TY - JOUR
T1 - Do polluting firms suffer long term? Can government use data-driven inspection policies to catch polluters?
AU - Lo, Chris K.Y.
AU - Tang, Christopher S.
AU - Zhou, Yi
N1 - Funding Information:
The authors are grateful to Professors Edward Anderson, George Ball, Christian Blanco, Robert Bray, Ryan Buell, Felipe Caro, Karen Donoghue, Zhu Feng, Robert Hayes, Steve Gilbert, Maria Ibanez, Guomeng Lai, Retsef Levi, Kevin Linderman, Doug Morrice, Antonio Moreno, Mark Pagell, Ivan Png, Anna Saez de Tejada Cuenca, Nicolo Secomondi, Kingshuk Sinha, Rachna Shah, Bradley Staats, Ioannis Stamatopoulos, Jay Swaminathan, Michael Toffel, Jan Van Mieghem, Ovunc Yilmaz, Xuying Zhao, Karen Zheng and seminar participants at Carnegie Mellon University, Harvard Business School, Kellogg School of Management, MIT Sloan School of Management, ESMT Berlin, University of Hong Kong, University of Minnesota, University of Notre Dame, and University of Texas at Austin for their constructive suggestions on our early versions of this paper.
Publisher Copyright:
© 2022 Production and Operations Management Society.
PY - 2022/12
Y1 - 2022/12
N2 - Do firms suffer from negative long-term business performance after being exposed for violating environmental regulations? We empirically examine this question using all 1542 environmental incidents committed by 418 public Chinese manufacturers listed on the Shanghai/Shenzhen Stock Exchange from 2004 to 2013. We use the Coarsened Exact Matching to match a sample firm with (exposed) environmental incidents with a control firm that exhibits the same characteristics in our sample. Our comparative analysis reveals that relative to control firms, firms with (exposed) environmental incidents have poor business performance (e.g., sales growth, market share, returns on sales, and returns on assets) over a 5-year period after being exposed. Using our training samples (data from 2004 to 2012), we also develop a predictive model and a “risk” scoring system to characterize the likelihood of a Chinese manufacturer violating environmental regulations in 2013. Specifically, we use publicly available financial data to identify factors (e.g., firm age, total assets, percentage of government ownership, and past environmental incidents) to predict which firm is more likely to violate environmental regulations. Using our training samples (data from 2004 to 2012), we can expose over 71% of the violations in 2013 by inspecting only 21.5% of the firms with risk scores above the top 80 percentile. Given the long-term penalty and the potential for the Chinese government to use our predictive model as a building block for developing a more effective inspection policy, the number of environmental incidents in China will decline.
AB - Do firms suffer from negative long-term business performance after being exposed for violating environmental regulations? We empirically examine this question using all 1542 environmental incidents committed by 418 public Chinese manufacturers listed on the Shanghai/Shenzhen Stock Exchange from 2004 to 2013. We use the Coarsened Exact Matching to match a sample firm with (exposed) environmental incidents with a control firm that exhibits the same characteristics in our sample. Our comparative analysis reveals that relative to control firms, firms with (exposed) environmental incidents have poor business performance (e.g., sales growth, market share, returns on sales, and returns on assets) over a 5-year period after being exposed. Using our training samples (data from 2004 to 2012), we also develop a predictive model and a “risk” scoring system to characterize the likelihood of a Chinese manufacturer violating environmental regulations in 2013. Specifically, we use publicly available financial data to identify factors (e.g., firm age, total assets, percentage of government ownership, and past environmental incidents) to predict which firm is more likely to violate environmental regulations. Using our training samples (data from 2004 to 2012), we can expose over 71% of the violations in 2013 by inspecting only 21.5% of the firms with risk scores above the top 80 percentile. Given the long-term penalty and the potential for the Chinese government to use our predictive model as a building block for developing a more effective inspection policy, the number of environmental incidents in China will decline.
KW - business analytics
KW - China
KW - environmental incidents
KW - social responsibility
UR - http://www.scopus.com/inward/record.url?scp=85138729977&partnerID=8YFLogxK
U2 - 10.1111/poms.13861
DO - 10.1111/poms.13861
M3 - Journal article
AN - SCOPUS:85138729977
SN - 1059-1478
VL - 31
SP - 4351
EP - 4363
JO - Production and Operations Management
JF - Production and Operations Management
IS - 12
ER -