Summary:
China's economy is entering a new stage driven simultaneously by population aging and rapid technological progress. On the one hand, population aging has intensified labor market imbalances and pushed up labor costs; on the other hand, breakthroughs in artificial intelligence, industrial robotics, and other hard technologies, together with deep digital transformation, are reshaping the configuration of production factors. These dual transformations provide strong incentives and momentum for enterprises to pursue workforce replacement by machines. Current academic research on the economic consequences of workforce replacement by machines remains nascent. At the macro level, existing literature mainly focuses on its impact on labor markets, finding that workforce replacement can generate both “substitution effects,” which reduce labor demand, and “scale effects,” which increase it. At the micro level, studies indicate that workforce replacement can improve production efficiency by reducing costs and stimulating innovation. However, little attention has been paid to its impact on corporate financing activities, even though financing costs are critical to the implementation of workforce replacement by machines. Because workforce replacement by machines typically involves large-scale, long-term capital investments, equity capital costs have become a key channel to ease rigid funding requirements. Examining whether and how workforce replacement affects equity capital costs not only helps reveal investors' attitudes toward firms developing new quality productive forces through such transformation, but also tests the efficiency of capital market resource allocation. Theoretically, workforce replacement may reduce equity capital costs by enhancing production efficiency, improving firms' adaptability to market changes, and lowering operational risks; it may also improve internal controls, reduce the risk of material misstatements, and enhance earnings information quality, all of which further decrease equity capital costs. Conversely, acquiring large amounts of machinery may raise operating and financial risks, highlighting the need for rigorous empirical analysis. Using data from Chinese A-share listed companies, this study empirically examines the effect of workforce replacement by machines on equity capital costs. The results show that workforce replacement significantly reduces firms' equity financing costs, and this finding remains robust under various tests. Mechanism analysis suggests that this effect operates through reduced business risk and improved earnings information quality. Further heterogeneity analyses reveal that the effect is more pronounced when firms or regions exhibit higher levels of digitalization, when labor protection regulations are stricter, and when a larger share of the workforce is low-skilled, indicating that workforce replacement interacts with contextual factors to reduce equity capital costs more effectively. This study provides empirical evidence on how productivity transformation affects capital markets and demonstrates that capital markets can support the development of new quality productive forces and the pursuit of high-quality economic growth. The contributions of this paper are threefold. First, it expands research on the microeconomic consequences of workforce replacement by machines by showing that it significantly lowers equity financing costs, indicating that capital markets can recognize and reward firms' transformation toward new quality productive forces, thereby providing empirical support for the role of capital markets in facilitating high-quality economic transition. Second, it explores dual mechanisms: through enhanced production efficiency and reduced operational risk, as well as improved internal controls and earnings information quality, that explain how workforce replacement lowers equity capital costs. Third, it examines heterogeneity from the perspectives of digital technology adoption and workforce composition, offering practical implications for firms implementing workforce replacement strategies.
[1]柏培文和张云,2021,《 数字经济、人口红利下降与中低技能劳动者权益》,《经济研究》第5期,第91~108页。 [2]杜亚光、何瑛和田马飞,2023,《工业机器人应用对审计收费的溢出效应——来自制造业上市公司的证据》,《上海财经大学学报》第6期,第104~118页。 [3]冯玲、袁帆和刘小逸,2023,《工业机器人与企业创新——来自中国制造业企业的证据》,《经济学(季刊)》第4期,第1264~1282页。 [4]胡晟明、王林辉和朱利莹,2021,《工业机器人应用存在人力资本提升效应吗》,《财经研究》第6期,第61~75+91页。 [5]李建强、高翔和赵西亮,2020,《最低工资与企业创新》,《金融研究》第12期,第132~150页。 [6]李建伟,2020,《我国劳动力供求格局、技术进步与经济潜在增长率》,《管理世界》第4期,第96~113页。 [7]李磊、王小霞和包群,2021,《机器人的就业效应:机制与中国经验》,《管理世界》第9期,第104~119页。 [8]毛新述、叶康涛和张頔,2012,《上市公司权益资本成本的测度与评价——基于我国证券市场的经验检验》,《会计研究》第11期,第12~22+94页。 [9]孟晓俊、肖作平和曲佳莉,2010,《企业社会责任信息披露与资本成本的互动关系——基于信息不对称视角的一个分析框架》,《会计研究》第9期,第25~29页。 [10]倪静洁和郭檬楠,2023,《工业机器人应用如何影响企业内部控制质量?》,《经济与管理研究》第6期,第19~37页。 [11]王化成、张修平、侯粲然和李昕宇,2017,《企业战略差异与权益资本成本——基于经营风险和信息不对称的中介效应研究》,《中国软科学》第9期,第99~113页。 [12]王永钦和董雯,2020,《机器人的兴起如何影响中国劳动力市场?——来自制造业上市公司的证据》,《经济研究》第10期,第159~175页。 [13]叶康涛和陆正飞,2004,《中国上市公司股权融资成本影响因素分析》,《管理世界》第5期,第127~131+142页。 [14]游家兴和刘淳,2011,《嵌入性视角下的企业家社会资本与权益资本成本——来自我国民营上市公司的经验证据》,《中国工业经济》第6期,第109~119页。 [15]Acemoglu, D., 2002. “Technical Change, Inequality, and the Labor Market”, Journal of Economic Literature, 40(1): 7~72. [16]Acemoglu, D., and Restrepo, P., 2020. “Robots and Jobs: Evidence from US Labor Markets”, Journal of Political Economy, 128(6): 2188~2244. [17]Acemoglu, D., Lelarge, C., and Restrepo, P., 2020. “Competing With Robots: Firm-level Evidence from France”, AEA Papers and Proceedings, 110: 383~388. [18]Anderson, M. C., Banker, R. D., and Janakiraman, S. N., 2003. “Are selling, General, and Administrative Costs ‘sticky’?”, Journal of Accounting Research, 41(1):47~63. [19]Autor, D. H., 2015, Why are there Still so Many Jobs? The History and Future of Workplace Automation”, Journal of Economic Perspectives, 29(3): 3~30. [20]Botosan, C. A., 1997. “Disclosure Level and the Cost of Equity Capital”, Accounting Review, 323~349. [21]Chen, C. X., Lu, H., and Sougiannis, T., 2012. “The Agency Problem, Corporate Governance, and the Asymmetrical Behavior of Selling, General, and Administrative Costs”, Contemporary Accounting Research, 29(1): 252~282. [22]Chen, Z., Harford, J., and Kamara, A., 2019. “Operating Leverage, Profitability, and Capital Structure”,Journal of Financial and Quantitative Analysis, 54(1): 369~392. [23]Dechow, P. M., Sloan, R. G., and Sweeney, A. P., 1995. “Detecting Earnings Management”, Accounting Review, 193~225. [24]Graetz, G., and Michaels, G., 2018. “Robots at Work”, Review of Economics and Statistics, 100(5):753~768. [25]Holmstrom, B., 1989. “Agency Costs and Innovation”, Journal of Economic Behavior & Organization, 12(3): 305~327. [26]Kuzmina, O., 2013. “Employment Flexibility and Capital Structure: Evidence From a Natural Experiment”, Management Science, 69(9): 4992~5017. [27]Lennox, C., Wu, X., and Zhang, T., 2016. “The Effect of Audit Adjustments on Earnings Quality: Evidence from China”, Journal of Accounting and Economics,61(2-3): 545~562. [28]Li, J., Hu, A., Chen, W., and Fang, S., 2024. “Social Security Fee Reduction, Industrial Robots, and Labor Income Share”, Journal of Asian Economics, 94: 101788. [29]Qiu, J., Wan, C., and Wang, Y., 2024. “Labor-saving Innovations and Capital Structure”, Journal of Corporate Finance, 84: 102510. [30]Yoo, Y., Boland Jr, R. J., Lyytinen, K., and Majchrzak, A., 2012. “Organizing for Innovation in the Digitized World”, Organization Science, 23(5): 1398~1408.