Abstract
The relationship between environmental regulation and labor demand has been mixed. Few research studies have considered the impact of cross-regional environmental regulations on the labor market, even though this is notable. We adopted a geographic regression discontinuity (RD) approach to investigate the labor market impact of the cross-regional environmental regulation “2 + 26” Cities Special Emission Limit Program for Air Pollutants. We found that the policy significantly boosts the labor demand of polluting firms rather than a simple inter-industry transfer, mainly through increasing investment in fixed assets and cost offset mechanisms. Further analysis of the structure of labor demand shows that the increasing workforce is primarily low-quality labor accompanied by wage loss. The government should recognize the risks of welfare loss from low-quality work due to environmental regulation and direct polluting firms to innovate rather than reduce costs by increasing demand for low-quality labor. This facilitates a better understanding of the labor market response to environmental policy from the perspective of cross-regional environmental regulation.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
2 The “2 + 26” cities are centered in Beijing and Tianjin, including Shijiazhuang, Tangshan, Handan, Xingtai, Baoding, Cangzhou, Langfang and Hengshui in Hebei Province, Taiyuan, Yangquan, Changzhi and Jincheng in Shanxi Province, Jinan, Zibo, Jining, Dezhou, Liaocheng, Binzhou and Heze in Shandong Province, and Zhengzhou, Kaifeng, Anyang, Henan Province, Hebi, Xinxiang, Jiaozuo and Puyang City.
3 Before using DID, to ensure the validity of the method, we need to perform a parallel trend test as shown in Appendix 2. The results show that the parallel trend hypothesis holds.
4 Due to the continuous policy cycle after 2013, when President Xi took office, we selected data from listed firms from 2013–2020 and matched with corporate recruitment data using firm name, latitude, longitude and address.