discharge standards in the Lake Tai, Jiangsu region of China (Liu et al., 2017). Only a few studies have examined the impact of market-based envi­ ronmental regulations on employment (Chan et al., 2013; Ferris et al., 2014; Curtis, 2018). Importantly, possibly due to the lack of data, prior literature is almost exclusively concentrated in developed country set­ tings such as the U.S. and EU where pollution levels are relatively low. Whether and how the market-based environmental regulation affects employment in China, the world’s largest developing country, is still unclear. The core objective of this study is to identify the causal effect of a market-based environmental regulation on firm’s labor demand. We introduce the characteristics of China’s “SO2 emissions trading pilot program” to construct a “quasi-natural experiment”. The SO2 emissions trading program is implemented in 11 provinces in China, different from the SO2 allowance trading program in the U.S. and the Emissions Trading Scheme (ETS) in the EU, which cover their entire respective territories. The program comprises regions in various geographical lo­ cations, and the selected provinces differ in economic development level significantly, with the eastern provinces being the most developed and the western provinces the least. Moreover, the central government chooses the provinces for the pilot program without much influence from local governments. Given the top-down nature of the program design, the program can be considered as a quasi-natural experiment. A difference-in-differences (DID) identification framework is constructed to compare the labor demand between the firms located in the program provinces (treatment group), and the firms in the non-program prov­ inces (control group). As many developing and emerging economies, China is often regar­ ded as having weak environmental enforcement due to poor legal in­ stitutions, and thus effectively enforcing regulations may be especially challenging (Hering and Poncet, 2014). Therefore, one issue we need to address before examining the employment effects of the SO2 emissions trading program is whether the program is effective in reducing SO2 emissions. Our results show that the program was accompanied by large reductions in SO2 emissions of the regulated cities. Moreover, we find that the SO2 emissions trading program significantly increases firm’s labor demand and there are some lagging effects. We further confirm through mechanism analysis that this positive employment effect is driven by the expansion of firm’s production scale. These results suggest that the China’s SO2 trading program makes a double dividend in environment and employment. Our results hold up to a battery of robustness tests, such as instrument variable approach using the dis­ tance to the nearest mine as instrument variables for treatment status, the placebo test with a random assignment of the pilot provinces, the inclusion of region-specific time, the exclusion of carbon emissions trading program, a counterfactual check for pre-existing trend and the control for firm avoidance behavior. This paper contributes to the literature in two important ways. First, to the best of our knowledge, this study constitutes the first attempt to investigate the impact of the emissions trading program on labor de­ mand in a developing country. Prior work that examines the impact of environmental regulations on labor demand mostly focus on commandand-control regulations (Neuwahl et al., 2008; Gray et al., 2014; Liu et al., 2017; Sheriff et al., 2019). A few studies that examines market-based environmental regulations are conducted in developed countries, such as the U.S. and EU (Chan et al., 2013; Ferris et al., 2014; Curtis, 2018). We note, however, it is open question whether the emissions trading program may affect labor demand in a developing country. Second, despite the existing literature that emphasizes the role of

environmental regulations for preserving environmental quality and human health, the complex relationship between regulations and employment continue to be a matter of debate (Curtis, 2018). In this paper, we exploit a quasi-natural experimental design to identify the employment effects of a market-based environmental regulation (namely emissions trading) in China. We find evidence that the emis­ sions trading program significantly increases firm’s labor demand, which is inconsistent with the study by Liu et al. (2017) that examined the impact of China’s command-and-control regulation on labor de­ mand. This study attempts to broaden the understanding of environ­ mental regulation, which could provide us with more insights and evidence concerning the design of regulations in pollution prevention and control. In addition, China’s emissions trading program could pro­ vide useful lessons for environmental policy-making in other developing countries. This encourages developing countries to actively apply market-based approaches to address local and global environmental challenges. Section 2 provides literature review and institutional background of the SO2 emissions trading program. Section 3 introduces the data. Sec­ tion 4 presents empirical methodology and the results. Section 5 reports the robustness tests. Section 6 conducts the mechanism analysis, fol­ lowed by concluding remarks in Section 7. 2. Related literature and institutional background 2.1. Environmental regulation and labor demand The impact of environmental regulation on labor demand has received great attention in the past decades. Mcneilla and Williamsb (2007) argue that there is a need for ecological economists to pay attention to the effect of sustainable development policies on the employment. Since developed countries strengthened environmental regulations in the 1970s in response to serious pollution problems, the concern that regulations would lead to employment loss has been emerging. For instance, in a 1990 poll, one-third of respondents reported that their jobs were threatened by environmental regulations (Mor­ genstern et al., 2002). However, some scholars believe that the employment loss caused by environmental regulations is an “over­ statement”, and the employment creation caused by environmental regulations is usually ignored (Goodstein, 1994). To date, the directional impact of environmental regulations on labor demand has still been ambiguous a priori (Liu et al., 2017). By increasing marginal cost of production, compliance with envi­ ronmental regulations may cause output to fall, thereby reducing de­ mand for labor inputs. Greenstone (2002) examines the impact of the Clean Air Act Amendments (CAAA) in the U.S. on manufacturing employment, and finds that the number of jobs in nonattainment counties facing stricter environmental regulations reduced by about 590,000 from 1972 to 1987. Similarly, using the revision of the Clean Air Act by the EPA in 1990, Walker (2011) estimates the effects of the new and stricter pollution standards on employment. He also finds that the gross manufacturing employment falls by about 15% in nonattain­ ment counties relative to attainment counties. Gray et al. (2014) find that plants in the pulp and paper industry, which were regulated by EPA’s Cluster Rule, decrease employment in a small scale (about 3%– 7%), but these effects are not always statistically significant. More recently, Sheriff et al. (2019) find that the 1990 change in ozone Na­ tional Ambient Air Quality Standards (NAAQS) regulations significantly reduces the employment of power plants located in nonattainment areas. Liu et al. (2017) analyze the impact of wastewater discharge standard on textile printing and dyeing (TPD) enterprises in China. They find these enterprises experience about 7% reduction in their labor de­ mand when faced with stricter regulation. However, environmental regulations could also increase the labor demand. Specifically, plants may require more labor for installation, operation and maintenance of pollution control equipment to comply 2

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with environmental regulations. Indeed, many studies argue that envi­ ronmental regulations have a positive impact on labor demand. For example, Berman and Bui (2001), taking the air pollution regulations of the south coast air quality management district (SCAQMD) in the U.S. as an example, find that stricter environmental regulations do not always lead to a decrease in refinery employment and may even slightly in­ crease labor demand. Bezdek et al. (2008) estimate the economic impact � standards in of the increase in corporate average fuel economy (CAFE) the United States. They find that the stricter environmental standards have a positive impact on the economy and created 300,000 jobs. Barrett and Hoerner (2002) report that many policies aimed at improving en­ ergy efficiency and reducing carbon emissions significantly improve the economic efficiency. It was projected that these environmental policies would create 660,000 jobs in 2010 and 1.4 million jobs in 2020. As discussed above, studies focusing on the impacts of commandand-control environmental regulations on labor demand have been still controversial. Unlike previous studies, we investigate the employ­ ment effects of a market-based environmental regulation (such as environmental taxes and emissions trading), where there is little related literature focusing on it. Martin et al. (2014) explore the impact of UK’s carbon tax policy and find that the policy has no significant impact on manufacturing employment. Yamazaki (2017) finds that the impacts of British Columbia’s carbon tax policies on employment differed by in­ dustries, and the overall employment effects are positive and statisti­ cally significant. However, Chi (2018) in a recent study finds that the same carbon tax policy lead to about 1.3% decrease in the overall employment.

employment effect in developing countries. This study provides new evidence that links market-based regulations and firm’s labor demand in China. 2.3. Institutional background The high concentrations of SO2 emissions due to fossil fuel con­ sumption have contributed a lot in degrading the air quality in Chinese cities (World Bank, 2007; Hering and Poncet, 2014). To address this issue, the Chinese government has been regularly implementing new environmental regulations. Specifically, the State Council first issued the Law on Prevention and Control of Atmospheric Pollution in 1987, high­ lighting that the emission of sulfide gas in the production process should be equipped with desulfurization device or other desulfurization mea­ sures. In 1995, the Law on Prevention and Control of Atmospheric Pollution was amended; the restriction of high sulfur coal and the management of thermal power plants were added to the law. In 1996, the emission standards of atmospheric pollutants were issued, in which the emission limit of SO2 was clearly defined, and it began to set a total emission limit of SO2. In 1998, in order to curb SO2 pollution and the increasing acid rain problem, the State Council implemented the two control zones (TCZ) policy and set the specific reduction targets for SO2 emission. However, the implementation of the early SO2 emission reduction policy was not successful. According to the 2006 report of the Ministry of Environmental Protection of China, SO2 emissions reached a historical high in 2005. The main reason may be that environmental laws and regulations have inhibited the enthusiasm of firms to control for pollu­ tion, and the impacts of these policies on SO2 emissions are mostly temporary (Chen et al., 2018). In 2007, the Ministry of Finance and the Ministry of Environmental Protection (MEP) approved implementation of the emissions trading program in 11 provinces, including Jiangsu, Tianjin, Zhejiang, Hubei, Chongqing, Hunan, Inner Mongolia, Hebei, Shaanxi, Henan and Shanxi (see Fig. 1). The 11 pilot provinces account for 27% of the nation’s territory, 42.8% of the GDP, and 50.2% of industrial SO2 emissions in 2007 (China Statistical Yearbook, 2008). Under the program, all pilot provinces have set up emissions trading centers to facilitate trading among firms, and they have also issued regulations and guidelines for the management of emissions trading. The SO2 emissions trading program is implemented as follows. First, the Ministry of Environmental Protection in China delimits the total allowable emissions (i.e., the cap) for a stated time period. Each prov­ ince is then allocated an emissions quota according to its actual emis­ sions in a base year, and the total permits issued equal to the total emission cap. The allocation of the total quantity of emissions is every five years. Second, firms have to purchase their initial emission allow­ ance (or “permit”) from the local Environmental Protection Bureaus (EPBs), which is responsible for inspecting the purchase applications and deciding whether to issue permits to them. Polluters should apply for the number of permits according to their production plan, and the total number of applications must not exceed the amount of pollutant emissions approved by the environmental impact assessment require­ ment. What is important is that polluters only allow trading their per­ mits with other polluters in the province, and all transactions are conducted through the online platform of the emissions trading center. The EPBs will punish the polluters who exceed their purchased permits by means such as fine, prohibition to emit, and revocation of operating licenses. In addition, the emissions trading center will repurchase sur­ plus pollutant discharge permit at the same price of purchase. Third, the operation of the emissions trading is mainly dominated by the market. While the provincial governments set the benchmark price for SO2 emission trading, the real transaction price is regulated by the market and far exceeds the benchmark price. Table 1 summarizes the implementation of the emissions trading program in each pilot area. We find that the real transaction price ex­ ceeds the benchmark price in most provinces. The transaction volume in

2.2. Emissions trading and labor demand As an important instrument of market-based environmental regula­ tions, emissions trading was proposed in 1968 by Dales. The right to discharge pollutants is legalized through legislation and distributed to the polluter in the form of compensation or free, allowing such rights to be traded through market, so as to achieve the purpose of reducing emissions and protecting environment (Dales, 1968). As Coase (1960) noted, when the property rights are clearly defined, the transaction costs tend to be small or even zero, and the market could always achieve Pareto optimality in resource allocation. Montgomery (1972) demon­ strates that the emissions trading program has the characteristic of achieving pollution control target at the lowest cost. Few studies investigate the effect of emissions trading on labor market. Anger and Oberndorfer (2008) find that the allocation of first-stage quotas within the EU ETS have no significant impact on employment of regulated firms in Germany. Abrell et al. (2011) use more than 2000 firms in Europe to study the relationship between the first and second stages of EU ETS and firm competitiveness. They find that while EU ETS leads to a reduction in CO2 emissions, but it has no significant impact on employment. Chan et al. (2013), using data of 5873 units in 10 European countries from 2001 to 2009, find that the EU ETS has no significant impact on firm’s labor demand. Ferris et al. (2014) find little evidence that the SO2 trading program in the U.S. leads to a significant decrease in labor demand of power plants. A recent study by Curtis (2018), however, shows that NOx trading program in the U.S. significantly reduce the labor demand of regulated firms. In sum, the existing literature on the impact of emissions trading on labor demand is mainly from developed countries such as the EU and the U.S. Yet, the employment effects of emissions trading remain understudied because weak institutions are generally considered a key impediment to advanced market-based environmental regulations in developing countries (Hanna, 2010). Identifying whether and how market-based regulations affect labor demand is important, since many developing economies with relative weak institutions are facing not only the world’s worst environmental challenges and but also economic growth pressure. Lower income, larger rural-urban mobility, and increased environmental awareness may all contribute to a different 3

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Fig. 1. The geographical distribution of China’s SO2 trading program pilot areas.

et al., 2018), and China’s SO2 emissions have exceeded the sum of OECD countries in 2013 (Liu et al., 2018). This paper uses a sample of firms that emits SO2, which were listed at Shanghai and Shenzhen Stock Ex­ changes during 2004–2016. The following three steps are used to determine the samples and collect data. First, we determine the industries that emits SO2. According to the China Environmental Statistical Yearbook 2014, we identified the in­ dustries with SO2 emissions are concentrated in three sectors – mining, manufacturing, and electricity – which accounted for almost all of China’s SO2 emissions (99%). However, most electricity firms are stateowned, and emissions allowances are only traded among them. There­ fore, we only focus on mining and manufacturing firms, and SO2 emis­ sions of these two industries account for about 61% of the total emissions. Second, we identify the listed firms that emit SO2 in the mining and manufacturing industries. According to the Rules on China’s Environ­ mental Information Disclosure promulgated by the China Securities Reg­ ulatory Commission in 2008, firms in the mining and manufacturing industries must disclose SO2 emission information in their annual re­ ports. Therefore, we collected information on SO2 emissions from the annual disclosures and corporate social responsibility (CSR) reports of the listed firms in the two industries. Specifically, if the firm meets any of the following conditions, we will include the firm into our sample: 1) Disclosure of any SO2 emission information; 2) Whether SO2 emissions meet the emissions permit requirements; 3) Whether desulfurization equipment or desulfurization technology has been installed/adopted; 4) Whether the production investment includes raw materials such as coal. To avoid the influence of abnormal, we follow Du et al. (2014) to delete

Table 1 Trading centers and aggregate statistics by province. Pilot provinces

Benchmark price (1000 Yuan/ton)

Real price (1000 yuan/ ton)

Accumulated transactions (100 million Yuan)

Jiangsu Zhejiang Hubei Chongqing Hunan Inner Mongolia Hebei Shanxi Henan Shaanxi

2.24 2 3.99 4.88 15 2.5

19.2 12.4 9.3 13 15 2.5

4.23, by Dec. 2016 7.73, by Jun., 2014 0.26, by Dec. 2014 2.96, by Jun., 2016 2.02, by Jun., 2017 2.1, by Dec. 2016

5 18 4.9 6

5 18 4.9 11

0.61, by Dec. 2013 18, by Dec. 2016 1.11, by Nov., 2013 7.53, by Dec. 2016

Note: Data is compiled from published information from the trading center of each pilot province. The data for Tianjin is unavailable.

each province has increased quickly in recent years. The accumulated transactions in Shaanxi and Chongqing reached 753 and 296 million yuan by 2016, respectively. 3. Data description 3.1. Sample and data China has been the world’s largest SO2 emitter since 2005 (Yang 4

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special treatment (ST) and *ST firms. Because ST and *ST firms indicate that a listed firm has suffered losses for two or three consecutive fiscal years, and these firms carry the risk of being delisted. And then, we exclude firms with missing values. After the above process, we ended up with 203 listed firms, which selected from 15 2-digit Chinese Industry Classification (CIC) industries. There are 84 firms in the program provinces and 119 firms in the non-program provinces. The sample distribution is reported in Table A1. Third, based on the codes of listed firms determined above, we further collected the firm-level financial information from the China Stock Market and Accounting Research (CSMAR) database, where data is taken from firm annual reports. The remaining provincial-level and city-level data used in this paper are separately obtained from the China Statistical Yearbook and China City Statistical Yearbook for years between 2004 and 2016.

(Wagner, 2002). We follow Sheng et al. (2019) to use a dummy variable as the proxy for export behavior, i.e., Export equals 1 if a firm has export business, otherwise it equals 0. Marketization: Fierce competition is more likely to drive toward improving their production efficiency and expanding their production scale to enhance competitiveness (Sheng et al., 2019). We use local marketization level to measure the market competition. 3.3. Descriptive statistics We first present initial graphical evidence on the validity of the quasi-experimental design. As shown in Fig. 2, the trends of employment between treatment group and control group were almost parallel before 2008. After the regulation, the treatment group experienced a faster increase in employment than the control group, which preliminarily suggested that the emissions trading program has a positive impact on corporate employment. Then, Table 2 reports the descriptive statistics of key variables. As shown in Panel A, we could find that the differences between the min­ imum and maximum are always large. The Panel B indicates that the mean values of these variables vary greatly before and after the program.

3.2. Measurement of key variables 3.2.1. Outcome variable Firm’s labor demand: Following previous studies (Gray et al., 2014; Liu et al., 2017), we use the number of employees to measure the firm’s labor demand. 3.2.2. Treatment variables and control variables Emissions Trading Program: This is a dummy variable that equals 1 if a firm is located in the program provinces of the SO2 emissions trading policy and equals 0 otherwise. There are 11 program provinces: Jiangsu, Zhejiang, Tianjin, Hubei, Hunan, Henan, Shanxi, Chongqing, Shaanxi, Hebei, and Inner Mongolia. Other provinces are regarded as nonprogram areas. SOE: It indicates the ownership of a firm, i.e., SOE equals 1 if a firm is state-owned, otherwise it equals 0. Compared with private firms, stateowned firms tend to have a higher share of labor income and advantages in financing, capital use, and production technology. This might lead to different demands on labor across different ownership structure (Brandt et al., 2012). Capital density: As Tavares and Teixeira (2005) argued, it is measured by the ratio of total fixed assets of the firm to the number of employees at the end of the year. Capital intensity reflects to what extent that the firm’s production depends on capital or labor. Capital-intensive and labor-intensive firms would have large differences in the production process and labor demand. Firm age: There is a big difference in labor demand between young and old firms (Liu et al., 2017), older firms with qualifications might be more competitive and attractive to workers than younger one. Income tax: On the one hand, firm income tax might increase the capital cost and reduce the net profit, thus inhibiting firm’s labor de­ mand (Harden and Hoyt, 2003). On the other hand, income tax also reflects the business performance of firm to a certain extent. This means that income tax might be conducive to expanding production and in­ crease labor demand. Average wage: Gray et al. (2014) shows that the local labor market conditions may affect the recruitment decisions of firm. We use local average wage to measure the labor market conditions and test its impact on labor demand. Sales expenses rate: The increase in selling expenses rate could enhance the firm’s future performance (Srinivasan et al., 2009; Joshi and Hanssens, 2010), thereby promoting firm expansion and increasing its labor demand. Operating profit: Firm’s operating profit reflects its operation per­ formance. The better the operation performance, the more advanta­ geous the firm is in attracting labor force and expanding production (Greenstone, 2002). Export: Export behaviour is often closely related to employment of firms. Export firms usually have an increased level of business activities and higher profits, thus having greater incentives for scaled expansion

4. Empirical test 4.1. Effectiveness of SO2 emissions trading program Before identifying the employment effects of SO2 trading program, we first examine whether the program was effective in reducing SO2 emissions. To address this issue, we estimate the following DID model based on the city level data: LnðSit Þ ¼ θ0 þ θ1 timet * programi þ λXit þ γi þ μt þ εit

(1)

where i indexes cities and t indexes years. The dependent variable Ln (Sit) represents the natural logarithm of industrial SO2 emissions. programi is a dummy variable and equals 1 if the city i is located in the emissions trading program provinces, otherwise it equals 0. timet equals 1 for each year after 2007, otherwise it equals 0. The coefficient on timet *programi is the standard difference-in-differences estimator, reflecting the difference between the treatment group and the control group before and after 2007. If the trading program leads to reductions in industrial SO2 emissions, the coefficient of interaction term should be significantly negative. Xit controls for a set of covariates that capture technology investments (log), total population (log), Foreign direct in­ vestment (log), and per capita GDP (log). γi is the city fixed effects, controlling for any permanent differences across cities such as geographic features and natural endowments. μt is the time fixed effects, capturing any unobserved year-specific impacts such as macroeconomic shocks, business cycles, and fiscal and monetary policies that are common to both program areas and non-program areas. is the error term. Standard errors are clustered at the province level to deal with the potential heteroskedasticity and serial correlations. The estimates of the impact of the trading program on SO2 emissions are reported in Table 3. Column (1) presents the estimates that only control for year and city fixed effects. In column (2), we incorporate control variables. The estimates in both columns are negative and sta­ tistically significant, suggesting that China’s SO2 emissions trading program is effective in reducing pollution emission. In addition to examining the environmental performance of the emissions trading program, we also examined whether the emissions trading program affected the city level industrial employment. As shown in columns (3) and (4) of Table 3, industrial employment in the program areas experiences a significant increase compared to that in the nonprogram areas. According to the point estimate in column (4), indus­ trial employment in the program cities increases by approximately 5

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Fig. 2. Average change in firm employment. Table 2 Descriptive statistics of variables.

Table 3 Impact of SO2 emissions trading program on SO2 emissions and industrial employment.

Panel A: Descriptive Statistics Variable

Observation

Mean

Std. dev

Min

Max

Ln (Emp) SOE Capital density Firm age Income tax Average wage Sales expense rate Operating profit Export Marketization

2639 2639 2639 2639 2639 2639 2639 2639 2639 2639

8.157 0.697 2.340 14.335 0.079 39.60 0.021 0.315 0.547 6.617

1.205 0.460 7.963 5.431 0.210 20.495 0.045 1.017 0.498 1.872

2.303 0 0.096 3 1.814 11.855 0 8.022 0 0.23

11.280 1 287.510 46 3.828 122.749 0.603 15.652 1 10.92

Variable

time*program Ln (investment in technology) Ln (total population)

Pre-program (2004–2007)

Post-program (2008–2016)

difference

7.923 0.734 1.977 10.140 0.052 20.599 0.011 0.239 0.483 7.018

8.260 0.681 2.501 16.640 0.091 48.045 0.026 0.350 0.575 6.439

0.337*** 0.053*** 0.524 6.500*** 0.039*** 27.446*** 0.015*** 0.110*** 0.091*** 0.578***

Ln(IndEmp)

Ln(IndEmp)

(1)

(2)

(3)

(4)

0.184*** (0.059)

0.177*** (0.057) 0.087* (0.045) 0.428 (0.415) 0.002 (0.012) 0.137 (0.099) 0.500 (3.054) Y Y 0.800 3396

0.093*** (0.035)

0.087** (0.035) 0.051** (0.020) 0.575* (0.333) 0.004 (0.007) 0.083 (0.051) 8.391*** (2.457) Y Y 0.942 3396

Ln (per capita GDP) Constant City fixed effects Year fixed effects R-squared Observations

3.932*** (0.045) Y Y 0.799 3396

13.872*** (0.020) Y Y 0.941 3396

Note: Robust standard errors in parentheses are clustered at the province level. Significance: ***p < 0.01, **p < 0.05, *p < 0.1.

operating profit, export and marketization. ηc controls for the industry fixed effects, γi accounts for the province fixed effects, and μt captures the year fixed effects. εijt is the error term. Standard errors are clustered at the province level. The estimated results are reported in Table 4. Column (1) presents the estimates for the SO2 trading program on the firm’s labor demand with control variables and year fixed effects. Column (2) adds province fixed effects and column (3) adds industry fixed effects. Columns (4) further incorporates both the year and firm fixed effects that control for permanent differences across firms. All the estimated results consis­ tently show that the SO2 emissions trading program has a significantly positive impact on the firm’s labor demand. The point estimates, which reported in column (3), indicate that the firms in the program provinces experience a 16% increase in labor demand relative to firms in the nonprogram provinces. These are some differences in our results compared to other related studies. On the one hand, our findings are different from the study by Liu et al. (2017) that investigated the employment effect of China’s waste­ water discharge standards. The reason for the inconsistent conclusions is the difference in the types of regulatory policies. The emissions trading program is a market-based regulation, while wastewater discharge standards is a command-and-control regulation. Both theoretical and

Note: Significance: ***p < 0.01, **p < 0.05, *p < 0.1. The unit of income tax and operating profit is billion yuan, the unit of average wage is thousand ton.

8.7%. This means that China’s SO2 emissions trading program achieves the double dividend of coexistence of employment growth and envi­ ronmental protection at city level. 4.2. Impact of SO2 emissions trading program on firm’s labor demand 4.2.1. Benchmark regression To examine the employment effects of the SO2 emissions trading program at the firm level, we estimate the following DID model for the nature logarithm of the number of corporate employees (LnðEmpijt Þ) LnðEmpict Þ ¼ β0 þ β1 timet * programi þ λXict þ γi þ μt þ ηc þ εict

Ln(SO2)

Ln (FDI)

Panel B: Differences in the mean value of the variables before and after the program

Ln (Emp) SOE Capital density Firm age Income tax Average wage Sales expense rate Operating profit Export Marketization

Ln(SO2)

(2)

where j indexes firms, c indexes industries, and the definitions of i, t, timet , and programi are the same as Eq. (1). The coefficient β1 of the interaction term represents the effect of the SO2 trading program on the firm’s labor demand. Xijt is a set of control variables, including firm age, SOE, capital intensity, income tax, sales expenses rate, average wage, 6

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significant, it means that there might be a significantly different trend in employment between the treatment and control groups before the emissions trading program. Fig. 3 plots the estimated coefficients of the interaction terms and their associated 95% confidence interval. Firstly, β2004 , β2005 , β2006 are statistically insignificant, suggesting that there is no signifi­ cant difference in the trend between program firms and non-program firms before the implementation of the policy. Furthermore, we find that the estimated coefficients became statistically significant in 2012 and continued to be positive with increasing magnitude. These results imply that there are some lagging effects of the SO2 emissions trading program on firm’s labor demand. A possible explanation is that some pilot program provinces, such as Hunan, Hubei, Henan, and Shaanxi, have no large-scale emissions trading until 2011. This might also lead to insignificant employment effects at the early stage of the program. Consistent with our anticipation, as the emissions trading market became more and more active, the impact on firm’s labor demand was increasing.

Table 4 Estimates of impact of SO2 emissions trading program on firm’s labor demand. Variable

Ln (Emp)

time*program program SOE Capital density Firm age Ln (income tax) Ln (average wage) Sales expense rate Ln (operating profit) Export Marketization Constant Year fixed effects Province fixed effects Industry fixed effects Firm fixed effects R-squared Observations

(1)

(2)

(3)

(4)

0.165** (0.069) 0.008(0.135) 0.586*** (0.118) 0.012 (0.007) 0.009 (0.014) 0.010*** (0.004) 0.131 (0.234) 8.697*** (2.016) 0.006** (0.002) 0.485*** (0.132) 0.010 (0.034) 8.352*** (2.159) Y N N N 0.309 2639

0.160** (0.061)

0.161** (0.061)

0.143** (0.061)

0.417*** (0.126) 0.011 (0.007) 0.005 (0.017) 0.010*** (0.003) 0.643* (0.317) 8.348*** (1.951) 0.005** (0.002) 0.529*** (0.144) 0.015 (0.042) 0.794 (3.260) Y Y N N 0.390 2639

0.358*** (0.128) 0.011 (0.007) 0.010 (0.015) 0.010*** (0.003) 0.604* (0.317) 8.950*** (1.751) 0.004* (0.002) 0.457*** (0.117) 0.014 (0.043) 2.281 (3.196) Y Y Y N 0.492 2639

0.303 (0.191) 0.004** (0.002) 0.031 (0.033) 0.004* (0.002) 0.462 (0.305) 5.526*** (1.506) 0.001 (0.001) 0.103*** (0.036) 0.006 (0.048) 3.337 (2.715) Y N N Y 0.202 2639

5. Robustness tests In this section, to address other possible concerns, we further check the robustness of benchmark results. 5.1. Placebo test To address the concern that our results might be driven by omitted variables, we conducted a placebo test by randomly assigning the pilot program across all provinces in the country (Li et al., 2016; Cai et al., 2016). Specifically, we first randomly select 11 provinces from the total 31 provinces in our sample and assign pilot status programflase to the firms in these provinces, while the firms in other provinces as the control group. Then, we introduce a new regressor time*programflase , and re-estimate equation. In order to avoid contamination by any rare events, we conduct the random assignment 500 times. Fig. 4 presents the distribution of estimated coefficients of the 500 random sample and their associated p-values. We find that the distri­ bution is centered around zero and most of estimates’ p-values are larger than 0.1, which means that our estimates are unlikely to be driven by omitted variables (Li et al., 2016).

Note: Robust standard errors in parentheses are clustered at the province level. Significance: ***p < 0.01, **p < 0.05, *p < 0.1.

empirical work has shown market-based regulation, such as the emis­ sions trading, to be less costly than command-and-control regulation (Chan et al., 2013; Curtis, 2018). In this case, emissions trading program could increase labor demand by providing firms with greater flexibility in arranging production rather than reducing output. On the other hand, our study has different findings compared to previous studies that examined the impact of EU ETS on employment (e. g., Anger and Oberndorfer, 2008; Chan et al., 2013; Ferris et al., 2014). A possible explanation is that the free or generous allocation of emission allowances within the EU ETS largely undermines firms’ incentives to engage in emission reduction activities, thereby EU ETS has a limited or insignificant impact on employment (Chan et al., 2013). In contrast, the Chinese government has formulated stricter guidelines to facilitate the SO2 emissions trading program, such as requiring polluters to purchase initial emission allowances, establishing emission trading centers, and setting benchmark prices.

5.2. Instrumental variable estimation To further check whether our results are biased to omitted variables, we follow Cai et al. (2016) to adopt an instrumental variable approach. We employ the average distance between a firm and the nearest coal mine as the instrument for the treatment status. There are two reasons why we selected the average distance as the valid instrument. On the one hand, in order to save transportation costs, firms (especially energy-intensive firms) will choose to locate close to energy sources such as coal, which means that those firms tend to be clustered in coal producing areas, and thereby these areas are more likely to be selected as part of the emissions trading pilot area. Therefore, we expect that the average distance is negatively correlated with the treatment status. On the other hand, the average distance is the same for all the firms in a province and does not vary over time, which meets the exogenous condition of the valid instrument. The regression result of the first stage is shown in Table A2. The results of the first stage show that the instrumental variable is signifi­ cantly negatively correlated with the treatment variable at the 1% level, and the F statistic is larger than the critical value (F ¼ 10), indicating that the instrumental variable is a strong predictor of treatment status. The second stage results of the estimates which reported in column (1) of Table 5 show again that the SO2 emissions trading program has a sig­ nificant positive impact on firm’s labor demand.

4.2.2. Measuring dynamic treatment effects To examine the dynamic treatment effects on firm’s labor demand, we follow Chen et al. (2018) to adopt the event study approach and set the following Eq. (3). 2016 X

LnðEmpict Þ ¼

βt programi * Yearst þ λXict þ γi þ μt þ ηc þ εict

(3)

t

where Yearst represents a vector of year dummies, t indexes years, and the values of s were from 2004 to 2016. If t ¼ s , then yearst ¼ 1 , otherwise yearst ¼ 0. We take 2007 as the base year, so the value of s does not include the year of 2007. Other variables are defined as same as in the model (2). We especially concerned with the coefficient βt which measures the differential change in firm’s labor demand between the treatment firms and control firms in year t relative to the base year. Note that the estimates βt also help us to check the common trends of the DID identification strategy. If β2004 , β2005 , β2006 are statistically 7

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Journal of Environmental Management 254 (2020) 109789

Fig. 3. Estimated dynamic treatment effects.

trading pilot program. However, TCZ policy is unlikely to confound the observed impact from our analysis because 84.3% of polluting firms in the TCZ had met the target level for SO2 emissions in 2000 (Cai et al., 2016). For another, Beijing, Tianjin, Shanghai, Chongqing, Hubei, Guangdong and Shenzhen were designated by the National Develop­ ment and Reform Commission as pilot areas for carbon emissions trading program in October 2011. Three of these 7 program provinces (Tianjin, Chongqing and Hubei) are also covered by the SO2 emissions trading program. Thus, as an additional robustness check, we exclude the sample of the overlapping provinces and firms from the analysis. In column (2) of Table 5, we find a similar estimate in the regressions with the smaller sample and the employment effects remain positive and statistically significant. This demonstrates that our estimates are not affected by the pilot policy of carbon emissions trading. Fig. 4. Placebo Test. Note: The purple curve plots the kernel density distribu­ tion of the estimates, while the blue dots are associated p-values. The red line indicates that the p-value is equal to 0.1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

5.4. Controlling for regional linear time trend The employment in different regions may follow different trends for various reasons. To control for the aggregate time trend, as Chen et al. (2018), we included regional linear line trends in the benchmark regression. Specifically, we first categorized the 30 provinces into three groups based on the regional economic development according to the China Statistical Yearbook, where three groups include Eastern, Central, and Western China. Furthermore, we added a set of region-by-year fixed effects along with all the other variables in our benchmark Eq. (2). As

5.3. Ruling out confounding effects of carbon emissions trading program One might be concerned that the observed impacts from our analysis may come from the other simultaneous environmental policies, such as the SO2 Two Control Zones (TCZ) policy and the carbon emissions Table 5 Robustness tests. Variable time*program Constant Firm controls Year fixed effects Province fixed effects Industry fixed effects Region-by-year fixed effects R-squared Observations

Ln (Emp) (1)

(2)

(3)

(4)

(5)

(6)

0.722* (0.374) 2.974 (4.570) Y Y Y Y N 0.480 2639

0.191*** (0.059) 1.762 (3.180) Y Y Y Y N 0.490 2496

0.171** (0.076) 2.330 (3.289) Y Y Y Y Y 0.493 2639

0.141** (0.064) 3.197 (3.382) Y Y Y Y N 0.511 2522

0.059 (0.066) 13.279*** (4.589) Y Y Y Y N 0.500 812

0.150** (0.066) 3.410 (3.270) Y Y Y Y N 0.534 2639

Note: Robust standard errors in parentheses are clustered at the province level. Significance: ***p < 0.01, **p < 0.05, *p < 0.1. Firm controls include SOE, capital density, ownership, firm age, income tax, average wage, sales expense rate, operating profit, export and marketization. 8

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Journal of Environmental Management 254 (2020) 109789

shown in column (3) of Table 5, controlling for region-by-year fixed effects leaves the estimated coefficient on labor demand unaffected, suggesting that our estimates are not affected by the aggregate trends.

significant, suggesting that the firms in the emissions trading program provinces experience a significant expansion in production scale relative to firms in the non-program provinces. In addition, we also employ firm’s fixed assets to proxy for production scale. As shown in column (3) and column (4). We find that the key coefficients are still statistically positive. Thus, we argue that the emissions trading program expands firms’ production scales and thereby drive firms to employ more workers.

5.5. Accounting for firm avoidance behavior Compared with those in non-program areas, firms in program areas tend to greater compliance pressure. One concern is that some firms, especially those with higher pollution, may move from the program areas to non-program areas so as to avoid compliance costs, leading to biased estimations. To reduce the probability of the bias from avoidance behaviors, we excluded all firms that have changed the firm’s manufacturing address during our sample period, and re-estimated Eq. (2). The estimated results are reported in column (4) in Table 5. We find that the coefficient on time*program remains positive and statistically significant.

7. Conclusion and policy implications This paper uses the DID identification framework to estimate the impact of China’s SO2 emissions trading program on firm’s labor de­ mand. Based on firm-level data from the mining and manufacturing industries, our results show that the emissions trading program signifi­ cantly increases the labor demand of regulated firms. The results remain consistent across a battery of robustness tests such as instrumental variable estimation, placebo test, the inclusion of region-specific time, counterfactual check, the exclusion of carbon emissions trading program and firm avoidance behavior. Furthermore, our mechanism analyses find that the positive employment effect is driven by the expansion of production scale. This study also has important policy implications. To start with, our findings shed light on the positive impact of market-based environ­ mental regulation on firm’s labor demand. The government needs to pay more attention to the role of market-based environmental regulation (such as the emissions trading program) in environmental governance. For example, Chinese government should further expand other marketbased environmental regulations, such as CO2 ETS, water rights trading, and energy rights trading, to address to the serious environmental challenges. In addition, the policy design and implementation of China’s SO2 ETS could provide useful lessons for other developing countries, where facing the double pressures of coexistence of environmental protection and employment. This encourages developing countries to actively apply market-based approaches to address local and global environmental challenges. Future research could proceed in the following directions. First, it will be interesting to study the impact of emissions trading program on labor demand at the regional or industry level. This could help to explore the shift of employment across industries and regions for identifying the net employment effects of environmental regulation. Second, future research could investigate whether emissions trading program affects labor demand through the entry and exit of firms.

5.6. Counterfactual check for pre-existing trend Another potential concern is that our estimates was not driven by the trading program, but by the pre-existing differences between the treat­ ment and control group. To address this concern, following Chen et al. (2017), we use a subsample with the period from 2004 to 2007 and suppose 2006 as the adoption time of the treatment. In other words, the years 2004 and 2005 are the false pre-program and the years 2006 and 2007 are the false post-program. The estimated results are shown in column (5) of Table 5. We find that the estimated coefficient on time* program is insignificant, which reveals that there are no systematic dif­ ferences in the time trends between the treatment and control group. 5.7. Robustness with respect to influential outlier To check whether the results were driven by some particularly influential outliers, we winsorized at the top and bottom 1% for all continuous variables and re-estimated Eq. (2) with full set of control variables. The estimates is reported in column (6) of Table 5. We find again that the coefficient on time*program remains significantly positive. 6. Mechanism analysis Our results show that the SO2 emissions trading program signifi­ cantly increases firm’s labor demand, which confirms the substitution effects driven by environmental regulations, as we have discussed pre­ viously. In this section, we further explore the possible mechanisms from the perspective of substitution effect. Emissions trading program is a flexible market-based environmental regulation. According to Porter hypothesis, the flexible market-based environmental regulation can improve technological innovation and firm competitiveness (Porter and Van der Linde, 1995). First, the market-based environmental regulations (e.g., the emissions trading program) could provide greater flexibility for firms to arrange produc­ tion so as to reduce emissions (Yang et al., 2017), rather than forcing firms to reduce output, to suspend operation or even to close down. Second, previous studies find evidence that the emissions trading pro­ gram could stimulate firm technological innovation (e.g., Borghesi et al., 2015; Calel and Dechezlepretre, 2016), which help firm achieve tech­ nological leadership and expand market share (Dechezlepr^etre and Sato, 2017). Therefore, under the emissions trading program, firms have greater incentives for scaled expansion and thereby increasing labor demand. To test the potential mechanism, we estimate the treatment effects on firm’s production scale. Firm’s output and firm’s production scale are often closely related (Sheng et al., 2019). We use firm’s revenue, a variable directly related to firm’s output, to proxy for the production scale. As reported in the columns (1) and (2) in Table 6, we find that the coefficients of interaction term are all positive and statistically

Table 6 Mechanism analysis. Variable

time*program Constant Other controls Year fixed effects Province fixed effects Industry fixed effects Firm fixed effects R-squared Observations

Ln (Revenue)

Ln (Revenue)

Ln (Fixed assets)

Ln (Fixed assets)

(1)

(2)

(3)

(4)

0.231* (0.129) 20.79*** (0.596) Y Y Y

0.215* (0.117) 19.48*** (0.252) Y Y N

0.278* (0.146) 19.93*** (0.638) Y Y Y

0.263** (0.129) 18.82*** (0.327) Y Y N

Y

N

Y

N

N 0.503 2639

Y 0.412 2639

N 0.432 2639

Y 0.343 2639

Note: Robust standard errors in parentheses are clustered at the province level. Significance: ***p < 0.01, **p < 0.05, *p < 0.1. Other controls include firm age, SOE, operating profits, firm export, ROA, Asset-liability ratio. 9

Journal of Environmental Management 254 (2020) 109789

S. Ren et al.

Acknowledgements

this research was also supported by the Fundamental Research Funds for the Central Universities of Central South University of China (Grant No. 2018zzts096).

We acknowledge the financial support from the National Natural Science Foundation of China (Grant Nos. 71974205, 71431006), and

Appendix

Table A1 Firm samples by industry Industry

Industry code

Number of listed firms

Obs.

Industry

Industry code

Number of listed firms

Obs.

Coal mining and washing Black metal mining Mining auxiliary activities Food manufacturing industry Textile industry Paper and paper products industry

B06 B08 B11 C14 C17 C22

13 4 1 2 7 10

169 52 13 26 91 130

B07 B09 C13 C15 C18 C25

3 10 1 5 1 10

39 130 13 65 13 130

Chemical raw materials and chemical products manufacturing Chemical fiber manufacturing industry Non-metallic mineral products industry

C26

37

481

Oil and gas extraction Non - ferrous metal mining industry Agricultural and sideline food processing industry Wine, beverages and refined tea manufacturing Textile and apparel, apparel industry Oil processing, charred and nuclear fuel processing industry Pharmaceutical manufacturing industry

C27

11

143

C28 C30

4 19

52 247

C29 C31

3 14

39 182

Non - ferrous metal smelting and rolling processing industry General equipment manufacturing industry Automotive Manufacturing

C32

18

234

Rubber and plastic products industry Ferrous metal smelting and rolling processing industry Metal products industry

C33

3

39

C34

3

39

Special equipment manufacturing industry

C35

5

65

C36

5

65

C37

3

39

Electrical machinery and equipment manufacturing Total

C38

5

65

Railways, ships, aerospace and other transportation equipment manufacturing Computer, communications and other electronic equipment manufacturing

C39

6

78

203

2639

Table A2 First stage of instrumental variable estimation Variable

First stage Ln(Emp)

Distance*time

1.162*** (0.054) 0.238 (1.004) 10.201 Y Y Y Y 2639

Constant F-statistics Firm controls Year fixed effects Province fixed effects Industry fixed effects Observations

Note: Robust standard errors in parentheses are clustered at the province level. Significance: ***p < 0.01, **p < 0.05, *p < 0.1.

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