人工智能与中国要素报酬分配——基于GTAP模型的分析

Abstract
Figure/Table
References
Related Citation (5)

Download: PDF (8007 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

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

Received: 12 December 2019

PACS:

F742

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Li Xia
	Tu Taotao
	Lei Zekui

Cite this article:

Li Xia,Tu Taotao,Lei Zekui. Artificial Intelligence and Factor Income Distribution in China——An Analysis based on GTAP Model[J]. , 2020, 1(9): 133-144.

URL:

http://www.zgkjlt.org.cn/EN/ OR http://www.zgkjlt.org.cn/EN/Y2020/V1/I9/133

[1]AGHION P,JONES B F,JONES C I.Artificial intelligence and economic growth[R].NBER Working Papers,2017.
[2]BRYNJOLFSSON E,MCAFEE A,SPENCE M.New world order:labor,capital,and ideas in the power law economy[J].Foreign affairs,2014,93 (4):44-53.
[3]AUTOR D H,SALOMONS A.Does productivity growth threaten employment? “robocalypse now”[R].MIT Working Paper,2017.
[4]STIGLITZ J E,KORINEK A.Artificial intelligence,worker-replacing technological progress and income distribution[R].NBER Working Papers,2017.
[5]曹静,周亚林.人工智能对经济的影响研究进展[J].经济学动态,2018 (1):103-115.
[6]MANYIKA J,CHUI M,MIREMADI M,et al.A future that works:automation,employment and productivity[M].San Francisco:McKinsey Global Institute,2017.
[7]FREY C B ,OSBORNE M A.The future ofemployment:how susceptible are jobs to computerization[J].Technological forecasting and social change,2017 (114):254-280.
[8]SCHUMPETER J.The theory of economic development[M].Cambridge,MA:Harvard University Press,1911.
[9]HANSON R.Economic growth given machine intelligence[R].Technical report,Berkeley:University of California,2001.
[10]DECANIO S J.Robots and humans——complements or substitutes[J].Journal of macroeconomics,2016,49:280-291.
[11]BENZELL S G,KOTLIKOFF L J,LAGARDA G,et al.Robots are us:some economics of human replacement[R].NBER Working Papers,2017.
[12]BRYNJOLFSSON E,ROCK D,SYVERSON C.Artificial intelligence and the modern productivity paradox:a clash of expectations and statistics[R].NBER Working Papers,2017.
[13]ACEMOGLU D,RESTREPO P.The race between machine and man:implications of technology for growth,factor shares and employment[R].MIT department of economics working paper,2017:16-05.
[14]AUTOR D H.Why are there still so many jobs? The history and future of workplace automation[J].Economic perspect.2015,29:3-30.
[15]GOLDIN C,KATZ L F.The origins of technology—skill complementarity[J].The quarterly journal of economics.1998,113: 693-732.
[16]GRILICHES Z.Capital—skill complementarity[J].Review of economics and statistics.1969,51:465-468.
[17]ACEMOGLU D ,RESTREPO P.Robots and jobs:evidence from US labor markets[R].NBER working papers,2017.
[18]VERMEULEN B,KESSELHUT J,PYKA A,et al.The impact of automation on employment:just the usual structural change[J],Sustainability,2018,10 (5):1666-1688.
[19]ACEMOGLU D,RESTREPO P.Artificial intelligence,automation and work[R].NBER working papers,2018.
[20]AUTOR D H,LEVY F,MURNANE R J.The skill content of recent technological change:an empirical exploration[J].The quarterly journal of economics,2003,118 (4):1279-1333.
[21]HEMOUS D,OLSEN M.The rise of the machines:automation,horizontal innovation and income inequality[J].SSRN electronic journal,2014(1):1-41.
[22]ACEMOGLU D.Directed technical change[J].The review of economic studies,2002,69 (4):781-809.
[23]陆菁,刘毅群.要素替代弹性、资本扩张与中国工业行业要素报酬份额变动[J].世界经济,2016,39 (3):118-143.
[24]陆雪琴,章上峰.技术进步偏向定义及其测度[J].数量经济技术经济研究,2013 (8):20-34.
[25]郝枫,盛卫燕.中国要素替代弹性估计[J].统计研究,2014,31 (7):12-21.
[26]白重恩,钱震杰,武康平.中国工业部门要素分配份额决定因素研究[J].经济研究,2008 (8):16-28.
[27]VAN-DER-MENSBRUGGHE D.The standard GTAP model in GAMS,Version 7[J].Journal of global economic analysis,2018,3 (1):1-83.
[28]BRITZ W,VAN-DER-MENSBRUGGHE D.CGEBox:a flexible,modular and extendable framework for CGE analysis in GAMS[J].Journal of global economic analysis,2018,3 (2):106-177.