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Actions speak louder than words: environmental law enforcement and audit fees

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Abstract

Using the staggered establishment of environmental courts in China, we study the effect of environmental law enforcement on audit fees. We find that companies’ abnormal audit fees increase significantly after the establishment of a specialized environmental court strengthens environmental law enforcement. Our cross-sectional analyses show that the increase in abnormal audit fees is greater for companies with worse environmental performance and for those in heavily polluting industries. We then assess the channels through which environmental courts affect companies’ audit fees and find that the effect of the courts on fees is driven by both audit effort and audit risk and the establishment of a particular type of environmental court (an independent environmental adjudication division). Finally, our results reveal that public concern about environmental protection plays a substitutive role for environmental courts in affecting the increase in audit fees. Our findings suggest that environmental courts aimed at strengthening environmental laws and regulations alter firms’ and auditors’ behaviors and decisions, having unintended spillover effects on audit pricing.

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Data Availability

Our data will be provided upon request.

Notes

  1. See detailed statistics at “Environmental Quality in China—statistics & facts,” Statista.

  2. According to the 2021 World Air Quality Report, of 1,374 cities in East Asia, approximately 11% (143 cities) had recorded annual average PM2.5 concentrations seven times higher than World Health Organization (WHO) standards. All these cities were located in China. Hotan, located in southwestern **njiang, had the country’s worst pollution levels, at around 101 µg/m³, more than 20 times than the WHO guideline. In addition, as much as 90% of the country’s groundwater is contaminated by toxic waste, as well as farm fertilizers, which means that about 70% of rivers and lakes are unsafe for human use.

  3. The establishment of environmental courts is a top-down reform initiated by China’s Supreme People’s Court. Initially pilot environmental courts were set up by local people’s courts with the support of the Supreme People’s Court to address cases of severe environmental pollution. As of 2014, more than 100 such courts had been established in approximately 60 cities across 20 provinces and municipalities. They have witnessed a surge in caseload since their establishment.

  4. Environmental issues involve public and private interests; cases involve the coordination of litigation and alternative dispute resolution as well as the support of substantive and procedural rules. Thus, it is necessary to address environmental cases in specialized trials. Environmental issues are also scientifically complicated: it is difficult to identify victims and perpetrators as well as to ascertain a causal link between pollution and damage. This leads to the need for special rules for environmental litigation. However, traditional judges may only possess general legal qualifications that are inadequate for handling such cases.

  5. In 2011, the city of Haikou established three environmental courts, and the city of Chongqing established two.

  6. See https://baijiahao.baidu.com/s?id=1622210139430385290&wfr=spider&for=pc for a report by East Money Net (accessed April 30, 2021).

  7. China Judgements Online (https://wenshu.court.gov.cn/), which is hosted by the Supreme People’s Court of the People’s Republic of China, issues effective judgment documents from people’s courts at all levels. As of April 18, 2021, the total number of documents on China Judgements Online had reached 119 million.

  8. Examples of compliance costs include costs to collect, manage, and report energy and emissions data for all operations, including costs associated with internal and external auditing of carbon data, internal and external legal advice, contract amendments to support compliance with legislation, management time to interpret and assess the impact of legislation, the establishment of new policies and procedures, and the development of human resources to support a carbon management system. Indirect consequences include potential increases in the price of energy or materials, government penalties, potential extra fees for accessing debt facilities, and the loss of competitive advantages among firms located in regions without an emissions trading scheme (Busch and Hoffmann 2007; Sato et al. 2007; Schneider 2011).

  9. ENV_COURT here is equivalent to the interaction term TREAT×AFTER, where TREAT is a dummy variable that equals 1 for firms in an environmental court jurisdiction and 0 for other firms. AFTER is a dummy variable that equals 1 for the post-environmental court period and 0 for the pre-period. Because there is no within-firm variation in TREAT and no within-year variation in AFTER when we control for firm and year fixed effects, the coefficients of TREAT and AFTER are automatically dropped. This explains why our main model Eq. (1) has only the difference-in-differences term ENV_COURT.

  10. MacKinnon and Webb (2017) point out that the more unequal the number of observations for each cluster, the harder it is to derive consistent estimates of standard errors. In our study, the distribution of observations in each city is highly unbalanced. For example, the minimum and maximum numbers of observations in a city are five (Suqian) and 226 (Nan**g), respectively. Therefore, we cluster standard errors at the client company level in our main regression model. Nonetheless, in analyses not reported here, we also cluster standard errors at the city level and find similar results.

  11. In Eqs. (1) and (2), some variables overlap, whereas others are different. To ensure the robustness of our results, we conduct additional analyses including only the variables unique to Eq. (2). We find results consistent with our baseline regression analysis, which confirms the reliability of our findings. Alternatively, we also add one common variable, SIZE, as it is commonly controlled for in many studies during both stages of the analysis (Beck et al. 2019; Bentley et al. 2013; deHaan et al. 2013; Hribar et al. 2014; Messier et al. 2011). The results of this extended analysis align with our main analysis, further supporting the validity of our conclusions.

  12. In 2011, the city of Haikou established three environmental courts, and the city of Chongqing established two.

  13. We also randomly draw 800 and 1,600 observations from the 2,583 observations and repeat the simulations. The findings remain consistent.

  14. These external awards can indicate best practices and reflect recognition of firms’ superior environmental performance, because third-party expert reviewers evaluate a company’s compliance with applicable environmental laws and regulations, environmental strategy, management, use of resources, and impact on society and the environment (e.g., water pollution, carbon and pollution emissions, and waste consumption and discharge).

  15. Alternatively, we measure environmental litigation risk using three monetary-based proxies: the natural logarithm of the amount involved in lost lawsuits (LITLOSEMONEY; representing the financial value of lawsuits lost by the plaintiff), the ratio of the amount involved in lost lawsuits to the total amount involved in lawsuits (Ratio_LITLOSEMONEY), and the amount involved in lost lawsuits divided by total assets (Ratio_LITLOSEMONEY_Size). The regression results consistently show that the variable (ENV_COURT) loads with significant and positive coefficients, which indicates a significant increase in the amount of litigation expenses for companies following the implementation of environmental courts. The results indicate that, in cities with environmental courts, environmental lawsuits not only are significantly more frequent but also more costly. The specialized nature of the courts likely influences the outcomes of these lawsuits, potentially resulting in larger settlements or damages awarded compared to cases handled in traditional courts. These findings further confirm that the establishment of environmental courts leads to an increase in environmental litigation risk.

  16. We specifically examine the potential impact of environmental courts on the reputation of auditors. To do this, we create the variable MEDIA_AUDITENVLIT, calculated as the natural logarithm of the number of media reports containing negative coverage of environmental litigation involving auditors. This variable captures the media’s attention to and perception of auditors’ involvement in their clients’ environmental litigation. By quantifying the number of negative media reports, we gain insights into the potential reputational risks auditors may face from environmental lawsuits. When we replace the dependent variable with MEDIA_AUDITENVLIT, we find that the coefficient of ENV_COURT remains positive and statistically significant (0.012, p < 0.10). This analysis indicates that, as environmental courts are introduced, the media’s negative coverage of environmental litigation involving auditors increases. This suggests that the establishment of the courts amplifies the reputational risks faced by auditors, which leads to higher audit fees.

  17. To validate our proposition, we conduct a separate analysis to explore the relationship between two proxies for public concern, ECL and ENGO, and abnormal audit fees. The results consistently demonstrate that the coefficients of both ECL and ENGO are positive and statistically significant. This finding supports our argument that heightened public concern is linked to an increase in environmental risks, which prompts auditors to charge more in response to these risks. As public awareness of environmental issues increases, companies may face greater demands to address environmental risks and enhance transparency, which leads to additional costs in the form of higher audit fees.

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Acknowledgements

We are grateful for the helpful comments of Stephen H Penman (editor), an anonymous reviewer, Donghui Wu, Yu** Zhao, Yue He, Huihui Shen, **zi Wang, Qihui Gong, and seminar participants at **amen University. Jiaxing You acknowledges the financial support from the Major Program of National Social Science of China (23&ZD072) and the National Natural Science Foundation of China (72272125). **ting Wu acknowledges the financial support from the National Natural Science Foundation of China (72302131) and the Natural Science Foundation of Shandong Province (ZR2023QG078). Corresponding author: Jiaxing You, E-mail: jxyou@xmu.edu.cn.

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Appendix 1 Forty-one bills related to the environment enacted by the Chinese government

Appendix 2 Information on the collection of court judgment data

We collect court judgment data from China Judgement Online (CJO), an official online database managed by the Supreme People’s Court of China. To enhance judicial transparency and strengthen oversight, the Supreme People’s Court introduced “Provisions of the Supreme People’s Court on the Issuance of Judgments on the Internet by the People’s Courts” on July 1, 2013. These provisions mandate that all court judgments, except those involving national secrets, trade secrets, or personal privacy, be made public through CJO within seven working days of their issuance. Users can easily access the database through a simple registration process and download judgments. The availability of up to 17 filters, such as case type, case name, issuance year, location, judgment type, court level, and more, allows users to refine their searches. CJO has become the largest global repository of judgments, with a total of 119 million lawsuit documents available as of April 18, 2021. The website also offers access to historical judgments dating back to 1997, ensuring a comprehensive collection of legal information.

From this vast collection of 119 million lawsuit documents, we identify a total of 9,287,624 lawsuits in which the defendants are companies. Next we define an environmental lawsuit as one that references at least one of 41 specific environmental bills we identify (see Appendix 1) as its legal basis. This results in a total of 41,981 environmental lawsuits.

Appendix 3 Definitions of variables

Appendix 4 The establishment of environmental courts in different cities

Appendix 5 Various matching methods

  1. (1)

    New propensity score matching

In the main regression, we strictly follow previous studies (e.g., Shipman et al. 2017) and use propensity score matching to identify a one-to-one matched control group for the treatment group. Although the application of this method results in no significant overall differences between the treatment and control groups based on the existing control variables, there are still significant differences in two individual variables (i.e., SOE and GDPPC) between the two groups. To address this, we first transform the GDPPC variable into categorical variables based on decile (GDPPC_C) and then use the variables SOE and GDPPC_C for precise matching of the treatment group with the control group. Subsequently, we adopt a similar approach for other control variables in the main regression. This leads to a final sample of 2,532 observations: 1,317 observations in the treatment group and 1,215 observations in the control group. The regression results for this updated propensity score matched sample are reported in Column (1) of Appendix Table 14. The coefficient of ENV_COURT is positive and significant at the 5% level, which is consistent with our baseline regression analysis.

  1. (2)

    Entropy balancing and coarsened exact matching

Although propensity score matching is widely used, it has been criticized for increasing imbalance, inefficiency, model reliance, and bias (e.g., Shipman et al. 2017). To address the issues associated with the use of method, we also use two alternative matching methods: coarsened exact matching and entropy balancing.

First, we use the entropy balancing method developed by Hainmueller (2012). This method is considered an extension of traditional propensity score matching, where unit weights are estimated through logistic regression. Hainmueller and Xu (2013) point out that entropy balancing can improve balance between the treatment and control groups, reducing the model’s reliance in estimating the effect of the treatment group. Specifically, we use the same control variables as in the main regression for matching. The results in Column (2) of Appendix Table 14 reveal a significant regression coefficient of 0.041 for ENV_COURT (p < 0.05).

Second, DeFond et al. (2017) suggest that coarsened exact matching can overcome the problems of propensity score matching by using random matching, in which matched samples have better covariate balance than unmatched samples. Following previous studies (Cen et al. 2018; DeFond et al. 2017), we use the quintiles of five variables—SIZE, LEV, LIQUIDITY, ROA, and GDPPC—for matching, which results in 1,892 firm-year observations: 946 observations in the treatment group and 946 observations in the control group. Column (3) of Appendix Table 14 presents the results based on the coarsened exact matched sample. They show a positive and significant regression coefficient for ENV_COURT (0.043, p < 0.05). These findings suggest the robustness of our main inference to the matching method.

Table 14 Robustness tests using alternative matching methods

Appendix 6 Oster test and permutation test

  1. (1)

    Oster test

Although we attempt to reduce estimation bias by controlling for observable variables through various matching methods, potential systematic differences (e.g., unobservable latent omitted variables) may still introduce bias into our conclusions. To address this, we use a simulation-based approach, following Oster (2019) and Donohoe et al. (2022), to establish bounds for the β coefficients. We use two parameter values (i.e., R2 and 𝛿) derived from the simulation: the upper bound for β is determined by assuming a 130% increase in the R2 from estimating model (1) after accounting for unobservable factors that may correlate with both environmental courts and abnormal audit fees. In addition, unobservable factors are assumed to be at least as significant as observable, controlled factors in model (1) (i.e., equal selection of observables and unobservables; 𝛿 = 1). The lower bound is the estimated β from the model before these parameter assumptions are incorporated (i.e., β in Column [2] of Table 4). Appendix Table 15 Panel A presents the results of these tests, which indicate that the “true” β is likely bounded at [0.030, 0.045]. Oster (2019) proposes two criteria for assessing the robustness of estimated β coefficients: whether the bound (1) falls within the 99.5% confidence interval for the coefficient and (2) excludes zero. In this case, the likely bounds for β [0.030, 0.045] fall within the 99.5% confidence interval for β in Column (2) of Table 4 [0.002, 0.089], and the bounding estimate excludes zero, which indicates that the estimated β coefficient in Table 4 is not likely driven by unobservable shocks that are at least as important as the observable, controlled covariates. Column (2) reports that 𝛿 = 1.996, which suggests that unobservable variables must be more than approximately twice as important as observable, controlled factors to produce no treatment effect (i.e., β = 0). Given that model (1) already controls for several factors, year fixed effects, and firm fixed effects, it is unlikely that such unobservable factors drive our main results.

  1. (2)

    Permutation test

 

To further validate the impact of the timing of environmental courts (ENV_COURT) on our results, we perform a permutation test by randomizing the timing of ENV_COURT within each firm 1,000 times and re-estimating model (1) for each randomized iteration to obtain simulated β coefficients. Panel B of Appendix Table 15 displays the results, indicating that the observed β coefficient in Column (2) of Table 4 (our main result) is an extreme outlier among the simulated β coefficients. Specifically, the number of times the simulated β coefficients from the permutation test are more positive than the observed β is only 1 out of 1,000 iterations, corresponding to a likelihood of randomly observing the β coefficient obtained in Column (2) of Table 4 of about 0.1%. This permutation test result strongly suggests that the timing of our treatment variable ENV_COURT, rather than other events around the environmental courts period, significantly contributes to the increase in abnormal audit fees.

Table 15 Oster test and permutation test

Appendix 7 List of Chinese newspapers consulted

Appendix 8 Determining media tone

In our study of media tone in the Chinese setting, we use machine-based algorithms. Machine-based algorithms are more accurate than the traditional bag-of-words approach in determining tone. The bag-of-words method only considers the frequency of word occurrences without capturing the sequence of words or semantic relationships.

To do this, we expand on the word list established by You et al. (2018). Our word list comprises 127 ambiguous words with varying interpretations in different contexts, 103 negative words, 288 weak modal verbs, 523 strong modal verbs, 1,771 passive words, and 1,894 positive words.

To calculate media tone, we follow the specific procedure outlined below:

  • Step 1: Segmenting Articles

    We start by segmenting the whole article into individual sentences using punctuation marks, such as commas, periods, and others.

  • Step 2: Estimating Sentence Tone

    a. Positive and Negative Words

  • Within each sentence, we identify positive and negative words and assign a score of 1 to each positive word and − 1 to each negative word. To obtain a more nuanced score, we adjust the score of each positive or negative word based on its context.

    • If a strong modal verb precedes a positive/negative word, we multiply the score by 1.5.

    • If a weak modal verb precedes a positive/negative word, we multiply the score by 1.

    • If a negative word precedes a positive/negative word, we multiply the score by − 1.

  • Step 3: Calculating Positive and Negative Media Scores

  • Step 4: Calculating TONE for Each Report

    b. Ambiguous Words and Combinations

    In the accounting literature, encountering phrases akin to “increasing inventory” or “reducing liabilities” is a frequent occurrence. These expressions convey essential financial information and illuminate shifts in a company’s financial condition. However, attributing the inherent tone solely to specific keywords like “increasing” or “reducing” can sometimes result in misunderstandings. To better discern the tone conveyed by such phrases, we’ve classified these expressions into two distinct categories of ambiguous terms, and we examine ambiguous words or combinations thereof within every sentence. This allows us to delve deeper and consider the context within which these phrases are used. Specifically, the first category pertains to terms related to input and output. Terms like “profit,” “revenue,” and “output,” which are associated with output, are categorized as conveying positive meanings in ambiguity. Conversely, terms like “cost,” “loss,” “expenses,” and “inventory,” linked to input or consumption, are categorized as conveying negative meanings in ambiguity. The second category includes words that can form phrases with the first category of words. Terms like “increase,” “expand,” reflecting increments, are classified as conveying positive meanings in ambiguity. Terms like “increase,” “decrease,” reflecting decrements, are categorized as conveying negative meanings in ambiguity.

    Subsequently, we apply the following rules to determine the tone: First, positive combinations, such as “negative (from the first category of ambiguous terms) + negative (from the second category of ambiguous terms),” receive a score of 1. For example, “reducing costs” or “lowering inventory.”

  • Also, “positive (from the first category of ambiguous terms) + positive (from the second category of ambiguous terms),” receive a score of 1. For instance, “increasing profits” or “raising output.”

  • Second, negative combinations, such as “positive (from the first category of ambiguous terms) + negative (from the second category of ambiguous terms),” receive a score of -1. For example, “decreasing profits” or “lowering output.”

  • Similarly, “negative (from the first category of ambiguous terms) + positive (from the second category of ambiguous terms),” also receives a score of -1. For instance, “increasing inventory” or “escalating losses.”

  • c. Adjusting Scores with Modals and Exclamation Marks

  • We use the presence of modal words or exclamation marks to adjust the scores further.

    • If a strong modal precedes a combination of ambiguous words, we multiply the score by 1.5.

    • If a weak modal precedes a combination of ambiguous words, we multiply the score by 1.

    • If a negative word precedes a combination of ambiguous words, we multiply the score by − 1.

    • If a sentence ends with an exclamation mark and the nearest word is identified as positive (negative), we multiply the score by 1.5 (–1.5).

  • d. Calculating Sentence Scores

    We add up the adjusted scores for each positive, negative, and ambiguous word within a sentence to obtain the overall positive score and overall negative score for that sentence.

  • Step 3: Calculating Positive and Negative Media Scores

  • We add up the overall positive scores of all sentences to obtain the positive media score for a specific firm-year (POSITIVE). Similarly, we sum up the overall negative scores of all sentences to obtain the negative media score for a particular firm-year (NEGATIVE).

    Step 4: Calculating TONE for Each Report

    We calculate TONE for each report using the positive and negative media scores as follows:

    $$\text{TONE}\_\text{MACHINE}=\frac{POSITIVE-NEGATIVE}{POSITIVE+NEGATIVE}$$

    If TONE_MACHINE is less than 0, we classify the report as negative; if TONE_MACHINE is greater than 0, we classify the report as positive; if TONE_MACHINE is exactly 0, we classify the report as neutral.

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Wu, X., Luo, L. & You, J. Actions speak louder than words: environmental law enforcement and audit fees. Rev Account Stud (2024). https://doi.org/10.1007/s11142-024-09823-x

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