How does climate regulatory risk influence labor employment decisions? Evidence from a quasi-natural experiment

IF 5.2 1区 经济学 Q1 ECONOMICS 中国经济评论 Pub Date : 2024-07-06 DOI:10.1016/j.chieco.2024.102236
William Mbanyele , Hongyun Huang , Linda T. Muchenje , Jun Zhao
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Abstract

We exploit a green lending mandate as a quasi-natural experiment and estimate its effect on labor investment inefficiency of firms with high carbon risk. We document that heightened climate regulatory risk through mandatory green lending requirements motivates firms with higher carbon risk to adjust their labor investments to levels supported by economic fundamentals. We especially show that climate regulatory risk lowers labor investment inefficiency by curbing overinvestment in labor. This impact is more concentrated among firms that are more dependent on banks for liquidity, firms with severe financial constraints, and those with more institutional investors. After the green credit policy, we also observe an increase in bank lending costs and a reduction in loan maturities for carbon-intensive firms. Overall, our findings suggest that climate bank lending regulation is one of the major channels through which climate risks get embedded in labor employment decisions. In particular, green lending regulatory costs can have significant effects on corporate labor investment efficiency.

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气候监管风险如何影响劳动力就业决策?来自准自然实验的证据
我们利用绿色贷款强制要求作为准自然实验,估算其对高碳风险企业劳动力投资低效率的影响。我们记录了通过强制性绿色贷款要求提高气候监管风险,促使碳风险较高的企业将其劳动力投资调整到经济基本面支持的水平。我们特别表明,气候监管风险通过抑制劳动力的过度投资,降低了劳动力投资的低效率。这种影响更多集中在流动性更依赖银行的企业、财务约束严重的企业以及拥有更多机构投资者的企业。绿色信贷政策实施后,我们还观察到碳密集型企业的银行贷款成本增加,贷款期限缩短。总之,我们的研究结果表明,气候银行贷款监管是气候风险嵌入劳动力就业决策的主要渠道之一。特别是,绿色贷款监管成本会对企业劳动力投资效率产生重大影响。
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来源期刊
中国经济评论
中国经济评论 ECONOMICS-
CiteScore
10.60
自引率
4.40%
发文量
380
期刊介绍: The China Economic Review publishes original works of scholarship which add to the knowledge of the economy of China and to economies as a discipline. We seek, in particular, papers dealing with policy, performance and institutional change. Empirical papers normally use a formal model, a data set, and standard statistical techniques. Submissions are subjected to double-blind peer review.
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