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作 者:余玲铮[1] 魏下海[2] 孙中伟 吴春秀 Yu Lingzheng
机构地区:[1]华侨大学经济与金融学院 [2]华侨大学经济发展与改革研究院 [3]华南师范大学政治与公共管理学院 [4]上海财经大学公共经济与管理学院
出 处:《管理世界》2021年第1期47-59,M0004,236,共15页Journal of Management World
基 金:国家自然科学基金面上项目(项目批准号:71873048);教育部人文社科研究规划基金项目(项目批准号:18YJA790086);广东省自然科学基金(项目编号:2016A030310256)的阶段性研究成果。
摘 要:当前,以工业机器人为载体的新技术日益接手以往由人工执行的任务,将改变劳动力市场的技能需求和工资分配方式。本文通过理论模型表明,由于工业机器人与不同工作任务具有不同程度的替代弹性,其中,对可编码和重复性的常规任务存在替代性,对高级认知和社会互动的非常规任务具有互补性,工业机器人的引入促使非常规任务相对工资增长,从而非常规/常规任务工资差距扩大。利用制造业"企业—工人"匹配数据的经验证据证明了这一点,工业机器人使非常规任务工资大幅增益,工业机器人所体现的技术进步是任务偏向型(TBTC),而非传统意义的教育技能偏向型(SBTC)。此外发现,在私营、外资企业以及出口贸易企业中,工业机器人对任务工资差距影响尤为明显。工资差距更多来自于非常规能力的价格效应而不是禀赋效应。本文有助于理解当前我国沿海地区如火如荼的"机器换人"现象及所产生的劳动力市场影响,并提供相应的政策启示。As industrial robots technologies gradually took over the tasks that were previously performed manually,the restructuring demand for skills and wage distribution methods have been reshaped in the labor market.The theoretical model in this study shows that industrial robots have different degrees of substitution elasticity of labor for different tasks.In particular,robots replace the routine tasks of coding and repetitiveness,but they are a complement to the non-routine tasks requiring more cognitive and social interaction.The introduction of industrial robots has led to an increase in the wages of non-routine tasks,hence widened the wage gap between non-routine and routine tasks.We further test our theoretical implication by“enterprise-worker”matching data.The results suggest that industrial robots increase the wages for non-routine tasks significantly,and the technological advance led by industrial robots is task-biased(TBTC),rather than educational skill-biased(SBTC).In addition,industrial robots have a significant impact on the task wage gap among private,foreign-invested and export enterprises.The wage gap is attributed to the price effect of non-routine capability but not the endowment effect.This paper is useful to understand the current“machine substitution”phenomenon in the coastal areas of China and its labor market im pact,and the results shed light on the corresponding policy insights.
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