Impact of environmental labeling certification on firm performance: Empirical evidence from China

Journal Pre-proof Impact of environmental labeling certification on firm performance: Empirical evidence from China Huwei Wen, Chien-Chiang Lee PII:

S0959-6526(20)30248-1

DOI:

https://doi.org/10.1016/j.jclepro.2020.120201

Reference:

JCLP 120201

To appear in:

Journal of Cleaner Production

Received Date: 3 December 2019 Revised Date:

17 January 2020

Accepted Date: 19 January 2020

Please cite this article as: Wen H, Lee C-C, Impact of environmental labeling certification on firm performance: Empirical evidence from China, Journal of Cleaner Production (2020), doi: https:// doi.org/10.1016/j.jclepro.2020.120201. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

CRediT author statement Jan. 17, 2020 Huwei Wen: Conceptualization, Methodology, Software, Data curation, Writing- Original draft preparation. Chien-Chiang Lee: Visualization, Investigation, Supervision, Writing- Reviewing and Editing.

Impact of environmental labeling certification on firm performance:

empirical evidence from China

Huwei Wen a,b,

Chien-Chiang Lee a,b, *

a. Research Center of the Central China for Economic and Social Development, Nanchang University, Nanchang, 330031, China b. School of Economics and Management, Nanchang University, Nanchang, 330031, China

Funding: We acknowledge the financial support from the Jiangxi Natural Science Fund Management Science Project (2018BAA208004), and Jiangxi Humanities and Social Sciences Key Research Base Project of University (JD18016). Conflict of Interest: The authors declare that they have no conflict of interest.

*

Corresponding author. Chien-Chiang Lee, Distinguished Professor, School of Economics and

Management, Nanchang University, Nanchang, Jiangxi, China. Contact email: [email protected] (C.-C. Lee). These authors contributed equally to this study and share first authorship.

Impact of environmental labeling certification on firm performance:

empirical evidence from China

ABSTRACT Environmental labels are designed to signal environmental and health information on products throughout their life cycle to consumers and other stakeholders. Investigating A-share listed manufacturing firms in China for the 2004-2018 period, this paper sheds new light on the nexus of environmental labeling certifications and firm performance. We find that there is a grouping difference in firm performance between treatment firms and comparison firms, no matter whether measured by ROA, Tobin’s Q, or TFP. Based on the difference-in-difference (DID) regression, we show that manufacturing firms significantly increase their financial performance and productivity after obtaining environmental labeling certifications. Our research also reveals that the effects of environmental labels on firm performance stem from both the labeling effect and technical factor. The results are robust when we use the micro-level DID model. Further examination shows that the intervention effect is mainly driven by the mechanism of price markup rather than by market share.

KEYWORDS:

environmental

labels;

difference-in-difference; intervention effect.

1

firm

performance;

matching;

1. Introduction Humans are becoming increasingly concerned about many environmental issues that impact them both directly and indirectly. Various instruments of environmental regulation are used to reduce pollution and mitigate its adverse impact on one’s health (Lee et al., 2009; Lee et al., 2010). Although public awareness in China over environmental issues is increasing, the problem of indoor pollutants caused by industrial products is still serious domestically. According to the survey data by the Association of Chinese Interior Environmental Monitoring Center, deaths in China caused by indoor pollution are already over 100,000 per year. It has thus become a major challenge for the China government on how it should encourage enterprises to produce environmental products and solve the problem of indoor pollution. Pollutants can be classified into three categories: global pollutants (e.g. carbon emissions and ozone depleting products), local pollutants (e.g. water pollutants, sulfur dioxide, and harmful gases), and indoor pollutants (e.g. volatile pollutant, product noise, and harmful chemicals). According to the traditional economic literature, externalities and information asymmetry can prevent market solutions to environmental problems (Reinhardt, 1999; Spulber, 1988). For global pollution and local pollution, the government must become involved in dealing with the externalities via command-and-control policies or other market-oriented instruments. However, information asymmetry is impacting pollution control to a greater degree. On the one hand, public supervision or other stakeholders adopt corresponding regulatory actions based on environmental information about enterprises (Mao and Wang, 2018). On the other hand, the essence of indoor pollution is mainly information asymmetry between producers and consumers (Wang et al., 2015). In this case, a voluntary approach, especially environmental information disclosure, may be more effective at controlling pollution. Environmental labeling certifications (ELCs) are designed to signal environmental information to stakeholders and to reduce information asymmetry between consumers

2

and producers (Van Amste et al., 2008). Some better-known examples of environmental labeling include forest and marine resource products, organic foods, non-fresno freezers, water-based latex paints, biodegradable washing powders, and so on (Amacher et al., 2004; Carlson and Palmer, 2016). China has initiated its own ELC program in 1994, which has been widely adopted by manufacturing enterprises since 2007 due to policy intervention (Liu et al., 2019), as detailed in Section 2.1. The program has experienced an increase in participating enterprises of more than 13 times from 2007 to 2017. One question arises as to whether the growing trend is consistent with a firm’s internal motivation to improve performance. Despite the rapid development of China’s ELC program, many manufacturing firms (over 20%) have quit the program. This phenomenon indicates that the program is encountering some challenges, and its effect on firm performance is not clearcut. With the increasing popularity of environmental labels, the extant literature has investigated the motivation of manufacturing firms that engage in voluntary certification (e.g. Gavronski et al., 2008; Shen and Qin, 2011) as well as the impact of voluntary environmental certification on purchasing behaviors (e.g. Chen et al., 2018; Roheim et al., 2011). Previous research has presented the motivations from two perspectives: the external pressure from legal concerns and stakeholders as well as the proactive reaction to internal resources in expectation of future business concerns (Gavronski et al., 2008). In other words, manufacturing firms may have a profit motivation to participate in a labeling program. There is also strong evidence that consumers have a higher willingness to pay for environmentally friendly products (Chen et al., 2018). The above literature naturally arouses the interest of this article about whether manufacturing firms benefit both financially and productively from obtaining environmental labels. Although the environmental performance of environmental certifications is relatively certain, unfortunately, the financial or economic performance is still controversial (He et al., 2015). Some studies have found that environmental certification improves the return on assets, Tobin’s Q, and other financial indicators

3

(He et al., 2015; Nishitani, 2011; Treacy et al., 2019), whereas other studies have also found a neutral or even negative effect on financial performance (King and Lenox, 2001). The extant literature is mainly concerned about ISO 14001 certification, which refers to a firm’s social responsibility for pollution with externality, and its effects on firm performance rely on stakeholders such as government, financial institutions, and other manufacturers, while it has little to do with consumers. Therefore, the effect relies on complex factors and always varies with divergent research settings (King and Lenox, 2001; He and Shen, 2019). As for environmental labeling certification, which is a consumer-oriented program, it increases legitimacy, differentiates strategy, and reduces information asymmetry, thereby enhancing a firm’s economic performance (see Wang et al. (2015) for a detailed discussion). Unlike ISO 14001, labeling certification not only reduces the information asymmetry with general stakeholders, but also conveys environmental or health information to consumers about a product’s life-cycle, especially indoor pollution information (Galarraga Gallastegui, 2002). Therefore, consumers always tend to idealize environmental labeling products, while conversely environmental labels may have a positive impact on firm performance (Sörqvist et al., 2015). Although Wang et al. (2015) discussed a similar topic, our paper, which has done a lot of work on causal identification and effect decomposition, provides more abundant evidence of the nexus between environment labels and firm performance. Wang et al. (2015) used corporate data in the period from 2002 to 2005 without the policy of green procurement and found that environmental labels have limited influence on financial performance. Our paper uses a new dataset of Chinese A-share manufacturing firms in the period from 2004 to 2018, investigates the causality of environmental labels and a firm’s economic performance, and determines the mechanisms underlying the findings. We extend the previous research primarily in three ways. First, we utilize the quasi-natural experiment of China’s ELC program to identify the effect of environmental labels. The difficulty of causal identification about the relationship

4

between environment labels and firm performance may be incurred by the potential endogeneity problem (He et al., 2015; Potoski & Prakash, 2005). In this paper we employ the matching method and difference-in-difference (DID) regression to capture the treatment effect without the risk of bringing in confounding bias. We also decompose the intervention effect into labeling effect and technical factor and explore the question over whether there is a labeling effect for environmentally friendly products. Second, we look into the mechanism of how environmental labels affect firm performance - that is, price markup vs. market share. The result shows that the intervention effect mainly stems from the mechanism of price markup rather than market share. In other words, environmental initiative of ELCs has only enlarged the acceptance for customers who are already exhibiting environmental awareness. This result has important implications for environmental policy and program reform. Third, this paper enriches the literature on corporate environmental behavior and voluntary environmental regulation. The extant literature in this field focuses on the effect of ISO14000 series, environmental management system, and green supply chain (He et al., 2015; Li et al., 2019). They cover mainly environmental problems in the production and circulation stages of manufacturing firms and for global and local pollution, while scant literature targets environmental problems in the consumption stage of products and indoor pollution. China’s ELC program in our paper is consumption-oriented, voluntary environmental regulation, and mainly for indoor pollution. The rest of the article is organized as follows. Section 2 introduces the background of China’s ELC program and explains the rationale mechanism of how it can affect firm performance. Section 3 describes the methodology and the data of our study. Section 4 provides the empirical results and checks the mechanisms. Section 5 presents some discussions, and Section 6 concludes.

5

2. Background and theory 2.1. Environmental labeling certifications in China By awarding labels and certificates to related manufacturers in accordance with certain environmental standards, and environmental label (also called “Eco-label” or “Green Label”) certifies via a third-party organization that the process of producing, using, recalling, and disposing of manufacturers’ products is in compliance with environmental requirements (Zhao and Xia, 1999; Jin, 2006). Environmental labeling certification has gradually become a worldwide popular voluntary environmental regulation since it first originated in Germany at the end of the 1970s. The Ministry of Ecological Environment Protection (MEEP) of the People’s Republic of China initiated its own program of environmental labeling certification in 1994 and authorized an independent organization named China Environmental United Certification Center (CEC) in charge of the certification of environmental labels. China’s ELC program aims to facilitate green consumption and standard environmental products. It aims to signal environmental and health information to stakeholders about the manufacturing products in their life cycles and benefits the manufacturers of eco-friendly products. The program provides environmental standards for automobiles, electronic devices, decorative materials, textile, clothing, packaging supplies, paper products, daily chemicals, and light industry products. At present, there are more than 100 types of environmental labeling for products in China. Among the 100 types, the majority are related to the daily life of residents, showing that environmental labels have gradually been recognized by the public, bringing better social and economic benefits, and encouraging manufacturing enterprises to actively apply for labeling certification. Before the Ministry of Finance and the Ministry of Ecological Environment Protection jointly released the first batch of List of Government Procurement of Environmental Labeling Products in December 2006, there were few manufacturing firms participating in the environmental labeling program. Thereafter, the growth of 6

green government procurement has surged in China, with types of environmental labeling products on the government procurement list rising from 14 to 59 since its implementation. Starting from April 1, 2019, all types of environmental labeling products are included in the public procurement list. According to statistical data from the Ministry of Finance, the total amount of government procurement of eco-friendly with ELCs hit 715.45 billion CNY over the period from 2008 to 2015. In 2017, China spent more than 171 billion yuan on green procurement, accounting for 90.8% of public procurement. The policy intervention of green government procurement has turned out to be quite effective, has become an important factor in China’s sustainable development strategy, and has played an important role in guiding the whole society toward environmental protection. According to the statistical data of the Ministry of Ecological Environment Protection, the number of environmental labeling certifications has experienced an increase of more than 13 times in ten years, from 458 in 2007 to 6524 in 2017, as shown in Figure 1. Precisely because of the green procurement policy, more and more manufacturing firms want to join China’s ELC program, with a total of nearly 35,000 ELCs having been approved by September 2018. 7000

1

6000

0.8

5000

0.6

4000

0.4

3000

0.2

2000

0

1000

-0.2

0

-0.4 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Environmental Label Certifications Growth Rate

Fig. 1. The growth of environmental labeling certifications in China. Note:

Statistics for the year 2018 end on September 30.

7

Figure 2 displays the spatial distribution of environmental labeling firms. As shown in the figure, environmental labeling certified enterprises in China show a downward trend in the east, central, and western regions, while mainly distributed in the eastern coastal areas. According to statistics, the number of environmental labeling certified enterprises in the eastern region accounts for 75.9% of all labeled firms, while the proportion of environmental labeling certified enterprises in the central region and western region are 12.3% and 11.8%, respectively. At the provincial level, Guangdong has the largest number of such enterprises, accounting for 20.27% of the total number. Eight provinces in the eastern region rank in the top ten, with Sichuan ranking seventh and Anhui ranking tenth. The characteristics of geographical agglomeration indicate that there may be label competition among manufacturing enterprises; in other words, firms may want to gain market performance by obtaining environmental labels. Therefore, it is necessary to investigate whether there is a label effect for the ELC program.

Fig. 2. The spatial distribution of environmental labeling certifications in China. Note:

We add up all certifications in the period from 2007Q1 to 2018Q3.

2.2. The difference between ELC and ISO 14001 As the most widely used environmental management system, the ISO 14001 standard has aroused great interest of researchers in the existing literature. Some people may be confused about the difference between ISO 14001 certification and 8

China’s environmental labeling certification. In fact, the ISO 14001 standard aims at the production environment of enterprises, and only those enterprises that meet the national standards are permitted to produce products. China’s ELC program implements the ISO 14024 and ISO 14021 standards, while ISO 14001 implements the ISO 14000 standards; in other words, China’s ELC is based on the ISO 14001 standard and is more stringent than it. Although it is possible for manufacturing enterprises to directly apply for an ELC without obtaining ISO 14001 certification, they should meet the ISO 14001 standard. Naturally, all manufacturing firms with ELCs should meet the requirement of the ISO 14001 standard. ISO 14001 reflects the responsibility of enterprises to society and mainly imposes environmental constraints in the production process. However, China’s ELC is a product certification that can only be accepted on the premise that all the environmental indicators of the enterprise meet the standards. It mainly refers to whether the pollution emissions related to health meets the national standards. As a summary, China’s ELC program is mainly responsible to consumers on the premise that enterprises embody social responsibility and their products are harmless to health. In addition, the ISO 14001 standard is applicable to all enterprises in various industries, and the ELC program is designed for specific products in some industries. Of course, the environmental labeling program has its own product labeling for each type of certified product. Therefore, it provides a better quasi-natural experiment for empirical analysis. 2.3. The theoretical influence of ELCs on firm performance Drawing on traditional perspectives of information asymmetry and externalities in the previous literature, we explore the static impact of environmental labels on a firm’s financial performance. Next, we discuss the impact of environmental labels on the financial performance and productivity of manufacturing firms from the perspective of dynamic competition. First, environmental labels may benefit a firm’s financial performance through the reduction of information asymmetry. It is always difficult to observe the information 9

of manufacturing products about environment, quality, and health, and there is information asymmetry between consumers and producers. Information asymmetry always causes two problems and eventually leads to the disappearance of environmentally friendly products. On the one hand, manufacturing firms of products that are unhealthy or harmful to the environment will put more efforts into marketing and then win in competition against environmentally friendly products in the product market. This is the problem of reverse selection. On the other hand, manufacturing firms will no longer be willing to engage in R&D activities or technology applications to produce environmentally friendly and healthy design products, which will generate moral hazard. Environmental labels, in theory, send clear signals to consumers about environmental stewardship and healthy products and validate the sustainability claims of manufacturing firms, especially for information about indoor pollution (Galarraga Gallastegui, 2002; Yenipazarli, 2015). If consumers prefer to purchase healthy and safe products, then environmental labels should positively correlate with a firm’s financial performance. In fact, customers are willing to pay premium prices not only for safer and healthier products, but also for green products provided by socially responsible manufacturers (Wang et al., 2015). Second, environmental labels may benefit a firm’s financial performance through internalizing the externalities of pollution. As discussed in the extant literature, environmental certification (e.g. ISO 14001 standard) will push a firm to internalize the externalities of pollution (He et al., 2015). The reason for this conclusion is that government and other regulatory organizations will take action based on a firm’s environmental certifications. For example, according to China’s policy of green government procurement, government agencies must purchase environmental labeling certified products. Other stakeholders such as financial institutions, investors, and suppliers also take on certain regulatory measures for social responsibility or other goals. Therefore, environmental labels will offset the costs of pollution control by reducing other costs - that is, the pollution cost is internalized for manufacturing

10

firms. Third, a firm’s financial performance and productivity will benefit from environmental labels through the impact of dynamic competition. Similar to the ISO 14001 certification and environmental management system, China’s ELC program may have positive impacts on firm innovation and green behavior; see Prieto-Sandoval et al. (2016), He and Shen (2019) for more details. Although the micro-mechanisms are complicated, we can elaborate upon some mechanisms more intuitively. Whether it is the external factor of stakeholders’ support for green products or the internal factor of improved financial performance, manufacturing firms will have more resources for innovation and long-term investment activities after obtaining environmental labels. Put briefly, environmental labels increase firms’ capabilities to facilitate environmentally sustainable activities. According to the resource-based view and the resource management perspective, the capabilities to facilitate environmentally sustainable activities encompass an ever-increasingly important competitive advantage for manufacturing enterprises (Cai and Zhou, 2014; Hart, 1995; He and Shen, 2019). Therefore, environmental labels improve a firm’s financial performance and productivity. Based on the above discussion, we hypothesize the following. Hypothesis: Environmental labels have a positive impact on a firm’s financial performance and productivity.

3. Methodology and data 3.1. Sample and data To conduct our research, we set up a unique panel database by matching environmental labeling certifications data to Chinese A-share listed manufacturing firms. First, we gather the data from the official website of the Ministry of Ecology and Environment of the People’s Republic of China (http://www.mee.gov.cn/).

11

Second, we analyze the two-digit-code industry distribution of environmental label certifications and divide these ELCs into two-digit-code manufacturing industries. The classification results are mainly composed of 17 manufacturing industries, as shown in Table A1 of Appendix. The top three industries in the number of ELCs are the Manufacture of Raw Chemical Materials (C26), the Printing and Reproduction of Recording Media (C23), the Manufacture of Furniture (C21), making up 24%, 20%, and 17% of certifications, respectively. The following five industries account for more than 2% each:

Processing of Timber and Manufacture of Wood (C20), Manufacture

of Rubber and Plastics(C29), Manufacture of Non-metallic Mineral Goods (C30), Electrical Machinery and Equipment (C38), Computers and Other Electronic Equipment (C39). Third, we match the ELCs data to A-share listed manufacturing firms belonging to the 17 manufacturing industries and obtain 102 treated manufacturing firms with ELCs. In the final step, we match the 102 treated manufacturing firms to other untreated firms at a ratio of 1:3 and describe the matching method below. As a result, our sample consists of 408 Chinese A-share listed firms in the manufacturing industry, of which 102 firms are involved in China’s ELC program. Although China’s environmental labeling program began in 1994, it has been widely adopted by enterprises since 2007, because of policy intervention. Moreover, the environmental labeling certification is valid for three years. For the purpose of the difference-in-difference design in this study, we limit our sample to the period starting from 2004, so that any firm in the treatment group has a three-year period before intervention for comparison. We set the sample to the period from 2004 to 2018. The financial data and performance data are gathered from the database of China Stock Market and Accounting Research (CSMAR), which is one of the most complete for Chinese A-share listed firms’ financial information. 3.2. Matching method The treatment group is made up of manufacturing firms that have experience in ELC (in other words, have adopted environmental technology in their products’ cycle 12

life), while the comparison group is free from ELC treatment. Given that the special treatments of environmental technology and its labeling certification are not randomly assigned to firms, the estimates using the full sample may suffer from selection bias. One of the usual ways to address this issue is to construct a comparison group by defining a group of matched firms that share important characteristics with firms in the treatment group. Following Treacy et al. (2019), we construct a matching group based on specific criteria. First, the sample firms in the two group have to be in the 17 manufacturing industries with similar characteristics in their main business. Second, we choose the following variables (or characteristics) for matching:

natural log of a

firm’s total assets, a firm’s degree of capital deepening, and the leverage ratio. In order to avoid performance fluctuations that might happen for a particular control firm, each firm in the treatment group is paired with a portfolio of control firms that meets the two matching criteria. We therefore construct the matched samples of the treatment group and comparison group based on a firm’s first-year data, next apply the panel regression model to estimate the difference in firm performance between the two groups, and then utilize the DID design to estimate the effects of environmental labels on the firms’ financial performance and productivity. The combination of matching method and DID regression with additional control variables makes it possible to capture the treatment effect without the risk of any confounding bias. 3.3. Model specification We divide the manufacturing firms in our sample into two groups according to whether they do or do not partake in environmental labeling certifications. In other words, the 102 listed manufacturing firms with ELCs belong to the treatment group, while the other firms belong to the comparison group. Accordingly, we define a grouping dummy variable, treatment group and

= 1, if the manufacturing firm belongs to the = 0 otherwise.

Following He et al. (2015), we first examine the grouping difference of financial performance and productivity between the treatment firms and comparison firms with 13

the following regressions: =

+

+

+

+

+

,

(1)

where i refers to firm; t refers to time; j is a subscript representing the 2-digit code for the industry;

is the proxy variable for firm performance;

vector of control variables;

is a K-dimensional

is the corresponding K-dimensional vector; and

is

an idiosyncratic error term. We also include 2-digit code dummies ( ) and year dummies ( ) to capture the industry and macro-economic conditions that might affect the firms’ financial performance and productivity. Accordingly, coefficient of

; if

is the

is significant and positive, then it means firms with

ELCs (treatment group) exhibit significantly higher indicators of economic performance. We employ the difference-in-difference design to investigate the effects of environmental labels on firm performance. By comparing the different effects of an exogenous shock on the treatment firms and the comparison firms, the DID method is well-known for its effectiveness in identifying causal relationships and thus is widely used in causal inference and policy effect studies. We define a time dummy variable = 1 if it is the treatment period and

( ) and set

= 0 otherwise. Following He

and Shen (2019), we define the variable ×

(

as the interaction item

) directly according to whether firm i’s ELC is in period of validity at

the practical level. We express the DID model used in the empirical analysis as: =! +" where dummy (

+ "#

+

+

+

,

(2)

is the interaction of the time dummy ( ) and the grouping ), and the corresponding coefficient "# is a regression-based DID

estimate for the treatment effect. Because time fixed effects have already captured macro-economic conditions and a time trend for firm performance, the dummy variable of

cannot be included. The coefficients of " and "# refer to the

self-selection effect and intervention effect, respectively. Because a firm’s individual data exist at a lower level nested within the treatment group, it is better to apply the micro-level DID model for policy evaluation (Ryan,

14

Burgess and Dimick, 2015). Therefore, we revise the above DID model of Equation 2 as follows for investigating the intervention effect in the empirical analysis: = ! + "$

+

+

+

,

(3)

where ! is the individual fixed effect at the firm level, which is incorporated to account for potential unobserved individual heterogeneity. It is worth noting that individual effects already capture the invariant characteristics of industry, and so industry dummies are no longer included in our DID model. In other words, we use the regression model with the panel fixed-effects and control variables to estimate the treatment effect so that unobserved time-invariant heterogeneity is allowed. The coefficient "$ represents the effect of environmental labels on firm performance. Once the coefficient of

is determined to be significantly positive, the

hypothesis of this study is established. 3.4. Variable and data description Our interest is in whether or not environmental labels help manufacturing firms to improve their financial performance and productivity. Thus, we consider three proxy variables for the measurement of the dependent variable. The first proxy variable, return on assets (ROA), is from the ratio of a firm’s annual net profit to its total assets as in Li et al. (2019), representing business performance in a product market. Because business performance reflects short-term profitability, it is easily interfered by accounting methods and other factors. One way to overcome the problem is that we choose Tobin’s Q as a second proxy variable, which is measured by the market value of equity divided by the book value; it is a financial market-based measure of a firm’s managerial performance (Bharadwaj et al., 1999). Following Borin and Mancini (2016), we also choose total factor productivity (TFP) as a reliable indicator of economic performance or productivity. We obtain the variable of a firm’s total factor productivity as a residual of input-output regression analysis by using the Cobb-Douglas production function in capital, labor, and intermediate input. The first two proxy variables represent financial performance, while the latter refer to productivity or technology. 15

Following Li et al. (2019), we consider the following control variables:

lnSize,

measured by the natural logarithm of a firm’s total assets; lnAge, measured by the natural logarithm of the years a firm has existed; Debt ratio, the ratio of a firm’s total debts to its total assets; State is a dummy variable, with the value of 1 for the firm being controlled by state-owned capital and 0 otherwise; Foreign is also a dummy variable, where we set foreign=1 if the firm is controlled by foreign capital and foreign=0 otherwise; lnLoan indicates long-term loans from banks and is divided by fixed assets; Capital-labor ratio is the ratio of fixed assets to labor quantity and reflects the firm’s capital intensity; and Lerner-industry is the price markup of the industry and reflects the degree of monopoly or bargaining power. It is worth noting that our empirical analysis avoids the endogenous problem of missing variables as we consider both firm-fixed effect and year-fixed effect. To analyze the mechanism of how environmental labels influence a firm’s financial performance and productivity, we also use two mediator variables, Sale and Lerner, respectively defined as the sales revenue and the price markup of firms. We display the descriptive statistics for the variables in this study in Table A2 of Appendix.

4. Empirical results To reveal the causal relationship between environmental labels and firm performance, we first test the grouping differences of economic performance between the treatment firms and comparison firms and put forward the problem of two-way causation. Second, we use the DID estimation by pooled panel regression to obtain a primary quantitative result that could capture the intervention effect and self-selection effect. Third, we provide the mic-level DID estimation by the firm fixed effect model to reduce endogeneity and to capture causality. Fourth, we decompose the intervention effect into technical factors and labeling effects. To further explore the heterogeneity of the policy effects, we divide our sample of treated firms into tryers and sticklers according to period of ELC application. Finally, we test the mechanism of market share and price markup for how environmental labeling certification affects 16

firm performance. Our empirical route is briefly shown in Figure 3. It should be noted that the samples of regression in our paper are the treated firms and the matched control firms.

Intervention effect

Labeling effect

Grouping

Heterogeneity Tryers vs. Sticklers

Causality

difference

Self-selection effect

Technical factor

Mechanism Markup vs. Share

Fig. 3. The route of our empirical analysis. Note: The dotted line indicates that there are other factors that go beyond the scope of this article.

4.1. The grouping differences of firm performance Based on Equation (1), Table 1 displays the OLS regression results of firm performance on the group dummy variable, and the coefficient of ECLfirm represents the grouping difference of firm performance. Since the values of the group dummy variable are time-invariant, the regression model cannot include the individual fixed effect for obtaining the regression coefficient of ECLfirm. Using the pooled panel regression, this paper estimates the model of Equation (1) by controlling for the industry and time fixed effects. In Column (1) and Column (2), we consider the difference in return on assets (ROA) between treatment firms and comparison firms. Column (1) controls the industry and time dummies, while Column (2) does not. Whether or not the model controls industry and time fixed effects, the coefficients of ECLfirm with similar values are significant and positive. In Column (3) and Column (4), we choose another variable, Tobin’s Q, for a firm’s economic performance. The regression coefficients of group dummy are also significant and positive, which is similar to the conclusion of ROA. As for productivity or TFP, the coefficients of group dummy ECLfirm are also significant 17

and positive. Comparing the coefficients of the group dummy variable in Table 1, we conclude that there are grouping differences of firm performance between these two groups of manufacturing firms, and firms in the treatment group exhibit significantly higher indicators of economic performance, whether measured by ROA, Tobin’s Q, or TFP.

Table 1. Grouping Differences of Firm Performance Variable

ROA

Tobin’s Q

TFP

(1)

(2)

(3)

(4)

(5)

(6)

0.950***

0.932***

0.241***

0.334***

0.085***

0.131***

(6.85)

(6.03)

(5.43)

(8.41)

(9.02)

(12.60)

0.993***

1.114***

-0.445***

-0.519***

0.064***

0.049***

(13.09)

(13.34)

(-15.62)

(-19.82)

(12.01)

(8.43)

-0.622***

-0.015

0.221***

-0.104**

0.047***

-0.030***

(-5.24)

(-0.11)

(4.93)

(-2.56)

(4.91)

(-2.82)

-8.878***

-9.652***

-1.522***

-1.293***

-0.150***

-0.149***

(-15.77)

(-14.69)

(-6.45)

(-6.62)

(-5.63)

(-5.74)

-0.355***

-0.604***

-0.126***

0.002

0.033***

0.060***

(-2.64)

(-4.37)

(-2.99)

(0.06)

(3.36)

(5.90)

1.294***

0.976***

0.081

0.159***

-0.012

0.029*

(5.63)

(4.23)

(1.11)

(2.60)

(-0.75)

(1.80)

-1.300

-0.838

-0.652*

0.488

-0.555***

-0.224**

(-1.14)

(-0.71)

(-1.77)

(1.57)

(-5.57)

(-2.28)

-0.874***

-0.832***

-0.029

-0.076***

0.053***

0.017**

(-9.24)

(-8.06)

(-0.99)

(-2.65)

(6.97)

(2.06)

0.268

-0.839

-0.924***

-0.256*

-0.696***

-0.596***

(0.52)

(-1.52)

(-5.80)

(-1.77)

(-17.73)

(-13.30)

8.521***

14.45***

1.785***

1.470**

0.405***

-0.071

(5.57)

(4.70)

(4.06)

(2.44)

(3.58)

(-0.38)

-2.232

-4.841***

12.190***

13.150***

-0.916***

-0.032

(-1.46)

(-2.62)

(23.32)

(25.93)

(-8.01)

(-0.22)

Year dummy

No

Yes

No

Yes

No

Yes

Industry dummy

No

Yes

No

Yes

No

Yes

F-statistic

91.00

31.41

157.30

101.61

106.14

49.46

R-squared

0.2531

0.3072

0.2722

0.5345

0.2145

0.2991

4052

4052

4044

4044

4052

4052

ECLfirm

lnSize

lnAge

Debt ratio

State

Foreign

lnLoan

Capital-labor ratio

Fixed-asset ratio

Industry-lerner

Constant

N Notes:

Figures in parentheses are robust cluster standard errors (clustered at industry levels). ***, **, and *

represent that the difference is significant at the 1%, 5%, and 10% levels, respectively.

As the regression sample consists of the treated firms and the matched firms, we 18

exclude some disturbance factors for the grouping differences. For example, only firms with a specific business can apply for ECL, and the characteristics of a specific business contribute to the grouping differences of economic performance between these two groups of firms. Therefore, the grouping differences of firm performance can be interpreted as the return of firm characteristics or intervention effect. We further note that treatment firms may have higher performance before or after obtaining the ELCs. In the former case, the improvement of economic performance is due to firm characteristics, especially green behavior before participating in the program. In another case, it is environmental labeling certification that improves firm performance - namely, the intervention effect. Therefore, we need to further investigate why manufacturing firms in the treatment group have higher performance.

4.2. Decomposition of grouping differences:

intervention effect vs. self-selection

effect We can identify the self-selection effect and intervention effect by employing the regression-based difference-in-difference design, which is based on Equation (2). Table 2 displays the results of difference-in-difference regression. As mentioned above, the coefficients of ELCfirm and ELCadoption represent the self-selection effect and intervention effect, respectively. It is worth noting that this paper aims to provide the empirical evidence of the intervention effect rather than the self-selection effect. Thus, our argument in the following about the self-selection effect may not be sufficient. We first investigate the impact of environmental labels on a firm’s financial performance and productivity. As shown in Table 2, the coefficients of ELCadoption are all positive and statistically significant at the 5% level except for Column (7) and Column (12). In Column (7), the regression does not control the industry and time fixed effects, meaning that the coefficient may suffer deviation in the regression results. In Column (12), the T value of ELCadoption is 1.36 and the P value is 0.177, denoting that the coefficient is greater than zero with high probability. The above 19

results indicate that manufacturing firms in the treatment group have significantly their financial performance and productivity after obtaining ELCs. In other words, it has an intervention effect

China’s ELC program and supports the study’s

hypothesis. As for the grouping differences of firm performance, they are still significant and positive after controlling the effect of policy intervention. As shown in Table 2, the regression coefficients of ELCfirm are significantly positive at the 1% level except for Column (9), indicating that economic performance is higher for the treated group even without environmental labeling certification, and manufacturing firms in the treatment group without ELCs have a higher performance, because of their own characteristics. We control the business characteristics and other common characteristics of the sample firms in the two groups by the matching method as described in Section 3.2. The coefficient of ELCfirm in Table 2 refers to other unobservable characteristics, especially green behavior of manufacturing firms. In any case, we can determine that there are unobserved differences in firm characteristics between the treatment group and the control group. Therefore, the grouping differences of firm performance between the two groups of firms are nfluenced by both the intervention and self-selection effects. Our model of Equation (2) does not include the invariant characteristics of manufacturing firms and may suffer two endogenous problems of the pooled panel regression. On the one hand, a firm’s decision to participate in the program is affected by these unobservable factors. As a result, the variable ELCfirm correlates to the idiosyncratic error term in Equation 2, and the coefficient of ELCfirm may be biased. This is why our study pays little attention to the self-selection effect, and we just conjecture that the technical factors may be responsible for this effect. On the other hand, the idiosyncratic error term in Equation (2) may also be related to the variable of ELCadoption, and the coefficient of ELCadoption will fail at capturing the intervention effects. Put briefly, the self-selection effect leads to the DID estimator, which is based on the pooled panel model, being biased.

20

Table 2. Results of Difference-in-difference Regression Variable

ROA

Tobin’s Q

TFP

(7)

(8)

(9)

(10)

(11)

(12)

0.867***

0.636***

0.066

0.228***

0.041***

0.120***

(4.88)

(3.24)

(1.09)

(4.30)

(3.10)

(8.51)

0.163

0.583**

0.341***

0.209***

0.087***

0.022

(0.68)

(2.34)

(4.38)

(3.11)

(5.44)

(1.36)

0.990***

1.108***

-0.452***

-0.521***

0.063***

0.049***

(12.98)

(13.28)

(-15.82)

(-19.92)

(11.66)

(8.38)

-0.633***

-0.019

0.198***

-0.105***

0.041***

-0.030***

(-5.33)

(-0.14)

(4.42)

(-2.58)

(4.25)

(-2.83)

-8.860***

-9.611***

-1.485***

-1.279***

-0.140***

-0.148***

(-15.71)

(-14.65)

(-6.32)

(-6.55)

(-5.28)

(-5.68)

-0.351***

-0.610***

-0.118***

0.001

0.035***

0.060***

(-2.61)

(-4.42)

(-2.81)

(-0.00)

(3.58)

(5.87)

1.288***

0.938***

0.068

0.145**

-0.015

0.028*

(5.60)

(4.04)

(0.92)

(2.36)

(-0.95)

(1.71)

-1.277

-0.846

-0.601

0.486

-0.542***

-0.224**

(-1.12)

(-0.72)

(-1.64)

(1.56)

(-5.50)

(-2.29)

-0.876***

-0.825***

-0.033

-0.074***

0.052***

0.017**

(-9.25)

(-8.01)

(-1.12)

(-2.58)

(6.88)

(2.09)

0.276

-0.842

-0.909***

-0.259*

-0.692***

-0.596***

(0.53)

(-1.53)

(-5.72)

(-1.79)

(-17.72)

(-13.31)

8.441***

14.430***

1.611***

1.460**

0.362***

-0.071

(5.52)

(4.67)

(3.68)

(2.42)

(3.19)

(-0.38)

-2.118

-4.815***

12.420***

13.160***

-0.855***

-0.031

(-1.37)

(-2.61)

(23.60)

(26.10)

(-7.44)

(-0.21)

Year dummy

No

Yes

No

Yes

No

Yes

Industry dummy

No

Yes

No

Yes

No

Yes

F-statistic

83.02

30.89

145.69

99.69

104.74

48.97

R-squared

0.2532

0.3083

0.2758

0.5358

0.2190

0.2994

4052

4052

4044

4044

4052

4052

ELCfirm

ELCadoption

lnSize

lnAge

Debt ratio

State

Foreign

lnLoan

Capital-labor ratio

Fixed-asset ratio

Industry-lerner-

Constant

N Notes:

Figures in parentheses are robust cluster standard errors (clustered at industry levels). ***, **, and *

represent that the difference is significant at the 1%, 5%, and 10% levels, respectively. The model is pool OLS regression with dummy variables.

Although this paper adopts matching methods to mitigate a potential threat from the endogenous problem, the intervention effect will be influenced by the self-selection effect as long as the coefficient of the grouping variable is significant in Equation (2). Therefore, we utilize the advantages of the micro-level difference-in-difference model or the two-way fixed effect model and then investigate the causality between 21

environmental labeling certification and firm performance, or the intervention effect. Table 3. Results of Micro-level Difference-in-difference Regression ROA

Variable ELCadoption lnSize lnAge Debt ratio State Foreign

Tobin’s Q

TFP

(13)

(14)

(15)

(16)

(17)

(18)

0.832**

0.855**

0.275***

0.217**

0.084***

0.071***

(2.48)

(2.54)

(2.68)

(2.51)

(3.32)

(2.73)

0.379*

0.484**

-0.736***

-0.842***

0.0321**

0.012

(1.74)

(2.07)

(-9.31)

(-12.56)

(2.08)

(0.76)

-1.491***

-0.697

0.927***

0.032

0.202***

0.037

(-4.43)

(-1.28)

(7.64)

(0.25)

(6.81)

(0.96)

-7.921***

-7.636***

-0.900**

-0.685***

-0.148***

-0.137***

(-8.05)

(-7.65)

(-2.45)

(-2.73)

(-3.14)

(-2.71)

-0.779

-0.906

-0.089

-0.004

0.019

0.044

(-1.36)

(-1.62)

(-0.43)

(-0.02)

(0.59)

(1.37)

0.327

0.015

0.059

-0.151

0.021

0.043

(0.81)

(0.03)

(0.37)

(-1.07)

(0.42)

(0.86)

2.273

1.460

-0.828*

-0.253

-0.135

-0.060

(1.26)

(0.82)

(-1.72)

(-0.63)

(-0.93)

(-0.42)

-0.470**

-0.422**

0.016

-0.010

0.030*

0.024

(-2.57)

(-2.36)

(0.29)

(-0.21)

(1.73)

(1.29)

-3.702***

-3.375***

-0.901***

-0.829***

-0.627***

-0.571***

(-3.15)

(-2.78)

(-2.85)

(-2.77)

(-7.23)

(-6.33)

9.867**

4.296

16.07***

19.56***

-0.312

0.416

(2.56)

(0.97)

(10.83)

(14.45)

(-1.10)

(1.22)

Year dummy

No

Yes

No

Yes

No

Yes

Industry lerner

No

Yes

No

Yes

No

Yes

F-statistic

26.70

13.34

22.01

71.36

38.87

18.88

R-squared

0.1929

0.2345

0.2206

0.4739

0.1565

0.2099

4052

4052

4044

4044

4052

4052

lnLoan Capital-labor ratio Fixed-asset ratio Constant

N Notes:

Figures in parentheses are robust cluster standard errors (clustered at industry levels). ***, **, and *

represent that the difference is significant at the 1%, 5%, and 10% levels, respectively. The micro-level DID model is a two-way fixed effect panel model. Column (14), Column (16), and Column (18) are the DID results after controlling the year dummy.

Table 3 displays the regression results of the micro-level difference-in-difference model. Whether the interpreted variable or firm performance is ROA, Tobin’s Q, or TFP, we find that the coefficients are significantly positive at the 5% level, and that ELC adoption significantly improves a firm’s financial performance and productivity. Therefore, China’s ELC program has an intervention effect for the treatment firms, as 22

the results of DID and Micro-level DID consistently support the hypothesis of this study. Profit motivation is one of the aims for these firms to participate in the program.

4.3. Decomposition of intervention effects: labeling effect vs. technical factor A potential shortcoming of the previous analysis is that it does not distinguish between the labeling effect and dynamic impact of ELC intervention on a firm’s financial performance such as ROA and Tobin’s Q. In fact, intervention of the ELC program will affect a firm’s behavior via complicated mechanisms after a firm obtains the label. For example, it may increase green innovation activities through some micro-mechanisms (see Prieto-Sandoval et al. (2016) for more details) and further improve firm performance. However, we do not know if the intervention effect of financial performance is due to its own dynamic behavior or market response, especially the reactions of consumers who prefer environmentally friendly products. In this part, we choose the variable of productivity, TFP, as a proxy for technical factors and then decompose the intervention effect into technical factors and labeling effects. Table 4 displays the decomposition results based on DID regression. According to the decomposition results, we conclude that both the labeling effect and technical factors contribute to improved financial performance. The coefficients of TFP are all significantly positive at the 1% level, and the coefficients of ELCadoption are smaller than they are in Table 2 and Table 3. This verifies that the dynamic mechanism of the ELC program affects financial performance - namely, the technical factor or dynamic productivity contributes to improved financial performance. We also find that the coefficients of ELCadoption are almost all significantly positive at the 10% level when we control the technical factors, TFP. Therefore, the intervention of China’s ELC program has a labeling effect.

23

Table 4. Decomposition of Intervention Effects Variable

ROA (19)

Tobin’s Q

(20)

(21)

ELCfirm

ELCadoption

TFP

lnSize

lnAge

Debt ratio

State

Foreign

lnLoan

Capital-labor ratio

(22)

(23)

(24)

0.247

0.179***

(1.38)

(3.47)

0.572

0.623*

0.577**

0.232**

0.187**

0.152**

(1.62)

(1.77)

(2.39)

(2.28)

(2.19)

(2.18)

3.105***

3.267***

3.452***

0.494***

0.395***

0.405***

(7.85)

(8.15)

(14.26)

(5.00)

(4.95)

(6.41)

0.279

0.445*

0.934***

-0.752***

-0.847***

-0.531***

(1.29)

(1.89)

(11.56)

(-9.50)

(-12.54)

(-20.83)

-2.119***

-0.818

0.173

0.827***

0.017

-0.079*

(-6.25)

(-1.55)

(1.31)

(7.01)

(0.13)

(-1.96)

-7.463***

-7.190***

-8.898***

-0.826**

-0.630**

-1.261***

(-7.19)

(-6.72)

(-14.02)

(-2.19)

(-2.43)

(-6.67)

-0.837

-1.049*

-1.002***

-0.098

-0.021

0.007

(-1.51)

(-1.96)

(-7.57)

(-0.49)

(-0.10)

(0.20)

0.263

-0.127

0.904***

0.049

-0.168

0.128**

(0.59)

(-0.27)

(4.11)

(0.30)

(-1.15)

(2.04)

2.692

1.654

-1.998*

-0.763

-0.230

0.488

(1.53)

(0.96)

(-1.78)

(-1.60)

(-0.58)

(1.61)

-0.564***

-0.499***

-0.806***

0.001

-0.019

-0.127***

(-2.95)

(-2.64)

(-8.16)

(0.02)

(-0.41)

(-4.91)

-1.756

-1.508

1.266**

-0.590*

-0.602**

-0.254*

(-1.61)

(-1.33)

(2.32)

(-1.85)

(-2.02)

(-1.78)

10.84***

2.936

-5.524***

16.23***

19.40***

14.36***

(2.90)

(0.66)

(-3.24)

(11.02)

(14.42)

(29.18)

Year dummy

No

Yes

Yes

No

Yes

Yes

Industry characteristics

No

Yes

Yes

No

Yes

Yes

F-statistic

27.82

16.07

47.25

21.06

69.20

150.27

R-squared

0.2267

0.2914

0.3291

0.2396

0.4825

0.5179

4052

4052

4052

4044

4044

4044

Fixed-asset ratio

Constant

N Notes:

Column (21) and Column (22) are the DID results of Equation (2), and other columns are the micro-level

DID model based on Equation (3). The variables of industry characteristics are different according to model selection.

4.4. Heterogeneity effect for tryers and sticklers Another interesting thing is that there are two types of enterprises in the treatment group: tryers and sticklers. Tryers are firms that participated in the environmental labeling program for only one term of validity (three years), and Sticklers are firms that have adopted environmental labeling for more than three years. We find that 57% 24

of firms in the treatment group have adopted the environmental labeling for more than one validity period. It is well-known that China’s ELC program is a market-oriented environmental regulation, and that the firm’s decision whether to participate in this program is related to the effect of the program on firm performance. To explore the heterogeneous effects, we divide our sample firms into Tryers and Sticklers and examine the treatment effect separately for each of the two subsamples based on the micro-level DID model. The corresponding results are in Table 5.

Table 5. Effects of ELCs on firm performance for Tryers and Sticklers Variable

ELCadoption

lnSize

lnAge

Debt ratio

State

Foreign

ROA

Tobin’s Q

TFP

(19) Tryer

(20) Stickler

(21) Tryer

(22) Stickler

(23) Tryer

(24) Stickler

0.418

1.064***

0.242

0.218**

0.063

0.072**

(0.71)

(2.66)

(1.41)

(2.20)

(1.55)

(2.24)

0.383

0.582**

-0.875***

-0.849***

0.019

0.012

(1.51)

(2.38)

(-12.60)

(-11.74)

(1.05)

(0.75)

-0.606

-0.760

0.058

0.054

0.039

0.036

(-1.03)

(-1.36)

(0.39)

(0.40)

(0.92)

(0.90)

-7.683***

-7.356***

-0.748***

-0.719***

-0.191***

-0.133**

(-7.04)

(-7.16)

(-2.70)

(-2.65)

(-3.91)

(-2.56)

-0.611

-0.968*

0.101

-0.004

0.056

0.042

(-1.22)

(-1.77)

(0.50)

(-0.02)

(1.64)

(1.32)

0.040

0.020

-0.107

-0.157

0.033

0.044

(0.08)

(0.05)

(-0.74)

(-1.10)

(0.54)

(0.86)

2.132

1.655

-0.209

-0.196

0.050

-0.014

(1.07)

(0.88)

(-0.49)

(-0.45)

(0.32)

(-0.10)

-0.384*

-0.403**

0.001

0.000

-0.001

0.022

(-1.86)

(-2.24)

(0.01)

(-0.00)

(-0.04)

(1.12)

-3.594***

-2.985**

-0.938***

-0.878***

-0.493***

-0.538***

(-2.68)

(-2.42)

(-2.91)

(-2.85)

(-5.02)

(-5.79)

15.36***

15.17***

0.789

0.532

-0.079

-0.065

(4.20)

(4.34)

(0.94)

(0.73)

(-0.33)

(-0.33)

5.852

1.790

20.050***

19.550***

0.548

0.416

(1.23)

(0.38)

(14.84)

(13.43)

(1.43)

(1.20)

Yes

Yes

Yes

Yes

Yes

Yes

F-statistic

12.03

13.04

67.36

63.93

15.25

16.42

R-squared

0.2137

0.2252

0.4912

0.4951

0.1883

0.1946

3379

3741

3375

3737

3379

3741

lnLoan

Capital-labor ratio

Fixed-asset ratio Industry Lerner

Constant Year dummy

N Notes:

Figures in parentheses are robust cluster standard errors (clustered at industry levels). Both firm-fixed and

year-fixed effects are included. 25

There are significant differences of intervention effect between the two types of treatment firms. The intervention effect on the treated firms in China’s ELC program is significant, while the effect of tryers is not significant. As shown in Table 5, the coefficients of ELCadoption are all positive for both tryers and sticklers, but the coefficient is consistently significant at the 5% level only for sticklers, implying the ELC program may have positive effects for both sticklers than tryers and only the effects on sticklers are significant, which may be for two reasons. First, a firm’s decision to participate in the program could because of a strong motive for profit. If firms fail to improve performance after participating in the program, then they will withdraw from it. Second, the intervention effect needs more time to be impactful. Of course, the first case may be more acceptable. 4.5. The mechanism of the intervention effect To reveal the mechanism of how China’s environmental labels can improve firm performance, we examine the intervention effects of the ELC program on a firm’s business income and price markup for the full sample and for the tryer and stickler subsamples. We use the variables lnSale and Lerner previously defined to represent a firm’s

market

share

and

price

markup

and

report

their

micro-level

difference-in-difference regression results in Table 6. China’s ELC program has a positive impact on a firm’s price markup, and the effect is significant on sticklers, with coefficients of ELCadoption at 0.019 and significant at the 1% level. This finding implies with the adoption of environmental labels that the price markup to manufacturing firms significantly increases, where the increase is more to sticklers than to tryers. In contrast, although the intervention effects of the ELC program on business income are all positive in both the full sample and the two subsamples, all of the effects are insignificant at the 10% level, suggesting the influence of the program on the market share of firms is insignificant or slight. The above findings provide evidence that China’s environmental labeling program does promote firm performance mainly through the price premium of manufactured 26

products, while it has no effect on market share. One explanation from the consumer’s perspective is that environmental labels increase the product’s premium to demanders who prefer green products, while it does not attract consumers who are unwilling to pay for green behavior. Put briefly, the environmental initiative of ELCs has only enlarged the acceptance for customers who are already exhibiting environmental awareness..

Table 6. Effects of ELCs on business income and price markup Panel A: Lerner

Panel A: lnSale

Variable (25) Full sample

(26) Tryer

(27) Stickler

(28) Full sample

(29) Tryer

(30) Stickler

0.014**

0.005

0.019***

0.013

0.029

0.010

(2.33)

(0.38)

(2.83)

(0.55)

(0.70)

(0.35)

0.024***

0.023***

0.024***

-0.028*

-0.036*

-0.027*

(5.06)

(4.68)

(4.92)

(-1.81)

(-2.24)

(-1.68)

-0.019**

-0.019*

-0.015

0.094**

0.103*8*

0.077*

(-2.08)

(-1.76)

(-1.64)

(2.36)

(2.67)

(1.89)

-0.090***

-0.086***

-0.086***

0.131**

0.093

0.122**

(-5.16)

(-4.64)

(-4.78)

(2.17)

(1.62)

(1.98)

-0.033***

-0.033***

-0.035***

-0.007

0.008

-0.008

(-2.87)

(-2.72)

(-2.98)

(-0.18)

(0.21)

(-0.21)

-0.005

-0.004

-0.005

0.018

0.009

0.016

(-0.50)

(-0.34)

(-0.48)

(0.62)

(0.25)

(0.56)

0.078**

0.100**

0.082**

-0.365***

-0.306***

-0.328***

(2.03)

(2.32)

(2.01)

(-3.53)

(-2.83)

(-3.15)

-0.002

-0.001

-0.001

-0.071***

-0.063***

-0.065***

(-0.50)

(-0.35)

(-0.36)

(-5.38)

(-4.23)

(-4.77)

-0.085***

-0.095***

-0.086***

0.364***

0.354***

0.389***

(-4.15)

(-4.29)

(-4.09)

(4.93)

(4.34)

(5.14)

0.383***

0.390***

0.377***

0.351**

0.518***

0.363**

(6.68)

(5.25)

(6.43)

(2.05)

(2.80)

(2.02)

-0.275***

-0.267***

-0.295***

1.752***

1.778***

1.673***

(-3.02)

(-2.72)

(-3.22)

(5.30)

(5.18)

(4.97)

Yes

Yes

Yes

Yes

Yes

Yes

F-statistic

13.44

12.31

12.67

10.12

8.33

10.03

R-squared

0.2018

0.1974

0.2021

0.0348

0.0290

0.0281

4041

3373

3375

4052

3379

3741

ELCadoption

lnSize

lnAge

Debt ratio

State

Foreign

lnLoan

Capital-labor ratio

Fixed-asset ratio

Industry lerner

Constant Year dummy

N Notes:

Figures in parentheses are robust cluster standard errors (clustered at industry levels). Both firm-fixed and

year-fixed effects are included.

27

5. Further discussion 5.1. Interpreting the results from a literature perspective Similar to the results of the extant literature (Heras-Saizarbitoria et al., 2011; Boiral and Henri, 2012), firms with better performance are more likely to participate in environmental initiatives, indicating that the performance differences between treated firms and untreated firms should be carefully investigated. The main conclusion of this paper is that environmental label certifications significantly impact the performance of listed enterprises, which seems to contradict the results of Wang et al. (2015). They argue that environmental labeling certifications only have limited impacts on small and unlisted firms, which are threatened by organizational legitimacy and information asymmetry. In fact, the green procurement policy in 2007 is an important factor for ELCs’ effects on large and listed firms, whereas Wang et al. (2015) used a sample spanning from 2000 to 2005. Our results are also different from the literature on ISO 14001, as that certification has an uncertain impact on firm performance (Heras-Saizarbitoria et al., 2011; He and Chong, 2015; San et al., 2015). As discussed earlier, ELC offers concrete financial performance, because it is consumer-oriented, whereas ISO 14001 certification is social-oriented. This paper presents an interesting result that the intervention effect of ELCs is mainly driven by the mechanism of price markup rather than by market share. Existing research has demonstrated that consumers willingly pay more for labeled products (Bjørner et al., 2004), thus supporting the conclusion that environmental labeling certification could increase price markup. We also conclude that environmental initiative of ELCs has only enlarged the acceptance for customers who are already exhibiting environmental awareness., which is consistent with the findings of the existing literature whereby only consumers who have had experience in purchasing environmental-labeled products are willing to pay more for those products with ELCs (Shen, 2012). In general, we find some novel conclusions that do not conflict with the extant literature. 28

5.2. Re-examining performances from an environmental perspective It is also possible that the ELC program may also play an important role at improving environmental quality. Above all, with respect to the estimated increase of financial performance, it may increase green innovation and productivity through some micro-mechanisms. Beside, as an environmental initiative more stringent than ISO 14001, China’s ELC program also has requirements for the environmental performance of enterprises. However unfortunately, there are few indicators of environmental performance for listed firms. Thus, we only take environmental social responsibility as a measure for a firm’s environmental performance and present the results in Table A3 of Appendix. Except for Column (4), where the coefficient is insignificant and the T value is 1.64, the results in Table A3 are consistent with our conclusions related to economic performance. Overall, manufacturing firms do significantly improve their environmental performance after obtaining environmental labeling certifications.

6. Conclusion How to effectively encourage a firm’s environmental behavior and the nexus between environmental behavior and firm performance have been extensively discussed by scholars and politicians in recent years. To develop a strong market-oriented regulation instrument, China has implemented a series of environmental policies, among which its ELC program, aimed at encouraging firms to become more involved in environmental behavior and promoting the development of green technologies, has received ever-growing attention since 2007. This paper investigates the effects of China’s ELC program on firm performance using the matching and DID methods, which can intuitively identify causal relationships owing to the use of DID method and at the same time effectively reduce the selection bias caused by matching.

29

By investigating listed manufacturing firms in China for the 2004-2018 period, this paper provides evidence for the grouping differences of firm performance between a treatment group and comparison group,

whether the performance indicator

is measured by ROA, Tobin’s Q, or TFP. Our research reveals that the grouping differences of firm performance stem from both the intervention effect and self-selection effect. On the one hand, manufacturing firms have significantly increased their financial performance and productivity after obtaining environmental labeling certification - namely, the intervention effect. On the other hand, firms in the treatment group also have higher financial performance and productivity compared to firms that do not have environment labels or are not in the validation period. It means that the treatment firms are different from the comparison firms, and the difference may come from unobservable characteristics, especially green behavior of manufacturing firms. The study thus presents strong evidence for the significantly positive effects of environmental labels on financial performance and productivity, and both labeling effect and technical factors contribute to improved financial performance. We also divide our sample firms into tryers and sticklers. Findings show that only the intervention effect for sticklers is significant. Further research notes that the intervention effect mainly impacts firm performance through the mechanism of price markup rather than market share. According to our empirical analysis, China’s ELC program has significantly improved firms’ financial performance and productivity. Conversely, profitability incentives encourage them to participate in the program and engage in green behavior in order to adhere to the program requirements. Therefore, to meet the grand goal of becoming a green manufacturing country, the China government should use such a market-oriented instrument that can compensate for the cost of green technology and improve firm performance. Our findings also reveal two potential problems of China’s ELC program. First, not all manufacturing firms that have engaged in the environmental labeling program have

30

improved their economic performance, thus preventing potential firms from participating in the program. Second, the intervention effect is mainly due to the mechanism of price markup, rather than the mechanism of market share. In other words, China’s ELC program only strengthens a firm’s pricing power for its environmental products, while it does not attract more consumers to choose environmental products.

ACKNOWLEDGEMENTS The authors are grateful to the Editor and two anonymous referees for helpful comments and suggestions. These authors contributed equally to this study and share first authorship.

31

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34

Appendix Table A1. Industry distribution of firms in our sample 2-digit code Chinese Industrial Classification

Sample firms Treated firms

C17 C18 C20 C21 C22 C23 C24

Manufacture of Textile Manufacture of Apparel, Footwear and Caps Processing of Timber and Manufacture of Wood Manufacture of Furniture Manufacture of Paper and Paper Products Printing and Reproduction of Recording Media Manufacture of Articles for Culture and Sport Manufacture of Raw Chemical Materials Manufacture of Chemical Fibers Manufacture of Rubber and Plastics Manufacture of Non-metallic Mineral goods Manufacture of Metal Products Manufacture of General Purpose Machinery Manufacture of Special Purpose Machinery Manufacture of automobile Electrical Machinery and Equipment Computers and Other Electronic Equipment

4 9 7 20 12 9 11 58 11 37 45 7 27 4 35 51 61

1 3 7 16 4 5 5 8 1 11 8 2 2 1 5 8 15

C26 C28 C29 C30 C33 C34 C35 C36 C38 C39 Total

All industries

408

102

Table A2. Descriptive statistics Variable

Obs

Mean

Std. Dev.

Min

Max

Matched

Treated

ROA

4,069

4.095

4.184

-5.385

13.688

3.794

5.030

Tobin’s Q

4,061

1.918

1.369

0.404

6.352

1.941

1.848

TFP

4,064

1.066

0.314

0.533

2.348

1.036

1.162

lnSize

4,069

21.822

1.096

19.206

27.386

21.674

22.282

lnAge

4,069

2.525

0.494

0.000

3.526

2.522

2.533

Debt ratio

4,069

0.423

0.201

0.010

3.262

0.412

0.459

State

4,057

0.346

0.476

0.000

1.000

0.321

0.424

Foreign

4,069

0.061

0.239

0.000

1.000

0.064

0.052

lnLoan

4,069

0.033

0.061

0.000

0.527

0.033

0.033

Capital-labor ratio

4,064

12.507

0.905

7.370

16.488

12.544

12.390

Fixed-asset ratio

4,069

0.262

0.154

0.000

0.820

0.266

0.248

Industry-lerner

4,069

0.083

0.041

-0.073

0.458

0.082

0.085

Mediator

Sale

4,069

0.668

0.307

0.254

1.394

0.630

0.788

variables

Lerner

4,060

0.091

0.076

-0.073

0.306

0.088

0.098

Dependent variables

Independent variables

35

Table A3. Effects of ELCs on environmental performance Variable

(1)

(2)

(3)

(4)

(5)

(6)

1.620***

1.573***

1.050**

1.068**

(5.58)

(5.29)

(2.48)

(2.47)

0.885*

0.778

1.253**

1.236**

(1.87)

(1.64)

(2.55)

(2.52)

ELCfirm

ELCadoption 0.927***

0.857***

0.921***

0.848***

0.746***

0.667***

(8.59)

(7.31)

(8.53)

(7.24)

(4.56)

(3.97)

0.087

-0.232

0.107

-0.217

0.724

0.509

(0.32)

(-0.84)

(0.39)

(-0.78)

(1.26)

(0.88)

lnSize

lnAge -1.634***

-1.550***

0.111

(-3.10)

(-2.96)

-0.17

1.521***

1.517***

0.881*

(5.44)

(5.43)

(1.86)

0.637

0.582

0.191

(1.49)

(1.36)

(0.26)

4.875*

4.769*

0.725

(1.93)

(1.89)

(0.23)

Debt ratio

State

Foreign

lnLoan -19.73***

-17.37***

-19.69***

-17.26***

-16.99***

-14.96***

(-8.60)

(-6.99)

(-8.55)

(-6.94)

(-4.68)

(-4.01)

Yes

Yes

Yes

Yes

Yes

Yes

0.1448

0.1566

0.1460

0.1575

0.1405

0.1489

3146

3135

3146

3135

3146

3135

Constant Year dummy R-squared N Note:

Firm-fixed effects are included in Column (35) and Column (36).

36

Disclosure statement Title: Impact of environmental labeling certification on firm performance: empirical evidence from China The authors declare that we have no relevant or material financial interests that relate to the research described in this paper.

December 13, 2019