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Contaminated Heart: Does Air Pollution Harm Business Ethics? Evidence from Earnings Manipulation

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Abstract

We investigate whether air pollution harms business ethics from the perspective of earnings manipulation, which exerts a real effect on the economy and social welfare. Using a large sample and a comprehensive air quality index in China, we find that firms located in cities with more severe air pollution exhibit higher levels of discretionary accruals and are more likely to restate their financial statements, consistent with exposure to air pollution leading to more earnings manipulation. We further provide causal evidence using propensity score matching and a discontinuity regression design (RDD) exploiting the Qinling Mountain–Huai River Heating Policy Line, which exogenously leads to more air pollution to cities located immediately north of the Line but not those in the south. Our findings are robust to controlling for weather conditions and alternative samples and measures of air pollution and earnings manipulation. Overall, this study unveils how the ecological environment shapes business ethics.

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Notes

  1. We use “earnings manipulation” and “earnings management” interchangeably in this paper.

  2. The combination of overall severe air pollution with substantial variation in air pollution levels across regions in China provides a powerful setting to test our prediction. See Sect. 3.1 for more details.

  3. See, for instance, Dechow et al. (2010) and Bishop et al. (2017) for comprehensive reviews of the earnings management literature.

  4. Although poor performance is implicated as a determinant of earnings management in many prior studies, some research suggests that firms with good performance may also engage in earnings management. For instance, Healy (1985) finds that managers engage in downward earnings management when their bonuses have reached the maximum due to good firm performance. While Harris and Bromiley (2007) find that bonuses have no impact on firms’ financial misrepresentation, their results suggest that the probability of restatements is increasing in firm performance. We believe this alternative nonetheless works against us finding significant results. We thank an anonymous reviewer for leading us to this discussion.

  5. Our inference remains the same if we follow Kothari et al. (2005) or Dechow and Dichev (2002) to estimate discretionary accruals, or if we follow Chen et al. (2018b) and include explanatory variables of the modified Jones model as control variables in Eq. (2) that we use for the hypothesis testing.

  6. See Table 1 Panel B for the industry classification scheme.

  7. We focus on the case of restatements that correct the overstated financial position or performance (Ahluwalia et al. 2018) and are not tax driven. Expanding to all restatements does not alter our inference.

  8. All Chinese firms have a fiscal year that runs from January 1 to December 31. Our results remain similar when we measure AP from January 1 to the date of financial statements issuance, as shown in Table 9.

  9. The results do not change when we exclude observations for which the parent company’s total assets are less than 60%, 70%, 80%, and 90% of those of the consolidated company. For brevity, we do not tabulate these results, but they can be made available upon request.

  10. Due to the lack of data, we are not able to test for financial restatement of parent companies.

  11. The polluting industries include: B-Mining, C1-Textile, Clothing & Fur, C3-Papermaking & Printing, C4-Petroleum, Chemistry & Plastic, C6-Metal & Nonmetal, C8-Medicine & Biologics, D-Utilities.

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Acknowledgements

The authors wish to thank Hanwen Chen, Costel Istrate (AFC discussant), Nian Liu, Chong Wang, Xi Wu, and the participants of the 2019 French Accounting Association (AFC) Annual Congress and research seminar at ESSEC Business School for their helpful comments and suggestions on earlier versions of this paper. Charles Cho also acknowledges the financial support provided by the Erivan K. Haub Chair in Business & Sustainability and the Global Research Network program through the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2016S1A2A2912421). Siyi Liu acknowledges the financial support provided by the research grant from the National Natural Science Foundation of China (No. 71902030). Daoguang Yang acknowledges the financial support provided by the research grant from the National Natural Science Foundation of China (Nos. 71932003, 71702030, and 71790604).

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Authors and Affiliations

  1. Schulich School of Business, York University, Toronto, Canada

    Charles H. Cho

  2. Cass Business School, City, University of London, London, UK

    Zhongwei Huang

  3. UIBE Business School, University of International Business and Economics, Beijing, China

    Siyi Liu & Daoguang Yang

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  1. Charles H. Cho
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Appendix: Definitions of variables

Appendix: Definitions of variables

Variables

Definitions

DA = 

Discretionary accruals, equal to the absolute value of the residual estimated from the modified Jones model by each industry-year with at least 15 observations

RESTATE = 

Restatement, an indicator variable taking a value of 1 if the t year’s financial restatement is restated, excluding the tax and upwards adjustment, and 0 otherwise

AP = 

Air pollution, which is calculated as the mean value of the daily Air Quality Index (AQI) in the cities in which the firm is headquartered from Jan 1 to Dec 31, divided by 100. The higher the value, the more severe the air pollution

SOE = 

Ownership, an indicator variable taking a value of 1 if the firm is state-owned, and 0 otherwise

SIZE = 

Firm size, which is calculated as the natural logarithm of total assets

BM = 

Ratio of book value to market value, which is calculated as the book value of total assets divided by the market value of total assets

ROA = 

Profitability, which is equal to net income divided by total assets

OCF = 

Operating cash flow, which is calculated as the operating cash flow scaled by total assets

LEV = 

Leverage, which is calculated as total liabilities divided by total assets

SEG = 

Operation complexity, calculated as the natural logarithm of one plus the number of subsidiaries

AGE = 

List years, equal to the number of years a company has been listed

LOSS = 

Indicator variable taking a value of 1 if the firm reports a loss, and 0 otherwise

ST = 

Listing status, an indicator variable taking a value of 1 if the firm is specially treated (ST), and 0 otherwise

OREC = 

The intensity of other accounts receivable, calculated as the other accounts receivable scaled by total assets

CROSS = 

Cross-listing, an indicator variable taking a value of 1 if the firm is listed in a capital market outside mainland China as an A-share, and 0 otherwise

FSR = 

The percentage of shares hold by the largest shareholder, calculated as the number of shares the largest shareholder holds divided by the number of total shares

DUAL = 

Duality, an indicator variable taking a value of 1 if the firm’s CEO is also the chair of the board of directors, and 0 otherwise

DIST_CSRC = 

Distance between the firm’s headquarters and CSRC (China Securities Regulatory Commission)

BRD = 

Board of directors’ size, which is calculated as the natural logarithm of the total number of directors on the board

IDR = 

Independent director ratio, which is calculated as the number of independent directors divided by the total number of directors on the board

BIG4 = 

Big 4 audit firm, an indicator variable taking a value of 1 if the firm is audited by a Big 4 audit firm, and 0 otherwise

GDP = 

The macroeconomic condition, measured as the GDP growth rate of the city where the firm is located in

MKT = 

The degree of marketization, measured as the Marketization Index of the province in which the firm is located in year t

DA parent

Discretionary accruals, calculated based on the parent statements data, equal to the absolute value of the residual estimated from the modified Jones model by each industry-year with at least 15 observations

AP_ANO = 

Air pollution, which is calculated as the mean value of the daily Air Quality Index (AQI) in the cities in which the firm is headquartered from Jan 1 to the date of issuing financial reports, divided by 100. The higher the value, the more severe the air pollution

AP_REG = 

Air pollution, which is calculated as the mean value of the daily Air Quality Index (AQI) in the cities in which the firm’s registered address is from Jan 1 to Dec 31, divided by 100. The higher the value, the more severe the air pollution

AP_RATE = 

Air pollution level, calculated as the mean value of daily air pollution rating (from 0 to 5 indicating excellent air quality to heavy pollution)

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Cho, C.H., Huang, Z., Liu, S. et al. Contaminated Heart: Does Air Pollution Harm Business Ethics? Evidence from Earnings Manipulation. J Bus Ethics 177, 151–172 (2022). https://doi.org/10.1007/s10551-021-04762-y

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