人工智能与中国要素报酬分配——基于GTAP模型的分析

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摘要基于标准GTAP模型的框架,本文探究人工智能冲击对中国要素报酬分配的影响。在本文中,人工智能既体现为资本增强型技术进步,又表现为资本—劳动要素替代弹性的增加。在此基础上,讨论人工智能的深化、广度、强度等冲击对中国要素报酬分配的影响。模拟结果表明:在人工智能冲击中国要素报酬分配的传递路径中,人工智能的深化和广度是影响要素报酬的核心因素,而人工智能的强度、非熟练与熟练劳动力替代弹性对要素报酬的变动均不具有根本性影响。具体而言,世界范围的人工智能深化有利于中国资本要素报酬增加,而不利于中国的劳动要素报酬;由中国引领的人工智能深化同时有利于中国的资本和劳动报酬;由美国引领的人工智能深化既不利于中国的资本报酬,也不利于中国的劳动报酬。此外,人工智能强度、非熟练与熟练劳动替代弹性不会显著改变人工智能深化影响中国要素报酬的变动方向。

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关键词 ：人工智能, 资本增强型技术进步, 要素替代弹性, 要素报酬

Abstract：Based on the framework of standard GTAP model,the paper examines the impact of artificial intelligence on factor income distribution in China.Artificial intelligence is not only reflected in capital-augmenting technical change,but also in the increase of elasticity of substitution between capital and labor.Then,this paper discusses the influence of the deepening,scope and intensity of artificial intelligence on the factor income distribution in China.The result shows that the deepening and breadth of artificial intelligence are the core factors affecting factor income in China.However,the intensity of artificial intelligence,substitution elasticity between unskilled and skilled labors have no significant impact on evolving of factor income.Specifically,worldwide deepening of artificial intelligence is beneficial to China's capital income,but unfavorable to labor income in China.Besides,the deepening of artificial intelligence led by China is beneficial to China's capital and labor income.Furthermore,the deepening of artificial intelligence led by the United States is unfavorable to both capital and labor income in China.In addition,intensity of artificial intelligence and substitution elasticity between unskilled and skilled labors will not significantly affect the distributional impact of artificial intelligence deepening in China.

Key words： Artificial intelligence Capital-augmenting technical change Factor elasticity of substitution Factor income

收稿日期: 2019-12-12

PACS:

F742

基金资助:国家自然科学基金项目 “偏向型技术进步与农业要素收入分配:基于理论和实证的分析” (71503092),国家自然科学基金项目 “极端气候下中国水资源对粮食安全影响的风险评估和弹性对策研究” (71461010701),中央高校基本科研业务费专项资金资助项目 (2662020JGPY010)。

通讯作者: 涂涛涛

作者简介: 李霞 (1995-),女,云南曲靖人,华中农业大学经济管理学院博士研究生;研究方向:农产品贸易。

引用本文:

李霞, 涂涛涛, 雷泽奎. 人工智能与中国要素报酬分配——基于GTAP模型的分析[J]. 中国科技论坛, 2020(9): 133-144.
Li Xia, Tu Taotao, Lei Zekui. Artificial Intelligence and Factor Income Distribution in China——An Analysis based on GTAP Model. , 2020(9): 133-144.

链接本文:

http://www.zgkjlt.org.cn/CN/Y2020/I9/133

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